Remember: There’s no such thing as a “Free” way

Congestion pricing is a win-win strategy and the only way to truly reduce traffic congestion

The urban transportation problem is a hardy perennial: no matter how many lanes we add to urban freeways, traffic congestion is just as bad, if not worse than ever. In the face of “free” road travel, induced demand means that supply side strategies like widening freeways simply encourage more people to drive, and congestion gets worse. Houston successively widened its Katy Freeway to 23 lanes, but only found that congestion got worse and travel times got longer.

Fortunately there is a solution: peak-hour road pricing. It’s been successfully implemented in cities around the world, from London, to Singapore, to Milan to Stockholm. Charging a modest price for the use of roads at the peak hour encourages a small fraction of drivers to change their route or mode, with the result that traffic levels and travel times improve for remaining users. And while there’s always skepticism and resistance to charging for something that everyone pretended was free, once these systems are put in place, they have broad popular support because they actually make travel better and easier. Stockholm’s system was initially opposed by the public, but after six months of operation, was approved in a popular referendum by 51 percent; today more than 70 percent of Stockholm residents support the system.

The economics of this problem are simple and straightforward: as long as we charge a zero price for the use of scarce and valuable peak hour road capacity, more people will use it than it can handle. Ultimately, we end up rationing road space by the degree of road user’s desperation and their tolerance for delays. As we’ve suggested at City Observatory, it’s as if we’re trying to run the urban road system the way Ben & Jerry’s runs their ice cream business the one day of the year that they give cones away for free, and with similar results: People line up around the block for “free” ice cream, which is actually only available to those with the patience to wait long periods in line. There’s no way for Ben and Jerry’s to build enough ice cream stores to meet the demand for free cones–and free ice cream everyday would soon bankrupt the company.

This is one instance where charging a price for a “free” good will make things better for everyone. Here are two important things to keep in mind. First, not everyone attaches the same value to a peak hour roadway trip. Second, traffic is non-linear.  Here’s why these things matter.

One working assumption seems to underly popular discussions about peak hour travel: It’s that somehow everyone who is using the road now “needs” to be on the road at that time. But in fact, a big fraction of travelers have options. They could travel at  a different time (a little earlier, or a little later), they could take a different route or mode (like bus or bike), or they could combine or re-route trips. Some people would find it just as convenient to travel at a different time or to a different destination, and while others might forego a trip altogether.  Perhaps a majority of those currently traveling at the peak hour would find it difficult or inconvenient to change, but many people could be flexible, if they had the incentive to do so. They key question is how many people would have to change their behavior to get a positive result.

That’s the second key fact: The answer is that only a few people would need to forego current peak hour car trips to get a big improvement in congestion. A critical feature of traffic congestion is that it’s non-linear.  What that means in practice is that roads work really, really well, right up to the point where they get congested. A highway with heavy traffic moving along at 45 to 50 miles per hour carries the most vehicles of any roadway. But add just a few more cars, and the traffic level reaches a tipping point, where things slow down, and the road actually loses its ability to move people. The key to making roads work well is to keep them just below this critical tipping point. The good news here is reducing traffic volumes just 5 to 10 percent is all that is needed in many instances to keep the roads moving.

We’re not talking about big charges either:  in most cases, modest toll levels trigger big changes in commuting patterns. Louisville, Kentucky recently started tolling the I-65 crossing over the Ohio River. Regular commuters pay a toll of $1 per crossing; and tolls have reduced the volume of cars using the I-65 crossing by more than 40 percent. Today, you encounter almost zero traffic at rush hour leaving downtown Louisville.  (As it turns out, Louisville wasted roughly a billion dollars widening this bridge, because they doubled it’s capacity before levying a toll; if they’d tolled first, they would have discovered they had plenty of road space).

So to make our roads work better, all we need are tolls that persuade maybe one in ten travelers to change their behavior. And that’s what peak hour pricing does: sends a signal to those people who have alternatives or who least value traveling at that time on that route, to do something different. The result is that every other traveler gets a big benefit in terms of improved performance.

A well-designed system of road pricing could be an enormous win-win:  It would shorten travel times, and actually improve the carrying capacity of the highway system. It would eliminate the need for expensive, environmentally destructive and usually wasteful road-widening projects. The funds from road pricing could be used–as they are in London–to fund other transportation alternatives. There’s really no such thing as a “freeway,” and a correctly priced road system would be better for all of us.


Renters move up-market

What to make of the high credit scores of new renters in some markets: alarm bell or success signal?

RentCafe–one arm of Yardi Matrix, a real estate data and services firm–has a very interesting new data series on the credit scores of successful and unsuccessful apartment seekers in different cities.  Rent Cafe runs a tenant screening service on behalf of landlords, and a key variable that they report to their clients is a household’s credit score (which ranges from a low of 300 to a high of 800).  Rent Cafe’s research has shown a strong correlation between a strong credit score and a high probability of having your rental application approved.  Nationally, if you have a credit score over 750, your chances are 98%, if it’s between 500 and 600, then you have only about an 67% chance of getting the apartment.

In some markets–like Boston, San Francisco and Seattle–successful rental applicants have sterling credit. In these top cities, new renters have credit scores over 700.

