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Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.


May 04, 2016


Construction Spending Update

Looking at the latest construction spending report can be an informative exercise, despite the fact that the data lag other releases, because it bundles together various measures of construction activity for one comprehensive look. The latest report, released on May 2, revealed continued growth in construction spending. Private construction spending increased 8.5 percent on a year-over-year basis. The breakdown of growth by segment shown in chart 1 reveals that private residential (the sum of new single-family, multifamily, and residential improvements) and private nonresidential spending contributed almost equally to this increase (4.0 and 4.5 percent respectively).i

Chart-one

Growth in private residential and nonresidential spending from the year-earlier level has persisted since July 2011, but how does the level of spending compare to the previous cycle? The seasonally adjusted annual rate of private nonresidential spending has rebounded to a level just 1.8 percent below its previous peak. Private residential construction spending, on the other hand, remains 35.8 percent below its previous peak. With that said, after zooming out to look at spending over the entire horizon of the series and adjusting for inflation (see chart 2), it doesn't seem particularly wise to judge the health of construction spending relative to the past peak. In hindsight, the last peak was clearly an aberration, especially for residential spending.

Chart-two

Using this longer-running and inflation-adjusted time series to help put current spending in context, it's hard not to notice that the level of private nonresidential spending has surpassed the level seen in earlier peaks (the most recent peak excluded) while private residential spending now looks to be about on par with levels seen in earlier peaks. This surface-level comparison is a bit short-sighted, as this is not a mean-reverting time series. An upward trend in aggregate real construction spending seems perfectly reasonable as the population and economy grow over time.

Shifting focus to the dashed trend lines in chart 2, we see that spending on residential construction has yet to catch up with trend but is much closer than when compared with the previous peak, while spending on nonresidential construction is at a level that exceeds its trend.

Takeaways

Two high-level questions emerge after reviewing the latest construction spending data. First, does construction spending really provide a comprehensive look at construction? The construction spending data could confound the underlying trend because it reflects activity, costs, and timing of payment (for some categories). Data on activity (that is, square feet and units under construction) for all subcategories are not available, but charts 3 and 4 (below) provide some indication for the trend in residential and some categories of nonresidential construction activity.

Chart-three

Chart-four

The construction of single-family and multifamily units as well as the square footage under way for warehouse and office properties have all resumed upward trajectories. Because these measures of construction activity tell a consistent story with the spending data, they provide some reassurance that the costs aren't the primary driver of the growth in construction spending.

Second, does the recovery in real estate still have legs? This one is hard to say for certain but, taking the construction spending and construction activity data together, it seems fairly likely that there is still room for growth.

Photo of Jessica DillJessica Dill, economic policy analysis specialist in the Atlanta Fed's research department

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i Private nonresidential spending is comprised of lodging, office, commercial, health care, educational, religious, amusement and recreation, transportation, communication, power, and manufacturing structures.

May 4, 2016 in Affordable housing goals | Permalink

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April 27, 2016


Teachers Teaching Teachers: The Role of Networks in Financial Decisions

Nearly every homeowner goes through the process of refinancing a mortgage at least once, and usually several times. The process itself can be rather daunting, especially for someone experiencing it for the first time. Determining the optimal time to refinance, the best lender to refinance with, and the best mortgage product to refinance into are all fairly complicated decisions, even for a research economist like me who studies housing and mortgage markets for a living.

Fortunately, in my case, I was able to draw on the experiences of an older relative who had refinanced numerous times and was willing to provide advice and, more importantly, a referral to a fantastic mortgage broker.  The importance of social networks and peer effects in the refinancing decision is something that many housing economists have long believed in, largely based on anecdotal evidence. Now, a new study has come out that confirms this belief using a unique data set of school teachers and a novel empirical design that cleanly identifies the influence of peer effects on refinancing decisions. The paper, titled "Teachers Teaching Teachers: The Role of Networks on Financial Decisions," is written by Gonzalo Maturana (Emory) and Jordan Nickerson (Boston College). It was presented at a housing finance conference that our very own Center for Real Estate Analytics held in New Orleans back in December (a copy of the agenda and links to the presentations are available here). In addition, I recently discussed the paper at the Midwest Finance Association meetings held in Buckhead last month (a copy of my discussion slides can be found here).

