220 Week 2 A /For WIZARD KIM
2014 V42 2: pp. 472–496
DOI: 10.1111/1540-6229.12030
REAL ESTATE ECONOMICS
The Influence of Fannie and Freddie on Mortgage Loan Terms Alex Kaufman*
This article uses a novel instrumental variables approach to quantify the effect that government-sponsored enterprise (GSE) purchase eligibility had on equi- librium mortgage loan terms in the period from 2003 to 2007. The technique is designed to eliminate sources of bias that may have affected previous studies. GSE eligibility appears to have lowered interest rates by about ten basis points, encouraged fixed-rate loans over ARMs and discouraged low documentation and brokered loans. There is no measurable effect on loan performance or on the prevalence of certain types of “exotic” mortgages. The overall picture suggests that GSE purchases had only a modest impact on loan terms during this period.
In 2011, over 75% of all mortgages that were originated in the United States— over $1 trillion worth—passed through the hands of the Federal National Mort- gage Association (Fannie Mae) and the Federal Home Loan Mortgage Cor- poration (Freddie Mac) (Inside Mortgage Finance 2012). These institutions, known as the Government-Sponsored Enterprises (GSEs), have traditionally been private corporations with a public charter, operating with the implicit backing of the U.S. government.1 Their mission, as defined in their charters, is to promote stability, liquidity and affordability in the U.S. mortgage market. The GSEs are meant to accomplish these goals by purchasing mortgage loans on the secondary market, which they then package into securities or hold in portfolio. In September 2008, the GSEs’ implicit government backing became explicit when in the throes of the financial crisis and facing possible bankruptcy, both Fannie and Freddie were placed in conservatorship by their regulator, the Federal Housing Finance Agency (FHFA). The cost to taxpayers of their bailout has been estimated at $317 billion so far (Congressional Budget Office 2011).
*Board of Governors of the Federal Reserve System, Washington, D.C. 20551 or alex.kaufman@frb.gov.
1Technically the term Government-Sponsored Enterprise also applies to the 12 Federal Home Loan Banks, which are much smaller than Fannie Mae and Freddie Mac. For simplicity in this article, the term “GSE” is used to refer only to Fannie and Freddie.
C© 2013 American Real Estate and Urban Economics Association
The Influence of Fannie and Freddie 473
Given the GSEs’ vast scale, the liability they represent to taxpayers and the decisions that must soon be made about their future, it is crucial to understand how exactly they affect the mortgage markets in which they operate. Unfortu- nately, modeling GSE activity and estimating its effect is a challenge. Fannie and Freddie are for-profit enterprises bound by a government-mandated mis- sion that is likely at odds with their profit motive (Jaffee and Quigley 2011). As such, it is unclear what they maximize. Furthermore, they are large relative to the market. How they affect consumer outcomes, each other and the rest of the market depends upon details of market structure. For instance, Passmore, Sparks and Ingpen (2002) show that whether or not lower capital costs (due to the implicit government subsidy) are ultimately passed on to borrowers in the form of lower mortgage rates depends crucially on the degree of competition or collusion between Fannie and Freddie, which is theoretically ambiguous.2
The GSEs’ huge market share may also affect their behavior in other ways. Bubb and Kaufman (2009), for instance, explore how the GSEs’ size may allow them to incentivize mortgage originators using a toolbox of strategies that is unavailable to private-label securitizers.
In addition to these theoretical challenges, empirical estimation of the GSEs’ impact on outcomes such as interest rates, default rates and contract structures faces at least three important obstacles: externalities, selection bias and sorting bias.
Externalities can arise because GSE purchase activity may affect the equilib- rium characteristics of all loans that are eligible for GSE purchase, including loans that are not purchased by the GSEs ex post. Just as the presence of an orthodox Jewish community in the United States has encouraged most large food manufacturers to produce foods according to kosher dietary standards, the presence of Fannie and Freddie may change prevailing loan standards. If one were to try to estimate the effect of orthodox Jews on food standards by comparing the food that they purchase with food purchased by other people, one would incorrectly conclude that they have little effect because non-Jews also tend to buy kosher food. To the contrary, it is likely that without orthodox Jews, no one would buy kosher food because manufacturers would not bother to follow kosher standards.
2In the Passmore, Sparks and Ingpen (2002) model, it is even possible that the estab- lishment of the GSEs can raise equilibrium interest rates. For this to happen, it must be the case that the GSEs behave collusively and that the liquidity of mortgage-backed securities issued by private-label institutions is lowered because the market share of the GSEs cuts into private securitizers’ economies of scale.
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Analogously, it is not enough simply to compare the characteristics of GSE- bought loans and non-GSE-bought loans.3 GSE purchase eligibility may affect the characteristics of both groups of loans. Instead, the ideal experiment is to compare loans in two similar markets: one in which the GSEs can make purchases and one in which they cannot.4 The difference in mean characteristics between loans in one market and loans in the other will be an estimate of the effect of GSE purchase eligibility on these outcomes.
Second, estimates of the effect of GSE eligibility may suffer from selection bias. Due to the GSEs’ government mandate, the loans Fannie and Freddie can buy are not a random subset of all loans. GSE-eligible mortgage loans, on average, differ along several dimensions, including loan size and borrower creditworthiness, from loans purchased by private-label securitizers or left in the portfolio of originating lenders. Such selection must be separated from the true treatment effect of GSE eligibility.
Third, to the extent that GSE purchase eligibility may lead to loan terms that are more (or less) favorable to borrowers, potential borrowers may adjust their loan attributes in order to qualify for (or avoid) loan categories that the GSEs are likely to buy. Such customer sorting is another potential source of bias. If borrowers that sort into GSE-eligible loans are different from other borrowers, and if those differences influence the features of the loans they receive—for instance, due to preferences or risk-based pricing—then customer sorting will lead to biased estimates of GSE treatment effects.
