What City Observatory did this week

1.  More evidence job growth is shifting to city centers.  A recent paper by Nathaniel Baum-Snow and Daniel Hartley has some interesting data on the pattern of job growth in the nation’s largest metropolitan areas. They find that while suburban area job growth greatly outpaced that of the central city in the 1990s – basically, the further away from the core you were, the faster you grew – that this pattern totally changed in the decade 2000 to 2010. (Chart shows job growth rate by distance to the CBD, in kilometers).

2.  Vancouver’s foreign-buyer tax didn’t slash home prices. This summer, the Province of British Columbia imposed a 15 percent tax on the sale of residences to foreign buyers. Some press accounts described the tax increase as an instant cure for home price inflation, claiming it resulted in an overnight 16 percent decline in home prices. In fact, Vancouver home prices were basically flat; we explain how journalists made a fundamental error in examining the data; one that is unfortunately all too common in reporting on real estate trends.

3.  The most interesting neighborhood in the world.  Google Maps has added a new feature, areas of interest, which show up as peach-colored blotches. Its not entirely clear how Google selects these areas, and some observers are concerned that there may be hidden biases at work. Using City Observatory’s Storefront Index, we’ve looked to see how the clustering of consumer-facing retail and service businesses corresponds to the places Google characterizes as interesting.  Storefront clusters closely correspond to the areas google flags as “interesting.”

4.  Where is ridesharing growing fastest?  A new report from the Brookings Institution uses federal data on so-called non-employer businesses to track the growth of independent contractors in the “rides and rooms” segment of the gig economy. We use the Brookings data as a proxy for the penetration of transportation network companies like Lyft and Uber in the top 50 metropolitan markets. The leaders are New York, San Francisco and Washington.

This week’s must reads

1.  The under-appreciated benefits of public housing. Public housing has long been associated with negative consequences. It’s emblematic of concentrated poverty, and it’s widely believed that large-scale, high density public housing projects breed social pathologies.  But a new paper from John Haltiwanger and colleagues challenges that view.  Part of the problem of assessing the impact of public housing is the problem of “selection effects” – people who are chosen to live in public housing, by definition, are low income, and suffer from many challenges.  The key question is, once chosen to be in public housing, how do children do? Haltiwanger, et al overcome this selection effect problem by looking at the different outcomes for different children within a single household based on the amount of time they live in public housing. Comparing siblings allows the researchers to net out the effects of families (parental education, income, employment, etc), and judge the separate impact of housing itself.

While the impacts are different for boys and girls, the study shows that public housing actually benefits children of poor families over their lifetimes, as measured by income and likelihood of incarceration, (girls see larger gains in income and lower rates of incarceration than boys). This holds for both traditional public housing and housing vouchers. In large part this seems likely to be an income effect: families getting vouchers or living in public housing spend less on housing than they would if they rented in the private market, without subsidies, and therefore have more resources for all their other expenses, which may translate into better outcomes for their children. As we’ve noted at City Observatory, the big problem with our housing programs for the poor is their lack of scale: fewer than one-in-four households that technically qualify for housing assistance get anything. And this study suggests that there could be substantial benefits from expanding the reach of these programs.  The full report is available from the National Bureau of Economic Research (NBER): Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing.

2.  Tales of Rent Control  John McNellis tells us what rent control looks like from the perspective of a small landlord. His family inherited a small 5-unit San Francisco apartment building which fell under the city’s rent control program. Two of the apartments turned over after they inherited the building, but three tenants were ones who were in place. Rent control created a strong adversarial relationship between the landlord and tenants; one tenant even insisted all communication be in writing. Eventually, McNellis’s family tired of the burden of being landlords, and opted to sell . After the sale, McNellis reports finding out that three of his nominal tenants had actually sublet their apartments — at market rates, “Our apartments were thus rented at market rates: just not by us.” Tens of thousands of apartments, particularly smaller complexes that make up the “missing middle” housing are owned not by big corporations, but by smaller part-time and amateur landlords. Rent control creates strong incentives for them to take their capital and invest elsewhere, with the paradoxical result that the landlord business is further dominated by more ruthless and efficient corporate owners.

New knowledge

1.  Housing, Interest Rates and Inequality:  Writing at VoxEU, economist Gianni LaCava describes the results of his research looking at the relationship between home prices, interest rates and economic inequality. Digging in to state level data, LaCava confirms what Thomas Piketty and Matthew Rognlie have argued:  that the distribution of income and wealth in the United States has become more unequal, and that it has been the growth of housing wealth, in particular, that has been the big driver of inequality.  His work also shows that housing prices have accelerated most in those states with the most constrained (i.e. least elastic) housing supply, and that inequality accelerated as real interest rates declined steadily from the 1990s onward. If we’re concerned about inequality, LaCava argues, we need to pay more attention to the distribution of housing wealth, and the “imputed” income that homeowners receive.

2.  Home prices and economic mobility in California. California’s Legislative Analyst has produced some of the most insightful examinations of the connections between housing markets and the economy.  A new paper by LAO looks at the connection between high home prices in coastal counties (around San Francisco and Los Angeles) and the inland counties of the state.  It finds that historically, incomes were converging between the lower income inland counties and the higher income coast counties. But that pattern has reversed in recent years, and that’s attributable to higher housing prices on the coast. The lack of housing has forced more people to live in the interior counties, where economic opportunities and productivity are lower. And significantly, as measured by residual income—the amount of income left after paying housing and transportation costs—incomes are actually lower in these “cheaper” counties, meaning that these families would be economically better off if they could live in one of the coastal counties.

What City Observatory did this week

1.  Cities for Everyone:  Our Birthday Wish.  October 15 marked City Observatory’s second birthday. We reviewed some of the highlights of the past year, focusing on the growing evidence of the economic resurgence building around the nation’s cities. For the coming year, we’re planning on focusing on what it takes to build and maintain diverse inclusive communities:  cities for everyone.

2.  The Price of Parking.  Using data from the website Parkme, we’ve constructed an index of typical monthly parking costs in the nation’s largest metropolitan areas. While the median price in large cities is around $200 per month, there’s huge variation. Prices range from more than $700 per month in New York to less than $30 monthly in Oklahoma City. See how your city’s parking prices compare to others.

3.  Cities and the Price of Parking. Using our city parking price index, we look at the relationship between parking prices and transportation behavior in different metropolitan areas.  Our analysis shows a strong correlation between parking prices and transit use: people are much more likely to take transit in cities with expensive parking. Parking prices also correlate closely to the penetration of ride-hailing businesses like Lyft and Uber. The population-adjusted number of transportation service non-employers (a proxy for ride sharing businesses) is highest in cities with the most expensive parking. Parking, as it turns out, is a surrogate form of road pricing, with parking charges discouraging peak period car trips to urban centers and shifting travel to other modes. The more widespread deployment of ride-hailing may transform the role that parking prices play.

4.  The Myth Rich Cities/Poor Suburbs. There’s a new narrative about cities that claims that we’ve already experienced a great inversion, with poverty in the suburbs and wealth in the cities. Despite the resurgence of city populations in the past two decades, however, it’s still the case that poverty, especially concentrated poverty, is disproportionately found in cities, and that suburbs, especially newer and more distant ones, have much lower rates of poverty. At current rates of change it will be many decades before city and suburban poverty rates are equal. Rather than assuming that any movement of better educated and higher income people into cities makes their problems worse, we ought to look to see how we can leverage the re-investment in cities in away that increases opportunity and maintains diversity.

This week’s must reads

1.  Implementing a carbon tax: politics makes strange enemies. In less than three weeks, Washington State voters will decide the fate of a proposed carbon tax. I-732 would impose a $25 per ton carbon tax, and use the proceeds to reduce the state’s sales taxes and pay rebates to low income families, as well as cutting some business taxes. A key aspect of the plan is that it is revenue-neutral—with the funds raised by the tax entirely returned in the form of tax cuts. While that’s a feature for some advocates, it’s a fatal bug to other interests, which is why, surprisingly, some of the strongest opposition comes from a number of environmental and social justice groups. Writing at Vox, David Roberts tells the story of a how a progressive economist’s market-based pricing solution for climate change has run into a buzz-saw of political opposition from groups who’d like to see carbon tax revenue used to fund a slew of clean energy and transition support programs.  The practical difficulties of engineering an agreed-upon policy approach to climate change – among groups that agree something needs to be done –likely foreshadows the conflicts that will play out when this issue finally reaches the national stage, which one hopes will be sooner rather than later.

2.  Achieving economic integration:  Tales from Chappaqua.  Bill and Hilary Clinton live in (for the next little while at least) in Chappaqua, New York, an up-scale suburb in Westchester County. In addition to its famous residents, Chappaqua is also ground zero for the battle to build more affordable housing in an area that has traditionally been zoned almost exclusively for relatively expensive single-family homes.  Politco relates the history of the city’s exclusionary policies, and describes the present day conflicts in trying to site affordable multi-family housing.  As President Obama’s recent endorsement of “YIMBY”-Yes in my backyard zoning reforms indicates, this may become more of a national issue in the months ahead.

3.  A road much less traveled. Between Austin and San Antonio, there’s a brand new freeway where you can cruise along—legally—at 85 miles per hour. And you’ll find that it’s nearly free of traffic. And that’s the problem: the SH 130 toll-road, built by one of those vaunted “public-private partnerships” we hear so much about, is careening into bankruptcy. The San Antonio Express-News calls the project “a monument to failure” and tells the story of how wildly optimistic traffic estimates and federal loan guarantees led to the construction of a billion dollar highway, that nobody seems to think is worth paying to drive on.

New knowledge

Historical Maps of Redlining.  And this week, we have some “old knowledge” in a new form.  A team of researchers from three universities, including the University of Richmond, the University of Maryland, Virginia Tech and Johns Hopkins, has digitized the maps and neighborhood descriptions compiled by the Home Owners Loan Corporation (HOLC) in the 1930s.  These maps show the redlining of many urban neighborhoods that pre-figured decades of disinvestment and decline. In addition to the maps, you can also read individual, type-written descriptions of particular neighborhoods.

Although the HOLC maps are often themselves blamed for redlining and disinvestment, its actually more likely that they mostly codified widespread community attitudes about older, poorer and minority neighborhoods.  The maps and descriptions were compiled by mortgage lenders, developers and real estate appraisers.  And some academic evidence suggestions that HOLC and others still made loans in the redlined areas—although at higher interest rates.  The maps and the narrative are well-worth a read:  they provide a real historical context for thinking about the way “neighborhood stigma” can become the kind of self-fulfilling prophecy that has long term economic consequences.

What City Observatory did this week

1.  Bubble Logic.  A major and persistent change in the housing market from a decade ago has been the decline in the number of “trade-up” home-buyers. While some fret that recent first-time homebuyers have become locked in to so-called starter homes, we point out that in many ways, trade-up demand was a product of the unsustainable housing bubble of the last decade. The lingering effects of the bubble’s collapse, and an enduring change in expectations about home price inflation strongly suggest we won’t see a resurgence of trade-up demand anytime soon.

2.  Why a housing lottery won’t solve our affordability problems.  Inclusionary zoning programs require developers to set aside some units in new developments for low and moderate income households. Because tens of thousands of households are potentially eligible for a few hundred units, cities face the practical problem of choosing who gets to buy or rent these cut-price homes. Sally French tells the story of how she entered, and won, San Francisco’s housing lottery, and was able to buy a new condo for about one-third the going rate. That’s great for her—and a relative handful of others—but is hardly a scalable solution to our housing affordability problems. Her experience shows that you also need a good deal of savvy and persistence to negotiate the lottery process.

3.  Are integrated neighborhoods stable? Its long been popular to think of the process of neighborhood change as being characterized by “tipping points.” Once the demographics of a neighborhood start changing, from say mostly white, to more mixed race, it tends to “tip” to being entirely a community of color, because many whites may not feel comfortable if they’re not a majority.  New evidence of neighborhood change shows that once established, mixed race neighborhoods are in fact stable.

4.  A memo for Stockholm.  Next Monday, we’ll learn the name of the latest Nobel laureate in economics. We think a good case could be made that the award should go to Paul Romer, recently named as the new chief economist for the World Bank. Romer’s seminal contributions to New Growth Theory have long been recognized by his academic peers—and have important implications for urban economic policies. And in recent days, Romer has been making a strong case that the profession’s approach to macroeconomic policy has become profoundly unscientific and needs to be fundamentally re-thought.  This is the kind of thoughtful and provocative speaking truth to power that the Nobel prize ought to recognize.

This week’s must reads

1. How we talk about pedestrian deaths.  More than 4,000 pedestrians are killed in car crashes each year, and the way their deaths are reported in the media obscures the systemic nature of this problem.  In an essay at Streetsblog, Angie Schmidt points out that press stories routinely call deaths “accidents,” tend to blame the victims, describe the car, rather than its driver as the cause of the crash, and fail treat design of streets as a factor in the deaths. We know that multi-lane arterials and streets that encourage high travel speeds are responsible for a disproportionate share of pedestrian deaths.

2. Thinking hard about infrastructure investment. The two major party Presidential candidates may agree in some general way about infrastructure, but urban economist Ed Glaeser does not.  In an Interview with Vox, Glaeser points out we need to re-think the way we invest in infrastructure. Current approaches tend to systematically neglect maintenance in favor of shiny new projects, infrastructure investments are seldom subjected to serious cost-benefit analysis, and actual users rarely pay for the costs of projects they benefit from—which in some cases amplifies inequality. And in the case of roads, building more capacity without implementing some form of road pricing simply stimulates more demand—the fundamental law of road congestion.

3. Making mixed use developments legal.  President Obama got a lot of press in the urbanist world last week with the release of the White House’s housing toolkit—essentially a list of recommended policy changes that states and cities could undertake to allow more density. Writing this week in the Washington Post, Jonathan Coppage points out that the federal government could play a key role here as well, by changing its guidelines on residential mortgages to make it easier to include commercial space in residential buildings – think ground floor shops with apartments above.  Currently, federally purchased or guaranteed loans can only go to projects with no more than 15 to 25 percent non-residential uses, effectively precluding this important form of financing for developments that are less than four stories in height.

New knowledge

1.  Concentrated Poverty in the Wake of the Great Recession.  The Brookings Institution’s Elizabeth Kneebone and Natalie Holmes have sifted through the 2010-2014 five-year American Community Survey to track the growth of concentrated poverty in the US. Concentrated poverty is defined in their work as neighborhoods with a poverty rate of 40 percent or higher.  Their key finding:  since 2009, the number of people living in these extremely poor neighborhoods has increased 34 percent, from 8.7 million to 13.7 million.  That comes on top of a big increase in concentrated poverty since 2000; the number of people living in these high poverty neighborhoods in the US has more than doubled since 2000.  Concentrated poverty disproportionately affects people of color: blacks are nearly five times likelier than whites to live in neighborhoods of concentrated poverty. And despite the much noted increase in the number of people living in poverty in the suburbs, concentrated poverty is much more common in cities: about one in four poor persons in cities lives in a neighborhood of concentrated poverty, compared to about 1 in fourteen poor persons living in the suburbs. The Brookings report has detailed data on the top 100 metropolitan areas.

2.  US consumers pay some of the highest real estate commissions in the world.  A new survey of real estate broker commissions in 17 countries around the world shows that the US pays an average commission of about 5.5 percent, compared with about 1.5 to 3.0 percent in other high income countries.  While commissions in the US have declined slightly from an average of about 6.0 percent in 2002, the declines in other nations have on average been much sharper. Commissions in Canada have fallen from about 4.5 percent a decade ago, to about 3.0 percent per day.  Lower real estate commissions would make houses more affordable for everyone and lower transaction costs for real estate sales would make it easier for households to move to new homes and new neighborhoods.

What City Observatory did this week

1. Where are African-American entrepreneurs?  A new Census Bureau survey, undertaken in cooperation with the Kauffman Foundation provides a detailed demographic profile of the owners of the nation’s businesses. It reports that there are about 108,000 African-American owned businesses with paid employees (i.e., not counting self-employed entrepreneurs). We look at the distribution of African-American owned businesses in the fifty largest metropolitan areas, and find some surprising patterns.

2.  Counting people and cars with Placemeter.  We review our experiences using Placemeter—a web cam that when pointed at a road or sidewalk, counts the number of cars, bikes and pedestrians. Its an inexpensive and flexible technology that puts traffic counting within easy reach of businesses, individuals and neighborhood groups. This kind of “little data” can help democratize the planning process.

3.  How much will autonomous vehicles cost?  Its easy to be captivated by the fast pace of technological developments in autonomous vehicles. But the big question that economists—and urbanists—should be focusing on is: how much will these vehicles cost? We assemble some of the studies that have been made so far. They show that initially fleets of autonomous vehicles might cost about a dollar mile to operate. But, as the technology matures and the business scales up, costs are likely to fall.  Many estimates fall in the range of 30 to 40 cents per mile—well below the cost of today’s conventional, privately owned cars.

4.  The price of autonomous cars: Why it matters. Part 2 of our examination of the likely cost of autonomous vehicles examines the potential impacts of fleets of inexpensive AVs on the nation’s cities. At 30 to 40 cents per mile, AVs would be highly competitive not just with cars, but many transit trips as well. The key limiting factors will continue to be the highly peaked demand for urban transportation and the limited capacity of city streets. A la carte per mile pricing could make travelers more willing to consider alternate modes on a trip-by-trip basis, and may reduce car ownership. Fleets of autonomous vehicles may also disrupt the business models—and fiscal stability—of road-building and transit operating agencies.

This week’s must reads

1.  President Obama on zoning:  OK urban policy wonks, try to get accustomed, however briefly to the glare of the national media spotlight. On Monday, President Obama proclaimed himself a YIMBY (Yes in my backyard) as his administration weighed in on how local zoning decisions affect housing affordability.  The administration released a “toolkit” of policy ideas that will be very familiar to City Observatory readers, calling for fewer limitations on building more densely, as a way of lessening housing costs. Importantly, the toolkit calls for communities to consider eliminating minimum parking requirements.  To paraphrase Vice President Biden, this is a big deal.

2.  The high price of affordable housing.  One component of the solution to housing affordability is building more subsidized housing. But in practice the practical impact of subsidized units is limited due to very high construction costs.  In Portland, alt-weekly Willamette Week reports that the city is contracting with non-profits to rehab existing housing units at a cost more than double the per square foot cost of new construction. Private builders say the city fails to put much weight on cost-effectiveness and that it chooses “cool projects with lots of expensive bells and whistles.” The article estimates that if the city could reduce its costs of construction by even 10 percent, it could have built an additional 1,400 units of affordable housing in the past decade.

3. The mythology of HOT lanes.  The most widespread practical application of road-pricing is the growing implementation of high-occupancy toll lanes. At Streetsblog, Kevin Posey asks some hard questions as to whether HOT lanes are living up to the high policy expectations that have been set. He appraises the claims that HOT lanes reduce congestion in general purposes lanes, whether they overtax the capacity of high occupancy lanes, and whether they encourage (or discourage) transit and carpooling.

New knowledge

1.  The impact of carbon taxes.  Since 2008, British Columbia has had a real, live carbon tax. The tax has been gradually raised and now works out to about $30 per ton (rule of thumb: when it comes to carbon taxes, a dollar a ton works out to about 1 cent per gallon–when burned a gallon of gas produces about 20 pounds of carbon dioxide).  Werner Antweiler and Sumeet Gulati of the University of British Columbia explore how the carbon tax has influenced driving and vehicle purchases in the province. They find that the carbon tax has accounted for about half of the 15 percent decline in per capita gasoline consumption in BC since 2008, and is the product of both somewhat more efficient vehicles and less driving.

2.  Oil prices and housing markets.  A new working paper from the Federal Housing Finance Administration looks at how oil prices affect the pattern of home prices within metropolitan areas. Urban economists have long documented the presence of a “rent gradient”: home and land prices tend to decline as distance to the central business district increases. Using zip code level data on home prices, William Larson and Weihua Zhao show that as oil prices increase—increasing the cost of transportation—the rent gradient gets steeper (prices for homes closer to the center appreciate relative to more peripheral homes.

3.  Central neighborhood change.  Nathanial Baum-Snow and Daniel Hartley undertake an elaborate statistical decomposition of the factors driving population change in urban centers in a new working paper from the Federal Reserve Bank of Chicago. Looking at very small downtown core areas (a 2 kilometer/1.6 mile radius around the central business district), they look at who’s moving in and who’s moving out.  While population in these centers declined in the aggregate from 1980 to 2000, they rebounded sharply from 2000 to 2010, driven primarily by the in-migration of white, college-educated residents. Less educated minorities moved out of these neighborhoods throughout the entire 1980-2010 period.

Chart shows change in fraction of adults with a college degree, by distance (kilometers) from the center of the central business district, by decade.

What City Observatory did this week

1. America’s most creative metros, ranked by Kickstarter campaigns. One of the most popular ways to raise funds for a new creative project–music, a video, an artistic endeavor, or even a clever new product–is Kickstarter. Website Polygraph.cool has created an impressive visualization of nearly 100,000 kickstarter campaigns. We use that data to rank US metros by number of kickstarter campaigns per capita. The unsurprising leaders: Austin, Portland and San Francisco. See how your city compares, and use Polygraph’s data visualization to identify the top indie entrepreneurs in your area.

2. Successful cities and the civic commons.  Cities are more than just collections of businesses, buildings and infrastructure:  the social fabric of cities–they way they enable us to easily connect with one another–is important both to their economic function and to the civic realm. As we pointed out in our report Lost in Place, in many ways the social fabric of cities has been stressed and torn by growing segregation and privatization of many parts of our lives, from travel to entertainment to leisure. But there are growing signs of a revival of investments in the public realm that try to strengthen the social and civic functions of cities. Knight Foundation and others have launched a new initiative to reimagine the civic commons, with targeted funding for five cities around the country.

3. Caught in the prisoner’s dilemma of local planning. While the principle of local control has a lot of political resonance and popular support, when it comes to meeting our housing challenges, in creates a terrible conundrum. While every neighborhood in a city or metropolitan area would benefit from more affordable housing if greater density were more widely allowed, each individual neighborhood is reluctant to be the first (or only) place that allows more density, for fear that it alone will bear the brunt of change. This prisoner’s dilemma dominates many local zoning fights and is very much in evidence as New York tries to implement its new mandatory inclusionary zoning program.

4. Lessons in supply and demand: Housing Market edition. We recognize that many people bristle at the mention of economic terminology, but in our view its hard to make sense of our current housing affordability problems without explicitly thinking about supply and demand. Specifically, the demand for urban living has increased rapidly, and continues to do so; meanwhile the supply of great urban neighborhoods–and housing in those neighborhoods–has grown only slowly. The inevitable result is higher rents. Tackling our “shortage of cities” is a fundamental challenge.

This week’s must reads

1. Another NYC Affordable Housing Project gets shot down. In New York City, a proposal to build 209 units of affordable housing in Queens in an area currently zoned for manufacturing has apparently died, due to opposition by the local city councilor. As we’ve noted, the Achilles heel of Mayor de Blasio’s mandatory inclusionary zoning program is the need for project-by-project up-zonings. So far, in both of the cases that have come forward, the up-zonings have provoked neighborhood outcry, and led local city councilors to oppose the project, which given the City Council’s deference to issues in member’s own districts, is the kiss of death. Yet more evidence that hyper-localism in decision-making makes it extraordinarily difficult to tackle housing affordability.

2. The Jane Jacobs Centennial. Writing at the New Yorker, Adam Gopnick uses his review of two recently published biographies of Jane Jacobs to assess her contribution to our understanding of cities. He argues that some of her insights haven’t weathered the test of time well, but in many ways, her work continues to be as fresh and provocative as when she wrote it. And in important ways its prescient about the situation we now find ourselves in, as Gopnick notes: “The new crisis is the ironic triumph of Jacobs’s essential insight. People want to live in cities, and when cities are safe people do. Those with more money get more city than those with less.”

3. Bonus Must Watch: City Observatory’s Daniel Kay Hertz on Chicago Newsroom. You’ve read his commentaries here on City Observatory, now you can watch him on video as well, discussing a range of issues from urban sprawl, to changing demographics, and even optimal bus-boarding process (the latter really get him going). A full hour of urban wonkiness.

New knowledge

1. How zoning has re-shaped American cities.  For decades, the conjecture among many academics was that zoning simply ratified the kinds of land use patterns that were already in place. But a new study of Chicago comparing land use patterns prior to the adoption of that city’s zoning code in 1923 with current development shows that zoning strongly influenced subsequent development. In a new NBER working paper, entitled Zoning and the Economic Geography of Cities, Allison Shertzer, Tate Tinam and Randall Walsh, examine parcel level data on land uses and market values from the 1920s and look to see how they are related to today’s development patterns. Their key findings: zoning does have an impact, and may be more influential in the location of different activities than either geography or transportation networks.  They also find that exclusive residential zoning tends to drive up home prices. In addition, zoning seems to have greatly reduced mixed use development: in 1922, 82 percent of the developed blocks in Chicago had at least some commercial activity.

2. How city center service-exporting businesses drive the UK economy. A new report  Trading places: Why firms locate where they do from the UK Centre for Cities looks at the location and growth of different industries. It divides businesses into those that export goods, those that export services and those that serve local demand. This is especially important for the service exporting sector which has powered the UK economy, as goods production as continued to decline. Fully 32 percent of Britain’s high-skilled jobs in service exports are located in city centers, more than double the proportion (14 percent) of all jobs. The report concludes with some pointed advice for new Prime Minister Theresa May: “The geography of Britain’s jobs and firms means that supporting growth in our cities will become increasingly important for improving the performance of the national economy.”

2. The 2016 Census Planning Database.  The Census Bureau has released its annual compendium of geographically detailed data on population demographics and housing designed for use by planning technicians. This isn’t new information per se (the most recent data is taken from the 5-year American Community Survey results from 2010-2014). What it does do is assemble this information in a form–with census tract and block group estimates, and with baseline comparisons for similar geographies to Census 2010.  Even by Census Bureau standards this is a giant mass of data; the national block group data is a 160 MB file.

It’s been widely noted that poor neighborhoods tend to bear a disportioncate share of the exposure to environmental disamenities of all kinds. In the highway building era of the 1950s and 1960s, states and cities found it cheaper and politically easier to route new roads through poor neighborhoods, not only dislocating the local populace, but exposing the remaining residents to higher levels of air pollution. So, as environmental justice advocates regularly point out, we’ve made policy decisions that shift the burden of pollution on to the poor.

Its widely recognized that environmental pollution (like other disamenities, such as high crime rates) depresses property values and rents.  If a neighborhood is highly polluted or crime-ridden, people with the economic wherewithal to move elsewhere typically will. When they abandon dirty or dangerous places, the rents fall, and by definition, the residents of these neighborhoods disproportionately become those who lack the resources to afford a better alternative:  the poor.  While it is undoubtedly the case that polluting activities tend to locate near poor neighborhoods, it also turns out to be the case that the poor end up living in more polluted places.

A new study from the St. Andrews University–“East Side Story: Historical Pollution and Persistent Neighborhood Sorting“–by Stephan Heblich, Alex Trew and Yanos Zylberberg provides an interesting historical perspective on this process.  It has long been noted that the “East End” of many industrial cities is the location of the greatest concentrations of poverty. In these cities, the prevailing wind direction is from the West, with the result that smoke and other air pollutants from the city tend to be most severe in the East (and air quality is generally better in the West).  By digitizing data on the location of Victorian-era smokestacks, and combining that data with modern atmospheric modeling, the authors were able to estimate 19th Century pollution levels by neighborhood, and examine the correlation between concentrations of poverty and air pollution.  (They proxied income levels by looking at the occupational composition of different neighborhoods, an approach akin to that used by Richard Florida).

The study shows that variations in pollution levels are significant factors in explaining the distribution of poverty within cities in the 19th century.  The authors conclude:

The negative correlation is both economically and statistically significant at the peak of pollution in 1881: pollution explains at least 15% of the social composition across neighborhoods of the same city.

This, of course, is an interesting finding in its own right, but there’s more.  Since the peak of unfettered coal burning a century ago, Britain, and other countries have done a lot to reduce air pollution.  Many of the mills and powerplants that produced all that Victorian pollution are long since gone, and the air in these formerly polluted neighborhoods is much cleaner. What’s interesting is that those 19th century levels of pollution are still correlated with concentrations of poverty today.  The authors find that 1881 pollution levels are a statistically significant explainer of the distribution of poverty in levels in the past decade.

This suggests that pollution played a critical role in initially establishing the concentration of poverty in these neighborhoods, but that once established, poverty was self-reinforcing. While pollution was the initial dis-amenity that attracted the poor and discouraged the rich; once the neighborhood was poor, poverty itself became the dis-amenity that fueled this sorting process.  Another study using historical data on marshes in New York, finds a similar historical persistence of poverty.  Economist Carlos Villereal has an interesting paper entitled “Where the Other Half Lives: Evidence on the Origin and Persistence of Poor Neighborhoods from New York City 1830-2012.” He finds that in the 19th century, the lower-lying marshy areas of Manhattan were regarded as less desirable, and generally were concentrations of poverty.  Many of these same patterns persist even today.

Ownership and Sorting

The St. Andrews study offers one other surprising insight about neighborhood change. One factor that over time ameliorated the concentration of poverty in UK cities was the construction of “council housing”–what we in the US would call public housing. In general, council housing was constructed in a very wide range of neighborhoods, was in public ownership, and was rented out to its tenants.  Because it was built in both the legacy polluted/poor neighborhoods and in less poor neighborhoods, it had the effect, over time, of reducing concentrated poverty. One of the reforms of the Thatcher era was shifting council housing to an ownership model–transferring title to tenants, and then letting them decide to stay, or to sell the property to others.  The St. Andrews study shows that the shift to the ownership model actually reinforced the concentration of poverty, as owners of former council houses in desirable, low-pollution neighborhoods sold them to higher income households. Meanwhile, council housing in formerly polluted, and chronically impoverished neighborhoods weren’t so attractive to higher income households, and so remained in the hands of lower income families. While the initial owners of the Council housing benefited financially from being able to sell their appreciated homes, the formerly affordable housing was no longer available to other families of modest means, and as a result, these neighborhoods became more economically homogeneous.  As the authors conclude:

While the original intent of Thatcher’s policy was to reduce inequality by providing a route for working class households to step on the housing ladder, its consequence appears to have been to lengthen the shadow of the Industrial Revolution and set back the slow decay of neighborhood sorting. Our estimates suggest that about 20% of the remaining gradient between polluted and spared neighborhoods can be attributed to this reform.

The St. Andrews study is an eclectic and clever combination of history and economics. The authors have pioneered some fascinating techniques for digitized historical data, had shed additional light on the tipping point dynamics, and even managed to include references to the evolution of moths in response to coal pollution. It’s well worth a read.

Editor’s Note:  Thanks to Daniel Kay Hertz for flagging Carlos Villereal’s New York City study.

Its apparent to almost everyone that the US has a growing housing affordability problem. And its generating more public attention and public policy discussions. Recent proposals to address housing affordability in California by Governor Jerry Brown and in New York, by Mayor Bill de Blasio have stumbled in the face of local opposition. Its a delicate moment in housing policy debates.

So now we’re being told, by our very smart friends at the Sightline Institute, that we ought not to talk about urban housing problems using the terms “supply and demand.”  Excuse us if we politely, if firmly–and wonkily–choose to disagree.  Housing affordability problems, in Seattle, San Francisco, and just about everywhere have everything to do with supply and demand.

OK, sure:  for general audiences, saying supply and demand may cause some people’s eyes to glaze over, and for others, it may be taken as a sure sign that one has succumbed to a heartless neo-liberal paradigm.  For many people, we know, any mention of economics reminds them of a painfully unpleasant under-graduate course. And Sightline has prudent advice about how to talk about the problem in the media.  They say:

Avoid supply and demand language; opt instead for messages that describe the housing shortage, such as building enough homes and plenty of housing; and a range of housing choices

But for us at City Observatory, this is a teachable moment. The demand for cities and for great urban neighborhoods is exploding.  Americans of all ages, but especially well-educated young adults are increasingly choosing to live in cities.  And in the face of that demand, our ability to build more such neighborhoods and to expand housing in the ones that we already have is profoundly limited, both by the relative slowness of housing construction (relative to demand changes), and also because of misguided public policies that constrain our ability to build housing in the places where people most want to live, to the point in many communities, we’ve simply made it illegal to build the dense, mixed-use, walkable neighborhoods that widely regarded as the most desirable.

Our key urban problems–housing affordability, concentrated poverty, gentrification, long commutes–are all either directly caused or significantly worsened by this imbalance between housing supply and demand.  

But there’s a studied disbelief in many media outlets that market forces have anything to do with housing. NIMBY’s believe that blocking new construction will keep prices down, when the opposite is true.  As a result, we paradoxically pursue strategies that make housing affordability problems worse.

Two recent bits of evidence remind us that supply and demand are very much at work.  A terrific analysis, written by Financial Times reporter Robin Harding and echoed by Vox’s Matthew Yglesias shows how even in a big dense city, increasing supply to meet demand keeps prices in check. In Tokyo, one of the world’s largest and densest metropolises, housing prices have barely budged in the past two decades, because Japan makes it relatively easy to build new housing. Local jurisdictions and neighbors don’t have effective veto power over new development, so when demand increases, supply responds relatively rapidly, and as a result house prices remain much more affordable.

Closer to home, Yardi Matrix is a real estate data research firm tracks and regularly reports on changes in rents and housing occupancy in major markets around the nation.  They’ve noted nearly 300,000 new apartments will be completed this year. As a result, in many markets supply is finally catching up to demand, and rental price inflation is going down in places like San Francisco, Denver, Austin and Houston.  For example, after seeing double digit growth in rents for several years, rents in San Francisco are up just 3.5 percent in the last 12 months, according to Yardi. In some places, such as the oil patch, where demand has declined due to layoffs in the energy sector, rental prices have actually declined.

While these trends are hopeful signs, and while they clearly illustrate that the market forces of supply and demand are very much at work, there’s still much to be done to re-work our public policies to address affordability, urban livability and equity.  We don’t expect the demand for urban living to abate any time soon–in fact, there’s good reason to believe that it will continue to increase.  And it’s still the case that we have a raft of public policies – from restrictions on apartment construction and density, to limits on mixed use development, to onerous parking requirements, and discretionary, hyper-local approval processes – that make it hugely difficult to build new housing in the places where it’s most needed.

Many of the problems we encounter in the housing market are a product of self-inflicted wounds that are based on naive and contradictory ideas about how the world works.  We believe that housing should both be affordable and a great investment (which is an impossible contradiction), and we tend to think the laws of supply and demand somehow don’t apply to one of the biggest sectors of the economy (housing). At their root, our housing problems–and their solutions–are about understanding the economics at work here. So in our view, it’s definitely time to talk about supply and demand.

One of the cleverest adaptations of web-technology is the development of crowd-sourced funding for new products and business ideas.  The biggest of these crowd-sourced funding platforms is Kickstarter, which since its launch in 2009, has generated funding for ideas like the pebble smartwatch, the “coolest” cooler and a revival of the Mystery Science Theater 3000 television series.  Most Kickstarter campaigns are for relatively small amounts, raising between $1,000 and $10,000 and are popular ways of raising funding for creative projects ranging from music albums and films to games and art.  Since its inception, Kickstarter has raised more than $2.6 billion for more than 112,000 projects.

The website Polygraph.cool has used data on the location and industry of Kickstarter campaigns to create a city by city, industry-by-industry visualization of these business plans.  Using data from more than 90,000 Kickstarters, their interactive infographics display the relative size of the campaign (based on money pledged to each project), and projects are color coded by industry (music, film and video, design, publishing, art, theater and games, among other categories).

In these diagrams, the overall size of each cities constellation of dots corresponds to the volume of funding raised via Kickstarter, and the size of each dot represents the value of pledges.  The concentration of colors of each diagram is indicative of the industrial focus of Kickstarter campaigns in that city. You can mouse over any dot on the diagram to see details on the particular project.

Not surprisingly, the nation’s largest metro areas (New York and Los Angeles) account for the largest number of Kickstarter campaigns.  New York has more than 11,000 Kickstarters; Los Angeles has more than 8,000. The campaigns in some cities are heavily skewed toward local industrial specializations–Nashville’s for example, consists mostly of music projects (the red dots in the chart above).  Its worth exploring the infographics for different cities to see the number and variety of projects funded in different locations.

At City Observatory, we focus on metropolitan areas, so we’ve tabulated Polygraph.cool’s Kickstarter data by metro area (aggregating multiple cities within a metro, such as Tempe and Scottsdale along with Phoenix, and Cambridge and Lawrence along with Boston).  To get an idea of the relative importance of Kickstarter to each metro economy, we’ve computed the number of Kickstarter campaigns in each metropolitan area per 10,000 population. As you can see, there’s wide variation among metropolitan areas.

Perhaps unsurprisingly, many of the usual suspects of the creative economy show up at the top of the chart. On a per capita basis, Austin, Portland and San Francisco have the highest number of Kickstarter campaigns, with between 80 and 100 campaigns per 100,000 population. Among metropolitan areas with one million or more population, the median metropolitan area has about 20 campaigns per 100,000 population. The metro areas with the fewest Kickstarter’s per capita include Riverside, Virginia Beach and Hartford, each of which have fewer than 8 campaigns per 100,000 population.

As the authors of the Polygraph visualization note, many of our conventional yardsticks for measuring the creative economy are dominated by data sources that capture large scale enterprises, but not grass roots and D-I-Y activities. Because Kickstarter has few barriers to entry, and is accessible even to individual artists, its one way to measure creative efforts that simply don’t show up in other sources. So have a look at the Kickstarter data for your city, to see how it stacks up.

What should cities do to tackle growing housing affordability problems? Is inclusionary zoning a good way to provide more affordable housing, or will it actually worsen the constrained housing supply that’s a big cause of higher rents?  

In the next few months, the city of Portland, Oregon will be considering the terms of a new inclusionary zoning (IZ) policy. Like similar policies in other cities, the Portland IZ proposal will likely require developers of new multi-family housing projects to set aside some portion of newly built units to be rented at a discount from market rates. Earlier this year, the Oregon Legislature repealed the state’s ban on inclusionary housing requirements. (Oregon and Texas were reportedly the only two states that explicitly prohibited mandatory inclusionary zoning).

On September 12, the Northwest Chapter of the Urban Land Institute held a forum to discuss inclusionary zoning.  I was one of the panelists speaking at this event:  here’s a quick synopsis of my remarks and some observations about the presentations and discussion.

First, despite the enthusiasm among legislators and housing advocates for inclusionary zoning, there’s precious little evidence that it’s had a meaningful impact on alleviating the shortage of affordable housing in major US cities.  As we’ve reported at City Observatory, at least to date, these programs have produced remarkably few units in some of the nation’s largest and strongest real estate markets.

The bigger concern about inclusionary zoning is that it tends to drive up the cost of building new housing, thereby restricting supply, and actually aggravating market-wide affordability problems.  While the comparative handful of new units set aside for low or moderate income households are visible, there is an invisible cost in the form of units not built, and consequently, higher market rents for everyone. Whether, and how much, inclusionary zoning drives up costs is a subject of intense debate.

Mike Wilkerson of ECONorthwest presented a summary of his firm’s recent analysis of inclusionary policies (commissioned by the Urban Land Institute). They constructed pro-forma financial assessments of several common housing types (three-story stacked flats, “four-over-one” podium apartments and concrete and steel apartment towers) and examined their financial feasibility under a range of assumptions about market rents, land costs, incentives (tax breaks and density bonuses), and set-aside levels. The full report is well worth a read–and we’ll explore it in detail in a future commentary.

In his remarks, Wilkerson used the ECONorthwest analysis to examine the likely impacts of the proposed inclusionary zoning in the Portland market.  Portland has experienced some of the fastest rental growth in the nation in the past year, especially in close-in urban neighborhoods.  A key takeaway from Wilkerson’s remarks:  inclusionary zoning requirements are likely to skew developer choices away from higher density construction (like apartment towers) and toward lower density development (stacked flats and podium development). The combination of higher construction costs and higher needed market rents for towers means meeting IZ requirements is disproportionately burdensome for  denser construction. This is especially important in Portland, and its downtown and central neighborhoods, where the city’s comprehensive plan envisions high rise density as key method for meeting expected population growth.  On its face, the ECONorthwest findings should give policymakers in Portland pause about moving forward with inclusionary zoning.

But there’s an additional wrinkle. As thoughtful and comprehensive as the ECONorthwest analysis is, it is still just one firm’s pro-forma model of development costs. And there’s a good deal of uncertainty about some key assumptions that necessarily drive this kind of analysis.  

The ECONorthwest study joins a growing body of research that attempts to model current development costs and the impacts of inclusionary (and other) requirements on the cost and likelihood of housing development. We profiled several of these at City Observatory in July. Each of these analyses represents a solid, fact-based effort, but they come to quite different conclusions about whether and under what circumstances inclusionary requirements are feasible. As a result, no one knows for sure what will happen when these policies are implemented.

Uncertainty is a big risk.

A big challenge with Portland’s proposed inclusionary zoning program is that no developer can know, with any certainty, how big a cost the inclusionary zoning requirements impose, or how easy or difficult will be the process to get development approved under the new rules. As experience with New York’s new mandatory inclusionary zoning has shown, the entire program can be tripped up in the project-level approval process. As a result, many developers are likely to take a wait-and-see attitude–to let others go first, and then make an investment decision when the costs and contours of the new system are better understood.  The effect is almost certain to be a fall off in housing investment. Moreover, this could happen even if the program itself is well-designed and has incentives and cost offsets that lower developer costs; until this is proven in practice, it’s likely to be a deterrent to investment. And paradoxically, because less new housing will be built, prices are likely to be higher than they otherwise would be–worsening the affordability problem (except for those fortunate enough to get reduced price apartment).

It may be that the city will decide that inclusionary zoning requirements are fundamentally at odds with its development objectives (higher density in the urban core) or judge them to be counterproductive to solving the affordability problem.  But if it does decide to go ahead with an inclusionary zoning ordinance, there are several design features that might minimize the risk of the potential negative effects of such a policy. First, it could consider a long phase-in of the inclusionary requirements, to give developers time to carefully study and fully understand the costs and implications of the policy, and the effects of its incentives. Second, the city could establish a simple and clear approval standards for inclusionary projects; discretionary approvals and complex review processes are likely to magnify uncertainty.  Third, at least initially, it should set a relatively low in lieu-fee for developers who want to opt-out of building inclusionary units on-site. The availability of the in-lieu fee gives developers financial certainty that they know the costs the ordinance imposes, and is therefore likely to lessen the chilling effect on investment associated with the uncertainty of a new program. The fee could escalate over time to increase the incentive to build housing on site, once the costs and effectiveness of the program are demonstrated.

As we’ve frequently said at City Observatory, the underlying problem of affordability in Portland (and around the country) stems from our shortage of cities.  Simply put, the demand for urban living is growing much faster that the supply of great urban spaces. This is a strong market signal that we need to be improving urban neighborhoods in more places, and building more housing in the places that are already in high demand. There’s growing evidence in many markets that the supply response–building more apartments–is beginning to blunt the rate of rent increases. The last thing any city concerned about affordability should do is get in the way of increasing housing supply.

A standard rule of medicine–which turns out to be good advice for public policy, as well–is “first, do no harm.” Portland’s City Council would be well advised as it considers an inclusionary zoning policy to design one that doesn’t, even inadvertently, sidetrack the current supply response in the housing market.

What City Observatory did this week

1. Counting Women Entrepreneurs.  The Census Bureau has just released the results of its new survey of entrepreneurs, and we report its key findings on the extent and geography of women-owned businesses.  There are more than 1.1 million women-owned businesses with more than 5 million employees; about one in five businesses is now headed by a woman owner.  Female-headed businesses are still more common in the West and in some Southern cities than in the Northeast and Midwest.

2.  Back to School:  Three charts that make the case for cities.  Recognizing that students may not be the only one’s who may have taken a summer break, City Observatory offers a quick refresher on the economic case for urbanism. It comes in the form of three charts that show the growing importance of walkability to commercial land values, the key role that transit access places in promoting residential values, and more data that show the movement of young adults to city centers.  These three charts underscore the economic momentum behind the move to cities.

3.  Why the median rent is a misleading indicator of housing affordability. Its pretty standard practice to use median rent (or housing prices) to compare the affordability of housing in different locations.  But this misses the key fact that while some cities and neighborhoods have a wide variety of housing, others are much more homogenous. It turns out that having more variety generally means greater affordability. We show how to use data on the 25th percentile of rents as an alternative measure of affordability.

The week’s must reads

1. Welcome to Uberville. Are transportation  transportation network companies (aka ride-sharing) the solution to transit’s last mile problem or an existential threat to public transit service? Altamonte Springs, Florida, a suburb of Orlando has formed a partnership with Uber and is subsidizing some trips. At The Verge, Spencer Woodman examines how this new public private arrangement is playing out, and what it might mean for other communities.

2. Parking reform hits bureaucratic resistance in Seattle.  Parking requirements have increasingly been identified as a key contributor to rising rents and fights over neighborhood parking impacts are a key objection to higher density. As part of its housing affordability “grand bargain,” Seattle endorsed the establishment of parking benefit districts, which would establish pay parking in some areas and return a portion of the net revenues to local neighborhoods. The idea is to promote more efficient use of on-street parking, and buffer local residents from the impact of pay parking. Pilot implementation of the idea has been stymied by bureaucratic opposition from the city’s transportation department. Sightline Institute’s Alan Durning describes the controversy and offers up a  point-by-point rebuttal of the objections that the agency has raised to the parking benefit district plans.

3.  The geography of poverty in Boston. Concentrated poverty amplifies all of the negative effects of poverty on the well-being and economic opportunities of the poor. The Boston Globe explores how the location of public housing and the allocation of Section 8 housing vouchers actually reinforces the segregation of the poor. More than two-thirds of public housing and vouchers go to house families in neighborhoods classified as “low” or “very low” opportunity areas. The opposition to a more balanced distribution of affordable housing is a key factor in perpetuating these historic patterns. The Globe story is a terrific combination of data-based reporting, first-hand accounts of how a better neighborhood can change a family’s opportunities, and insights into the seemingly insurmountable obstacles that reluctant jurisdictions can employ to block affordable housing.

New knowledge

1. Forty years of zip code level home price data. The Federal Housing Finance Agency has posted four decades worth of zip code level data on housing prices.  FHFA has calculated a quality-adjusted, repeat-sales home price index for 1975 through 2015. The data also include an interactive map that allows quick comparisons of home price changes. This map shows changes since 2000, with the biggest increases colored red. Dark green represents the biggest declines.

