Monday, October 16, 2017

Far From Static, Rural Home Prices are Dynamic and Growing in Most of the Country

by Alexander Hermann
Research Assistant
While many believe that home prices in rural areas are largely stagnant, this is not the case, according to a new Joint Center analysis from the Federal Housing Finance Agency (FHFA). Rather, non-metro home prices are dynamic, highly variable, and growing—much like home prices for the nation as a whole.

Nationally, home prices grew significantly over the last half-decade, following years of decline in the aftermath of the housing crisis. But these broad indicators mask significant variation by region, market, and even neighborhood. While these trends were discussed extensively in this year's State of the Nation's Housing report and on our blog, those analyses focused almost exclusively on the roughly 62 million homeowners living within metropolitan areas, where 83 percent of the country's owner-occupied units are located.

But what about home prices in the largely rural, non-metro areas that are home to about 15 percent of the population? Due to limited data availability, these areas often are ignored in discussion about trends in home prices. A new Joint Center analysis of the FHFA House Price Index, which measures price changes from the sale, refinancing, and appraisal of the same properties, seeks to fill this gap. In particular, the analysis examined house prices for all counties outside of Metropolitan Statistical Areas (MSAs). (Metro-area counties are those that contain an urbanized area of at lease 50,000 persons plus any adjoining counties that commuting patterns show are economically integrated with their metropolitan neighbors.)

Several key findings emerge from this analysis, most notably:

  • Rather than stagnating, home prices outside the metropolitan areas grew considerably between 2000 and 2016. In nominal terms, non-metro home prices grew 58 percent and real non-metro home prices grew nearly 15 percent. Moreover, by the fourth quarter of 2016, nominal non-metro and rural home prices were two percent above their pre-recession peak—the same as national home prices at the same point. However, when adjusted for inflation, home prices in non-metro areas were still 11 percent below their peak, which again, is somewhat similar to national patterns (Figure 1).





















Note: The US non-metro index is a weighted-average of state non-metro HPIs, with each state's value weighted by its share of non-metro detached single family housing units. Real home prices are adjusted for inflation using the CPI-U for All Items less shelter. 
Source: JCHS tabulations of the Federal Housing Finance Agency, All-Transactions House Price Index.


  • While significant, these increases were more modest than the gains experienced by the nation as a whole. Nationally, real home prices grew 23 percent in 2000-2016, about 8 percentage points more than prices in non-metro areas (Figure 2). The difference is largely due to the more modest cyclicality of non-metro home prices movements during and after the recession. In the immediate aftermath of the housing crisis, national home prices fell severely for several years before starting to rise steadily in early 2012. In contrast, home prices in non-metro areas were mostly stagnant from 2011 to 2014, and, compared to metro areas, have grown less quickly since. As a result, between 2012 and 2016, the percentage-point increase in non-metro home prices was greater than the percentage-point increase in statewide prices only in Alaska, Hawaii, Mississippi, Montana, and Nebraska.





















Note: The US non-metro index is a weighted-average of state non-metro HPIs, with each state's value weighted by its share of non-metro detached single family housing units. Real home prices are adjusted for inflation using the CPI-U for All Items less shelter. 
Source: JCHS tabulations of the Federal Housing Finance Agency, All-Transactions House Price Index.


  • Rural home price trends by state vary widely. While non-metro home prices within states generally change in ways that are similar to the broader state-wide trends, increases in rural areas did not always trail overall increases in their states (Figure 3). Rather, from 2000 to 2016, the increase in non-metro house prices actually exceeded statewide prices increases in 24 of the 47 states where at least some part of the state was not in an MSA. (Three states—Delaware, New Jersey, and Rhode Island—do not have non-metro areas.)

    The gaps were largest in North Dakota (13 percentage points greater), Nebraska (12 percentage points greater), South Dakota (11 percentage points greater), New Mexico (7 percentage points greater), and Louisiana (6 percentage points greater). In contrast, the increases in statewide prices most exceeded rural prices in coastal states, where prices in metropolitan areas have grown significantly. The gaps were greatest in California (26 percentage points), Hawaii (23 percentage points), Virginia (22 percentage points), Oregon (19 percentage points), and Maryland (18 percentage points).

























  • Non-metro home prices rose more slowly in the run-up to the housing crisis, and rarely fell as far in the aftermath. Between 2000 and 2007, real non-metro home prices increased by 28 percent, far less than the 41 percent increase for all single-family homes. However, in about half of the 47 states with non-metro areas, the percent increase for all single-family homes. However, in about half of the 47 states with non-metro areas, the percent increase in non-metro home prices exceeded states-wide home-price gains in the run-up to peak. After the crash, real national home prices fell below 2000 levels briefly in 2012, while non-metro prices remained about three percent above their 2000 levels. Moreover, this pattern held true in most states. In only 10 of 47 states were the recessionary-low home prices (relative to 2000) in non-metro areas lower than for the state overall.

  • Unclear signals for the future. While this analysis shows that non-metro house prices generally follow national patterns, since the recession price growth in rural areas has been slower than in the nation as a whole. The homeownership rate in non-metro areas was also about 71 percent in 2015, nearly 9 percentage points higher than in metro areas. These differences held across racial and ethnic groups, as well as for low- and moderate-income households. Collectively, this indicates that these markets merit close attention in the coming years.

Monday, October 2, 2017

The Negative (and Positive) Spillovers of Concentrated Foreclosure Activity in New York City

by Kristin L. Perkins
Postdoctoral Fellow
Foreclosures have negative effects not only for the people who lose their homes, but also for the neighborhoods where they lived.

In an article that recently appeared in Urban Affairs Review, Michael J. Lear, Elyzabeth Gaumer, and I conclude that, at least in New York City, neighborhood foreclosure activity during the peak of the Great Recession was associated with an individual property's risk of foreclosure, but not in the way that most previous research has assumed. These findings suggest that financial institutions' practices during the foreclosure process may have contributed to how quickly neighborhoods recovered from that crisis above and beyond the institutions' roles in causing it. The research focused on two key phases in the foreclosure process, an early phase when a lender filed a foreclosure notice after a property owner missed some mortgage payments, and a later phase when properties were actually scheduled to be auctioned. (In New York, the latter process often occurs more than a year after the former one.)