To illustrate this point with a fanciful example, imagine that GSE purchase eli- gibility lowers interest rates by 20 basis points, and GSEs follow a government- mandated rule that they will only buy loans made to people who live in red houses. Suppose further that potential borrowers who know this rule and are savvy enough to paint their homes red are also, on average, better credit risks (in a way that is apparent to a loan underwriter but not to an econometrician with limited data) and so would naturally receive loans that are cheaper by 15 basis points, regardless of house color. If we were to estimate the effect of GSE eligibility on interest rates using the idiosyncrasies of the house color rule, we would incorrectly find that it is 35 basis points because we would have conflated the true treatment effect with the sorting effect.
3Data sources such as FHFA (www.fhfa.gov/Default.aspx?Page=313), Inside Mortgage Finance (2012) and Lender Processing Services all suggest that between a fifth and a quarter of all securitized conforming loans during this period were bought by private- label securitizers. 4Estimates of the conforming/jumbo spread can be thought of as approximations to this ideal experiment. What matters is whether a loan is conforming and thus eligible for purchase, not whether it was, in fact, purchased.
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This article estimates the equilibrium treatment effect of GSE purchase eligi- bility on interest rates, loan delinquency rates and mortgage contract features using an instrumental variables regression discontinuity design meant to ad- dress externalities, selection bias and sorting bias. The strategy takes advantage of the interaction of two features of the mortgage market: the conforming size limit and the ubiquity of 20% down payments.
By law, the GSEs are only allowed to buy loans smaller than the conforming loan limit, an upper bound that varies from year to year. In 2006 and 2007, for instance, the limit was $417,000 in the continental United States. Loans that exceed the conforming size limit are referred to as jumbo. This purchase rule is fairly rigorously observed: in 2007, for instance, the GSEs bought 88% of all loans in the $5,000 window just below the conforming size limit, but only 3% of loans in a similar window just above the limit.5
Researchers can potentially overcome two of the three previously mentioned sources of bias—externalities and selection—by exploiting the discontinuity in GSE intervention across the conforming size limit. By comparing loans made in a segment of the market where GSEs dominate (the conforming market) with otherwise similar loans made in a segment of the market where GSEs do not operate (the jumbo market), one can obtain estimates that incorporate the externalities of GSE purchases on the rest of the market. Also, because the GSE purchase eligibility is discontinuous while other relevant loan features (absent any sorting effects) vary smoothly with loan size, loans just above the thresh- old form a natural comparison group for loans just below (see, for example, DiNardo and Lee 2004). A regression discontinuity design can therefore be used to overcome bias due to loan selection.
However, a comparison of loans just above and below the conforming loan limit may still be biased due to customer sorting. Indeed, histograms such as Figure 1 suggest that customers bunch just below the conforming loan limit, choosing a larger down payment to avoid getting a jumbo loan. If borrowers that do this are unobservably different from borrowers that do not, estimates of the GSE treatment effect that use this discontinuity will be contaminated by sorting. Indeed, if sorting on unobservables is similar to sorting on observables (Altonji, Elder and Taber 2005), then the evidence is stark: the average credit score of borrowers in the sample who are just below the conforming cutoff is nearly 45 points higher than it is for those just above the cutoff. It thus appears that more-creditworthy borrowers are better able to take advantage of conforming loans.
5This and other statistics cited in text, unless otherwise noted, estimated using data from Lender Processing Services (LPSs).
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Figure 1 � Histogram of loan origination amounts for 2006–2007 continental U.S. subsample.
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Continental US 2006−2007 Histogram of Origination Amount
Note: The vertical line is the $417,000 conforming size limit.
To address simultaneously all three sources of bias, this article uses a slightly different approach. Rather than directly compare loans above and below the conforming loan limit, I instrument for whether a loan is conforming using a discontinuous function of home appraisal value. As will be explained in detail in the Estimation Strategy section of this article, certain features of the loan origination process ensure that at particular home appraisal values, the chance that a borrower gets a conforming loan jumps significantly. In particular, above some appraisal values, it is impossible to get a conforming loan without putting more than 20% down, inducing a jump in the number of jumbo loans at those values. Evidence suggests that these key appraisal values are not salient to either lenders or borrowers, and there is little evidence of manipulation of appraisals around these values.
This article thus compares prices and attributes of loans made to borrowers whose homes happen to be appraised just below one of these values with those of borrowers whose homes happen to be appraised just above. I argue that the resulting differences are most plausibly attributed to the different rates at which these borrowers get conforming rather than jumbo loans. Because GSE purchase eligibility is the essential difference between the conforming and
The Influence of Fannie and Freddie 477
jumbo markets, this quasi-random assignment to the conforming loan market allows for a clean estimate of the equilibrium impact of GSE purchase eligibility on loan attributes.
Using this method, I find only modest impacts of GSE activity. For a sample of loans originated between 2003 and 2007, I estimate that GSE purchase eligibility lowered interest rates in the conforming market by 8–12 basis points, which is slightly smaller than previous estimates of the conforming/jumbo spread. I find no significant effect on loan default or foreclosure rates. GSE activity appears to have promoted fixed-rate mortgages over adjustable-rate mortgages: I estimate an increase of 5.3 percentage points on a base of 61.9% fixed-rate loans. It also appears to have discouraged low documentation loans and loans bought through a broker. I find no effect on debt-to-income ratios, nor on the prevalence of contract features such as prepayment penalties, negative amortization, interest-only loans and balloon loans.