2. The Transport DataBook.  Yonah Freemark of Transport Politic has compiled an impressive list of trend and ranking data for urban transportation. You’ll find data and clearly presented charts on vehicle miles traveled, modes of travel, transit ridership, operations and finances. Yonah regularly updates these charts as new data becomes available.

3.  Where the kids are.  Chicago public radio station WBEZ has taken block-level Census data and used it to identify the pattern of households with and without children under 18.  You’ll see a recurring pattern here:  households without children (light blue dots) tend to predominate in city centers; households with children (yellow dots) are disproportionately in less central locations.  You can zoom to any location in the country.The Week Observed is City Observatory’s weekly newsletter. Every Friday, we give you a quick review of the most important articles, blog posts, and scholarly research on American cities.

Our goal is to help you keep up with—and participate in—the ongoing debate about how to create prosperous, equitable, and livable cities, without having to wade through the hundreds of thousands of words produced on the subject every week by yourself.

If you have ideas for making The Week Observed better, we’d love to hear them! Let us know at jcortright@cityobservatory.org or on Twitter at @cityobs.

We’ve all been paying a lot more attention to developments in Britain since June’s Brexit vote. As we noted at the time, some of the same kinds of political divides that play out in America—between globally-integrated, knowledge driven cities and more rural areas that are older, less-educated—also happen in Britain. (Population density helps explain the red blue division in the US and the leave/remain divide in England.)

The UK’s Centre for Cities, a London based think tank studies many of the same issues on the other side of the Atlantic that we find so interesting at City Observatory. We were particularly struck by one of their recent reports looking at economic development strategies for cities. Much of what is said in this report could be said with equal force — if in slightly different English — for cities in the U.S.

Paul Swinney and Elli Thomas of the Centre have written a strongly historically grounded description of urban economies, entitled A Century of Cities: Urban Economic Change Since 1911. Looking at economic trends over the past 100 years, they draw a stark contrast between cities that were dominated by mass production, and which have clung to older manufacturing industries, and those cities that embraced services and build a knowledge economy.

In Britain, this plays out as a North/South divide. London and other smaller cities in the South, like Reading and Brighton, developed a strong service sector and thriving new industries. The North the Midlands and Wales, all clung to manufacturing, and relatively low skill jobs like call centers and distribution. Not only did incomes in the South outpace those in the rest of the country, so did job creation: For every job created in the North, Midlands and Wales, 2.3 jobs were created in the South.

Swinney and Thomas offer sharp and clear policy advice. Three takeaways from their report make every but as much sense on this side of the Atlantic as in the UK. From their report:

1. Improving the skills of the workforce. Knowledge businesses require high skilled workers. The ease with which they can recruit these workers is a key determinant of where they locate.

2. Supporting innovation. High-skilled workers don’t just work anywhere – they cluster in successful cities. This is because a worker isn’t more productive just because of the qualifications that he or she holds, but also because of the workers he or she works with and the institutions that he or she works in. The ‘knowledge networks’ that workers are part of are place specific, and cities need to be able to facilitate innovation and the creation of new ideas via their knowledge networks to increase long-run productivity.

3. Dealing with the scars of industrial legacy. The 21st century economy requires less employment space – an office has a smaller footprint than a factory. And this employment space tends to be in a different part of the city – jobs in our most successful cities have been concentrating in their city centres. This shift has left large swathes of empty land and buildings in some cities, so encouraging density of employment should be done alongside dealing with land remediation.

All three of these points mirror analyses we’ve done here at City Observatory. Businesses are increasingly choosing locations based on worker availability, and, for the first time in decades, jobs in city centers are growing as fast or faster in than the suburbs.

The critical factor is a city’s ability to reinvent itself and its economy in the face of major technological or economic changes. In many ways the argument made here is similar to one that Harvard’s Ed Glaeser has made about New York and Boston compared to other rust-belt cities in the US. Places that embraced trade and openness and had a well-educated population have been much more successful in adapting to industrial change than more insular, less-educated places.

Or, more simply put: Nostalgia is not an economic strategy. Economies don’t go backwards, and efforts to forestall, or reverse fundamental economic changes are usually costly and ineffective. The challenge of economic strategy is to plan for the kind of economy we’re likely to have in the future, not pine for the restoration often-imaginary glory of an economy past.

And that, in a way, is a sub-text of the Brexit vote: a majority of voters, dismayed with the changes wrought by globalization and epitomized by the European Union, and with their patience worn thin by the lingering effects of the worst economic downturn in eight decades, not surprisingly voted for the past. Meanwhile, the young, the best-educated, and those living in cities voted to remain, and move forward. The question of whether we go forward, or try to go back, is one that is equally relevant on both sides of the Atlantic.

Its a truism that the zip code that you are born in (or grow up in) has a lot to do with your life chances. If you’re born to a poor household, a neighborhood with safe streets, good schools, adequate parks and public services, and especially some healthy and successful peers and neighbors has a material impact on whether you rise out of poverty or are trapped in it.  While this message often gets presented in fatalistic tones, there’s a growing body evidence that suggests how we build our cities, and specifically, how good a job we do of building mixed income neighborhoods that are open to everyone can play a key role in reducing poverty and promoting equity.

The big problem the US confronts is that poverty has grown increasingly concentrated over the past four decades. Our Lost in Place report showed the number of poor people living in urban census tracts with a poverty rate of 30 percent or higher has doubled since 1970; and today more of the urban poor live in neighborhoods where a high fraction of their neighbors are also poor. As difficult as it is to be poor, its harder growing up in a place where many or most of your neighbors also face these challenges.

New research shows that neighborhood effects—the impact of peers, the local environment, neighbors—contribute significantly to success later in life. Poor kids who grow up in more mixed income neighborhoods have better lifetime economic results. This signals that an important strategy for addressing poverty is building cities where mixed income neighborhoods are the norm, rather than the exception. And this strategy can be implemented in a number of ways—not just by relocating the poor to better neighborhoods, but by actively promoting greater income integration in the neighborhoods, mostly in cities, that have higher than average poverty rates.

In the New York Times, economist Justin Wolfers reported on groundbreaking work by Eric Chyn of the University of Michigan that found previous research may have understated the effect of neighborhoods on lifetime earnings and employment. The paper shows that moving low-income children in very poor neighborhoods to less poor neighborhoods can have a major positive effect on their life chances.

Most media outlets have covered this story as reinforcing the importance of “mobility programs”: that is, policies that encourage residents of very low-income neighborhoods to move to more economically integrated areas, usually with some form of direct housing assistance like vouchers. And the ability to move to neighborhoods with good amenities and access to jobs, without having to pay unsustainable amounts for housing or transportation, is a crucial part of creating more equitable, opportunity-rich cities.

But the coverage may be missing the other half of the policy equation: Chyn’s paper adds to the evidence about the value of mixed-income neighborhoods in general, not just mobility. That means it’s just as important that cities find a way to invest in low-income neighborhoods to bring opportunity to them, rather than simply trying to move everyone out.

Why this research is so important

The results of the voucher demonstration illustrate that there can be large benefits from even modest changes in economic integration. The average household moved about 2 miles from their previous public housing location, and still lived in a neighborhood that had a higher than average poverty rate. Chyn’s results show the effects of moving from neighborhoods dominated by public housing (where the poverty rate was 78% on average), to neighborhoods that had poverty rates initially 25 percentage points lower, on average. Most participants still lived in neighborhoods with far higher levels of poverty than the typical American neighborhood. But compared to their peers who remained in high poverty neighborhoods, they enjoyed better economic results later in life.

In this chart, you can see the growing earnings benefit to children who left very low-income neighborhoods in their adult years.

This study—on the heels of a widely-cited study led by Harvard economist Raj Chetty released last year—adds even more heft to the growing body of evidence that helping people with lower incomes move to mixed-income neighborhoods can play a huge role in spreading economic opportunity.

The new research improves on older studies by getting rid of an important confounding factor that affected some earlier research by more closely replicating a true “natural experiment.”

The experiment was made possible by the decision to demolish large scale public housing in Chicago in the early 1990s. The families dislocated from the old style public housing—which were in neighborhoods of extremely concentrated poverty—had to find new housing. The Chicago Housing Authority (CHA) provided the families with vouchers to move to privately operated rental housing, typically in neighborhoods with far lower levels of poverty. The kids who moved to new lower-poverty neighborhoods saw a significant increase in their lifetime earnings compared to otherwise similar kids who remained in the public housing that wasn’t torn down.

This natural experiment has an important advantage over the “Moving to Opportunity” (MTO) housing experiment conducted by the federal government in the 1990s. In MTO, public housing households had to apply for a voucher lottery. This created the possibility that the people who had applied were particularly motivated and able to make the transition to a new neighborhood. That would mean that even those households that lost the lottery might have better-than-average outcomes, reducing the gap between those who moved and those who didn’t, and making the effect of moving appear smaller than it really was.

But unlike MTO, the participants in the CHA relocation program were not self-selected. They represented a more or less random cross-section of public housing residents, and so the differences between the outcomes of treatment groups (those who got vouchers) and those who didn’t (control groups) could be treated as purely the result of the voucher program.

The policy implication: Mixed-income neighborhoods promote opportunity

But it’s important to put this finding in a broader context. Evidence about mobility programs, in turn, are part of a larger body of research that neighborhoods matter for economic opportunity. While the focus has been helping people leave neighborhoods with high concentrations of poverty, it’s also possible to bring investments and resources to these communities.

Of course, when that happens, it often happens in conjunction with—or even because of—a return of middle- and upper-income people to the neighborhood. In other words, gentrification.

For some, that’s enough to reject that policy avenue. But some research suggests we ought to give it another look. While news from neighborhoods in San Francisco and Brooklyn, where incredibly high levels of demand and tight supply have led to spiraling housing costs, makes it sound like gentrification inevitably and utterly displaces all a neighborhood’s residents, other research suggests that displacement is far less widespread than commonly thought. While housing costs can be an issue, a recent study from the Philadelphia Federal Reserve suggests that displacement is much less common than we might expect—and another study of New York public housing residents in gentrifying areas showed an increase in earnings and school test scores.

This research also occurs against a backdrop of widening inequality and economic segregation. And inequality has an important spatial dimension: low-income and high-income households are increasingly segregated from one another in separate neighborhoods.

While the spatial response, as we’ve said, has focused on mobility, enabling the poor to move to higher income neighborhoods is challenging for a number of reasons. The raison d’etre of many suburbs is exclusion—using zoning requirements to make it essentially impossible for low income households to afford housing—and efforts by outside organizations or governments to reduce these barriers have been difficult. If we want to make the biggest difference in economic integration, we need to try to integrate low-income neighborhoods as well as high-income neighborhoods.

Neighborhoods for everyone

Taken together, the new Chyn results add to the growing body of literature on neighborhood effects and strongly suggest that we ought to be looking for all kinds of opportunities, large and small, to promote more mixed-income neighborhoods. Even the small steps—like lowering the poverty rate in a kid’s neighborhood from 75 percent to less than half—pays clear economic dividends.

But we also need to remember that integration isn’t just about moving around people with low incomes. We can reinvest in neighborhoods of concentrated poverty in ways that improve quality of life and enhance opportunity in place.

If so many people live in suburbs, it must be because that’s what they prefer, right? But the evidence is to the contrary.

One of the chief arguments in favor of the suburbs is simply that that is where millions and millions of people actually live. If so many Americans live in suburbs, this must be proof that they actually prefer suburban locations to urban ones. The counterargument, of course, is that people can only choose from among the options presented to them. And the options for most people are not evenly split between cities and suburbs, for a variety of reasons, including the subsidization of highways and parking, school policies, and the continuing legacies of racism, redlining, and segregation. One of the biggest reasons, of course, is restrictive zoning, which prohibits the construction of new urban neighborhoods all over the country.

But does zoning really act as a constraint on more compact, urban housing? Sure, some skeptics might say, it appears that local zoning laws prohibit denser housing and walkable retail districts. But in fact, city governments pass such strict laws because that’s what their constituents want. Especially within a metropolitan region with many different suburban municipalities, these governments are essentially competing for residents and businesses. If there were real demand for denser, walkable neighborhoods, wouldn’t some municipalities figure out that they could attract those people by allowing that type of development?

A 2005 study by Jonathan Levine—and explored further in Levine’s 2006 book, Zoned Out—seeks to answer this question. Are local governments just responding to “market” demand in ensuring that new development is low-density and auto-oriented? Or is there really pent-up demand for more urban neighborhoods that can’t be satisfied because of zoning?

Levine looks for the answer in two contrasting metropolitan areas: Boston and Atlanta. Boston, as a much older region, has a relatively higher number of dense, walkable neighborhoods, while in Atlanta, which mostly boomed after World War Two, urban neighborhoods are much more scarce. Levine hypothesizes that if dense housing is adequately supplied to match people’s preferences, you should find a pretty good match between the kinds of places people say they’d like to live, and the kinds of places they actually do live. But if zoning really creates a “shortage of cities,” then the greater the shortfall of urban neighborhoods, the worse the matchup between stated preferences and actual living arrangements.

This is an important wrinkle to the “revealed preference” arguments of many defenders of the suburban status quo. Recent Census population figures sparked what were only the latest of a long line of scuffles over whether, or to what extent, the “back to the city” movement is real. But if Levine’s argument is correct, measuring demand for urban areas simply by how many people end up living there is flawed, because some people who would like to live in more compact neighborhoods can’t do so because there aren’t enough to go around.

To begin his analysis, Levine classified neighborhoods in both the Boston and Atlanta metro areas according to their level of “urban-ness” on a five-point scale, with “A” neighborhoods being the densest and most urban, and “E” being the most sprawling and exurban. Levine and his researchers then conducted a survey of residents in each of the zones, asking about their housing preferences and satisfaction with their current housing situation.

In Boston, about 40 percent of respondents said they preferred denser, more pedestrian-friendly neighborhoods, while in Atlanta, just under 30 percent of respondents did so. (Auto-oriented neighborhoods were preferred by 29 percent of people in Boston and 41 percent of people in Atlanta, with remaining respondents neutral.)

And how well did these preferences match actual behavior? Well, in Boston—where neighborhoods in the three most urban categories made up over half of all housing—83 percent of people with strong preferences for urban neighborhoods lived in one of these three urban zones. In Atlanta—where the same top three urban categories make up barely over 10 percent of all housing—just 48 percent of people with strong preferences for urban neighborhoods lived in an urban zone.

In fact, all down the line, people whose stated preferences were more urban were much more likely to actually live in an urban neighborhood in the Boston area than in the Atlanta area—suggesting that in Atlanta something might be preventing them from satisfying their preferences. At the same time, people who expressed preferences for the most auto-oriented neighborhoods were able to satisfy that demand the vast majority of the time in both regions—about 95 percent of those in Atlanta, and 80-90 percent of those in Boston. More rigorous tests prove that this difference is statistically significant.

This seems like strong evidence that there is a “shortage of cities” in Atlanta. Why, otherwise, would there be such a gap between the number of people who satisfy their preferences for urban neighborhoods in the Boston and Atlanta metro areas—and much smaller gaps between people who can satisfy their preferences for more car-oriented areas?

If this is correct, it helps explain a other issues we see. If urban neighborhoods are undersupplied compared to demand for them, we would expect to see urban housing go to the people willing to outbid other households, increasing prices relative to auto-oriented neighborhoods, which are more plentiful. In a place like Atlanta, lots of urban housing would have to be built before this bidding war could be ended, returning prices to a “normal” market level.

It’s also notable that this kind of “shortage of cities” can occur even where there is no overall housing shortage. Atlanta, for example, is not a particularly high-cost region, but it has mostly added new housing on the suburban periphery. So while there’s no bidding war for housing in the metropolitan area as a whole, there is a bidding war for more urban housing, making walkable neighborhoods more expensive than they would have to be. Boston is almost the opposite: walkable neighborhoods appear to be less undersupplied relative to auto-oriented neighborhoods, but the region as a whole has very expensive housing, suggesting that the total supply of housing is too low. Boston could help bring down housing prices by building any housing at all—auto-oriented or more walkable. (Though walkable housing would have lower total location costs.)

Levine’s study ought to be known by anyone who works in urban planning or housing. It’s one of the strongest pieces of evidence that “revealed preferences”—the choices that people actually make about where to live—actually reveal the limited choices that people are given as a result of restrictive land use laws.

If so many people live in suburbs, it must be because that’s what they prefer, right? But the evidence is to the contrary.

One of the chief arguments in favor of the suburbs is simply that that is where millions and millions of people actually live. If so many Americans live in suburbs, this must be proof that they actually prefer suburban locations to urban ones. The counterargument, of course, is that people can only choose from among the options presented to them. And the options for most people are not evenly split between cities and suburbs, for a variety of reasons, including the subsidization of highways and parking, school policies, and the continuing legacies of racism, redlining, and segregation. One of the biggest reasons, of course, is restrictive zoning, which prohibits the construction of new urban neighborhoods all over the country.

But does zoning really act as a constraint on more compact, urban housing? Sure, some skeptics might say, it appears that local zoning laws prohibit denser housing and walkable retail districts. But in fact, city governments pass such strict laws because that’s what their constituents want. Especially within a metropolitan region with many different suburban municipalities, these governments are essentially competing for residents and businesses. If there were real demand for denser, walkable neighborhoods, wouldn’t some municipalities figure out that they could attract those people by allowing that type of development?

A 2005 study by Jonathan Levine—and explored further in Levine’s 2006 book, Zoned Out—seeks to answer this question. Are local governments just responding to “market” demand in ensuring that new development is low-density and auto-oriented? Or is there really pent-up demand for more urban neighborhoods that can’t be satisfied because of zoning?

Levine looks for the answer in two contrasting metropolitan areas: Boston and Atlanta. Boston, as a much older region, has a relatively higher number of dense, walkable neighborhoods, while in Atlanta, which mostly boomed after World War Two, urban neighborhoods are much more scarce. Levine hypothesizes that if dense housing is adequately supplied to match people’s preferences, you should find a pretty good match between the kinds of places people say they’d like to live, and the kinds of places they actually do live. But if zoning really creates a “shortage of cities,” then the greater the shortfall of urban neighborhoods, the worse the matchup between stated preferences and actual living arrangements.

This is an important wrinkle to the “revealed preference” arguments of many defenders of the suburban status quo. Recent Census population figures sparked what were only the latest of a long line of scuffles over whether, or to what extent, the “back to the city” movement is real. But if Levine’s argument is correct, measuring demand for urban areas simply by how many people end up living there is flawed, because some people who would like to live in more compact neighborhoods can’t do so because there aren’t enough to go around.

To begin his analysis, Levine classified neighborhoods in both the Boston and Atlanta metro areas according to their level of “urban-ness” on a five-point scale, with “A” neighborhoods being the densest and most urban, and “E” being the most sprawling and exurban. Levine and his researchers then conducted a survey of residents in each of the zones, asking about their housing preferences and satisfaction with their current housing situation.

In Boston, about 40 percent of respondents said they preferred denser, more pedestrian-friendly neighborhoods, while in Atlanta, just under 30 percent of respondents did so. (Auto-oriented neighborhoods were preferred by 29 percent of people in Boston and 41 percent of people in Atlanta, with remaining respondents neutral.)

And how well did these preferences match actual behavior? Well, in Boston—where neighborhoods in the three most urban categories made up over half of all housing—83 percent of people with strong preferences for urban neighborhoods lived in one of these three urban zones. In Atlanta—where the same top three urban categories make up barely over 10 percent of all housing—just 48 percent of people with strong preferences for urban neighborhoods lived in an urban zone.

In fact, all down the line, people whose stated preferences were more urban were much more likely to actually live in an urban neighborhood in the Boston area than in the Atlanta area—suggesting that in Atlanta something might be preventing them from satisfying their preferences. At the same time, people who expressed preferences for the most auto-oriented neighborhoods were able to satisfy that demand the vast majority of the time in both regions—about 95 percent of those in Atlanta, and 80-90 percent of those in Boston. More rigorous tests prove that this difference is statistically significant.

This seems like strong evidence that there is a “shortage of cities” in Atlanta. Why, otherwise, would there be such a gap between the number of people who satisfy their preferences for urban neighborhoods in the Boston and Atlanta metro areas—and much smaller gaps between people who can satisfy their preferences for more car-oriented areas?

If this is correct, it helps explain a other issues we see. If urban neighborhoods are undersupplied compared to demand for them, we would expect to see urban housing go to the people willing to outbid other households, increasing prices relative to auto-oriented neighborhoods, which are more plentiful. In a place like Atlanta, lots of urban housing would have to be built before this bidding war could be ended, returning prices to a “normal” market level.

It’s also notable that this kind of “shortage of cities” can occur even where there is no overall housing shortage. Atlanta, for example, is not a particularly high-cost region, but it has mostly added new housing on the suburban periphery. So while there’s no bidding war for housing in the metropolitan area as a whole, there is a bidding war for more urban housing, making walkable neighborhoods more expensive than they would have to be. Boston is almost the opposite: walkable neighborhoods appear to be less undersupplied relative to auto-oriented neighborhoods, but the region as a whole has very expensive housing, suggesting that the total supply of housing is too low. Boston could help bring down housing prices by building any housing at all—auto-oriented or more walkable. (Though walkable housing would have lower total location costs.)

Levine’s study ought to be known by anyone who works in urban planning or housing. It’s one of the strongest pieces of evidence that “revealed preferences”—the choices that people actually make about where to live—actually reveal the limited choices that people are given as a result of restrictive land use laws.

We confess:  we’re data geeks.  We love data that shows how cities work, and that give depth and precision to our understanding of policy problems.  But truth be told, most data we — and other analysts — work with is second-hand:  its data that somebody else gathered, usually for some other purpose,  that uses definitions and methods that don’t quite capture what we’re trying to get at.  Information about employment and wage trends, for example, is gleaned from tax and administrative records.  Working with this “secondary” data means that you’re always trying to extract meaning from something that doesn’t exactly measure what you’re interested in; this is why data analysis is often as much an art as it is a craft.

So when a new technology comes around that let’s us generate our own personalized, customized data, we get pretty excited.  (For you foodies, its kind of like going from only having store-bought industrial tomatoes, to growing sun-ripened heirloom varieties in your own backyard–sort of).  But what we’re talking about here is our own, home-grown, artisanal data.

Placemeter, a New York based startup has developed image processing software to count cars, people and bikes in urban environments. Their technology uses existing cameras (it can analyze a stream of real time data) or you can buy one of their Placemeter devices (currently $90) or use any fairly standard wi-fi enabled web-cam. Placemeter then sells their monitoring and data services for between $30 and $90 per month per location counted (locations can be a street, sidewalk, building entrance or area).

For the past several months we’ve been experimenting with the company’s Placemeter device. Placemeter is a small, plastic box, slightly larger than a deck of playing cards.  It has a camera, a wifi tranceiver and an embedded processor.  It attaches to the inside of a window and is powered by a small AC-power brick.  You point it out the window, connect it to your local wifi network, and then log into a web page that lets you see the camera’s eye-view of the area outside your window.  Using your web-browser, you identify cordons (Placemeter calls these “turnstiles”) by using a mouse to draw lines on the image from the camera.  Then Placemeter starts counting the number of people, bikes and vehicles that pass through the turnstile.  It logs this information, by turnstile and by direction, and also provides a handy set of diagnostics that let you view counts by hour or by day and by direction.  (You can also export a CSV formatted log file that can be used by any standard spreadsheet or analysis program). The company has squarely addressed privacy concerns: the Placemeter analyzes the data stream in real time, and doesn’t store personally identifying information or images, just the results of its own computations of the numbers of people and vehicles it has counted.

For our CityObservatory test, we positioned the sensor to face a residential street with two-way traffic and with paved sidewalks on either side of the street.  As shown in the grainy, low-resolution photo below–captured through the Placemeter device camera, we established three turnstiles, one for the street, and one for each sidewalk.  Turnstiles are shown as bright green lines. A vehicle is just crossing Eastbound through the turnstile that crosses the roadway. Our camera has an oblique view across the street.

Placemeter Turnstiles on a Residential Street (webcam image)

A key limitation of our version of Placemeter is that it doesn’t distinguish between the type of object that goes through a turnstile:  so whether its a car, truck, or bicycle, any vehicle traveling through our street-centered cordon would be recorded as a single vehicle.  On a street with a marked bike lane, it would be possible to establish separate turnstiles for the general travel lane and the bike lane, but accurate counts would require a camera angle that allowed you to see both lanes clearly.  Placemeter has recently come out with a new version of its software that is designed to distinguish between people, bikes and cars; we have not tested that version yet.

Placemeter’s dashboard shows hour by hour counts and provides a historical baseline computed from the previous three weeks of data.  Here’s a chart for early July.  The weekday traffic shows a common “double hump” pattern, reflecting morning and evening rush hours and a mid-day dip.  Weekend traffic is lower, with a single mid-day to evening peak. Traffic on this residential street falls to nearly zero over night.  The much more subdued traffic (especially in the morning hours) is apparent on the July Fourth holiday:  Instead of the usual 257 vehicles between 8am and 9am, there were just 50.

In addition to these hourly counts, Placemeter’s dashboard also summarizes counts by day and direction in a bar chart format.  Here what that looks like:

To judge the accuracy of the Placemeter vehicle counts, we compared them to a vehicle count conducted by the City of Portland.  The most recent city data were from January 2015, and were recorded on five weekdays on the same street and block face as that covered by our Placemeter setup.  For comparison purposes, we examined our daily data for the same week in January 2016 (one year later).  The city statistics show an average daily traffic of 5,233 vehicles while our Placemeter count is 4,843 ADT.  The two counts also show a similar directional pattern (the city figures show a 52%/48%westbound to eastbound split; our figures also show 52%/48%.

We’re awash in claims about how big data will transform the world. But there’s also an important role for little data, especially when its data that can be precisely focused on the places and issues you find interesting.

It levels the playing field for bikes and pedestrians. As we’ve pointed out, when it comes to some modes of transportation, especially biking and walking, they’re effectively invisible and therefore disenfranchised from policy discussions, simply because we have no data about them.  We’re awash in data about how many cars are traveling and how fast–or slowly–they travel, but have precious little information about the use of active modes.

Placemeter can be a boon to planners and transportation agencies.  Its less expensive–and by our very crude reckoning–roughly as accurate as other methods of traffic counting.  It provides basic analysis tools and archived data.  Placemeters are flexible and easy to set up, and its possible to use existing web-cams to generate data.

It can be a terrific tool for evaluating projects and events.  One of the toughest evaluation tasks is judging the impact of limited duration events (rallies, festivals and concerts, traffic associated with football games).  Placemeter lends itself to recording activity in very small geographies–like the entrance to a building or a single pathway in a park.

Finally, while its not free, its also not terribly expensive.  So this provides the opportunity to democratize access to data. If a civic group, a neighborhood organization, or just an individual citizen wants to get their own data on how a roadway or other public space is being used, they don’t have to depend on anyone else to get it.  Easy access to lots of “little data” may be just as disruptive as the much ballyhooed “Big data.”

City Observatory believes in using data to understand problems and fashion solutions. But sometimes the quantitative data that’s available is too limited to enable us to see what’s really going on. And incomplete data can lead us to the wrong conclusions.

Our use of data is subject to what we call the “drunk under the streetlamp” problem: An obviously intoxicated man is on his hands and knees on the sidewalk, under a streetlamp. A passing cop asks him what he’s doing. “Looking for my keys,” the man replies. “Well, where did you drop them?” the cop inquires. “About a block away, but the light’s better here.”

When it comes to transportation, we have copious data about some things, and almost nothing about others. Plus, there’s an evident systematic bias in favor of current modes of transportation and travel patterns. The car-centric data we have about transportation fundamentally warps the field’s decision-making. Unless we’re careful, big data will only perpetuate that problem—if not make it worse.

Sometimes Qualitative Data is More Informative

To understand why qualitative data can sometimes tell us more, let’s look at some documentation about the way one American transportation system performs.

Three recent essays from people walking in Houston make it clear that, there, the infrastructure and land use patterns that facilitate safe walking simply don’t exist. The following excerpts are snapshots from a large body of qualitative evidence showing that, in many U.S. cities, walking is a hellish experience.

Writing in Texas Monthly, in an essay entitled “Where the Sidewalks End,” Sukhada Tektel describes her experiences adapting to Houston after living in Mumbai and Toulouse:

Nothing could have prepared me for the disconnectedness of this oil-and-gas mecca: no clear city center, pitiable public transportation, and, most strikingly, no place to walk…For as far as the eyes can see, there are only cars and not a single person on foot.

David Yearsely wrote a different essay, albeit with a similar title (“Where the Sidewalk Ends”), describing wandering about Houston’s downtown and Third Ward while visiting for an organ music gathering. Even traversing the city’s upscale River Oaks district, he describes long, sidewalk-less stretches outside the walled enclaves of the busy four-lane San Felipe Avenue. In ten miles of walking, he encountered only two other pedestrians, both walking their dogs.

At the Houston Chronicle, David Dorantes wrote, “I want to walk, but Houston won’t let me.” Like many migrants to the Bayou City, he has lived in other places where walking is a normal part of everyday life. But not in Houston:

Nowhere else have I ever experienced such fear when walking in the street. I don’t mean that I’m afraid of the people who I meet on the sidewalk. I mean that walking in Houston is a horrific adventure, a pleasure endangered.

It’s unfair to pick on Houston. Large parts of most American cities, and especially their suburbs, constitute vast swaths of hostile territory to people traveling on foot. Either destinations are too spread out, or there just aren’t sidewalks or crosswalks to support safely walking from point to point. Moreover, walking is so uncommon that drivers have become conditioned to behave as if pedestrians don’t exist, making streets even more foreboding.

From the standpoint of the data-reliant transportation engineer, the problems encountered by Dorantes, Yearsley, and Tektel are invisible—and therefore “nonexistent.” Because we lack the conventional metrics to define and measure, for example, the hardships of walking, we don’t design and enforce solutions or adopt targeted public policies.

But when it comes to car traffic, we have parking standards, traffic counts, speed studies, and “level of service standards.” There is simply no comparable vocabulary or statistics for walking or cycling. Traffic engineers will immediately tell us when a road is substandard, or its pavement has deteriorated, or its level of service has become (or might someday become) degraded. We have not collected a parallel array of metrics to tell us that it isn’t similarly as safe, convenient, or desirable to walk or bicycle to common destinations. The American Society of Civil Engineers’ Infrastructure Report Card grades roads chiefly on vehicle congestion and delay (using dubious data, in our estimation). And, as we’ve pointed out, the U.S. DOT’s proposed performance measures for urban transportation further codify this bias by making vehicle delay the chief indicator of how well roads work. The logical result, as Smart Growth America has argued, is that we will end up with a system that optimizes every street for fast-moving cars, with—predictably—negative effects on walking.

The personal stories of pedestrians in Houston are rich and compelling in their detail, but lack the technocratic throw-weight of quantifiable statistics or industry standards to drive different policies and investments in our current planning system.

Will the move to “smart” cities make this worse?

Last month, the U.S. Department of Transportation announced that Columbus, OH, was the winner of its Smart Cities Challenge, beating out six other cities around the country. Google’s city planning subsidiary, Sidewalk Labs, promised to work with the winning city to deploy a wide array of big data and communication tools in order to better plan and operate transit systems. While the Guardian speculated that Google is securing a central position for its technologies in urban transportation markets, we have a different concern.

Sidewalk Labs has sketched out Flow, a flashy new data system for transportation. According to its own descriptions and press reports, it will help cities optimize traffic and parking. Clearly, Flow is primarily concerned with vehicles (cars and transit vehicles alike). But there’s no indication how it will address the movement of people on foot and on bicycles. It’s ironic that an entity called Sidewalk Labs appears more concerned with cars than with pedestrians.

As the old adage goes: If you don’t count it, it doesn’t count. That premise becomes vastly more important the more we define problems in big-data terms. New technology promises to provide a firehose of data about cars, car travel, car delay, and roadways—but not nearly as much about people. This is a serious omission, and should give us pause about the application of “smart” principles to cities and transportation planning.

It will likely amplify the bias that already favors counting cars, but not people. Consider New York City, perhaps the most pedestrian-oriented place in the nation. New York gathers data on pedestrian activity in a twice-annual survey (which counts pedestrian traffic on two different days in May and September at 100 locations). Contrast that with its system that reports vehicle traffic speeds in real time at more than 300 locations.

This isn’t simply a matter of somehow instrumenting bike riders and pedestrians with GPS and communication devices so they are as tech-enabled as vehicles. An exacting count of existing patterns of activity will only further enshrine a status quo where cars are dominant. For example, perfectly instrumented count of pedestrians, bicycles, cars in Houston would show—correctly—little to no bike or pedestrian activity. And no amount of calculation of vehicle flows will reveal whether a city is providing a high quality of life for its residents, much less meeting their desires for the kinds of places they really want to live in.

The fundamental problem is that we’ve designed our cities for the people moving through them, rather than for the people living, working, and being in them. We’re obsessed with getting there rather than being there.

If we want cities that are truly walkable and bikeable–that can become great places to be rather than easy corridors to travel through–we have to listen to more than big data. We need a framework that considers a wide array of evidence of what we’ve done and what we’ve left undone; of what we are, and what we aspire to be. Simply grafting more technology on to today’s imbalanced system will not accomplish this.

Educational attainment explains two-thirds of the variation in economic success among metropolitan areas.

Each additional percentage point increase in the four-year college attainment rate increases metro per capita income by $1,500

We’re increasingly living in a globalized, knowledge based economy.  In that world, the single most important factor determining a region’s economic success is the education and skills of its population. If you’re concerned about urban economic development, the one thing you should be focusing on, laser-like, is educational attainment.  Raising a metro area’s educational attainment is the key to raising productivity, living standards and incomes. Our core metric for assessing the importance of a well-educated population is to look at the relationship between per capita incomes and the four-year college attainment rate, a relationship we call the “Talent Dividend.”

We’ve been tracking these data, and today we’re updating them to reflect the latest information from the Census Bureau’s just-released 2018 American Community Survey.  We’ve paired this information with the Bureau of Economic Analysis’ estimates of per capita income in each metropolitan area for 2018. The following chart plots the relationship between per capita personal income (on the vertical axis) and the fraction of the adult population who have completed at least a four-year college degree (on the horizontal axis).  Each dot on the chart represents one of the nation’s metropolitan areas with at least 1 million population (53 of them).  You can mouse-over a dot to see the corresponding metropolitan area and its educational attainment rate and per capita income.

There’s a strong, positive correlation between educational attainment and per capita income.  The metro areas with the highest levels of education have the highest levels of per capita personal income.  Cities like San Francisco, Boston and Washington have the highest levels of per capita income and the best-educated populations. Cities like Riverside and Las Vegas have low levels of educational attainment and correspondingly lower levels of per capita income. The coefficient of deterimination of the two variables–a statistical measure of the strength of the relationship–is .65, which suggests we can explain about two-thirds of the variation in per capita personal income among metropolitan areas, simply by knowing what fraction of their adult population has a four-year degree. Most cities lie close the to regression line; a few outliers have plausible explanations for their over or under performance. San Francisco and San Jose lie far above the regression line, and are super-charged (and expensive) high performers.  Raleigh and Austin have incomes lower than their educational attainment would predict, but also have populations that skew very young, and therefore have lower incomes.

This chart tells you the most important thing you need to know about urban economic development in the 21st century: if you want a successful economy, you have to have a talented population. Cities with low levels of educational attainment will find it difficult to enjoy higher incomes; cities with higher levels of educational attainment can expect greater prosperity. As Ed Glaeser succinctly puts it: “At the local level fundamentally the most important economic development strategy is to attract and train smart people.” And critically, because smart people are the most mobile, building the kind of city that people want to live in is a key for anchoring talent in place. And, importantly, the economic research shows that the benefits of higher educational attainment don’t just accrue to those with a better education: people with modest education levels have higher incomes and lower unemployment rates if they live in metro areas with higher average levels of education.

The data presented here imply that a 1 percentage point increase in the four-year college attainment rate is associated with about a $1,500 per year increase in average incomes in a metropolitan area, an increment we refer to as the Talent Dividend.  This cross-sectional relationship suggests that if a metropolitan area were to improve its educational attainment by one percentage point on a sustained basis, that it would see a significant increase in its income.

Over time, the strength of this relationship, and the size of the talent dividend effect has been increasing.  When we computed the relationship using 2010 data, the correlation coefficient was .60 and the size of the talent dividend was $860 (in current dollars).  These data suggest that educational attainment has become even more powerful in determining economic success than just a few years ago.

Education is a stronger predictor of economic success today than ever before. That’s true for individuals, for private businesses, for communities, and for metropolitan economies.  The better educated you are, the more likely you are to be prosperous in a knowledge-based economy. Not only do well-paid and fast growing technology jobs go disproportionately to the better educated, but better educated workers tend to be more adaptable and more innovative, which better prepares them to cope with a changing economy.  The policy lessons for city leaders are clear: a successful economy depends on doing a great job of educating your population, starting with your children, and also building a community that smart people will choose to live in.

Last week, the Washington Post reported that the U.S. District Court in Washington, D.C., has ordered new ridership projections for the proposed Purple Line light rail line, which will connect a series of Maryland suburbs. Like any multi-billion dollar project that serves a densely settled metropolitan area—and this one connects some of its wealthiest suburbs—there’s bound to be controversy. But today, we’ll ignore the substantive debate over the merits of the proposed alternative and focus instead on the technical issue of projecting future ridership on which this case turned.

The court’s decision was based on the fact that the state, and the FTA, have failed to update ridership projections since 2009. The plaintiffs argued that rail ridership on the Washington Metropolitan Area Transit Authority’s Metro system has declined every year since then, and that the system’s recent safety, budget, and operational woes are threatening to push ridership even lower.

The state of Maryland and the Federal Transit Administration, the Purple Line’s project sponsors, argued in response to the plaintiffs that because WMATA is a separate transit system, its ridership is not germane to assessing the environmental impacts of the Purple Line, which will be operated by the Maryland Transit Administration. The court rejected these claims, noting that more than a quarter of the expected riders of the Purple Line are also expected to use the WMATA Metro system.

U.S. District Court Judge Richard J. Leon determined that the failure to update the ridership forecasts in light of WMATA’s troubles was “arbitrary and capricious.” In his opinion, the two agencies were “cavalier” and failed to update ridership forecasts in the face of changed circumstances, which “raises serious concerns about their competence as stewards of nearly a billion dollars of federal taxpayer’s funds.”

The case is Friends of the Capital Crescent Trail v. FTA, Civil Case #14-01471. The decision was handed down on August 3. A copy of Judge Leon’s memorandum of opinion from August 3 is available here.

Maryland officials have said they’ll appeal the District Court’s ruling, so it’s not yet certain that this case will be the law of the land. But in our view, if this ruling stands, it has an important implication for transportation planning—especially the construction of highway expansion projects. The underlying principle here is that big investments ought to be based on forecasts—whether of future traffic or ridership—that fully reflect all the information we have at hand on how travel patterns are changing, and are likely to change in the future. The track record for highway traffic forecasts has, if anything, been far worse than the problems flagged for WMATA’s Metro.

Around the nation, state departments of transportation have routinely overestimated the growth of automobile traffic and used these exaggerated projections to justify billions of dollars of new roads. The State Smart Transportation Institute analyzed an aggregation of state traffic forecasts prepared annually by the USDOT. SSTI’s analysis showed that 20-year projections overestimated future traffic volumes in every single year states’ reports could be compared against data on actual miles driven by Americans.

As we and others have noted, there’s been a sea-change in American travel habits, influenced by changing gasoline prices and a generational change it attitudes toward travel and urban living. Yet many state forecasting models are still grounded in unexamined extrapolations of trends dating back to the 1990s. In Friends of the Capital Crescent Trail v. FTA, Judge Leon cited press reports on the impact of Metro safety problems on ridership. If courts take judicial notice of the contemporaneous developments that are materially changing travel patterns, we hope that they will cast a similar light on the abundant evidence about changing travel behavior —and insist that highway projects also fully consider these changing circumstances. There’s a precedent: As we reported last year, a federal court in Wisconsin struck down the approval of a highway project there based on flawed and outdated traffic projections.

A overwhelming amount of assumptions, inertia, and potential bias are buried in traffic projections. At a time of rapid shifts in travel behavior, markets, and technology, and especially in response to climate change, we should be especially wary of projects based on models that are opaque extrapolations of outdated trends. It’s a good thing that the National Environmental Policy Act requires project sponsors to take note of changing circumstances. Hopefully, as Judge Leon observed in his opinion, this ought to be simply a matter of common sense.

Educational attainment explains two-thirds of the variation in economic success among metropolitan areas.

Each additional percentage point increase in the four-year college attainment rate increases metro per capita income by $1,250

For a long time, we’ve been exponents of what we call “The Talent Dividend,” the idea that raising a metro area’s educational attainment is the key to raising productivity, living standards and incomes. Our core metric for assessing the importance of a well-educated population is to look at the relationship between per capita incomes and the four-year college attainment rate.

We’ve been tracking these data, and today we’re updating them to reflect the latest information from the Census Bureau’s just-released 2016 American Community Survey.  We’ve paired this information with the Bureau of Economic Analysis’ estimates of per capita income in each metropolitan area for 2016. The following chart plots the relationship between per capita personal income (on the vertical axis) and the fraction of the adult population who have completed at least a four-year college degree (on the horizontal axis).  Each dot on the chart represents one of the nation’s metropolitan areas with at least 1 million population (53 of them, according to the 2015 Census tabulations).  You can mouse-over a dot to see the corresponding metropolitan area and its educational attainment rate and per capita income.

As you’ll immediately notice, there’s a strong, positive correlation between educational attainment and per capita income.  The metro areas with the highest levels of education have the highest levels of per capita personal income.  Cities like San Francisco, Boston and Washington have the highest levels of per capita income and the best-educated populations. Cities like Riverside and Las Vegas have low levels of educational attainment and correspondingly lower levels of per capita income. The coefficient of deterimination of the two variables–a statistical measure of the strength of the relationship–is .67, which suggests we can explain two-thirds of the variation in per capita personal income among metropolitan areas, simply by knowing what fraction of their adult population has a four-year degree. Most cities lie close the to regression line; a few outliers have plausible explanations for their over or under performance. San Francisco and San Jose lie far above the regression line, and are super-charged (and expensive) high performers.  Raleigh and Austin have incomes lower than their educational attainment would predict, but also have populations that skew very young, and therefore have lower incomes.

This chart tells you the most important thing you need to know about urban economic development in the 21st century: if you want a successful economy, you have to have a talented population. Cities with low levels of educational attainment will find it difficult to enjoy higher incomes; cities with higher levels of educational attainment can expect greater prosperity. As Ed Glaeser succinctly puts it: “At the local level fundamentally the most important economic development strategy is to attract and train smart people.” And critically, because smart people are the most mobile, building the kind of city that people want to live in is a key for anchoring talent in place. And, importantly, the economic research shows that the benefits of higher educational attainment don’t just accrue to those with a better education: people with modest education levels have higher incomes and lower unemployment rates if they live in metro areas with higher average levels of education.

The data presented here imply that a 1 percentage point increase in the four-year college attainment rate is associated with about a $1,250 per year increase in average incomes in a metropolitan area, an increment we refer to as the Talent Dividend.  This cross-sectional relationship suggests that if a metropolitan area were to improve its educational attainment by one percentage point on a sustained basis, that it would see a significant increase in its income.

Over time, the strength of this relationship, and the size of the talent dividend effect has been increasing.  When we computed the relationship using 2010 data, the correlation coefficient was .60 and the size of the talent dividend was $860 (in current dollars).  These data suggest that educational attainment has become even more powerful in determining economic success than just a few years ago.

Education is a stronger predictor of economic success today than ever before. That’s true for individuals, for private businesses, for communities, and for metropolitan economies.  The better educated you are, the more likely you are to be prosperous in a knowledge-based economy. Not only do well-paid and fast growing technology jobs go disproportionately to the better educated, but better educated workers tend to be more adaptable and more innovative, which better prepares them to cope with a changing economy.  The policy lessons for city leaders are clear: a successful economy depends on doing a great job of educating your population, starting with your children, and also building a community that smart people will choose to live in.

The biggest single factor determining the success of a city’s economy is how well-educated is its population. As the global economy has shifted to knowledge-based industries, the jobs with the best pay have increasingly gone to those with the highest levels of education and skill.

For a long time, we’ve been talking about the talent dividend–how much an area’s college attainment rate is correlated with its per capita income. Since its such an important touchstone for policy, we think its worth taking a close look at what the data say about the strength and importance of this relationship.

Today, we’ve pulled together the latest metro area data–for 2014–from the Census Bureau (on educational attainment) and from the Bureau of Economic Analysis (on per capita income).  The following chart plots the relationship between per capita personal income (on the vertical axis) and the fraction of the adult population who have completed at least a four-year college degree (on the horizontal axis).  Each dot on the chart represents one of the nation’s metropolitan areas with at least 1 million population (53 of them, according to the 2014 Census tabulations).  You can mouse-over a dot to see the corresponding metropolitan area and its educational attainment rate and per capita income.

As you’ll immediately notice, there’s a strong, positive correlation between educational attainment and per capita income.  The metro areas with the highest levels of education have the highest levels of per capita personal income.  Cities like San Francisco, Boston and Washington have the highest levels of per capita income and the best-educated populations. Cities like Riverside and Las Vegas have low levels of educational attainment and correspondingly lower levels of per capita income. The coefficient of deterimination of the two variables–a statistical measure of the strength of the relationship–is .67, which suggests we can explain two-thirds of the variation in per capita personal income among metropolitan areas, simply by knowing what fraction of their adult population has a four-year degree.

This chart tells you the most important thing you need to know about urban economic development in the 21st century: if you want a successful economy, you have to have a talented population. Cities with low levels of educational attainment will find it difficult to enjoy higher incomes; cities with higher levels of educational attainment can expect greater prosperity. As Ed Glaeser succinctly puts it: “At the local level fundamentally the most important economic development strategy is to attract and train smart people.” And critically, because smart people are the most mobile, building the kind of city that people want to live in is a key for anchoring talent in place. And, importantly, the economic research shows that the benefits of higher educational attainment don’t just accrue to those with a better education: people with modest education levels have higher incomes and lower unemployment rates if they live in metro areas with higher average levels of education.

The data presented here imply that a 1 percentage point increase in the four-year college attainment rate is associated with about a $1,100 per year increase in average incomes in a metropolitan area.  This is what we call the Talent Dividend.  This cross-sectional relationship suggests that if a metropolitan area were to improve its educational attainment by one percentage point on a sustained basis, that it would see a significant increase in its income.

Over time, the strength of this relationship, and the size of the talent dividend effect has been increasing.  When we computed the relationship using 2010 data, the correlation coefficient was .60 and the size of the talent dividend was $860 (in current dollars).  These data suggest that educational attainment has become even more important in determining economic success than just a few years ago.