To Rent Cafe, the high scores in these cities are a kind of alarm bell. You apparently need really great credit to rent an apartment in these places.

What Credit Score Do You Need to Rent an Apartment? Insanely High, If You’re in Boston or San Francisco

But there’s another possible view: it’s likely that high credit scores signal just how robust the local economies are in these communities, and furthermore, how much households with strong credit are choosing to rent rather than to buy.  Given that most of these communities have very expensive home prices, it may be that many credit-worthy households who might otherwise prefer to buy a home end up renting.  And because Rent Cafe is reporting the average credit score, the average renter in these cities just has better credit.

Conversely, in some other cities, if you have a high credit score, you may find it more affordable to purchase a home rather than renting. This selection effect would explain why renters have much higher average credit scores in high-priced markets than in low-priced markets.

It’s also likely that credit scores are rising because the personal financial situation of the typical renter is getting better.  Rent Cafe notes that the credit score of the typical approved applicant has increased about 12 points in the past three years, from 638 in 2014 to 650 this year. This is consistent with other evidence showing steady income gains in most metro economies. We’ve reported on national data at City Observatory. Oregon economist Josh Lehner has a series showing income gains for the Portland area (which ranks 8th in Rent Cafe’s list of metros with the highest credit scores for renters). Rising incomes are making housing more affordable.


Clearly, the rental market is very competitive in places like Boston and San Francisco, meaning landlords can pick and choose among tenants with a strong financial background; but the very strong credit scores of successful rental applicants in these cities  is as much an indication of strong local economies  (and prosperous tenants) as it is of the choosiness of local landlords. In the long run, the growing average creditworthiness of tenants may reflect the limited opportunities for homeownership in these increasingly expensive markets, where households with sterling credit who would be buyers almost anywhere else, find themselves renting instead. A new report issued by the Philadelphia Federal Reserve Bank shows a continued decline in the number of first-time home buyers nationally, a trend consistent with rising average credit scores among renters.

You’l want to take a look at the full report. The RentCafe report has detailed information on average credit scores of successful and unsuccessful apartment seekers in 100 of the nation’s largest cities. As we’ve noted, the data are drawn from RentCafe’s own client work, and so may be influenced by how complete and representative a sample of properties it covers in each market.

Uber and Lyft: A dynamic duo(poly)?

Will two firms produce enough effective competition to benefit consumers?

The use ride-hailing services continues to grow in the US, and while there are a range competitors in some markets, like New York, in most places, nearly all ride-hailing is dominated by Uber and Lyft. The good news for consumers is that fierce competition for market share between these firms is helping to keep fares low, and promote steady innovation and change.

While our view has long been that we want to encourage a range competitors–”Let a thousand Ubers Bloom,” we said–that doesn’t seem to be happening yet. Austin, which was temporarily evacuated by both companies after a local voter-approved measure which enacted some restrictions (such as background checks for drivers), seemed to be a keen example of how new entrants could step in and fill the void. But this summer, after the Texas Legislature pre-empted Austin’s municipal laws, Uber and Lyft re-entered the market with a vengeance, and have apparently recovered most of their lost market share. Local startups are struggling against the bigger firms brand recognition and and deep pockets. One local startup, RideAustin, saw its business decline 62 percent after Uber and Lyft came back to town.

In the longer term, the burgeoning number of businesses developing autonomous vehicles are potential competitors for Lyft and Uber. Waymo, the Google/Alphabet subsidiary, has just launched its fully driverless cars in Phoenix, and as they scale up, they threaten to be a major disruptor in this market. But that development seems to still be a ways off.  Will two firms in this industry be enough to assure vigorous price competition, and provide consumers with good value? Let’s look to see what’s happened here in the past few months.

Will a duopoly be enough?

Uber has been the dominant firm in the ride-hailing market since its inception, but in recent months there are signs that its dominance is ebbing. The company has experienced a series of widely publicized gaffes, ranging from sexual harassment claims against its executives, to a video of the company CEO disparaging one of its drivers, to the ultimate resignation of founder Travis Kalanick. The bad publicity, coupled with a #deleteUber campaign has had an impact. During most of the past calendar year, rival ride-hailing firm Lyft has grown faster, and picked up market share. Nationally, estimates are that Uber’s market share has fallen from more than 80 percent to less than 75 percent.

But the national statistics obscure a fiercer battle in many cities. Instead of being a weak also-ran, Lyft appears to becoming a sizable competitor in many markets.  Data compiled by credit card analytics firm Second Measure estimates the market share of the two companies in different cities around the country. As reported in Recode, these data show strong improvements in Lyft’s position in many markets. Across the dozen markets shown here, Lyft has recorded 10 point or better gains in share in three-quarters of them. In general, Lyft has made the biggest inroads in major West Coast markets; in Portland, Lyft has a 45 percent market share, making it a very close rival to Uber.

Numbers on red bars show percentage point increase in Lyft market share from December 2016 to September 2017.