This article joins a growing literature that attempts to measure the impact of GSE intervention on residential mortgage markets. Previous work has largely focused on determining the effect of GSE intervention on contract interest rates. McKenzie (2002) performs a meta-analysis of eight studies that attempt to quantify the size of the conforming/jumbo rate spread and concludes that the spread has averaged 19 basis points over the years 1996–2000.6 Studies in this literature generally run regressions in which a “jumbo” dummy is the coefficient of interest, and they control for observables that covary with jumbo status. Though extremely useful, such studies are potentially vulnerable to selection bias and sorting bias. Later studies, such as Passmore, Sherlund and Burgess (2005) and Sherlund (2008), yield similar estimates in the 13–24 basis point range while attempting to address sources of bias better.7
Another important strand of the literature has attempted to determine the effect of GSE intervention on the supply of mortgage credit. Ambrose and Thibodeau (2004) use a structural model to argue that subsequent to the establishment in 1992 of a set of “Affordable Housing Goals” for the GSEs, the total supply of credit increased slightly more in metropolitan areas with higher proportions of underserved borrowers. Bostic and Gabriel (2006) investigate the same set
6Studies include Hendershott and Shilling (1989), ICF Incorporated (1990), Cotterman and Pierce (1996), Ambrose, Buttimer and Thibodeau (2001), Naranjo and Toevs (2002), U.S. Congressional Budget Office (2001), Passmore, Sparks and Ingpen (2002) and Pearce (2002). 7Sherlund (2008), for instance, uses geographic location to control for unobserved borrower characteristics.
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of housing goals but use the regulation’s definition of what constitutes a “low- income neighborhood” to compare areas that the GSEs were supposed to target with areas where they had no particular mandate, finding no effect of GSE targeting on outcomes such as homeownership rates and vacancy rates.
This article contributes to this literature in two ways. First, its estimation strategy is designed to eliminate biases that may have affected previous studies. Second, it expands the set of outcomes examined to include contractual forms and features, as well as measures of loan performance.
Since the original version of this article appeared, Adelino, Schoar and Sev- erino (2011) and Fuster and Vickery (2012) have used similar methodologies instrumenting for conforming status using appraisal limits in order to study re- lated research questions. Adelino, Schoar and Severino (2011) exploit changes in the conforming limit over time in order to study the effect of GSE loan purchases on house prices, while Fuster and Vickery (2012) use the post-2007 credit freeze in order to estimate the effect of GSE purchases on the supply of fixed-rate mortgages during times of financial distress.
The next section presents a brief history of the GSEs and provides background on conforming loan limits. The Estimation Strategy section describes the es- timation strategy in greater detail, while the Data and Specifications section discusses the dataset and the econometric specifications used. The Results section presents results, and the last section concludes.
Background
History of the GSEs
The Federal National Mortgage Association (Fannie Mae) was established in 1938 as a federal agency fully controlled by the U.S. government (Fannie Mae 2010). Its mission was to provide liquidity in the mortgage market by purchasing loans insured by the Federal Housing Administration (FHA). In 1948 that mandate was expanded to include loans insured by the Veterans Administration, and by the early 1950s Fannie Mae had grown to such a point that pressure mounted to take it private. In 1954, a compromise was reached whereby Fannie privatized but was still controlled by the government through Treasury ownership of preferred stock. Fannie was also granted special privileges, such as exemption from local taxes, which it maintains to this day.
The Housing and Urban Development Act of 1968 took the privatization of Fannie Mae a step farther, splitting it by spinning off its functions buying FHA- and VA-insured loans into the wholly government-controlled Ginnie Mae, while
The Influence of Fannie and Freddie 479
preserving the rest of its business in the now supposedly fully private Fannie Mae.8 However, Fannie Mae continued to enjoy implicit government backing for its debt.
In 1970, the government chartered the Federal Home Loan Mortgage Corpora- tion (Freddie Mac) as a private company. Its mission—buying and securitizing mortgages to promote liquidity and stability—was similar to Fannie Mae’s mis- sion, though initially Freddie Mac was only meant to buy mortgages originated by savings and loan associations. With time this distinction eroded. Like Fannie Mae, Freddie Mac was perceived by most as having the implicit backing of the government.
In the wake of the savings and loan crisis, Congress in 1992 passed the Federal Housing Enterprises Financial Safety and Soundness Act, which established the Office of Federal Housing Enterprise Oversight (OFHEO) as the new regulator for the GSEs. The act also expanded the GSEs’ mandate to improve access and affordability for low-income borrowers by creating the affordable housing goals studied in Ambrose and Thibodeau (2004) and Bostic and Gabriel (2006). The rules require the GSEs to buy a certain proportion of their loans from households defined as mid or low income and from neighborhoods defined as low income.
The GSEs’ market share ballooned throughout the 1990s and early 2000s. During this time, both institutions expanded their loan purchases and securi- ties issuance, and they also began holding more MBS and mortgage loans in portfolio, which they financed by issuing debt.9 Spurred by competition from private-label securitizers, in the mid-2000s, the GSEs began expanding their operations into the subprime and Alt-A mortgage markets, which they had tra- ditionally avoided. With the collapse of the housing bubble in mid-2007, the GSEs’ subprime MBS holdings put them at risk of insolvency. The Housing and Economic Recovery Act (HERA) of 2008 replaced the regulator OFHEO with FHFA and granted it the power to place the GSEs in conservatorship, which FHFA did in late 2008, finally making explicit the government’s long- standing implicit backing of GSE debt. Since then the GSEs have been held in conservatorship, and their future remains uncertain.
8An often-cited reason for this division is that a 1968 change in public accounting rules made it so that additions to Fannie Mae’s balance sheet would be treated as public expenditures. Privatizing Fannie Mae made federal debt appear smaller. 9Lehnert, Passmore and Sherlund (2008) investigate whether the expansion of the GSEs’ portfolios were a major force affecting the mortgage rate and conclude it was not.