We’ve also mapped the metro level data on the talent dividend relationship.  On this map, the color of each circle corresponds to a metropolitan area’s level of educational attainment (red circles have lower than average educational attainment among these metros, blue circles higher educational attainment).  The size of circles is proportional to an area’s per capita income; larger circles indicate higher per capita income.  (You can mouse over any metro area to see the educational attainment and per capita income figures for that metro area).

Education is a stronger predictor of economic success today than ever before. That’s true for individuals, for private businesses, for communities, and for metropolitan economies.  The better educated you are, the more likely you are to be prosperous in a knowledge-based economy. Not only do well-paid and fast growing technology jobs go disproportionately to the better educated, but better educated workers tend to be more adaptable and more innovative, which better prepares them to cope with a changing economy.  The policy lessons for city leaders are clear: a successful economy depends on doing a great job of educating your population, starting with your children, and also building a community that smart people will choose to live in.

Readers of the Aug. 19 Week Observed: here’s the piece you’re looking for.

Here’s a puzzle: If 89 percent of Apple’s ideas are invented in the U.S., why is 92 percent of its profit overseas?

The link between local economies and tax bases has long been obvious and physical. Companies paid property taxes on their buildings and equipment, people and businesses paid sales taxes on their purchases, and employers and employees paid taxes on their income. But the economy is changing, and the link between economic activity and taxes has changed.

Perhaps no products are a better examples of this trend than Apple’s iPhone and MacBookAir laptop computer. If you look at the polished aluminum backside of any Air, you’ll find an emblazoned slogan: “Designed by Apple in California.” That imprint has a strong basis in fact.

Between 2011 and 2015, Apple got 7,527 patents worldwide, according to the U.S. Patent and Trademark Office. Of these, 6,716 were in the United States, and 6,143 were in California.

The next line on the back of the MacBookAir is “Assembled in China.” Stories of the globalization of production of high-tech devices abound. Many reports have chronicled the conditions of the tens of thousands of workers employed by contractors like China’s FoxConn, which produce all manner of technological devices. But the labor and even the components in most tech devices reflect only a small portion of their value. The cost and profitability of devices stems not from their physical elements or the labor used to assemble them, but from the superior software and design features—or, the intellectual property generated by Apple.

As The Economist explains, “Apple, it’s worth pointing out, continues to capture most of the value added in its products. The most valuable aspects of an iPhone, for instance, are its initial design and engineering, which are done in America.” InformationWeek estimates, “The cost of parts and labor to assemble an iPhone 6S, for example, cost an estimated $160, compared to a selling price of $399.”

However, even though Apple does 90 percent of its creative work in the United States, it pays very little in U.S. taxes. Instead, Apple attributes a disproportionate share of its earnings to the Republic of Ireland. Nobel laureate and former World Bank economist Joseph Stiglitz has called Apple’s accounting “fraudulent,” telling Bloomberg:

“Here we have the largest corporation in capitalization not only in America, but in the world, bigger than GM was at its peak, and claiming that most of its profits originate from about a few hundred people working in Ireland—that’s a fraud.”

How does Apple do it? The exact mechanics are hidden in the company’s tax filings in the U.S. and Ireland, but the basic mechanism at work here is transfer pricing. Apple has transferred ownership of its intellectual property (its patents and software designs, and perhaps even its brand and logo) to its overseas subsidiaries. Obviously, Ireland is not a major center for Apple’s technology development. According to USPTO, between 2011 and 2015, Apple received exactly zero patents for work performed in Ireland. By contrast, at least some other U.S. technology companies, including IBM, Intel, Microsoft, and Google, all reported Irish patents.

Nonetheless, Apple’s operations in the U.S., and throughout the rest of the world, make payments (including royalties and purchases) to foreign affiliates in, say, Dublin, in to make use of the intellectual property. The foreign affiliate records its income and pays any applicable taxes in its low-tax or no-tax jurisdictions.

This strategy shifts profits from the United States, where they might be taxed at the high statutory 35 percent corporate tax rate, to places like Ireland, where taxes are much lower and can be deferred. As a result, Apple’s profits have piled up overseas. Today, $215 billion of Apple’s $232 billion cash hoard is located outside the United States.

Apple is hardly alone in pursuing such strategies to avoid U.S. taxes. Facebook is facing an IRS claim for billions in back taxes for a single year, based on its strategy of shifting its intellectual property to the Cayman Islands.

Because so many companies are doing this, the cumulative reduction in American taxes paid by American corporations is significantly reducing federal revenue. The most comprehensive analysis of tax avoidance is by University of California’s Gabriel Zucman, author of The Hidden Wealth of Nations. Zucman estimates that these tax avoidance tactics cost the U.S. Treasury $130 billion annually in lost revenue.

A bizarre side effect of these tax-avoiding machinations showed up last month in the form of a breathtaking report that in 2015, the Gross Domestic Product of Ireland had increased an astonishing 26.3 percent over the prior year. This Irish miracle, as it turns out, has almost nothing to do with local economic activity and everything to do with the nation’s tax haven status, which artificially inflates the size of Ireland’s measured GDP.

The economy is increasingly dominated by global, knowledge-based companies whose profits are largely attributable to their intellectual property. The current system of taxation rewards Apple, Google, Facebook, and others for offshoring their patents, brands, or proprietary designs.

While the primary implications are for the U.S. Treasury, it’s also likely that shifting taxable income outside of the United States shortchanges states and cities as well. Most state tax systems piggyback on important aspects of the federal system, and state revenue departments are even more hamstrung in dealing with the high-powered lawyers and accountants of global firms than is the IRS.

As more sectors of the economy are disrupted by big firms using brands and patents, it may be harder for states and cities to collect taxes. This could also put smaller local firms at a competitive disadvantage. A city or state may have the legal ability to collect taxes from a local coffee shop, a bookstore, or a taxi company, but if a Starbucks, an Amazon, or an Uber attributes its profits to foreign domiciled intellectual property, that’s likely to be beyond the understanding, much less the reach, of local tax collectors.

This is a global problems that affects local economies and local governments, but it’s almost entirely beyond their ability to influence. At some point national governments—and international cooperation—will be required to establish clear ground rules and a level playing field in a world increasingly characterized by global, knowledge-based competition.

Just say the words “urban renewal” and you immediately conjure up images of whole neighborhoods–usually populated by poor families and people of color being dislocated by big new publicly funded development projects. It seems like a relic of the past.  But it appears to be getting a new lease on life in the suburbs. For a couple of years now, we’ve been following the story of Marietta, Georgia, where local officials used $65 million in taxpayer funds to buy up and begin demolishing some 1,300 apartments along Franklin Road. This is a striking case where the displacement of low income families was an explicit objective of public policy, rather than the side-effect of a changing real estate market. The tale raising some interesting questions about how we talk about neighborhood change, and whether we’re really open to economic integration in all places–city and suburb.

In a few weeks, the new Atlanta United franchise of Major League Soccer will kick off its inaugural season. The team’s been preparing at the verdant practice facility in the Atlanta suburbs.

What isn’t apparent from this picture is the fact that hundreds of old but serviceable apartments were demolished to make way for these spacious fields. The Marietta apartment complexes had been built in the 1960s, and, when new, were a preferred upscale location for single professionals and young married couples. Over the decades, the apartments aged and the mix of occupants changed. Franklin Road shifted to families with increasingly modest incomes—a process housing economists call filtering, which is the primary source of affordable housing throughout the nation. Along the way, the economic and and racial makeup of the apartments transformed from nearly 90 percent white in 1980, with a poverty rate around five percent, to 20 percent white in 2010, with a poverty rate of nearly 25 percent.

Despite the usefulness of filtering, which increased the diversity of suburban Marietta, the city perceived these units as growing concentrations of poverty and, thus, a problem. So it used the proceeds of a voter-approved bond measure to purchase and begin demolishing the housing complexes. It’s worth noting that no one ever claimed that the buildings themselves were a problem, despite their age. Rather, it had everything to do with the demographics of their occupants. In all, the city’s plan calls for acquiring and demolishing almost 10 percent of the city’s multi-family housing stock.

Caution:  This post contains graphic images of housing displacement. Viewer discretion is advised.

Marietta’s plan is proceeding apace. The city re-christened Franklin Road as “Franklin Gateway” to signal change. It began demolishing the apartments last year. Here, we’ve used imagery from Google Maps show what’s happened where one of these complexes—the Woodland Park Apartments—once stood.  This is what they looked like in 2011.

Early this year, the demolition was nearly complete. All that remains of  the old apartment complex is its driveway, a partial brick wall and metal gate, and two patches of evergreen shrubbery, flanked by a stand of pine trees.

The latest Google imagery shows the city’s unfolding plan for development. The site will become the training facility for Atlanta United, the area’s new major league soccer team. For use of the 32 acres formerly covered by apartments, the team will pay the city $1 per year for the first ten years of a thirty-year lease.

The demolition of the apartments on Franklin Road represents a kind of national blind spot when it comes to talking about neighborhood change. In any large city—say New York, Los Angeles or Washington—the wholesale demolition of affordable housing to provide discounted land for new businesses would undoubtedly be treated as the most pernicious form of gentrification. But because it happens in a suburb, somehow it doesn’t count, or at least isn’t objectionable.

Perhaps this reflects a deeply ingrained but seldom-voiced bias in our views about place: Suburbs are for rich, mostly white people. Cities are for poorer people and people of color. Anything change that runs counter to this worldview (like gentrification of a Brooklyn neighborhood, or efforts to build affordable apartments in suburbs like Marin County) is an affront to the order of things. The apparent prevalence of this outlook shows just how hard it will be to make progress on economic integration.

Last summer, we told here the story of Marietta, Georgia, where local officials used $65 million in taxpayer funds to buy up and begin demolishing some 1,300 apartments along Franklin Road. This is a striking case where the displacement of low income families was an explicit objective of public policy, rather than the side-effect of a changing real estate market. The tale raising some interesting questions about how we talk about neighborhood change, and whether we’re really open to economic integration in all places–city and suburb.

The Marietta apartment complexes had been built in the 1960s, and, when new, were a preferred upscale location for single professionals and young married couples. Over the decades, the apartments aged and the mix of occupants changed. Franklin Road shifted to families with increasingly modest incomes—a process housing economists call filtering, which is the primary source of affordable housing throughout the nation. Along the way, the economic and and racial makeup of the apartments transformed from nearly 90 percent white in 1980, with a poverty rate around five percent, to 20 percent white in 2010, with a poverty rate of nearly 25 percent.

Despite the usefulness of filtering, which increased the diversity of suburban Marietta, the city perceived these units as growing concentrations of poverty and, thus, a problem. So it used the proceeds of a voter-approved bond measure to purchase and begin demolishing the housing complexes. It’s worth noting that no one ever claimed that the buildings themselves were a problem, despite their age. Rather, it had everything to do with the demographics of their occupants. In all, the city’s plan calls for acquiring and demolishing almost 10 percent of the city’s multi-family housing stock.

Caution:  This post contains graphic images of housing displacement. Viewer discretion is advised.

Marietta’s plan is proceeding apace. The city re-christened Franklin Road as “Franklin Gateway” to signal change. It began demolishing the apartments last year. Here, we’ve used imagery from Google Maps show what’s happened where one of these complexes—the Woodland Park Apartments—once stood.  This is what they looked like in 2011.

Early this year, the demolition was nearly complete. All that remains of  the old apartment complex is its driveway, a partial brick wall and metal gate, and two patches of evergreen shrubbery, flanked by a stand of pine trees.

The latest Google imagery shows the city’s unfolding plan for development. The site will become the training facility for Atlanta United, the area’s new major league soccer team. For use of the 32 acres formerly covered by apartments, the team will pay the city $1 per year for the first ten years of a thirty-year lease.

The demolition of the apartments on Franklin Road represents a kind of national blind spot when it comes to talking about neighborhood change. In any large city—say New York, Los Angeles or Washington—the wholesale demolition of affordable housing to provide discounted land for new businesses would undoubtedly be treated as the most pernicious form of gentrification. But because it happens in a suburb, somehow it doesn’t count, or at least isn’t objectionable.

Perhaps this reflects a deeply ingrained but seldom-voiced bias in our views about place: Suburbs are for rich, mostly white people. Cities are for poorer people and people of color. Anything change that runs counter to this worldview (like gentrification of a Brooklyn neighborhood, or efforts to build affordable apartments in suburbs like Marin County) is an affront to the order of things. The apparent prevalence of this outlook shows just how hard it will be to make progress on economic integration.

Back in 2009, in the darkest days of the Great Recession, Federal Reserve Chair Ben Bernanke attempted to reverse the economic pessimism that gripped the nation. He pointed to what he called “green shoots,” small bits of good news around the country. To him, the green shoots showed that the economy was turning around, the economic winter was ending, and spring was around the corner. Bernanke was right. That year, job losses slowed, then stopped, and the economy began growing again.

Today, we turn our attention to Detroit to look for evidence of green shoots in its economy. Detroit and its long period of wrenching industrial change and urban decline has long been the locus of conversation. More recently, the media has emphasized efforts to revive Detroit. The city has embarked on the construction of a downtown light rail line. Quicken Loans conspicuously moved its headquarters downtown in 2010 and has steadily expanded its footprint. In the past few months, a Niketown has opened and a Shake Shack has been announced. These are visible signifiers of progress and renewal.

At City Observatory, we judge the success of revitalization efforts based on whether they move the needle of key economic statistics. And there’s growing evidence based on our analysis of employment statistics for Wayne County (which encompasses the city) that things are changing in a positive direction in Detroit, confirming the anecdotal signs on its streets.

A core measure of economic growth is the number of jobs in the local economy. By that standard, the late 1990s and the early 2000s were an unrelenting tide of bad news for Detroit. Between 2001 and 2010, Detroit lost more than 200,000 jobs. Total payroll employment declined by 24 percent. Even though the national economy and employment expanded for most of the decade, employment in Detroit declined every year between 2001 and 2010. The Great Recession simply amplified these job losses.

During this time, the city was in dire straights, both politically and financially: Its Mayor Kwame Kilpatrick had been removed from office in a scandal, the city’s financial administration had been turned over to a state-appointed administrator, and in 2013, the city entered the nation’s largest municipal bankruptcy.

But since 2010, Detroit’s economy has turned around. Employment totals for Wayne County bottomed out at about 690,000 jobs, then started growing again. Detroit has recorded year-on-year increases in employment every year since then, and is continuing to do so in 2016. Today the city has 50,000 more jobs than it did in the depths of the recession.

There’s encouraging news, too, in the kind of growth that’s occurring. The details of Detroit employment growth point to a small rebound in the traditional manufacturing sector, as well as some much-welcomed growth in other knowledge-based sectors of the economy.

The industry detail shows that some sectors of the Detroit economy (Wayne County in particular) are now performing better than their counterpart industries nationally over the past year, March 2015-2016. For example, while manufacturing employment has slipped nationally, it’s up nearly 1 percent in Detroit over the same time period—positive news after so many years of decline. Information industries and professional and business services, two stalwarts of the knowledge economy, have both recorded faster job growth in Detroit than the country overall in the past year. And financial services employment has grown at a 7.7 percent clip, four times faster than in the nation as a whole.

Still, not everything’s rosy: Wayne County is growing more slowly (+1.4 percent) than the surrounding suburban counties (+2.7 percent) and the nation as a whole (+2.0 percent). Some of this reflects continuing, difficult adjustments. For example, government employment is down 1.0 percent in Detroit, and up about 0.5 percent nationally.

Altogether, the employment data provides hopeful signs that the Detroit economy has put its worst economic days in the rearview mirror, and is starting to build a stronger economic future. Some of this undoubtedly has to do with the national economic recovery. However, it’s important to remember that during the last national economic growth cycle (2001 to 2008), Detroit was actually losing jobs when the nation was gaining them—evidence of the region’s structural economic problems. Even though the city and region have a long way to go, they are now headed in the right direction.

About the data

Please note that for this analysis, we use federal data for Wayne County, Michigan, which is centered on Detroit and includes the adjacent cities of Dearborn and Livonia, but does not include the surrounding suburban counties that make up the balance of the Detroit metropolitan area. The Bureau of Labor Statistics, which compiles these data, doesn’t compile monthly or annual city level statistics that would let us track these changes. These data are BLS Series ID SMS26198040000000001, and are available at from the BLS website.

Making a market for shared mobility services

Yesterday, we urged cities to think hard about how they can craft the rules for the transportation network companies that offer “ride sharing” systems to maximize competition, and encourage innovation and low prices.  “Let a thousand Ubers bloom,” we said.

The rules and regulations that cities set for ride sharing — everything from taxes, to accessibility requirements, to safety measures (like fingerprinting drivers) — can have an impact on whether ride sharing ultimately becomes an effective monopoly or cozy duopoly that is profitable for firms but offers limited choices for riders, or whether there’s an open system where new entrants can continually be as disruptive to incumbents as Uber and Lyft currently are to inefficient taxi systems.

Here’s our initial list of things cities should be considering to encourage a competitive, dynamic marketplace for ride sharing services.

1. Assure ride sharing information is open and accessible. Ride share operators make their money using a scarce and expensive public asset: streets. They should be required to provide information about the trips they take, the areas they serve, and the fares they charge so that the public can understand how ridesharing affects the community—for good and for ill. Disclosing information on fares and service can also encourage competition, enabling comparisons between cities to see if consumers are getting a good deal. It can also provide a basis for deterring anti-competitive practices, like price discrimination. Boston and San Francisco have negotiated agreements with Uber to provide data on trip origins and destinations.
2. Set some basic ground rules, including certification for drivers. If there are some minimal standards that assure rider safety and transparent information and pricing, consumers may be more willing to try smaller and newer firms. One possible reason that Uber and Lyft opposed fingerprinting drivers was that it diluted the value of their brand reputation by leveling the playing field, assuring that all persons using a ride sharing service were driven by similarly vetted drivers.
3. Encourage bidding for travel. We’re all used to using services like Kayak to shop for the best airfares or bidding on eBay in real time auctions. Uber and Lyft act as price-setters, dictating a single price for trips, regardless of who’s traveling or who’s providing the ride. They then allocate drivers to riders and riders to drivers. It’s possible to imagine a more free-wheeling auction system, where riders could bid for trips, and drivers could compete to provide service. Such services might help make it easier for smaller rivals to break into the market, and would give customers more leverage.
4. Don’t inadvertently privilege large size with regulatory set ups. Virtually any regulatory requirement will impose a higher burden on small firms and startups than on larger, established businesses.  For example, requirements that all ride share operators offer a certain number or proportion of wheelchair-accessible vehicles. A provision that assesses a fee to cover the cost of these services—or gives operators the option of paying a fee in lieu of equipping vehicles, would lower the barriers to entry.
5. Insist on multiple options when integrating ride sharing with public transit. It’s increasingly apparent that ride sharing services could be a logical complement to fixed route transit in lower density locations and during off peak hours.  Transit operators should resist the temptation to enter into exclusive deals with a single ride share provider. Public transit is, in most places, a monopoly—but it’s under public control, and subject to a fair degree of scrutiny.
6. Restrict or prohibit fare and driver compensation schemes that lock users into a single service. In Boston, Uber is offering 1 cent rides on UberPool—provided you buy a $40 monthly pass. While undoubtedly a money loser, this UberPool pricing model effectively discourages those who sign up from using alternative providers. Similarly, Uber and Lyft frequently offer much more favorable compensation to drivers who drive full time for only one service or the other: again, the idea being to lock drivers into one service, and reduce their competitors’ market share. If drivers truly operate as “independent contractors,” they should be free to secure business through any network, and to set their own prices.

Recently, we profiled Paul Romer, who has just been appointed as the Chief Economist for the World Bank.  One of the key insights of his New Growth Theory is that we ought to be open to a range of different institutional set-ups in order to encourage experimentation and promote economic growth.  That’s exactly the attitude we ought to bring to ride sharing.  Having different cities around the country develop various ways of regulating this industry is likely to prompt a range of different business models and a faster pace of innovation.

While hardly an exhaustive list, these six ideas illustrate the important ways that setting the rules of the game for the ride sharing industry are likely to influence competition, innovation and customer choice.  Ride sharing and urban transportation are evolving quickly; we should aim to build on this momentum.

Today we’re getting really wonky. Paul Romer, who’s currently at New York University’s Marron Institute has just been appointed to be the chief economist for the World Bank. Personnel decisions involving technocratic positions at global NGOs is about as wonky as it gets, of course. But this is a genuinely interesting development, especially if you’re passionate about cities, and economies, as we are.

First, an introduction:  Romer is a prolific and wide-ranging economist.  He’s most famous for his work in creating what’s come to be called “New Growth Theory” (NGT).  It bears a much longer and more precise description, but briefly, NGT focuses on the critical role that creating new ideas plays in driving long term economic growth.  The reason we become more prosperous over time, is not because we accumulate more stuff, but because we continuously generate new and better ways of making use of the finite materials around us.  A critical property of ideas are that they are “non-rival”–you and I can equally make use of an idea without diminishing its utility to one another.  Romer pointed out that non-rivalry is crucial for driving growth, and for some pretty technical reasons, it also means that unfettered free markets can’t automatically generate the conditions that produce long term growth.  As a result, the kinds of institutional arrangements we create, both nationally and locally, are very important to whether we experience growth or not.

New Growth Theory has an important implication for cities.  Cities are, as Jane Jacobs argued decades ago, the crucibles and laboratories where new ideas — what Jacobs called “new work” gets created.  The combination of a diverse population, frequent interaction, and the right set of rules or institutions is what makes economies grow–and these forces play out most dramatically in cities.  (For a much longer explanation of NGT and its policy implications you can read a report I wrote for the US Economic Development Administration).

Romer explained this all in a quite accessible article written for the World Bank 25 year ago, entitled “Two Strategies for Economic Development:  Using Ideas and Producing Ideas.”  This article demonstrates Romer’s keen ability to translate complex economic arguments into simple and powerful metaphors.  He illustrates the difference between traditional views of economic growth and the knowledge-driven growth of new growth theory by contrasting two child’s toys.  The conventional model is the Play-Dough Fun Factory.  It combines capital (the plastic press and dies) with labor (a child’s arm) and raw materials (clay) to produce tubes, I-beams, and other shapes.  This model (and the very math-ey versions of it used by economists) are good for thinking about production efficiency and allocation, but don’t help much to explain how growth happens. 

In contrast, New Growth Theory visualizes the growth process much as if it were a child’s chemistry set.  It turns out that there are so many different possible combinations of even a few handfuls of ordinary chemicals, that its simply impossible for a manufacturer to verify that all of the ways they might be mixed would turn out not to be hazardous or explosive (which for many children is the chief motivation for playing with chemicals).  That’s a downside for chemical companies, their risk analysts, attorney and insurance companies, but its got a surprisingly optimistic implication for long run economic growth.

The point is that prosperity is driven by the nearly inexhaustible opportunities to create new ideas–new combinations of things–that produce useful products and services.  The trick is figuring out the kinds of institutional arrangements that will prompt people to undertake the experiments that will generate these ideas. This has a critical implication for cities, as Romer explains:

“As the world becomes more and more closely integrated, the feature that will increasingly differentiate one geographic area (city or country) from another will be the quality of public institutions. The most successful areas will be the ones with the most competent and effective mechanisms for supporting collective interests, especially in the production of new ideas.”

In recent years, Romer has been a strong advocate of cities, and has pointed out the direct relationship between urbanization and economic and productivity growth.  Across countries, within countries, and over time, a higher degree of urbanization is strongly correlated with greater economic output.

The question going forward is what we might do to harness the growth potential of cities as places that offer new ways to do things and develop ideas.  He proposed the idea of “Charter Cities” — de novo city-states that would experiment with new institutional arrangements, looking to generate the kind of growth that we’ve seen in places like Singapore, Hong Kong and Shenzen.  An abortive attempt to do actually try this out in Honduras actually died stillborn–for reasons that illustrate what it will take to really make such a proposal work.  

More recently, he’s also made the case that creating new cities would be one of the ways that Europe might better respond to its refugee crisis:  HIs argument, in a nutshell:

1.  It takes only a few cities, on very little land, to accommodate tens or hundreds of millions of people.

2. Building cities does not take charity. A city is worth far more than it costs to build.

3. To build a city, do not copy Field of Dreams. (“Build it and they will come.”) Copy Burning Man. (“Let them come, and they will build it.”)

The World Bank is the dispenser, not just of billions of dollars in loans for less developed nations, but is also the dispenser of the the conventional wisdom, especially when it comes to cutting edge development strategies.  Its notions about the processes and strategies that can stimulate economic growth have immediate, practical and widespread implications.

Romer is a brilliant and original thinker, and is an economist who is willing to fully explore the policy implications of his theoretical work, and regularly comes up with ideas that make us look differently at the world.  He’s somebody who sees cities at the center of the solution to many of the globe’s most pressing problems. We’re excited to see what he does at this new job.

Suddenly, we’re awash in calculators. Housing calculators.

If you’re a Baby Boomer, you remember the day you saw your first electronic calculator. It had an electronic display–red or green light-emitting diode segments, usually eight or ten of them that would display numbers, arithmetic operators and a decimal point. They had a few hard-to-press chicklet type keys, but they would add, subtract, multiply and divide with a speed and accuracy that was previously unavailable. Precise math suddenly became easier. (And if you typed in 07734 and turned it upside down it looked like is was saying “hELL0.”)

In the past few months, we’ve seen the advent of a new generation of calculators–housing calculators, aimed at helping us understand the complex dynamics of financing and affording housing. Like the early days of the electronic pocket calculator, there are a lot of competing brands and different designs. Each of these calculators looks at the interplay of different factors that influence the feasibility of building new housing, embracing a range of purely private sector considerations (construction costs, interest rates, rents) and some public policies as well (inclusionary zoning, parking requirements, height limits, planning processes). All are designed as generalized “what if” models, and specifically disclaim their use for investment purposes.

The latest of these is the Urban Institute’s new “Affordable Housing: Does it Pencil Out” website, released today. In theory, these tools ought to give us a clearer picture of the factors influencing housing affordability and how we might make some progress in tackling this problem. Here’s a quick thumbnail of it, and three other examples of the genre.

Urban Institute: Does it Pencil Out?

The Urban Institute’s calculator estimates construction costs and rents for apartments built in Denver, which it characterizes as a fairly typical metropolitan area. You are given the choice of modeling a 50 or 100 unit apartment building, and you can see how varying the level of rent charged and some key development costs (like interest rates, land costs, construction costs, and operating costs) influence the profitability of a proposed development. The site’s key conclusion: It’s very difficult to build housing that’s affordable to anyone below 100 percent of area median incomes without some sort of subsidy.

Terner Center, UC Berkeley: Will Housing be Built

The Terner Center’s calculator takes a slightly different approach than the other calculators presented here, and as its name suggests, offers up its estimates of the probability that a particular housing development will go forward under different assumptions about financing, affordability requirements, rents, construction costs and approval processes. It’s calibrated using data from Oakland California. The Terner Center model has a rich set of controls that let you explore the impacts of varying inclusionary zoning requirements, parking requirements, and uniquely, the impact of a more attenuated approval process.

Cornerstone Partnership: Inclusionary Calculator

Cornerstone Partnership, a housing advocacy network, has created its own tool which lets the user select the number of units to build, construction costs, the cost of land, parking requirements, interest rates and rents, and other variables. The model then estimates the total cost of the project and whether it is profitable. Projects that generate more than a ten percent rate of return for the investor are judged feasible. Unlike the other calculators presented here, you have to register with the website to use this calculator.

Citizen’s Housing and Planning Council (NY): Inside the Rent

This calculator, built for New York City, allows the user to see the factors that influence the rental cost of new apartment construction in different neighborhoods in New York City. Between land, construction, soft costs and financing, new apartments in a mid-rise building in a typical neighborhood have a sticker price of around $500,000; and unsubsidized rents for these newly constructed units run at more than $4,000 a month. A unique feature of this calculator is its effort to estimate the cost of paying prevailing wages for construction and upkeep.

Some thoughts on the state of the art in housing calculators

While billed as calculators or tools, each of these is actually a gussied-up, html-coded quantitative model. Like all models, each is only as good as the assumptions that it’s based on. An ideal model is transparent about what its assumptions are, and enables the user to test those assumptions. But frequently models–especially complex models–make it difficult know exactly which assumptions are driving its conclusions. Different modelers will choose different assumptions–which may be buried deep in a model’s structure–which may unfortunately conceal biases.

These calculators have some similarities: They let you vary key financial parameters, like the price of land, rent levels, interest rates, construction costs, the amount of affordable housing included, and the extent of the public subsidy. They’re particularly useful for exploring the tradeoffs and costs of different policies; parking requirements and construction delays can move an otherwise likely and feasible development into the risky or unprofitable categories.

What’s difficult — and maddening, though not surprising — is how difficult it is to compare the results of the different calculators. They use varying terminology and definitions, and seem to have a wide range of assumptions. They are individually complex, and produce results that are framed differently, so that one can’t easily say how two calculators would appraise a project with the same inputs.

For example, the Urban Institute’s Will it Pencil and Cornerstone’s Inclusionary Calculator seem to produce very different messages about housing affordability. The authors of Cornerstone Partnership model use it to support their claim that developers can profitably build additional units of affordable housing with modest or no subsidies while remaining profitable. CityLab summarized the model’s conclusions as: “A new tool shows that developers can profit by building affordable housing almost anywhere.” In contrast, the authors of the Urban Institute’s model essentially say that it’s unprofitable to build any amount of affordable housing without substantial subsidies. They say: “Without the help of too-scarce government subsidies for creating, preserving, and operating affordable apartments, building these homes is often impossible.”  It’s not immediately apparent from looking at the two calculators which of these conclusions is the most accurate.

What’s needed here, is a kind of Consumer’s Guide to housing calculators. It would be useful, for example, if we had a standardized “benchmark” development: a certain number of units, and certain land cost, rent level and other parameters, than could be plugged into each model, and then we could see what kind of outputs each model produced for the same development.

The best that can be said at this point is that while the calculators we have don’t definitively answer the question, they do a good job of framing the variables that we need to pay attention to in discussing affordability. They’re also helpful in exploring the tradeoffs between policy objectives, for example, how increased parking requirements or longer approval processes lower the likelihood that housing projects will move forward. These calculators are in their infancy–two of them are self-described “betas”–and we hope that the people who’ve built them continue to develop and refine them, and research and debate the assumptions on which they’re based.

It’s an article of faith in some quarters—well, most quarters—that in the United States, owning a home ought to be a surefire way to build wealth. Whether it’s presidents, anti-poverty groups, foundations, or realtors, we’re always being told that that homeownership is the foundation of the American dream, and a key way secure one’s financial future.

For a long time, it certainly worked out that way for a lot of households. Real home prices in the US outstripped inflation by a wide margin. Home equity rose. If you bought a house in the 1960s, pretty much anywhere in the US, it was worth a lot more two or three decades later. Collectively, and after adjusting for inflation, the real value of home equity owned by US households increased from 1 trillion in 1960 to 2 trillion in 1975 to 8 trillion before finally peaking at more than 14 trillion in 2006.

Because of this record, we’re told that promoting more widespread homeownership is an effective way to lift low-income families out of poverty, and to help racial and ethnic minorities—who’ve long had lower than average rates of homeownership, to build wealth.

The implication is that the 40 percent or so of American households that don’t now own homes would be better off, all things equal, if they were able to buy a home. But as the standard investment disclaimer goes, “past performance is no guarantee of future results.” That warning is especially true for low income households and minorities who are now renters.

Why Homeownership is Risky for Low Income Households

Housing can be a good investment if you buy at the right time, buy in the right place, get a fair deal on financing, and aren’t excessively vulnerable to market swings. Unfortunately the market for home-ownership is structured in such a way as to assure that low-income and minority buyers meet none of these conditions. For these Americans, there’s no guarantee that homeownership builds wealth; in fact, tends to be a risky proposition that often produces financial hardship.

First, you have to buy at the right time. The old adage is to buy low and sell high. Jordan Rappaport of the Kansas City Federal Reserve Bank estimates that buying has outperformed renting on a financial basis only about half the time since the 1970s; so those who buy during the  “wrong” half stand to be worse off. And low-income and minority buyers tend to be disproportionately drawn into the market at these “wrong” times.

That’s because the best time to buy, from a wealth-building perspective, is when housing prices are low and growing sluggishly. But generally, such times coincide with limited credit availability: home lenders ration credit according to credit score, and only the “best” borrowers have access to home loans when prices are low.

As the experience of the last housing bubble showed, low-income and minority buyers came into the market most strongly as lending standards were relaxed, relatively late in the cycle. Those who bought in 2001 (when the market was depressed) fared very differently that those who bought in 2006 (at the height of the bubble). Easy credit nominally made homes more affordable, but also drew ever more borrowers into the market to bid up the prices of homes until the bubble popped. Because of this inherent quality of the credit cycle, the poorest borrowers are drawn into the market at the worst time to buy—when prices are at their highest.

Second, you have to buy in the right place. Opportunities for home appreciation vary enormously, not only by region of the country, but by neighborhood within metro areas. Ethnic minorities tend to buy in neighborhoods that have lower rates of home price appreciation. Zillow’s Skylar Olsen analyzed the data on home price trends by race and ethnicity. They show that Black and Hispanic households experienced bigger declines in home values as the housing bubble collapsed, and a slower rebound as it recovered, leaving them worse off that the typical white homeowner. And that’s not a result of Black and Hispanic buyers being poor judges of neighborhood quality: In segregated housing markets, the behavior of whites to avoid Black and Hispanic neighborhoods means that it’s much more difficult for those communities to see consistently rising home values.

Third, you have to get a good deal on credit. The evidence is that low income borrowers and ethnic minorities pay, on average, higher interest rates. A 2006 study for HUD found that after controlling for household, property and loan characteristics, black households pay interest rates that are 21 to 42 basis points higher than whites, and hispanics pay rates than are 13 to 15 basis points higher. Federally guaranteed home mortgages must pay fees based on their riskiness, as measured by the mortgage’s loan-to-value ratio and the borrower’s credit score. Because minority buyers tend to have lower down payments and worse credit scores, it’s estimated that they pay guarantee fees that are 50 percent higher on average than white buyers. In addition, we know that low income and minority borrowers were the targets of predatory lenders. If you pay more for your mortgage, that raises the cost and lowers the returns to homeownership.

Finally, in order to build wealth with housing, you have to have the ability to weather economic cycles. Low income and minority families often have limited financial resources beyond the equity in their homes and therefore are poorly positioned to cope with financial setbacks—loss of a job, a major medical expense or home repair—and missing mortgage payments can quickly push them into default. And highly leveraged home buyers (who get 90% or greater mortgages) are vulnerable to lose their entire investment in the face of even a modest decline in home prices. As Atif Mian and Amir Sufi have documented, conventional US mortgage loans are a risky, one-sided bet for borrowers.

As Patrick Bayer, Fernando Ferreira, and Stephen Ross have demonstrated, minority homebuyers are drawn into the market relatively late in the credit cycle, have limited financial resources on which to draw in the event of economic problems and are disproportionately more likely to default on their loans, even after controlling for differences in credit scores, down payments, and neighborhood characteristics. As a result, they conclude:

“Our study raises serious concerns about homeownership as a vehicle for reducing racial wealth disparities. …Homeownership may be especially risky for households with a low initial level of wealth (savings) or fewer family resources on which to draw when hit with an adverse economic shock.“

If you buy at the wrong time, if you buy in the wrong place, if you pay too much for for the money you borrow and don’t have the financial wherewithal to weather economic turbulence, chances are that home ownership could turn out to be a wealth-destroying, not a wealth-building, proposition. It clearly was in the aftermath of the collapse of the housing bubble.

What City Observatory did this week

1. Economist Paul Romer Joins the World Bank.  Paul Romer, a leading exponent of the New Growth Theory has been hired as chief economist for the World Bank. We explore how his thinking about the role of knowledge-driven growth and the key role of cities in fostering institutional and technological innovation might influence the Bank’s strategies. The good news here is that Romer is one of the most plain-spoken economists around, illustrating his theories with play dough and a children’s chemistry set (Really!).

2. Housing Cost Calculators. There’s a growing array of web-based tools that let you dig into the cost components of housing construction and development to better understand why the rent is, as they say, “so damn high.” We review four different housing cost calculators, including the latest entry from the Urban Institute, plus models from UC Berkeley’s Terner Center and the Cornerstone Partnership. Our verdict: while the models are useful at illustrating some of the key variables and tradeoffs in the development process, there’s a huge amount of complexity here, and many critical assumptions built into the models are not readily apparent, especially for casual users.

3.  The Party Platforms on Housing. Housing and housing affordability are top of mind for local governments, but where do they show up in the national political agenda.  We take a close look at what the Republican and Democratic party platforms have to say about housing.  The parties both express support for homeownership, but seemingly part company on zoning.  The Republican platform lambastes the Affirmatively Furthering Fair Housing Rule as “social engineering” (an epithet which could easily be applied to most zoning).  The Democratic platform makes a vague call for easing local barriers to building affordable rental housing.

4. The Triumph of the City and the Twilight of the Nerdistans.  The big news in San Francisco this week is that Facebook is exploring a major expansion in the city.  The center of gravity of the Bay Area’s tech sector is decidedly shifting north, propelled by the desire of tech workers to live in great urban neighborhoods–and the fierce competition by tech firms to hire these workers. The wave of corporate expansions in downtown areas, and the growing flow of venture capital to startups in city centers signals the ebbing of the suburban nerdistan model of development.

The week’s must reads

1.  After a century is zoning obsolete? This past week marked the 100th anniversary of zoning in the US, dated from the adoption of New York City’s first zoning code. At BloombergView, Justin Fox notes that, as it has been applied, particularly since the 1970s, zoning has been implicated as a contributor to segregation, inequality and housing unaffordability.  He looks back at the historical roots of zoning, unearths some of the archaic views on which it is based, and concludes that if its not time to kill zoning altogether, then a radical overhaul is probably in order.

2.  Cities Alive: Towards a walking world.  London-based ARUP architects have produced a colorful, encyclopedic guide that builds a strong case for promoting more walkable cities, along with case studies of 80 successful projects. This compendium draws from the work of Jan Gehl, Janette Sadik-Khan, Jeff Speck and others, to explain why walking is beneficial to health, communities and the economy, and to demonstrate the practical steps toward promoting walking. That said, the report is primarily about tactics and projects; it only briefly touches on how we might make strategic changes to the policies and institutions that lead to auto-dominated urban landscapes.  While there’s copious detail on parklets, there’s only passing mention of pricing and economic incentives, and no reference to eliminating or reducing parking requirements.

3. More evidence for Portland’s Green Dividend.  At BikePortland, Michael Andersen shows that per capita car ownership in Portland has declined, taking nearly 40,000 cars off the roads, compared to the rate of vehicle ownership in 2007.  He estimates that less driving is saving Portland residents $138 million annually in vehicle operating costs, money that mostly gets re-spent in the local economy.

New knowledge

1. The Outlook for the Homeownership Rate. In the aftermath of the collapse of the housing bubble, homeownership has fallen to rates not seen in decades. The University of Pennsylvania’s Susan Wachter and Arthur Acelin conclude that a combination of demographics and economics likely may continue to depress homeownership rates going forward. Homeownership is down most for young adults, who will represent a growing share of the population over the next two decades.  The report also points to tougher lending standards as a brake on homeownership.

2.  The impact of Seattle’s minimum wage on earnings, employment and business activity. A team at the University of Washington, led by economist Jake Vigdor, has been using employment tax records to track the impacts of the City of Seattle’s minimum wage, which rose to $11 per hour for many employers. They find that since the wage increased, hourly earnings for low wage workers are up by more than $1 per hour, but much of the effect is attributable to the strong local economy, rather than the wage law. There’s been a small reduction in hours worked: the employment rate for low wage workers in Seattle declined about 1 percent.  Despite fewer hours of work, average worker earnings are up. The study finds no net impact on business success: the failure or exit rate of businesses hasn’t changed appreciably, and the closure of local businesses has been more than offset by new business starts.

3.  Has the Great Recession depressed the rate of productivity growth? A new paper from the National Bureau of Economic Research shows that in the face of weak demand, businesses have limited incentives to incur the up-front costs associated with developing R&D, and deploying new ideas and technology. The result is that the state of the macro-economy lowers the rate of productivity growth and decreases the total capacity of the economy–something economists call an “endogenous” effect, which is a key reason why recovery from financial crises is so slow.

The Week Observed is City Observatory’s weekly newsletter. Every Friday, we give you a quick review of the most important articles, blog posts, and scholarly research on American cities.

Our goal is to help you keep up with—and participate in—the ongoing debate about how to create prosperous, equitable, and livable cities, without having to wade through the hundreds of thousands of words produced on the subject every week by yourself.

If you have ideas for making The Week Observed better, we’d love to hear them! Let us know at jcortright@cityobservatory.org or on Twitter at @cityobs.

What City Observatory did this week

1. Homeownership:  A failed wealth creation strategy.  Its an article of faith that owning a home is the most reliable route to wealth building in the US.  But this hasn’t been true over the past decade, and its especially problematic for low income households and minorities. The housing market is structured so that they buy at the wrong time, in the wrong place, pay a higher price and face far greater risk.

2. Homeownership can worsen inequality.  Policies that promote homeownership have worked far better for the wealthy than the poor, with the result that wealth inequality related to housing has actually grown in the past decade.  Low income households experienced greater value declines and more frequent foreclosures; higher income households continue to experience gains in home equity, with the result that the top twenty percent of households had, on average 9 times as much home equity as the typical household in 2010, up from a five-fold difference in 1990.

3.  Housing can’t be a good investment and affordable.  There’s a fundamental contradiction between the two pillars of US housing policy. In order to be a good investment, it has to increase steadily in value over time.  But rising home values are synonymous with diminished affordability. Until we confront this contradiction squarely, we’ll have real difficulty making progress on either front.

4.  Changes.  We bid adieu and bon chance to our colleague Daniel Hertz, who’s taken a new position with a public policy think tank in Chicago. His thoughtful analysis and clear voice have defined City Observatory, and we’re delighted that he’s agreed to continue to provide monthly contributions. Thanks, Daniel!

The week’s must reads

1. Car-sharing reduces traffic.  Susan Shaheen and her research team at the University of California Berkeley used fleet data and surveys of users of Daimler’s Car2Go car-sharing service.  They estimate that each additional vehicle in the ride-sharing fleet leads to 9 to 11 fewer cars on the road, and greenhouse gas emissions per user decline substantially. Users report selling existing cars, or avoiding buying cars, and can utilize car-sharing on an as-needed basis to supplement transit, biking and walking. This is strong evidence that “transportation as a service” will be more efficient than our current model of having each household own one or more cars.

2. Making the buses run on time in NYC.  For the past several years, bus ridership in New York City has been dwindling, even as subways have become increasingly crowded.  The Transit Center released a detailed report–“Turnaround: making recommendations on how to get buses moving faster, including re-designing and straightening routes, electronic payment, all-door boarding, and dedicated lanes. It’s a list of ideas that could be applied in nearly all cities. Conspicuous by its absence is one other idea: charging private vehicles using the public roadway in peak hours to reduce traffic and speed buses.

3. Demographic Headwinds for Job & Housing Growth?  A new report from John Burns Real Estate, a consultancy, predicts much slower job growth in the years ahead, due to lower growth in the working age (20-64) population. They forecast that this group, which grew 1.0 to 1.5 percent per year in the past decade, will grow at less than 0.5 percent annually through 2024. The result:  labor shortages and likely subdued demand for housing. Some provocative data here.

New knowledge

1. Segregation and the Financial Crisis.  NYU’s Furman Institute has posted another one of its series of well-structured discussions of key urban policy topics. This one explores an essay by Jacob Faber which suggests that the size of the housing market collapse was shaped heavily by segregation.  In reply, Steve Ross acknowledges the key role of segregation in shaping housing market outcomes, but argues the decline was much more broad based.

2. Public Support for Road Finance Alternatives.  Everyone, it seems, wants more and better roads, but no one actually wants to pay for them is the short summary of Indiana University’s Denvil Duncan’s paper exploring survey evidence on support for road finance alternatives.  Of five considered options, none gets majority support; higher gas taxes (favored by 29%) or tolls (34%) are the least unpopular.  Raising the income tax is the least favored. The title of this paper “Searching for a Tolerable Tax,” gives away the game; the full paper is available (paywall) from the Public Finance Review.

3. How Housing Market Fluctuations Affect Young Households.  In hot housing markets, high prices keep younger households in the rental market, and also seem to lead to lower rates of marriage and child-bearing, according to research from economists Luc Laeven and Alexander Popov. They look at across market variations in home prices in the US, and conclude that housing booms tend to disadvantage young households, while benefiting older homeowning households.

The Week Observed is City Observatory’s weekly newsletter. Every Friday, we give you a quick review of the most important articles, blog posts, and scholarly research on American cities.

Our goal is to help you keep up with—and participate in—the ongoing debate about how to create prosperous, equitable, and livable cities, without having to wade through the hundreds of thousands of words produced on the subject every week by yourself.

If you have ideas for making The Week Observed better, we’d love to hear them! Let us know at jcortright@cityobservatory.org, dkhertz@cityobservatory.org, or on Twitter at @cityobs.

Earlier, we wrote about the first fatal crash of a partly-self-driving car. A Tesla, operating on autopilot mode, failed to detect a semi-trailer crossing in its path, and the resulting collision killed its human driver.

The crash has provoked a great deal of discussion in the media about safety data, the potential for future technology, and the problems of human-interaction with partially automated systems. A key challenge is that current (and at least foreseeable) implementations of self-driving technology are likely to require human supervision and intervention, and the man-machine combination likely poses its own risks. The seeming inerrancy of the machine systems in route circumstances may lull humans into a sense of complacency or inattention, with disastrous consequences when the machine fails. (Indeed, according to at least one account the victim of the Florida crash was watching a Harry Potter video at the time of the crash).

While we may instinctively regard the safety issues surrounding autonomous vehicles as being primarily technological in nature, they also depend critically on institutional arrangements we establish and the policy choices we make about transportation and public space.  Safety will be determined as much by rules of the road as by any safety device.

To many achieving safety will come in the form of fully automated vehicles that would eliminate any human role in the driving process. Not only are such vehicles still years—if not decades—away, but then there’s the challenge navigating a transitional period in which self-driving vehicles share the road populated mostly with human-piloted vehicles.

To avoid these problems altogether, Jerry Kaplan writing in the Wall Street Journal, has suggested that self-driving vehicles be given their own infrastructure. Lanes on highways would be reserved for self-driving vehicles.

