The Urban Institute’s Housing Finance Policy Center has released its November 2017 Housing Finance at a Glance Chartbook. The Introduction looks out how this summer’s big storms have pushed up delinquency rates:
The Federal Reserve’s Devin Bunten has posted Is the Rent Too High? Aggregate Implications of Local Land-Use Regulation. It is a technical paper about an important subject. It has implications for those who are concerned about the lack of affordable housing in high-growth areas. The abstract reads,
Highly productive U.S. cities are characterized by high housing prices, low housing stock growth, and restrictive land-use regulations (e.g., San Francisco). While new residents would benefit from housing stock growth in cities with highly productive firms, existing residents justify strict local land-use regulations on the grounds of congestion and other costs of further development. This paper assesses the welfare implications of these local regulations for income, congestion, and urban sprawl within a general-equilibrium model with endogenous regulation. In the model, households choose from locations that vary exogenously by productivity and endogenously according to local externalities of congestion and sharing. Existing residents address these externalities by voting for regulations that limit local housing density. In equilibrium, these regulations bind and house prices compensate for differences across locations. Relative to the planner’s optimum, the decentralized model generates spatial misallocation whereby high-productivity locations are settled at too-low densities. The model admits a straightforward calibration based on observed population density, expenditure shares on consumption and local services, and local incomes. Welfare and output would be 1.4% and 2.1% higher, respectively, under the planner’s allocation. Abolishing zoning regulations entirely would increase GDP by 6%, but lower welfare by 5.9% because of greater congestion.
The important sentence from the abstract is that “Welfare and output would be 1.4% and 2.1% higher, respectively, under the planner’s allocation.” Those are significant effects when we are talking about real people and real places. The introduction provides a bit more context for the study:
Neighborhoods in productive, high-rent regions have very strict controls on housing development and very limited new housing construction. Home to Silicon Valley, the San Francisco Bay Area is the most productive and most expensive metropolitan region in the country, and yet new housing construction has been very slow, especially in contrast to less-productive large cities like Houston, Texas. The evidence suggests that this slow-growth environment results from locally determined regulatory constraints. Existing residents justify these constraints by appealing to the costs of new development, including increased vehicle traffic and other types of congestion, and claim that they see few, if any, of the benefits from new development. However, the effects of local regulation extend beyond the local regulating authorities: regions with highly regulated municipalities experience less-elastic housing supply. (2, footnotes omitted)
The bottom line, as far as I am concerned, is that localities that are attempting to deal with their affordable housing problems have to directly address how they go about their zoning. If the zoning does not support housing construction, then no amount of affordable housing incentives will address the demand for housing in high growth places like NYC and San Francisco.
Michael Bader and Siri Warkentien have posted an interesting mapping tool, Neighborhood Racial & Ethnic Change Trajectories, 1970-2010. They had set out to answer the question:
how have neighborhoods changed since the Civil Rights Movement outlawed discriminatory housing? We study how neighborhood racial integration has changed during the four decades after the legislative successes of the Civil Rights Movement. We were unsatisfied with previous studies that focused mostly on defining “integrated” and “segregated” neighborhoods based on only on whether groups were present. We thought that the most interesting and important changes occur within “integrated” neighborhoods, and we set out to identify the common patterns of those changes.
We used a sophisticated statistical method to identify the most common types of change among Blacks, Latinos, Asians and Whites in the metropolitan neighborhoods of the four largest cities in the U.S.: New York, Los Angeles, Chicago, and Houston. We were disappointed to learn that many integrated neighborhoods were actually experiencing slow, but steady resegregation — a process that we call “gradual succession.” The process tended to concentrate Blacks into small areas of cities and inner-ring suburbs while scattering many Latinos and Asians into segregating neighborhoods throughout the metropolitan area.