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Conforming Loan Limits
By law, the GSEs are only allowed to purchase loans smaller than the conform- ing loan limit (Federal Housing Finance Agency 2010). The conforming loan limit varies by both year and location. Prior to 2008, the size limit increased at most once a year and was constant across all locations within the continental United States and Puerto Rico.10
In 2008, the passage of HERA retroactively changed the conforming size limits of loans originated after July 1, 2007, allowing the GSEs to guarantee more loans. Because the act passed in 2008, it is unlikely that the retroactive changing of the conforming limit in some areas affected loans terms at the time of origination.11 Our only variables measured after origination, default and foreclosure are likely functions of house price appreciation, loan terms and borrower credit risk, and as such they would not be expected to be affected directly by retroactive eligibility for GSE purchase. After HERA, it is no longer the case that all continental U.S. locations are treated equally—the Act designated a set of “high-cost” counties with higher conforming loan limits.
Estimation Strategy
The estimation strategy in this article employs a discontinuous function of home appraisal value as an instrument for conforming loan status. Appraisal value is related to conforming status for obvious reasons: more expensive houses are more likely to require mortgage loans larger than the conforming limit. However, the relationship between appraisal value and conforming loan status is not smooth. It is discontinuous because loan-to-value (LTV) ratios of exactly 80 (equivalent to a down payment of 20%) are extremely modal in the U.S. mortgage market. An LTV of 80 is common in part because borrowers are typically required to purchase private mortgage insurance (PMI) for loans above 80 LTV. In addition, 80 is considered “normal” and may function as a default choice for many people who would otherwise choose a different down payment. Figure 2 provides a histogram of the LTV ratios of first-lien mortgage loans, illustrating the importance of 80 LTV.
10Hawaii, Alaska, Guam and the U.S. Virgin Islands were considered “high-cost areas” and had a conforming limit 50% higher than the rest of the country. 11If the law’s passage were anticipated, there could be an influence. However, even if passage were anticipated, the exact formulas determining which counties were affected may not have been anticipated. If such anticipation did occur, it would tend to bias the results of this article toward zero. The data over this period show bunching of loans at the limits that were in force at the time of origination but not at the retroactively imposed limits, suggesting that the law changes were not anticipated.
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Figure 2 � Histogram of LTV ratios for the 2006–2007 continental U.S. subsample. 0
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Continental U.S. 2006−2007 Histogram of Loan−To−Value Ratios
To see why the widespread use of 80 LTV induces a discontinuity in the relationship between appraisal value and conforming status, note that the LTV ratio equals the origination amount divided by the appraisal value. In order to have an LTV of 80 while staying under the conforming limit, a home cannot be appraised at more than the conforming limit divided by 0.8. For a conforming limit of $417,000, for instance, this appraisal limit, as I will refer to it, would be $417,000/0.8 = $521,250. Borrowers with homes appraised above $521,250 must choose whether to put 20% or less down and get a jumbo loan or put greater than 20% down and get a conforming loan; conforming loans with 20% down payments are impossible for such borrowers. Because of the stickiness of 80 LTV, borrowers whose homes are appraised above this appraisal limit are discontinuously more likely to get a jumbo loan. Figure 3 illustrates the first- stage relationship between appraisal value and jumbo status for the 2006–2007 subsample.
Effectively, the empirical strategy compares the loan terms of borrowers whose homes were appraised just below the limit with those whose homes are ap- praised just above. The only difference between these two groups is that those in the former group have a discontinuously higher likelihood of ending up with
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Figure 3 � Proportion of loans smaller than the conforming limit, by home appraisal amount, for 2006–2007 continental U.S. subsample.
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Continental U.S. 2006−2007 Percent in Conforming Market by Appraisal Amount
Note: The vertical line is the $521,250 “appraisal size limit” equal to the conforming limit divided by 0.8.
a conforming loan.12 The resulting difference in loan terms is then scaled by the size of the difference in the likelihood of getting a conforming loan in order to yield the appropriate two-stage least squares IV estimate of the causal impact on loan terms of being in the conforming market.
So long as borrowers do not sort themselves by finely manipulating values around the appraisal limit, this method will be unbiased. How easy is it to manipulate appraisal values? Dennis and Pinkowish (2004) provide an overview of the home appraisal process. Independent appraisals are needed because a mortgage lender cannot rely on selling price as a measure of the collateral value of the home. Typically, the lender or mortgage broker contracts a third party to provide an appraisal (Hutto and Lederman 2003). Borrowers are not allowed to contract appraisers themselves for fear they will shop around for
12The likelihood of getting a conforming loan does not change from 0 to 1; instead, it increases by about 8.8 percentage points. Such a situation is typically referred to as a “fuzzy” regression discontinuity.
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an appraiser willing to inflate the appraisal and thus lower the borrower’s LTV. The appraiser estimates the probable market value of the home by taking into account the neighborhood, the condition of the home, improvements to the home and recent sale prices of comparable homes in the area. Appraisals usually cost $300–$500, and the fee is paid by the borrower when the loan application is filed.
When applying to refinance, the appraisal value is the sole determinant of the denominator of LTV. For home purchase loans, however, the denominator of LTV is the minimum of the appraisal value and the purchase price.13 Borrowers purchasing a home might therefore ignore the formal appraisal and attempt to manipulate the purchase price instead. If such manipulation happened on a large enough scale, it would create customer sorting and potentially bias the results. However, such manipulation can be observed: it would create a lump of borrowers with “appraisals” just below the appraisal limit. As will be shown in the Data and Specifications section, there appears to be no bunching around the appraisal limit, suggesting that such manipulation did not occur on an appreciable scale.
Borrowers aside, appraisal manipulation by the lender remains a concern. Anec- dotal evidence suggests lenders sometimes leaned on appraisers to inflate values to make loans more attractive for resale on the secondary market.14 Appraisers unwilling to inflate values may have seen a loss of business as a result. Such manipulation may indeed have occurred, but it is only relevant for this article if it occurred across the particular appraisal limit used in the regression dis- continuity. If the efforts of lenders to encourage appraisal inflation were less targeted, targeted at another goal or occurred in small enough numbers, such manipulation would not pose a threat to the empirical strategy. The lack of bunching around the appraisal limit (again shown in the Data and Specifica- tions section of this article) suggests that lenders’ manipulation of appraisals around this particular limit was not a widespread phenomenon.