Taking the initiative in this way would better foster innovation and let the free market work its magic. What might such a plan look like? Perhaps we could start by reserving high-occupancy-vehicle lanes or certain roads at specific times for automated vehicles.

It’s breathtaking that neither the Kaplan—nor apparently his editors at the Wall Street Journal—grasped the glaring contradiction between “free market” and “government prohibiting non-automated vehicles from using roads.”

There’s no such thing as a free market for transportation: transportation hinges directly on public policy, particularly spending on roads and the rules that govern their use. A technologically advanced car is essentially useless without a network of public roads on which it can operate. The (largely) private market for vehicles depends directly on a public policy of building roads and regulating them in ways favorable to vehicle travel.

How we get from today’s technology to tomorrow’s, whatever form it takes, will be very much about public policy choices. That’s the way its always been.  It’s worth spending a moment thinking back about this process. In the early days of the automobile, the rules of the road were quite different. There were no traffic signals, and wagons, streetcars, and pedestrians freely mixed on city streets. In the earliest days, some cities required cars be preceded by a flag-bearer to warn other travelers of its approach.

A key victory of what Vancouver’s Gordon Price calls “motordom” came in literally re-writing the rules of the road. By law and custom, other users were marginalized or completely excluded from the roadway. Pedestrians not crossing at marketed crosswalks were branded “jay-walkers”—a derisive and entirely manufactured term, designed to shame what had long been common behavior on city streets.

There’s no such thing as a free market for roads

While it’s cloaked in the rhetoric of markets, Kaplan’s call to dedicate a portion of the public right of way exclusively to autonomous vehicles is really the latest incarnation of Asphalt Socialism: We ought to give massive public subsidies to private vehicle movement, privilege these cars over other forms of transportation, and generally subordinate the quality of place to the movement of vehicles.

It may make sense for fully automated vehicles to have their own right of way: but if they do, they ought to pay for the privilege. The root of many of our transportation and urban problems is the consistent under-pricing, and consequent massive subsidies to private vehicle transportation. Our decision to subsidize freeway construction in cities—ostensibly to speed travel—has chiefly led to much more sprawling development patterns. Its an open question whether fleets of cheap, comfortable authonomous vehicles would lead people to choose to commute longer distances from even more far-flung locations.  Subsidized infrastructure for automated vehicles would recapitulate this mistakes of the past with a whole new set of technologies.

Likewise, the advent of driverless vehicles raises important issues about the legal liability for damages when car crashes occur.  Will driverless vehicle manufacturers or software providers or map makers be subject to legal recourse under product liability laws?  At least one study has suggested a fairly sweeping legal framework that would shift liability to vehicle owners.  How the legal architecture of liability is re-written will likely have a profound impact on the deployment of this technology.

The advent of highly instrumented vehicles should instead be treated as an opportunity to revisit archaic and failed choices about how we regulate, price and pay for roads. If vehicles had to pay for the cost of the public roads on which they travel (and the public street space that is routinely used for free car storage), drivers would make very different decisions about when, where and how much to travel.  Realizing the safety and other benefits that can potentially come from self-driving vehicles will be just as much a matter of working out the right public policies as it is tackling the technological challenges.

Last week, sadly, two tragic deaths represented unfortunate, but predictable firsts in transportation. They are also reminders that despite the very real potential benefits of new technology, operating large metal objects at high speeds is an inherently dangerous activity, and public safety is best served by reducing people’s exposure to the risk—which means designing urban spaces to minimize necessary driving and keep most vehicular traffic traveling at low speeds.

On May 7, Joshua Brown had his Tesla sedan operating in auto-pilot mode crash into a semi truck that turned across his path on a four-lane Florida highway. Neither driver nor operator reacted to the truck, the car’s self-driving mechanism apparently fooled by the low contrast between the truck and a bright sky, or its own programming that led it to disregard large metal rectangles as highway signs. As Tesla put it: “Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied.” (The passive voice here suggests autonomous vehicles are well on their way to developing the key driving skill of minimizing responsibility when crashes occur).

Then on July 1, bike sharing recorded its first fatality. In Chicago, 25-year old Virginia Murray riding a Divvy bike was struck by a flat-bed truck as they both turned right from Sacramento on Belmont in the city’s Northwest side.

Until these crashes, both technologies had enviable safety records. But as they become more widely used, it was a statistical certainty that both would fail. What lessons can we draw from these tragedies?

Gizmodo hastened to point out that the Tesla, while using its lane-keeping, object-detection and speed-maintaining functions, was not truly a fully autonomous vehicle. (Tesla’s system requires the driver to keep his or her hands on the wheel, or the car starts slowing down). Arguably, the Tesla’s systems don’t have all of the functionality or redundancy that might be built into such vehicles in the future. To Gizmodo, the Tesla crash is just a strong argument for fully autonomous vehicles—humans can neither be counted upon to intervene correctly at critical moments, and in theory, vehicle-to-vehicle communication between the Tesla and truck could have avoided the incident entirely.

In its press release on the crash, Tesla pointed out that its vehicles, collectively, now have recorded one fatality in 130 million miles of auto-piloted driving, which compares favorably with a US average fatality rate of one fatality per 94 million miles driven.

In a subsequent tweet-exchange in response press coverage of the Florida crash, Tesla CEO used that disparity to claim that if all cars worldwide were equipped with the auto-pilot function it would save half a million lives per year. Elon Musk upbraided a Fortune reporter, insisting he “take 5 mins and do the bloody math before you write an article that misleads the public.”

But the math on safety statistics hardly supports Musk’s view, for a variety of reasons, as pointed out in Technology Review. So far, the sample size is very small: until Tesla has racked up several tens of billions of miles of driving, it will be hard to say with any validity whether its actual fatality rate is higher or lower than one in 94 million. Second, it’s pretty clear that current Tesla owners only use the autopilot function in selected, and largely non-random driving situations, i.e. travelling on freeways and highways. Limited access freeways, like Interstates, are far safer than the average road; in 2007, the crash rate on Interstates was one fatality per 143 million miles driven (100 million divided by .70). The most deadly roads are collectors and local streets, where auto-pilot is less likely to be used.

Fatality Rates Per Million Miles Traveled, 2007

Statistically, it’s far too early to make any reasonable comparisons between this emerging technology and human drivers. But our experience with managing risk and safety with other technologies suggest that the problem will be daunting. As Maggie Koerth-Baker pointed out at FiveThirtyEight, the complexity of driving and of coping with every possible source of risk—and selecting the safest action—is mind-boggling. Plus, computers may not make the same mistakes as humans, but that doesn’t mean that they won’t sometimes act in ways that lead to crashes.

Part of the problem is that the very presence of safety systems may lull drivers into a false sense of security. Crashes, especially serious ones, are low-probability events. Humans may be very leery about trusting a machine to drive a car the first few times they use it, but after hundreds or thousands of repetitions, they’ll gradually believe the car to be infallible. (This logic underlies the Gizmodo argument in favor of full autonomy, or nothing).

This very process of believing in the efficacy the safety system can itself lead to catastrophes. Maggie Koerth-Baker describes the meltdown of the Three Mile Island nuclear reactor. It had highly automated safety systems, including ones designed to deal with just the abnormalities that triggered its accident. But they interacted in unanticipated ways, and operators, trusting the system, refused to believe that it was failing.

While some kinds of technology—like vehicle-to-vehicle communication—might work well in avoiding highway crashes, there’s still a real question of whether autonomous vehicles can work well in an environment with pedestrians and cyclists—exactly the kind of complex interactions with un-instrumented vulnerable users that resulted in Virginia Murray’s death in Chicago.

Increasingly, safety problems affect these vulnerable users. Streetsblog reported that the latest NHTSA statistics show that driver deaths are up six percent in the past year, pedestrian deaths are up 10 percent and cyclist deaths are up 13 percent, reversing a long trend of fewer deaths, and making 2015 the deadliest year on the road since 2008.

For the time being, it’s at best speculative to suggest that all of these deaths can be avoided simply by the greater adoption of technology. And as many observers have noted, today’s technology, while impressive and developing quickly, is far from achieving the vision of full vehicle autonomy; some robotics experts predict self-driving cars may be 30 years away. With present technology, as we’ve noted more driving means more deaths. The most reliable way to reduce crash-related deaths is to build environments where people don’t have to drive so much, and where cyclists and pedestrians aren’t constantly exposed to larger, fast moving and potentially lethal vehicles even when making the shortest trips. That’s something we can actually do with the technology that exists today.

At City Observatory, we love fat reports full of data, especially when they shed light on important urban policy issues. Last week, we got the latest installment in a long-running series of annual reports on housing produced by Harvard’s Joint Center on Housing Studies (JCHS). The State of the Nation’s Housing, 2016—aka SONH2016—presents copious details on the housing situation. This year, it particularly highlights the growing problem of housing affordability. (We summarized last year’s report here.)

There’s a lot to digest in this report. Our review focuses on four big issues, how the report describes affordability, its analysis of recent developments in the housing market, the outlook for homeownership as a wealth building strategy, and the implications of all this for housing policy.

Housing Affordability

SONH2016’s headline finding is the growing number of households spending half or more of their income on housing. Using the 30 percent of income standard, the report estimates the number of rental households with affordability problems has increased by 3.6 million since 2008. The report continues to use the 30 percent of income standard even though there are real questions as to whether it really reflects affordability, or addresses the housing/transportation cost tradeoff. And the number of households spending 50 percent or more of their income on rent has increased by 21 million to 11.4 million over that same time period (p. 4).

We agree that housing affordability is a widespread and serious problem. But what seems lacking from SONH2016 is a thorough diagnosis of the causes of the affordability problem and an appropriately scaled set of solutions. There’s passing reference to local land use controls as a contributing factor to the problem of housing affordability: “High rents reflect several market conditions, including a limited supply of land zoned for multifamily use and a complex approval process that adds to development costs.” (p. 4) But the report emphasizes the impact of land use controls on building affordable housing, and aside from this brief mention, offers little comment—and no policy recommendations—about how to ameliorate the effects of supply constraints by changing local zoning.

While SONH2016 does a thorough job of amassing statistics that chronicle the growth and extent of affordability issues, it spends relatively little time describing policy actions that might make a dent in the problem. On the last full page of the report, the authors offer their outlook on housing challenges. The note that only modest additional resources have been made available to support low income housing construction —we’re told (p. 36) the biggest program in the past decade is the recent authorization of the $174 million Housing Trust Fund (which works out to about $16 for each of the 11 million households spending 50 percent or more of their income on housing). In effect, we’re presented with a daunting problem without any commensurate solution.

The Housing Market

The report tries to sound some optimistic notes about about single-family homeownership. But they come off as weak reeds in the face of what continues to be a highly depressed sector of the economy. The report highlights an increase in single family housing construction (p. 1: “new home construction was up by a healthy 11 percent”) but later concedes that the total number of homeowners, and the homeownership rate, has continued to fall (p. 16).

On the plus side, the report helps dispel a commonly reported misperception about the growth of McMansions. The data show that though large homes (greater than 3,000 square feet) have become a bigger share of the market, that has more to do with the utter collapse of the market for small homes than it does with any expansion of the appetite for large ones. In fact, McMansion-sized homes are still being built at just half the rate of a decade ago (200,000 units per year compared to 400,000 at the peak). (See p. 8). As we’ve explained at City Observatory, the “McMansion/Multi-Millionaire” ratio has actually been falling.

Very much to its credit, SONH2016 calls out the growing economic segregation of US housing. It says that poverty in the US has become more concentrated, and bluntly calls out the location of public housing and exclusionary local zoning as major causes of this problem (p. 35).

Homeownership, Wealth and Home Prices

State of the Nation’s Housing continues to promote homeownership as a reliable wealth-creating strategy, what it calls “the wealth building potential of sustained home ownership” (p. 21) The report cites data for the net wealth gains of homeowners who managed to hang on to their homes, but combines data from those who bought over the entire decade (1999 to 2009), which conceals the key fact that those who bought in say 2001 or 2002 had a very different experience than those who bought at the height of the bubble. The report omits the fact that homeowners collectively lost something on the order of $7 trillion in the housing bubble, and that these losses fell disproportionately on low income and minority households. And, as Calculated Risk’s Bill McBride points out, a decade after the peak of the housing bubble, real, inflation-adjusted home prices are still 17 percent below their peak.

This series of State of the Nation’s Housing reports have been been issued for nearly three decades. Over the years, there have been a number of recurring themes. Concerns about housing affordability have been hardy perennials. A decade ago, concerns about affordability were also in the report’s headlines, although for very different reasons. The 2006 State of the Nation’s Housing dealt primarily with how rising home prices were reducing affordability for homebuyers. (This particular aspect of the affordability problem was corrected by the popping of the housing bubble, something that that year’s SONH didn’t forsee.)

In 2006, with the nation’s housing bubble about to burst, it offered confident reassurance: “large house price declines seem unlikely for now . . . the long term outlook for housing is bright . . . STRONG DEMAND FUNDAMENTALS . . . with each generation exceeding the income and wealth of its predecessor, growth in expenditures on home building and remodeling should match if not surpass the current pace . . . (State of the Nation’s Housing 2006, page 2). Now, nearly a decade later, the housing sector’s contribution to GDP remains fully one-third lower than its historic average (SONH 16, Figure 12).

The point here isn’t that so much the SONH made a bad forecast, but more than it gave bad advice. Far from being a reliable source of wealth generation, housing was a very risky proposition that was about to impose huge costs of millions of households. Prior to 2007, one might argue that a 20 or 30 percent drop in home prices was improbable.  But in light of the human and personal costs of the last housing bubble, it seems like the report ought to be a bit more cautious in its approach to the question of whether, for whom and under what circumstances home ownership is a good investment.

Federal Housing Policy

There are some surprising omissions from the report. You’ll find no mention of the biggest and most expensive federal housing subsidy programs: the favorable tax treatment of owner-occupied housing, in the form of the mortgage interest deduction, the exclusion of imputed rental income and favorable treatment of capital gains on housing. Collectively, these policies amount to a $250 billion annual subsidy for homeownership, which thanks to a falling homeownership rate, is going to a smaller fraction of American households each year.

It’s also interesting to note that at a time when SONH16 frets that our nation’s housing problems are being aggravated by “dwindling federal subsidies” (p. 31), that federal resources for housing are “dwindling” (page 31), the value of the federal tax subsidies has increased sharply. According to the Congressional Budget Office, the combined value of favorable tax treatment for owner occupied housing increased by $39 billion between fiscal years 2013 and 2015.

If we’re really concerned about housing affordability, we have to do more than periodically trumpet alarming statistics. We really ought to plainly identify root causes, and spell out the public policy choices that we’ve made — through local zoning and public subsidies — and talk about the scale of the effort required to materially change the situation.

A casual reader of the SONH16 may come away with the impression that federal housing policies are ineffectual and under-funded, but that’s hardly the case. We have a well-funded federal housing policy that works quite well—if you agree that its intended purpose is to provide generous support, particularly for the wealthiest households, to own their own homes.   In our view, a more comprehensive framing of federal housing policy–one that encompasses tax subsidies to homeownership–provides a much more useful context to understanding our housing problems and formulating commensurate solutions.

If you’re interested in housing, particularly the tale of the tape when it comes to a wide range of housing statistics, SONH 16 is an invaluable resource. But when it comes to thinking about housing policy, and specifically, identifying the root causes of our affordability problem and the nature and scale of the policy solutions that would need to be undertaken to actually move the needle on the indicators presented here, you’ll have to look elsewhere.

Last summer, we flagged a fascinating study by Fitch Investment Advisers which tracked twenty five years of home price data, stratified by the “urbanness” of housing. Fitch showed that particularly since 2000, home prices in neighborhoods in the center of metropolitan areas increased in value relative to all other metropolitan housing. We termed the price premium that central neighborhoods command “the Dow of Cities” because like the Dow Jones index, it serves as an indicator of the market valuation of urban locations.

Last month, the Federal Housing Finance Agency (FHFA) produced a new data series, a repeat sales index of housing. (For more about ways of measuring housing prices, check out our stats guide.) They analyzed over 100 million property transactions from 1975 through 2015 and produced an index of home prices that can be used to track neighborhood level price changes, and to disaggregate the effects of location from other factors (like home size). A new research paper—Local House Price Dynamics: New Indices and Stylized Facts—based on that data, authored by Alexander Bogin, William Doerner, and William Larson looks at the relationship between urban location and price increases within metropolitan areas. You’ll find a summary of the report and maps for several metropolitan areas in Emily Badger’s Wonkblog analysis.

Their key finding is that more centrally located homes—those closer to a metropolitan area’s central business district—have experienced higher rates of appreciation over the past twenty five years. You can see those findings in the following chart, which shows the appreciation rate by zip code, between 1990 and 2015, based on distance to the central business district. The results are surprisingly strong: in large U.S. metropolitan areas, homes within 5 miles have appreciated, in real terms, at an annual rate of about 1.5 to almost 2.0 percent per year over the past 25 years, while homes 10 or more from the center have appreciated at a fraction of one percent per year. (See the green line in figure four). In smaller metropolitan areas (those with fewer than 500,000 housing units), the relationship is nearly flat, suggesting that the big gains in home values in the center have been in the largest metropolitan areas.

Maps of several metropolitan areas make the pattern clear. In these maps, zip codes with the highest relative rates of housing appreciation have the darkest blue shading. Those zip codes with the lowest relative levels of appreciation have the lightest shading. In each case, appreciation rates are normed to metropolitan level averages. For example, the Washington Post prepared the following map of housing price appreciation in Portland, showing that the neighborhoods with the highest levels of appreciation are in or very near the center of the metropolitan area, and the rates of appreciation are lower on the suburban periphery. Similar patterns show for metropolitan areas like Houston, Minneapolis, Chicago, Denver and Phoenix.

Map Credit: Washington Post, from FHFA data.

The authors are making their complete data set of 5-digit zip code level data available for download. You can also explore on-line maps of individual metropolitan areas which allow you to see the rate of price appreciation since 1990 and 2000.

As the authors note, the size and detail of the FHFA transaction database gives it some technical advantages over other ways of measuring home prices. The FHFA data covers more transactions that private databases like the Case-Shiller data used by Fitch, and has the added advantage of being publicly available. And in contrast to analyses of area home price data (such as Zillow’s zip code level price estimates), is able to distinguish between changes in home price and home quality. That said, the FHFA analysis substantially confirms the trends spotted using these other data sources.

The effect is clear, but what’s the cause?

While there’s little question about the trend—housing closer to the city center is appreciating significantly relative to that in peripheral suburbs—there’s still some debate about the causes. One of the FHFA studies authors, William Larson, maintains that the shift in demand back to cities doesn’t necessarily represent a change in preferences, but is instead driven simply by growing higher incomes, traffic congestion, improved urban amenities, and reduced crime. While economists generally want to explain everything in the context of market variables—and we include ourselves in this category—this seems like a stretch. As we’ve shown in our studies of the Young and Restless, well-educated young adults are today dramatically more likely to choose to live in close-in urban neighborhoods than their predecessors of 10, 20 and 30 years ago. Moreover, the growth urban amenities is as much a result as a cause of the shift to urban living—the growing number of well-educated urban residents provides the demand for restaurants and the experience economy. In a forthcoming paper, Jesse Handbury and Victor Couture attribute the rise of central city property values directly to “a diverging preference for consumption amenities” particularly by well-educated workers.

It’s also doubly hard to square the evidence on accessibility, commute times, and travel modes with a claim that preferences haven’t changed. Central locations have always had an accessibility advantage to jobs, congestion and travel times have not increased appreciably—and in fact have decreased in recent years. And as one indicator of a fundamental change in preferences, far more Americans choose to cycle to work in cities today than a decade or two ago; something they could have easily done then—but didn’t, most likely because that generation had a different preference for driving as opposed to cycling; just as it has a different attitude toward urban living. Indeed, the willingness of consumers to pay higher prices to live in cities today, relative to the price of suburban living, is a key indicator of the change in values we attach to great urban places.

And, as we’ve noted before, it’s an indication that we have, nationally “a shortage of cities”—the demand for housing in urban neighborhoods is rising faster than the supply, resulting in steadily escalating relative home prices in dense, central neighborhoods. It’s both a major contributor to the growing challenge of housing affordability and an economic signal that we ought to be doing more to build additional housing in cities.

Last week’s big news was Britain’s decision, via referendum, to leave the European Union. The results of the vote lead Prime Minister Cameron to resign and sent markets reeling, and it’s still unclear what the ultimate economic and political effects will be. For some keen, if depressing, insight on the ramifications of Brexit, you may want to read this essay by Fusion’s Felix Salmon.

Analyses of the vote in Britain point up the sharp generational, educational and geographic cleavages that produced the outcome. Older and less well-educated voters favored leaving, younger and more highly-educated voters preferred to remain the the European Union. As the BBC reported, those 18 to 24 voted almost 3 to 1 in favor of remaining; a majority of those over 45 voted to leave:

There was a strong correlation between the education level of an area, and its vote on the referendum. According to data analyzed by The Guardian, the highest educated areas voted most strongly to remain; the least well-educated areas tended to vote to leave.

And similar to our own red state/blue state divisions—a topic we’ve explored at City Observatory—there’s a strong geographic split in the vote in the UK, especially in England. (All of Scotland and most of Northern Ireland favored staying in the EU, by sizable margins.) As Emily Badger has pointed out in the Washington Post, there’s an urban/rural divide in the election returns. London and its environs, and Liverpool and Manchester supported remaining by substantial margins. The more rural parts of England voted to leave. The Washington Post’s map casts the vote in familiar red/blue hues, with the remain vote shown as blue and the leave vote shown as red.

Another way to look at this question is to consider the relationship between population density and voting patterns. Studies of the US showed a strong correlation: higher density counties were much more likely to vote for Barack Obama than Mitt Romney in 2012. To dig deeper into this question, we gathered data on the population density of English electoral districts and compared it to the fraction of local electors voting to remain in the European Union. Density data are the number of eligible electors (voters) in each of England’s 380 electoral districts, divided by the number of hectares in the district (electors per hectare). These data come from the Local Government Boundary Commission. We obtained election returns for each district from the UK Electoral Commission.

This chart shows the relationship between each electoral district’s density (shown on the horizontal axis, densest areas to the right of the chart) with the fraction of the voters favoring remaining in the European Union (vertical axis, higher values corresponding to a greater percentage of voters favoring remaining). Since there are varying numbers of voters in each district, we’ve displayed these results as a bubble chart, with the size of each bubble corresponding to the number of votes cast in each electoral district.

In general, the denser an electoral district, the more likely it was to was to vote to remain in the EU. All of the electoral districts with densities of 40 or more electors per hectare voted to remain. The pattern for lower density districts was more diffuse, but on average those with the lowest levels of density cast their ballots for leaving.

The geographic, demographic and generational polarization around issues like Brexit may be a sign of our times. In a world still being reshaped by globalization and technological change—and still working to cope, fitfully, with the aftermath of the worst economic downturn in eight decades, there’s a division between those who greet the future with optimism and those who are looking to return, or cling to, a seemingly fast disappearing past. Many of the fault lines are defined by place, with many city economies drawing and attracting talent, flourishing with the growth of global connectedness. But others, especially in smaller towns and rural areas haven’t gained so much from change. As Britain’s Brexit election shows us, the divisions are deep. Addressing them will be a major issue for all of us.

Over the past three weeks, we’ve introduced the “sprawl tax”—showing how much more Americans pay in time and money because of sprawling urban development patterns. We’ve also shown how much higher the sprawl tax is in the US than in other economically prosperous countries, and how sprawl and long commutes impose a psychological, as well as an economic burden. Today, we’ll take a close look at how ignoring the sprawl tax distorts our view of the cost of living in different regions and neighborhoods.

As one old saying goes, an economist is someone who knows the price of everything and the value of nothing. It’s often claimed that some places, often sprawling Sunbelt cities, have a lower cost of living, based usually on observations about lower housing prices. And judged solely from the sticker price of new homes, the argument has some merit.

But as our aphorism about economists implies, there is a lot more to this question than just one set of prices. If you’ve followed our series on the sprawl tax, you know that living in some cities—those with cheap average housing costs, like Houston or Dallas or Birmingham—also carries with it a heavy, and largely ignored cost in the form of the “sprawl tax”: much higher transportation costs. In short, we tend to fixate on the price of something we can easily measure (housing) and simply leave out the value of something that is much less obvious (sprawl and longer commutes)

How big is the sprawl tax, relative to the supposed cost of living differences among metropolitan areas? Quite large as it turns out: enough to erase much of the supposed cost advantages that low density settlement is supposed to offer.

We know that within metropolitan areas, there’s a strong tradeoff between rents and home prices and typical commute distances. Low density housing, at a long remove from the city center and most metro area jobs, commands lower prices, reflecting in large part the added transportation costs implied by more far-flung and less accessible locations. Others, notably the Center for Neighborhood Technology through its H+T calculator, have addressed the tradeoff between housing costs and transportation costs within metropolitan areas. Our analysis here uses the sprawl tax in concert with Bureau of Economic Analysis estimates of housing costs differences at the metropolitan level to examine this tradeoff.

The Sprawl Tax and Cost of Living Differences

In the following chart, the vertical axis shows the sprawl tax: higher values mean that a region’s residents pay more for transportation costs and travel time as a result of sprawl. The horizontal axis shows the rent differential: how much more (or less) the typical resident pays in annual rent/housing costs compared to the typical large metropolitan area. Areas to the right have higher rents; areas to the left have lower rents. And, as discussed in the post-script, at least some portion of the difference in rents reflects real quality of life differences between cities; but for now we use this as an index for housing cost comparisons.

The chart illustrates a range of different combinations. Four metro areas with the highest sprawl taxes (Atlanta, Nashville, Dallas, Houston) all have lower than average housing costs. For example, according to the BEA estimates, the annual cost of housing in Houston is $850 per person less than the national average. But the typical Houston household would face a sprawl tax based on longer commute distances of about $2,900 per worker which would essentially wipe out the superficial price advantage from housing.

The nation’s most expensive housing markets—including San Francisco, San Jose, New York, and Washington—have housing costs that are considerably higher than the national average, with per person annual rental costs exceeding the national average by $5,000, up to almost $10,000 in San Jose and San Francisco. But these cities have much lower than average sprawl taxes. Workers in San Francisco, San Jose and New York pay less than $500 per year in sprawl taxes. This amount doesn’t fully offset the added rental costs, but combined with the quality of life differences between high prices and low prices cities, makes the differences much smaller.

And for many cities, adding the sprawl tax essentially erases the supposed cost of living advantage from cheaper housing. For example, Houston’s housing cost is about $1,100 per person less than Portland’s ($845 below the large metro average compared to $318 above). But Portland’s sprawl tax ($871) is $2,000 less, per worker, than Houston’s ($2,877), which for many households will more than eliminate the housing price advantage.

A sensible discussion of the real differences in the cost of living between places has to look just past the prices of a few commodities and big ticket items, to consider the intrinsic value consumers attach to great urban environments, the variety and convenience of consumption opportunities in compact urban centers and the sprawl tax the consumers must pay.

Methodology: How we calculated living cost differentials

The best measure we have of inter-metropolitan differences in consumer prices is the Bureau of Economic Analysis Regional Price Parities (RPP) estimates. These estimates, prepared from the data used to construct the nationwide consumer price index, confirm some of our fundamental intuitions about differences in costs of living between communities. The cost of goods varies little among places. The difference between relatively expensive and relatively inexpensive cities (that is, the 75th percentile and 25th percentile) in the cost of goods is just 2.5 percent. For services, prices vary somewhat more, about 8 percent. So what’s really making up the difference in cost of living? No surprise: housing, which varies by more than 30 percent.

Regional Price Parities for Metropolitan Areas of Over 1 Million, 2013

Source: Bureau of Economic Analysis, City Observatory calculations. Weight is the share of these items in the RPP calculation. Index values are for the entire nation, i.e. U.S.=100. Mean and interquartile range are weighted by metro area.

Here are BEA’s estimates of the rent price index for the 51 largest U.S. metropolitan areas (those with a population of one million or more). The data are indexed to the national average rent (U.S. = 100). On average, rents are about seven percent higher in large metropolitan areas (i.e. the index for the median metropolitan areas of the 51 largest is 107). Rental prices in San Jose and San Francisco are 80 to 90 percent higher than nationally; rents in Louisville and Birmingham are 25 to 30 percent lower than the national average.

Because housing costs are the biggest source of variation in the measured cost of living among large metropolitan areas, we use the BEA data to estimate how much more, or less, a typical household pays for housing, based on the difference in rents among metropolitan areas. BEA estimates the rents as a combination of the actual rent paid by renters, and the “imputed rent” which is the value of housing services received by households that own their own homes). We compute the difference in the income paid for housing among metropolitan areas by observing the difference between the average rental price parity for the 51 largest metropolitan areas (107) and the actual value for each metropolitan area. We multiply that difference by the per capita personal income of the area and the share of per capita income devoted to rent (estimated by BEA at 20.7%).

A Postscript: Two Big Challenges with Comparing Living Costs

We present the estimates of housing cost differentials here, and compare them to the magnitude of the sprawl tax to illustrate how important urban form is to our economic and personal well-being. But its also worth noting that conventional cost comparison measures understate two important economic advantages of cities.

First, as with other commodities, differences in prices often signal the value that consumers attach to different objects. Housing may be more expensive in San Francisco or Hawaii than in Omaha or Idaho, but much of this difference reflects the value that we attach to being in a vibrant city or a sunny, tropical climate. A two bedroom apartment near the beach in Maui or on Nob Hill in San Francisco represents an entirely different set of amenities than a similarly sized apartment in Fresno or Fargo. Indeed, a whole class of economic analysis—hedonic regression—uses these price differences to estimate the value of natural and manmade amenities.

Second, there’s an inherent limitation in trying to compare different places that have widely different sets of attributes and consumption opportunities. Typical estimates of cost-of-living differences will look only at a single commodity (like housing) or a very limited market basket of simple goods and services, and use these to compute differences in living costs between places. The assumption is that consumers or households buy the exact same mix and quantity of goods and services wherever they live. Economists have cast serious doubt on the validity of these simple-minded price comparisons. It turns out, consumers attach value to having convening access to a big range of goods and services, something you find mostly in cities. Columbia’s Jessie Handbury has shown that the greater mix, variety and convenience of shopping opportunities in larger cities means that consumers actually enjoy lower prices for the particular market basket of goods they prefer in larger cities than in smaller ones, contrary to the notion that small town prices are lower. BEA’s Regional Price Parities which imply that New York’s prices for goods are 8.8 percent higher than the national average aren’t adjusted for quality and variety. According to Handbury’s estimates, when you make that adjustment, prices in larger cities are actually lower than in smaller ones.

What are cities worth? More than big private companies, as it turns out: The value of housing in the nation’s 50 largest metropolitan areas ($22 trillion) is more than double the value of the stock of the nation’s 50 largest corporations ($8.8 trillion).

Market capitalization is a financial analysis term used to describe the current estimated total value of a private company based on its share price. It’s a good rough measure of what a company is worth, at least in the eyes of the market and investors. The market capitalization—or “market cap,” as it is commonly called—is computed as the current share price of a corporation multiplied by the total number of shares of stock outstanding. In theory, if you were to purchase every share of the company’s stock at today’s market price, you would own the entire company.

In roughly similar fashion, we can compute the market capitalization of cities—or at least of their housing stock. We start with Zillow’s estimate of the market value of owner-occupied housing in each of the nation’s largest metropolitan areas which is computed by estimating the current market price of each house in a metropolitan area, and sum that value over all of the owner occupied houses. We also estimate the value of rental housing. For rented units we use a commonly accepted technique of estimating current values based on the income generated from rent. (Americans paid about $535 billion in rent in 2015, according to data compiled by Zillow; we can use this data and some financial formula to estimate the value of rental housing. Details of this calculation are explained below.) Then we add together the value of all owner-occupied housing and the value of rental housing to compute the total market cap of housing in each metropolitan area in the US.

Together, the 50 largest publicly traded private corporations in the United States had a market capitalization of $8.8 trillion at the end of 2015. The total market value of housing in 2015 in the 50 largest metropolitan areas was $22 trillion. For reference, the gross domestic product—the total value of all goods and services produced in the US in 2015—was estimated at $18 trillion. It’s hard to find things measured in trillions of dollars, so we’ve juxtaposed GDP against the market cap of housing and businesses. Keep in mind that the GDP is a flow (trillions of dollars per year) while the value of corporations and housing is a stock (trillions of dollars in value at one-point in time).

The following table shows the market value of housing in each of the nation’s 50 largest metropolitan areas and the current market capitalization of the nation’s 50 largest publicly-traded private sector businesses.

For metro areas, the value of housing is divided into two components (owner-occupied housing) shaded blue, and rental housing (shaded orange).

The most valuable company is Apple, with a market cap of $541 billion; the most valuable metro area is New York, where the market value of owner-occupied and rental housing is $2.9 trillion—more than five times higher. The current market value of Apple is about the same as the current market value of housing in Seattle (the twelfth most valuable market on our list).

Some modest-sized metros have housing that’s worth as much as the entire value of some very well-known corporations: IBM’s market cap ($128 billion) is about equal to Indianapolis housing ($138 billion). Orlando’s housing ($208 billion) is valued at more than 25 percent over all of Disney ($164 billion). Three Seattle-based companies (Microsoft, at $418 billion; Amazon, at $285 billion; and Starbucks, at $84 billion) are worth more combined ($787 billion) than all the housing in Seattle (about $617 billion).

The differences are smaller at the bottom end of our two league tables. The fiftieth largest firm, the oil services company Schlumberger, is worth about $15 billion more than the fiftieth most valuable metro housing market, Buffalo: $82 billion versus $67 billion.

It may seem strange to compare the market value of houses with companies, but this exercise tells us more than you might think. Just as the share price of a corporation reflects an investor’s expectations about the current health and future prospects of a company, the price of housing in a metropolitan area also reflects consumer and homeowner attitudes about the quality of life and economic prospects of that metropolitan area. So, for example, as the price of oil has fallen, weakening growth prospects in the oil patch, it’s quickly translated into less demand and weaker pricing for homes in Houston. Just as stock market investors purchase and value stocks based on the expectation of income (dividends) and capital gains from their ultimate sale, so too do homeowners (and landlords)—they count on the value of housing services provided by their home as well as possible future capital gains should it appreciate.

In fact, these two commodities—housing and stocks—are among the most commonly held sources of wealth in the United States. And while the financial characteristics of the two investments are dramatically different the underlying principle is the same, making market cap is a useful common denominator for assessing the approximate economic importance of each entity.

Each day, the financial press reports the market’s assessment of the value of individual firms, through their stock prices. But viewed through a similar lens, the housing markets of the nation’s cities are by this financial yardstick an even bigger component of the nation’s economy.

Technical Notes

How we computed the value of rental housing. In real estate, the value of rental housing is usually estimated using a “cap rate” capitalization rate, that approximates the rate of return on capital that real estate investors expect from leasing out apartments. To estimate the current market value of apartments, we take Zillow’s estimate of the total amount of rent paid in each market and deduct 35% to estimate “net operating income”—the amount the investor receives after paying maintenance, other operating expenses, and taxes—and then we divide this number by a capitalization (cap) rate of 6%. Both of these figures (net operating income and capitalization rates) are rough estimates—values vary across different times of properties, different markets, and over time with financial conditions (such as with the change in market interest rates).

Many thanks to Zillow’s Chief Economist Svenja Gudell and Aaron Terrazas for doing the hard work here of estimating property values and rental payments. For more keen insights on housing markets, follow their work at Zillow’s Real Estate and Rental Trends blog.

What City Observatory did this week

1. Does Rent Control Work: Evidence from Berlin.  Economists are nearly unanimous about rent control:  they think it doesn’t work. Berlin’s recent adoption of a new rent control scheme in 2015 provides a new test case to see if they’re right.  An early analysis of the Berlin program shows that it’s done little to reduce rents, and even though the program was intended to address affordability problems for low and moderate income households, most of the benefits have gone to those renting the most expensive apartments.

2. Does Cyber-Monday mean package delivery Gridlock tuesday?  The growing volume of e-commerce has led some pundits to worry that city streets will be clogged by delivery vehicles.  But while we are getting many more packages at our homes, the growth of actual truck traffic has been much slower, in large part because growing volumes produce economies of scale for shippers.  More packages mean higher delivery density, more stops per mile traveled, and less energy, pollution and labor per package delivered.  In addition, e-commerce purchases mean fewer shopping trips. On balance, e-commerce is likely to reduce, rather than increase overall traffic.

3. Destined to disappoint: Housing lotteries.  The demand for affordable housing is so great and the supply of subsidized housing so small, that cities frequently have to resort to lotteries to allocate units to deserving households. An analysis of New York City’s lotteries for the past three years showed that nearly half of all winners fell into the 25 to 34 year old age category, leading to speculation that the lottery is somehow tilted in favor of young adults.  We look at the population that’s likely to be seeking an rental apartment in New York, and find little discrepancy between the population and lottery winners.  The bigger problem with lotteries is that so few units are available:  Fewer than two-tenths of all the households moving to an apartment in New York in the past year were lottery winners.

4. Why biotech strategies are often 21st Century snake oil.  Cities and states around the nation have invested hundreds of millions of dollars in public funds in efforts trying to make themselves the next hub of biotechnology.  But like many biotech ventures themselves, this is a high-cost, high-risk undertaking. In one particularly epic example, a small town in Minnesota spent more than $30 million in state and federal funds on highway improvements for its biotech park, based in large part on the assurances that a prominent national biotech analyst could provide a $1 billion venture fund.  You’ll never guess what happened next.

Must Read

It’s been an incredibly prolific week for “must read” articles, so we’re highlighting a few more than usual.  We have two very insightful commentaries on road safety and inclusionary zoning, and four articles dissecting the results of the November 8 national election (hopefully we’ve reached peak political post-mortem).

1. The real reason the US has so many traffic deaths.  The surge in crashes and traffic deaths in the past few years has re-kindled concern about road safety, and prompted a wave of media reports pointing a finger of blame at texting while driving.  At City Observatory, we’ve been skeptical of this explanation. Now, Vox has published a comprehensive essay from by Norman Garrick, Carol Atkinson-Palombo, and Hamed Ahangari , reminding us of the big structural reasons why American traffic deaths are so much higher than in other countries–and it has almost nothing to do with texting.  Not only do Americans drive many more miles (or kilometers, if you prefer), but added driving has been spurred on by cheaper gas prices.  Garrick and his co-authors conclude that the recent increase in crash rates and deaths is almost fully explained by the decline in gas prices and lower unemployment rates. That’s not to say texting in a car is a good idea, but our road safety problems are more fundamental and deep-seated.

2. In many cities, inclusionary zoning–mandating that those building new housing in cities include a fixed proportion of affordable units–is seen as an easy way to force developers to solve the affordability problem, at no cost to the public.  Writing at the Sightline Institute, Dan Bertolet and Alan Durning consider whether inclusionary zoning is the most promising or most counter-productive strategy for tackling this problem.  They argue that uncompensated inclusionary zoning–where the costs of added units are borne entirely by the developer–simply pushes up the market price of housing, reduces the number of new units built, and actually makes housing affordability problems worse.  In theory, they say that if developers costs are compensated or offset (by some combination of density bonuses, faster permit approvals, lessened parking requirements or tax breaks) that these negative effects could be reduced or eliminated.  While that’s likely to be true, the very practical question that gets begged in this analysis–and in most IZ debates–is whether these “offsets” are large enough to truly cover the higher costs.  This article is a thoughtful exploration of many of the points that come up in debates over inclusionary zoning.  Its an absolute must read for anyone who cares about housing affordability.

3. The election, by metro area.  Mapping how America’s metro areas voted.  Richard Florida breaks out the election returns by metropolitan area, finding that most large metropolitan areas voted for Hillary Clinton, while most smaller ones voted for Donald Trump.  Clinton won more than three-quarters of the votes in the San Francisco metro area and more than two-thirds of the votes cast in the San Jose, Washington and New York metro areas.  Of the nation’s largest metropolitan areas (those with a million or more population), ten  of them (including Oklahoma City, Dallas, Pittsburgh and Cincinnati) awarded at least a majority of votes to Donald Trump; in five other metropolitan areas Trump won a plurality of the Presidential vote.

4. The election, by productivity.  In “Another Trump-Clinton Divide,” the Brookings Institution’s Mark Muro slices county-level election returns by gross domestic product.  He finds that the most economically productive counties in the US (again, overwhelmingly in large metro areas) tended to vote strongly for Hillary Clinton.  In all, the counties that voted blue in 2016 accounted for 64 percent of US GDP, compared to only 36 percent of GDP in red counties.  The economic disparity between red and blue counties has apparently widened.  In the  similarly close 2000 presidential election, counties that voted for Al Gore produced 54 percent of US GDP, compared to 46 percent for the counties who voted for George W. Bush. (Imagine electoral votes were apportioned by economic output).

5. The election, by tech-based economic development.  The Economist’s “Graphic Detail” feature further sharpens this economic view of politics by looking at how tech-dominated counties voted in the election.  (There’s a fair amount of overlap here between high tech counties and high productivity ones).  Their summary:  “In counties that favoured Democratic presidential candidates between 2000 and 2016, employment in high-tech industries grew by over 35%. In Republican-leaning counties, such employment actually fell by 37%. Today, there are more than three times as many high-tech industry workers in places that voted for Hillary Clinton as there are in those that favoured Mr Trump.”  Something on the order of 90 percent of the nation’s employment in computer manufacturing, software publishing and information services is located in counties that voted Democratic in the 2016 election.

New Knowledge

1. The 500-pound gorilla in US retailing is the fast-growing e-commerce behemoth, Amazon.  There’s little question that its growth has had a significant effect on the retail landscape, contributing first to the decline of independent bookstores, and more recently, it is argued, to the overall shrinkage of the number of retail establishments in the US.  A new report from the Institute for Local Self-Reliance–Amazon’s Stranglehold: How the Company’s Tightening Grip is Stifling Competition, Eroding Jobs, and Threatening Communities, —  takes a comprehensive and critical look at Amazon’s growth and impacts.  There’s a huge amount of information here, addressing everything from the growth of e-commerce and Amazon’s market share, to working conditions in Amazon warehouses, and the competitive effect.  While the report’s tone can be a bit hyperbolic, and its title and chapter heads leave little doubt as to the authors’ feelings  — “monopolizing the economy, undermining jobs and wages, weakening communities” — there’s plenty of hard data as well.

2.  More evidence on lead and crime. A growing body of research points to the substantial role that exposure to lead played in determining crime rates in US cities. While much of the research examines the correlation between atmospheric lead (from burning leaded gasoline) and the rise and subsequent decline in urban crime rates, a new study takes a look at a different source of exposure: lead water pipes. Many cities routinely used lead water pipes at the end of the 19^th century, and by comparing crime rates in cities with lead and iron water pipes, James Feigenbaum and Christopher Muller are able to tease out the connection between lead exposure and city crime. In their paper “Lead Exposure and Violent Crime in the Early Twentieth Century, they show that cities with lead water pipes had crime rates that were 24 percent higher than cities that didn’t use lead.

Thanks to technological innovations, our lives are in many ways better, faster, and safer: We have better communications, faster, cheaper computing, and more sophisticated drugs and medical technology than ever before. And rightly, the debates about economic development focus on how we fuel the process of innovation. At City Observatory, we think this matters to cities, because cities are the crucibles of innovation, the places where smart people collaborate to create and perfect new ideas.

While the emphasis on innovation is the right one, like any widely accepted concept, there are those who look to profit from the frenzy of enthusiasm and expectation.

Around the country, dozens of cities and many states have committed themselves to biotech development strategies, hoping that by expanding the local base of medical research, that they can generate commercial activity—and jobs—at companies that develop and sell new drugs and medical devices. There’s a powerful allure to trying to catch the next technological wave, and using it to transform the local economy.

Over the past decade, for example, Florida has invested in excess of a billion dollars to lure medical research institutions from California, Massachusetts and as far away as Germany to set up shop in the Sunshine State. Governor Jeb Bush pitched biotech as a way to diversify Florida’s economy away from its traditional dependence on tourism and real estate development.

Of course it hasn’t panned out; Florida’s share of biotech venture capital—a key leading indicator of commercialization—hasn’t budged in the past decade. And several of the labs that took state subsidies are down-sizing or folding up their operations as the state subsidies are largely spent. Massachusetts-based Draper Laboratories (which got $30 million from the state) recently announced it was consolidating its operations at its Boston headquarters and closing outposts in Tampa and St. Petersburg—in part because they were apparently unable to attract the key talent that they needed. The Sanford-Burnham Institute, which got over $300 million in state and local subsidies, is contemplating leaving town and turning its Orlando facilities over to the local branch of the University of Florida.

And while Florida’s flagging biotech effort might be well-meant but unlucky, in one recent case, the spectacular collapse of a development scheme has to be chalked up to outright fraud. As the San Francisco Chronicle’s Thomas Lee reports, both private and public investors have succumbed to the siren song of biotech investment. Last month, the Securities and Exchange Commission issued a multi-million dollar fine, and a lifetime investment ban, to Stephen Burrill, a prominent San Francisco-based biotech industry analyst and fund manager. Burrill diverted millions of dollars meant for biotech startups funds to his personal use. Not only that, but Burrill was a key advisor to a private developer who landed $34 million in state and federal funds to build a highway interchange to service a proposed biotech research park in rural Pine Island, Minnesota, based on Burrill’s promise he could raise a billion dollar investment fund to fill the park with startups. In the aftermath of the SEC action, Burrill is nowhere to be found, and the Elk Run biotech park sits empty.

But puffery and self-dealing are nothing new on the technological frontier or indeed, in the world of economic development. The most recent example, biomedical equipment maker Theranos, which claimed that it had produced a new technology for performing blood tests with just a single drop of blood. The startup garnered a $9 billion valuation, and conducted nearly 2 million tests before conceding that its core technology didn’t in fact work. Theranos has told hundreds of thousands of its patients that their test results are invalid. As ZeroHedge’s Tyler Darden relates, the company rode a wave of fawning media reports that praised its disruptive “nano” breakthrough technology (WIRED) and lionized its CEO as “the world’s youngest self-made female billionaire” and “the next Steve Jobs.” All that is now crashing to earth.

When it comes to biotech breakthroughs, consumers, investors and citizens are all easy prey for the hucksters that simultaneously appeal to our fear of illness and disease and our hope—borne from the actual improvements in technology—that theirs is just the next step in a long chain of successes. Investors pony up their money for biotech—even though nearly all biotech firms end up money losers, according to the most comprehensive study, undertaken by Harvard Business School’s Gary Pisano. And as my colleague Heike Mayer and I pointed out nearly a decade ago, it’s virtually impossible for a city that doesn’t already have a strong biotech cluster to develop one now that the industry has locked into centers like San Francisco, San Diego and Boston.