Although New York City's housing market fared better than many other markets during the recession, it was not immune to the problems associated with that downturn. Illustratively, in 2007 and 2008, there were nearly 14,000 foreclosure filings annually, double the number in 2004. In 2009, the number increased to over 20,000. The number of foreclosure auctions, however, was much smaller, ranging from 3,000 to 4,500 a year between 2007 and 2009.

This foreclosure activity was also highly concentrated. Between 2007 and 2009, over half of the city's foreclosure filings occurred in just nine of the city's 55 sub-borough areas (SBAs), all of them in Brooklyn or Queens. Moreover, over half of the city's auctions took place in just six SBAs. Not all specific areas with the highest concentration of foreclosure auctions, however, were among the areas with the highest concentration of foreclosure filings. Rather, the share of foreclosure filings that result in scheduled foreclosure auction varied considerably across boroughs, from less than 10 percent being scheduled for auction in parts of Brooklyn to more than 40 percent in parts of Queens.

My coauthors and I hypothesized that the number of scheduled foreclosure auctions surrounding a property that had received a foreclosure filing is positively associated with the likelihood of that property itself reaching auction, net of other factors. Since foreclosure filings do not necessarily involved a transfer of ownership, however, we thought they might not have as strong an association as auctions may have.

Drawing on data about individual property and neighborhood characteristics in addition to foreclosure activity, we found that levels of neighborhood foreclosure activity in both phases were, in fact, associated with an individual property's outcome, but in different directions. Holding constant individual property and neighborhood characteristics, as the number of nearby properties with a foreclosure filing increases, the probability that an individual financially-distressed property will be scheduled for foreclosure auction decreases. This pattern is reversed for properties in the later phase of the foreclosure process. As the number of nearby properties scheduled for auction increases, the probability that an individual financially-distressed property will be scheduled for foreclosure auction also increases (See Figure).



These findings also suggest that at least in New York, banks and loan servicers may have delayed the processing of foreclosures in areas with larger numbers of properties with foreclosure filings. They may have done so because foreclosed properties may not sell as quickly or as profitably as in more desirable areas where there are fewer distressed properties and/or where foreclosed properties sell right away. Future research should examine these practices in more detail, not only in New York, but in other states as well and, in doing so, underscore the importance of financial institution practices not just in the lead up to the Great Recession, but throughout the recovery process as well.

Thursday, September 28, 2017

Successful Collaboration in Community Development: Easier Said Than Done

by Alexander Von Hoffman
Senior Research Fellow
What are the keys to successful collaborations of nonprofit housing organizations? A remarkable attempt to form a novel alliance by five such groups in western New York State reveals several keys to an effective collaboration. Each of the five organizations -- NeighborWorks® Rochester, West Side (Buffalo) Neighborhood Housing Services, Niagara Falls Neighborhood Housing Services, Arbor Housing and Development, and NeighborWorks® Home Resources -- were long-established in their geographic areas. Moreover, each belonged to the network of NeighborWorks® America, a congressionally chartered nonprofit corporation that provides grants, technical assistance, training, and organizational assessment to housing and community development organizations. Their experiences, which are documented in a recent Joint Center case study, shows both the problems and the possibilities of putting the idea of collaboration into action.

Buffalo, New York


Before going into details, it bears mention that it has become almost an axiom in the community development field that nonprofit organizations must "collaborate" if they are going to survive, much less transform low-income communities. And the idea of collaboration is appealing: two or more organizations agree to coordinate activities in a systematic way -- as opposed to carrying out a one-time joint venture. Such collaborations can range from a temporary partnership to outright mergers (or anything in between). But many practitioners and scholars believe such initiatives can address a host of serious problems. For most community development organizations, money is always short, and especially so in recent years as the federal government has reduced funding for the Community Development Block Grant (CDBG) program. In addition, many nonprofit groups appear to be financially weak, undersized, relatively unproductive, organizationally stagnant, or some combination of the above. By sharing business lines, programs, and administrative functions, smaller and financially weaker groups could become more efficient and possibly tap the resources and knowledge of stronger organizations. If so, they could stabilize their finances and begin to grow, which would allow them to devote more time and attention to serving their low- and moderate-income constituents effectively.

But as the new case study documents, putting these ideas into practice can be difficult. After extensive discussions, in 2012 the leaders of five western New York State groups devised the concept of a "collaborative merger." In this structure, each organization would become a subsidiary division of a new central organization. As subsidiaries, the five groups would maintain their separate identities, offices, and geographic service areas while increasing their capabilities and expanding the types and volume of business. The central organizations, which would be overseen by board members from each of the participating groups, would provide core administrative functions, and, in doing so, bring the efficiency and resources of a large organization to the work of what had been separate smaller groups.

Just as the groups were about to create the new entity, however, the alliance came to an abrupt halt. Many factors contributed to the breakdown of the process. The biggest obstacle was the difficulty of bringing five disparate groups together under a common structure. The organizations covered starkly different kinds of geographic territories. Three of the organizations (NeighborWorks® Rochester, West Side Neighborhood Housing Services (Buffalo), and Niagara Falls Neighborhood Housing Services) were rooted in cities. The other two (Arbor Housing and Development, and NeighborWorks® Home Resources) were rural entities with geographically extensive service areas.

Moreover, there were significant differences in the organizations' programmatic offerings. Arbor, the largest of the five groups not only provided residential services for people with special needs and victims of domestic violence, it also developed and managed low-income housing. The other four groups were traditional "neighborhood housing service" groups that emphasized homeownership counseling, lending, and community engagement. Over time, key staff and board members of the housing service organizations became increasingly concerned that if the alliance went forward, their organizations would lose their identities and be less able to perform their core functions. Ultimately, the concerns became so great that the groups' leaders decided not to proceed with the planned alliance.