Another potential cause of concern about the estimation strategy is the avail- ability of outside financing that is not observable in the dataset. During the 2003–2007 period, it became tolerated practice to fund down payments with second-lien mortgages. These so-called “silent seconds” were often 15-LTV (or even 20-LTV) second-lien mortgages on an 80-LTV first-lien mortgage. Be- cause the data do not allow for the linkage of first- and second-lien mortgages
13In the case of home purchase loans, the “appraisal value” variable provided by Lender Processing Services is equal to this minimum. 14See, for instance, “In Appraisal Shift, Lenders Gain Power and Critics,” New York Times, 18 August 2009.
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made on a given property, it is likely that a significant portion of the 80-LTV loans seen in the data were, in fact, supplemented by a second-lien mortgage at the time of origination.
However, the invisibility of these seconds does not present a problem for the estimation strategy. Such seconds are the means by which some borrowers managed to stay within the conforming size limit. Borrowers who use seconds may be different, both observably and unobservably, from borrowers who do not. However, the IV strategy used here does not compare borrowers with and without seconds—instead, it compares the entire group of borrowers below the appraisal limit with the entire group above. Both below and above there are some borrowers who, if appraised above the appraisal limit, would take second loans in order to stay within the conforming limit on their first loans. These borrowers would get conforming loans no matter what their appraisal values are, so they are effectively netted out. There are also some borrowers who would not use seconds, perhaps because such seconds were unavailable or were already maxed out, or the borrowers were unaware or uninterested in them. These borrowers would get a conforming loan if appraised below the limit but a jumbo loan if appraised above. In the terminology of instrumental variables, these are the “compliers”—the ones whose behavior is affected by the instrument. The methodology used here provides an unbiased estimate of the local average treatment effect of being in the conforming market on the compliers.
Though appraisal manipulation and silent seconds are unlikely to present prob- lems for the estimation strategy, at least four limitations of the strategy should be mentioned. First, this method is not appropriate for studying the GSEs’ effect on loan terms during the financial crisis itself. From late 2007 onward, there was a collapse in the jumbo loan market. Though this itself suggests that the GSEs may have played an important role ensuring access to credit during the crisis, the tiny number of jumbo loans in the 2008–2011 period eliminates the control group necessary for the estimation strategy. In effect, there is no longer a first-stage relationship between appraisal value and jumbo status because there are, to a first approximation, no longer jumbo loans. This article therefore focuses on the period 2003–2007 and estimates the effects of GSE activity during noncrisis times.
Second, all estimates apply to borrowers taking loans near the conforming loan limit. Despite the fact that the sample period of 2003–2007 saw an unprece- dented extension of large mortgage loans to poorer borrowers, it is still the case that most borrowers taking loans close to the conforming limit were relatively affluent. Therefore, this estimation strategy is not able to address the question
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of what effect GSE purchase eligibility may have had on the loan terms of less affluent borrowers.
Third, this strategy is ill-suited to estimating the GSEs’ effect on access to mortgage credit. The continuity that we see in the loan density function across the appraisal limit suggests that there is little GSE effect on credit availability, at least for more affluent borrowers in the noncrisis 2003–2007 period. How- ever, developing a formal test of this proposition would necessitate adapting a density discontinuity estimation approach such as McCrary (2008) for use in an instrumental variables framework. Such an exercise might be of little use in any event, as GSE credit access effects might be expected most strongly for less affluent borrowers or during crises.
Lastly, these estimates cannot be interpreted as more general estimates of the effects of loan securitization. Though the proportion of conforming loans displays a discontinuity around the appraisal limit, the securitization rate itself does not display a discontinuity (though it does change slope). The results should instead be interpreted as the effects on price, contract structure and default of being in a segment of the market eligible for purchase by the GSEs.
Data and Specifications
Data
The data used in this article come from Lender Processing Services Applied Analytics, Inc. (LPS).15 These are loan-level data collected through the coop- eration of mortgage servicers, including the ten largest servicers in the United States.16 The data cover over half of all outstanding mortgages in the United States and contain more than 32 million active loans. Key variables include origination amount, home appraisal amount, loan terms, securitization status and monthly payment performance.
The analysis sample contains first-lien, non-FHA, non-VA insured mortgage loans backed by owner-occupied, single-family homes and originated between the years 2003 and 2007. To be included in the sample, both the origination amount and the appraisal value must be $1,000,000 or less. Table 1 provides
15These data are often referred to by the name McDash. Lender Processing Services acquired McDash Analytics in November 2008. 16Mortgage servicers fulfill a role similar to building superintendents: they collect payments from borrowers and pursue accounts that are delinquent. A mortgage loan’s servicing rights are often sold separately from rights to that loan’s stream of payments. All the mortgages in the LPS dataset were either originated by one of its participating servicers or have had their servicing rights sold to one of these servicers.
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Table 1 � Summary statistics.
Full Sample Near Appraisal Limit
Mean S.D. Obs. Mean S.D. Obs.