At first glance, biotech development strategies seemed like political losers: you incur most of the costs of building new research facilities and paying staff up front, and it takes years, or even decades for the fruits of research to show up in the form of breakthroughs, products, profits and jobs. No Mayor or Governor could expect to still be in office by the time the benefits of their strategy were realized. But as it turns out, the distant prospects of success always enable biotech proponents to argue that their efforts simply haven’t yet been given enough time (and usually, also resources) to succeed. And likewise, no one can pronounce them failures. When asked why the struggling Scripps Institute in West Palm Beach hadn’t produced any of the spin off activity expected, local economic developers had a read explanation, reported the Palm Beach Post:

“Biotech officials urge patience and repeat the mantra that a science cluster needs decades to evolve. “This takes a lot of time to develop,” said Kelly Smallridge, president of the Business Development Board of Palm Beach County.”
“The biotech bonanza Jeb Bush hoped for? It didn’t go as planned,” Palm Beach Post, June 15, 2015

So rather than being a liability, the long gestation period of biotech emerges as a political strength. Apparently, you’ve got to give the snake oil just a little bit more time to kick in.

In the first installment of our “Sprawl Tax” series, we explained how laws and patterns of development that make our homes, businesses, and schools farther apart cost us time and money—on average, nearly $1,400 a year per commuter in America’s 50 largest metropolitan areas. In the second installment, we showed how the Sprawl Tax is levied much more heavily on Americans than our international peers, with US commuters paying a much larger proportion of their income on transportation and spending much more time on their trips to and from work than people in other wealthy countries.

Today, we want to talk about another cost of sprawl, and the greater distances it forces us to travel: Our quality of life. Powerful evidence suggests that longer commutes make us individually less happy and less healthy, in addition to having detrimental effects on our communities. In recent years, behavioral economics has made great strides in determining how different factors influence our happiness. Consistently, this literature finds that long commutes are strongly associated with lower levels of “subjective well-being”—the technical term that researchers use to describe “happiness.”

One study from Germany, for example, calculated that reducing one’s daily commute time from 23 minutes each way (the German average) to zero minutes would produce an increase in happiness equal to about an 18 percent increase in income. Research in other countries, including the United States, has produced similar results.

In a survey of working women in Texas, behavioral economist Daniel Kahneman and his collaborators found that time spent commuting had the lowest positive ratings of all daily activities.

Other studies have confirmed that commute distances are correlated with happiness and health. The Gallup Healthways Index shows that Americans with longer commutes report lower levels of subjective well-being. The data also show that long commutes are correlated with a higher incidence of back pain, obesity, and high cholesterol.

Source: Gallup

We also have detailed data from a survey taken by the state of Connecticut. For nearly every income group, self-reported well-being declined as commute distance increased. The chart below shows that relationship. The power of commuting distance was such that low-income households (making under $30,000) with a roundtrip commute of 40 minutes or less reported being as happy as households making roughly twice as much money (between $50,000 and $75,000), but with commutes of 80 minutes or more.

It’s not a surprise, then, that average commute times are also correlated with satisfaction with the local transportation system itself. Using data from a survey of homeowners commissioned by Porch, an online home improvement information firm, and the median commute length as calculated by the Brookings Institution, we can see a strong negative correlation between metro area commute times and satisfaction with the region’s transportation system: the longer the median commute, the less satisfied homeowners are.

Conversely, it turns out that transportation satisfaction is almost completely uncorrelated with “congestion”—at least as it’s often measured. As you can see below, the Urban Mobility Scorecard ratings of metropolitan traffic congestion calculated by the Texas Transportation Institute bear almost no relationship to whether homeowners report being satisfied with their region’s transportation system. If anything, congestion is associated with more satisfaction.

Taken together, this analysis suggests that overall commute distances—and not traditional measures of traffic congestion—are the chief factor influencing homeowner perceptions about transportation.

Finally, there is evidence that longer commutes have social, as well as personal, costs. Robert Putnam reported that each additional ten minutes of commute time reduces social capital—things like church-going, civic participation, club attendance—by 10 percent.

As we’ve shown, Americans around the country bear the financial burden of the sprawl tax. But sprawling car dependent development patterns don’t just end up costing us time and money. The long commutes they engender also make us less happy. They’re correlated with lower levels of mental and physical health, and reduce our social capital. Among metropolitan areas, long commutes—and not traffic congestion—are what we find least satisfactory about our transportation systems.

In our first post on the “Sprawl Tax,” we’ve explored the ways that our decisions about how to build American cities have imposed significant costs—in money, time, and quality of life—on all of us. We pay more to drive more, spend more time traveling instead of being at our destinations, and as a result deal with more stress, than we would if our destinations weren’t so widely separated from one another.

But the sprawl tax isn’t equally costly for everyone. We’ve shown how cities that are more compact enable their residents to spend significantly less time and money on transportation than less compact cities.

And what about beyond our borders? When we compare the typical American to her counterpart in other rich countries, it’s clear that the sprawl tax is a national concern, dragging down our disposable income and free time relative to residents of other countries.  To illustrate the international dimension of the sprawl tax, we draw on data on travel time and transportation spending compiled by Stephen Redding and Matthew Turner in a recent paper on the connections between transportation infrastructure and urban form.

Household Budgets

The table below shows the fraction of household income that people in 15 European countries, Canada, and the US countries spent on transportation from 2005 to 2009, as well as how much time the average worker in each of these countries spends on a daily roundtrip commute. Let’s take the spending side first.

During this time, the average American household spent approximately 18 percent of its budget on transportation. Among the other 16 countries, none spent more than 16 percent, and the average spending level was just 12.8 percent. That means a typical US household spent about five percentage points more of its income on transportation than the residents of other developed countries—which translates to about $1,500 every year.

Here’s how we arrive at this figure: In 2007 (the median year of the estimates presented above) disposable household income in the United States was $31,000 compared to an average of $21,000 in these other nations, according to estimates from the World Bank, based on purchasing power parities that adjust for price differences between countries. If US households spent the same share of their incomes on transportation as did the households in the typical high income country, they would have spent about $1,500 less (.05 * 31,000) on transportation.

Commute Time

The average American worker spent about 51 minutes commuting between home and work and back again—more than all but one other country in the sample. That exception was Canada, where the typical worker spent 63 minutes on their commute—but the rest were lower, all the way down to Portugal, where roundtrip commutes took up just 29 minutes per worker per day. For the other 15 nations examined the average commute time was 39 minutes—about 12 minutes less per day than in the US.

This means that over the course of a year with 250 working days, the typical American commuter spends about 51 more hours (12 minutes times 250 days) commuting to and from their place of work than workers in other high income countries. Valued at $15 per hour, the additional cost of commuting to US workers comes to $770 per worker worker per year.

The International Sprawl Tax

Compared to other high income nations, we spend about $1,500 per household on transportation costs and about $770 per worker more on commute time costs. While there are many reasons for these disparities, in large part, they reflect American cities’ more sprawling development patterns, and the represent a sprawl tax that is paid by Americans.

There’s little question that housing affordability is a growing problem in many cities around the country. Rents have been rising faster than incomes, especially for low- and moderate-income households.

One of the most widely touted policy responses is “inclusionary zoning,” which requires developers who build new housing to set aside at least a portion (typically 10 to 20 percent) of new units that will be sold or rented for less than the market price.

In many respects, inclusionary zoning seems like a win-win, free lunch policy: by making developers pay for new affordable housing, these new homes don’t directly cost taxpayers a dime. But developers have to make up the cost of these below-market units somewhere, and typically it will be by passing the costs on to the buyers of the market rate units in their development. At least one study* suggests that this results in higher prices. In some cases, cities offer density bonuses to developers to ease the financial burden of constructing below market units, but it’s far from clear that the bonuses cover the additional costs, plus the uncertainty and negotiation that attends these frequently discretionary approval processes adds to costs.

But the larger problem with inclusionary zoning requirements is that they may simply not be up to the scale of the problem. Although dozens of jurisdictions have enacted inclusionary zoning requirements, they simply haven’t produced many units of housing. Consider New York City’s decade-old policy. In many ways, New York ought to be a perfect place for inclusionary zoning, which tends to do best in hot real estate markets. But in one of the nation’s hottest housing markets, in its largest city, inclusionary zoning produced about 2,800 units of affordable housing its its first decade—about 280 per year, in a metropolis of over eight million people.

Most inclusionary zoning programs are much smaller, and cities have less leverage with developers because market-rate development is not nearly as profitable as it is in robust markets like New York. A recent compendium of inclusionary zoning programs showed that only six cities nationally operated inclusionary zoning programs that had produced more than 100 units per year, and just one jurisdiction—Montgomery County, Maryland, a high income suburb of Washington, DC—accounted for nearly half of all inclusionary zoning units.

The fundamental problem with inclusionary zoning is also its primary advantage: it asks for, and receives, virtually no taxpayer money. But skimming off the top of developer profits is almost by definition an inadequate source of funding for affordable housing, particularly in places like New York and San Francisco where the need is most acute. All newly built housing is generally a fraction of one percent of a city’s housing market in any given year; housing that triggers inclusionary requirements is less than that; and you then have to reduce that number by 80 to 90 percent to get to the 10 to 20 percent set-aside of affordable units. It’s not an accident that Montgomery County has built so much inclusionary housing, relatively speaking—it’s also built vastly more housing, period, than most cities, nearly doubling its population since 1970. How many inclusionary housing advocates in other parts of the country are eager for such a breakneck pace of development?

Solutions, then, are likely to require some actual tax money. One possibility: dedicate a portion of the added property tax revenue from new real estate construction to subsidizing affordable housing. Portland, Oregon has dedicated about a third of such revenues to affordable housing, and has built more than 2,300 units of affordable housing in one neighborhood near downtown—nearly as much as New York’s affordable housing ordinance has produced in the five boroughs of New York. Portland has dedicated $67 million on tax increment funds over the next decade to support affordable housing in the city’s fast changing neighborhoods. Also, unlike inclusionary zoning, using tax increment financing doesn’t have the undesirable side effect of driving up the price of market rate housing or constricting the supply of market rate units.

Ultimately, a solution that addresses the scale of the nation’s affordability problems will have to tackle the nation’s highly skewed subsidies to homeownership by higher income households. The combination of the mortgage interest deduction, property tax deduction, capital gains exemption and the non-taxation of imputed rents amounts to a federal subsidy to owner-occupied housing on the order of $250 billion per year, most of which goes to the nation’s highest income households. There’s a lot we could do: like expand funding for rental vouchers, which reach only 22 percent of those who qualify. Or tap the capital gains that accrue to homeowners (in substantial part due to the constriction of housing supply by zoning regulations. But it should be clear that feel good programs like inclusionary zoning are mostly a token response to a problem of much more substantial dimension.

* See: Schuetz, Meltzer & Bean, Silver bullet or trojan horse? The effects of inclusionary zoning on local housing markets in the United States, Urban Studies 2011;48(2):297-329. The authors note that most inclusionary zoning programs have had a modest scale relative to housing markets, and conclude: “Results from suburban Boston suggest that IZ has contributed to increased housing prices and lower rates of production during periods of regional house price appreciation. In the San Francisco area, IZ also appears to increase housing prices in times of regional price appreciation, but to decrease prices during cooler regional markets. There is no evidence of a statistically significant effect of IZ on new housing development in the Bay Area.”

In auto-land, pedestrians are just one more patented gimmick away from being safe

The dominant approach to automobile safety has, for many years, been the quintessential technical fix. Some combination of new technologies (anti-lock brakes, collapsible steering columns, crush zones, multiple air bags, etc) will make cars safer and safer (well, at least for their occupants).  And soon, self-driving cars will (it is hoped) eliminate human errors that produce most crashes. 

Still the humble pedestrian remains under-engineered for this brave new world of technologically assured density. But that’s changing.

Some months back, we noted that Google unveiled drawings of a novel plan to coat the exterior of self-driving cars with a special adhesive that would cause any pedestrians the vehicles struck to adhere to the car rather than being thrown by the impact.  Now, Automotive News is sharing reports of a new General Motors patent that would put airbags on the outside of cars to deflect errant pedestrians.

Whether it would be better to find oneself stuck to the car that struck you, or being pushed aside by an exploding airbag, is far from clear. But let it not be said that automotive engineers and major corporations are the only ones who can come up with such far-fetched ideas. Here at City Observatory, we’ve come up with our own concepts for, if you will, lessening the impact of cars on pedestrians. In the interest of safety and advancing the state of the art, we’re putting our ideas into the public domain, and not patenting any of them.

Personal airbags. Airbags are now a highly developed and well-understood technology. Most new cars have a suite of frontal impact, side curtain and auxiliary airbags to insulate vehicle passengers from collisions. The next frontier is to deploy this technology on people, with personal airbags. Personal airbags could have their own sensors, inflating automatically when the pedestrian was in imminent danger of being struck by a vehicle.

Rocket Packs. While a sufficiently strong adhesive might keep a struck pedestrian from flying through an intersection and being further injured, perhaps a better solution would be to entirely avoid the collision in the first place by lifting the pedestrian out of the way of the collision in the first place. If pedestrians were required to wear small but powerful rocket packs, again connected to self-driving cars via the Internet, in the event of an imminent collision, the rocket pack could fire and lift the pedestrian free of the oncoming vehicle.

We offer these ideas partly in jest, but mostly to underscore the deep biases we have in thinking about how to adapt our world for new technology.  Let’s be clear: while these are labeled “pedestrian safety” technologies, they are really about making the environment better for cars and driving. Like crosswalks and electronic “walk-don’t walk” signs, these are not technologies that are needed in pedestrian-only environments. No matter how crowded, no mall, stadium, or concert hall has stoplights for pedestrians.

It has long been the case with private vehicle travel that we’ve demoted walking to a second class form of transportation. The advent of cars led us to literally re-write the laws around the “right of way” in public streets, facilitating car traffic, and discouraging and in some cases criminalizing walking. We’ve widened roads, installed beg buttons, and banned “jaywalking,” to move cars faster, but in the process making the most common and human way of travel more difficult and burdensome, and make cities less functional. And at some point, the existence of these kinds “pedestrian protection” technologies becomes a basis for rationalizing making the physical environment even more hostile to actual humans. The ultimate objective of much engineering practice seems to be to exterminate walking (if not walkers). As one Washington State engineer bluntly described Seattle’s 1950s era transportation plan: “Pedestrians, who are a constant hazard to city driving, are entirely removed.”

Everywhere we’ve optimized the environment and systems for the functioning of vehicle traffic, we’ve made places less safe and less desirable for humans who are not encapsulated in vehicles. A similar danger exists with this kind of thinking when it comes to autonomous vehicles; a world that works well for them may not be a place that works well for people.

Not all of our problems can be solved with better technology. At some point, we need to make better choices and design better places, even if it means not remaking our environment and our communities to accommodate the more efficient functioning of technology.

Thanks to Matt Cortright for providing the diagrams for our proposed pedestrian protection devices.

For many, it’s all but a certainty that our world will soon be full of self-driving cars. While Google’s self-driving vehicles have an impressive safety record in their limited testing, it’s just a matter of time until one is involved in a serious crash that injures someone in a vehicle, or a pedestrian.

So, in a way, it’s good news that Google is devoting some of its considerable intellectual energy to figuring out ways that we might lessen the seriousness of pedestrian injuries in the event of such collisions. Earlier this month, Google unveiled plans for a novel plan to coat the exterior of self-driving cars with a special adhesive that would cause any pedestrians the vehicles struck to adhere to the car rather than being thrown by the impact.

Whether it would be better to find oneself stuck to the car that struck you, rather than being pushed aside, is far from clear. But pedestrian safety in a world of self-driving cars is clearly an issue that needs to be dealt with.

Here at City Observatory, we’ve come up with our own concepts for, if you will, lessening the impact of autonomous cars on pedestrians. In the interest of safety and advancing the state of the art, we’re putting our ideas into the public domain, and not patenting any of them.

Pedestrian Shock Bracelets. Most pedestrians are already instrumented, thanks to cell phones, and a large fraction of pedestrians have fit-bits, apple watches and other wearable, Internet-connected devices. We propose adding a small electroshock device to these wearables, and making it accessible to the telematics in autonomous vehicles. In the event that the autonomous vehicle’s computer detected likelihood of a car-pedestrian collision, it could activate the electroshock device to alert the pedestrian to, say, not step off the curb into the path of an oncoming vehicle.

Personal airbags. Airbags are now a highly developed and well-understood technology. Most new cars have a suite of frontal impact, side curtain and auxiliary airbags to insulate vehicle passengers from collisions. The next frontier is to deploy this technology on people, with personal airbags. Personal airbags could have their own sensors, inflating automatically when the pedestrian was in imminent danger of being struck by a vehicle.

Rocket Packs. While a sufficiently strong adhesive might keep a struck pedestrian from flying through an intersection and being further injured, perhaps a better solution would be to entirely avoid the collision in the first place by lifting the pedestrian out of the way of the collision in the first place. If pedestrians were required to wear small but powerful rocket packs, again connected to self-driving cars via the Internet, in the event of an imminent collision, the rocket pack could fire and lift the pedestrian free of the oncoming vehicle.

We offer these ideas partly in jest, but mostly to underscore the deep biases we have in thinking about how to adapt our world for new technology.

It has long been the case with private vehicle travel that we’ve demoted walking to a second class form of transportation. The advent of cars led us to literally re-write the laws around the “right of way” in public streets, facilitating car traffic, and discouraging and in some cases criminalizing walking. We’ve widened roads, installed beg buttons, and banned “jaywalking,” to move cars faster, but in the process making the most common and human way of travel more difficult and burdensome, and make cities less functional.

Everywhere we’ve optimized the environment and systems for the functioning of vehicle traffic, we’ve made places less safe and less desirable for humans who are not encapsulated in vehicles. A similar danger exists with this kind of thinking when it comes to autonomous vehicles; a world that works well for them may not be a place that works well for people.

Consider this recent “Drivewave” proposal from MIT Labs and others to eliminate traffic signals and use computers to regulate the flow of traffic on surface streets. The goal is to allow vehicles to never stop at intersections, but instead travel in packs that create openings in traffic on cross streets that allowed crossing traffic to flow through without delay. Think of two files of a college marching band crossing through one another one a football field.

It’s thoroughly possible to construct a computer simulation of how cars might be regulated to enable this seamless, stop-free version of traffic flow. But this worldview gives little thought to pedestrians—the video illustrating drivewave doesn’t show any pedestrians, although the project description implies they might have access to a new form of beg button to part traffic flows to enable crossing the street. That might be technically feasible, but as CityLab’s Eric Jaffe pointed out, “it would be a huge mistake for cities to undo all the progress being made on human-scale street design just to accommodate a perfect algorithm of car movement.”

Not all of our problems can be solved with better technology. At some point, we need to make better choices and design better places, even if it means not remaking our environment and our communities to accommodate the more efficient functioning of technology.

Thanks to Matt Cortright for providing the diagrams for our proposed pedestrian protection devices.

What the obsession with technological fixes says about how we fail to prioritize people in cities

In the best traditions of engineering, clever minds are working on new technologies that can prevent or reduce the carnage on our nation’s roadways. A couple of years ago, we note that Google had patented a technology to coat self-driving cars with a special adhesive that would cause any pedestrians the vehicles struck to adhere to the car rather than being thrown by the impact.

Appalled, and with tongue firmly in cheek, we offered up three of our own equally absurd technological fixes for the pedestrian “problem,” including vehicle-activated shock bracelets that would paralyze pedestrians before they could jay-walk, rocket packs that could lift errant pedestrians out of a vehicle’s path, and perhaps most fancifully, pedestrian mounted air-bags, that could cushion pedestrians struck by vehicles.

But, as is often said, truth is stranger than fiction.  Because its now apparently the case that General Motors (and several other automobile manufacturers) are patenting passenger airbags, although their concept is that the airbags be mounted on the outside of vehicles, rather than on the pedestrian.  Here’s the drawing for GM’s idea, from the Automotive News:

Ultimately, the purpose of such technologies is not to make cities safer for people, but to make them more universally available to cars and car travel. It acknowledges that we’re going to design our urban space around stroads that inherently put fast-moving cars in conflict with people on foot. It has long been the case with private vehicle travel that we’ve demoted walking to a second class form of transportation. The advent of cars led us to literally re-write the laws around the “right of way” in public streets, facilitating car traffic, and discouraging and in some cases criminalizing walking. We’ve widened roads, installed beg buttons, and banned “jaywalking,” to move cars faster, but in the process making the most common and human way of travel more difficult and burdensome, and make cities less functional.

Everywhere we’ve optimized the environment and systems for the functioning of vehicle traffic, we’ve made places less safe and less desirable for humans who are not encapsulated in vehicles. A similar danger exists with this kind of thinking when it comes to autonomous vehicles; a world that works well for them may not be a place that works well for people. As CityLab’s Eric Jaffe pointed out, “it would be a huge mistake for cities to undo all the progress being made on human-scale street design just to accommodate a perfect algorithm of car movement.”

Not all of our problems can be solved with better technology. At some point, we need to make better choices and design better places, even if it means not remaking our environment and our communities to accommodate the more efficient functioning of technology.

Thanks to Matt Cortright for providing the diagrams for our proposed pedestrian protection devices.

A trio of reports released in the past week provide new data showing the economic strength of the nation’s cities.

Whether we look at population growth, new business formation, or job creation, big cities, urban centers and close-in urban neighborhoods are big drivers of national growth. While the data are drawn from different sources and use slightly different geographies, the messages are quite similar.

The Economic Innovation Group, a Washington-based think tank, used county-level data from the Census Bureau’s County Business Patterns program to chart net new business formation and job growth. Over the period 2010-2014, the 20 counties with the largest increase in new businesses (all big urban counties) accounted for a majority of all net new business formation in the current economic expansion. The 20 counties with the largest job increases accounted for 28 percent of all new jobs. This represents a dramatic turnaround from previous expansions in the 1990s and early 2000s, when smaller, less populous counties tended to grow faster. The dwindling rate of new firm formation is a topic of growing concern as we think about the nation’s long term growth prospects; in the current recovery, a few large metropolitan areas have played a dramatically disproportionate role in fueling new business activity.

In the 1990s, there was a strong negative correlation between county population and job growth rates, meaning that less populous counties grew much faster than more populous ones. But in the past four years, that relationship has reversed. Small counties are growing the most slowly; larger counties are growing more rapidly. Counties with a million or more residents grew only half as fast as those with fewer than 100,000 residents in the mid-90s (7.7 percent vs. 16 percent); and the larger counties grew more than twice as fast (9.9 percent vs 4.4 percent) since 2010.

The Brookings Institution’s Bill Frey crunched the numbers from the latest (2015) Census population estimates to track population growth in the nation’s 50 largest metropolitan areas, comparing growth rates in the principal city in each metropolitan area, with the remaining jurisdictions. As Frey notes, principal cities—the largest municipality in each metropolitan area—are growing faster than the remaining portion of metropolitan areas, reversing a long-standing pattern of suburban growth outpacing city growth. Cities have grown faster than suburbs since 2010.

And at City Observatory, we’ve released our updated figures on central city job growth. Using fine-grained establishment level data from the Census Bureau’s Local Employment and Housing Dynamics (LEHD) database, we’ve plotted employment change for the three mile radius surrounding the center of the central business district in the nation’s large metropolitan areas. While job growth in urban centers lagged well behind suburbs a decade ago, job growth rates urban centers are today very similar to those in more suburban locations. Bloomberg View columnist Justin Fox addressed the findings of our report in a recent column, and highlighted the pattern of change over time:

While these three reports draw on different data sources, and use somewhat different geographies (large counties, principal cities and a three-mile radius) they tell very similar stories about the persistence of urban-led population and economic growth in the US at least through the middle of the present decade.

More jobs, more businesses, more people. These three reports add to a growing body of data suggestion that large metropolitan areas and urban centered economies are increasingly driving national economic prosperity.

As we’ve noted before, using city and county boundaries to measure differences between “cities” and “suburbs” and particularly to make comparisons across metropolitan areas can be problematic. City and county boundaries often don’t correspond well to patterns of urbanization, and the scope of a largest county or principal city varies widely across metropolitan areas.

The US faces a shortage of cities. More and more Americans, especially talented, young workers with college degrees, are looking to live in great urban locations. As we’ve explored at City Observatory, the demand for urban living has increased faster than the supply of great urban spaces—with the predictable result that the price of land is appreciating faster in cities. We’ve pointed to a growing body of data—stronger residential price growth in urban cores, faster appreciation for homes in more walkable areas, a more rapid growth of office rents in walkable locations—all of which signal the growing market value of living and working in cities.

This trend also clearly manifests itself in the residential rental marketplace. New data from multifamily market analyst RealPage shows that apartments in big cities have seen higher rent increases than in smaller ones.

The RealPage data enable us to divide the US top 100 US markets into three broad groups: the nine hottest coastal markets, the remaining 41 of the 50 largest metropolitan areas, and 50 smaller metropolitan areas. Data shown here are for the fourth quarter of 2008 and 2015 and are expressed in inflation-adjusted dollars. Percentages shown are the percent increase in real, inflation-adjusted rents over the entire seven-year period.

The most obvious point is that the large, hot markets are showing the biggest increases, at all price points. The nine markets are all on the East Coast (New York, Boston, Washington) and West Coast (Southern California, the San Francisco Bay area and Seattle). In the nine hot markets, median rents increased about 13 percent, compared to 8 percent for other metro areas in the top 50 and just 2 percent for metros ranked 50th through 100th in size.

As interesting as the pattern of change is across markets, the change within metro markets is also instructive. In every size market, rents are increasing fastest in the highest priced segment of the market place. RealPage reports the average rents for the median apartment, as well as the apartments representing the 25th and 75th percentiles of the marketplace. Think of the 75th percentile as representing the cutoff for the top one-quarter “nicest” apartments, and the 25th percentile representing older, less desirable and more affordable apartments. (We explored the importance of looking at all segments of the rental marketplace to understand affordability a few months back; in many ways, having a broad range of price points within the market is a better indicator of affordability than the median).

The most expensive apartments in the most expensive cities are seeing the fastest rate of price appreciation. For example, in the top nine coastal markets, rents for the 75th percentile apartment increased by about 23 percent, while apartment rents for the median and 25th percentile apartments increased only about half as fast (10 percent and 12 percent respectively). What this signals is that the demand for urban living is being fueled by the preferences of high income households rather than simply a generalized increase in rents that affects every segment of the marketplace equally.

The next 41 largest metropolitan areas–those rounding out the list of the fifty largest metropolitan areas have lower rates of price appreciation overall, but exhibit the same within metro pattern of greater price increases at the higher end of the market. Rents at the 75th percentile increased about 13 percent between 2008 and 2015, but only about 8 percent for the median apartment and 7 percent at the 25th percentile.

What this signals is robust demand for high-end apartments in the nation’s largest and hottest markets. As RealPage economist Shane Squires points out in his commentary on this data, much of the demand for new, high-end product is in walkable locations in cities, where it doesn’t compete directly with more affordable (but far less accessible) suburban locations. This is also consistent with the hypothesis offered by Edlund, Machado and Sviatchi, that time-pressured, dual income households are increasingly willing to pay higher rents in urban centers for better accessibility to jobs and consumption opportunities.

The RealPage data provide nuanced insights into rental price trends that aren’t fully reflected in the usual measures that talk only about the “median” apartment. They show that rental prices are changing at very different rates in different markets, and at different price points with metropolitan markets. This more detailed view of where–and why–rents are increasing should be a useful guide to policy makers as they consider how to define and grapple with housing affordability problems.

(RealPage makes its estimates based on data drawn from lease transactions covering about 10 million apartments nationwide.)

There’s more evidence that housing market supply is beginning to catch up to demand in a way that is likely to moderate rent increases. Nothing, it seems, is more infuriating to those caught in a market of steady rent hikes that being lectured by some economist that what is needed to resolve the problem is an increase in supply. Nice to know, but that’s not going to pay the rent any time soon.

But just as after a long winter there are some early signs of spring, there are a few hopeful indicators from housing markets that the long promised relief from increased supply is starting to show up, at least in a small way. Today we look at two recent market reports, one national, and one quite local, that are beginning to indicate a market shift.

There’s no denying that rents in the US have escalated over the past several years. Overall rents are up 4.6 percent in the past year, and the national rental vacancy rate has plunged from more than 10 percent to about 7 percent, signaling that their are relatively more tenants bidding for every available apartment. As a result the share of households spending more than 30 percent of their income on housing has increased.

For the past several years—and for a variety of reasons—we’ve seen a surge in demand for rental properties. Some of that had to due, especially initially with the collapse of the housing bubble, and several million households being moved, quite involuntarily from the ownership market, into renting. At the same time, younger adults have been much more likely to rent that previous generations, and seem especially enamored of centrally located, walkable apartments in great urban environments. The net effect is that the demand for rental housing has risen steadily.

At City Observatory, we think there are two fundamental, and widely under-appreciated facts about housing markets. First, that when supply does catch up to demand, rent increases soften. Second, supply almost always moves much slower than demand. The supply of rental housing has responded only slowly; and has mostly struggled to keep up with increasing demand.

In the past few months there’s growing evidence that supply is starting to catch up. The market analysts at REIS follow national trends in apartment construction tracking delivery (the completion of new apartments) and absorption (how many newly completed apartments get leased. Absorption is a “net’ figure: the difference between the number of previously vacant apartments that get leased and the number of previously occupied apartments that become vacant over any time periods. The difference between the completions and absorptions drives vacancy rates. When lots more apartments get leased than new apartments are built, vacancy rates fall; when completions outpace absorptions, vacancy rates rise.

REIS tracks these numbers on an annual basis, and their estimates for the past three decades are shown here:

REIS analysts are reading the data to suggest that construction of new apartments is finally starting to have an impact on the market. REIS’s Scott Humphreys says: “It’s official: developers are finally building more apartments than there are renters to fill them.”

Similarly, at the blog Calculated Risk, Bill McBride reports on data from the National MultiFamily Housing Council (NMFC) which tracks vacancy rates for apartments around the country. Their data show that “market tightness” has been trending downwards for the past couple of years, and leads McBride to conclude that “it appears supply has caught up with demand—and I expect rent growth to slow.”

These data illustrate an important fact about supply and demand: Demand is highly volatile and can change quickly, while supply responds only slowly. Consider: the first decade of the 2000s was mostly a pretty bad time to be a landlord. A steady supply of new apartments was being built, but net absorptions fell short of the number of completions. In fact, at the height of the housing bubble, net absorptions were negative: more households moved out of apartments than moved in.

In the last two quarters of 2015, according to REIS data, completions outstripped net absorption by about 13,000 units in the third quarter and by nearly 15,000 units in the fourth quarter, with the result that the national vacancy rate ticked upward.

While national trends provide a helpful background, like politics, all housing is local. In an important sense, there really is no “national” apartment marketplace: apartments built in Cheyenne Wyoming really aren’t good substitutes for apartments in San Francisco. While there are broad national trends, the trajectory of supply and demand can play out differently in different local markets. The collapse of oil prices, for example, has dramatically altered the demand/supply balance in the oil patch town of Williston, ND, with the result that rents are off more than 20 percent from a year ago.

The evidence from one of the nation’s tightest housing markets, Boston, suggests that supply may be getting closer to catching up with growing demand. The Boston Globe reports that in the fourth quarter, rents in Boston increased by just one-tenth of one percent from the previous year, the smallest increase in five years. About 3,800 units came on line in 2015, and about another 5,000 are in the development pipeline. The news from Boston echoes reports from markets as diverse as Seattle, Denver, and Houston, that the growing number of new properties being completed is producing at least temporarily higher vacancy rates and more favorable rental offerings for tenants.

Nobody’s predicting a glut of unoccupied apartments—either in Boston, or nationally—that will push rents down. But slowly, and inexorably, the supply of housing is catching up to the demand, both in the aggregate and in the specific places where demand has grown most rapidly. It’s a reminder that if policy enables housing supply to expand, relief from higher rents can be delivered through the market.

Fundamentally, the nation’s housing affordability problems are due to demand outpacing supply:  there’s more demand to live in some cities–and especially in great urban neighborhoods–than can be met from the current supply of housing, especially apartments. As demand surges ahead of supply, rents get bid up, which is the most visible manifestation of the affordability problem. But higher rents are also a signal to developers that they ought to build more housing, and when and where local zoning allows it, they do.  Over the past two years, rising rents have produced a surge of new apartment construction, and there’s growing evidence–in some markets, and at least for the short-term–these increments to supply are moderating the rate of rent hikes.  

There’s no denying that rents in the US have escalated over the past several years. Overall rents are up 4.6 percent in the past year, and the national rental vacancy rate has plunged from more than 10 percent to about 7 percent, signaling that their are relatively more tenants bidding for every available apartment. As a result the share of households spending more than 30 percent of their income on housing has increased.

Nothing, it seems, is more infuriating to those caught in a market of steady rent hikes that being lectured by some economist that what is needed to resolve the problem is an increase in supply. Nice to know, but that’s not going to pay the rent any time soon.  But just as after a long winter there are some early signs of spring, there are a few hopeful indicators from housing markets that the long promised relief from increased supply is starting to show up, at least in a small way. Today we look at recent market reports, national and  local, that provide some evidence that the predicted market forces are at work.

First, in New York, home to some of the most expensive real estate in the nation, there’s growing evidence that rent inflation is easing, at least at the high end of the rental market.  According to Bloomberg, the inventory of vacant, for-rent apartments in New York has increased by between 20 and 30 percent in the past year.  As a result, nearly a quarter of all apartments listed for rent in Manhattan in October included landlord-offered incentives or concessions, the highest level recorded in the past decade. Figuring in the value of these concessions, brokers estimate that median rents in Manhattan fell by about one percent, year-over-year. Even in Brooklyn, where rents have been rising rapidly, the number of for-rent units with landlord concessions increased from 8 percent to 12 percent in the past few months.

The same pattern observed in New York is starting to play out around the country.  Market analyses by Freddie Mac (the federal mortgage agency) and by real estate analytics firm Axiometrics suggest that in most metro areas around the country, rent increases are moderating, and that this trend is likely to continue in the near future.  Comparing first half 2016 rent growth with the same period in 2015, the data show that rent increases are slowing in most markets.  In 28 of the markets shown below, rent increases are slower now than in the previous year; in three markets they are about the same, and in eight markets, rents are accelerating.  (In the chart, white outlined bars indicate 2015 rent growth, and blue bars represent 2016 rent growth).  Rent increases are significantly lower in 2016 than the previous year in Portland, Oakland, San Jose, Boston, Austin, Houston and Philadelphia, to name a few.

Looking forward, Axiometrics predicts that for the next couple of years, rent increases will moderate.  Rents increased about 4.6 percent nationally in 2015, and are expected to rise a further 3.4 percent in 2016. But the outlook for 2017 is for an increase of 2.1 percent.

While national trends provide a helpful background, like politics, all housing is local. In an important sense, there really is no “national” apartment marketplace: apartments built in Cheyenne Wyoming really aren’t good substitutes for apartments in New York. While there are broad national trends, the trajectory of supply and demand can play out differently in different local markets.  But even in San Francisco, where rental inflation has been severe, and where its famously difficult to build new housing, there’s an indication that added supply is also moderating rent increases:  according to Freddie Mac, rents there have increased only about 0.9 percent over the past year, just a fraction of the increases being recorded a year ago. And while much of this is attributable to rising supply, at least some of the problem is what the analysts call “renter fatigue”–renters simply can’t afford higher rates than now being charged.

Nobody’s predicting a glut of unoccupied apartments that will push rents down. But slowly, and inexorably, the supply of housing is catching up to the demand, both in the aggregate and in the specific places where demand has grown most rapidly. It’s a reminder that if policy enables housing supply to expand, relief from higher rents can be delivered through the market.

Earlier this week, we introduced the Storefront Index, a measure of the location and clustering of customer-facing retail and service businesses. A primary use of the index is to identify places that have the concentration of retail activity that we generally associate with a vibrant neighborhood commercial area, and that can support a high level of walkability.

It’s also possible to construct the Storefront Index at different points in time to measure the growth or change in neighborhood commercial activity. For example, we presented data for Portland’s Alberta Street commercial neighborhood in 1997 and 2014. This neighborhood is situated in Northeast Portland, which has historically been home to some of the city’s lowest income households, and which has had a high share of the region’s African-American population.

The data show the substantial growth in storefront businesses over that time period.

As we noted in our report, storefronts are not important simply as commercial destinations or focal points for pedestrian activity. These retail and service businesses are an important source of jobs. To explore the connection between storefronts and jobs, we gathered data from the Census Bureau’s LEHD program for this same neighborhood. LEHD uses administrative records to provide very geographically detailed (block-by-block) estimates of the number of persons employed throughout the U.S. These data show a dramatic increase in local jobs over this time period. Firms on and near Alberta Street employed about 650 workers in 2002; by 2014 that had nearly tripled to 1,838. (We excluded the administration sector from these tabulations to exclude employment reported by local temporary help and employment leasing firms whose employees actually work outside the local neighborhood).

In the case of Alberta Street, the flourishing of the local storefront businesses has been associated with a significant increase in local employment—not just in retail and services businesses, but in a wide range of other sectors. Tracking the presence of storefronts over time can be an indicator not just of a neighborhood’s sidewalk vitality, but employment strength.

Except for boomers, we’re all less likely to be buying new cars today

One of the favorite “we’re-going-to-debunk-the-claims-about-millenials-being-different” story ideas that editors and reporters seem to love is pointing out that millennials are actually buying cars.  Forget what you’ve heard about bike-riding, bus-loving, Uber-using twenty-somethings, we’re told, this younger generation loves its cars, even if they’re a bit slow realizing it. Using a combination of very aggregate sales data and usually an anecdote about the first car purchase by some long-time carless twenty-something, reporters pronounce that this new generation is actually just as enamored of car ownership as its predecessors.  

The latest installments in this series appeared recently in Bloomberg and in the San Diego Union-Tribune. “Ride-sharing millennials found to crave car-ownership after all,” proclaimed Bloomberg’s headline.   “Millennials enter the car market — carefully,” adds the San-Diego Union Tribune.  San Diego’s anecdote is 32-year old Brian, buying a used Prius to drive for Uber; Bloomberg relates market research that shows that young car buyers especially like sporty little SUVs, like the Nissan Juke.  Like other studies, Bloomberg relies on a vague reference to aggregate sales figures by generation:  “Millennials bought more cars than GenXers,” we are told.  

Earlier this year, and previously, in 2015, City Observatory addressed similar claims purporting to show that Millennials were becoming just as likely to buy cars as previous generations. Actually, it turns out that on a per-person basis, Millennials are about 29 percent less likely than those in Gen X to purchase a car.  We also pointed out that several of these stories rested on comparing different sized birth year cohorts (a 17-year group of so-called Gen Y with an 11-year group of so-called Gen X). More generally though, we know that there’s a relationship between age and car-buying. Thirty-five-year-olds are much more likely to own and buy cars than 20-year-olds. So as Millennials age out of their teen years and age into their thirties, it’s hardly surprising that the number of Millennials who are car owners increases. But the real question is whether Millennials are buying as many cars as did previous generations at any particular point in their life-cycle.

This is a question that economists at the Federal Reserve Bank turned their attention to in a study published this past June.  Christopher Kurz, Geng Li, and Daniel Vine, used detailed data from JD Power and Associates to look at auto buying patterns over time, controlling for the age of car purchasers.  (Their full study, “The Young and the Carless,” is available from the Federal Reserve Bank). Here’s their data showing the number of car purchases, per 100 persons in each of several different age groups.

These data show a number of key trends.  First, the data confirm a pronounced life-cycle to car purchasing:  those under 35 purchase very few new cars; car purchasing peaks in the 35 to 45 age group, and then declines for those over 55.  Second, the state of the economy matters.  Especially compared to 2000 and 2005, auto purchasing declined sharply for all age groups in 2010 (coinciding with the Great Recession) and has rebounded somewhat since then as the economy has recovered.  Third, as of 2015, auto purchasing was lower for all age groups under 55 years of age than it was in either 2000 or 2005.  Fourth, the big factor in driving car sales growth in the past decade was the over 55 group (increasingly swelled by the aging Baby Boom generation).  Car sales for the over 55 crowd fell proportionately less during the great recession, and are at a new high (5.7 per 100 persons over 55).  There’s clearly been an aging of the market for car ownership.  The authors summarize this data as follows:

In summary, the average age of new vehicle buyers increased by almost 7 years between 2000 and 2015. Some of that increase reflected the aging of the overall population, but some of it reflected changes in buying patterns among people of different age groups. The most relevant changes in new vehicle-buying demographics over this period were a decline in the per-capita rate of new vehicle purchases for 35 to 54 year olds and an increase in the per-capita purchase rate for people over 55.

Kurz, Li and Vine look at the relationship between the decline in auto sales to these younger age groups and other economic and demographic factors.  They find that declining sales are correlated with lower rates of marriage and lower incomes; that is to say:  much of the decline in car purchasing among these younger adults can be explained statistically by the fact that un-married people and people with lower incomes are less likely to buy new vehicles, and as a group, there are relatively more un-married people and lower incomes among today’s young adults.  

Their argument is essentially that if young adults today got married at the rate that they did in early generations, and if they earned as much as previous generations, that their car buying patterns would be statistically very similar to those observed historically.  While the authors cite this as evidence that young adults taste for car-buying may not be much different that previous generations, in our view, this has to make some strong assumptions about the independence of later marriages and lower marriage rates and changed attitudes about car ownership. While those who do marry may exhibit the traditional affinity for car ownership, it may be that those who delay marriage (or who never marry) have different attitudes about cars.  In addition, there’s growing evidence that the relative weakness of generational income growth may persist for some time, lowering the demand for car ownership.

Hot on the heels of claims that Millennials are buying houses come stories asserting that Millennials are suddenly big car buyers. We pointed out the flaws in the home-buying story earlier this month, and now let’s take a look at the car market.

The Chicago Tribune offered up a feature presenting “The Four Reasons Millennials are buying cars in big numbers,” assuring us that millennials just “got a late start” in car ownership, but are now getting credit cards, starting families and trooping into auto dealerships “just like previous generations.”

Similar stories have appeared elsewhere. The Portland Oregonian chimed in: “Millennials are becoming car owners after all.”

Not quite a year ago, we addressed similar claims purporting to show that Millennials were becoming just as likely to buy cars as previous generations. Actually, it turns out that on a per-person basis, Millennials are about 29 percent less likely than those in Gen X to purchase a car.

We pointed out that several of these stories rested on comparing different sized birth year cohorts (a 17-year group of so-called Gen Y with an 11-year group of so-called Gen X). After applying the highly sophisticated statistical technique known as “long division” to estimate the number of cars purchased per 1,000 persons in each generation, we showed that Gen Y was about 29 percent less likely than Gen X to purchase a car.

More generally though, we know that there’s a relationship between age and car-buying. Thirty-five-year-olds are much more likely to own and buy cars than 20-year-olds. So as Millennials age out of their teen years and age into their thirties, it’s hardly surprising that the number of Millennials who are car owners increases. But the real question—as we pointed out with housing—is whether Millennials are buying as many cars as did previous generations.

The answer is no.

Auto industry analysts at the National Automobile Dealer’s Association—who have a very strong stake in the outcome—are pretty glum about sales prospects of the Millennial generation. NADA’s economist Steven Szakaly predicts it will take four Millennials to equal the sales impact of a single Boomer. This is due to a combination of factors, including Millennials’ weaker income and job prospects, and lower propensity to drive and own cars. Its also the case that waiting longer to buy one’s first car means that one is likely to own fewer cars over a lifetime, and as with housing there’s no evidence that young adults are catching up to previous generations as they age.

As we said last year, the real gold standard for intergenerational comparisons would be to look at the rate of car ownership for different generations when they were at the same point in their life-cycle, i.e., look at the car buying habits of Boomers when they were in late twenties and early thirties (during the seventies and eighties), and compare them with the habits of Gen X (25-34 in the nineties) and Millennials (25-34 from 2005 onward). Alas, we don’t have that data.

But we do have another indicator: the fraction of young adults who have drivers licenses.

Michael Sivak and Brandon Schoettle at the University of Michigan’s Transportation Research Institute analyzed US DOT data, and found big declines in the rate at which young adults get driver’s licenses. At every age up to 50 years old, a smaller fraction of U.S. adults is getting a driver’s license than a few decades ago. Today, only about 60 percent of 18-year-olds have a licence, compared with about 80 percent in the 1980s. Even among those in their late twenties and early thirties the licensing rate is down 10 or more percentage points from earlier decades. And the declines appear to be persisting and continuing as time goes on: Between 2008 and 2014, the rate of licensing declined from 82.0 percent to 76.7 percent among 20 to 25-year-olds.

Another bit of evidence comes from financial markets. The Federal Reserve Bank of New York studied credit records to determine what fraction of persons in each age group took out an auto loan. While the total volume of automobile lending declined sharply between 2003 and 2015, the decline was most pronounced among younger age groups. Lenders made about 25 auto loans per 100 persons in their mid thirties in 2003, but only about 17 loans per 100 the same age cohort in 2015. Only those 65 or older are more likely to take out a car loan today than in 2003.

When it comes to America’s much storied romance with the car, it’s apparent that ardor has cooled, especially among Millennials. We’re buying fewer cars, we’re taking out fewer car loans, we’re waiting longer to get a driver’s license, and fewer of us are ultimately doing so.

We’ve just gotten our first look at the new US Department of Transportation performance measurement rule for transportation systems. The rule (nearly three years in gestation, since the passage of the MAP-21 Act) is USDOT’s attempt to establish performance measures to guide investment and operation of the nation’s urban transportation system. One of the criticisms—fair, in our view—of the nation’s transportation system is that there are few, if any, quantitative standards against which performance can be measured, and against which the merits and results of alternative policies and investments can be judged. The new DOT standards aim to do just that. In the next few weeks, we’ll all have an opportunity to weigh in on whether these standards help address this problem.

Standards like these may seem like technocratic trivia. But we’ve routinely witnessed obscure and seemingly innocuous rules of thumb about things like road width or parking requirements end up profoundly shaping our cities—and often in ways we don’t like. Getting these performance measures right can help push transportation investment in a direction that supports more successful cities. Getting them wrong runs the risk of repeating past mistakes. The proposed rules are voluminous, running to more than 400 pages in all—and incredibly detailed. So it will take some time to dig into them. But at first glance, we have some reactions. We’ll take a closer look in the days ahead, and revise and extend our comments as we (and others) go through the minutiae here.

