Real Estate Research provided analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog included the Atlanta Fed's Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
Comments are moderated and will not appear until the moderator has approved them.
Please submit appropriate comments. Inappropriate comments include content that is abusive, harassing, or threatening; obscene, vulgar, or profane; an attack of a personal nature; or overtly political.
In addition, no off-topic remarks or spam is permitted.
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.
Real Estate Research Search
- Affordable housing goals
- Credit conditions
- Expansion of mortgage credit
- Federal Housing Authority
- Financial crisis
- Foreclosure contagion
- Foreclosure laws
- Governmentsponsored enterprises
- Homebuyer tax credit
- House price indexes
- Household formations
- Housing boom
- Housing crisis
- Housing demand
- Housing prices
- Income segregation
- Individual Development Account
- Loan modifications
- Monetary policy
- Mortgage crisis
- Mortgage default
- Mortgage interest tax deduction
- Mortgage supply
- Multifamily housing
- Negative equity
- Positive demand shock
- Positive externalities
- Rental homes
- Subprime MBS
- Subprime mortgages
- Supply elasticity
- Upward mobility
- Urban growth