Origination Amount ($) 212,322 129,932 14,941,284 303,385 88,241 162,235 Appraisal Value ($) 308,559 191,472 14,941,284 458,768 50,650 162,235 Jumbo 0.095 0.294 14,941,284 0.089 0.285 162,235 FICO Score 711.6 61.9 12,733,244 722.3 56.0 139,257 LTV Ratio 72.0 16.9 14,815,612 65.9 16.9 161,282 Interest Rate (%) 6.25 1.38 14,284,352 6.01 1.35 153,771 Adjustable Rate Mortgage 0.279 0.448 14,812,239 0.354 0.478 160,722 ARM Teaser Rate (%) 5.34 2.26 4,116,418 4.88 2.15 55,110 Prepayment Penalty 0.133 0.34 14,593,905 0.152 0.359 159,565 Interest-Only Allowed 0.126 0.332 14,941,284 0.175 0.38 162,235 Negative Amortization 0.05 0.219 14,941,284 0.066 0.248 162,235
Allowed Balloon 0.009 0.092 14,941,283 0.009 0.096 162,235 Brokered 0.31 0.462 9,866,479 0.327 0.485 106,208 Low or No Documentation 0.32 0.466 8,117,111 0.379 0.485 87,858 Debt-to-Income Ratio 34.9 13.4 10,033,173 35.2 12.7 112,091 61+ Day Default 0.107 0.309 14,941,284 0.098 0.297 162,235 Foreclosure 0.071 0.258 14,941,284 0.065 0.246 162,235
Notes: Sample of first-lien, non-FHA insured, non-VA insured loans made to borrowers with owner-occupied single-family residences between the years 2003 and 2007. The sample contains only loans with both origination amount and appraisal value $1,000,000 or less. Near Appraisal Limit contains the subset of loans that fall in the $5,000 band on either side of their own appraisal limit. Interest Rate defined as contract interest rate for fixed-rate mortgage loans, and as post-teaser margin plus index for adjustable rate mortgage loans. Index value taken at time of origination. 61+ Day Default and Foreclosure equal to 1 if loan ever attains that status within a 36-month window following origination.
summary statistics for this sample of approximately 14.9 million mortgage loans. The numbers for the full sample are broadly consistent with statistics found in studies using other data sources.17 The rightmost columns provide averages for loans that fall within a $5,000 band on either side of their appraisal limit. This provides a base rate against which the size of the regression estimates can be judged.18
17Direct comparisons with other studies are difficult because of variation in sample selection. Mayer, Pence and Sherlund (2009), for instance, cover the same time period but focus more on the Alt-A and subprime markets than the present study does. 18Because this base rate is calculated using loans near the appraisal limit, the vast majority of which are conforming, this rate should be interpreted as the rate that exists with GSE intervention, while this rate minus the regression point estimate yields the rate that would exist in the absence of the GSEs.
The Influence of Fannie and Freddie 487
Figure 1 presents a histogram of loan frequency by origination amount for the continental United States in the years 2006 and 2007.19 Visual inspection con- firms that there is an atom of borrowers positioned just below the conforming size limit of $417,000. The figure also displays evidence of rounding. Dollar amounts ending in even $5,000, $10,000 and $50,000 increments are more common than other amounts. The presence of rounding makes formal anal- ysis of the discontinuity (as in McCrary 2008) unreliable. However, because $417,000 falls between tick marks (where we would expect to find a smooth density despite rounding,) and because the density there is larger than in any other bin, the atom is very likely not an artifact of rounding. It appears that some borrowers are bunching just below the limit in order to avoid jumbo loans.
Bunching below the limit can only create bias if borrowers below the limit are different from borrowers above the limit. LPS data contain limited information about borrower characteristics, but they do contain one important measure: credit (FICO) score. Taking our 2006–2007 continental U.S. sample, the aver- age FICO score of borrowers in the $5,000 bin just below the conforming limit of $417,000 is 740.9, while the average FICO of borrowers in the $5,000 bin just above is only 696.5. This swing of nearly 45 FICO points represents a very sizable drop-off in credit quality. Though we can explicitly control for observ- ables such as FICO score, this sorting on observables suggests that there may be sorting on unobservables as well. This motivates the use of an instrumental variables specification based on appraisal value.
Figure 4 presents a histogram of loan frequency by appraisal value for the same sample. Again, there is evidence of rounding, this time making it difficult to determine visually whether there is an atom. Figure 5 provides a close-up of the area around the $521,250 cutoff, which confirms that there is no evidence of abnormal bunching. The average FICO score of borrowers in the $5,000 bin just below the cutoff is 719.6, while the average FICO score of borrowers in the bin just above is 719.3. It thus appears that appraisal value is not meaningfully compromised by borrower sorting, and that it is a valid running variable for our regression discontinuity analysis.
19Because the conforming loan limit varies by year and location, histograms using the full sample are not easily interpretable—they overlay several different conforming limits. However, the 2006–2007 continental U.S. subsample has a single conforming limit ($417,000) and so is easily visually interpreted. For the sake of interpretability, all figures in this article use the 2006–2007 continental U.S. subsample, while all regression estimates use the full 2003–2007 sample. Estimates using the 2006–2007 continental U.S. subsample and estimates using the full sample are nearly identical.
488 Kaufman
Figure 4 � Histogram of home appraisal amounts for 2006–2007 continental U.S. subsample.
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Continental U.S. 2006−2007 Histogram of Appraisal Amount
Note: The vertical line is the $521,250 “appraisal size limit” equal to the conforming limit divided by 0.8.
Specification
The instrumental variables regression discontinuity specification used in this article fits a flexible polynomial on either side of the appraisal cutoff and measures the size of the discontinuity using a dummy variable taking value 1 for observations below the cutoff. The first-stage specification is
X i = α0 + α1 Zi + f (APPi) + g(APPi) ∗ Zi + α2Si + υi,
where X i is an indicator for whether the loan origination amount is under the conforming limit, f (·) and g(·) are seventh-order polynomial functions of appraisal amount, Zi is an indicator for whether the appraisal amount is under the appraisal limit and Si is a vector of control variables including refinance status, dummies for FICO score in five-point bins and over 600,000 dummies for every ZIP code/month of origination combination in the dataset, allowing us to control for local market conditions extremely flexibly.20 Although the
20These variables were chosen because they are all pretreatment variables with respect to home appraisal. Other variables, such as loan-to-value ratio or whether the loan is
The Influence of Fannie and Freddie 489
Figure 5 � Detail of histogram of home appraisal amounts for 2006–2007 continental U.S. subsample.