A companion article in Recode examines the connection between the #deleteUber campaign and consumer use of the two services.  According to Second Measure’s data, most ride hailing customers exclusively use just one of the two leading services (71 percent exclusively use Uber, 19 percent exclusively use Lyft).  Only about 10 percent of ride-hailing customers use both. While customer loyalty to a single firm is something that every company no doubt wants to cultivate, the sharp change in the market share of the two companies is good evidence that switching costs are relatively low. Thus, even though only a small fraction of customers are regular comparison shoppers (using both services routinely), those who rely primarily on one or the other can easily switch if they’re dissatisfied. Low switching costs are one of the keys to getting the benefits of competition in a duopoly situation.

An intensely competitive duopoly in ride-hailing is likely to help assure better rates and better services for consumers than would be the case if one company achieved monopoly status. If customers are dissatisfied over prices, the service the receive or the company’s policies, they can vent their displeasure by switching to the alternative. This market share shift however suggests that the market valuation attached to Uber–almost more than $70 billion last year–may be far too high. While we may not see a thousand flowers blooming, an effective duopoly may offer the next best thing.

The end of the housing supply debate (maybe)

Slowly, the rhetorical battle is being won, as affordable housing advocates acknowledge more supply matters

There’s been a war of words about what kind of housing policies are needed to address the nation’s affordability problems. Economists (from the White House to academe) argue that increasing housing supply is essential. Low income housing advocates of many stripes push for efforts to pay via taxes or require via regulations that more units be built specifically for low and moderate income households.

Everyone agrees that much of the affordability problem is due to national policies that provide massive subsidies to homeownership by the wealthy (mostly through the tax code) and parsimonious and chronically underfunded programs that provide subsidies for the poor (which reach less than a quarter of those technically eligible), a fair share of responsibility lies in the hand of local governments.  Is the key relaxing zoning limits to allow more market rate housing, or does it require regulating or subsidizing more affordable units into existence.

The battle cry of the low income housing advocates is “you can’t build your way to affordability.” As Bloomberg’s Noah Smith put it, among these advocates:

 . .  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.

Sightline Institute has tackled that notion directly. Not only can  you build your way to affordable housing, in fact, building more supply may be the only effective way to reduce the pressure that is driving up rents and producing displacement. There’s ample evidence for this position, but there’s still the strong sense that addressing our housing problem by building more high end housing is a cynical and ineffective kind of “trickle down” economics.”

Building more market rate housing isn’t so much about “trickle down” as it is building enough new housing to keep higher income households from moving down-market and bidding up the price of older housing that would otherwise be affordable to moderate and lower income households. When there isn’t enough supply, demand from higher income households floods down to older housing stock, driving up rents and reducing housing options for those with lesser means. Which, as why, as we’ve observed, in some markets, modest 1950’s-era ranch homes are a mainstay of affordability, while in others, they cost more than a million bucks.

When supply does catch up to demand, rents tend to fall across the market. Last month, we showed how the completion of thousands of new, market rate apartments in Portland was having the knock-on effect of creating a growing number of vacancies and a flood of “FOR RENT” signs in the city’s older apartment stock. Rent increases, which were measured in the double digits eighteen months ago, have gone negative.

As Sightline Institute’s clever musical chairs metaphor makes clear, it doesn’t matter whether you add fancy overstuffed arm-chairs or or simple folding metal chairs to the game, both make it equally likely at the end of the day that there will be a closer match between chairs and hind-ends than otherwise.

What about “filtering up?”

The latest salvo in the rhetorical battles over the merits of expanding market rate housing supply comes from Miriam Axel-Lute, writing for Shelterforce.  Her latest article “Trickle Up Housing: Filtering does go both ways” makes the case that building new market rate housing does somewhat ameliorate displacement and affordability issues, that building more new low and moderate income is a more direct and powerful solution. The argument here is that if we build more housing for the poorest among us, that will free up some units for other perhaps slightly less poor households.

She buttresses the case for “trickle-up” housing by citing a study from U. C. Berkeley’s Karen Chapple and Miriam Zuk, that claims that building affordable units is twice as effective in reducing displacement as building more market rate housing. The exact claim, quoted from Chapple and Zuk is:

“At the regional level, both market-rate and subsidized housing reduce displacement pressures, but subsidized housing has over double the impact of market-rate units”

“Double the impact” sounds more like a pitch for a new and improved laundry detergent than a calculated analysis of housing policy options, but it did pique our curiosity.  How did Chapple and Zuk determine the relative effectiveness of these two policies?

As it turns out, their work is a response to a widely cited analysis developed by the California Legislative Analyst’s Office (LAO), which looked at the connection between displacement and housing construction in the Golden State.  The LAO’s conclusion was that building more market rate housing reduced displacement. Chapple and Zuk questioned the LAO report for omitting the possible positive effects of building more subsidized housing. They ran a regression analysis looking at both market rate and subsidized housing and controlling for the impact of a number of other factors, including age of the housing stock, racial/ethnic composition of the population, and education. They find that building more market rate units decreases measured displacement, as they put it:

Consistent with the LAO Report, we find that new market-rate units built from 2000 to 2013 significantly predict a reduction in the displacement indicator . . .

They also find that the construction of more subsidized housing also results in a reduction of the displacement indicator.  The claim of “twice the impact” comes from comparing the coefficients of the two variables, the coefficient on market rate housing is -.002; the coefficient for subsidized housing is -.005.  So it is literally the case that the coefficient of one is more than twice as large as the other.