That was not the end of the story, however. Following the original idea, albeit on a smaller scale, the three urban-oriented neighborhood housing service groups (NeighborWorks® Rochester, West Side Neighborhood Services, and Niagara Falls Neighborhood Housing Services) merged to form a new organization called NeighborWorks® Community Partners. Meanwhile, Arbor, which continued to be a NeighborWorks® member, has not only thrived, but has also expanded its service area as far as Pennsylvania and Albany, NY. The fifth organization, NeighborWorks® Home Resources, remained in business under the name Rural Revitalization Corporation, but has left the NeighborWorks® network.

The experiences of these five organizations not only underscores the importance of building trust among partners in any collaboration, it also offers several lessons for those interested in collaborating with other entities. First, prospective collaborators might do well to begin by collaborating on actual programs before they start building a grand organizational structure. Second, collaborators should take time to develop a common vision, which means wrestling honestly with with the differences that separate the participating groups. Third, and related to the above, communication - open and constant - is essential, as is the full and committed participation of all of the involved parties. The leaders of such efforts must go to great lengths to ensure that everyone - including staff and board members from all the organizations - understand and support the collaborative effort.

Finally, everyone must understand that bringing existing groups into a new organizational arrangement is not business as usual. It is an act of creation that will change the status quo. Such a collaboration requires extraordinary care to ensure that the participants recognize the process and the outcome as legitimate. And this in turn means it is essential to tackle difficult questions about management, sharing leadership, and the roles and responsibilities of staff and board members sooner rather than later.

Tuesday, September 19, 2017

Are Integrated Neighborhoods Becoming More Common in the United States?

by Jonathan Spader and
Shannon Rieger
The share of the population living in racially and ethnically integrated neighborhoods in the US has increased since 2000, according to our new Joint Center research brief. However, most Americans continue to live in non-integrated neighborhoods, and evidence suggests that some of the recent increases in integration may be the temporary byproduct of gentrification and displacement.

The new brief, "Patterns and Trends of Residential Integration in the United States Since 2000," assesses whether the nation's increasingly diverse population is fostering the growth of integrated neighborhoods or whether the choices people make about where to live are reinforcing existing lines of segregation and exclusion. Specifically, we use data from the 2000 Census and the 2011-15 American Community Survey (the most recent data available at the census tract level) to describe the number, stability, and characteristics of integrated neighborhoods.

Because there is no single measure for identifying integrated neighborhoods, our analyses applies two commonly-used definitions to define integration. The first approach—which we refer to as "no-majority neighborhoods"—defines integrated neighborhoods as those where no racial or ethnic group accounts for 50 percent or more of the population. While this definition identifies neighborhoods with a plurality of races and ethnicities, it may exclude some neighborhoods with relatively high levels of integration relative to the median neighborhood in the United States. For example, under this definition, a census tract that is 49 percent black and 51 percent white would be classified as non-integrated.

The second definition—which we refer to as "shared neighborhoods"—uses a broader definition of integration, identifying neighborhoods as integrated if any community of color accounts for at least 20 percent of the tract population AND if the tract is at least 20 percent white. While this definition might be expanded to include neighborhoods in which any two groups account for at least 20 percent of the tract's population, this definition requires that the neighborhood population be at least 20 percent white because of whites' long history of exclusionary practices as well as attitudinal surveys suggesting that, on average, whites are less willing that other groups to live in integrated neighborhoods.

Both definitions suggest that the number of integrated neighborhoods—and the share of the US population living in integrated neighborhoods—increased between 2000 and 2011-15 (a time when the white, non-Hispanic share of the population fell from 69.1 percent to 62.3 percent). The number of "no-majority neighborhoods" increased from 5,423 census tract in 2000 to 8,378 in 2011-15, and the share of the US population residing in such tracts increased from 8.0 percent in 2000 to 12.6 percent in 2011-15 (Figure 1).

Similarly, the number of "shared neighborhoods" increased from 16,862 tracts in 2000 to 21,104 tracts in 2011-15, and the share of the US population residing in "shared" tracts increased from 23.9 percent in 2000 to 30.3 percent in 2011-15. These figures are higher than the estimates for "no-majority" tracts, reflecting the broader definition of integration used to define "shared neighborhoods." Nonetheless, both definitions show increases in integration between the 2000 Census and the 2011-15 ACS.



Notes: "No-majority neighborhoods" are census tracts in which no racial or ethnic group accounts for 50 percent or more of the population. "Share d neighborhoods" are census tracts in which whites account for 20 percent or more of the population and any community of color accounts for 20 percent or more of the population. 
N=71,806 Census tracts.

While the share of the population living in integrated neighborhoods has increased since 2000, most Americans continue to live in non-integrated areas, with white individuals the least likely to live in integrated areas. While 12.6 percent of the total US population lives in one of the 8,378 "no-majority" tracts, these neighborhoods include just 7.2 percent of the nation's whites, compared to 20.3 percent of blacks, 20.3 percent of Hispanics, 30.9 percent of Asians, and 19.5 percent of individuals of other races and ethnicities (Figure 2).

A similar pattern is present within "shared neighborhoods." While 30.3 percent of the total US population lives in one of the 21,104 "shared neighborhoods," these neighborhoods include just 22.9 percent of the nation's whites, compared to 43.0 percent of blacks, 42.8 percent of Hispanics, 44.8 percent of Asians, and 36.5 percent of individuals of other races and ethnicities.



Note: Estimates show the percent of each group that live in integrated neighborhoods. White, black, Asian, and Other are non-Hispanic.

The research brief provides more specific details about the relative composition of integrated and non-integrated neighborhoods by race, ethnicity, and other demographic characteristics. Additionally, it describes the stability of integrated neighborhoods between 2000 and 2011-15, as well as the geographic distribution of integrated neighborhoods across central cities, suburbs, and non-metropolitan areas.

