Is it random, or is it Zumper?

Pay no attention to Zumper’s claims about rent trends

Zumper claims rents for one-bedroom and two-bedroom apartments are moving in opposite directions in about a fifth of all markets

There’s a lot of hyperventilation in the media about falling rents in different places in the US.  It’s certainly likely that in the midst of the worst and most abrupt economic downturn in more than a century, and now, with the expiration of expanded unemployment insurance benefits that we’d see some downward pressure on rents.

If Zumper’s data is to be believed, rents are collapsing in San Francisco and New York (down 11 and 7 percent, respectively) and exploding in Cleveland, Columbus, St. Louis, Indianapolis and Detroit (all up more than 14 percent year over year).  But the wild fluctuations across markets should immediately raise questions about how these data are calculated.

But please, oh, please, do not look to the Zumper real estate data to tell you anything meaningful about what’s happening.  We’ve been over this before, several times.  Zumper aggregates listings of current rentals from various sources and reports on the median asking rents at any moment in a city.  The trouble with this methodology, as we’ve pointed out, is that its highly susceptible to “composition effects.”  If a new building is completed in a city and adds 100 or 200 high priced apartments to the mix in the marketplace, Zumper treats this as an increase in rents.  If low priced apartments rent quickly and high priced ones linger on the market, this too drives up the apparent rent–but not because rents have gone up market-wide, but only because the composition of “for lease” apartments has changed.  The same thing works in reverse as higher value units get rented–they drop out of the for lease category, and aren’t counted, so it appears that the rents in the pool of available apartments has gone down.

Because there are constant shifts in the set of apartments that are for lease at any one time, Zumper’s method really is measuring that shift in composition, rather than changes in market prices.

There’s plenty of evidence from Zumper’s own data to show how erratic and non-sensical its measures are.  Zumper separately reports prices for one-bedroom and two-bedroom apartments.  In general, when rents are rising (or falling) in a market, one would expect these two measures to move synchronously (i.e. rising or falling by about the same amount, especially over longer periods, like a year).  But Zumper’s data show little correlation between year over year trends in one-bedroom and two-bedroom apartments.

The clearest indicator of Zumper’s craziness is that in 18 of the 100 markets for which it reports data, the price trends for one-bedroom and two-bedroom apartments are moving in opposite directions over the last 12 months.  For example, in Oakland, California, Zumper says rents for one-bedroom apartments fell by 3.5 percent, while rents for two-bedroom apartments went up by 6.6 percent.  That’s a ten percentage point difference in the price trajectory of the larger and smaller apartments. The same pattern of increased rents in one category and decreases in the other holds for Washington, Minneapolis, New Orleans, Pittsburgh, Raleigh, Columbus and a dozen other markets.

Year-over-year change in rents reported by Zumper, via

In these 18 markets, the absolute value of the difference in reported rent changes between one- and two-bedroom apartments is not small:  it averages more than 8 percent, which is larger than the typical year-over-year change within categories for these markets. The big variance in rent trends between one- and two-bedroom apartments within a market is a pretty clear indication that Zumper’s method is biased by these composition effects.

Okay, business journalists, we get it.  Someone sends you a press release with a ream of data (with decimal points!) that ranks US cities.  You don’t have the time to look into their methodology, you’ve got a deadline. But you can do much better than this.  Other sources of data (we like Zillow and ApartmentList.Com) report much better behaved data based on much more transparent methodologies.  If you care about understanding what’s really happening in real estate markets, don’t rely on Zumper.




The way we measure housing affordability is broken

This week, we’re running a three-part series on the flawed way that we measure housing affordability. This post looks at exactly what’s wrong with one of the most common ways we determine what “affordable” means. Tomorrow, we’ll look at an alternative measure, and on Wednesday, we’ll examine the particular challenges of understanding “affordability” for owner-occupied homes.

Given how much time media outlets, policy shops, and community groups have spent talking about America’s affordable housing crisis over the last few years, you might think that we’ve at least settled on a pretty good way to define what housing affordability actually is. After all, how can we talk about solving a problem if we don’t have a reliable way of determining who’s suffering, and where, and why?

Unfortunately, you’d be wrong.