One of the main innovations in the paper is the data set that the authors compile. They start with administrative data on public school teachers in Texas. These data contain detailed demographic information, employment information (the school district and school where each teacher works and the exact employment dates), and, most importantly, information on each teacher's daily class schedule.

For example, the authors know the exact time of the classes that each teacher is scheduled to teach as well as the exact timing of all teachers' break periods. The teachers' data are then matched to a public voting records database in order to obtain the exact street addresses of the teachers' places of residence. Finally, armed with the street addresses, the authors are able to merge the data with public property records. The property records come from county deed registries in Texas and contain detailed information on property transactions (addresses, names of the buyers and sellers, and property characteristics obtained by tax assessors) as well as information on every mortgage that is originated in the state (the type of mortgage—purchase or refinance, the loan amount, the interest rate type—fixed or adjustable, and the identity of the lender). Thus, the authors are left with a data set that contains detailed information on the refinancing decisions of Texas public school teachers (the timing of the refinances, characteristics of the loans, and the identities of the lenders), and detailed information on the employment history and status of the teachers including the exact campus where each teacher works, and the exact daily schedule that each teacher follows.

Armed with this unique data set, the authors implement a strategy to test whether one teacher's decision to refinance influences other teachers' refinancing decisions who are part of that teacher's same "peer group." The term "peer group" typically refers to the group of people that an individual interacts with on a frequent basis and thus, whose economic or financial decisions are most likely to influence those of the individual.  There are two major challenges that this study along with every other empirical study on social interactions and peer effects must confront with respect to peer groups. The first challenge is determining exactly what constitutes a given individual's "peer group" in a particular context, and then identifying those groups in the data. The second challenge is finding peer groups that an individual is randomly assigned to rather than groups that an individual explicitly chooses to join. This latter challenge is especially crucial, but very difficult to overcome in a non-experimental setting, as individuals typically choose which groups to associate with and the factors that determine those choices are often unobservable to the researcher and hence, can lead to severe omitted variable bias that conflates inference.

In Texas, teachers apply for jobs in a specific school district, but then are more-or-less randomly assigned to specific schools within the district. Therefore, one teacher peer group that the authors consider in the paper is the set of teachers who work in the same school. This peer group is rather large, however, so it is unclear how much interaction actually occurs between teachers in the same school. To address this issue, the authors use their detailed information on teacher schedules and identify groups of teachers in the same school that have significant overlap in their respective break schedules (at least 40 minutes of overlap in off-periods each day). The idea is that if two teachers are on break together fairly often, then it is more likely that they will directly interact with each other and discuss aspects of their lives including their financial decision making. This is a particularly compelling strategy because teachers often spend their break periods in the faculty lounge, near other teachers on break, which maximizes the potential of significant social interaction.

Using this detailed information on teacher schedules and the data on mortgage refinancing from the property records, the authors define their main variable of interest to be the number of teachers with significant overlap in break periods (at least 40 minutes per day) who have refinanced their mortgage debt within the previous three-month period. They then estimate a regression to determine whether an individual teacher's choice to refinance is influenced by the number of teachers in her peer group who had previously refinanced their mortgages. The results show that this indeed the case. Specifically, a one standard deviation increase in the percentage of a teacher's peer group who refinanced their loans with the previous three months is found to increase the likelihood that an individual teacher in the peer group refinances his or her loan by around 6.5 basis points. While 6.5 basis points does not sound like a large amount, it corresponds to almost 10 percent of the unconditional monthly hazard of refinancing in the data (which is approximately 56 basis points), so the effect is nontrivial.