Taken together, this evidence offers support for the conclusion that the number of integrated neighborhoods has increased in recent years. However, it also highlights that this conclusion is subject to two important caveats. First, some portion of the increase in integration reflects neighborhood change processes associated with the gentrification and displacement pressures affecting the central areas of many cities. While some of these neighborhoods may become stably integrated areas, it is not yet clear how many of the newly integrated neighborhoods will become stably integrated and how many will eventually become non-integrated areas.

Second, integrated neighborhoods remain the exception rather than the rule in the United States. The 2011-15 ACS shows that fewer than one in three Americans lives in a shared neighborhood, with just 12.6 percent living in "no-majority neighborhoods." As the country moves toward a population in which people of color are projected to be a majority by the middle of the century, further growth will be necessary for such changes to produce a more inclusive society.

Wednesday, September 6, 2017

Rebuilding Housing in Harvey’s Aftermath: Two Lessons from Hurricanes Katrina and Rita

by Jonathan Spader
Senior Research Associate
As floodwaters finally subside in Houston, and as Florida residents prepare for Irma, residents, civic leaders, and policymakers can glean two important lessons from the intensive efforts to rebuild homes and communities after Hurricanes Katrina and Rita, two devastating storms that hit the U.S. in back-to-back succession in 2005. 

First, rebuilding residential properties is a lengthy process likely to take several years. Second, the rebuilding process will be especially lengthy for rental properties (as compared to owner-occupied homes), which could greatly affect the 950,000 renters (who account for 41 percent of households) in the greater Houston metropolitan area, as well as additional renters affected by Hurricane Harvey in elsewhere in Texas and in other states. The slower pace of rental rebuilding is due to several factors including both renters’ dependence on property owners to rebuild rental housing units and historical differences in the availability and terms of federal aid for rental property owners as compared to homeowners.

To be sure, the need for emergency assistance and shelter for displaced residents will continue for weeks to come. Nevertheless, Congress is already starting to discuss an aid package. Moreover, the extensive damage (and the need to reauthorize the National Flood Insurance Program before September 30) may spur new efforts to develop policies and programs to support housing recovery in the wake of future natural disasters. As policymakers, civic leaders, and local residents begin to focus on the rebuilding process, they might want to keep the following in mind.

Extensive flooding from Hurricane Harvey in Southeast Texas. Air National Guard photo by Staff Sgt. Daniel J. Martinez

1. Rebuilding residential properties takes time.

An initial lesson from Hurricanes Katrina and Rita is that the rebuilding process takes time, with many properties continuing to show observable damage several years after the storms had passed. In early 2010—almost five years after both hurricanes made landfall—a HUD-commissioned study that I worked on surveyed the exterior conditions of properties damaged by those storms. The survey produced representative estimates of the rebuilding outcomes of properties that experienced “major” or “severe” damage—defined by FEMA as $5,200 or more in storm-related damage—that were located on significantly-affected blocks—defined as a city block on which three or more properties experienced “major” or “severe” damage.

The survey found that 17 percent of hurricane-damaged properties in Louisiana and Mississippi still showed substantial repair needs as of early 2010, almost five years after the storms had hit. Almost half these properties did not meet the U.S. Census Bureau’s definition of a “habitable structure,” a housing unit that is closed to the elements with an intact roof, windows, and doors and does not show any positive evidence (e.g. a sign on the house) stating that the unit was condemned or was going to be demolished. Only 70 percent of hurricane-damaged properties in Louisiana and Mississippi were rebuilt by early 2010, and 13 percent contained cleared lots in which the damaged property had been removed from the parcel (Figure 1).

In the case of Hurricanes Katrina and Rita, the properties that still were damaged included some whose owners had received rebuilding grants through federal programs designed to aid housing recovery. The largest source of assistance following the 2005 hurricanes was the $18.9 billion special Community Development Block Grant (CDBG) appropriations passed by Congress between 2005 and 2008. Some portion of the properties with remaining damage likely also reflect abandonment by owners who moved elsewhere in the wake of the hurricanes. For such properties, funding for demolition, rehabilitation, and land banking may be necessary to transition the properties to a new use, and potentially to support efforts to encourage residents to rebuild in areas with lower flood risks.

Notes: Sample is representative of properties in Louisiana and Mississippi that experienced major or severe hurricane damage and that were located on significantly-affected blocks. Rebuilt structures are residential structures that do not show substantial repair needs as defined in Turnham (2010). Cleared lots contain an empty lot or a foundation with no standing structure. Damaged structures are residential structures that show substantial repair needs—and include all uninhabitable structures. Uninhabitable structures are residential structures that do not meet the Census definition of habitability. 

2. Rental properties were rebuilt more slowly than homeowner properties.

A second lesson from the rebuilding process following Hurricanes Katrina and Rita is that rental properties were rebuilt more slowly than owner-occupied homes. This likely was due to several factors. While homeowners directly control the rebuilding progress of their home, renters are dependent on landlords’ rebuilding decisions. Smaller “mom-and-pop” landlords may also be slower to rebuild investment properties if their own home is also damaged. And policymakers have been wary of providing rebuilding assistance to rental property owners who did not purchase sufficient insurance.

Following Hurricanes Katrina and Rita, both Louisiana and Mississippi used the CDBG special appropriations for disaster recovery to create rebuilding assistance programs for homeowners and small rental property owners. (Texas, which faced less damage from Hurricanes Katrina and Rita, created only a homeowner program.) In both Louisiana and Mississippi, the homeowner programs covered much of the difference between the estimated cost to rebuild and the amount available to the homeowner from insurance and other rebuilding-assistance programs. Conversely, the grant programs for 1-4 unit small rental properties included a more complex set of eligibility requirements that included commitments for the rebuilt units to be rented to qualifying low- and moderate-income tenants. The result was that few rental property owners applied for and received rebuilding assistance, compared to widespread take-up of the homeowner assistance programs. While concerns about the incentive effects associated with bailing out under-insured investors are reasonable, a secondary effect was to reduce the number of rebuilt properties available to renters.