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Appraisal Amount ($)
Continental U.S. 2006−2007 Histogram of Appraisal Amount: Detail
Note: The vertical line is the $521,250 “appraisal size limit” equal to the conforming limit divided by 0.8.
appraisal limit varies by year and location, all data are pooled by recentering the data such that, for each year and location, the relevant appraisal limit is equal to zero. This allows the full 2003–2007 sample to be run in a single regression. Table 2 provides a summary of the applicable conforming limits and appraisal limits for all years and locations in the sample.
The second-stage specification is
Yi = β0 + β1 �
X + h(APPi) + k(APPi) ∗ Zi + β2Si + ∈i,
where Yi is an outcome, such as the interest rate, and �
X i is the predicted value from the first stage. The effect on outcome Yi of getting a loan in the conforming
fixed or adjustable rate, are omitted because they are determined posttreatment. How- ever, including these variables does not meaningfully change the results. Additionally, dummies are included for whether the appraisal value is an exact multiple of $5,000 or $1,000 in order to account for any potential reporting effects related to the rounding seen in the data.
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Table 2 � Conforming loan limits and appraisal limits.
Standard Areas High-Cost Areas
Conforming Limit Appraisal Limit Conforming Limit Appraisal Limit
2003 $322,700 $403,375 $484,050 $605,063 2004 $333,700 $417,125 $500,550 $625,688 2005 $359,650 $449,563 $539,475 $674,344 2006 $417,000 $521,250 $625,500 $781,875 2007 $417,000 $521,250 $625,500 $781,875
Notes: High-Cost Areas are defined during the sample period as Alaska, Hawaii, Guam and the U.S. Virgin Islands. The standard limit applies to the continental United States and Puerto Rico. During the sample period, the high-cost limit is always 50% larger than the standard limit. Appraisal Limit is defined as the applicable conforming limit divided by 0.8.
market as opposed to the jumbo market is estimated by the coefficient β1. The estimate can be thought of as a local average treatment effect of GSE activity on those borrowers who would not respond to slightly higher appraisals by increasing their down payments above 20% in order to stay in the conforming market.
Many of the outcome variables (Yi ) used in this study are binary, suggesting a probit or logit specification. However, the size of the dataset (nearly 15 million observations) coupled with the number of independent variables (over 600,000) makes such an estimation impractical. For this reason, a linear probability model is used instead.
Results
As a first step, Figure 3 confirms that there is power in the first stage by presenting a scatterplot of percent conforming against appraisal value for the continental United States in 2006 and 2007. Visual inspection shows a clear discontinuity at the appraisal limit of $521,250. Virtually all borrowers with homes appraised at $521,000 end up with conforming loans, whereas borrowers with homes appraised at $521,500 are discontinuously more likely to get jumbo loans. Table 3 shows the results of the first-stage regression using the full sample. There is a discontinuity of 8.8 percentage points, significant at the 1% level, in whether or not the borrower gets a conforming loan.
Tables 4 and 5 present the regression results. Each coefficient in the tables represents a separate instrumental variables regression, each using appraisal value as the running variable and including the complete set of control variables.
The Influence of Fannie and Freddie 491
Table 3 � First stage.
Below Conforming Limit
Above Appraisal Limit (α) −0.088*** t-stat (−111.6) Base Rate 0.969 N 14,941,284
Notes: First-stage regression of conforming status on a dummy indicating whether a loan is above the appraisal limit. Controls include a seventh-order polynomial on either side of the appraisal limit, dummy variables for every combination of ZIP code and origination month, as well as refinance status and FICO score in five-point bins. Base Rate is the sample average in the $5,000 band below the appraisal limit. Standard errors in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Table 4 � Price and performance.
Interest Rate ARM Teaser Rate (Basis Points) (Basis Points) 61+ Day Default Foreclosure
β −10.07*** 4.59*** 0.008 0.007 t-stat (−9.7) (5.6) (0.9) (0.8) Base Rate 600.94 487.89 0.098 0.065 N 14,284,352 4,116,418 14,941,284 14,941,284
Notes: Each cell is an instrumental variables regression of the dependent variable on conforming status, instrumenting for conforming status with appraisal value. Controls include a seventh-order polynomial on either side of the appraisal limit, dummy variables for every combination of ZIP code and origination month, as well as refinance status and FICO score in five-point bins. Interest Rate defined as contract interest rate for fixed-rate mortgage loans, and as post-teaser margin plus index for adjustable-rate mortgage loans. Index value taken at time of origination. 61+ Day Default and Foreclosure equal to 1 if loan ever attains that status within a 36-month window following origination. Base Rate is the sample average in the $5,000 band on either side of the appraisal limit. Standard errors in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Table 4 shows that the estimate of the jumbo/conforming spread is ten ba- sis points. This is about half the size of many estimates in the literature. There are at least two possible explanations for this difference. First, if previ- ous estimates suffered from borrower sorting (specifically, more-creditworthy borrowers choosing conforming loans over jumbo loans), this would tend to bias those estimates upwards. An estimate that eliminated this source of bias would be smaller. Most previous estimates, such as those in McKenzie (2002), made no attempt to control for sorting bias.
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Table 5 � Contract features.
Adjustable Pre-Payment Interest Negative Rate Penalty Only Amortization
β −0.053*** −0.014 0.003 0.008 t-stat (−6.0) (−1.6) (−0.3) (0.9) Base Rate 0.354 0.152 0.175 0.066 N 14,812,239 14,593,905 14,941,284 14,941,284
Balloon Brokered Low Documentation DTI Ratio
β 0.003 −0.049*** −0.078** 2.633 t-stat (−0.4) (−4.1) (−2.2) (1.5) Base Rate 0.009 0.327 0.379 35.196 N 14,941,283 9,866,479 8,117,111 10,033,173
Notes: Each cell is an instrumental variables regression of the dependent variable on conforming status, instrumenting for conforming status with appraisal value. Controls include a seventh-order polynomial on either side of the appraisal limit, dummy variables for every combination of ZIP code and origination month, as well as refinance status and FICO score in five-point bins. Low Documentation includes no documentation loans. Base Rate is the sample average in the $5,000 band on either side of the appraisal limit. Standard errors in parentheses. Sample sizes vary due to missing data for some dependent variables. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.