As an illustration, picture yourself as an employee of a local supermarket, making $1,500 a month. You live with a friend in an outlying neighborhood, and your share of rent is $400, plus $300 a month for car expenses. After all that, you have $800 a month left over – which dwindles pretty quickly between child care, groceries, and prescriptions. When you get sick or your car breaks down, you can’t avoid racking up some credit card debt.

The front page of Craigslist for apartments in San Francisco.
The front page of Craigslist for apartments in San Francisco.


Across town, a man who works as a VP in marketing makes $8,000 a month. He pays $3,000 in rent for a brand new loft apartment near downtown. Because he can walk to work and takes public transit most other places, he buys a monthly pass for $100 and doesn’t own a car. After those costs, he’s got $4,900 to spend every month, which buys lots of nice meals out and international vacations while leaving room for healthy retirement savings.

You’re having trouble making rent, and the marketing VP can make their payments easily. But according to our most common standard of housing affordability, it’s the VP who’s rent-burdened, and you’re doing fine.

That’s because those standards rely on a simple ratio: if you pay more than 30% of your income in housing costs, your housing is unaffordable. If you don’t, it’s not. And the supermarket worker pays just 27% ($400 of $1,500), while the marketing VP pays 38% ($3,000 of $8,000).

The supermarket/VP story is an extreme example, but it demonstrates several of the fundamental problems with the 30% threshold as a measure of housing affordability.

1. Equity. Most obviously, it doesn’t take into account that, depending on how much money you start with, leaving 70% of your income for all non-housing expenses may be plenty – or not nearly enough. Affluent people have the luxury of deciding whether to spend relatively large proportions of their incomes to buy housing in a better location, or with particular amenities, without sacrificing other necessities like food or clothing. Low-income people generally don’t. In that way, comparisons between people with different earnings can turn out misleading or unfair, as in the example above.

Craigslist apartments in Boston.
Craigslist apartments in Boston.


But it can also fail in analyzing the burden of housing costs on people with similar incomes. Not everyone, after all, has the same non-housing obligations: for a healthy, childless twentysomething, a salary of $40,000 might easily cover housing, food, insurance, and other necessities. But someone who has to do much more non-housing spending – because of a chronic medical condition, say, or children with special needs – might struggle on the same income.

2. Other location-based costs. On top of that, there’s increasing recognition that housing choices are closely tied to other costs, which need to be considered part of the package. In other words, the cost of housing is less relevant than the total cost of a location. By far the most important of these other costs is transportation. While housing closer to the center of a metropolitan area is often more expensive, it also requires less driving – and often no driving at all, thanks to public transit – which saves a lot of money. According to Harvard’s Joint Center for Housing Studies, low-income people who manage to spend less than 30% of their income on housing actually end up paying $100 a month more on getting around, which eats into their savings, and sometimes erases them entirely.

Some organizations, like Chicago’s Center for Neighborhood Technology, have tried to take this into account. CNT’s H+T Index shows the total housing and transportation costs for various locations, set against a combined affordability standard of 45% of income. That’s a major step forward – but using a ratio like 45% still has all the other problems of the 30% ratio we’ve already covered.

3. Quality of housing. The 30% threshold can’t tell us anything about what a given household is getting for their money. Few of us would say that affordable housing needs are met by homes that are low in cost but lacking in basic modern amenities like heating or indoor plumbing. While those problems are now relatively rare in major metropolitan areas, many cities have a stock of affordable housing that is predominantly located in neighborhoods with high crime rates, failing schools, few options for fresh food, or other major quality of life issues. Do that housing satisfy our need for affordability?

Craigslist apartments in Memphis.
Craigslist apartments in Memphis.


This is an especially important question if we care about housing for its effects on opportunity and mobility. As recent research from Raj Chetty has reinforced, the kind of neighborhood you live in can dramatically change your prospects for living a comfortable middle-class life. It seems odd, in light of those findings, to measure housing access without taking into account whether that access includes communities that offer a shot at economic stability in addition to cheap rent.

In conclusion, the way that we currently measure housing affordability – a simple 30% ratio of cost to income – is simply inadequate to the task. It fails to give us an equitable picture of who is in need and who isn’t; fails to consider the total cost of a location, missing housing-dependent payments, like transportation, that can add a significant burden to low-income households; and fails to consider questions of housing and neighborhood quality that exert significant influences on the life chances of the people who live there.