Figure 2 displays the share of hurricane-damaged properties on significantly-affected blocks that received a rebuilding grant through the CDBG-funded homeowner and small rental programs, along with the share of homeowner and small rental properties that were rebuilt by early 2010. The results show that 58 percent of hurricane-damaged homeowner properties in Louisiana and Mississippi received a rebuilding grant, compared to 10 percent of small rental properties. While this rental figure is limited to 1-4 unit small rental properties, a GAO report similarly found that federal assistance through CDBG, the Individual and Households Program, and the Home Disaster Loan Program together reached only 18 percent of all damaged rental units (including units in larger multi-family buildings), compared to 62 percent of damaged homeowner units. The rebuilding outcomes documented in the HUD-commissioned survey also showed sizable gaps, with 74 percent of homeowner properties rebuilt by early 2010 compared to 60 percent of rental properties.


A final question for policymakers is whether to use this opportunity to create a permanent program to support housing recovery following natural disasters. While Congress has relied on the CDBG program for this purpose since the early 1990s, its role is currently defined by the special appropriations legislation drafted following each individual disaster. Making disaster recovery a permanent function of the CDBG program (or creating some other permanent program for housing recovery) would allow HUD to develop permanent regulations and program guidance in anticipation of future disasters. While it is too late for this change to benefit victims of Hurricane Harvey, it might improve preparedness for the next disaster.

Friday, August 25, 2017

Managing Rapid Urbanization: Lessons from Mongolia

by David Luberoff
Senior Associate
Director
The capital city of Mongolia, Ulaanbaatar, provides a useful lens for understanding tensions that arise from rapid urbanization, according to two recent journal articles reporting on research funded in part by the Joint Center's Student Research Support Program.

In the first piece, which appeared in International Planning and Development Review, Raven Anderson (formerly a research fellow in the Social Agency Lab at the Harvard Graduate School of Design) and Michael Hooper (an associate professor of urban planning at the GSD) explain that because of an economic boom, a series of harsh winters, and the decline of the rural economy, Ulaanbaatar grew dramatically over the last two decades. In response, a diverse array of domestic and international organizations converged on the city to help address the environmental, spatial, and social challenges created by the rapid growth.

Anderson and Hooper, interviewed representatives from 18 of these organizations and learned that while there is agreement about the city's main challenges there is a lack of consensus about how to tackle them. The result, they note, was a "proliferation of plans, in which local organizations' perspectives are often given relatively little attention."

In the second article, which appeared in the Journal of Urbanism, Anderson, Hooper, and Allie Aldarsaikhan Tuvshinbat (also a former researcher at the Social Agency Lab) use interviews with about 120 residents to explore some of the differing perspectives. They found that while there were relatively high levels of support for increases in unit-level density and for apartment living, the majority of interviewees also favored low land-use density.

The tension between the two findings, they note, is "a central theme in the results. The limited appeal of high-density land use likely reflects Mongolian cultural attitudes towards land and open space. These attitudes are generally not reflected in the global 'compact city' models that are promoted by international organizations and which appear to be driving the current city masterplan and other formal planning efforts in the city."

The divergence between residents' views and the organizations' desires, note Anderson and Hooper in the first article, "is likely to further reduce the city's ability to effectively cope with rapid urban growth."

Friday, August 18, 2017

Who Owns Rental Properties, and is it Changing?

by Hyojung Lee
Postdoctoral Fellow
Institutional investors own a growing share of the nation's 22.5 million rental properties and a majority of the 47.5 million units contained in those properties, according to the US Census Bureau's recently released 2015 Rental Housing Finance Survey (RHFS). The changes are notable because virtually all of the household growth since the financial crisis has occurred in rental units, with more than half of the growth occurring in single-family rental units.

According to the RHFS, individual investors were the biggest group in the rental housing market in 2015, accounting for 74.4 percent, or 16.7 million rental properties, followed by limited liability partnerships (LLPs), limited partnerships (LPs), or limited liability companies (LLCs) (14.8 percent); trustees for estates (4.1 percent); and nonprofit organizations (1.6 percent) (Table 1). However, because the share of rental properties owned by individual investors tends to decrease with the property size, individual investors owned less than half (47.8 percent) of rental units, followed by LLPs, LPs, or LLCs (33.2 percent), trustees for estates (3.3 percent), real estate corporations (3.3 percent), and nonprofit organizations (3.2 percent).


Source: Rental Housing Finance Survey, 2015.

When combined with data from the 2012 RHFS and the 2001 Residential Finance Survey (RFS), the new data also show that the number and share of rental properties owned by institutional investors increased for all types of properties between 2001 and 2015 (Figure 1). For example, while about a third of properties with 5 to 24 units were owned by non-individual investors in 2001, that share soared to 47 percent in 2012 and about two-thirds in 2015. Similarly, about 66.1 percent of properties with 25 to 49 units were owned by institutional entities in 2001, which rose to 77 percent in 2012 and about 81 percent in 2015.


Source: Residential Finance Survey, 2001; Rental Housing Finance Survey, 2012 and 2015.
Note: The condominiums and mobile homes the 2001 RFS were excluded as they are excluded in the 2012 and 2015 RHFS. Single-family units were not counted in the 2012 RHFS.

While individual investors (often called "mom-and-pop landlords") still owned about three-quarters of all single-family rental properties in 2015, the share of those properties owned by institutional investors rose from 17.3 percent in 2001 to 24.5 percent in 2015. However, during this time, many individual landlords reportedly created their own LLCs and transferred ownership of their property to protect themselves from liabilities and take advantage of tax benefits. As a result, the figures for single-family rentals may understate the number of mom-and-pop landlords.