In addition to testing whether increased refinancing by a teacher's peers influences that teacher's own decision to refinance, the study looks at whether there is a tendency for teachers within the same peer group to use the same lender. This is a natural extension since it would seem likely that during the course of discussing their refinancing experience with each other, teachers would share the identity of and their personal experience with the lender. We also know anecdotally that referrals are a large source of business for mortgage brokers. Sure enough, the authors find that teachers within the same peer group use the same lender to refinance at a significantly higher rate than would be the case if simple random chance were driving lender decisions. On average, teachers within the same peer group use the same lender approximately 8.2 percent of the time. Assuming a world in which there were no peer effects in refinancing behavior, and lenders were chosen randomly, teachers within the same peer group would be expected to use the same lender roughly 3 percent of the time. This difference is highly statistically significant, suggesting that teachers within the same peer group share their lender experiences and refer those lenders with whom they have had good encounters.

Broadly speaking, the results in the paper appear to confirm our belief that people tend to seek and receive advice on major financial decisions from individuals within their social network. In particular, determining the optimal time to refinance a mortgage and the best lender to perform the refinance with are complicated decisions with potentially large consequences, as mortgage debt accounts for the majority of total outstanding debt for many U.S. households.

While I find the results of the paper fairly convincing and believe the authors have implemented a very careful analysis, there is an important and open question of external validity. That is, should we generalize these results to other types of individuals besides just public schoolteachers in Texas, who, it turns out, are predominantly female and highly educated (approximately three-fourths of the sample has at least a bachelor's degree)? This is always an issue with studies that do not use a representative sample of the population, but in this case, there are huge advantages that the data set provides in facilitating the analysis of peer effects on refinancing behavior, which I think dominate the drawbacks of not having a representative sample.

Photo of Kris GerardiBy Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta.

April 27, 2016 in Affordable housing goals | Permalink

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January 06, 2015


Bringing Foreign Investment into Economically Distressed Markets: The EB-5 Immigrant Investor Program (Part II)

This is the second post in a two-part series on the EB-5 Immigrant Investor Program. EB-5 is a federal program designed to attract foreign investment to real estate projects in economically challenged markets. Part 1 provided an overview of the mechanics and impacts of EB-5. This post discusses some of the projects in the southeastern region and discusses four major issues facing the program.

EB-5 in the Southeast
The Southeast is home to more than 100 approved immigrant investor regional centers and more than 25 successfully developed and financed EB-5 projects. A recent report from the Initiative for a Competitive Inner City profiles the EB-5 program and describes the projects. Click on the map to see details of seven projects financed at least in part through the EB-5 program.

Real-estate-map Miami Fort Lauderdale Boca Raton Jupiter Natchez Clayton Anniston Cusseta Atlanta Columbus

Anniston, Alabama

Anniston Senior Housing Development involves redevelopment of a former army facility into a senior living community. Expected to open in 2015, this $30 million project includes $6 million from 12 international investors.

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Atlanta, Georgia

Homewood Suites by Hilton Hotels, opened in October 2014, is adjacent to Hartsfield International Airport. This $18 million project was financed primarily with EB-5 capital from 30 international investors.

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Boca Raton, Florida

Via Mizner Golf and Country Club is currently in the third phase of development, which is using EB-5 investment. The overall project cost is approximately $129 million, with 88 international investors providing $44 million in subordinate debt.

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Columbus, Georgia

Fairfield Inn & Suites and Courtyard by Marriott opened in 2012 and 2014, respectively. These projects combined into a $35 million development cost, with $7 million provided by international investors.

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Cusseta, Alabama

AJIN USA and its subsidiary WOOSHIN USA are auto body frame suppliers for Hyundai and Kia. The overall project cost for a manufacturing plant expansion was approximately $112 million, with EB-5 investors providing $41 million.

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Clayton, Georgia

Mountain Lakes Medical Center was renovated in 2012 using EB-5 financing. The second phase of the project will involve building a new hospital nearby and repurposing the existing hospital into a senior living facility. Overall, 31 international investors have provided $16 million.