Two key words: “Excessive” and “Expectations”

Initially, we’re focusing on three standards that the USDOT is proposing apply to metropolitan areas of a million or more population. The USDOT has also proposed separate standards for travel time reliability and freight that we’ll look at in a future post.

Briefly, the standards are outlined in the following table and further analyzed below. They deal with total hours of excessive delay, and the share of the Interstate system or national highway system where travel times don’t exceed 150 percent of a locally established “expectation.”

Performance Measures for Large Metropolitan Areas

Implementing these performance measures will require a heavy reliance on technology and a substantial investment. USDOT believes that it will be able to use speed data collected from vehicle telemetry, including cell phones to determine performance for individual street segments. While the technology is promising—it does have some limitations—an analysis of the performance of HOT lanes was flawed because it couldn’t distinguish between vehicles traveling in the tolled and free lanes. USDOT estimates that complying with the data collection and reporting requirements of this rule will cost states and local governments $165 to 224 million over the next decade.

Congestion: “Excessive hours of delay”

The core measure of whether a metropolitan area is making progress in addressing its congestion problem is what USDOT calls “annual hours of excessive delay per capita.”  This congestion measure essentially sets a baseline of 35 mph for freeways and 15 pmh for other roads.  If cars are measured to be traveling more slowly than these speeds, the additional travel time is counted as delay.  The measure calls for all delay hours to be summed and then divided by the number of persons living in the urbanized portion of a metropolitan area.

The proposed measure is, in some senses, an improvement over other measures (like the Texas Transportation Institute’s Travel Time Index) that compute delay based on free flow traffic speeds (which in many cases exceed the posted speed limit). But despite its more realistic baseline, this measure suffers from a number of problems:

—This is all about vehicle delay, not personal delay. So a bus with 40 or 50 passengers has its vehicle delay weighted the same amount according to this metric as a single occupancy vehicle.

—This ignores the value of shorter trips. As long as you are traveling faster than 15 miles per hour or 35 on freeways, no matter how long your trip is, the system is deemed to be performing well.

Interstate and National Highway System performance: “Peak hour travel times meet expectations”

If “meets expectations” sounds a bit squishy for a federal planning standard, it’s because it is.

Under the rule, state DOTs or Metropolitan Planning Organizations (MPOs) would establish “expectations” for how long (or how fast) trips would take on each segment of a metropolitan area’s major freeways and highways. Segments which experienced peak hour travel times that were 50 percent more than these “expected” travel times would be deemed to be congested. The metric would track the share of a region’s highway segments that didn’t experience this level of congestion-related delay.

The pivotal policy question here is what are “expected” travel times. Here the USDOT simply punts: It’s up to state DOTs or metropolitan planning organizations to make this call. As the DOT says: “Under this proposed approach, FHWA does not plan to approve or judge the Desired Peak Period Travel time levels or the policies that will lead to the establishment of these levels.”

In effect, this means that performance metrics are likely to vary widely from place to place. These performance measures beg the essential question of what constitutes a reasonable expectation of travel times. As we’ve pointed out, it’s a regular occurrence in daily life that Americans have come to tolerate very different levels of delay for the same service at different times, for example, when they order their morning coffee, as we documented in the Cappuccino Congestion Index. In our first reading, it’s also not clear whether states and MPOs can adjust expectations over time. It’s an interesting question: Should we adjust our expectations as conditions change, or once established, should “expected” travel times be an unchanging baseline against which performance is measured?

The “expectations” terminology begs a larger question as well: congestion reduction measures are seldom free. It costs money to expand capacity, improve transit, or implement other measures that might reduce travel times at the peak hour. The big question is whether the value commuters attach to such potential travel time savings come anywhere close to being commensurate with the cost of achieving expectations. Because USDOT offers no guidance or guidelines as to what might constitute reasonable expectations for travel times, and because they’re unmoored from any standard of cost effectiveness, this performance standard is likely to be of limited usefulness.

In addition, the “peak hour travel times meet expectations” measure is, of course, a variant of the classic travel time index that we—and others—have long critiqued. One of its chief problems is the denominator. In this case, the denominator is the size of a region’s highway system (in DOT parlance “segment length”.) The indicator is the percent of Interstate (or NHS) roadways that aren’t congested. So—at least in theory—if a region expands its “segment length” by building new, under-utilized highway capacity, it can improve the ratio of uncongested to congested roads—thus improving its performance. Conversely, a metropolitan area that doesn’t increase the segment length of its Interstate (or NHS system) in the face of increasing travel seems likely to see a decline in its rated performance. As a result, this measure seems to impart a strong “build, baby, build” bias to the indicators.

DOT whiffed on greenhouse gases

Despite some hopes that the White House and environmentalists had prevailed on the USDOT to tackle transportation’s contribution to climate change as part of these performance measures, there’s nothing with any teeth here. Instead—in a 425 page proposed rule—there are just six pages (p. 101-106) addressing greenhouse gas emissions that read like a bad book report and a “dog-ate-my-homework” excuse for doing nothing now. Instead, DOT offers up a broad set of questions asking others for advice on how they might do something, in some future rulemaking, to address climate change.

Three ideas for what DOT might have done

1. Make VMT per capita a core measure. Vehicle Miles Traveled (VMT) per capita is strongly correlated with important transportation system outcomes. It’s correlated with total system costs, costs to households, greenhouse gas emissions, crashes, injuries and fatalities.
2. Shift from excess travel time to total travel time. A total travel time measure, which recognizes the value of shorter trips, even when they occur at somewhat lower speeds better recognizes the economic and environmental value of more compact development patterns. Implement a “total travel time” measure that computes total travel time per resident, and gives equal weight to measures that reduce the distance of trips and the need for travel, especially at the peak hour, when it will have the greatest effects on congestion.
3. Establish a separate methodology for transit delays. How much additional time do transit riders incur from transit systems that don’t have average running speeds of some reference number (like DOTs 35 MPH for freeways and 15 MPH) for roads, or locally established expectations. The amount of this delay could easily be calculated from transit system operating records and ridership counts.

Our objective in writing about these standards is to encourage others to take a close look, and help provide a robust discussion of this important policy. We invite your comments and corrections—and we’ll update and add to this post as we learn more about the rule. Stay tuned.

Hey reporters! We know you love rankings, especially ones that show some measure of widely shared pain, like traffic congestion or rent increases.

And some people, armed with a database and an infographic are more than happy to feed your hunger for this type of analysis.

But please: Stop using Abodo’s rent numbers. They’re wrong. They’re meaningless.

We’ve documented why these numbers are wrong. Abodo computes an average based on the apartments contained in its listings database. But Abodo has only a partial, un-represenative, and constantly changing sample of the marketplace. They don’t cover all apartments, and their monthly estimates are biased by composition effects: the apartments included in their sample in one month aren’t necessarily comparable to the apartments listed in their sample in the next month, and so changes in average prices reflect not overall inflation, but the different kinds of apartments that are for rent each month. As a result, Abodo rental inflation estimates fluctuate wildly from month to month.

Here’s a classic case of why you should ignore them:

Abodo said that in March, 2016, Portland, Oregon had the highest level of rent increases in the country, up 14 percent from February.

Then, the next month, Abodo reported that rents in April 2016 in Portland were down seven percent over the previous month, and this represented the fifth biggest decline in the nation.

And Colorado Springs, according to Abodo, showed exactly the reverse trajectory: it was one of the ten biggest losers in March 2016 (down 10 percent compared to February), but then had the fifth biggest increase in April (up 13 percent).

There’s nothing real or meaningful about either of these data points, or about the precipitous (and utterly absurd) changes they seem to imply. If you take Abodo at face value—and clearly no one should—Portland’s rental price inflation problem essentially disappeared in the last four weeks. And Colorado Springs when from catastrophic free-fall to boom in the same brief time period.

Abodo’s listings driven rent estimates are essentially a random number generator when used to calculate month-over-month changes in rental price inflation. Shame on Abodo for producing them in the first place. And shame on any journalist who credulously repeats them.

Acoustical engineers talk about a “signal-to-noise” ratio.  What we have here is almost all noise and no signal.

Housing affordability and rental price inflation are real issues, but they’re not ones that Abodo’s data sheds any light upon. There are lots of reliable sources of data on rent levels and on rent inflation. We’ve set about compiling a friendly user’s guide to these data. So reporters, if you care about helping people understand what’s going on in the local housing market, please use these, or similar, resources.

A Hollywood staple of the 1930s and 1940s was the story of a plucky band of young kids—usually led by Mickey Rooney and Judy Garland—who, their dreams of making it on Broadway dashed by some plot twist, decide to stage a show of their own. They would find a barn or a warehouse, sing and dance and tell a few jokes on a makeshift stage, and then some theatrical bigshot standing in the wings would snap his fingers—and in the final scene, we’d see Mickey and Judy opening on the Great White Way.

If they remade one of those movies today, the plot would be nearly the same, but would undoubtedly revolve around software.

Case in point: Last month, New York University’s Rudin Center and the Transit Center hosted a one-day hackathon to come up with ideas for improving bus service on Staten Island. Like many places, transit routes on the island are mostly a slightly evolved hodge-podge based on historical streetcar lines, with service levels and timetables that have changed incrementally over the years. New York City’s fifth borough is its most suburban, and its residents have long average commutes, and a wide range of destinations, including Manhattan, Brooklyn and New Jersey.  

The Hackathon was held March 5 and drew a solid cadre of laptop-wielding, data-infused programmers, who labored all day to produce a software and presentations to show how the buses could indeed be better. The competition’s $2,000 Grand Prize went to Sri Kanajan for a map the visualized the concentration of bus trip origins and destinations.

While colorful and interesting, the map is chiefly a restatement of the problem, rather than a proposed solution. It’s useful to have a data-driven, visual representation of the Staten Island origins (dispersed) and Manhattan destinations (concentrated, mostly in the financial district and Midtown), of current bus riders. And, from all accounts, the hackathon generated a healthy discussion of the key problems that need to be addressed in improving transit service.

In our view, anything that gets people talking about buses is generally a good thing. Buses are the underappreciated work-horses of the public transit system, and anything we do that gets them running more efficiently can produce widespread benefits. Case in point: Houston’s rearrangement of its bus routes to implement a gridded, timed-transfer system, providing more frequent service to the most heavily patronized destinations.

And we’re the last ones to argue that only expensive experts can have good ideas. It’s often very productive to have bright outsiders with a fresh perspective re-frame questions and brainstorm new ideas. But at some point—and optimizing transit routes is one of them—a level of expertise and practical experience that even the best programmer isn’t going to deduce from even the biggest data—at least not in the first attempt.

But maybe the bigger problem is that there’s a certain tyranny in the limitations of existing data: current transit ridership data reflects, in part, the pattern and quality of the current service system. There’s some necessary risk-taking and abstraction to apply the lessons from experience in say, other cities, in order to be able to imagine the potential opportunities of a service model for which there is no current local exemplar. In addition, the organizers of the hackathon decreed that all proposals for rearranging transit service had to be “cost-neutral”—a condition that begs the question of what level of resources ought to be devoted to bus service (an especially ironic issue, given the billion-dollar-plus price tag of other New York City transit investments like the $1.4 billion Fulton Center and $4 billion Calatrava WTC station).

The blinders clamped on by the limits of existing data or institutionally siloed problem definitions isn’t just the province of plucky volunteers. Last month we pointed out that design firm IDEO’s efforts to reimagine transportation on behalf of one of its corporate clients was plagued with a fundamental failure to correctly (and broadly define the problem) which led them to overlook thinking about the important roles that urban form and location choices could play in addressing transportation needs.

A couple of years back, a Brooklyn based startup, then called “Significance Labs,” proudly unveiled its smartphone app to enable the poor to prepare applications for food stamps, trumpeting it as a way for the poor to more readily access nutrition benefits. Its founder, a former Facebook and Linkedin veteran, said he wanted to extend SNAP to the “80 percent of the poor” who own smartphones. It would help them cope with the ordeal of applying for SNAP benefits. Next City related their business plan:

“We’ve all been annoyed at some point by navigating government services like waiting in line at the DMV, or filing taxes or applying for a passport,” says Chen. “For low-income Americans, it’s no longer just an annoyance, it’s a necessity. The system, despite being more important for these Americans, isn’t any easier for them.”

Never mind that according to Pew, aggregate smartphone for the entire population was 68 percent in 2015 (and considerably lower for the poor). People who are poor, and who have difficulty getting SNAP benefits are pretty much the least likely among us to own and use smartphones. There’s precious little evidence that the application process is a very big barrier to accessing SNAP benefits: eligible non-recipients are disproportionately elderly and disabled or lack language skills—barriers that seldom correlate strongly with smartphone ownership, much less use.

What’s especially egregious is the implication that it is the “app” that’s solving the hunger or poverty problem. The secret sauce here is not the app, but the dollar value of SNAP benefits. And the problem with SNAP benefits is not that there isn’t an app, but that Congress has been steadily whittling away at the population eligible for benefits and reducing the value of the benefits themselves. Defining this as primarily a technology problem distracts attention from this central issue.

More generally, it’s far from clear that privately developed apps are necessarily a good way to provide access to public services. (While there are plenty of useful, free apps—Google maps has seamlessly integrated transit directions in most cities, thanks to the widespread adoption of open data standards for transit schedules). One article praising Easy Food Stamps called it a “Turbo Tax” for the SNAP program. Turbo Tax has certainly simplified the process of filing one’s taxes, for a price. But the company has also fiercely defended its turf: lobbying Congress to prevent the IRS from offering automatic simple and free tax preparation services, something that could replace the 1040EZ form.

At some point, this kind of techno-centrism is more than just tone-deaf. Blogger Alex Payne diagnosed this particular behavior in claims that BitCoin—the cybercurrency—would help the un-banked poor:

Silicon Valley has a seemingly endless capacity to mistake social and political problems for technological ones, and Bitcoin is just the latest example of this selective blindness. The underbanked will not be lifted out of poverty by conducting their meager daily business in a cryptocurrency rather than a fiat currency.

Open data, apps and technology are all important tools for addressing a range of problems. We really ought to use technology to broaden the audience and inject fresh perspectives into a wide range of discussions. But we shouldn’t let our fascination with technology lead us to believe that there are cheap and easy solutions to complex problems. Technology is usually a complement to, and not a substitute for resources, programs and investments. Open tech needs to be viewed as a way of expanding the discussion, bringing in new perspectives and widening the range of considered alternatives, rather than being treated as a panacea.

So let’s definitely hack away. But if you’re looking to figure out a real challenge for programmers, here’s a thought: instead of spending a billion dollars widening I-75 in Detroit, let’s have a freeway hackathon.

It’s been debunked, right? Though we’ve long been told that millennials want to live in cities, renting rather than owning, and biking instead of driving, a new round of articles are here to tell us that all of that is a myth: as soon as they find their financial footing, young people are buying homes in the suburbs just like previous generations.

These latest claims about housing are based on a new National Association of Realtors study. Based on its annual surveys of recent homebuyers, the NAR reports that millennials now make up 35 percent of those who had purchased homes in the past year, up from 32 percent in 2014, and 31 percent in 2013.

But, if you’ll forgive the paraphrase, these numbers—they do not mean what the NAR thinks they mean.

The basic problem is that the NAR—and other, similar reports (for example, about car-buying habits)—are confusing lifecycle effects with generational effects. Lifecycle effects describe how people’s behavior changes predictably as they get older. A generational effect describes how a cohort of people born at a certain time are different from another cohort of people born at a different time: so, for example, how a millennial behaves at age 25, compared to how previous generations—gen x-ers or boomers—behaved at age 25.

Life Cycle vs. Generational Effects

So when the NAR reports that millennials are buying more homes as they get older, we have to ask if this is a lifecycle effect or a generational one. After all, even if millennials are less likely to buy homes compared to earlier generations, it would be very strange if millennials bucked the well-established lifecycle effect of being more likely to buy a home in their 30s compared to their 20s.

The NAR statistics about market share can’t answer this question, because they compare the home buying habits of 18- to 33-year-olds in 2013 with 20- to 35-year-olds in 2015. For market share to remain the same across these samples, the 34- and 35-year-olds added in 2015 would probably have to buy homes at the same rates as the 18- and 19-year-olds who were dropped. But people in their mid-30s have always bought more homes than people just out of high school, and that lifecycle effect doesn’t signal any shift away from the underlying generational decline in homebuying.

In effect, all the NAR has proven is that, when they are older, millennials buy more homes than when they were younger. But as we’ll show below, at any given age, millennials are still less likely to be homeowners than previous generations at the same age.

Comparing Millennials to Previous Generations

To check for generational change, you have to compare identical age cohorts over time. And if you do that, it’s clear that millennials are, in fact, less likely to buy a home than earlier generations. Homeownership for every cohort under age 65 is depressed well below historic levels, especially for people now in their thirties, and this decline continuing, not reversing.

This chart shows how homeownership rates have varied by age for two years: 2001 and 2014. You can see that in both years, homeownership rates increase steeply in the for persons in their late 20s and early 30s, and then grow more slowly as people age into their 50s or 60s.

But it’s also clear that at every age below 60, homeownership rates are now significantly lower than they were in 2001. If we chart the difference itself, it looks like this:

For those aged 21 to 60, homeownership rates have fallen by six to 10 percentage points since 2001. The biggest declines have been for those in their thirties. (Note, too, that while some accounts make sharp distinctions between generations, this chart shows how continuous and consistent is the relationship between age and the decline in housing tenure, with the behavior of the oldest millennials flowing into that of the youngest gen-Xers.)

To distinguish between a lifecycle change and generational change, it can also be helpful to focus on a single age group over a long period of time. In the following chart, we look at the homeownership rates of 32-year-old and 34-year-old heads of household in each year from 2001 to 2014.

There are two obvious takeaways. First, 34-year-olds are more likely to be homeowners than 32-year-olds (the blue line is higher than the red line). Second, over time, the homeownership rate of both 34-year-olds and 32-year-olds has gone down (both lines slope down to the right). Again: This signals a generational change in home buying tendencies. Both 32- and 34-year-olds are less likely in 2014 (by a wide margin) to be homeowners than they were in 2001. And if there were evidence that in the last few years millennial home buying tendencies were reverting to those of previous generations, it would show up here, as a reversal or upswing in these lines. But they continue to slope down, indicating that homeownership, far from rebounding to historic patterns, is continuing to become less common among this generation than its predecessors.

We can use the change in age-specific home-ownership propensities to compute how many fewer millennials own homes today than would have been the case if they had behaved as previous generations did. To do this, we multiply the 2001 home-ownership rate for each age group by the number of household heads in 2014, and compare the the predicted number of homeowners in each age group with the actual number reported by the census. The calculation is shown in the following table. In all, we estimate that there are 1.7 million fewer millennial home-owning households today than would have been the case if homeownership were as prevalent today as it was in 2001.

“Just delaying” homeownership results in big change in real estate

Finally, there’s a statistical claim being made here that Millennials are just like other generations in their home buying habits and preferences, they’re just buying homes and moving to the suburbs later in life. There are two problems with this argument. First, a one- or two-year change in the average age of first home purchase significantly reduces the number of homeowners permanently. It automatically means that those who own homes will be homeowners for about 2.5 to 3 percent less of their lifetime—over the whole population, over several decades that translates into a permanent decline in the homeownership rate. Second, the implication is that somehow, though they wait longer to buy their first home, eventually, they’ll catch up to historic levels of homeownership . . . someday. But there’s no evidence for this “catch up” theory—in fact, homeownership levels are dropping for everyone up through about age 60. American’s are both delaying homeownership until they’re older, and buying fewer homes over their lifetimes—which together represent a huge change in how US housing markets work.

Far from debunking the story about Millennial home-buying habits being different, the latest data confirm that serious, long-term changes are afoot. We’re well past the dark days of the 2009 recession. The economy has been growing again for six years, and home buying rates at all ages remain depressed well below historic levels—no more so that for those Millennials in their mid-30’s who are fully ten-points less likely to be homeowners than the Gen-Xers of 2001 were in their mid 30s.

Data Notes:

These data are taken from the American Community Survey for the years indicated. There are many different definitions of what constitutes the “millennial” generation: for these tabulations we follow NARs definition of those born between 1980 and 2000.

America’s pedestrians are in pain.

Every day, tens of millions of Americans waste tens of thousands of hours stuck waiting on the side of streets for car traffic to get out of their way. We estimate that the annual value of time lost waiting to walk totals $25 billion annually.

Today, City Observatory announces the launch of our latest data product: the Pedestrian Pain Index (PPI). Following the techniques developed over the past thirty years by the highway-oriented Texas Transportation Institute (TTI), PPI uses similar methods and assumptions —to calculate the amount of time pedestrians lose each year having to wait their turn to cross streets to allow cars to proceed.

We attribute 100 percent of pedestrian wait time as “delay” due to automobiles for two reasons. First, our methodology mirrors exactly that used by the TTI, which counts traffic delay as any slowdown in traffic below the level that motorists enjoy at so-called “free flow speeds,” even if the free flow speed is higher than the posted speed limit. Second—and perhaps more importantly—pedestrians are only forced to wait at intersections because of vehicle traffic. In pedestrian-only environments, there is no need for “Don’t Walk” signs. In that sense, traffic lights and crosswalks are not walking infrastructure—in places without cars like inside shopping malls or in Venice, Italy, there is no need to have signals to tell people when they can walk or paint lines to show people where they can walk.

There’s little question that walking has been made a second-class form of transportation—and that pedestrians regularly feel the pain of being subordinated to automobiles. One of the best examples is “beg buttons” can delay law-abiding pedestrians up to a minute and a half in order to cross a city street—a point illustrated by Gizmodo.

Here’s how we came up with our PPI estimate. According to data tabulated by John Pucher and his colleagues from the the most recent National Household Transportation Survey, the typical American spends about 112 hours walking about 37.7 miles per year. We estimate that out of a typical walk, a pedestrian spends about five percent of their time waiting for traffic, either as they cross the street an un-signaled location, or waiting for a traffic signal. Our five percent estimate corresponds to waiting about 55 seconds during the average 18.5 minutes that each American walks on a daily basis. For those in low-traffic, low-density areas, these 55 seconds will likely be an overestimate; in urban settings with traffic lights on most corners—where a disproportionate share of walking occurs—55 seconds will be an underestimate.

We multiply our daily delay estimate of 55 seconds per person by 365 days and by the roughly 300 million Americans five years of age or older to come up with an estimate of about 1.6 billion hours of pedestrian delay experienced by Americans annually. Valuing that delay at $15 per hour—a figure somewhat lower than that used in studies of automobile congestion delay—produces a total estimate of $25.2 billion in time lost in pedestrian pain waiting for automobiles.

The Pedestrian Pain Index is a first, rough approximation of the time lost by pedestrians due to automobile traffic. Constructing this index is complicated by the fact that, unlike the case for automobile travel, we have very limited data on walking travel. As Tom Vanderbilt put it in Slate, “Walking in America is a bit like sex: Everybody’s doing it, but nobody knows how much.” It’s a classic instance of the old adage “if you don’t count it, it doesn’t count.” Lacking any data about pedestrians in most settings, the costs and consequences of land use and engineering decisions on walking are simply invisible——and therefore ignored.

Traffic engineers have begun to recognize that the waits imposed on by signals on pedestrians impose major costs and discourage people from walking. The National Association of City Transportation Officials (NACTO) writes in the Urban Street Design Guide:

Long signal cycles, compounded over multiple intersections, can make crossing a street or walking even a short distance prohibitive and frustrating. This discourages walking altogether, and makes streets into barriers that separate destinations, rather than arteries that stitch them together.

According to the 2012 National Traffic Signal Report Card (yes, there really is such a thing: it gives us a D+), the United States has about 311,000 traffic signals (about 1 for every 1,000 Americans), with an estimated replacement cost of about $83 billion. Most of these signals control pedestrian travel, as well as vehicles. Pedestrians face delays not just at traffic signals, but when crossing roads at un-signalized intersections, and when crossing mid-block (as is frequently necessitated by the serpentine, uninterrupted roadways found in most US suburbs).

Those of you who regard this as a bit of early April data-whimsy, think again. If anything, the estimates presented here profoundly understate the costs travel time costs that our auto-centric transportation system imposes on those who would like to walk. Recent national survey data collected by Jennifer Dill and her colleagues at Portland State University show that walking is a highly valued form of transportation. Two-thirds of Americans of all ages agreed or strongly agreed with the statement “I like walking.” Younger Americans preferred walking to driving, with the share of Millennials saying they like to walk outpacing those who agreed they liked driving by 12 percentage points.

Significantly, the most commonly cited barrier to walking (identified by two-thirds of the entire sample) was the relative remoteness of destinations——and destinations are more remote because they are scaled to the size of automobile market-sheds, and because parking requirements (coupled with bans on mixed use zoning) mean that it is uneconomical or illegal to build communities that are convenient for walking. The growing demand for walkable communities, coupled with their relatively short supply is one of the key reasons that values for walkable residential and commercial areas have been rising faster than for auto-dependent locations.

Like last year’s Cappuccino Congestion Index, the Pedestrian Pain Index illustrated that armed with a modicum of data and a few assumptions, one can easily craft an impressive (or at least impressive sounding) estimate of the dollar cost of some delay that we face in our lives. But being able to monetize delay is not the same thing as saying it’s worth spending scarce public resources to remedy.

In a complex, crowded, and interconnected world, no system can be designed so that no user ever experiences a moment of delay. While it is possible to tally and monetize the value of time spent waiting, that doesn’t necessarily mean that the problem is a serious one, it would be—dare we say it “foolish”—to insist we ought to spend scarce public resources to lessen what are in many cases mostly private costs. That’s something to remember the next time you hear anyone quoting impressive sounding numbers from the Texas Transportation Institute—or anyone else—about the billions and billions lost to traffic congestion.

A new measure purports to gauge city attractiveness by measuring whether local college graduates stick around. But these raw numbers can be a misleading indicator, and we’ll show how it can be adjusted to more accurately measure how good a job a city is doing of producing and retaining talent.

There’s powerful evidence that the educational attainment of population is the single most important factor affecting a region’s economic success. We’ve observed that you can explain about 60 percent of the variation in per capita incomes among metropolitan areas simply by knowing what fraction of the adult population has completed a four-year college degree. While there are many ways to increase a region’s talent base, one core strategy is doing a great job in educating your own young people—and then building the kind of community that they will want to stay in.

Does retaining local graduates mean you’re stemming brain drain?

But measuring the migration of talented workers can be tricky. In a recent article in CityLab, “The U.S. Cities Winning the Battle Against Brain Drain,” Richard Florida presents some findings on the tendency of college graduates to stay in the metropolitan areas where they got their degrees. Using data cleverly assembled by the Brookings Institution’s Jonathan Rothwell and Siddharth Kulkarni from LinkedIn profiles, Florida shows which cities have the highest and lowest levels of retention of college graduates.

Some of the results are, at least at first glance, surprising. According to the Brookings figures, the Detroit metro has retained 70.2 percent of its graduates—one of the highest figures in the nation. This seems surprising, because the Detroit metro area actually experienced a 10 percent decline in the number of 25- to 34-year-olds with a four-year degree between 2000 and 2012 (as we documented in our report, “Young and Restless”).

Conversely, fast-growing tech powerhouse and hipster haven Austin, Texas ranks among the ten lowest cities, handing on to just 38.2 percent of its recent college graduates.

What’s going on here?

Well, it turns out that this particular set of college graduate retention statistics tell us a lot more about the size and characteristics of the local higher education system than they do about the attractiveness of the local city, either in terms of its amenities or its job prospects. In other words, it’s more about the supply of college graduates produced by local colleges and universities than the demand of college graduates for living in a particular city.

Different cities have different kinds of higher ed systems

To understand why, think about two kinds of cities. In a college town like Madison, WI or State College, PA—or even larger cities with high concentrations of college students, like Boston or Austin—the local colleges or universities are effectively a big export industry, producing far more degrees than the local economy demands, and then shipping them out to a statewide, regional or national market. Students come to Austin from all over to get a degree at the University of Texas, and many return to their hometowns—or relocate somewhere else for a job—immediately after graduating.

In other cities, the local colleges and universities aren’t so “export-oriented.” In these cities, local higher education mostly serves the local market. As a result, graduates in these cities are more likely to remain in the city where they studied, because that’s where they started out. These cities will have a much higher “retention rate” than export-oriented higher education markets, but that has everything to do with who’s coming in, and much less to do with how attractive graduates find the city when they get out. The key here is that the difference is in thehigher education institutions, not the cities they’re located in.

As part of our research for the Talent Dividend Prize—a competition funded by the Kresgeand Lumina Foundations—to see which US metropolitan area could achieve the largest increase in the number of two- and four-year college degrees awarded to local students over a four-year period, we assembled data from the Integrated Post Secondary Education Data System (IPEDS) on the number of college degrees awarded in large metropolitan areas. Among the 50 largest metropolitan areas, the number of BA and higher degrees awarded in 2012 varied from a low of 2.8 (in Riverside, California) to a high of 17.5 (in Phoenix, Arizona). The typical large metropolitan area grants about 8 BA or higher degrees per 1,000 population on an annual basis.

In the following table, we’ve matched our BA degree award rate data with the information provided by Brookings on the BA retention rate for the ten highest rated and ten lowest rated metropolitan areas.

There’s an obvious pattern here: Metropolitan areas with high levels of BA retention have very small higher education establishments (as measured by the number of BAs awarded per 1,000 population. Conversely, metro areas with low levels of BA retention have, on average, much higher levels of BA award granting. This is exactly what we’d expect when thinking about cities that are home to large universities that attract many students from elsewhere.

To get a better sense of whether a metro area is experiencing a brain drain or a brain gain, when can combine these data. The final column of the tables does that by multiplying the number of BA degrees issued per 1,000 population by the BA retention rate. This is a rough estimate of the number of additional BA degree holders (per 1,000 population) who reside in a metro area after graduation.

These data come closer to our intuition about which places are gaining talent. Larger metropolitan areas (New York, Los Angeles) have relatively high rates of local BA growth (5.9 and 5.2 per 1,000 population respectively. Cities with strong tech economies, like San Jose (5.7) also do well on this measure. Conversely, economically challenged places don’t do as well—Detroit’s local BA per 1,000 population rate is 2.9; even though it does a relatively good job of retaining those who graduate locally, the size (or at least output) of local higher education institutions is so small (relative to the size of the region), that it is not gaining talent as much as the other areas on this list.

So, as it turns out, the retention rate of college graduates is at best an incomplete indicator of whether cities are stemming the tide of a brain drain (or not). If local higher education institutions are small, and chiefly serve students from local high schools, a high retention rate is not necessarily a sign of success. Conversely, if your area colleges and universities are large and attract students from around the nation, a low retention rate may not be a sign that you’re doing poorly.

Cities matter even before graduation

As these data make clear, the competition for talent begins long before students receive their bachelor’s degree. The number and kind of local colleges and universities is a decisive asset in positioning a city to attract talent.

At least a portion of the brain drain dynamic occurs when students decide where they are going to college. For metros like Detroit, where there are relatively fewer local universities than in the typical metropolitan area, more students are going to leave the local metro area to get a degree. And even if a high number of those who study locally stick around (as appears to be the case) that effect can easily be swamped by those who leave for college and never return. (Detroit’s college and universities award about 4 degrees per 1,000 population annually; compared to about twice that many for the typical metropolitan area). Because local universities and colleges are so small, relative to the average, Detroit has to retain 100 percent of its graduates to have as many new BA degree holders, proportional to its population, as a metro see half of its graduates migrate away.

For some students, the city in which their university is located can be an important factor in deciding where to enroll. Part of Philadelphia’s “Campus Philly” recruiting program—aimed at out of town students— has been to promote the city’s urban amenities as one of the advantages of choosing one of its many local colleges and universities. The program follows up with activities and internships that look to connect students to the community while in school and after graduation.

Nurturing, attracting and retaining talent are all mutually reinforcing strategies for bolstering the regional economy. Cities need to pay attention to the size and quality of their colleges and universities, as well as to build the kind of communities that they (and other well-educated persons) will want to live in. Because this process is so multi-faceted, no single measure captures all of the dynamics at play. What we’ve provided here shows that a simple retention rate is not enough to understand whether a city is doing well in attracting and retaining college grads.

Data notes

The numbers for Phoenix are something of an anomaly. The University of Phoenix, the nation’s largest distance learning institution reports its BA degrees to IPEDS as being awarded in the Phoenix metropolitan area, even though nearly all of its students are located in other metropolitan areas. This tends to greatly exaggerate its local output of graduates.

The number of young adults is increasing, not declining, and a larger share of them are living in cities.

Yesterday’s New York Times Upshot features a story from Conor Dougherty–”Peak Millennial? Cities Can’t Assume a Continued Boost from the Young.” It questions whether the revival in city living is going to ebb as millennials age, and the number of persons turning 25 decreases in the years ahead.

In our view, the story offers two mistaken premises: first, that the growth of young adults in cities has been driven primarily by the large size of the Millennial generation, and second, that the affinity of young adults for cities is waning. Our research shows that neither of these premises is true. The movement of young adults to the city has been gathering steam for more than 25 years, and the number of young adults in cities was actually increasing during the 1990s–at a time when the number of 25 to 34 year olds nationally was actually declining. And the relative preference of young adults for city living continues to increase.

The argument that as millennials age they will increasingly move to the suburbs mistakenly conflates life-cycle effects with generational change. As individuals age, the likelihood that they’ll live in different places changes. After high school, there’s a large migration to college towns. Young adults, starting out in their careers are disproportionately likely to rent and live in cities compared to other Americans. As they get older, find partners and have children, they’re more likely to own homes and live in the suburbs. This general pattern of succession holds for most recent generations as they age. But what’s different–and important–is how many people are in each generation, and how long they remain in each stage in this process. What’s happening now is that today’s young adults–the so-called Millennial generation–are both more numerous than the immediately preceding generation and are demonstrating a greater propensity to spend a larger share of their early adult years living in cities.  That’s the essence of what our research (and that of many others) shows has been happening.

The Upshot story takes issue with this thesis in two respects.  First, building on an argument advanced by USC’s Dowell Meyers––which we addressed when it first came out–Upshot says that the fortunes of cities will wane because the number of persons turning 25 years of age will decline slightly in the next decade. Second, Upshot says that as individual millennials get older, they’ll tend to move to the suburbs in greater numbers.

The essence of the Upshot story is two claims: (1) that the impact of millennials on cities will decline because their numbers will decrease, and (2) that their propensity to choose to live in urban settings will decline. Let’s consider each of these ideas in turn.

Numbers

The number of 25-34 year old millennials will increase by about 3 million over the next 7 years; this is the stage in the life cycle when they are most likely to live in cities.

Is the move to cities being buoyed by the rising number of millennials, and will their numbers decline soon, and therefore cause a decline in cities?

What’s interesting is that in 2015—for the first time—Millennials (those born between 1980 and 2000) constitute all of the persons aged 25-34. At the time of the last decennial census (2010), about half of the 25- to 34-year-old age group was composed of people in the tail end of generation X, and half were the early wave of Millennials. So strikingly, what the Census data shows is that the total number of 25 to 34 year olds in the US will increase from now through 2024. This chart shows the Census Bureau’s estimates of population aged 25 to 34 based on historical data through 2014 and its projections through 2035.

During the 1990s the number of 25 to 34 year olds actually declined as Baby Boomers aged out of this age group and were gradually replaced by the numerically smaller Generation X. Between 2000 and 2010 the number of Gen-Xer 25-34 year-olds increased slowly, with all of the aggregate increase in this age group being recorded after 2008. So, to the extent there was a movement back to the city in the 1990s, and the first half of the 2000-2010 decade, it was propelled not by an aggregate increase in the number of 25-to-34 year olds in the nation, but the changing relative preference of young adults for urban locations.

So, what we—and others—have recorded as the movement of young adults back into cities has, until very recently, little to do with the size (or preferences) of the Millennial generation. In fact, as they turn 25, and as they now dominate this age cohort, the next decade will be the time when the Millennial generation’s effect on cities will be most fully felt. Rather than declining, the number of 25-to 34 year olds in the United States will increase each year from now through 2024, rising from 44.1 million in 2015 to 47.6 million in 2024. In reality, the Millennial wave of urbanism is just now hitting the beach.

The outlook after 2024 (when the 25-34 year olds will increasingly be “post-Millennials”) is not for a dramatic demographic collapse. Rather than a peak, the young adult population stabilizes at a fairly high plateau above 47 million 25 to 34 year olds through 2035. So there is little basis for forecasting a decline in the key population group that has driven urban growth.

Preferences

With each passing year, 25-34 year olds, especially those with a four-year college degree or more education are more likely to live in close-in urban neighborhoods than other Americans.

Are young adults becoming less likely to live in cities?

At City Observatory, we’ve tracked the carefully tracked the location of urban residents in America by age group over the past three decades.  We’ve measured the relative preferences of young adults for close-in urban neighborhoods (census tracts within three miles of the center of the central business district).  The relative preference is the probability that a young adult will live in a close in neighborhood compared to the probability than any other resident of any age would live in such a neighborhood. These figures are drawn from Table 5 of our Young and Restless report; we’ve computed relative preference by dividing the probability that a person aged 25 to 34 lives within a three-mile radius of the center of the CBD of one of the 51 largest metropolitan areas, and compared it to the probability that the average resident of a metropolitan area live in this radius. If 11 percent of 25 to 34 year olds live in the 3 mile radius, and 10 percent of the population as a whole lives inside that radius, the relative preference is 10 percent (11 percent/10 percent)=110 percent, meaning that a 25 to 34 year old is 10 percent more likely than the typical resident to live in this area.

Since 1980, the relative preference of young adults for close-in neighborhoods has increased steadily. In 1980, young adults were 10 percent more likely than all metro residents to live in these neighborhoods; in 1990 12 percent more likely, in 2000 32 percent more likely; and in 2010, 25 to 34 year olds were fully 51 percent more likely to live in close-in neighborhoods than other metro residents. The relative preference of 25 to 34 year olds with a four-year degree to live in such neighborhoods was even higher:  over 100 percent in 2010.

Another way of looking at this is examining census data on where young adults live.  The University of Virginia’s Luke Juday has prepared a revealing set of charts that show the concentration of the young adult population by distance to the city center for all of the nation’s 50 largest metro areas.  He has comparative data for 1990, 2012 and 2015.  The data–just as in the statistics cited above–show that in the aggregate, the share of young adults living in close-in neighborhoods has increased and the share living in more distant neighborhoods has decreased. The orange line shows the share of young adults (age 22 to 34) as a share of the population in 1990.  The magenta and teal a lines show the share of young adults in 2012 and 2015 (respectively).  The share of young adults living in close in neighobrhoods (which had been higher in the center in 1990) increased over the next two decades. The share living in the suburbs declined. The steepening of this gradient is clear evidence of a growing relative preference of young adults for central locations.

This essential finding is actually the core of several well regarded academic papers.  Edlund, Machado and Siviatchi show that well-educated prime-age workers are increasingly concentrated in neighborhoods closer to the center of the metropolitan area. Couture and Handbury have replicated our findings, reporting:

More young adults are migrating to cities

Finally, almost in passing, the Upshot article resurrects a claim made in 2015 by FiveThirtyEight.com’s Ben Casselman, which had asserted that more young adults were now moving from cities to suburbs than vice versa, and claiming “Whether by choice or economic circumstance, young Americans are still more likely to leave the city for the suburbs than the other way around.”  We had a lengthy back-and-forth with Casselman at the time, but the University of Virginia’s Luke Juday pointed out that the data that Casselman used from the Currently Population Survey effectively missed millions of young adults, and disproportionately missing those living in cities. Juday’s analysis shows that actually the reverse is true–young adults are increasingly moving to cities:

Over the past 5 years about 3 million more Americans age 20-29 moved from suburbs to principal cities than from cities to suburbs, with last year being the largest net gain for cities yet.

The Takeaway: More Young Adult Urban Growth is Coming

The number of 25 to 34 year olds—the key group driving urban living, will not decline, but will grow between now and 2024. The urban wave we’ve experienced starting in the 1990s, and accelerating in the past decade wasn’t propelled by generational growth, so much as by a growing preference for urban living by young adults. The shift of young adults to cities, drawn by urban amenities, is increasingly confirmed by academic researchers, and is borne out by the latest Census data. 

Data Notes

The Census data for our of estimates of the 25 to 34 year old population come from three sources. Data for the period prior to 2010 comes from the archive of historical Census population estimates (https://www.census.gov/popest/data/historical/index.html).

Data for the period 2011 to 2014 comes from Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States, States, Counties, and Puerto Rico Commonwealth and Municipios: April 1, 2010 to July 1, 2014, Source: U.S. Census Bureau, Population Division, Release Date: June 2015. See: https://www.census.gov/popest/data/. Census projections of the population by year and age for the period 2015 through 2035 come from the 2014 Census Population Estimates series http://www.census.gov/population/projections/data/national/2014.html.

* Note the discontinuity in the data between 1999 and 2000 reflects the disparity between the Census Bureau’s annual intercensal estimates of the 25-34 year-old population and the actually higher number of 25-34 year olds enumerated by the 2000 decennial Census. It’s likely that the actual number of 25-34 year olds was underestimated in the intercensal estimates, during a period of significant immigration).

It’s an eye-catching, convention-tweaking claim: We’ve reached peak Millennial. And, so the argument goes, because Millennials have hit their “peak,” it’s time to junk all these crazy theories about Millennials not wanting to own cars, and not buying homes, especially in the suburbs. Sure, they had a youthful dalliance with city living, and the numbers of city-dwellers was temporarily pushed up by the now receding demographic wave, but now city living is now bound for a fall.

That, in a nutshell, is the argument being made by USC Professor Dowell Myers, who made the “peak Millennial call” in a lecture at the University of Texas in February. His argument was picked up and amplified by the Kinder Institute in “What if City-Loving Millennials Are Just a Myth?”, and most recently echoed by CityLab’s “Have U.S. Cities Reached ‘Peak Millennial’?

Have we hit peak Millennial? Does “peak Millennial” actually mean anything? Are we looking at a demographic ebb-tide for city living?

As it turns out, the answer to all of these questions is no.

We’ll explain the answers at greater length below, but the short synopsis is this:

The roughly 75-million-person group often called “Millennials” (those born 1980 to 1999) are now between 15 and 34 years of age. Just this year, for the first time, these Millennials made up 100 percent of those persons aged 25-34 (the age cohort that’s been fueling city growth). The number of 25 to 34 year-olds (all Millennials) will continue to increase from now through 2024, growing from 44.1 million to 47.6 million. Their impact on housing markets, in particular, is only beginning to be felt, and will grow in the decade ahead.

There’ll be more 25-to-34-year-old Millennials Every Year through 2024

Let’s start at the beginning. The core claim about the “peak” made by Professor Myers is based on one factoid: the highest number of births recorded in any year of the Millennial generation (those born between 1980 and 2000) occurred in 1990. The number of Millennials born in years after 1990 declines (slightly). So, by Myer’s math, the number of Millennials turning 25 has peaked in 2015. Myers core graph—reproduced here from Ryan Holyfield’s Kinder Institute blog, has a peculiar representation of the Millennial “peak.”

What it shows is the number of births in the US in each year from 1960 to 2013. Between 1980 and 2000, there’s a very soft peak in 1990, when 4.2 million people were born, as compared to an average of 3.8 million over the rest of the period. The people born during that soft peak turned 25 last year, which is what Myers is referring to as “peak Millennial.”

But why Myers picked age 25 to represent the “peak” of anything is unclear. For most young adult Americans, 25 is just the age where most (though not all) have finished their formal education, fewer than half have married, and most still don’t have children. It may be for some the end of an extended adolescence, but for most it’s essentially early onset adulthood.

More importantly, one year is never representative—it’s better to look at a larger age cohort. So as we go forward with our analysis, we’ll look at a 10 year wide age cohort, and lean on the Census Bureau’s forward-looking projections of the US young adult population. And in fact, we think it’s much more useful to talk about specific age cohorts (persons 25 to 34) than it is to talk about birth cohorts (those born in a particular time period), especially in undertaking time series analysis of economic trends. As we pointed out in our commentary debunking the National Association of Realtors claims about home buying trends, trying to deduce inter-generational changes by comparing Millennials when they are very young to those same people when they are older, inherently produces misleading results.

At City Observatory, we’ve focused keenly on the key 25- to 34-year-old age group. They’re highly mobile, likely to change jobs and homes, have generally completed their education, and have what economists would call “recent vintage human capital.” For all these reasons—and because they generally command lower wages than more experienced workers—they compose the dream demographic of fast-growing companies. They’re the most likely to move across state lines, and their migration decisions play a disproportionate role in determining which places experience a brain gain, as opposed to a brain drain.

What’s interesting is that in 2015—for the first time—Millennials (those born between 1980 and 2000) constitute all of the persons aged 25-34. At the time of the last decennial census (2010), about half of the 25- to 34-year-old age group was composed of people in the tail end of generation X, and half were the early wave of Millennials. So strikingly, what the Census data shows is that the total number of 25 to 34 year olds in the US will increase from now through 2024. This chart shows the Census Bureau’s estimates of population aged 25 to 34 based on historical data through 2014 and its projections through 2035.

During the 1990s the number of 25 to 34 year olds actually declined as Baby Boomers aged out of this age group and were gradually replaced by the numerically smaller Generation X. .Between 2000 and 2010 the number of gen xer 25 – 34 year-olds increased slowly, with all of the aggregate increase in this age group being recorded after 2008. So, to the extent there was a movement back to the city in the 1990s, and the first half of the 2000-2010 decade, it was propelled not by an aggregate increase in the number of 25-to-34 year olds in the nation, but the changing relative preference of young adults for urban locations.

So, what we—and others—have recorded as the movement of young adults back into cities has, until very recently, little to do with the preferences of the Millennial generation. In fact, as they turn 25, and as they now dominate this age cohort, the next decade will be the time when the Millennial generation’s effect on cities will be most fully felt. Rather than declining, the number of 25-to 34 year olds in the United States will increase each year from now through 2024, rising from 44.1 million in 2015 to 47.6 million in 2024. In reality, the Millennial wave of urbanism is just now hitting the beach.

The outlook after 2024 (when the 25-34 year olds will increasingly be “post-Millennials”) is not for a dramatic demographic collapse. Rather than a peak, the young adult population stabilizes at a fairly high plateau above 47 million 25 to 34 year olds through 2035. So there is little basis for forecasting a decline in the key population group that has driven urban growth.

The Preference of Young Adults for Urban Living is Increasing

As far as cities and city living are concerned, we’re just now seeing the full impact of Millennials in this key 25-34 year old age group. And the 25 to 34 year old age group will be composed entirely of Millennials for the next decade (and then the oldest Millennials (those born in the late 1990s) will age out of this young adult category in the early 2030s. So Millennials and their preferences—whatever they are—will essentially determine the behavior of the “young adult” demographic for the next 15 years, or so.