Finally, the 2015 RHFS also provides useful information about when these changes occurred. Overall, non-individual investors accounted for about 16 percent of rental properties acquired from 1980 to 2004. That changed dramatically in the years after the financial crisis. Non-individual investors bought 28 percent of rental properties sold between 2010 and 2012 and 49.3 percent sold between 2013 and 2015 (Figure 2). Moreover, this shift was particularly pronounced for properties with 1 to 4 units (compared to those with 5 or more units).


Source: Rental Housing Finance Survey, 2015.

Despite potential implications for both renters and the broader housing market, there have been relatively few studies assessing the impacts of changing ownership patterns for rental properties. However, some research suggest that the changes are more than just paperwork. Illustratively, a 2016 discussion paper published by the Federal Reserve Bank of Atlanta reported that large corporate landlords and private equity investors of single-family rental homes in Fulton county, Georgia were far more likely to file eviction notices than small landlords in the county. Hopefully, the changes documented in the 2015 RHFS will spur additional research on how ownership patterns affect such key issues as rental affordability, housing instability, and the upkeep of rental units.

Friday, August 11, 2017

Pay for Success: Opportunities and Challenges in Housing and Economic Development

by David Luberoff
Senior Associate
Director
Pay for Success (PFS) initiatives have received widespread attention in the United States over the past several years. These outcomes-based projects – which generally do not pay service providers and government entities until and unless they achieve certain agreed upon outcomes – hold great promise in a variety of fields, including housing and community development, notes Omar Carrillo Tinajero in a new working paper jointly published by NeighborWorks® America and the Joint Center for Housing Studies. In the paper, Carrillo, a 2016 Edward M. Gramlich Fellow, notes that PFS projects may offer important opportunities to break down funding silos, devise innovative new ways to address pressing problems, and compel providers to focus on the results of an intervention. However, he adds, “because their complexity makes them at present difficult to structure and finance, PFS projects are likely to be useful only in limited circumstances, which means the PFS model should therefore be used judiciously and carefully.” Moreover, he notes, “the interest in and discussion about PFS projects has highlighted approaches that could be carried out by the public sector without the structure of PFS arrangements.”

To better understand how this approach could be used to address housing and community development issues, Carrillo examines three projects: 
  • The Denver Supportive Housing Social Impact Bond Initiative, which focused on providing supportive housing for individuals who are both frequently in jail and often go to emergency medical services in Denver.
  • The Chronic Homelessness PFS Initiative, which aims to provide 500 units of permanent supportive housing for up to 800 of the 1,600 people currently experiencing homelessness in Massachusetts.
  • Project Welcome Home, an initiative in Santa Clara County, California focused on providing housing and supportive services for 150-200 chronically homeless individuals in the Silicon Valley over six years.
In the paper, Carrillo reviews the goals of each initiative and describes the metrics that will be used to decide whether and how much providers will be paid.  He also offers detailed descriptions about how each initiative was organized, funded, and evaluated.

The initiatives, he writes, “are promising, especially as they promote an emphasis on outcomes and begin to streamline services from various government sources.” However, he also cautions that “it is not immediately obvious that their benefits outweigh their costs,” particularly the extensive time and resources needed to develop and oversee the initiatives. He adds that it may be possible for the public-sector to adopt many PFS approaches (particularly their focus on outcomes, and the need for better data systems to measure those outcomes) without developing the complex structures and systems needed to establish and oversee an effective PFS.

“Though PFS sounds promising,” he concludes, “putting a project together can entail logistical difficulties and substantial transaction costs. Because of these challenges, the PFS model should be used judiciously. In particular, it could be a promising strategy for situations in which addressing problems requires coordination of a variety of disparate sources of public funding which, for various reasons, are difficult to use in a coordinated fashion.”

However, he adds, “we should not lose sight of the overall problem that PFS programs address: the need to provide services to as many people as possible, in the most effective way possible. It seems difficult to conceive of increased funding for these much-needed resources from the federal government, and state and local governments will continue to find themselves pressed for solutions to deliver evidence-based services. The PFS movement has pushed public-sector entities to focus more heavily on outcomes and, in doing so, to consider more multi-pronged approaches for addressing key issues.”


Monday, August 7, 2017

Significant Improvements in Energy Efficiency Characteristics of the US Housing Stock

by Elizabeth
La Jeunesse

Research Analyst
Compared to 2009, single-family homes built before 1980 are now better insulated, have relatively newer heating equipment, and are more likely to have undergone an energy audit. These and other energy-related characteristics of the owner-occupied stock, shown in Table 1, are consistent with the expanding size of the home improvement industry over the past few years, with particular growth in energy-sensitive projects. Homeowners' annual spending for related projects—including roofing, siding, windows/doors, insulation and HVAC—expanded from $50 billion to nearly $70 billion over 2009-2015.



The transformation of the existing US housing stock toward greater energy efficiency also reflects a wave of energy-related incentives for HVAC and building envelope upgrades put in place following the rise of energy prices in the mid-2000s. At the federal level, one of the biggest initiatives was the Obama administration’s American Recovery and Reinvestment Act of 2009, which extended and strengthened tax credits for energy improvements to existing homes, including insulation, windows, roofs, water heaters, furnaces, boilers, heat pumps, and central air conditioners.