Two market rate houses reduce displacement as much as one affordable house

But let’s step back and consider what that means in practice. What is measured in each case is the number of new market rate homes and the number of new subsidized homes built in a community over a decade. Put another way, the construction of one, new market rate home has almost half as much impact on measured displacement as building a subsidized home. What this means in practice is that two or three new $600,000 single family homes or condominiums built in the Bay Area in the last decade or so reduced displacement in the region by as much as building a new subsidized unit. On its face, this study puts to rest the old saw that building more market rate housing leads to more displacement: it doesn’t. In fact, building more market rate housing is an effective anti-displacement strategy.

In addition to effectiveness, we also have to consider cost. If government’s faced a costless choice between so many market rate units and and equal number of subsidized units, and the only policy objective were reducing displacement, the answer would be clear. But building subsidizing housing is hugely expensive for the public sector. As we pointed out last month, in the Bay Area, new subsidized housing in San Francisco, and in the East Bay costs as much as $700,000 per unit.

There’s a further point to be made here:  one of the key reasons that “affordable” housing is so expensive to build in places like San Francisco is that land prices are very high because of zoning restrictions. As a result, the same policies that facilitate market rate housing–more density, fewer parking requirements, clear and certain approval processes–would also make it less expensive to build affordable housing.

So it is literally true that building more subsidized units for the lower income households is more powerful in reducing displacement. But its tremendously expensive as well. What the data here confirm, is what economists–and musical chairs aficionados–have long maintained: increasing supply is critical to solving the housing affordability problem.


More evidence of the Dow of cities

The premium that households pay to live in cities relative to suburbs and rural areas continues to increase

Three years ago, we introduced the term “Dow of cities.” It’s a riff on the Dow Jones Industrial Average (DJIA), which is a broad-based summary measure of stock market valuation. The idea behind the Dow of cities is that the relative prices that people pay for housing in cities compared to suburbs constitutes a powerful indicator of the value attached to urban living. Over the past couple of years, we’ve assembled data from a variety of sources tracking the growth of urban home values relative to suburban ones. Some data has come from Fitch Investment Advisers, other data was compiled by Columbia University economists Lena Edlund, Cecilia Machado, and Michaela Sviatchi.

Our latest take on the Dow of cities comes from Zillow. Zillow tracks home prices throughout the nation, and its economists regularly make presentations about the health and outlook for local real estate markets. Zillow principal economist Aaron Terrazas recently made such a presentation to the Virginia Beach Marketing Forum last month. The whole presentation is worth a look, but we were particularly taken by one chart in his slide deck, which we’ve reproduced in part here.  It shows the average national price, measured by Zillow’s Home Value Index, of houses in urban areas (green), suburban areas (blue) and rural areas (black).

Source: Zillow, Aaron Terrazas, (Slide 9)

The data clearly show the inflation and collapse of the housing bubble a decade ago, and the recovery in house prices since then.  (Nominal house prices have recovered to pre-bubble levels today). The Zillow data also confirm a trend we’ve consistently seen from other sources. Since the late 1990s urban home prices have grown faster than suburban ones.  In the late 1990s, according to Zillow, urban and suburban homes commanded about the same price. Over the past 17 years, urban home prices have steadily pulled away from suburban ones, even as the housing bubble grew and deflated. Today, the typical urban home commands more than a $75,000 premium over the typical suburban home.

In an important sense, what this shows is that the past two decades have been a bull market for cities. City home values are now fully one-third higher than suburban ones. In stock market terms, a portfolio of urban housing has outperformed a portfolio of suburban housing. And meanwhile, the value of rural housing has fallen relative to cities, with the typical rural home being worth only a little over half as much as the typical urban home.

Underlying these trends is the growth of knowledge-based industries in cities and the growing demand for urban living. Households tend to have better economic prospects in cities because that’s where highly paid professional, technical and service jobs are growing. In addition, the desire for urban amenities, and for dense, interesting, walkable neighborhoods is also fueling the demand for urban living. Finally, as we’ve noted, there are important constraints to expanding the supply of housing in cities, and in building more great urban neighborhoods. As a result, the demand for urban living has outpaced the supply of housing in urban neighborhoods, producing a shortage of cities.

Using Yelp to track economic growth

We review Yelp’s new index for rating local economies:  It’s a good start

For a long time, the only comprehensive and reliable means we’ve had of tracking and comparing economic activity across state and regional economies has been official government statistics, such as those compiled by the Census Bureau and the Bureau of Labor Statistics. While these data have many virtues, there are often significant lags between the time data is collected and the time it is reported, especially for data with high levels of geographic or industry detail. In addition, as a rule, government data strictly protect the confidentiality of individuals, and so suppress data that might the identity or precise location of a particular household or business.

The advent of big data, in the form of massive databases augmented with crowd-sourced information, adds a new dimension to our ability to track and measure local economies. One of the most exciting sources is Yelp, which tracks and publishes user reviews of millions of businesses. Yelp has just introduced its new “local economic outlook” which rates cities and neighborhoods based on their “economic opportunity.” The rankings are based on Yelp’s extensive data, and are summarized in the form of national rankings of cities (and a parallel rankings of the top 50 neighborhoods).