While it’s fashionable to describe the trend toward city living as something caused by the unique preferences of Millennials, the shift toward city living both predates the maturation of the Millennial generation—and promises to continue as their places among the young adult age group are taken by the post-Millennials (or whatever name is attached to the succeeding generation).

The relative preference for urban living for 25 to 34 year-olds has been increasing over the past two decades.

• 1980: 10 percent
• 1990: 12 percent
• 2000: 32 percent
• 2010: 51 percent

(These figures are drawn from Table 5 of our Young and Restless report; we’ve computed relative preference by dividing the probability that a person aged 25 to 34 lives within a three-mile radius of the center of the CBD of one of the 51 largest metropolitan areas, and compared it to the probability that the average resident of a metropolitan area live in this radius. If 11 percent of 25 to 34 year olds live in the 3 mile radius, and 10 percent of the population as a whole lives inside that radius, the relative preference is 10 percent (11 percent/10 percent)=110 percent, meaning that a 25 to 34 year old is 10 percent more likely than the typical resident to live in this area.

Interestingly, the relative preference of young adults for urban living tripled during the 1990s—a time when the total number of 25- to 34-year olds was actually in decline in the US (down about 7.7 percent). And even though the number of 25- to 34- year olds was increasingly only slowly during the decade 2000 to 2010 (+3.2 percent), the preference for urban living grew substantially. In the coming decade (2015-2025) the size of the 25-34 year will growth by 7.7 percent.

Far from peaking, the Millennial generation is hitting the sweet-spot for urban living, plus their numbers will continue to grow, according to the Census, between now and 2024.

The implication of the Myers analysis is that the growth in urban living is tied somehow to the size of the Millennial generation rather than its growing relative preference for urban living. His thesis is that as the number of persons turning 25 declines by about 400,000 this will lead to a reduced number of Millennials moving to cities. But as the evidence of the 1990s shows, it’s entirely possible to see as sustained decline in the numbers of young adults and also to observe an increase in the relative preference of those young adults for urban living.

The Takeaway: More Young Adult Urban Growth is Coming

The number of 25 to 34 year olds—the key group driving urban living, will not decline, but will growth between now and 2024. The urban wave we’ve experienced starting in the 1990s, and accelerating in the past decade wasn’t propelled by generational growth, so much as by a growing preference for urban living by young adults.

Data Notes

The Census data for our of estimates of the 25 to 34 year old population come from three sources. Data for the period prior to 2010 comes from the archive of historical Census population estimates (https://www.census.gov/popest/data/historical/index.html).

Data for the period 2011 to 2014 comes from Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States, States, Counties, and Puerto Rico Commonwealth and Municipios: April 1, 2010 to July 1, 2014, Source: U.S. Census Bureau, Population Division, Release Date: June 2015. See: https://www.census.gov/popest/data/. Census projections of the population by year and age for the period 2015 through 2035 come from the 2014 Census Population Estimates series http://www.census.gov/population/projections/data/national/2014.html.

* Note the discontinuity in the data between 1999 and 2000 reflects the disparity between the Census Bureau’s annual intercensal estimates of the 25-34 year-old population and the actually higher number of 25-34 year olds enumerated by the 2000 decennial Census. It’s likely that the actual number of 25-34 year olds was underestimated in the intercensal estimates, during a period of significant immigration).

Yesterday, TomTom released its annual rankings of the levels of congestion in world and US cities. Predictably, they generated the horrified, self-pitying headlines about how awful congestion is in the top-ranked cities. Cue the telephoto lens shots of bumper-to-bumper traffic, and tales of gridlock.

As we’ve long pointed out, there are big problems with the travel time index TomTom and others use to compare congestion levels between cities. Most importantly, some cities have much shorter commute distances than others—meaning that even if traffic moves slower at the peak hour, people spend less time commuting. For example, Houston has an average commute distance of 12.2 miles, while Portland has an average commute distance of 7.1 miles, according to the Brookings Institution. So even if Portland’s “congestion index number” is slightly higher (26 percent) than Houston’s (25 percent)—at least according to TomTom—average commute times are much shorter in Portland because of its more compact land use patterns. In effect, the travel time index, expressed as a percentage of total commute times, discounts the pain of traffic congestion in sprawling, car-dependent cities. That’s why its a lousy guide for talking about how well transportation systems work. The same problems plague the rankings released by Inrix two weeks earlier.

Plus, as Felix Salmon pointed out a couple of years ago, the TomTom data has a special bias: it’s chiefly gathered from people who’ve bought the devices, who almost by definition are not typical commuters. It’s highly likely that they represent those who drive the most, and who drive most in peak traffic (hence the value of Tom-Tom’s services); but data gathered from these devices is not necessarily typical of the experience of the average commuter.

TomTom, as you know, is in the business of selling real-time traffic data and navigation assistance to motorists. So in many respects, its rankings may be less a serious and balanced effort to assess congestion than they are to drum up demand for its product. The company’s top three suggestions for coping with traffic congestion are use a real time navigation system (like a TomTom device), “dare” to try alternate routes suggested by . . . your Tom Tom device, and “check traffic before you leave”—you get the picture.

Refreshingly, TomTom admits in its press release that building more highway capacity will do nothing to alleviate the congestion it identifies—we doubt that this is the part of the study that highway advocates will share with a wider audience. Nick Cohn, the company’s “traffic expert,” tells us:

Traffic congestion is a fact of life for every driver. And as we reveal the latest Traffic Index results this year, we can see that the problem is not going away.

We should not expect our transport authorities to simply ‘build away’ congestion. Studies have shown over the years that building new motorways or freeways does not eliminate congestion.

Even though Nick Cohn apparently knows better—and really just wants us to buy his company’s product—what TomTom and its peers are doing is just feeding a profoundly distorted view of traffic congestion problems. Those in the highway lobby routinely use this kind of data to try and scare us into spending billions for new highway construction projects that are often un-needed, or which do nothing to reduce congestion.

Last year’s hit film The Big Short depicted various investors who, realizing that there was a housing bubble in the years before the 2000s crash, found ways to “short” housing, betting against the market and ultimately making a killing when the crisis hit. Looking forward, there’s a plausible case to be made that this might be the time for a “Big Short” in parking, as a confluence of the growing popularity of walkable neighborhoods and the arrival of self-driving cars may make our current levels of parking way over-supplied compared to demand in the near future.

There’s a lot of speculation that the advent of self-driving vehicles could create a huge surplus of parking. A recent paper by University of Texas Professor Kara Kockelman and her colleagues estimates that in urban environments, self-driving cars could eliminate the need for about 90 percent of parking. The theory is that fleets of on-demand autonomous vehicles would substitute for most private car ownership, that cars would nearly always be in use—and when not in use could be stored in peripheral low value locations—with the result that the demand for parking, especially in urban centers would collapse. If that’s the case, a whole lot of private parking structures may suddenly find themselves with fewer customers, less revenue, and a badly broken business model: exactly the conditions for “shorting” this industry.

So who, exactly is “long” in the parking market? Well, there are some private firms who build and operate parking lots. But in many places around the country, the entities that have made substantial future bets on parking are local governments. Since the 1930s, city governments have been borrowing money to build and operate municipal parking lots for public use. Most big cities operate a substantial parking enterprise. Not only to most communities provide copious amounts of under-priced parking in the public right of way—with devastating impacts on travel behaviour and urban form—but many cities build off-street parking lots and structures, often in central commercial districts. For example, The city of Los Angeles owns 118 parking facilities with more than 11,500 parking spaces. And cities have been regularly expanding the supply of parking, often relying on debt financing, on the expectation that parking revenues will be sufficient to cover the costs of bond interest and principal. For example, the City of Miami Beach is issuing $67 million in in revenue bonds to expand its convention center parking garage. Like home mortgages, circa 1999, this mostly seems like a boring, low risk business. Cities borrow money on the bond market and then pay it back out of parking revenues. And so far, at least, municipalities have had little trouble making payments.

Given that the expected lifetime of parking structures—and perhaps even more critically, the repayment period for the bonds used to finance them—is measured in decades, the potential advent of autonomous vehicles is a live issue. So what happens if there’s a sea change in the market for parking, and if parking revenues fall—or perhaps fail to live up to municipal expectations? A couple of recent case studies show that shortfalls in parking demand are not purely an academic concern.

In New York, the $238 million parking garages built next to Yankee Stadium has gone bankrupt—it failed to meet its expected occupancy levels—and the local government is out more than $25 million so far in expected revenue from the garage—in addition to more than $100 million in public subsidies that supported its construction.

In Scranton Pennsylvania, a local parking authority issued millions of dollars in bonds backed up by the city’s guarantee of its full faith and credit. When demand for parking slumped, the parking authority could no longer pay debt service, and in 2012 came to the city to make up the shortfall. Initially, the city balked at making the payments, but found its credit rating jeopardized, and ultimately relented, using other city funds to make the bond payments. Even so, the crisis hasn’t abated: demand is still depressed, the garages are deteriorating, and the city is now looking at demolishing the top levels of two of the older garages rather than repairing them.

The financial viability and implied risk of borrowing millions to build parking garages hinges directly on the accuracy of forecasts of the demand for parking. That issue is a live one in Portland, where the city’s urban renewal authority is issuing $26 million in bonds to finance an 425-space parking structure adjacent to the city’s convention center and a proposed headquarters hotel. The site is also adjacent to the city’s most traveled light rail lines and is served by the newly built streetcar. It is just a few blocks from an apartment building with the nation’s largest off-street bike parking facility.

But the big question, raised by the Portland Shoupistas, is whether, ten or 20 years from now, there will be any market for hundreds of additional off-street parking spaces in a neighborhood that already has 3,300 on-street and structured spaces.

Already, according to Bike Portland, car rental demand is lagging far behind growth in hotel occupancy. Visitors to Portland—and especially attendees at convention events—choose not to drive, and instead take advantage of the city’s diverse transit system. In a brilliant bit of statistical journalism, Bike Portland’s Michael Anderson pulled together data showing how even as the city has recorded increasing numbers of tourists and convention attendees, visitor car rentals have been in steady decline.

In addition to growing uncertainty about the demand for parking in the future, the other factor which makes it hard to answer our question about whether now is the time for a “Big Short” in parking is the paucity of data about our public sector parking infrastructure. In a growing world of big data and smart cities, one thing that is surprisingly difficult to find is the total number of municipally owned and operating parking lots and structures. While some data sources show the location of publicly accessible parking—like Parkme.com—they don’t provide data in a way that allows one to easily discern the total number of spaces in a city or their ownership.

One hint as to the scale of the municipal parking enterprise comes from the Census, which tabulates data on city budgets. It reports (2013 State and Local Government Finances) that in 2013, the total parking revenues of municipal governments nationally totaled $2.7 billion.

There’s a good chance that many of these parking lots will become stranded assets: expensive, debt-financed projects that no longer generate enough revenue to cover their costs of construction and operation. When we add in the considerable social costs of subsidized parking and driving, newly constructed parking structures in cities may be the urban equivalent of new coal-fired power plants: obsolete, value-destroying activities. There’s not a lot cities can do about previous decisions to take on debt to build parking garages, but going forward, it seems like they ought to take a very careful look at whether it’s a sound investment, or whether they’re setting themselves up to be on the wrong side of tomorrow’s “Big Short.”

Today, we spend a few minutes reviewing the recent history of housing markets in rural North Dakota. In a microcosm, we can see how the interplay of demand and supply drive housing market cycles. The speed and scale of changes in North Dakota dwarf what we usually see, but provide an illustration of the forces at work in many cities around the country.

For most of the past decade, the real estate market in Williston, North Dakota has been on an amazing tear. The region, home to the Bakken shale formation, has been the epicenter of the U.S. oil fracking industry. With sustained oil prices in the $100 a barrel range, everyone from global energy companies to independent producers have been drilling exploratory and production wells, and state’s oil output increased by a factor of ten, from about 100,000 barrels per day to more than 1,000,000 barrels per day.

Job growth quickly overwhelmed the local housing supply, spiking rents, and leading landlords to rent out travel trailers, garages, storage units, and outbuildings to oil workers—and those in the local service industry that grew in response to the population influx. Williston even became famous for “man camps”—quickly assembled fields of trailers and modular housing units, inhabited almost entirely by male oil workers.

But in the past year, everything has changed. First, the oil market has gone bust, with prices falling from more than $100 to recent lows of less than $38. In response, oil companies have drastically cut back on exploration and new well-drilling. The industry is shedding jobs.

Second—and importantly for our story—the local market has seen an incredible surge of new housing construction. The number of building permits issued in Williston grew ten-fold between 2009 and 2013.

The combination of flagging demand and a newly abundant supply has rental prices in Williston dropping like a rock. According to real estate analytics firm Zillow, average rents in the area have declined by 23.4 percent over the twelve month period ending in January 2016. Reuters reports that new apartments which were commanding rents of as much as $3,200 per month have now discounted rents sharply, added communal hot tubs and are providing free alcohol and snacks for residents.

The Williston experience provides a dramatic, but very clear, example of the dynamics of local real estate markets. The critical issue here is what you might call a “temporal mismatch” between demand and supply. Demand is the hare; supply is the tortoise. Demand can change in an instant—as quickly as new jobs open up, and as quickly as U-Hauls and moving vans deliver new residents to a city (or neighborhood). Supply takes time: planning, gaining financial and zoning approvals for new units, and then actually building and finishing out apartments and houses takes as much as 18 to 24 months. And when demand continues to change, supply can be struggling to keep up.

That’s just what happened in Williston. Developers did respond early on (building permits more than quadrupled in 2009 and then doubled in 2010) but demand grew even more, with the result that there continued to be a rise in rents.

Now, finally, due to the combination of flagging demand and the relentless (if comparatively plodding) increase in supply, the market is much closer to balance.

Williston is, in a supercharged microcosm, a metaphor for the housing market in US cities. In the past decade, the demand for rental housiing and urban locations has far outstripped the growth in supply. Lots more people have decided for economic reasons (or because they prefer city neighborhoods that they want to rent in cities. And housing supply, at least initially, has hardly budged—first because of the aftermath of the Great Recession, and still, in many cities because of local zoning restricts and slows the number of new housing units that can be built. But in true tortoise fashion, supply is beginning to catch up to demand. As we’ve seen in Denver, Seattle, and Washington, when a sufficient number of new apartments are built, they begin to shave rent inflation. The Williston story is also playing out in another oil-town: Houston, where new apartments are going begging and landlords are offering free first month’s rent to new tenants.

We think there are two key takeaways here. First, supply and demand do operate: building more housing is the key to addressing rental affordability. Second, housing markets are inevitably subject to a temporal mismatch between supply and demand. Unlike the neat whiteboard drawings of supply and demand curves that you may have seen in an undergraduate economics lecture, which can be erased and redrawn in a moment, in the real estate market, demand is fast, but new supply is slow.

At City Observatory, we’ve long been critical of some seemingly scientific studies and ideas that shape our thinking about the nature of our transportation system, and its performance and operation. We’ve pointed out the limitations of the flawed and out-dated “rules of thumb” that guide our thinking about trip generation, parking demand, road widths and other basics. One of the most pernicious and persistent data fables in the world of transportation, however, revolves around the statistics that are presented to describe the size, seriousness and growth of traffic congestion as a national problem. This week saw the latest installment in a perennial series of alarming, but actually un-informative reports about traffic congestion and its economic impacts.

The same old story

On March 15, traffic data firm Inrix released its 2015 Traffic Scorecard, ranking travel delays in the largest cities in Europe and North America. As is customary for the genre, it was trumpeted with a press release bemoaning the billions of hours that we waste in traffic. That, in turn, generated the predictable slew of doom-saying headlines:

• Houstonians wasted even more time in traffic in 2015, report shows
• Report: San Francisco has the third worst traffic in the country.
• U.S. Big Cities Now Among the Mos traffic jammed in the world

But at least a few journalists are catching on. At the Los Angeles Times, reporter Laura Nelson spoke with Herbie Huff from the UCLA transportation center who pointed out that “Aside from an economic downturn, the only way traffic will get better is if policymakers charge drivers to use the roads.”

And GeekWire headlined its story “Study claims Seattleites spend 66 hours per year in traffic, but some say that number’s deceptive” and reported Greater Greater Washington’s David Alpert as challenging the travel time index methodology used in the Inrix report.

Headlines aside, a close look at the content of this year’s report shows that on many levels, this year’s scorecard is an extraordinary disappointment.

As we’re constantly being told by Inrix and others, we’re on the verge of an era of “smart cities,” where big data will give us tremendous new insights into the nature of our urban problems and help us figure out better, more cost-effective solutions. And very much to their credit, Inrix and its competitors have made a wealth of real time navigation and wayfinding information available to anyone with a smart phone—which is now a majority of the population in rich countries. Driving is much eased by knowing where congestion is, being able to route around it (when that’s possible) and generally being able to calm down by simply knowing about how long a particular journey will take because of the traffic you are facing right now. It’s quite reassuring to hear Google Maps tell you “You are on the fastest route, you will arrive at your destination in 18 minutes.” This aspect of big data is working well.

By aggregating the billions of speed observations that they’re tracking every day, Inrix is in a position to tell us a lot about how well our highway system is working. That, in theory, is what the Scorecard is supposed to do. But in practice, it’s falling far short.

As impressive as the Inrix technology and data are, they’re only useful if they provide a clear and consistent basis for comparison. Are things measured in the same way in each city? Is one year’s data comparable with another? We and others have pointed out that the travel time index that serves as the core of the Inrix estimates is inherently biased against compact metropolitan areas with shorter travel distances, and creates the mistaken impression that travel burdens are less in sprawling, car-dependent metros with long commutes.

The end of history

For several years, it appeared that the Inrix work offered tremendous promise. They reported monthly data, on a comparable basis, using a nifty Tableau-based front end that let users track data for particular markets over time. You could see whether traffic was increasing or decreasing, and how your market stacked up against other cities. All this has simply been disappeared from the Inrix website—though you can still find it, with data through the middle of 2014, on an archived Tableau Webpage.

This year’s report is simply a snapshot of 2015 data. There’s nothing from 2014, or earlier. It chiefly covers the top ten cities, and provides a drill down format that identifies the worst bottlenecks in cities around the nation. It provides no prior year data that let observers tell whether traffic levels are better or worse than the year before. In addition, the description of the methodology is sufficiently vague that it’s impossible to tell whether this year’s estimates are in fact comparable to one’s that Inrix published last year.

Others in the field of using big data do a much better job of being objective and transparent in presenting their data. Take for example real estate analytics firm Zillow (like Inrix, a Seattle-based IT firm, started by former Microsoft employees). Zillow researchers make available and regularly update a monthly archive of their price estimates for different housing types for different geographies, including cities, counties, neighborhoods and zip codes. An independent researcher can easily download and analyze this data to see what Zillow’s data and modeling show about trends among and within metropolitan areas. Zillow still retains its individual, parcel-level data and proprietary estimating models, but contributes to broader understanding by making these estimates readily available. Consistent with its practice through at least the middle of 2014, Inrix ought to do the same—if it’s really serious about leveraging its big data to help tackle the congestion problem.

A Texas divorce?

For the past couple of years, Inrix has partnered closely with the Texas Transportation Institute (TTI), the researchers who for more than three decades have produced a nearly annual Urban Mobility Report (UMR). Year in and year out, the UMR has had the same refrain: traffic is bad, and it’s getting worse. And the implication: you ought to be spending a lot more money widening roads. Partly in response to critiques about the inaccuracy of the data and methodology used in earlier UMR studies, in 2010, the Texas Transportation Institute announced that henceforth it would be using the Inrix data to calculate traffic delay costs.

But this year’s report has been prepared solely by the team at Inrix, and has no mention of the the Texas Transportation Institute or the Urban Mobility Report in its findings or methodology. Readers of the last Inrix/TTI publication—released jointly by the two institutions last August—are left simply to wonder whether the two are still working together or have gone their separate ways. It’s also impossible to tell if the delay estimates contained in this year’s Inrix report are comparable to those in last year’s Inrix/TTI report. (If the two are comparable, then the report is implying that traffic congestion dropped significantly in Washington DC from 81 hours reported by TTI/Inrix last August, to the 75 hours reported by Inrix in this report).

Have a cup of coffee, and call me in the morning

As we pointed out last April, the kind of insights afforded by this kind of inflated and unrealistic analysis of costs—unmoored from any serious thought about the costs of expanding capacity sufficiently to reduce the hours spent in traffic—are really of no value in informing planning efforts or public policy decisions. We showed how, using the same assumptions and similar data about delays, one could compute a cappuccino congestion index that showed Americans waste billions of dollars worth of their time each year standing in line at coffee shops.

Inrix data have great potential, but a mixed record, when it comes to actually informing policy decisions. On the one hand, Inrix data was helpful in tracking speeds on the Los Angeles Freeway system, and showing that after the region had spent $1.1 billion to widen a stretch of I-405, that overall traffic speeds were no higher—seeming proof of the notion that induced demand tends to quickly erase the time-saving benefits of added capacity. In Seattle, Inrix’s claim that high occupancy toll lanes hadn’t improved freeway performance were skewered by a University of Washington report that pointed out that the Inrix technology couldn’t distinguish between speeds on HOT-lanes and regular lanes, and noted that Inrix had cherry-picked only the worst performing segments of the roadway, ignoring the road segments that saw speed gains with the HOT lane project.

This experience should serve as a reminder that by itself, data—even, or maybe especially, really big data—doesn’t easily or automatically answer questions. It’s important that data be transparent and widely accessible, so that when it is used to tackle a policy problem, everyone can be able to see and understand its strengths and limitations. The kind of highly digested data presented in this report card falls well short of that mark.

Our report card on Inrix

Here’s the note that we would write to Inrix’s parents to explain the “D” we’ve assigned to Inrix’s Report Card.

Inrix is a bright, promising student. He shows tremendous aptitude for the subject, but isn’t applying himself. He needs to show his work, being careful and thorough, rather than excitedly jumping to conclusions. Right now he’s a little bit more interested in showing off and drawing attention to his cleverness than in working out the correct answer to complicated problems. We’re confident that when he shows a little more self-discipline, scholarship and objectivity—and learns to play well with others—he’ll be able to be a big success.

Last week, the Washington Post published an article repeating an old-refrain in transportation journalism—the horror of long commutes.

According to the Post, more and more Americans are commuting longer and longer distances to work each day. There’s growing scientific evidence that long commutes are bad for your physical and mental health, reduce happiness, and even cut into civic participation.

But if you look closely at the data cited in the Post article, it’s pretty clear that long commutes are quite rare, and aren’t really becoming more common.

A 2013 Census study defined “mega commuters” as those traveling more than 90 minutes and more than 50 miles each way. They found that while mega commuting grew from about 1.6 percent of all commuters to 2.7 percent between 1970 and 2000, the share of such long commutes was flat to declining from 2000 to 2011.

Who are these mega-commuters? The Census report says that they’re most likely to be male, with a higher than average salary, older, and married to a spouse who doesn’t work. Also, most mega-commuters are commuting from one metropolitan (or micropolitan) area to another one—not just traveling from a very far-flung suburb to a business district in their own region.

In just the last two years, stories detailing the horrors of long commutes or describing strategies for coping have appeared in:

The Atlantic: The rise of the outrageously long commute

Fortune: 6 Ways to Survive a Hellishly Long Commute

Men’s Fitness: Long commutes can kill

US News: 3 Strategies for Surviving a Long Commute

While articles about mega commuting imply that it’s a stable, externally imposed lifestyle, we don’t know that mega commuting isn’t temporary, isn’t a lifestyle choice, and isn’t closely related to telecommuting for many of these workers. Census data are snapshots of a single point in time—if a person living in one metropolitan area accepts a far away job, and chooses to commute a long distance while looking for housing, but later moves closer to work, that wouldn’t be captured in the Census data.

It’s surprising how much attention mega-commuting gets given how uncommon it is. About eight times as many Americans have “micro-commutes”—they either work at home or have a commute of five minutes or less—as mega commutes. The 2014 American Community Survey reports that nearly 20 million Americans, about 16 percent of all commuters, have self reported commute times of 0 to 5 minutes. Instead of fretting about the problems of an extremely small group of commuters, maybe we should be thinking about how we build communities and arrange work so that at even larger fraction of the population can enjoy the benefits of micro-commutes. That would be the best way to reduce the “human cost” of commutes.

One of the regular findings of historical analyses of commuting times is that despite huge variations in wealth and technology, humans have generally commuted an average of about half an hour to work—an observation generally termed “Marchetti’s constant.” More formally, several scholars have modeled commuting behavior using a “travel time budget” to reflect these seemingly consistent time choices.

To be sure, some people, in some very large metropolitan areas, travel long distances to work—at least for a time. Whether these patterns are temporary or stable is another question. The author of a Grist story citing the Washington Post’s lament recorded that she, herself, once suffered a long period of driving excessive distances to work in North Carolina—before she decided to move to Seattle, where she now has a pleasant and relatively short walk to work.

Part of what this should highlight is the important role that personal choice plays in commuting. Most people consciously make choices about where they want to live, where they will look for work, and how long a commute they can endure. For some people, the appeal of a particular job, or the the special amenities of a particular house or neighborhood, and our tolerance for hours spent in a car or bus may mean that a long commute is a reasonable choice. For many households, the extra time a prime breadwinner spends commuting may be the functional equivalent of “sweat equity” because frequently by commuting a longer distance a family can afford a bigger house—a phenomenon real estate professionals call “drive ‘til you qualify.”

In a sense, house prices, home sizes and commute times are like the famous shop sign: “Low Price, High Quality, Fast Delivery: Choose Any Two.” It would be great if everyone could get big houses at low prices with short commutes, but in reality, in most large metropolitan areas every household has to make its own decisions about how to trade-off one or more of these characteristics to get more of the things it wants. And, as we never tire of pointing out, the demand for urban living (and shorter commutes), in the face of a relatively slowly expanding supply of great urban neighborhoods has lead to a shortage of cities. The solution to our travel problem may be more in building cities than building roads and transit.

While we think the Post has mis-stated the trend, it’s hard not to agree with the basic premise of the article: Americans waste lots of time commuting. Some of that is the product of personal choices—some of which may make sense, and other less so. But a lot of it has to do with how we build our communities, and the kind of options we create about where people can live, and how they can travel from home, to work and other common destinations.

Everyone interested in state or local economic development should read “Remaking Economic Development: The Markets and Civics of Continuous Growth and Prosperity.” In it, the Brookings Institution’s Amy Liu neatly synthesizes important lessons from the field about how metropolitan centered economic strategies are vitally important not just to revitalizing city economies, but to national economic progress. The report outlines a cogent list of lessons and sound advice for implementing a successful metro strategy.

There’s so much this report gets right that it’s difficult to find fault. But on a few key issues—mostly having to do with emphasis, rather than fundamentals—more could be said. Here are a six further thoughts about what remaking economic development ought to include, based on my own observations and experience.

Talent is central to economic development

“Remaking Economic Development” gives a vigorous nod to talent development as an economic strategy, but in our view, it should be front and center. We know that the educational attainment level of the population is the single most important factor shaping regional economic success: we can explain fully 60 percent of the variation in per capita incomes among metropolitan areas simply by knowing what share of the adult population has a four year degree. This relationship has grown steadily stronger over the past few decades, and promises to become even more important in the decades ahead.

While the report acknowledges the importance of education and skills, talent is third on the Brookings list of action principles. It should be number one, because it is something that applies everywhere, and without it, no economic strategy is likely to succeed. Unless you have a plausible approach to bolstering talent, anything else is irrelevant.

Placemaking is a key to anchoring talent

The Brookings report speaks to connections within the community as a broad umbrella for thinking about everything from widespread inclusiveness to infrastructure. But increasingly, placemaking—especially building great urban spaces and tackling issues of livability and housing affordability—is vital to attracting and retaining talent and growing the economy.

Placemaking is important because talent is mobile. Talented workers have choices of where to live, and are increasingly exercising their choices, disproportionately choosing to live in places that build great urban communities. The number of college-educated young adults is increasing twice as fast in close-in urban neighborhoods as in the rest of metro areas. That’s driven by the growing demand for dense, diverse, interesting, transit-served, bikeable, walkable neighborhoods. Companies are increasingly moving to be close to the workers living in (or seeking) these neighborhoods. Placemaking is essential to attracting and anchoring talent in place.

Exporting goods is best viewed as an indicator of success, rather than a tactic.

Brookings has worked with a number of cities, including Portland, to promote export strategies. There’s little question that a strong and growing export base is a correlate of a healthy economy. But simply telling cities to promote exporting glosses some important steps. In general, US-based firms and regional industry clusters aren’t successful because they export, they export because they are successful. In a high-cost location, facing global competition, US firms can be successful in global markets generally only if they are have demonstrably better products, more efficient production, and more continuous innovation. Portland’s largest exporter is Intel, which exports not because Portland has a particularly good export strategy or infrastructure (full disclosure: I was a state government official charged with trade policy for a dozen years in the 1980s and 1990s), but because Intel is utterly world-class in its research and manufacturing processes—regularly getting more patents for its Oregon-based technologies than from the rest of its US operations combined. The upshot: rather than focusing on raising exports, strategies should ask what it will take for a region’s industry clusters to be world class (better skills, improved technology, more entrepreneurs & innovation); these will be the places where the region should act.

In addition, especially for smaller and medium-sized firms, exporting is neither the best nor most profitable means to exploit global markets. Exporting can be risky and uncertain: smaller firms face formidable barriers to dealing with global logistics, trade finance, currency fluctuations, product localization, and market development. In many cases they may be better off licensing intellectual property or pursuing joint ventures with international partners, rather than exporting directly themselves. Note that Nike, based in Oregon, barely registers in the state’s export totals (and is actually a big net importer): but it’s a formidable global player because Portland is the hub of its design, marketing and finance functions.

I’d edit Brookings third principle for economic strategies to stress working to improve the health of traded sector clusters (the traded sector consists of businesses that sell their goods or services in competition with firms from other states or nations, regardless of whether they export them from their state of origin or the nation). Expanding exports is just one measure of how clusters are performing.

It’s better to have fewer goals than too many.

Brookings calls for economic development plans to have clear, measurable goals. No doubt this is good advice. But if you have 50 goals, you really don’t have any goals. Goals ought to help decision-maker set priorities. In practice, a laundry list of goals means that there no clear basis for choosing any one alternative action over others. A few key goals, including raising per capita income and assuring that opportunities to learn and earn are widely and equally available to everyone in the community, are key.

Strategy is about choosing what not to do.

There are a wealth of tactics, best practices, and exemplary case studies of how to do economic development. Brookings and others do a good job of cataloging such success stories, and retelling them to other cities. But while this can be informative, every city has its own distinct opportunities and liabilities, and what worked for one city, with one set of industries and resources at one time, may be simply irrelevant or unavailable to another city. As Brookings and others have documented, much economic development practice is rife with fads: witness the profusion of cities pursuing—at great expense, and with no evident results—the development of biotechnology industry clusters. It’s tempting to pursue a “one of each” economic development effort that shows that whatever set of model policies anyone has cataloged, your city has at least a token effort that qualifies. The essence of strategy is choosing—ruling out inappropriate or low-return efforts and focusing on the things that matter.

None of this is likely to work if federal macroeconomic policy doesn’t facilitate robust growth.

While it’s laudable, as well as necessary, that communities pursue their own economic strategies, it’s also important to recognize—especially from the perspective of those working in DC—that these are unlikely to be collectively successful unless national economic growth continues, and indeed accelerates. The backdrop to this entire policy environment is still a demonstrably weak recovery from the worst economic downturn in eight decades. The relatively small size and quick withdrawal of fiscal stimulus, and more recently, the Federal Reserve’s renewed hawkishness about non-existent inflation, signal that the macroeconomic environment in the next few years will work against many of these local economic initiatives. The increasingly metropolitan locus of competitive advantage may mean that a few places continue to prosper while many American metros languish—simply because the national economy isn’t expanding faster enough to power growth in any but the most adept and advantaged places. In addition to providing advice to Mayors and metro residents, it would be helpful if Brookings also spoke truth to the powerful in the federal government that all of these local economic development efforts hinge on a more ambitious macroeconomic policy.

There’s a growing recognition that many of the most important economic opportunities and decisions will be realized at the metropolitan level. “Remaking Economic Development” explains how past practice is simply inadequate to capitalizing on these opportunities and lays out the steps that cities (and metropolitan regions) will need to take. In many respects these efforts are still in their infancy, and more learning and evolution is needed (and will occur).

Additional disclosure: I’ve written three research papers published by Brookings on industry clusters and regional development, and for several years was a non-resident Senior Fellow at Brookings.

The big divides between rich and poor in the US are drawing increased attention, which is a good thing. Income inequality has been steadily growing in the US, and it’s a big problem.

As we’ve pointed out, this problem has an important spatial dimension as well. The concentration of poverty, in particular, amplifies all of the negative effects of poverty—and unfortunately, over the past four decades, the number of high poverty neighborhoods has been increasing. Poor people are now considerably more likely to live in neighborhoods where a large fraction of their neighbors are also poor.

But some of what’s being written about inequality at the city level is misleading, meaningless, or simply wrong.

There’s a kind of conundrum that confronts us when we talk about income inequality. Judged at a national level, a wide diversity of income levels is a bad thing. But in any particular neighborhood, having a diversity of incomes is pretty much the opposite: an indicator of economic integration. Conversely, lower levels of variation in income at the national level could be taken as a sign of a more equal society. But if there are very low levels of variation in income in a particular neighborhood, that’s pretty much a sure sign of strong economic segregation (whether that’s a neighborhood composed largely of the well-to-do or of the poor). The key point is this: while greater equality is generally a good thing at a national level, it can be a bad thing at a highly local level.

The reason of course is that at the neighborhood level, the distribution of income is shaped not by the overall distribution of income in the economy, but by the price of housing and the desirability of neighborhoods.

The confusion generated by this conundrum is very much in evidence in an article that appeared in Next City last week. Entitled “Five Charts that Detail Wealth and Inequality in U.S. Cities,” the article summarizes a new report by the Washington, DC-based Economic Innovation Group using a range of zip code level Census data to assess levels of economic distress among and within metropolitan areas.

The featured table in this report lists the “ten most prosperous cities.”

This is less a list of “most prosperous cities” than it is a list of “most exclusive suburbs.” In each case, these are suburban cities on the periphery of one of the nation’s larger and more successful metropolitan areas. The reason they score so low on the distress indicator is not because they’ve created lots of jobs, but because their land use planning systems and high priced housing effectively exclude poorer residents from locating there.

For example, consider Flower Mound, Texas. According to the Census Bureau’s “On the Map” data service, of the 32,152 workers who lived in the city in 2013, 28,482 (88 percent) worked outside the city limits. Flower Mound’s story is not about its localized economic success, but rather about being a bedroom community for relatively high income people who work somewhere else—and not being a place that many low income people can afford.

Describing such places as “the country’s most prosperous cities” isn’t so much wrong as it is incomplete and misleading.

And it diverts our attention from the fact that the creation of such exclusive enclaves is one of the factors that is amplifying the spatial economic segregation of metropolitan areas. Within a single metropolitan area—Phoenix—some suburban cities are classified as being prosperous and equal (Gilbert and Scottsdale) while another suburb a few miles away (Glendale) is among the most distressed.

This is clear when you look at the data in the EIG report: the two cities with the highest levels of “equality” are Cleveland and Detroit—essentially because poverty is so severe and widespread.

Just because we can compile data on income levels and economic inequality at the city level doesn’t mean that these are the useful units to use to assess or diagnose these problems.

In an important sense, municipalities are simply the wrong units for measuring economic performance—they don’t correspond to entire functioning economies, and they vary so widely in how their defined from region to region that comparisons simply aren’t meaningful.

Our storefront index shows where there’s a density of destinations to enable walkability

As Jane Jacobs so eloquently described it in The Death and Life of American Cities, much of the essence of urban living is reflected in the “sidewalk ballet” of people going about their daily errands, wandering along the margins of public spaces (streets, sidewalks, parks and squares) and in and out of quasi-private spaces (stores, salons, bars, boutiques, bars and restaurants).

Clusters of these quasi-private spaces, which are usually neighborhood businesses, activate a streetscape, both drawing life from and adding to a steady flow of people outside.

In an effort to begin to quantify this key aspect of neighborhood vitality, we’ve developed a statistical indicator—the Storefront Index (click to see the full report)—that measures the number and concentration of customer-facing businesses in the nation’s large metropolitan areas. We’ve computed the Storefront Index by mapping the locations of hundreds of thousands of everyday businesses: grocery and hardware stores, beauty salons, bookstores, bars and restaurants, movie theatres and entertainment venues, and then identifying significant clusters of these businesses—places where each storefront business is no more than 100 meters from the next storefront.

The result is a series of maps, available for the nation’s 51 largest metropolitan areas, that show the location, size, and intensity of neighborhood business clusters down to the street level. Here’s an example for Washington, DC. On this map, each dot represents one storefront business. This maps shows storefront businesses throughout the metropolitan area. In downtown Washington, there is a high concentration of storefronts; as one moves further out towards the suburbs, the number of storefronts diminishes, and storefronts are increasingly found arrayed only along major arterials, with a few satellite city centers (like Alexandria).

The Storefront Index helps illuminate the differences in the vibrancy of the urban core in different metropolitan areas. Here we’ve constructed identically scaled maps of the Portland and St. Louis metropolitan areas, zoomed in on their central business districts. The light colored circle represents a three-mile buffer around the center of downtown. In Portland, there are about 1,700 storefront businesses in this three-mile buffer—with substantial concentrations downtown, and in the close-in residential neighborhoods nearby. St. Louis has only about 400 storefront businesses in a similar area, with a smaller concentration of storefront businesses in its center, and fewer and less dense commercial districts in nearby neighborhoods.

The Storefront Index is one indicator of the relative size and robustness of the active streetscape in and around city centers. As this table shows, there’s considerable variation among US metropolitan areas in the number of storefront businesses with three miles of the center of downtown. New York and San Francisco have the densest concentrations of storefront businesses in their urban cores.

Maps of the Storefront Index for the nation’s 51 largest metropolitan areas are available online here. You can drill down to specific neighborhoods to examine the pattern of commercial clustering at the street level.

We also use the Storefront Index to track change over time, looking at the growth of businesses and street level activity in a rebounding neighborhood in Portland. There’s also strong evidence to suggest that concentrations of storefront businesses provide a conducive environment for walking. We’ve overlaid the storefront index clusters on a heat map of Walk Scores for selected metropolitan areas to explore the relationship between these two measures. While Walk Score includes destinations like parks and schools, as well as businesses, it also measures walkability from the standpoint of home-based origins, while our Storefront Index shows the concentration of commercial destinations.

City Observatory has developed the Storefront Index as a freely available tool for urbanists and city planners to use in their communities. The index material is licensed under a Creative Commons Attribution license (as is all City Observatory material), and a shape file containing storefront index information is available here.

Where are walkable local shopping districts in your city?

There are just six shopping days left until Christmas; while much of our shopping is done on-line or with at big box stores and national chains, many consumers look to support their local businesses during the holiday season.  Where, exactly, can you find the clusters of local shops that representative alternatives to getting malled?

Part of the answer comes from City Observatory’s Storefront Index–we’ve mapped the location of millions of customer-facing retail and service businesses in cities throughout the nation, with an emphasis on identifying clusters of businesses in close proximity to one another–where you could conceivably walk from one establishment to another (or several others).  One great feature of the Storefront Index is that it shows the pattern and density of retail activity throughout a metropolitan area.

Our Storefront Index includes a series of maps, available for the nation’s 51 largest metropolitan areas, that show the location, size, and intensity of neighborhood business clusters down to the street level. Here’s an example for Portland, Oregon. On this map, each dot represents one storefront business. This maps shows storefront businesses throughout the metropolitan area. In downtown Portland, there is a high concentration of storefronts; as one moves further out towards the suburbs, the number of storefronts diminishes, and storefronts are increasingly found arrayed only along major arterials.

A key message of the index is that retail businesses, especially small, local ones, don’t do well in isolation.  They thrive when they’re part of an environment that combines public spaces and private establishments in a dense, and mutually reinforcing array. A local book store, like Portland’s Broadway Books, is more likely to flourish if its located–as it is, near to restaurants, coffee shops, bakeries, florists, and other establishments that generate foot traffic and create a lively and enjoyable place to walk.

Broadway Books is located in one of these thriving l neighborhood business districts: Northeast Broadway.  We can use the Storefront Index map to zoom in to the local area to see the array of businesses lined up along both sides of the street, a walkable string of pearls for shoppers.

, 

As Jane Jacobs so eloquently described it in The Death and Life of American Cities, much of the essence of urban living is reflected in the “sidewalk ballet” of people going about their daily errands, wandering along the margins of public spaces (streets, sidewalks, parks and squares) and in and out of quasi-private spaces (stores, salons, bars, boutiques, bars and restaurants). These urban street spaces are especially active this time of year, when the usual flow of customers is swelled by holiday shoppers. 

You can use the Storefront Index to zoom in to any neighborhood in any of of the fifty largest metropolitan areas in the US; it will show you which places have a diverse array of storefronts in close proximity to one another, and which places have a paucity of such concentrations.

Maps of the Storefront Index for the nation’s 51 largest metropolitan areas are available online here. You can drill down to specific neighborhoods to examine the pattern of commercial clustering at the street level.

We also use the Storefront Index to track change over time, looking at the growth of businesses and street level activity in a rebounding neighborhood in Portland. There’s also strong evidence to suggest that concentrations of storefront businesses provide a conducive environment for walking. We’ve overlaid the storefront index clusters on a heat map of Walk Scores for selected metropolitan areas to explore the relationship between these two measures. While Walk Score includes destinations like parks and schools, as well as businesses, it also measures walkability from the standpoint of home-based origins, while our Storefront Index shows the concentration of commercial destinations.

City Observatory has developed the Storefront Index as a freely available tool for urbanists and city planners to use in their communities. The index material is licensed under a Creative Commons Attribution license (as is all City Observatory material), and a shape file containing storefront index information is available here.

As Jane Jacobs so eloquently described it in The Death and Life of American Cities, much of the essence of urban living is reflected in the “sidewalk ballet” of people going about their daily errands, wandering along the margins of public spaces (streets, sidewalks, parks and squares) and in and out of quasi-private spaces (stores, salons, bars, boutiques, bars and restaurants).

Clusters of these quasi-private spaces, which are usually neighborhood businesses, activate a streetscape, both drawing life from and adding to a steady flow of people outside.

In an effort to begin to quantify this key aspect of neighborhood vitality, we’ve developed a new statistical indicator—the Storefront Index (click to see the full report)—that measures the number and concentration of customer-facing businesses in the nation’s large metropolitan areas. We’ve computed the Storefront Index by mapping the locations of hundreds of thousands of everyday businesses: grocery and hardware stores, beauty salons, bookstores, bars and restaurants, movie theatres and entertainment venues, and then identifying significant clusters of these businesses—places where each storefront business is no more than 100 meters from the next storefront.

The result is a series of maps, available for the nation’s 51 largest metropolitan areas, that show the location, size, and intensity of neighborhood business clusters down to the street level. Here’s an example for Washington, DC. On this map, each dot represents one storefront business. This maps shows storefront businesses throughout the metropolitan area. In downtown Washington, there is a high concentration of storefronts; as one moves further out towards the suburbs, the number of storefronts diminishes, and storefronts are increasingly found arrayed only along major arterials, with a few satellite city centers (like Alexandria).

The Storefront Index helps illuminate the differences in the vibrancy of the urban core in different metropolitan areas. Here we’ve constructed identically scaled maps of the Portland and St. Louis metropolitan areas, zoomed in on their central business districts. The light colored circle represents a three-mile buffer around the center of downtown. In Portland, there are about 1,700 storefront businesses in this three-mile buffer—with substantial concentrations downtown, and in the close-in residential neighborhoods nearby. St. Louis has only about 400 storefront businesses in a similar area, with a smaller concentration of storefront businesses in its center, and fewer and less dense commercial districts in nearby neighborhoods.

The Storefront Index is one indicator of the relative size and robustness of the active streetscape in and around city centers. As this table shows, there’s considerable variation among US metropolitan areas in the number of storefront businesses with three miles of the center of downtown. New York and San Francisco have the densest concentrations of storefront businesses in their urban cores.

Maps of the Storefront Index for the nation’s 51 largest metropolitan areas are available online here. You can drill down to specific neighborhoods to examine the pattern of commercial clustering at the street level.

We also use the Storefront Index to track change over time, looking at the growth of businesses and street level activity in a rebounding neighborhood in Portland. There’s also strong evidence to suggest that concentrations of storefront businesses provide a conducive environment for walking. We’ve overlaid the storefront index clusters on a heat map of Walk Scores for selected metropolitan areas to explore the relationship between these two measures. While Walk Score includes destinations like parks and schools, as well as businesses, it also measures walkability from the standpoint of home-based origins, while our Storefront Index shows the concentration of commercial destinations.

City Observatory has developed the Storefront Index as a freely available tool for urbanists and city planners to use in their communities. The index material is licensed under a Creative Commons Attribution license (as is all City Observatory material), and a shape file containing storefront index information is available here.

A new Congressional Budget Office (CBO) report confirms what we’ve known for a long time: our nation’s system of assessing the costs of roads—and paying for their construction and maintenance—is badly broken.

Entitled “Approaches to Making Federal Highway Spending More Productive,” the new CBO report is a treasure trove of details about the recent history of transportation finance in the United States. Though couched in the careful technocratic language of the budget analyst—you’ll read about how alternative financial arrangements would enable better “performance” and create greater “efficiency”—the translation is straightforward: the big cause of our transportation problems is that we’re charging road users the wrong price.

Collectively, road users are paying too little for what they use, which is why taxpayers have had to chip in more than $140 billion over the past seven years to make up shortfalls in the Highway Trust Fund. The Trust Fund is the repository of gas taxes and other road user fees and is supposed to cover the cost of building and maintaining the nation’s roads. But the underlying problem isn’t just that there’s too little money: it’s that the way we allocate costs to users, and the way we distribute funding among alternative investments produces lousy results.

As the report puts it: “Spending on highways does not correspond very well with how the roads are used and valued.”

Translation: The price of roads is wrong. Drivers who use lots of expensive capacity (urban roads at peak travel times) don’t pay their costs, and money gets allocated to spending that produces limited value for the nation.

Under the current system of fuel taxes, all users pay basically the same amount whether they travel on highly congested roads or nearly empty ones. That means users have no incentive to adjust their travel times, routes, or modes to reduce the costs that their travel imposes on everyone else. The fact that many road users face prices that are far lower than the costs they impose on the system means that highways are over-used, and that there isn’t enough money to maintain or improve them. Getting prices right would lead to less peak demand (shifting travel to un-congested periods, when it can be accommodated with the existing infrastructure) and thus improving service for users who value travel time improvements.