While we reserve a healthy dose of pessimism about long-term integration, we also find neighborhoods experiencing long-term integration among Blacks, Latinos, Asians, and Whites. We call these “quadrivial” neighborhoods, which derives from Latin for the intersection of four paths. We thought that seemed appropriate given the often different paths different racial groups took to these neighborhoods. (emphasis in the original)
I was, of course, interested in the New York City map. While NYC is highly segregated, it was interesting to see the prevalence of these so-called quadrivial neighborhoods. The authors find that
About 20 million people call the New York metropolitan area home. The metro area is one of the most segregated in the United States and, as a result, New York has a large proportion of neighborhoods following stable Black and stable White trajectories. Some of the segregation came about because of White flight during the 1970s. Black segregation following this path clusters in the Lower Bronx, North Brooklyn, and in and around Newark, New Jersey.
Large-scale Latino immigration to the New York metro area has been relatively recent, and the number of recent Latino enclaves bears out that pattern. Neighborhoods experiencing recent Latino growth are scattered throughout suburban New Jersey, Long Island and northern New York neighborhoods. New York also experienced high levels of Asian immigration relative to other metropolitan areas. Neighborhoods experiencing recent Asian growth are scattered throughout the metropolitan region.
New York also contains a large number of quadrivial neighborhood and the highest proportion of White re-entry neighborhoods. The latter are found near transportation to Manhattan in the gentrifying areas of Jersey City and Weehawken, New Jersey and the Brooklyn terminals of the Manhattan and Williamsburg Bridges.
New York, therefore, contains the contradiction of containing a large number of segregating neighborhoods along with a distinct trend toward integration.
I am not sure that I have any insight to explain that contradiction, although Walt Whitman, Brooklyn’s poet, notes:
Do I contradict myself?
Very well, then I contradict myself,
(I am large, I contain multitudes).
S&P has posted a paper on Economic Factors That Affect Housing Prices. This is, of course, an important topic, albeit one that is an art as well as a science. While S&P undertook this analysis more for mortgage-backed securities investors than for anyone else, it certainly is of use to the rest of us. The paper opens,
The U.S. domestic housing market has experienced a 23% price increase since the beginning of the housing recovery in 2011. Many local housing markets are now close to or above their peak levels of 2006, which leads us to investigate whether the pace of home price appreciation (HPA) can continue at its current pace. In this paper, we (1) examine the economic factors that influence HPA and (2) forecast HPA for numerous geographic regions assuming various economic conditions over the next five years. While the aggregate national pattern in housing prices is an important reference, we need to examine housing prices at a more granular geographic level in order to understand regional housing market dynamics and learn how these are affected by local macroeconomic factors. This paper demonstrates that several economic variables are needed to predict average home price movements for each of 48 different U.S. metropolitan statistical areas (MSAs).
* * *
Factors that influence HPA can be difficult to predict. Therefore, residential mortgage backed securities (RMBS) investors frequently use a range of HPA projections to estimate their potential bond returns. With that in mind, for each MSA, we considered five separate hypothetical economic scenarios, ranging from an “Upside” forecast to an extreme “Stress 3” case. Interestingly, our Stress 3 case forecasts a 28% decline in HPI at the national level over the next five years, which corresponds roughly to the decline experienced in the last recession. Our “base case” scenario leads to forecasts at the national level of a 26% increase in HPI over five years. This represents what we believe to be the most likely economic forecast. (1-2)
S&P’s key findings include:
- Movement in HPA is primarily influenced by up to five variables, depending on the MSA: housing affordability, changes in shadow inventory, the unemployment rate, the TED spread [a measure of distress in the credit markets], and population growth.
- HPA in many MSAs has momentum, meaning that it depends on its level in the previous quarter of observation.
- The mortgage rate generally appears to have little predictive power in connection with home prices.
- Chicago, Houston, Boston, and San Francisco are projected to appreciate at a greater pace (45%, 40%, 27%, and 36%, respectively) than the 26% forecast for the nation as a whole over the next five years, and New York at a slower pace (21%). Columbus led all MSAs with a projected five-year HPA of 50%.
- Under our most pessimistic (Stress 3) scenario, Chicago is forecast to experience a greater decline in HPI (34%) over the next five years than the nation as a whole (29%), while New York, Boston, Houston, and San Francisco are projected to experience declines that are less severe than that of the nation (19%, 3%, 17%, and 16%, respectively). Markets that have been vulnerable in the past (Las Vegas, Phoenix, and Riverside) are projected to experience the greatest five-year declines under our Stress 3 scenario (66%, 68%, and 68%). The markets that show the greatest movements are the most sensitive to the five factors and frequently show the greatest upside and downside. (2-3, emphasis in the original)
I found the first and third bullet points to be the most interesting, as many pundits weigh in on the factors that affect housing prices. It will be interesting to see if further research confirms S&P’s findings.