What color is your city’s dot? (Yelp: Local economic outlook)

Yelp has done its homework. Ed Glaeser, the dean of the nation’s regional economists, has authored a paper with two other economists testing the validity of Yelp’s business count data against the Census Bureau’s County Business Patterns data (CBP).  CBP is generally only available with a lag of a year or more and so isn’t a good guide to what’s happening right now in regional economies.  Glaeser and his co-authors find that Yelp’s data does a good job of predicting future trends in CBP data.  There is some variability: Yelp’s counts tend to be most accurate in dense, well-educated and higher income areas.  Also–unsurprisingly–Yelp’s coverage and accuracy has been steadily improving over time, and more closely agrees now with official statistics than it did just a few years ago.

It’s fantastic to get this data, but at least in its first iteration Yelp provides only rankings and doesn’t publish specific numerical estimates. We don’t know, for example, whether the cities in the top ten are 1 percent, 10 percent or 50 percent better than the median. All we have, in effect, is a top-to-bottom ranking of cities according to Yelp’s economic opportunity index. As we’ve always maintained, simple rankings that omit scalar data tend to generate a lot of heat, but actually shed little light. The Yelp report would be much more useful and interesting if it reported the actual numerical values of their index, and updated these figures on a monthly or quarterly basis. Yelp’s Carl Bialik tells us via email that they are working to extend the opportunity scores for future quarters, including releasing more raw data. We’re looking forward to this.

Because it is different from most conventional measures of economic activity, and because it is so new, it’s still a bit difficult to know exactly what the opportunity index measures, and whether its survival probabilities reflect short-term or more enduring differences in economic climate. It’s apparent from the methodological explanation (below) that they are looking at the opening and closing of business establishments, and using this information to compute survival probabilities in particular areas. But since they don’t publish numerical values for individual cities, or precisely reveal the formula for computing the index, its hard to interpret the rankings, or see how the opportunity index squares with other widely used measures of local economic activity. As we see more of the detail from the index, and can calibrate it against other measures, we’ll get a better idea of what the index is signaling and what it means.

The Yelp data is a promising and tantalizing look at how big, crowd-sourced data can help us develop a more timely and nuanced understanding of local economic activity. While their current rankings are a good way to promote awareness of the data, we hope they’ll do even more in the months ahead to publish and regularly update specific metro market indicators that others can use.


The Week Observed, November 10, 2017

What City Observatory did this week

1. The growing premium for urban living. Three years ago, City Observatory introduced the term “the Dow of cities.” In essence, its the observation that the growth in city home prices relative to suburban ones is a good indicator of the relative value that people attach to urban living. And the data show a clear bull market for cities. The latest figures come from Zillow; their house price data show that prices for urban homes have far outstripped those in suburban areas, and the gap between the two continues to widen. Today, the typical urban home is worth almost a third more than the average suburban home. It’s strong evidence that there’s a continuing demand for urban living and shows that we have a shortage of cities.

2. Can we finally put an end to the housing supply debate? It’s a widely held view that building more market rate housing in cities somehow produces greater displacement, but that’s been debunked both by economic theory and practical experience. While some affordable housing advocates are arguing for more “trickle up housing” as a solution, the studies they cite actually show that market rate housing has a powerful, positive effect in reducing displacement. Building two new market rate housing units has nearly the same effect in reducing displacement as building a single affordable housing unit. And the key is market rate housing can be expanded at much lower cost; provided we have the policies that incentivize and allow more housing to be built where there is market demand.

Must read

1. Fires aren’t the only thing ravaging the California economy. UC Berkeley economist Enrico Moretti, writing in the New York Times argues that many of the Golden State’s problems–from inequality to the wildfire damage to sprawling suburban subdivisions–can be traced to a set of policies that cumulatively block the provision of housing in cities where its most needed. San Francisco continues to generate new jobs at a rate that far outstrips the increase in housing, largely because it has made building more densely so difficult and expensive. The result: the Bay Area has sprawled further and further, and at the margins intersects with ecologically sensitive (and fire prone) areas. Promoting more urban density would both increase economic opportunity, and benefit the environment.

2. Fully self-driving cars are here now (at least in Phoenix). Google autonomous vehicle subsidiary Waymo has announced that its deploying fully-self driving cars–vehicles with no stand-by human driver in Phoenix. From Waymo’s description it sounds like these vehicles have level 4 autonomy (driverless, but in a limited area) with initial deployment in suburban Chandler. As Waymo says: “Starting now, Waymo’s fully self-driving vehicles are test driving on public roads, without anyone in the driver’s seat. To date, Waymo vehicles have been operating on public roads with a test driver at the wheel. Now, in an area of the Phoenix metro region, a subset of our fleet will operate in fully autonomous mode, with Waymo as the sole driver.” The deployment of autonomous vehicles is happening faster than many thought possible, and in an environment with limited government regulation.