Despite recent progress, there is room for growth. As of 2015, 17 percent of single-family homes built prior to 1980 were still reported to have ‘poor insulation’, and only 11 percent had received an energy audit. By comparison, a recent profile of newly constructed homes (built after 2009) showed only 1 percent of residents reporting ‘poor insulation’—an impressively low share. Moreover, nearly 90 percent of new homes come with double- or triple-pane windows. Bringing older homes up to this higher standard will require significant investments to the existing stock.
At the same time, only 5 percent of new homes have smart thermostats—a relatively inexpensive but potentially high-payoff upgrade—and a similar share have energy-saving tankless water heaters. These lower shares suggest room for growth in energy-efficient technologies in new and old homes alike.
Renewable technologies, particularly solar energy, are also showing signs of growth. As of 2015, nearly 6 percent of recently built homes reported on-site solar generation, a relatively small share, but nearly triple the incidence in older homes. Thanks to the Consolidated Appropriations Act of 2016, US taxpayers can still claim a credit of up to 30 percent of expenditures for photovoltaic and solar thermal technologies placed in service in their homes. Several US states also now provide consumers with credits for net excess energy generation, further increasing the payoff for installing renewable energy systems.
With recent declines in energy prices, however, there is some question of whether homeowners still have strong incentives to pursue energy-efficiency improvements. Since 2015, the consumer price index for energy has hovered around 10 percent below its average for the prior ten years (2005-2014). If this trend continues, further progress in energy-related improvements will probably depend even more on consumer preferences and finances, in addition to changing building and product codes, and evolving industry standards. 
Data used in this analysis comes from a newly released 2015 Energy Information Administration survey that tracks the energy-related characteristics of all US residential units. Further results detailing energy consumption intensity (or usage per square foot) will be released in 2018, enabling deeper analysis into the evolution of energy efficiency in US homes.

Monday, July 31, 2017

Why is Moving to a New Home Worse for African-American and Hispanic Children than for White Children?

by Kristin Perkins
Postdoctoral Fellow
Compared to children who do not move to a new home, children who move are more likely to do worse in school, have more physical and mental health problems, and are more likely to be delinquent and use alcohol and drugs. In recent research that uses detailed data from the Project on Human Development in Chicago Neighborhoods, I find that African-American and Hispanic children showed more signs of anxiety and depression after they moved. I also find that, on average, Hispanic children demonstrated more aggressive behavior after they moved (Figure 1). White children in this sample, however, did not appear to be negatively affected by a move.
Figure 1.  



Why might moving be worse for African-American and Hispanic children than it is for white children? Perhaps non-white children are more likely to be exposed to violence or have fewer social supports in their homes and neighborhoods, which would make them more susceptible to the disruptive effects of a move? Neither of those factors, however, explained the negative effect of moving for African-American and Hispanic children (as measured by the Child Behavior Checklist, well-established scales that are frequently used as indicators of child behavior). A variety of other factors, such as being renters instead of homeowners and, for Hispanic children, their immigration history, also failed to explain the differences.

Another factor could be the differences between the types of neighborhoods that people are leaving and those they are entering. In general, most of the children in my sample whose families left their neighborhoods moved to a new neighborhood with similar characteristics. This is consistent with other research showing that it is uncommon for families to move to new neighborhoods that are radically different (in terms of poverty level and other characteristics) from the neighborhoods they are leaving. Given this, it's not surprising that among those moving to similar (or worse) neighborhoods, African-American and Hispanic children showed more signs of anxiety and depression, on average, after they moved.

I do, however, have suggestive findings that indicate that African-American children who moved to much better neighborhoods, within or beyond the city of Chicago, did not experience increases in anxiety and depression, unlike African-American children who moved to similar or worse neighborhoods. This finding is consistent with research on the Moving to Opportunity program showing better outcomes in some domains for children who moved from neighborhoods characterized by concentrated poverty to lower poverty neighborhoods.

These and similar findings from other studies of residential mobility and neighborhood effects have several possible implications for policymakers. The data suggest that the children most likely to experience negative effects of moves seem to be similar to children that Matthew Desmond's work on evictions shows are more likely to experience forced moves. If this is the case, the findings underscore the importance of efforts to prevent and reduce evictions and other forced moves.

The findings also suggest that policymakers pursuing programs that aim to improve neighborhood contexts by relocating families need to acknowledge the potential disruptive effects of residential mobility that could undermine the benefits of those moves. If further research confirm the suggestive results showing that the disruptive effects of residential mobility may differ depending on the characteristics of the destination neighborhood, then mobility programs should be designed to focus on efforts to move families to more advantaged neighborhoods.

Beyond mobility programs, policymakers might consider the extent to which other programs and policies unintentionally increase the number of moves that children make and thus increase the possibility of negative outcomes. As one example, it would be useful to determine if the Housing Choice Voucher Program's time limits for finding a unit to rent with a voucher unnecessarily result in temporary moves before a household finds a permanent unit.

Taken as a whole, such measures could potentially reduce negative outcomes among African-American and Hispanic children whose families have to move, particularly those who have to move frequently.

Monday, July 24, 2017

We're Finally Building More Small Homes, but Construction Remains at Historically Low Levels

by Alexander Hermann
Research Assistant
Census data released last month show that after years of stagnation, construction of smaller homes grew appreciably in 2016. New completions for homes under 1,800 square feet increased nearly 20 percent in 2016 to 163,000 units, the first significant growth since 2004 and the largest rise since the data series began in 1999.

This growth is significant because many first-time and lower-income homebuyers hope to purchase smaller homes, which are generally less expensive than larger ones. Moreover, historically-low levels of home construction over the last decade have led to declining inventories, decreasing vacancy rates, and increasing prices, as discussed in our latest State of the Nation's Housing report (Figure 1).


Even with the uptick in 2016, though, small-home construction remains 65 percent below the 464,000 units completed annually between 1999 and 2006, and comprises a much smaller share of newly-built housing than in the past. In 2016, small homes were 22 percent of single-family completions, well below their 37 percent market-share in 1999. In contrast, the share of large homes built grew from 17 percent in 1999 to 30 percent in 2016, while moderately-sized homes, which have consistently been the largest share of the market, have annually been 43-to-48 percent of all new single-family homes.

Construction of condos and townhouses, possible alternatives to smaller single-family housing, also remains low. Builders of multifamily properties continue to focus on the rental market where demand remains strong. Consequently, only 28,000 condos were started in 2016, a modest increase from the 26,000 starts in 2015 but much lower than the 53,000 starts averaged annually in the 1990s (Figure 2). Similarly, townhouse starts grew from 86,000 units in 2015 to 98,000 units in 2016. While this is more than double the number of starts from 2009 and comparable to the 95,000 units started annually in the 1990s, it is less than half the number started in 2005.