(Why, then, do we use it? This Bloomberg piece from last year, also pointing out the 30% ratio’s flaws, is probably correct that its durability has to do with simplicity.)

Tomorrow, we’ll look at an alternative way to measure housing affordability that addresses some of these problems.

Misleading Medians & the McMansion Mirage

A story published by the Washington Post’s Wonkblog last week made the headline claim that “The McMansion is back, and bigger than ever.”  The article says that new homes are an average of 1,000 feet larger than in 1982, and that the “death of the McMansion” has been highly exaggerated, as have claims that development is shifting to smaller, more urban and more walkable development. The Wonkblog article echoes an 2014 post in CityLab –“The Increasingly Bloated American Dream”–which claimed that “American homes are getting bigger and bigger.”

While the data seem to superficially support this argument, a closer reading shows that the apparent surge in McMansions is actually a bit of a statistical mirage. These analysts have overlooked a key limitation of the reported data. It’s actually the case that American homes are only getting bigger if one believes that people living in multi-family housing either aren’t Americans or don’t have homes.

If instead of looking at the median, we look at the actual number of houses built, a different story emerges. As with all single-family housing, the market for big houses remains depressed—housing starts of 4,000 square feet or more are down 59 percent from the peak and are lower now than they were in 2001.  Homebuilders built 137,000 of these huge homes in 2006, but only 56,000 in 2013, according to the Census Bureau.

The only reason these big houses have increased as a share of total new housing is because the market for affordable, smaller single family homes has done even worse. The smaller yet still catastrophic decline in McMansions is hardly evidence of a growing, or even a continuing consumer love-affair with big houses.

Medians are funny measures—they’re highly dependent on the composition of the population being measured. If the housing market were so bad that only Bill Gates had the wherewithal to build a house, the “median” new home would balloon to 66,000 square feet (the size of his Lake Washington mansion). While that’s an extreme example, that’s the kind of thing that has happened to the U.S. housing market since the bubble days of last decade.

When the housing market collapsed, the bottom fell out. The big decline has been in smaller houses. The apparent popularity of the McMansion is a statistical artifact of the misleading median in a still very depressed housing sector. If anything, the rising median size of new homes is more a testament to the continued growth of income inequality in the U.S., coupled with tougher (i.e. more realistic) lending standards by banks.

This becomes apparent when you look at the actual number of new houses built in the U.S. The growth in the share of new single family homes is not due to some burgeoning increase in the demand for McMansions—rather, it represented the bottom falling out of market for single-family homes. Since the housing bubble peaked in 2007, single-family housing construction is down 66 percent. The construction of 4,000 square foot and larger homes—the McMansions—is down 59 percent. Smaller single-family homes under 1,800 square feet are down 75 percent. Meanwhile, the number of multi-family homes constructed has been increasing steadily, and is now back to pre-recession levels. Multi-family housing now makes up 40 percent of new home starts, up from 20 percent a decade ago. If we recalculated the median new home size including both multi- and single-family homes, the increase in the McMansion share would look much smaller.

We’re far from having what by historical standards would be considered a “healthy” housing market. Total housing constructed over the past five years is lower than any five-year period in the past 50 years. Does anyone believe that if the single-family housing market boomed back to 1.5 million housing starts, that the demand would come proportionately from McMansions? Of course not: the only way to get unit growth in single family housing is by getting households of more modest means back into homeownership—if that ever happens. They will be buying smaller houses.

Unlike the old days of NINJA (no income, no job or assets) lending, where even those with poor credit could qualify for loans, today’s credit standards are much higher. The other key factor has been the demise of the trade-up market. Because most people buy their new homes in significant part with the accumulated appreciation on their existing home, the decline in home values meant that very few middle-income households were in any position to trade-up in the real estate market.

There’s another problem with this median measure: it only looks at single-family housing, not all housing. The one bright spot in the housing market is not in single-family homes, but in multi-family units. By excluding the smaller multi-family homes, this automatically biases the median measure upward.