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Fort Lauderdale, Florida

A Sonic fast food restaurant opened recently on Fort Lauderdale Beach. This project was financed with $4 million from 8 international investors.

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Jupiter, Florida

Harbourside Place river walk, entertainment plaza, marina, restaurants, and hotel. The $144 million Harbourside Place facility was built with partial EB-5 financing.

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Miami, Florida

The University of Miami Life Science and Technology Park phase I opened in 2011 and included a 250,000-square-foot life science building. The overall project cost for phase I was $107 million, with $20 million from 40 international investors.

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Natchez, Mississippi

Magnolia Bluffs Casino opened in 2012. The overall project cost was approximately $55 million, with 38 international investors providing $19 million.

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EB-5 successes are balanced by at least as many stories of failure, delay, unmet expectations, or, in the worst cases, fraud and litigation. One such project was the Green Tech Automotive project in Tunica, Mississippi, where a start-up car company had plans for a plant expansion to manufacture a new line of fuel-efficient vehicles. So far, promises of job creation and foreign investment in rural Mississippi have gone unfulfilled, and the federal government is investigating the transaction.

The experience of New Orleans with EB-5 offers another cautionary tale. In 2006, the city created a public regional center through partnership with an out-of-state real estate developer. According to legal complaints, investors have alleged that principals at the regional center committed fraud and diverted funds. The city has been entangled in litigation for several years now.

Four major issues facing EB-5
Last summer, the Initiative for Competitive Inner City (ICIC) hosted a conference on EB-5, "Impact Investing in Inner Cities: Putting Foreign Capital to Work through EB-5." Four major considerations facing the program emerged in presentations and conversations with experts attending the conference.

  • First is the limited availability of data on EB-5 projects, according to Brookings Institute and ICIC researchers, which impedes their ability to fully assess the program's impact. The U.S. Citizenship and Immigration Services annually collects data on EB-5 projects including which industries receive EB-5, the number of jobs each project creates or maintains, the total number of green cards approved, and the total number of petitions filed to remove immigration restrictions. But access to the data is limited. Many efforts are under way to centralize EB-5 data and to subject the program to more rigorous analysis.
  • Second, despite limited data being available to accurately assess the program's impact, some have observed that the communities envisioned as the targets of the EB-5 subsidy have benefited only marginally from the program. This argument takes two main forms: the first focuses on the impact of EB-5 on a particular place, and the other looks at the quality and quantity of jobs created. As to the first, EB-5 is intended as a finance tool for projects that create jobs, and especially projects that target economically distressed communities in rural areas or inner cities. In theory, EB-5 provides capital for projects where other financing options are not available. However, there is a perception that more projects than not are using EB-5 in questionably distressed places, including chain hotels in major urban markets and drive-in restaurants along major highways. Some projects—such as one building a golf club in Boca Raton—are taking place in areas that are clearly thriving. In terms of job creation, the program does not include specifications regarding the quality of jobs. Any job counts, including minimum wage-level retail, service, or construction jobs, for example. In addition, EB-5 specifies a threshold of 10 jobs per investor, which some perceive as too low, given the upside potential for foreign investors and their families.
  • Third, there seems to be limited alignment of local economic development priorities and EB-5 projects. EB-5 investors prefer public-private partnerships with local governments because, in addition to the leveraging effect, the public partner encourages greater transparency and accountability. Public-private partnerships also allow an investor to count jobs created by any public infrastructure improvements associated with private real estate financed with EB-5. For example, if a public road or sewer line must be extended in order to serve a new development, then the jobs created by public investment can count toward the EB-5 investor's job-creation requirement. So there are several incentives for EB-5 investors to support economic and community development priorities at the local level. However, according to Brookings Institute research, "regional centers and local economic development agencies lack coordination in their work, even though they share many similar goals." Such a lack of coordination may limit the deployment of subsidized capital into critical local improvement.
  • Finally, a complex network of unregulated intermediaries and brokers are driving up costs and fees and, according to some, discouraging investment. From the perspective of potential investors, the process is wrought with the potential for misdirection and fraud. For example, intermediaries and brokers typically receive a commission for every investor they attract to a project. Aggressive promotional tactics, misrepresentations, and exaggeration regarding the safety of an EB-5 investment are commonplace, according to Brookings.