However, the disparity between this estimate and previous estimates may also be due to other factors, such as differences in sample period. Passmore, Sherlund and Burgess (2005) show that the jumbo/conforming spread tends to widen when mortgage markets are tight but narrow when they are more liquid. Their analysis of the 1997–2003 period finds an average jumbo/conforming spread of 16 basis points. It is plausible that the smaller estimate reflects the looser mortgage market of the 2003–2007 period. Other estimates that find larger spreads, such as those in McKenzie (2002), also used earlier sample pe- riods.21 Hence, it is unclear to what extent the ten basis point estimate reflects the different methodology used in this article and to what extent it reflects the different time period. Ideally, one could differentiate between these ex- planations by using the current methodology on earlier data; unfortunately, coverage in the LPS dataset is scant prior to 2003, making a direct comparison impossible.
21The papers reviewed in McKenzie (2002) use data spanning the years from 1986 to 2000. It should be noted, however, that not all estimates in this period are so high. Naranjo and Toevs (2002), for instance, find a conforming/jumbo spread of eight basis points using data from 1994.
The Influence of Fannie and Freddie 493
While conforming status appears to push basic interest rates down, the estimate of its effect on introductory ARM teaser rates is positive 4.6 basis points. Why might teaser rates move in the opposite direction from other rates? One possibility is that lower teaser rates are associated with contracts that are more expensive in other ways. Bubb and Kaufman (2011) show that in a sample of credit card contracts, for-profit investor-owned credit card issuers were more likely to offer low teaser rates but high interest rates and penalties later on, while cards issued by credit unions have higher teaser rates but lower charges otherwise. Seen in that light, higher teaser rates and lower base rates may be a natural pairing.
Loans eligible for GSE purchase appear to enter default and foreclosure at the same rate as other loans—neither estimate is significant. A negative effect of GSE intervention on default would have been slightly more in line with prior work. Both Elul (2009) and Krainer and Laderman (2009) compare the delinquency outcomes of GSE-securitized loans and privately securitized loans, attempting to control for relevant risk characteristics, and they conclude that GSE-securitized loans generally perform better. However, these studies look at realized securitization status, not purchase eligibility, and they do not attempt to account for sorting bias.
Note that the interest rate effect, in the absence of any significant loan perfor- mance effect, suggests that the price difference is not simply due to less risky borrowers receiving a discount. It suggests instead that the price difference is a true effect of GSEs passing on the implicit government subsidy to borrowers.
Table 5 examines the effect of GSE eligibility on a number of mortgage contract features. There appears to be no effect on the prevalence of a number of “exotic” contract features: prepayment penalties, interest-only loans, loans allowing negative amortization and loans with balloon payments all have point estimates indistinguishable from zero. However, there is a GSE effect on at least three aspects of the contract. The conforming market appears to favor fixed-rate mortgages over adjustable-rate mortgages: the prevalence of adjustable-rate mortgages is estimated to drop by 5.3 percentage points. This result is consistent with the notion that GSEs encourage fixed-rate loans, though the magnitude of the estimated effect is small.22
The results further show that GSE activity lowers the prevalence of bro- kered loans by 4.9 percentage points and of low documentation loans by 7.8
22Using a similar methodology but slightly different data filters, time period and speci- fications, Fuster and Vickery (2012) find an insignificant effect of conforming status on the proportion of fixed-rate mortgages. As in this article, the authors conclude that GSE purchase eligibility had at most small effects on loan terms in the precrisis years.
494 Kaufman
percentage points. Both low documentation and the use of brokers have been associated with poor loan performance during the crisis. However, to the extent such a relationship is causal, it appears that the drops in low documentation and brokerage induced by GSE activity are not enough to have had an effect on default or foreclosure.
Conclusion
This article contributes to the literature on GSE intervention in the mortgage market in two ways. First, it employs a novel econometric strategy designed to produce estimates free of selection bias, sorting bias and externalities. Second, it expands the set of outcomes examined by including contract features and measures of loan performance. For borrowers with loans near the conforming limit, during the 2003–2007 period, GSE activity lowered interest rates by 8– 12 basis points, while modestly decreasing the prevalence of adjustable-rate mortgages, low documentation loans and loans originated through a broker. There was no measurable effect on loan performance or other loan terms.
As the postconservatorship future of Fannie and Freddie is debated, this set of modest effects should be weighed against the cost of government support of the GSEs, as well as the potential to achieve such outcomes through other means. The results of this article imply that, in noncrisis times at least, restructuring or even elimination of the GSEs might have little impact on the prevailing loan terms available to potential borrowers. However, this article supplies only a portion of the analysis necessary to judge the overall advisability of such re- forms. The GSEs may have important effects on credit availability, particularly during crisis times. They also may have larger effects on the loan terms that are offered to less affluent borrowers. More research on these questions is needed to complement the analysis offered here and paint a more complete picture of the likely effects of GSE reform.
I thank Ryan Bubb, Hess Chung, Josh Gallin, Claudia Goldin, Adam Guren, Karthik Kalyanaraman, Larry Katz, David Laibson, Shane Sherlund and Paul Willen for valuable discussions and comments. I am grateful to the Federal Reserve Bank of Boston for hosting me as I conducted a portion of this research. The analysis and conclusions are my own and do not indicate concurrence by other members of the Federal Reserve research staff or the Board of Governors.
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