3. Why Uber (and autonomous vehicles) can’t solve urban congestion. Streetsblog’s Angie Schmidt has a very insightful take on the inadvertent message in a new add by Uber. The add depicts a crowded Singapore Street with each person in a separate cardboard box. The implication, per Angie: “By stripping away the gloss, anonymity, and cultural connotations of car exteriors and leaving only their bulk, the ad brilliantly highlights why moving around in single-occupancy vehicles is so absurd in an urban context. There’s just not enough space for everyone to get around this way.” The image tells the story:

New knowledge

Community-based non-profits helped reduce crime. A new paper from Patrick Sharkey and co-authors–investigates the connection between community organizations and crime rates. While there have been many theories posited for the dramatic decline in crime rates in most US cities over the past 20 years, including more extensive policing, declines in lead levels, and other factors, Sharkey looks specifically at community based non-profits. Using data from the National Center for Charitable Statistics (NCCS), he finds that “every 10 additional organizations focusing on crime and community life in a city with 100,000 residents leads to a 9 percent reduction in the murder rate, a 6 percent reduction in the violent crime rate, and a 4 percent reduction in the property crime rate.” Their more detailed analysis suggests that the biggest impacts were a result of non-profits focusing on substance abuse and workforce development. (Hat tip to NYT’s Emily Badger).


In the news

The Washington Post takes the recent attack on Senator Rand Paul–who had three ribs broken by a neighbor who assaulted him over a dispute about appropriate yard-care practices in their gated subdivision–as evidence of the decline in social capital we explored in our report Less in Common. The Post quotes City Observatory’s Joe Cortright as saying: “Space and experiences became more private, fueled by suburban expansion, large lots, and the predominance of single-family homes.”

Human Transit‘s Jarrett Walker highlighted our recent story on the profusion of “FOR RENT” signs in Portland, confirming that building market rate housing influences the rents paid by everyone in the community.

The Week Observed, November 3, 2017

What City Observatory did this week

1. Rent control’s impact on the San Francisco housing market.   A new study from three Stanford economists dissects the impacts of rent control in San Francisco. Using a late-in-the-game revision of the rent control law (that extended controls to previously exempt 1-4 unit structures), and an impressive pile of big data, the study creates a quasi-experimental design that compares the rents, tenancy and fates of buildings that were exempt from rent control with those covered by it. The results: those lucky enough to get rent controlled apartments got economic benefits (savings worth about $2,300 to $6,600 per year). But the loss in rental income prompted landlords to renovate buildings (moving them up-market, and charging higher rents), to demolish some older buildings and replace them with new, unregulated ones, or to convert them to condominium or cooperative ownership. The net effect was to reduce the supply of rental housing, and drive up rents market-wide by 7 percent. The rent increases to those not covered by rent control effectively offset all of the savings to those who got rent controlled apartments.

2. Back at the Ranch. In the 1950s, America built millions and millions of ranch houses, typically a three bedroom, one bath, one floor home with about 1,200 square feet and a single car garage or carport. Coast-to-coast, the price of these homes was in the same rough price range, and was affordable to middle-class Americans. Half a century later, the economic fate of the ranch home varies dramatically by housing market. In many cities, the ranch home is the staple of affordability; a ranch house costs as little as $25,000 in Cleveland. But in other places, similar size and vintage ranch homes command more than a million bucks. Why some ranch homes are affordable now, and others aren’t tells us a lot about how housing markets work–or don’t–depending on the local economy and housing regulations.

3. The cost of federal pre-emption of autonomous vehicle regulation. States and cities are often described as the laboratories of democracy, the places where we develop innovative approaches for dealing with new problems. In a guest column, Noah Siegel lays out the case for giving local jurisdictions more latitude to develop the rules and policies that will guide the implementation of autonomous vehicles.

Must read

1. Jan Gehl on City Scale. ArchDaily has a short but insightful interview with Danish urbanist Jan Gehl, who once again, patiently reminds us of the importance of thinking about scaling our streets, buildings and public spaces to the bipeds who inhabit them. When spaces are built for human beings walking at five kilometers per hour, we feel safe and comfortable. When they’re engineered for vehicles going ten times faster, we are uncomfortable and alienated. Because walking is either impossible, unsafe, or uncomfortable (or all three), we walk less, which diminishes our health, and saps city streets of the human presence that makes them lively and interesting.

2. Philadelphia considers inclusionary zoning. Plan Philly’s Jake Blumgart has an analysis of Philadelphia’s proposed inclusionary zoning ordinance. The battle lines are predictably drawn between housing advocates, who see IZ as a way to get more units for low and moderate income households, and developers (and economists) who argue that the IZ requirements are likely to drive up the cost of development, and deter new construction (and by lowering housing supply, drive up rents). The Philadelphia ordinance is notable by requiring that some IZ units be affordable to households with very low incomes (30% of area median incomes), a burden that’s likely to have a strongly negative effect on development economics in a less than super-heated market like Philadelphia.

3. How is the housing market like musical chairs? Ever struggle to explain the basics of housing economics at a cocktail party? You’ll want to take a close look at the Sightline Institute’s new explainer video “Cruel Musical Chairs: Why the rent is so high.” It’s an apt metaphor for the housing market: If there are more households seeking houses than there are homes in the community, they’ll bid up the prices and those with the least resources (like the slow-moving in musical chairs) will be out of luck. The solution is straightforward: add more chairs (homes). The video subtly makes another key point: it really doesn’t matter whether you add fancy chairs or plain ones to the game, solving the availability/affordability problem hinges on their being more chairs, and the same applies to housing.

