Did Affordable Housing Legislation Contribute to the Subprime ...

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Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?

Authors Working Paper Number Revision Date Citable Link

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Andra C. Ghent, Rub?n Hern?ndez-Murillo, and Michael T. Owyang

2012-005D

December 2014

Ghent, A.C., Hernandez-Murillo, R., Owyang, M.T., 2014; Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?, Federal Reserve Bank of St. Louis Working Paper 2012-005. URL

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Real Estate Economics

Federal Reserve Bank of St. Louis, Research Division, P.O. Box 442, St. Louis, MO 63166

The views expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Federal Reserve System, the Board of Governors, or the regional Federal Reserve Banks. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment.

Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?

Andra C. Ghent, Rub?en Hern?andez-Murillo, and Michael T. Owyang

Keywords: Mortgages, Loan Performance, Community Reinvestment Act, GSEs.

Abstract We use a regression discontinuity approach and present new institutional evidence to investigate whether affordable housing policies influenced the market for securitized subprime mortgages. We use merged loan-level data on non-prime mortgages with individual- and neighborhoodlevel data for California and Florida. We find no evidence that lenders increased subprime originations or altered loan pricing around the discrete eligibility cutoffs for the GovernmentSponsored Enterprises' (GSEs) affordable housing goals or the Community Reinvestment Act. Although we find evidence that the GSEs bought significant quantities of subprime securities, our results indicate that these purchases were not directly related to affordable housing mandates.

This draft: December 9, 2014.

Ghent: W.P. Carey School of Business, Arizona State University; phone 480-965-4689; email aghent@asu.edu. Herna?ndez-Murillo: Research Division, Federal Reserve Bank of St. Louis; phone 314-444-8588; email: ruben.hernandez@stls.. Owyang: Research Division, Federal Reserve Bank of St. Louis; phone 314-444-8558; email owyang@stls.. The views expressed herein are those of the authors and do not reflect the official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Introduction

It is widely understood that the volume of subprime mortgages grew dramatically in the years immediately preceding the financial crisis of 2007-2008 and that a large fraction of these mortgages was transformed into private-label mortgage-backed securities (PLMBS).1 The volume of loan originations in the PLMBS market grew by about 750% between 2001 and 2005. This boom, and its subsequent implosion, had major consequences for the financial sector and the macroeconomy. Understanding the causes of this boom and bust may shed light on what drives asset booms and assist in preventing future financial crises.

Some observers have argued that affordable housing policy was a causal factor in the subprime crisis. For instance, writing in the Financial Times, Raghuram Rajan (2010) writes "[t]he tsunami of money directed by a U.S. Congress, worried about growing income inequality, towards expanding low income housing, joined with the flood of foreign capital inflows to remove any discipline on home loans." When asked about the cause of the financial crisis, Eugene Fama states that "the global crisis was first a problem of political pressure to encourage the financing of subprime mortgages" (Fama and Litterman, 2012). Greenspan (2010) also asserts that affordable housing policies played a key role in the subprime crisis.

While commentators are often not specific about precisely which affordable housing policies caused the crisis, the two main affordable housing policies for owner-occupied housing in the US are the Community Reinvestment Act (CRA) and the affordable housing goals for Fannie Mae and Freddie Mac. The CRA requires depository institutions to monitor and report the amount of mortgage lending they do in low income neighborhoods and to low income individuals. CRA examiners consider how much CRA-qualified lending the institution to be examined has done over the examination period but there are not firm targets for the percentage of lending that must be CRA-qualifying to receive particular evaluations. In contrast, the Government-Sponsored Enterprises (GSEs) have specific numerical targets for the share of their lending in low income neighborhoods, to neighborhoods with high shares of minorities, and to low income individuals. The Department of Housing and Urban Development (HUD) has responsibility to set the specific numerical targets for the GSEs in consultation with the GSEs and key congressional members.

1PLMBS are mortgage-backed securities (MBS) that are issued or guaranteed by an entity other than Fannie Mae, Freddie Mac, or Ginnie Mae.

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Loan may count towards more than one goal. However, depository institutions and the GSEs must comply with the requirements to lend to both low income individuals and in low income neighborhoods.

Importantly, the GSEs can satisfy their affordable housing goals by purchasing packages of securitized mortgages that they do not purchase as whole loans. They receive fractional credit towards their affordable housing goals for loans they acquire exposure to through purchases of MBS. Manchester (2008) shows that the GSEs generally purchased "goal rich" PLMBS during the subprime boom. In addition to their originations, depository institutions may count PLMBS toward their CRA obligations provided the MBS are structured as CRA-qualified securities.

While there was no substantive change in the CRA during the 2000s, there were changes in the GSEs' numerical targets for their affordable housing goals. Table 1 shows the evolution of the GSEs' affordable housing goals since 1996. There is a fairly substantial increase between 2000 and 2001 with the three subgoals increasing by six to eight percentage points. However, there is no change in the goals between 2001 and 2004. Between 2004 and 2005 there is a two percentage point increase in the Special Affordable Goal (SAG) and the Low-and-Moderate-Income Goal (LMIG) and a six percentage point increase in the Underserved Areas Goal (UAG). The UAG is a geographic goal as it regards loans made to borrowers living within particular Census tracts while the SAG and LMIG goals focus on loans made to borrowers with particular characteristics.