As part of the Atlanta Fed community and economic development program's efforts to promote the availability of capital in economically distressed communities in the Southeast, we will be examining specific tools and policies—like EB-5, and others—and sharing what we learn in this blog.

Will Lambe is a senior adviser with the Atlanta Fed's Community and Economic Development program, focusing on community development finance.

January 6, 2015 in Affordable housing goals | Permalink

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April 21, 2010


What role (if any) did the federal government and the GSEs have in the housing boom?

The government-sponsored enterprises (GSEs) have recently come under fire for possibly contributing to the housing and foreclosure crisis that has plagued our economy over the past few years. The GSEs are particularly appealing targets because of their controversial place at the forefront of the U.S. housing finance system over the past half century or so. (See background.) One argument focuses on the government's role in mandating that Fannie and Freddie extend mortgage credit in areas where they otherwise would not have, thus "forcing" the GSEs to make loans to borrowers likelier to default because of insufficient income, poor credit histories, or both. Namely, the GSE Affordable Housing Goals that Congress stipulated in the 1992 GSE Act requires Fannie and Freddie to use specific fractions of their loan purchase activity for certain underserved segments of the population.

The complicated issue of market factors versus Affordable Housing Goals
While this argument certainly sounds plausible, actually determining whether the federal government, through its regulation of GSE activities, had a causal effect on the mortgage boom and housing bubble is an extremely difficult task. GSE decisions about the number and type of loans to purchase and the locations of these purchases are almost surely influenced by market factors such as house prices, so it's quite possible that these market factors and not government mandates were what influenced Fannie and Freddie to expand their purchases into the underserved areas. Thus, in order to shed any light on this issue, one must first come up with a strategy to disentangle the effect of GSE activity on market variables due to government mandates from the effect of those variables on GSE activity.

In a recent paper, Federal Reserve Board economist Neil Bhutta has taken a stab at solving one aspect of this difficult issue: identifying the effect of government legislation on GSE activity. Bhutta's study, like several previous studies in the literature, exploits a discontinuity in the data caused by one of the GSE Affordable Housing Goals that was stipulated by Congress in the 1992 GSE Act. However, unlike the previous studies, Bhutta uses a more robust estimation technique and superior data coverage to arrive at a very different conclusion: He finds that one of the Affordable Housing Goals—the Underserved Areas Goal (UAG)—positively affected GSE loan purchase activity without crowding out other market participants such as the Federal Housing Administration (FHA) and private subprime mortgage lenders. In our opinion, Bhutta's analysis is very careful, and has provided an important first step toward answering the question of how much the GSEs contributed to the mortgage boom that preceded the crisis.

According to the UAG, Fannie and Freddie must purchase a certain fraction of their loans in low-income or minority census tracts. To be specific, a loan qualifies for the UAG if it is originated in a tract with median family income less than or equal to 90 percent of median family income in the metropolitan statistical area (MSA), or in a tract with minority population share of at least 30 percent and median family income less than or equal to 120 percent of the MSA median. Figure 1 in the Bhutta paper shows the UAG began at just over 20 percent in the mid-1990s and increased over time to almost 40 percent in 2006. Bhutta, and other authors who have studied this issue, hypothesize that to the extent that the UAG is binding, it will increase GSE loan purchases in the qualifying census tracts. This, in turn, may increase the supply of mortgage credit to low-income and minority households, depending on whether or not increased GSE purchases "crowd out" other lenders such as FHA and private, subprime originators.