This study includes the central cities of the 11 largest metropolitan areas in the U.S. (by population) from 2006 to 2013: Atlanta, Boston, Chicago, Dallas, Houston, Los Angeles, Miami, New York City, Philadelphia, San Francisco, and Washington, DC.
The number and share of renters rose in all 11 cities.
The rental housing stock grew in all 11 cities from 2006 to 2013, while owner-occupied stock shrank in all but two cities.
In all 11 cities except Atlanta, the growth in supply of rental housing was not enough to keep up with rising renter population. Mismatches in supply and demand led to decreasing rental vacancy rates in all but two of the 11 cities in the study’s sample.
The median rent grew faster than inflation in almost all of the 11 cities in this study. In five cities, the median rent also grew substantially faster than the median renter income. In three cities, rents and incomes grew at about the same pace. In the remaining three cities, incomes grew substantially faster than rents.
In 2013, more than three out of every five low-income renters were severely rent burdened in all 11 cities. In most of the 11 cities, over a quarter of moderate-income renters were severely rent burdened in 2013 as well.
From 2006 to 2013, the percentage of low-income renters facing severe rent burdens increased in all 11 cities in this study’s sample, while the percentage of moderate-income renters facing severe rent burdens increased in six of those cities.
Even in the cities that had higher vacancy rates, low-income renters could afford only a tiny fraction of units available for rent within the last five years.
The typical renter could afford less than a third of recently available rental units in many of the central cities of the 11 largest U.S. metro areas.
Many lower- and middle-income renters living in this study’s sample of 11 cities could be stuck in their current units; in 2013, units occupied by long-term tenants were typically more affordable than units that had been on the rental market in the previous five years.
In six of the cities in this study, the median rent for recently available units in 2013 was over 20 percent higher than the median rent for other units in that year, indicating that many renters would likely face significant rent hikes if they had to move. (4)
While this report does an excellent job on its own terms, it does not address the issue of location affordability, which takes into account transportation costs when determining the affordability of a particular city. It would be very helpful if the authors supplemented this report with an evaluation of transportation costs in these 11 cities. This would give a more complete picture of how financially burdened residents of these cities are.
MainStreet quoted me in Is Airbnb Making It Impossible For You To Rent That Dream Apartment?. It opens,
The accusation is blunt: Airbnb, say some, is sucking up apartment units that otherwise would be available to renters. In San Francisco, that claim is spoken so loudly – by so many politicians – a city agency just filed a report on it.
Similar claims are heard in Santa Monica, Calif., in Manhattan and some Brooklyn neighborhoods, a few areas in Seattle and also a sliver of Boston and adjacent Cambridge. True? False? Is that Airbnb host putting vacationers up in what should be your prime Greenwich Village flat?
Some think such accusations are just distracting from the main issue at hand: housing inventory shortages.
“It’s a diversion,” says Richard Green, the Lusk Chair in Real Estate at the University of Southern California. “Politicians are not dealing with what they should be dealing with to address housing unavailability so they are singling out Airbnb.” His nuanced point is that in most markets the number of Airbnb units is trivial and so whatever impact it has on apartment availability is minimal.
The San Francisco government report does not disagree: “the Budget and Legislative Analyst estimates that between 925 and 1,960 units citywide have been removed from the housing market from just Airbnb listings. At between 0.4 and 0.8%, this number of units is a small percentage of the 244,012 housing units that comprised the rental market in 2013.”
Read the San Francisco report. It said that under 1% of apartments have been removed from rental channels due to Airbnb. How important is that? What does it mean?
What is unique about San Francisco – also Manhattan and a few other places – is that apartment vacancy rates are fiercely low. In a recent survey, it stood at 4.1% in San Francisco and that means this is the type of town where would-be renters get in line early whenever a decent unit goes up for rent. Add back in those Airbnb units and, yes, that might be a happy day for some tenants. But not many.