4. Does Portland’s transit agency prioritize construction over service? In an essay  at Medium, local activist Andrew Reilly claims that “TriMet’s Budget is an elaborate shell game.” Reilly charges “It’s acting deliberately, ideologically, against the public interest: TriMet bosses intentionally use “funding constraints” as an excuse to avoid justified criticism of their priorities as a company.” Though it pleads poverty when it’s pressured by citizens to expand bus service or discount fares, it has used a significant amount of the revenue that’s potentially available to pay for transit service to be instead used to pay debt service for capital construction projects, like expansions to the light rail system. And it’s not chump change; the current TriMet budget identifies $85 million as “operating resources dedicated to capital.” These priorities are out of line with the interests of the region’s transit users, according to Reilly. It’s a provocative piece that raises important issues about how the region’s transit system should be planned and managed.

New knowledge

Redfin’s Residential Real Estate Data Center. Redfin is a national real estate brokerage, and one of the nation’s leading sources of housing market intelligence. (We’re big fans of WalkScore, their invaluable tool for quickly assessing the walkability of any house in America). Redfin tracks home sales, prices and inventory across the nation, and now makes that information available through an accessible and easy to use web dashboard. Here we’ve pulled out data on median sales prices in leading national markets for the five years 2012 through 2017.  You can also look at for sale inventory, price per square foot, the number of days a typical listing has been on the market, and other indicators. Much of the data is available at the zip code level, allowing you to track neighborhood level price changes.

Data provided by Redfin, a national real estate brokerage.

In the news

Oregon Business published Joe Cortright’s commentary on the Amazon HQ2 sweepstakes, “Why Amazon’s HQ2 will never come to Portland.” It’s not because Portland doesn’t have all the right stuff to be the headquarters of a fast-growing tech firm; it’s just that  it’s too similar, and too close to Seattle to effectively diversify Amazon’s ability to recruit talent.

StreetsblogUSA reposted our commentary on the progressivity of peak hour road pricing. The incomes of car commuters generally, and peak hour car commuters in particular, are much higher than for the rest of the population, so the incidence of congestion pricing is relatively progressive. In Portland, the median income of those commuting by car in the peak hour is roughly double that of those who commute to work by transit, biking or walking.

The Week Observed, November 17, 2017

What City Observatory did this week

1. Renter’s credit scores are rising. What does that mean? New data from RentCafe shows a noticeable increase in the average credit scores of successful applicants for rental housing. In some leading markets like San Francisco, Seattle, and Boston, those who land new apartments have sterling (700+) credit scores. One interpretation is that landlords are getting choosier and more demanding, and that you need a very high score to get an apartment. But its equally likely that rising credit scores for renters signify the strength of the local economy and the steadily improving household prosperity of many urban renters. It’s also the case that many creditworthy households in expensive housing markets can’t afford to buy a home, and by continuing to rent, raise the average credit score of renters.

2. YELP’s new local economic opportunity index. You’re probably most familiar with Yelp’s directory of local businesses and copious consumer reviews. Yelp has taken their massive database and started to use it to generate statistics on the economic outlook in different cities and neighborhoods.  Their first product is an opportunity index which looks at the survival probability of different kinds of businesses, and ranks the nation’s metros accordingly. In our view, its a great adjunct to traditional government data, especially because it is more timely. We’re looking forward to seeing this on a regular basis, plus getting additional details on how specific markets are performing.

New knowledge

New data trace the decline in first-time home-buyers. The Federal Reserve Bank of Philadelphia has developed new estimates of the homeownership rate in the US, drawing on credit records developed by Equifax. This measure complements existing data which is based in part on either surveys or government-backed mortgage originations. The data broadly confirm the declining trend home purchase by first time buyers, and provide additional detail by age group and other characteristics. Overall, first-time buyers now make up only about 35 percent of home buyers, down from roughly half prior to the housing bubble. The data also show a particularly strong decline in the younger age groups (25-34 and 35-44). Even as the economy has rebounded, there’s been little improvement in the number of first-time buyers in these age groups, perhaps signaling some long run effects of the housing bubble, Great Recession and rising student debt on patterns of homeownership.


Where should low-income housing go?

Is it better to build affordable housing in low income neighborhoods, or higher income neighborhoods?

A recent study has run the numbers, and argues that social welfare is optimized by putting affordable housing in very poor neighborhoods, rather than wealthier (and especially whiter) ones.

Authored by Rebecca Diamond and Timothy McQuade of the Stanford School of Business, the study really has two major conclusions. First, building affordable housing in very low-income neighborhoods creates major benefits for the surrounding area. Second, there are major social costs to placing affordable housing developments in higher-income neighborhoods, though they calculate that these costs are outweighed by the income benefits to the affordable housing residents.

While there are some valuable findings here, as you might imagine, we have a few issues.