So in large measure, the only healthy segment of the single-family market is for those with very high incomes. Even here, “health” is a relative thing. Compared to the peak of the housing bubble years, sales of McMansions were lower in 2013 than any year since 2001.

If anything, the growth of the median size of new houses is evidence of the continued and growing impact of income inequality. With growth in incomes occurring mostly among those with the highest incomes, it figures that to the extent there is demand for housing, it’s coming disproportionately from those in the highest income brackets who can afford larger homes, and who qualify for credit.

An accurate measure of the popularity of McMansions would look at the extent to which high-income households are buying large new houses. We don’t have a good annual public data series on wealth by household, but a number of private firms estimate the number of high-net-worth households that form the market for these very large single-family homes. The Spectrem Group has estimated the number of U.S. households with net financial worth of $5 million or more (exclusive of the value of their principal home). By their reckoning there are about 1.24 million such households in the U.S. The number fluctuates from year to year, chiefly due to changes in financial markets.

We can get a good contemporaneous gauge of the popularity of McMansions by dividing the number of new 4,000 plus square foot homes sold by the number of households with a net worth of $5 million or more: call it the McMansion/Multi-Millionaire ratio. (There’s no universally accepted definition of McMansion, but since the Census Bureau reports the number of newly completed single-family homes of 4,000 square feet or larger, most researchers take this as a proxy for these over-sized homes.)

The McMansion to Multi-Millionaire ratio started at about 12.5 in 2001 (the oldest year in the current Census home size series)—meaning that the market built 12 new 4,000 square foot-plus homes for every 1,000 households with a net worth of $5 million or more. The ratio fluctuated over the following few years, and was at 12.0 in 2006—the height of the housing bubble. The ratio declined sharply thereafter as housing and financial markets crashed.

Even though the number of high-net-worth households has been increasing briskly in recent years (it’s now at a new high), the rebound in McMansions has been tepid (still down 59 percent from the peak, as noted earlier). The result is that the McMansion/Multi-Millionaire ratio is still at 4.5–very near its lowest point. Relative to the number of high-net-worth households, we’re building only about a third as many McMansions as we did 5 or 10 years ago. These data suggest that even among the top one or two percent, there’s a much-reduced interest in super-large houses.

There are a couple of key lessons here for thinking about the state of the U.S. housing market. Don’t be fooled by the misleading median, and don’t overlook the big rebound in multi-family housing.

Is life really better in Red States (and cities)?

The red state/blue state divide is a persistent feature of American politics. Political differences among states are also associated with important economic differences, and a similar patterns hold across and within metro areas. Big cities are more likely to be blue, and smaller towns and rural areas are red. The more densely populated portions of every metro area are also more likely to be blue. Understanding and eventually bridging these fissures is a major challenge for the nation.

In an article in last week’s New York Times, urbanist Richard Florida seems to have, if perhaps only inadvertently, given aid and comfort to the persistent myth that people are somehow worse off in big cities compared with smaller towns and suburbs.

It could be that this impression is amplified by the headline writer’s provocative question: “Is life better in America’s Red States?” While he doesn’t directly answer this question, Florida seems to imply that because housing is on average cheaper in red states, people who live there must be better off.

But is it the case that cheap housing is a reliable marker of economic well-being?

While it’s true that average home prices are higher in blue states, it’s important to consider why that is, and what it signifies. First and most importantly, blue state housing prices are driven higher because incomes and economic productivity are higher in bluer states and bluer cities. GDP per capita tends to be higher in metro areas that favored President Obama’s re-election by the widest margin, as shown here:



Note: if you hover over the orange trend line, you will see that the p-value is low and significant at the 1% threshold. (The p-value measures the statistical likelihood that the relationship between vote margin and productivity –measured by GDP per capita–is different from zero).  It measures correlation and tells us nothing about causality.    You can see the now familiar red-blue pattern on the attached map; the size of circles for each city corresponds to GDP per capita:  

The question then is, are higher housing prices in blue places an indication that the standard of living is lower?

Focusing on dollars per square foot misses the important fact that, unlike our stone age ancestors, we don’t rely on shelter solely as a means of warding off the cold, dark and wild beasts. We don’t value houses just as boxes—location matters. The reason the price of a square foot of land in Manhattan is worth as much as an acre of farmland in North Dakota has everything to do with the access it provides to a range of services, experiences and goods.