The other unique feature: San Francisco, Manhattan and a very few other places attract large tourist populations, especially Millennials, and that has been a sweet spot for sharing economy rentals. Take tight supply, add in high hotel prices and a flood of tourists and there is the recipe for cries about any apartment that seems to be lost to the longterm tenant market.
In a lot of markets – from Phoenix to Houston – vacancy rates are already high, tourist numbers are low and nobody really thinks Airbnb is having any impact on local rentals.
But in some cities it just may be. Harry Campbell, TheRideShareGuy.com, said of Airbnb: it is “having a huge impact in coastal communities [of Los Angeles] like Venice/Santa Monica where mid level chain hotels can run upwards of $300-$400 a night. It just doesn’t make much sense for landlords to rent their apartments out traditionally when the profits are so much higher using Airbnb.” (Santa Monica, in mid May, enacted legislation banning short-term rentals such as Airbnb. Nobody knows how it will be enforced or if it will withstand legal challenges.)
At least one Portland, Ore. Airbnb host emailed Mainstreet to admit that two apartment units that had been rented to regular tenants are no longer. Explained that host: “From the point of view of a former landlord, the Airbnb experience is far superior. Airbnb guests are, on the whole, responsible, considerate and never late with rent since this is collected in advance by Airbnb.”
Either way, however, the calculus is not one-sided, not even in those premium markets like San Francisco. Green added: “You could also say that Airbnb is increasing the stock of affordable housing units by letting some keep their apartments by occasionally renting them out. It’s entirely possible Airbnb produces as many units as it loses.”
In that regard, listen to Kip (last name withheld) — a self-described 60+ woman living alone in Beverly Hills in a two bedroom apartment. A few times a month, said Kip, she rents it out through Airbnb. “That helps me with the cost of living,” she said. She stressed she would never take in a roommate but is happy with having guests a few nights a month. “It’s helped me boost my flagging income,” she said.
Christopher Nulty, an Airbnb spokesperson, had fighting words in response to the San Francisco report in particular.
“This comes from the same people who want to ban new housing in the Mission [a San Francisco neighborhood], ban home sharing and make San Francisco more expensive for middle class families,” he said. “Home sharing is an economic lifeline for thousands of San Franciscans who depend on the extra income to stay in their homes.”
So, who’s telling the truth?
“When evaluating claims about Airbnb, it is important to keep in mind whose ox is being gored,” said David Reiss, a professor at Brooklyn Law School. His point: In some cases, maybe Airbnb brings some harm. In other cases, it does good. Matters just aren’t simple or black and white.
NYU’s Furman Center and Capital One have produced an interesting graphic, Renting in America’s Largest Cities. The graphic highlights the growing trend of renting in urban communities, but also the increasing expense of doing so. The press release about this study provides some highlights:
- In 2006, the majority of the population in just five of the largest 11 U.S. cities lived in rental housing; in 2013, that number increased to nine.
- As demand for rental housing grew faster than available supply, rental vacancy rates declined in all but two of the 11 cities, making it harder to find units for rent.
- Rents outpaced inflation in almost all of the 11 cities. Rents Increased most in DC, with a 21 percent increase in inflation-adjusted median gross rent, and least in Houston, where rents were stable.
- In all 11 cities, an overwhelming majority of low-income renters were severely rent-burdened, facing rents and utility costs equal to at least half of their income.
- Even In the most affordable cities in the study, low-income renters could afford no more than 11 percent of recently available units.
- In five major cities, including New York, Los Angeles, San Francisco, Boston and Miami, moderate-Income renters could afford less than a third of recently available units in 2013.
Rental housing clearly has an important role to play in providing stable homes for American households, particularly in big cities. While rental housing has been the stepchild of federal housing policy for far too long, it is good that it is finally get some attention and resources.
I look forward to the Furman Center’s follow-up report, which will provide more detail than the graphic does. I am particularly curious about whether the researchers have addressed the difference between housing affordability and location affordability in the longer study. I would guess that the relative affordability of the cities in this study is greatly impacted by households’ transportation costs.