Bhutta’s paper draws different conclusion
The previous literature on this topic found little evidence of much of an effect. An et al. (2007) and An and Bostic (2008) previously studied the link between the UAG and housing market outcomes using a two-stage econometric approach and data from the 1990s. Their first stage results showed only weak evidence of a causal link between the UAG and GSE purchase activity. In fact, An et al. (2007) found lower GSE market shares in census tracts that qualified for the UAG. Gabriel and Rosenthal (2008) also studied the link between the UAG and GSE purchase activity using data through 2000, and found no statistically significant effect. So then what exactly distinguishes Bhutta's paper from these previous studies, and why does he come to a different conclusion?

First, and most importantly, Bhutta uses a different and, we would argue, superior econometric approach. He employs a regression discontinuity strategy that solves a serious misspecification issue in the studies mentioned above. Those studies essentially compared GSE purchase activity in qualifying census tracts to non-qualifying tracts, and controlled for a host of potentially important census tract variables like demographic trends and housing market characteristics.

However, the studies did not control for the fact that GSE purchase activity is correlated with the variable that the UAG is based on—the ratio of census tract median income to MSA median income, which we will refer to as the "assignment variable." Since the correlation is positive (higher median income, on average, makes an area more attractive to a potential lender), not controlling for it will lead to a negative bias in the estimated effect of the UAG on purchase activity. For example, holding all else constant, the GSEs are more likely to purchase a higher volume of loans in a census tract with an assignment ratio of 110 percent compared to a qualifying tract with a ratio of, say, 90 percent.

Bhutta controls for this effect in a couple of different ways. In one specification, he focuses on tracts that have assignment ratios within 5 percentage points of the qualifying ratio, and explicitly controls for the correlation between the assignment ratio and GSE purchase volume assuming a linear correlation structure. In a second specification, he uses census tracts with assignment ratios within 2 percentage points of the qualifying ratio, and does not control for the correlation (note that most of the prior studies used a window of 10 percentage points without controlling for the correlation).

The second difference is the data coverage used by Bhutta compared to the previous studies. Unlike those studies, Bhutta uses data through the mid-2000s, which is important because much of the increase in the UAG came after the year 2000. Thus, it is possible that the UAG was not binding before 2000, which would also explain the different conclusions.

When all is said and done, Bhutta finds a statistically significant, positive effect of the UAG on GSE purchases, but the magnitude of the effect is quite modest, at around 4 percent. That is, all else being equal, the GSEs purchase approximately 4 percent more loans in census tracts with assignment ratios just below the qualifying ratio versus tracts just above the ratio. In addition, he finds no evidence of crowding out, which suggests that the UAG may exert a causal impact on access to mortgage credit (as long as it is not a simple compositional effect whereby the GSEs are simply offsetting their increased purchases in qualifying tracts by lowering their purchases in non-qualifying census tracts).

Findings, though accurate, may not paint complete picture
While we believe the Bhutta paper is a very careful and rigorous analysis, there is some reason to suspect that its findings are a lower bound for the impact of the UAG. Bhutta uses Home Mortgage Disclosure Act (HMDA) data for his analysis, which is likely to under measure the number of loans purchased by the GSEs. HMDA identifies only mortgages purchased in the same calendar year of origination, so it is possible, if not likely, that a non-trivial fraction of GSE purchases take place with a significant lag from the date of origination. Bhutta attempts to address this issue by looking at the sample of loans eligible to be purchased by the GSEs separately, rather than considering only the loans that are actually purchased. However, the difficulty of determining which loans are eligible and which are not likely creates a significant amount of measurement error in this exercise.

A second, possibly more serious source of mismeasurement comes from the fact that the HMDA data don't contain information on the loans backing mortgage-backed securities that the GSEs purchased for their retained portfolios. These were not a trivial fraction of their total purchase activity and, if accounted for, could significantly add to the estimated effect of the UAG on GSE secondary market activity. Unfortunately, one would need to use loan-level GSE data, which is virtually impossible to obtain, at least up to now.

By Kris Gerardi, research economist and assistant policy adviser at the Atlanta Fed (with Boston Fed economists Christopher Foote and Paul Willen)

April 21, 2010 in Affordable housing goals, GSE, Mortgage crisis | Permalink

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