Table 1: The GSEs' Affordable Housing Goals over Time

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Goal

21% 24% 24% 24% 24% 31% 31% 31% 31% 37% 38% 38% 39%

UAG

FNMA FHLMC Actual Actual

28%

25%

29%

26%

27%

26%

27%

28%

31%

29%

33%

32%

33%

31%

34%

32%

42%

34%

44%

41%

44%

44%

40%

43%

42%

39%

Goal

12% 14% 14% 14% 14% 20% 20% 20% 20% 22% 23% 25% 27%

SAG

FNMA FHLMC Actual Actual

15%

14%

17%

15%

14%

16%

18%

17%

19%

21%

22%

23%

21%

20%

21%

21%

24%

23%

26%

24%

28%

26%

27%

26%

26%

23%

Goal

40% 42% 42% 42% 42% 50% 50% 50% 50% 52% 53% 55% 56%

LMIG

FNMA FHLMC Actual Actual

46%

41%

46%

43%

44%

43%

46%

46%

50%

50%

52%

53%

52%

51%

52%

51%

53%

52%

55%

54%

57%

56%

56%

56%

54%

52%

Notes: 1) Source, FHFA (2010). 2) UAG refers to the underserved areas goal, SAG refers to the Special Affordable Goal, and LMIG refers to Low-and-Moderate-Income Goal. 3) See text of paper for goal eligibility criteria.

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The existing literature finds little effect of affordable housing mandates on mortgage markets has not convinced the proponents of the view that affordable housing mandates caused the crisis. Early examples of the literature finding no or negligible effects include Ambrose and Thibodeau (2004) and Bostic and Gabriel (2006); see also the literature cited therein. Bhutta (2012), Bolotnyy (2014), and Moulton (2014) use a regression discontinuity approach and study loans that the GSEs could purchase as whole loans. Bhutta (2012) and Bolotnyy (2014) focus only on the UAG goal while Moulton (2014) studies all three GSE goals. While all three papers focus on the prime, rather than the subprime market, they all find small to no effects of the goals on loan supply. Reid and Laderman (2011) focus on the originator of the loan, since the CRA only applies to depository institutions, and show that the majority of subprime loans were originated by non-depository institutions. Using a regression discontinuity approach similar to ours, Bhutta (2011) shows that, over the 1994 2006 period, the CRA geographic goal had an economically and statistically significant effect on lending in large cities in the late 1990s and early 2000s but that the effect had disappeared by the mid-2000s. Agarwal, Benmelech, Bergman, and Seru (2012) look at whether the volume of loan originations changes around the CRA evaluation date and find significantly more originations in the three quarters before the evaluation date (not the evaluation period) and three quarters after the evaluation date. In contrast to Bhutta's (2011) findings, Agarwal, Benmelech, Bergman, and Seru (2012) find that these effects of the CRA on loan volume were largest during the subprime boom. See Reid et al. (2013) for a discussion of Agarwal, Benmelech, Bergman, and Seru (2012).

While some of the analysis in our paper uses a methodology similar to some of the previous literature, our formal empirical analysis addresses two important issues that previous literature does not address. Most importantly, all mortgages in our 2004-2006 sample were packaged into subprime PLMBS.2 To ensure that we look at only these mortgages, we match HMDA data to loan-level data from PLMBS deals. No previous paper has done this. As a consequence, previous work has been unable to assess whether affordable housing policies contributed to the subprime securities boom. Indeed, previous literature has only been able to use proxies for subprime such as whether HMDA defines the loan as high cost (e.g., Reid and Laderman, 2011; Moulton, 2014)

2Our data also contains loans that were packaged into "alt-A" deals. These deals were not marketed to investors as subprime deals. We use the term subprime for consistency with the common usage of the term subprime after the financial crisis and because, ex post, loans in "alt-A" deals performed more similarly to loans packaged into subprime securities.

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or was originated by an institution that was at one point in time designated by the Department of Housing and Urban Development (HUD) (Bhutta, 2012). In our matched data, we find that only about half of subprime mortgages were high cost loans. HUD discontinued publication of its list of designated subprime originators out of accuracy concerns suggesting that it may not be a strong proxy for a loan packaged into subprime securities. Second, our matched dataset reveals that the majority of mortgages in subprime PLMBS did not go to low income borrowers. Rather, we find that average stated borrower income for these loans is over $100,000 which is more than twice as high as the income of the census tract. Hence, any income falsification, as was done with low or no documentation mortgages, was more likely to be upwards rather than downwards.