The low levels of new construction have resulted in historically-low housing inventories, especially entry-level housing. According to data from CoreLogic, the supply of modestly-priced homes – those selling for 75-to-100 percent of the area's median list price – was below three months at the end of 2016, about half of the six months that generally represents a balanced market (Figure 3). Indeed, according to data compiled by Zillow, only a quarter of the homes for sale at the end of last year were in the bottom one-third of area homes by price, while half were in the top one-third.



Increased demand for entry-level housing and the corresponding uptick in smaller housing construction have already contributed to the growing number of first-time homebuyers in 2016. According to the National Association of Realtors, first-time homebuyers comprised 35 percent of home sales in 2016, up from 32 percent in 2015 but still below long-term historical rates, which are close to 40 percent of all buyers. Looking forward, increases in the supply of smaller homes, townhouses, and possibly condos could help address the growing demand for lower priced homes for first-time and low-income homebuyers.

Thursday, July 20, 2017

Steady Gains in Remodeling Activity Moving into 2018

by Abbe Will
Research Associate
Healthy and stable growth in home improvement and repair spending is anticipated for the remainder of the year and into the first half of 2018, according to our latest Leading Indicator of Remodeling Activity (LIRA), released today. The LIRA projects that annual increases in remodeling expenditures will soften somewhat moving forward, but still remain at or above 6.0 percent through the second quarter of 2018.

The remodeling market continues to benefit from a stronger housing market and, in particular, solid gains in house prices, which are encouraging owners to make larger investments in their homes. Yet, weak gains in home sales activity due to tight inventories in many parts of the country is constraining opportunities for more robust remodeling growth given that significant investments often occur around the time of a sale.

Even with some easing this year, the remodeling market is still expected to grow above its long-term averageOver the coming 12 months, national spending on improvements and repairs to the owner-occupied housing stock is projected to reach fully $324 billion.


For more information about the LIRA, including how it is calculated, visit the JCHS website.

Monday, July 17, 2017

The Effect of Debt on Default and Consumption: Evidence from Housing Policy During the Great Recession

by Peter Ganong and
Pascal Noel, Meyer Fellows
What is the effect of mortgage debt reductions that reduce payments in the long-term but not in the short-term? In a new paper using data from a recent government mortgage modification program, we find that substantial mortgage principal reductions that left short-term payments unchanged had no effect on default or consumption for "underwater" borrowers who owed more on their home than their homes were worth.

This finding is significant because the design of mortgage modification programs was a key question facing policymakers attempting to help struggling households during the Great Recession. Policymakers faced a choice between debt reductions that focused on borrower liquidity by temporarily reducing mortgage payments or debt reductions that focused on borrower solvency by permanently forgiving mortgage debt.

This normative policy debate hinged on fundamental economic questions about the effect of long-term debt obligations on borrowers' default and consumption decisions. While a large academic literature has examined the effect debt reductions that mix both short and long-term payment reductions, little is known about the specific effects of long-term debt obligations.

To help fill this gap, we compared underwater borrowers who received two types of modifications in the federal government's Home Affordable Modification Program (HAMP). Both modification types resulted in identical payment reductions for the first five years. However, one group also received $70,000 in mortgage principal reduction, which translated into increased home equity and substantial long-term payment relief. By comparing borrowers in each of these modification types, we were able to isolate the effects of long-run debt levels holding fixed short-run payments. An important feature of the policy we studied was that borrowers remained underwater even after substantial debt forgiveness.

To compare these borrowers, we built two new datasets with information on borrower outcomes and HAMP participation. Our first dataset matched administrative data on HAMP participants to monthly consumer credit bureau records from Transunion. Our second dataset used de-identified data assembled by the JPMorgan Chase Institute (JPMCI) that included mortgage, credit card, and checking account information for borrowers who received HAMP modification from Chase.

Using an empirical strategy called a regression discontinuity design, we found that principal reduction has no effect on default. The analysis exploited a cutoff rule in a model used by mortgage servicers to assign borrowers between the two modification types. While borrowers just above the cutoff were 41 percentage points more likely to receive principal reduction than those just below the cutoff, default rates were smooth at the cutoff, which indicates that principal reduction had little effect on default (Figures 1 and 2). We also estimated that even at the upper bound of our confidence interval, the government spent $800,000 per avoided foreclosure. This is over an order of magnitude greater than estimates of the social cost of foreclosures.

Figure 1.  

Figure 2.  



In the second part of our empirical analysis, we examined the effect of principal reduction on consumption by comparing the monthly spending of the two groups of borrowers over time. We showed that these two groups of borrowers were similar before modification on a broad range of observable characteristics, and that their credit card and auto spending measures were trending similarly in the months before modification. This means that the payment reduction group could be used as a valid counterfactual control group for the principal reduction group.

We found that $70,000 in principal reduction had no significant impact on underwater borrowers' credit card or auto expenditure (Figure 3). Although the spending of both groups stabilized after modification (consistent with the idea that short-term payment reductions helped borrowers), the group that received the additional principal forgiveness showed no differential effect. Rather, we estimated for each $1 of principal reduction received by borrowers, their total spending increased by only 0.2 cents. This is an order of magnitude smaller than the consumption response for average homeowners examined in prior studies, which typically have found spending increases between 4 and 9 cents per $1 of wealth increase.

Figure 3.



The inability of underwater borrowers to borrow against the housing wealth gains from principal reduction may explain why they were far less sensitive to housing wealth changes than borrowers in other economic conditions. Typically, housing wealth gains expand borrowers' credit access--in fact, prior research has found that equity withdrawal through increased borrowing may account for the entire effect of housing wealth on spending between 2002 and 2006. But if homeowners need positive home equity in order to borrow against their house, then principal reduction that still leaves borrowers underwater or nearly underwater will fail to free up collateral that can be used to finance new consumption. This limitation helps explain why policies to lower current mortgage payments were more effective than principal reductions at increasing consumer spending during the Great Recession.