For one, the kind of affordable housing the study looks at is targeted to people with incomes that are 50 percent or more higher than is typical in low-income neighborhoods. The paper looks at developments funded by the Low Income Housing Tax Credit, or LIHTC. LIHTC buildings generally target households making 60 percent of Area Median Income; the study’s authors estimate that in their sample, that averages about $40,000. By contrast, median income in the low-income neighborhoods they find benefit the most from new LIHTC buildings top out at about $26,000. And since $26,000 is the high end, most of these neighborhoods are actually poorer than that.

In other words, they’re talking about neighborhoods so poor that building low-income housing increases the community’s average income. That makes their finding that LIHTC projects increase housing prices by about 6 percent within 0.1 miles (yes, you read that right—more on the geographic range of the effects later) much less surprising. It also means that the finding is less about “affordable housing” per se and more specific to LIHTC, or other subsidies with similar income targets. It’s questionable whether the same results would hold for other kinds of subsidized housing targeted to lower-income households.

Second, it’s worth taking a second to underline exactly what the “costs” of LIHTC buildings are to higher-income neighborhoods. Diamond and McQuade find that LIHTC buildings don’t increase crime. And yet they also find that the average homeowner in such a neighborhood would pay nearly $4,000 to avoid having to live within 0.1 miles of a LIHTC building. But note that we said “homeowner”: renters appear to have no such preference. Even more curiously, this aversion to low-income housing only appears in higher-income neighborhoods with low Black and Latino populations.

What would create such a pattern? The authors have an idea. “If local residents have preferences over the demographics of their neighbors,” they write, “new in-migrants could make the neighborhood more or less desirable.” This may be the world’s politest way of saying “mostly white homeowners appear to be discriminating against Blacks, Latinos, and/or poor people.”

Now, that’s not necessarily a surprise: it confirms many years of research about how racism and the perception of the presence of lower-income people affect housing markets. But it raises a question that anyone in housing policy or urban planning needs to be able to answer: are preferences of advantaged groups for segregation—segregation that we know is harmful for lower-income people and people of color—just another legitimate interest that we need to weigh against the interests others might have in desegregation? As it happens, the authors estimated the gains of integration, and found that they outweighed the costs. But there’s no reason the numbers had to work out that way. If the model’s results had shown that the benefits of segregation to mostly white, mostly higher-income homeowners were greater than the costs to disproportionately Black and Latino lower-income households, would that mean they would have come out in favor of segregation?

Finally, the way the authors do try to quantify the benefits of integration is extremely limited. Their estimates are based on Raj Chetty et al’s findings about the increase in average lifetime earnings for low-income households in higher-income neighborhoods. Of course, we’re big fans of Chetty’s work, and we’ve cited it ourselves extensively. But it’s a huge mistake not to include other potential benefits in a cost-benefit analysis. Other studies, for example, have shown major improvements in mental health; you might also expect better educational outcomes, which arguably have value beyond simply their contribution to future income. There’s also the reduced likelihood of crime victimization; potentially shorter commutes; and so on. None of these are weighed in when the authors conclude that the benefits of building LIHTC in high-income areas are exceeded by the benefits of building in the very low-income neighborhoods we talked about earlier.

So what should we take away from all of this?

  1. Building LIHTC units in very poor neighborhoods may, in fact, be a kind of place-based development strategy with some payoffs, as reflected by rising home prices. But it’s not clear how far it goes as a broad strategy for revitalizing these neighborhoods. For one thing, the strongest gains are in a very small area—just 0.1 miles from the project—with quickly declining improvements beyond that. Moreover, just as their estimates of the benefits of integration are limited, so are their estimates of the costs of segregation. While they do find that LIHTC projects help lower crime, it’s not clear whether there are improvements on other indicators that residents are likely to care about beyond home prices: schools, local retail options, and so on.
  2. Are all preferences made equal? We can use home prices to quantify the preferences of homeowners—but that doesn’t mean we should weigh every kind of preference in the same way. It turns out that for many people, the presence of people of color or lower-income people is enough to cause them to value their homes less. Evaluating policy options always involves value judgments, and econometric models—while often helpful—are not a substitute.
  3. When we do use econometric models, we need to be aware of what’s being left out. This, in fact, is part of our value judgments, whether we’re aware of it or not. Do we value better mental health for low-income people? Do we value education beyond its income effects? Do we value giving people the option to live somewhere they otherwise couldn’t? If none of those are in the model, then we are effectively answering “no.”
  4. Leaving integration up to local governments is unlikely to be successful. This is a point we’ve made before, citing research by Michael Lens and Paavo Monkkonen that showed that metropolitan areas with more local power in development decisions are more segregated than ones where states play a bigger role. This study underlines that while many people in all sorts of neighborhoods value diversity and integration, some do not, and they are willing to pay thousands of dollars to avoid having low-income neighbors. (This may also be about the kind of “prisoner’s dilemma” of who might get “stuck with more than their fair share” of low-income housing. In fact, research suggests that when integration is widespread, the dynamics of neighborhood change are altered in ways that reduce the incentive for self-segregation fo the advantaged.) When those preferences are combined with hyper-local power over what kinds of housing gets built where, it’s inevitable that many jurisdictions and neighborhoods will create regulatory barriers to low-income people living in their communities: in other words, exclusionary zoning.