Our regression discontinuity approach identifies the effect of the act by looking at origination volumes, interest rates, and default rates near the goal thresholds. Because the goal thresholds are discrete, if the goals are binding we should expect to see, for example, lower interest rates on loans to borrowers with incomes just below the income thresholds for each of the borrower-specific goals than on loan loans to borrowers with incomes just above the income thresholds. We use this approach because it does not rely on the loan originator being the final holder of the loan. We find no evidence that affordable housing legislation affected the subprime market during the subprime crisis. Lending volumes, loan pricing, and default rates do not change in response to the goals. The point estimates are close to zero and the standard errors are small.

As the regression discontinuity approach identifies only local average treatment effects, we also adduce new relevant institutional details. In particular, we evaluate 100 randomly chosen prospectus supplements to assess how, if at all, they discuss how the loans satisfy affordable housing goals or whether the securities were CRA-qualified. We also use our sample of prospectus supplements to examine the extent to which MBS were tailored to satisfy GSE demand.

The institutional evidence indicates that it is highly unlikely that institutions were satisfying their affordable housing goals in the PLMBS market. In a random sample of 100 prospectus supplements for nonprime PLMBS that we examine, not a single prospectus ever mentions the GSEs' affordable housing goals or the CRA despite discussing at length numerous other characteristics of the loans in the pools. Put differently, none of the pools we examine were CRA-qualified. This finding is particularly strong evidence that the CRA did not affect the market since depository institutions can only get credit for purchases of PLMBS that are specifically structured as CRA-

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qualified. Consistent with the findings of Reid and Laderman (2011), we find that the majority of loans securitized in PLMBS were originated by non-depository institutions that were not subject to the CRA indicating the presence of substantial incentives to originate such loans by institutions that were not subject to the CRA.

However, the prospectus supplements reveal that the GSEs were major purchasers of PLMBS. Our review of the sample of prospectus supplements provides us with a rough estimate, 25%, of the GSEs' share of the market for the senior tranches of PLMBS deals. As such, it remains plausible that the GSEs encouraged subprime lending by purchasing large quantities of PLMBS. However, our results indicate that any role the GSEs played in the subprime crisis was not due to their affordable housing mandates.

Empirical Methodology

To assess whether affordable housing laws led to the subprime housing boom, we must first examine the mechanisms through which the change in laws could affect lending behavior. We investigate whether these laws led to a change in lender behavior to meet the programs' objectives. For example, changes in lending behavior could manifest as a relaxation in lending standards or a change in mortgage pricing. In this section, we outline each program's objectives. We then describe three channels through which lenders could respond to these regulations, thereby inducing a boom in subprime lending and securitization. We then test whether lender behavior did indeed change for these variables just below the programs' cutoffs.

The Affordable Housing Goals

The affordable housing goals for the CRA and the GSEs are actually seven separate goals. Two of the goals are CRA performance evaluation benchmarks and five are the GSEs' affordable housing targets. Some of the goals apply to borrowers living within a particular Census tract and some of the goals are specific to individual borrowers regardless of where they live. The loans that satisfy each of the goals are as follows:

1. CRA1: Loans to borrowers living in Census tracts with median tract to metropolitan statistical area (MSA) income of 80% or less.

2. CRA2: Loans to borrowers with incomes of 80% or less of the median MSA income.

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3. UAG1: Loans to borrowers living in Census tracts with a minority population of 30% or more and median tract to MSA income of 120% or less.

4. UAG2: Loans to borrowers living in Census tracts with median tract to MSA income of 90% or less.

5. SAG1: Loans to borrowers with incomes of 60% or less of the median MSA income.

6. SAG2: Loans to borrowers with incomes of 80% or less of the median MSA income and who live in Census tracts with median tract to MSA income of 80% or less.

7. LMIG: Loans to borrowers with incomes of 100% or less of the median MSA income.

Financial institutions subject to these regulations must meet both the borrower-specific and tract-specific goals. That is, the GSEs are given specific targets for each of the three goal areas (UAG, SAG, and LMIG) and depository institutions must satisfy both CRA1 and CRA2. As such, institutions cannot satisfy their goals solely by making loans to high income households that live in low-income neighborhoods. As Table 1 shows, the borrower-specific GSE goals were much closer to being binding in the mid-2000s than the geographic GSE goal.

None of our goal thresholds coincide with the major affordable rental program in the United States, the low income housing tax credit (LIHTC). See Baum-Snow and Marion (2009) for a discussion of the LIHTC. The CRA1 limit coincides with the moderate income definition for the community development block grant (CDBG) program of HUD. The CDBG program provides funds for a diverse set of community development projects such as public infrastructure, rehabilitating dilapidated homes, parks, homeless facilities, programs for battered spouses, employment training, and other services for low income communities. The funding amounts are not discretely determined by a goal threshold but, rather, are allocated "using a formula comprised of several measures of community need, including the extent of poverty, population, housing overcrowding, age of housing, and population growth lag in relationship to other metropolitan areas" (HUD, 2012). Furthermore, although the amount of funding each state and city receives depends on the portion of its population that is moderate income, the organizational unit that receives the funds is not a census tract but rather a state, county, or municipality. The program is also not related to funding for home ownership. It is thus highly unlikely that it affects our identification strategy below.

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