To an economist, if people are willing to pay a higher price for something—like housing in Manhattan or San Francisco or Honolulu—it’s a good indication that it has a higher value. A big part of the reason housing prices are higher in bigger cities than small ones is that we value the personal and economic opportunities that come from being close to lots of other people. As University of Chicago economist and Nobel Laureate Robert Lucas famously put it: “What can people be paying Manhattan or downtown Chicago rents for, if not being near other people?”

Harvard’s Ed Glaeser, author of Triumph of the City, has explored this theme in great depth. Increasingly, he argues, the biggest driver of city growth is the consumption advantages of living in cities, with close proximity to a wide range of goods, services, experiences, social interactions and cultural activities. This “consumer city” theory means that cities increase the well-being of their residents by facilitating all kinds of consumption. Indeed there are whole categories of goods, and especially services that are simply unavailable at any price outside major cities: think of everything from diverse ethnic restaurants to specialized medical care to cutting edge live art and music.

Provocative new work by Jessie Handbury shows once you adjust for the variety and quality of goods available in different places, the cost of living in big cities is actually lower than smaller cities. Her work looks at variations in the price and availability of food. It’s almost certain that differences in services are even more skewed in favor of city residents.

Moreover, looking just at differences in housing costs ignores important city advantages of density, proximity and convenience. Higher rents invariably provide city residents with better physical access to jobs, shopping, culture and social interaction. As Scott Bernstein and his colleagues at the Center for Neighborhood Technology have shown, savings in transportation costs in cities largely, and in some cases fully, offset differences in rents.

People who live in blue cities drive much less on average than those who live in red cities, and the savings in time and expense are substantial. My own work shows that residents of blue Portland Oregon drive about 20 percent less than in other large metro areas, saving them more than a billion dollars a year in transportation costs.

Florida makes one point that we all ought to pay attention to: as a nation we’d be much better off if we created more opportunities for people to live and work in blue cities. Because residents in big blue cities are so much more productive than otherwise identical workers in smaller red cities, we take a substantial hit to national economic productivity and growth. Enrico Moretti estimates GDP would be 13 percent or so higher if it weren’t for constrained population growth in these highly productive cities.

There’s an old adage that claims than an economist is someone who knows the price of everything and the value of nothing. Assuming that difference in house prices per square foot across metropolitan areas accurately reflects cost of living differences is arguably wrong. Cheap houses entail high costs for other things—like transportation—and to believe cheap houses automatically equals better quality of life misses the huge and tangible differences in the price and availability of a whole range of goods, services and experiences that make life nicer.

The political message here ought to be the high prices for blue cities generally, and the growing market premium for housing in dense, urban neighborhoods particularly, is a signal that Americans want more cities, and more opportunities for urban living. It’s a fair criticism of blue cities to say that they haven’t done a good enough job of making it possible for more people to live there—and this has a lot to do with local land use planning. But it has also been amplified by decades of federal subsidies to sprawling low-density development.

One final addendum on Richard Florida’s political analysis: as troubling as the persistent red/blue divide is among states and cities, it’s probably wrong to attribute the 2014 election results to this dichotomy. The huge fall off in turnout, especially among younger voters compared to 2012, is clearly the big driver of November’s red tide. Not only was 2014 the lowest off-year election turnout—only 37 percent—in six decades, but the electorate skewed far older in 2014 than in 2012. Voters over 65 made up 22 percent of voters in 2014, up from 16 percent in 2012; voters under 30 made up 13 percent of the electorate down from 19 percent in 2012. The 2014 red surge wasn’t so much geographic as it was demographic.

Our Shortage of Cities: Portland Housing Market Edition

The big idea: housing in desirable city neighborhoods in getting more expensive because the demand for urban living is growing. The solution? Build more great neighborhoods.

To an economist, prices are an important signal about value:  rising prices for an object or class of objects signal increasing value relative to other objects.  In our conventional supply and demand framework, rising prices are often symptomatic of a growing demand or a limited supply:  that consumers now want more of some commodity or product than is currently available in the market.