It’s also important to keep in mind that this report only addresses the direct financial costs to government for constructing and operating the highway system. There are also huge social and environmental costs—from air pollution, climate change, and injuries and deaths associated with crashes—that aren’t reflected in the prices that that roads users pay. In an earlier report, CBO estimated that trucks were subsidized to the tune of $57 to 128 billion a year because of these costs and road damage.

The CBO has three recommendations: price roads, especially to reflect congestion, allocate funds based on a cost-benefit basis, and link spending to performance. TheCBO points out that road pricing would not only provide badly needed funds, but would provide valuable information about which highway system improvements would generate the largest economic benefits. They report that according to FHWA, pricing might reduce the expenditure needed to achieve a given performance level by 30 percent.

And—almost in passing—the CBO report casts doubt on the accepted wisdom that highway building triggers economic growth. They say: “Research suggests that increase in economic activity from spending for new highways in the United States have generally declined over time.” Translation: highway investment experiences diminishing returns. The nation gets a big gain from building the Interstate Highway system when there was none, but each successive increment to the system produces a smaller and smaller return.

We’ll grant that critics might point out that other modes, like transit or biking or walking, don’t cover their own costs with user fees, either—there’s no sidewalk maintenance toll, and nor should there be. But there’s a critical difference between car travel and these other modes. Users of those modes don’t create the same costs, either, and not just because sidewalks cost a tiny fraction of a tiny fraction of what roads cost. Each additional driver, for example, creates congestion for every other driver on the road at the same time, up to the point that travel times can be doubled or tripled at peak use. Additional riders on a subway, on the other hand, create only very modest increases in travel times because of the time it takes for them to board—and perhaps none at all, if more ridership causes the transit agency to run more trains, and their boarding time is canceled out by less waiting time. We’re not confronting the cost of multi-billion dollar sidewalk investment projects due to peak hour congestion caused by under-priced foot traffic. Just as importantly, transit riders, bikers, and riders don’t create any, or very, very small amounts, of the major social costs of driving, from deaths and injuries in crashes to pollution.”

Simply pumping more money into the existing highway finance system will produce limited economic benefits. Many projects are only needed because drivers don’t confront anything close to the actual costs of the roads they drive on—and if they did, demand would be far smaller. Congestion pricing would improve the flow of traffic and enable us to meet the nation’s transportation needs at much lower costs. And investments in the highway system face real diminishing returns, so that additional money invested in highways produces less and less economic benefit.

As our name implies, we’re very focused on cities. We think cities are the key to solving many of the nation’s most challenging problems, from economic opportunity and social justice, to environmental sustainability. And we’re not alone: more and more, activists are looking to cities to take the lead on critical policy issues.

Cities should be fearless innovators. But at the same time, we have to recognize that there are some things that cities can’t do, there are some problems that transcend municipal boundaries in ways that defy effective or rational solution on a city-by-city basis. For some policy issues—including ones that play out in cities—there is simply no substitute for getting national policies right.

In a recent New York Times article, “Liberals turn to cities to Pass Laws and Spread Ideas,” Claire Cain Miller explained that progressives find a more receptive audience in the nation’s bluest political regions. It’s easier to try out controversial new policy ideas like paid parental leave in cities like San Francisco; and if a policy innovation can be shown to work at a smaller scale, it might lead to momentum to spread the policy nationally.

Viva la (Reagan) Revolution?

Progressives have increasingly turned to cities and states because of the seeming futility of engineering major changes in federal policy. If, prior to 1980, you had the expectation that the federal government was going to ride to the rescue in any major domestic policy arena, President Reagan sought to disabuse you of that notion. Three decades later, its apparent that many progressives have internalized this worldview.

With little hope or expectation that a federal government, hamstrung by chronic fiscal problems and paralyzed by deep partisan divisions would take any action, local activists have pushed cities and states governments to step forward to tackle problems, rather than waiting for a federal response that would never come. The shift to local, rather than national policy-making is, in effect, a concession to the Reaganite view of federalism.

With one notable exception—and we’ll return to this in a moment—federal domestic policy has consisted mostly of tokenism and misdirection. Token efforts that were too small to make any serious dent in a policy problem (like “Promise Zones”), or misdirection—the recently abandoned (or retooled if you like) “No Child Left Behind” act, that essentially imposed an overlay of federal regulation for states and local schools without providing new resources for K-12 education.

The single notable exception to the atrophy of national domestic policy is the Affordable Care Act, in which the federal government has re-written the rules for the health care sector, and put in place what seems to be (sorry skeptics) an irreversible move toward nearly universal health care coverage.

But with a nearly complete policy vacuum at the federal level in other policy areas, states and especially cities have upped their policy making game, on subjects as diverse as climate change, housing affordability, economic development and innovation.

Cities are clearly attractive arenas for policy innovation for a couple of reasons. Most importantly, there’s a political consensus. Most big cities are a deep, dark blue, so there’s little question of the political viability of some ideas—few push back in San Francisco or Boston if you fret about global warming or inequality.

To be sure, there are some issues of personal and individual rights and freedoms and local public health where cities are well-qualified and capable of implementing meaningful reforms, playing their fabled role as laboratories of democracy that test out solutions that can be emulated nationally. States and cities have taken important leadership roles in decriminalizing and legalizing marijuana, extending gay rights and marriage equality, limiting smoking in public places, and successful local policies pioneered in leading cities have lead to increasing policy adoption elsewhere.

Where better national policy is desperately needed

But even with best of intentions, there are some things that cities simply can’t do. Top of the list, the environment and the economy. Despite the noble efforts of cities and some states to push forward with greenhouse reduction goals and renewable portfolio standards, some form of carbon pricing (auctioning permits or taxing carbon) can only effectively be implemented at a federal level. Having Texas or Wyoming opt out of carbon limits would pretty much negate their impact, and puts a real brake on policies that meaningfully push costs on polluters. At some point, the learning and examples from local experimentation have to be rolled up to a national scale if we are to actually solve these problems.

Cities and states are increasingly concerned with promoting economic opportunity and raising wages. Many have devised useful programs to train workers, to provide better access to jobs for those cut off from them, and to legislate increases to minimum wages. But the success of all of these initiatives ultimately depends directly on the state of the national economy: the task of every city has been made harder by the stagnant economic growth which is a product of a contractionary federal fiscal policy, and which threatens to be pushed into recession at any time by a Federal Reserve Board that is still obsessed with fighting the imaginary inflation demons of the 1970s.

In many cases, changes to federal policy are essential simply to enable local experimentation. Some policies that seem to be amenable to local action have incentives that are so deeply embedded in federal law and regulation that it’s almost impossible to change. For example, the federal government dictates (and operates) the key features of the nation’s housing finance system from the term of mortgages and guarantees thereon, to a series of tax policies that prod families into homeownership. It also heavily subsidizes car and truck travel and highway expansion from general funds, and allocates money to local entities that are strongly oriented toward more road building.

Perhaps most fundamentally, local governments find it nearly impossible to directly tackle income inequality. Local governments that attempt income redistribution just impel the wealthy to secede to other jurisdictions; and our income inequality and segregation are actively worsened by exclusionary cities that use their police power to exclude the poor.

As well-intended and pragmatic as the shift to more favorable local venues seems, we think it is a mistake not to pursue strong national advocacy for specific federal policies that would help cities—even if there’s little chance of short-term adoption. What we need, in many cases is the policy framework that considers the root causes and scale of the problem, and which contributes to a dialog about what the nation as a whole needs to do—rather than relying solely on the courageous, but inherently limited efforts of individual cities. We’ve sketched out the case, for example, for shifting federal housing subsidies into vouchers or tax credits that would reach more of the nation’s rent burdened households. The ambivalence or latent hostility of existing federal policies is often a major headwind to local efforts to remedy these problems.

While it seems prudent in today’s political environment to “think globally and act locally,” we are quickly approaching the limits of what cities can do on their own to tackle big problems of inequality, housing affordability, environmental sustainability, and economic progress. Successful federalism hinges on a strong partnership, with a clear division of labor between the important roles to be played by both national and local governments. Increasingly, if cities are to continue to be crucibles of change, we’ll need new national policies that provide the frameworks, incentives and in some cases resources that are needed to realize the potential of solving major national problems by building more diverse, inclusive cities. As it turns out, 2016 is a presidential election year: Maybe now we should start having this conversation?

The most difficult design challenge is asking the right question

Not long ago, a feature article at the New York Times described how the design wizards at IDEO are helping stodgy old Ford Motor Company re-imagine how transportation might work in the future.

IDEO conceptualized the design task by sending groups of its employees to a restaurant a few miles away via different transportation modes, so they could assess the challenges each faces: the subway (too smelly), Divvy bikeshare (too dirty), and Uber (too expensive).

The hope is this exercise sheds light on how will enable IDEO to brainstorm clever new ways to get from point A to point B in the future. (We’re pretty sure the solution will involve apps.)

In our view, this is an epic fail for several reasons. First, it just gets certain things wrong. For example, according to the article, one of IDEO’s big complaints about the subway was the lack of cellphone reception—a problem the CTA had already announced it would be fixing when they took their ride in October, with full coverage in all subways rolled out in December.

But there’s a more fundamental problem: Is the optimal place for a quick work lunch four miles from IDEO’s office? If it is, isn’t the real problem here that a company has located its office in a place where its employees have to travel four miles just to get lunch and have a meeting? And isn’t the restaurant’s problem that it’s located in a place where its customers are four miles away?

And in fact, IDEO has already solved this particular design problem for itself. Fork and Tine, the restaurant they chose, might be four miles away, but there are dozens and dozens of restaurants within a quick walk of its downtown offices. City Observatory’s Chicago bureau chief, Daniel Hertz, spent an afternoon walking around IDEO’s West Loop neighborhood to find a bite to eat in the name of research. A five minute walk in one direction is Greektown, where you can get a quick gyro or souvlaki. A few minutes north of there is Randolph Street, one of the premier dining destinations in the city, with everything from high-concept burgers to Indian curries. Or, just about ten minutes straight north of IDEO’s offices is the French Market food court at a major commuter rail station, a hugely popular workday lunch spot. Not to mention that you pass at least a couple places to eat on every block on your way to any of these destinations.

It’s not a coincidence that there’s so much to do within a quick walk of IDEO’s offices—that’s almost certainly a major reason they chose to locate in the West Loop. Indeed, it’s a big reason that Chicago’s downtown has seen a steady stream of companies opening new or relocated offices there.

In other words, the issue here has at least as much—if not much more—to do with the design of cities than the relatively superficial features of different transport modes.

With rows of restaurants down the street from the IDEO offices, they don’t have to travel at all, save to walk a few hundred feet, saving them bundles of valuable time. So being in a dense urban location turns out to be the optimal design solution: relying as it does on the healthiest, least expensive, lowest carbon and most fully deployed transport technology in human history: walking. IDEO already knows this: that’s why they pay premium rents for their tidy, exposed-brick office space in the West Loop.

One of the subsidiary tasks IDEO assigned its testers was schlepping a couple of large shopping bags—to simulate, in some way how a busy person might have to mix some domestic errands with their business lunch. Fair enough. But if one lived in a mixed use neighborhood, where there was say a corner store or bodega down the block, one might easily handle all one’s shopping with more frequent but much more convenient walking trips to buy just a handful of necessities, rather than having perforce to carry a week’s worth of groceries because it was several miles to the big box store.

If we think about it correctly, dense, mixed use urban spaces are the ultimate design solution to our transportation problems. They provide low-cost, no-carbon, time-saving access to all manner of things that consumers want and need in their daily lives.

The real failure in design thinking here is IDEO viewing this task as primarily choosing between different transportation modes. Of course, they are free to frame this question however they—and their paying client—would like. But from a broader policy perspective—and from the perspective of citizens and consumers, we’d all be a lot better off if we’d make the design conversation about how we arrange our cities. And the design lens is often blinded by the look and feel of things, rather than comprehending basic, systemic issues: the quality of bus service, for example, has much more to do with schedule frequency, and running times, than the features of the transit system’s arrival time notification app: we think bus riders would be much more impressed with buses that arrived every ten minutes and made the trip faster thanks to dedicated lanes, than they would be by an app that told them the next bus was exactly 22 minutes from now.

Just focusing on transportation ignores and rules out the very substantial gains that could be made by better designing our cities for living. It’s hard to get the right answers under the best of circumstances. It’s just impossible to get the right answers if you ask the wrong questions.

A version of this commentary originally appeared at City Observatory in February 2016.

A breathless feature article at the New York Times describes how the design wizards at IDEO are helping stodgy old Ford Motor Company re-imagine how transportation might work in the future.

IDEO conceptualized the design task by sending groups of its employees to a restaurant a few miles away via different transportation modes, so they could assess the challenges each faces: the subway (too smelly), Divvy bikeshare (too dirty), and Uber (too expensive).

The hope is this exercise sheds light on how will enable IDEO to brainstorm clever new ways to get from point A to point B in the future. (We’re pretty sure the solution will involve apps.)

In our view, this is an epic fail for several reasons. First, it just gets certain things wrong. For example, according to the article, one of IDEO’s big complaints about the subway was the lack of cellphone reception—a problem the CTA had already announced it would be fixing when they took their ride in October, with full coverage in all subways rolled out in December.

But there’s a more fundamental problem: Is the optimal place for a quick work lunch four miles from IDEO’s office? If it is, isn’t the real problem here that a company has located its office in a place where its employees have to travel four miles just to get lunch and have a meeting? And isn’t the restaurant’s problem that it’s located in a place where its customers are four miles away?

And in fact, IDEO has already solved this particular design problem for itself. Fork and Tine, the restaurant they chose, might be four miles away, but there are dozens and dozens of restaurants within a quick walk of its downtown offices. City Observatory’s Chicago bureau chief, Daniel Hertz, spent an afternoon walking around IDEO’s West Loop neighborhood to find a bite to eat in the name of research. A five minute walk in one direction is Greektown, where you can get a quick gyro or souvlaki. A few minutes north of there is Randolph Street, one of the premier dining destinations in the city, with everything from high-concept burgers to Indian curries. Or, just about ten minutes straight north of IDEO’s offices is the French Market food court at a major commuter rail station, a hugely popular workday lunch spot. Not to mention that you pass at least a couple places to eat on every block on your way to any of these destinations.

It’s not a coincidence that there’s so much to do within a quick walk of IDEO’s offices—that’s almost certainly a major reason they chose to locate in the West Loop. Indeed, it’s a big reason that Chicago’s downtown has seen a steady stream of companies opening new or relocated offices there.

In other words, the issue here has at least as much—if not much more—to do with the design of cities than the relatively superficial features of different transport modes.

With rows of restaurants down the street from the IDEO offices, they don’t have to travel at all, save to walk a few hundred feet, saving them bundles of valuable time. So being in a dense urban location turns out to be the optimal design solution: relying as it does on the healthiest, least expensive, lowest carbon and most fully deployed transport technology in human history: walking. IDEO already knows this: that’s why they pay premium rents for their tidy, exposed-brick office space in the West Loop.

One of the subsidiary tasks IDEO assigned its testers was schlepping a couple of large shopping bags—to simulate, in some way how a busy person might have to mix some domestic errands with their business lunch. Fair enough. But if one lived in a mixed use neighborhood, where there was say a corner store or bodega down the block, one might easily handle all one’s shopping with more frequent but much more convenient walking trips to buy just a handful of necessities, rather than having perforce to carry a week’s worth of groceries because it was several miles to the big box store.

If we think about it correctly, dense, mixed use urban spaces are the ultimate design solution to our transportation problems. They provide low-cost, no-carbon, time-saving access to all manner of things that consumers want and need in their daily lives.

The real failure in design thinking here is IDEO viewing this task as primarily choosing between different transportation modes. Of course, they are free to frame this question however they—and their paying client—would like. But from a broader policy perspective—and from the perspective of citizens and consumers, we’d all be a lot better off if we’d make the design conversation about how we arrange our cities. And the design lens is often blinded by the look and feel of things, rather than comprehending basic, systemic issues: the quality of bus service, for example, has much more to do with schedule frequency, and running times, than the features of the transit system’s arrival time notification app: we think bus riders would be much more impressed with buses that arrived every ten minutes and made the trip faster thanks to dedicated lanes, than they would be by an app that told them the next bus was exactly 22 minutes from now.

Just focusing on transportation ignores and rules out the very substantial gains that could be made by better designing our cities for living. It’s hard to get the right answers under the best of circumstances. It’s just impossible to get the right answers if you ask the wrong questions.

After fourteen seasons, Discovery Channel’s always entertaining “Mythbusters” series ended last year. If you didn’t see the show-and it lives on at Youtube, of course–co-hosts Adam Savage and Jamie Hyneman constructed elaborate (often explosive) experiments to test whether something you see on television or in the movies could actually happen in real life. (Sadly, it turns out that you can’t make a bullet curve no matter how fast you flick your arm.)  

At City Observatory, we feel compelled to enter into this void, and we’ll start by doing our own urban myth-busting. First up: Does building new high-priced apartments, affordable only by middle- and upper-income families, make housing less affordable for lower income households?

We’ve heard this claim time and again in public hearings: new rental housing charges higher rents than existing apartments, and must therefore be making affordability problems worse. Business Week’s Noah Smith shared the lament of the misunderstood economist who confided in a progressive friend that he favored building new market-rate (i.e. high priced) housing in San Francisco:

I was talking to a friend the other day, a San Francisco anti-eviction activist, and said that allowing more housing construction in the city would be a great way to lower rents. She looked at me in horror, blinked and asked “Market rate?” I nodded. She was speechless.

My experience was far from unusual. To my friend and many others, it has become an article of faith that building market-rate housing raises rents, rather than lowers them.  The logic of Econ 101 — that an increase in supply lowers price — is alien to many progressives, both in the Bay Area and around the country.

Even Harvard University’s Joint Center on Housing Studies reprised this line in one of their recent reports: “50 percent of rental households make less than $34,000 per year, but only 10 percent of new multi-family units are affordable at this income.”

From this statistical observation, it’s a short leap to the conclusion that building new housing is part of the affordability problem. The Wall Street Journal reported that “much of the new supply is aimed at higher-income renters.” In May, the Journal ran a story claiming: “A focus by builders on high-end apartments helps explain why rents are soaring across the country.”

On its surface, this sounds terrible. But the key context missing here is that in the United States, we have almost never built new market-rate housing for low-income households. New housing—rental and owner-occupied—overwhelmingly tends to get built for middle- and upper-income households. So how do affordable market-rate housing units get created? As new housing ages, it depreciates, and prices and rents decline, relative to newer houses. (At some point, usually after half a century or more, the process reverses, as surviving houses—which are often those of the highest quality—become increasingly historic, and then appreciate.)

What really matters is not whether new housing is created at a price point that low- and moderate-income households can afford, but rather, whether the overall housing supply increases enough that the existing housing stock can “filter down” to low and moderate income households. As we’ve written, that process depends on wealthier people moving into newer, more desirable homes. Where the construction of those homes is highly constrained, those wealthier households end up bidding up the price of older housing—preventing it from filtering down to lower income households and providing for more affordability.

This isn’t theoretical: As we’ve discussed before at City Observatory, the vast majority of today’s actually existing affordable housing is not subsidized below-market housing, but market-rate housing that has depreciated, or “filtered.” Syracuse economist Stuart Rosenthal estimates that the median value of rental housing declines by about 2.2% per year. As its price falls, lower-income people move in. Rosenthal estimates that rental housing that is 20 years old is occupied, on average, by households with incomes about half the level of incomes of those who occupy new rental housing.

In its 2016 report on the state’s housing crisis, the California Legislative Analyst’s Office noted that as housing ages, it becomes more affordable. Housing that likely was considered “luxury” when first built declined to the middle of the housing market within 25 years. Take the 1960s-era apartments built in Marietta, a suburb of Atlanta: When they were new, they were middle to upper income housing, occupied by single professionals, gradually, as they aged, they slid down-market, to the point where the city passed an $85 million bond issue to acquire and demolish them as a way of reducing a concentration of low income households in the Franklin Road neighborhood. 

Another critical point is that if we don’t build more housing at the high end of the market, those households don’t just disappear, they take their demand “down-market” and bid up the price of housing that would otherwise filter down to middle and lower income households. That’s exactly what the Montgomery County Maryland housing department reports is happening there:

The shortage of rental housing at the high end of the market creates downward pressure on less affluent renters, the study found, because when higher-income households rent less expensive units, lower-income renters have fewer affordable choices. Cost-burdening is linked with this unbalanced market, especially at the lower end of the income spectrum.

Ironically, this problem persists in Montgomery County in spite of its widely touted inclusionary housing requirement that forces builders of new apartments to set aside a portion of them for low and moderate income households.

New Cars are Unaffordable to Low Income Households, too

Here’s another way to look at the connection between affordability and the price of new things: cars. (After houses, cars are frequently the most expensive consumer durable that most American’s purchase.)

Exactly the same thing could be said of new car purchases: Most new cars aren’t affordable to the typical household either—the average sale price of a new car is nearly $34,000.

In fact, using the same kind of approach that Harvard’s Joint Center for Housing Studies used to assess rental affordability, Interest.com reports that the median family can afford to buy the typical new car in only one large metropolitan area. Similar to the “30 percent of income” rule widely—and in our view inappropriately—used to gauge housing affordability, they assume that the typical household makes an 20 percent down payment, finances its purchase over four years and pays no more than 10 percent of its income for a car payment. They report in most metros that the typical family falls 30 to 40 percent short of being able to afford a new car. So most households deal with car affordability pretty much like they deal with housing affordability: by buying used.

When it comes to anything new and long-lived, higher-income households buy most of the output. According to Bureau of Labor Statistics data, households in the two highest income quintiles accounted for about 67 percent of the purchase of new cars in the US in 2001. New car buyers are getting progressively older, and are more likely to be high income. According to the National Automobile Dealers Association, the median new car buyer is 52 years old and has an income of about $80,000, compared to an average age of 37 and an income of $50,000 for the overall population.

But there’s no outcry about America’s “affordable car crisis.” The reason: high-income households buy newer cars; most of the rest of us buy used cars—which are more affordable after they’ve depreciated for a while.That’s even more true of housing, which is much longer lived. Nationally, 68 percent of the nation’s rental housing is more than 30 years old—so only about 10 percent of the nation’s renters live in apartments built in the last decade.

New houses, like new cars, are sold primarily to higher income households—and affordability comes from getting a bargain when the car (or house or apartment) has depreciated. Building more high priced new apartments, in fact, is critical to generate the filtering down of older housing that constitutes the affordable housing supply.

This myth is busted: building more high end housing doesn’t make housing less affordable.

The price of new housing is a poor gauge of housing affordability

Increasing housing supply over time, coupled with individual housing units moving down-market as they age, provides affordability

New cars are unaffordable to most households; used cars are the source of affordable driving

Discovery Channel’s always entertaining “Mythbusters” series ran for fourteen seasons before ending in 2016. If you didn’t see the show, co-hosts Adam Savage and Jamie Hyneman constructed elaborate (often explosive) experiments to test whether something you see on television or in the movies could actually happen in real life. (Sadly, you can’t make a bullet curve no matter how fast you flick your arm.)  

At City Observatory, we feel compelled to enter into this void, and we’ll start by doing our own urban myth-busting. Today: Does building new high-priced apartments, affordable only by middle- and upper-income families, make housing less affordable for lower income households?

We’ve heard this claim time and again in public hearings: new rental housing charges higher rents than existing apartments, and must therefore be making affordability problems worse.

Even Harvard University’s Joint Center on Housing Studies has reprised this line: “50 percent of rental households make less than $34,000 per year, but only 10 percent of new multi-family units are affordable at this income.”

From this statistical observation, it’s a short leap to the conclusion that building new housing is part of the affordability problem. The Wall Street Journal reported that “much of the new supply is aimed at higher-income renters.” The Journal also claimed: “A focus by builders on high-end apartments helps explain why rents are soaring across the country.”

On its surface, this sounds terrible. But the key context missing here is that in the United States, we have almost never built new market-rate housing for low-income households. New housing—rental and owner-occupied—overwhelmingly tends to get built for middle- and upper-income households. So how do affordable market-rate housing units get created? As new housing ages, it depreciates, and prices and rents decline, relative to newer houses. (At some point, usually after half a century or more, the process reverses, as surviving houses—which are often those of the highest quality—become increasingly historic, and then appreciate.)

What really matters is not whether new housing is created at a price point that low- and moderate-income households can afford, but rather, whether the overall housing supply increases enough that the existing housing stock can “filter down” to low and moderate income households. As we’ve written, that process depends on wealthier people moving into newer, more desirable homes. Where the construction of those homes is highly constrained, those wealthier households end up bidding up the price of older housing—preventing it from filtering down to lower income households and providing for more affordability.

This isn’t theoretical: As we’ve discussed before at City Observatory, the vast majority of today’s actually existing affordable housing is not subsidized below-market housing, but market-rate housing that has depreciated, or “filtered.” Syracuse economist Stuart Rosenthal estimates that the median value of rental housing declines by about 2.2% per year. As its price falls, lower-income people move in. Rosenthal estimates that rental housing that is 20 years old is occupied, on average, by households with incomes about half the level of incomes of those who occupy new rental housing.

In its 2014 report, the California Legislative Analyst’s Office noted that as housing ages, it becomes more affordable. Housing that likely was considered “luxury” when first built declined to the middle of the housing market within 25 years. Take the 1960s-era apartments built in Marietta, a suburb of Atlanta: When they were new, they were middle to upper income housing, occupied by single professionals, gradually, as they aged, they slid down-market, to the point where the city passed an $85 million bond issue to acquire and demolish them as a way of reducing a concentration of low income households in the Franklin Road neighborhood.

New cars are unaffordable to low income households, too

Here’s another way to look at the connection between affordability and the price of new things: cars. (After houses, cars are frequently the most expensive consumer durable that most Americans purchase.)

Exactly the same thing could be said of new car purchases: Most new cars aren’t affordable to the typical household either—the average sale price of a new car is nearly $34,000.

In fact, using the same kind of approach that Harvard’s Joint Center for Housing Studies used to assess rental affordability, Interest.com reported that the median family can afford to buy the typical new car in only one large metropolitan area. Similar to the “30 percent of income” rule widely—and in our view inappropriately—used to gauge housing affordability, they assume that the typical household makes an 20 percent down payment, finances its purchase over four years and pays no more than 10 percent of its income for a car payment. They report in most metros that the typical family falls 30 to 40 percent short of being able to afford a new car. So most households deal with car affordability pretty much like they deal with housing affordability: by buying used.

When it comes to anything new and long-lived, higher-income households buy most of the output. According to Bureau of Labor Statistics data, households in the two highest income quintiles accounted for about 67 percent of the purchase of new cars in the US in 2001. New car buyers are getting progressively older, and are more likely to be high income. According to the National Automobile Dealers Association, the median new car buyer is 52 years old and has an income of about $80,000, compared to an average age of 37 and an income of $50,000 for the overall population.

But there’s no outcry about America’s “affordable car crisis.” The reason: high-income households buy newer cars; most of the rest of us buy used cars—which are more affordable after they’ve depreciated for a while.That’s even more true of housing, which is much longer lived. Nationally, 68 percent of the nation’s rental housing is more than 30 years old—so only about 10 percent of the nation’s renters live in apartments built in the last decade.

New houses, like new cars, are sold primarily to higher income households—and affordability comes from getting a bargain when the car (or house or apartment) has depreciated. Building more high priced new apartments, in fact, is critical to generate the filtering down of older housing that constitutes the affordable housing supply.

This myth is busted: building more high end housing doesn’t make housing less affordable.

After fourteen seasons, Discovery Channel’s always entertaining “Mythbusters” series is coming to an end later this year. If you haven’t seen the show, co-hosts Adam Savage and Jamie Hyneman construct elaborate (often explosive) experiments to test whether something you see on television or in the movies could actually happen in real life. (Sadly, you can’t make a bullet curve no matter how fast you flick your arm.)  

At City Observatory, we feel compelled to enter into this void, and we’ll start by doing our own urban myth-busting. First up: Does building new high-priced apartments, affordable only by middle- and upper-income families, make housing less affordable for lower income households?

We’ve heard this claim time and again in public hearings: new rental housing charges higher rents than existing apartments, and must therefore be making affordability problems worse.

Even Harvard University’s Joint Center on Housing Studies reprised this line in their recent report: “50 percent of rental households make less than $34,000 per year, but only 10 percent of new multi-family units are affordable at this income.”

From this statistical observation, it’s a short leap to the conclusion that building new housing is part of the affordability problem. The Wall Street Journal reported that “much of the new supply is aimed at higher-income renters.” In May, the Journal ran a story claiming: “A focus by builders on high-end apartments helps explain why rents are soaring across the country.”

On its surface, this sounds terrible. But the key context missing here is that in the United States, we have almost never built new market-rate housing for low-income households. New housing—rental and owner-occupied—overwhelmingly tends to get built for middle- and upper-income households. So how do affordable market-rate housing units get created? As new housing ages, it depreciates, and prices and rents decline, relative to newer houses. (At some point, usually after half a century or more, the process reverses, as surviving houses—which are often those of the highest quality—become increasingly historic, and then appreciate.)

What really matters is not whether new housing is created at a price point that low- and moderate-income households can afford, but rather, whether the overall housing supply increases enough that the existing housing stock can “filter down” to low and moderate income households. As we’ve written, that process depends on wealthier people moving into newer, more desirable homes. Where the construction of those homes is highly constrained, those wealthier households end up bidding up the price of older housing—preventing it from filtering down to lower income households and providing for more affordability.

This isn’t theoretical: As we’ve discussed before at City Observatory, the vast majority of today’s actually existing affordable housing is not subsidized below-market housing, but market-rate housing that has depreciated, or “filtered.” Syracuse economist Stuart Rosenthal estimates that the median value of rental housing declines by about 2.2% per year. As its price falls, lower-income people move in. Rosenthal estimates that rental housing that is 20 years old is occupied, on average, by households with incomes about half the level of incomes of those who occupy new rental housing.

In its recent report, the California Legislative Analyst’s Office noted that as housing ages, it becomes more affordable. Housing that likely was considered “luxury” when first built declined to the middle of the housing market within 25 years. Take the 1960s-era apartments built in Marietta, a suburb of Atlanta: When they were new, they were middle to upper income housing, occupied by single professionals, gradually, as they aged, they slid down-market, to the point where the city passed an $85 million bond issue to acquire and demolish them as a way of reducing a concentration of low income households in the Franklin Road neighborhood.

New Cars are Unaffordable to Low Income Households, too

Here’s another way to look at the connection between affordability and the price of new things: cars. (After houses, cars are frequently the most expensive consumer durable that most American’s purchase.)

Exactly the same thing could be said of new car purchases: Most new cars aren’t affordable to the typical household either—the average sale price of a new car is nearly $34,000.

In fact, using the same kind of approach that Harvard’s Joint Center for Housing Studies used to assess rental affordability, Interest.com reports that the median family can afford to buy the typical new car in only one large metropolitan area. Similar to the “30 percent of income” rule widely—and in our view inappropriately—used to gauge housing affordability, they assume that the typical household makes an 20 percent down payment, finances its purchase over four years and pays no more than 10 percent of its income for a car payment. They report in most metros that the typical family falls 30 to 40 percent short of being able to afford a new car. So most households deal with car affordability pretty much like they deal with housing affordability: by buying used.

When it comes to anything new and long-lived, higher-income households buy most of the output. According to Bureau of Labor Statistics data, households in the two highest income quintiles accounted for about 67 percent of the purchase of new cars in the US in 2001. New car buyers are getting progressively older, and are more likely to be high income. According to the National Automobile Dealers Association, the median new car buyer is 52 years old and has an income of about $80,000, compared to an average age of 37 and an income of $50,000 for the overall population.

But there’s no outcry about America’s “affordable car crisis.” The reason: high-income households buy newer cars; most of the rest of us buy used cars—which are more affordable after they’ve depreciated for a while.That’s even more true of housing, which is much longer lived. Nationally, 68 percent of the nation’s rental housing is more than 30 years old—so only about 10 percent of the nation’s renters live in apartments built in the last decade.

New houses, like new cars, are sold primarily to higher income households—and affordability comes from getting a bargain when the car (or house or apartment) has depreciated. Building more high priced new apartments, in fact, is critical to generate the filtering down of older housing that constitutes the affordable housing supply.

This myth is busted: building more high end housing doesn’t make housing less affordable.

Increasing road capacity to reduce greenhouse gas emissions will backfire

Widening roads to reduce idling simply induces more travel and more pollution

Cities with faster travel have higher greenhouse gas emissions

Time for another episode of City Observatory’s Urban Myth Busters, which itself is an homage to the venerable Discovery Channel series “Mythbusters” that featured co-hosts Adam Savage and Jamie Hyneman using something called “science” to test whether commonly believed tropes were really true. In each episode, they would construct elaborate (often explosive) experiments to test whether something you see on television or in the movies could actually happen in real life. (Sadly, you can’t make a bullet curve no matter how fast you flick your arm.)  

In our first installment, we took on the oft-repeated claim that somehow building more housing for middle and upper income people made housing less affordable for lower income households. (It doesn’t).

Today’s claim comes from the world of transportation. As we all know, transportation is now the single largest source of greenhouse gas emissions. Here, when confronted with the need to do something to address climate change, the highway lobby likes to point out that cars emit carbon, and when they’re idling or driving in stop and go traffic, they may emit  more carbon per mile than when they travel at a nice steady speed. And of course, they have a solution for that: spend more money expanding capacity so cars don’t have to slow down so much. That’ll be great for the environment, or so the argument goes.

This claim has been invoked by highway advocates everywhere. Most recently, its been raised by officials  speaking in favor of spending upwards of a billion dollars on three freeway widening projects in the Portland area.  State Senator Lee Beyer argued that truck idling due to congestion was contributing to global warming. Here’s what Beyer told Oregon Public Broadcasting’s Think Out Loud program on April 18, 2017:

To the extent that we have congestion, in Portland for example, or anywhere else, but there particularly, if you look at the amount of exhaust those trucks are spewing into the air during that 52 hours while they sit in traffic, that may have more of a negative impact on the environment and more carbon release than we would gain solely through the low carbon fuels piece as its currently structured.

His argument was echoed by City Commissioner Amanda Fritz:

It seems likely the emissions from vehicles crawling in this section are worse than those at normal speed.

When he was appointed Director of the Oregon Department of Transportation in 2019, Kris Strickler trotted out the same tired claim:

…. it’s clear about 40% of the greenhouse gas emissions are from the transportation sector, so it’s an important aspect of the work we do.   I believe that there is no silver bullet, there is no single answer to address GHG emissions overnight.   And its something on our task list and our to-do list as a priority for us as we go forward and we need to attack it in multiple avenues.   One is, clearly, through design decisions that we can help to free up and move congested areas, because we know that cars sitting in traffic, frankly, emitting the emissions is not necessarily the best way to manage greenhouse gas reductions.

So is there any truth to the idea that reducing traffic congestion will lower vehicle emissions?

In place of the now retired duo of Adam and Jamie, we’ll turn this question over to Alex and Miguel–Alex Bigazzi and Miguel Figliozzi, two transportation researchers at Portland State University. Their research shows that savings in emissions from idling can be more than offset by increased driving prompted by lower levels of congestion.  The underlying problem is our old friend, induced demand: when you reduce congestion, people drive more, and the “more driving” more than cancels out any savings from reduced idling. As they conclude:

Induced or suppressed travel demand . . . are critical considerations when assessing the emissions effects of capacity-based congestion mitigation strategies. Capacity expansions that reduce marginal emissions rates by increasing travel speeds are likely to increase total emissions in the long run through induced demand.

In a companion paper, they look at a variety of data, including variations among metropolitan areas, changes over time in congestion and emissions, and corridor level estimates of traffic and emissions. In each case, they find that carbon emissions are strongly correlated with the length of travel and weakly correlated (or uncorrelated) with levels of congestion.

Specifically, metropolitan areas with high levels of congestion do not have higher levels of CO2 emissions per capita than ones with low congestion. They conclude there is “no relation.” But vehicle miles traveled is a strong correlate. Here’s a chart showing daily peak period hours of vehicle travel per peak period travel (on the horizontal axis) and CO2 emissions per peak period traveler per day. More driving is correlated with more carbon emissions.

Similarly, metro areas that had an increase in congestion (as measured by the Texas Transportation Institute’s Travel Time Index), didn’t see proportionate increases in CO2 emissions.  The following panel shows how the change in emissions per traveler between 2000 and 2010 for an array of 101 metropolitan areas related to changes in congestion (left hand chart), changes in hours traveled per person (center chart) and vehicle miles traveled (right hand chart). There’s essentially no relation between increases in congestion and per traveler emissions; but more hours of travel and greater distances traveled translate very directly into more carbon emissions.

There’s also another kicker to the speed/emissions relationship that you’ll never hear highway advocates mention. While its true that cars emit more carbon per mile while idling and in stop and go traffic than they do when cruising at 30 to 45 miles per hour, traveling at higher speeds is actually less fuel efficient and produces more CO2 per mile driven. Hence one of the strategies that we ought to employ is imposing stricter speed limits (say 55 miles per hour). This also means that the more we build roads that let people drive at higher speeds (60 to 70 miles per hour) the more we’re increasing global warming.

This myth is busted: adding more capacity might reduce idling a bit, but it will actually induce more driving, which will lead to higher, not lower carbon emissions.

And, a technological post-script: Automakers are now increasingly equipping their vehicles with stop-start technology, which automatically turns the engine off when the car stops moving, and then re-starts the engine when the driver takes her foot of the brake. This virtually eliminates idling emissions, not just in traffic, but at red lights too. Some 15 million European cars already have stop-start, a majority of cars sold in North America are predicted to have in the next few years. In addition, electric vehicles don’t idle when they’re stopped. So in the long run, if we want to reduce emissions from idling, a technical fix is in the works–no need to widen roads to address this source of pollution.

This commentary was originally published by City Observatory in 2017.  Sadly, six years later, people are still claiming that we can fight climate change by widening roads to reduce the amount of time people spend idling.

Increasing road capacity to reduce greenhouse gas emissions will backfire

Time for another episode of City Observatory’s Urban Myth Busters, which itself is an homage to the long-running Discovery Channel series “Mythbusters” that featured co-hosts Adam Savage and Jamie Hyneman using something called “science” to test whether commonly believed tropes were really true. In each episode, they would construct elaborate (often explosive) experiments to test whether something you see on television or in the movies could actually happen in real life. (Sadly, you can’t make a bullet curve no matter how fast you flick your arm.)  

In our first installment, we took on the oft-repeated claim that somehow building more housing for middle and upper income people made housing less affordable for lower income households. (It doesn’t).

Today’s claim comes from the world of transportation. As we all know, transportation is now the single largest source of greenhouse gas emissions. Here, when confronted with the need to do something to address climate change, the highway lobby likes to point out that cars emit carbon, and when they’re idling or driving in stop and go traffic, they may emit  more carbon per mile than when they travel at a nice steady speed. And of course, they have a solution for that: spend more money expanding capacity so cars don’t have to slow down so much. That’ll be great for the environment, or so the argument goes.

This claim has been invoked by highway advocates everywhere. Most recently, its been raised by officials  speaking in favor of spending upwards of a billion dollars on three freeway widening projects in the Portland area.  Sate Senator Lee Beyer argued that truck idling due to congestion was contributing to global warming. Here’s what Beyer told Oregon Public Broadcasting’s Think Out Loud program on April 18, 2017:

To the extent that we have congestion, in Portland for example, or anywhere else, but there particularly, if you look at the amount of exhaust those trucks are spewing into the air during that 52 hours while they sit in traffic, that may have more of a negative impact on the environment and more carbon release than we would gain solely through the low carbon fuels piece as its currently structured.

His argument was echoed by City Commissioner Amanda Fritz:

It seems likely the emissions from vehicles crawling in this section are worse than those at normal speed.

So is there any truth to the idea that reducing traffic congestion will lower vehicle emissions?

In place of the now retired duo of Adam and Jamie, we’ll turn this question over to Alex and Miguel–Alex Bigazzi and Miguel Figliozzi, two transportation researchers at Portland State University. Their research shows that savings in emissions from idling can be more than offset by increased driving prompted by lower levels of congestion.  The underlying problem is our old friend, induced demand: when you reduce congestion, people drive more, and the “more driving” more than cancels out any savings from reduced idling. As they conclude:

Induced or suppressed travel demand . . . are critical considerations when assessing the emissions effects of capacity-based congestion mitigation strategies. Capacity expansions that reduce marginal emissions rates by increasing travel speeds are likely to increase total emissions in the long run through induced demand.

In a companion paper, they look at a variety of data, including variations among metropolitan areas, changes over time in congestion and emissions, and corridor level estimates of traffic and emissions. In each case, they find that carbon emissions are strongly correlated with the length of travel and weakly correlated (or uncorrelated) with levels of congestion.

Specifically, metropolitan areas with high levels of congestion do not have higher levels of CO2 emissions per capita than ones with low congestion. They conclude their is “no relation.” But vehicle miles traveled is a strong correlate. Here’s a chart showing daily peak period hours of vehicle travel per peak period travel (on the horizontal axis) and CO2 emissions per peak period traveler per day. More driving is correlated with more carbon emissions.

Similarly, metro areas that had an increase in congestion (as measured by the Texas Transportation Institute’s Travel Time Index), didn’t see proportionate increases in CO2 emissions.  The following panel shows how the change in emissions per traveler between 2000 and 2010 for an array of 101 metropolitan areas related to changes in congestion (left hand chart), changes in hours traveled per person (center chart) and vehicle miles traveled (right hand chart). There’s essentially no relation between increases in congestion and per traveler emissions; but more hours of travel and greater distances traveled translate very directly into more carbon emissions.

There’s also another kicker to the speed/emissions relationship that you’ll never hear highway advocates mention. While its true that cars emit more carbon per mile while idling and in stop and go traffic than they do when cruising at 30 to 45 miles per hour, traveling at higher speeds is actually less fuel efficient and produces more CO2 per mile driven. Hence one of the strategies that we ought to employ is imposing stricter speed limits (say 55 miles per hour). This also means that the more we build roads that let people drive at higher speeds (60 to 70 miles per hour) the more we’re increasing global warming.

This myth is busted: adding more capacity might reduce idling a bit, but it will actually induce more driving, which will lead to higher, not lower carbon emissions.

And, a technological post-script: Automakers are now increasingly equipping their vehicles with stop-start technology, which automatically turns the engine off when the car stops moving, and then re-starts the engine when the driver takes her foot of the brake. This virtually eliminates idling emissions, not just in traffic, but at red lights too. Some 15 million European cars already have stop-start, a majority of cars sold in North America are predicted to have in the next few years. In addition, electric vehicles don’t idle when they’re stopped. So in the long run, if we want to reduce emissions from idling, a technical fix is in the works–no need to widen roads to address this source of pollution.

New data from the national traffic safety administration shows an ominous trend: traffic related deaths are up 11.3 percent for the first nine months of 2015, as compared to the same period a year earlier.

Although the NHTSA warns that the data are subject to revision, and cautions that it’s too early to discern the causes of this change, those who have been paying attention to the longer trend know that there’s every reason to believe we already have a suspect.

As we’ve noted before at City Observatory, the decline in gas prices that started in mid-2014 has led to an increase in driving, reversing a nearly decade-long trend of Americans driving fewer miles per person per day.

What’s striking about the new NHTSA numbers is that road crash deaths are increasing much faster than total miles driven. As a result, the number of deaths per mile driven—which has been declining for decades—jumped up in the first three quarters of 2015, from 1.05 deaths per 100 million miles to 1.10 deaths per 100 million miles.

But this isn’t a new relationship: the same kind of disproportionate change occurred when gas prices increased in 2007-08. At that time, miles driven fell sharply—and traffic deaths fell even faster. In 2008, total vehicle miles traveled declined by 0.7% and in 2009, they declined a further 1.5 percent.

This suggests that the relationship between driving and deaths is non-linear: a one percent increase in driving produces a much larger than one percent increase in deaths—actually, something like a three percent increase in deaths, based on these very partial data.

Why might deaths increase faster than miles driven? There are several reasons. First, we know that some traffic phenomena, like traffic congestion, are very non-linear: the roughly 3 percent decline in traffic in 2008 produced a 30 percent reduction in traffic congestion. Second, it may well be that the additional (or, in economist-speak, “marginal”) miles driven, and marginal drivers driving them, are for some reason less safe than the typical mile driven. If we take longer trips, we may drive on more dangerous roads, or at more dangerous times. Third, it may be that people are driving faster—some research showed that high fuel prices induced motorists to slow down—and speed is strongly correlated with road safety. Over at Streetsblog, Angie Schmitt reports on David Levinson’s theory that price sensitive teen drivers may drive more when gasoline is cheap, and that may account for some of the hypersensitivity of crash rates to apparently small changes in the total amount of driving.

We know that for many reasons, there are structural connections between cheaper gas, more miles driven, and more traffic fatalities. But some analysts want to downplay those structural issues and place the blame on driver behavior. One of the most widely offered explanations for the increase in crashes and fatalities is “distracted driving,” and especially the rise of text messaging. While there’s little question that texting and driving is dangerous, and contributes to many crashes, the numbers simply don’t support this theory.

Available data suggests that text messaging grew much more rapidly between 2008 and 2013—when traffic deaths were declining—than in recent years. One source, citing data collected by Nielsen, reports that text messaging increased by a factor of four between 2007 and 2009, but has grown only about 12-15 percent per year since 2011. Forrester reports messaging increased about 14 percent between 2010 and 2011. While one could wish for much better data about the volume of text messaging, the growth of text messaging seems to coincide with a period of safer driving.

What’s clear though is that the minor changes in the amount of driving that we do seem to have disproportionately large effects on traffic fatalities. Lower gas prices that encourage more driving produce proportionately larger increases in fatalities. And higher gas prices that reduced driving in 2007 and 2008, produced disproportionately large reductions in fatalities.

First-time home buyers play a critical role in the housing market. The influx of new households into the owner-occupied market is a key source of sales, and provides impetus for existing homeowners to move, liquidate their investment, or trade up to a bigger or better house. They’re the bottom of the home-buying pyramid.

The number of first-time homebuyers has been low since the Great Recession, in spite of recent improvements in housing affordability nationally (at least according to standard metrics like low interest rates and lower housing prices). Total sales of new and existing homes are still well below levels of a decade ago (from more than 8.5 million to 5.3 million), and the National Association of Realtors reports a historic low in the fraction of buyers that are first-timers (30 percent).