Trends in housing prices point to some significant shifts in consumer demand, especially in the value that consumers attach to urban, as opposed to suburban, locations.  The rising relative price of housing in cities is a strong indication of the growing demand for urbanity–and its unfortunate short supply.

Case in point:  Portland, Oregon.  Let’s take a quick glance back at housing prices in the Portland area over the past decade (courtesy of Zillow’s comprehensive archive of monthly housing price estimates).  For simplicity, we’ll look at four Portland metro area sub-markets–the central city of Portland (home to a little more than a quarter of the region’s population), and the region’s three principal suburban counties–Clackamas and Washington Counties in Oregon, and Clark County, Washington.  The following table shows single-family home prices for 2005, 2007 (the peak year for the region’s housing market), 2010 (the bottom of the bust) and the data for the latest quarter (3rd Quarter 2014).  To simplify comparison between the city and suburbs, I’ve calculated the un-weighted average price for for the three suburban counties.

In 2005, in the heyday of the housing bubble, the city of Portland’s housing prices were $236,000, on average about $20,000 lower than the three suburban jurisdictions, ranging from $3,000 lower than Clark County, to $30,000 lower than Clackamas County.  According to Zillow’s latest estimates the average Portland single family home is now worth about $309,000. Portland’s prices today are about $20,000 higher than the average of the three suburban counties, and its price level is equal to that of the Clackamas, the priciest suburban county.

Not only have houses in the City of Portland re-couped all the value lost to the collapse of the housing market, they are now worth on average about 6 percent more than they were at the peak of the housing bubble.  Meanwhile, the average suburban home is still about 7 percent below its peak price.

The verdict of this shift in housing markets is unequivocal:  housing in the city is now more valuable, and has appreciated faster than suburban housing.  In less than a decade, the city has reversed geographic polarity of the regional housing market:  the average city house sold at a nine percent discount to the average suburban house in 2005; today the average city house commands a seven percent premium.

There are doubtless many reasons for this shift.  We know that young, well-educated workers are increasingly choosing to live in close-in urban neighborhoods.  Over the past decade, the big increase in gasoline prices has made car-dependent suburban locations more expensive and less attractive than urban living.  My 2008 CEOs for Cities report “Driven to the Brink,” reported some early evidence that housing prices fell most on the suburban fringe, and held up best in urban centers.

The falling price of suburban housing relative to city housing is the most persuasive evidence possible about consumer preferences.  Citing the results of a recent opinion survey, some have claimed that Portland-area consumers allegedly prefer suburban locations to urban ones. But the fact that consumers are not willing to pay as much for suburban housing as they are for urban housing, and that while urban home prices are setting new highs, suburban prices are still well below their peaks, shows that the reverse is actually true:  consumers value urban single family housing more than its suburban alternative.

The rising price of urban housing is a market signal that housing in the city has value to consumers–and that we should be making more of it.  Prices are rising because the demand for city living is rising faster than we’ve expanded the supply of urban housing.  The clear public policy implication of the market data is that city and regional governments should be looking seriously at ways to expand the supply of urban housing.  And expanding supply in the city is especially important to addressing concerns about housing affordability:  unless supply expands, we can reasonably expect the growing demand for urban living to push prices still higher, reducing affordability.

While this commentary focuses on the city of Portland, there are good reasons to believe that the nation is experiencing a significant and growing shortage of cities as well.  As Americans rediscover–and recreate–the attractiveness of urban living, this shortage is likely to grow.  Paying close attention to the signals provided by the housing market is key to understanding the nature of this challenge and implementing appropriate solutions.

Technical Notes:  Data for this analysis were obtained from’s city and county ZHVI-Single Family Residential index.  Values are annual averages of monthly data, rounded to the nearest $1,000.  The suburban average presented here is the unweighted average of the price index values reported for the three suburban counties.  Since these four areas are all part of a single, larger metropolitan economy that shares the same industrial and job base, its pretty straightforward to interpret the change in relative prices among sub-markets as indicative of the relative change in consumer preference for these areas.  Also, because so little new single family housing has been constructed in the past several years, these numbers are not meaningfully skewed by the construction of a large number of new houses in any one jurisdiction.