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GSE Activity and Mortgage Supply in Lower-Income and Minority Neighborhoods: The Effect of the Affordable Housing Goals

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Abstract

I estimate the credit supply effect of the Underserved Areas Goal (UAG), which establishes GSE purchase goals for mortgages to lower-income and minority neighborhoods. Taking advantage of discontinuous census tract eligibility rules and abrupt changes in tract eligibility, I find some evidence of a small UAG effect on GSE purchases and mortgage originations, without crowding-out of FHA and subprime lending. The results also suggest that the GSEs exploit the law’s lack of precision-targeting, yielding effects that might diverge from the law’s intent.

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Notes

  1. The GSEs do not lend directly to consumers; they purchase “conforming” mortgages—loans that conform to relatively conservative credit characteristics—from lenders. The GSEs then pool these mortgages and sell mortgage-backed-securities (MBS) to investors, passing along the interest and principal payments from borrowers to investors less a credit guarantee fee. Investors perceive that the U.S. government backs this guarantee, giving the GSEs a competitive advantage. The GSEs also invest in their own MBS and other private-label MBS by issuing bonds at nearly a risk-free rate (Bernanke 2007).

  2. The penalty for failing to meet the goals might include bad publicity and eventual loss of Congressional support. Under certain circumstances HUD could issue cease and desist orders and assess civil money penalties.

  3. There are two other single-family goals, one that targets lower-income borrowers and another that targets very-low-income borrower in lower-income neighborhoods (see Manchester 2002 for more information).

  4. The sharp rise from 2004 to 2005 in the goal level and in purchases was due at least in part to the reclassification of tracts based on the 2000 Census (Manchester 2007)

  5. Of course, in light of the 2008 financial crisis and downfall of the GSEs, if the goals induced the GSEs to make unsound credit decisions the goals may be associated with a large net cost to society even though some targeted groups benefited.

  6. Bostic and Gabriel (2006) is similar to An et al. (2007), but focuses just on California. An and Bostic (2006) is similar to An and Bostic (2008), but looks at crowd-out of subprime rather than FHA lending. Ambrose and Thibodeau (2004) use MSA-level variation in population share residing in UAG-targeted tracts to identify the UAG’s effect between 1995 and 1998, and find a small impact.

  7. The authors suggest a finding of no effect may be because increased GSE purchasing crowds-out non-GSE purchases, as they discuss in another paper (2008b).

  8. Gabriel and Rosenthal’s (2008a) estimates are also negative, though not statistically significant. They use a five percentage point window, but, to my knowledge, do not control for the assignment variable.

  9. The exception is tracts that are part of the few newly formed MSAs between 1994 and 2002.

  10. 1990 tract definitions apply to HMDA data from 1992 to 2002, and 2000 tract definitions apply since 2003.

  11. HUD publishes a yearly “subprime lender list”, whose loans are considered subprime (www.huduser.org)

  12. Tract characteristics come from Census tract-level data distributed by Geolytics.

  13. Using triangular kernel weights that give most weight to data near the cutoff raises the point estimates in columns 5 and 6 slightly to 0.038 and 0.037, respectively.

  14. The point estimates in column 8 rises slightly with the inclusion a linear control function.

  15. The UAG could potentially affect the availability of mortgages for non-owner-occupied properties. I ran identical regressions for the (log) number of non-owner mortgages at the tract-level. The IV point estimate implies a 2.3% discontinuity, but it is not statistically significant. Non-owner mortgages are far fewer in number than those for owner-occupied units, and are more difficult to predict, leading to relatively high estimate variance.

  16. The discontinuity at 0.88 represents the conditional mean for tracts in the TM interval [86, 88] relative to that in the interval (88, 90] and therefore may reflect a heterogeneous UAG effect that is greatest in tracts right near the cutoff. However, I find it more reasonable to think that the effect would decline smoothly as TM decreases.

  17. This test will not reveal crowd-out that occurs within subprime lending institutions, that is, if subprime lenders increase conforming originations and reduce subprime originations. Similarly, the net positive increase in GSE-eligible lending may mask some crowd-out within prime lenders.

  18. Tracts with 88<TM≤90 averaged 679 originations in 1997–2002; deflating that amount by 0.034 is 23 originations.

  19. Just over 4.7 million loans were originated in sample tracts with TM between 0.70 and 0.90 in this period.

  20. The estimated UAG-effect on GSE purchases presented earlier is only slightly larger than that for GSE-eligible originations. Since the GSEs only purchase about 40% of eligible originations (Table 1), these estimates indicate that the level increase in originations is actually greater than the level increase in GSE purchases. One reason for this discrepancy is that the GSEs respond to the goals by purchasing seasoned loans that are not reported in HMDA (Federal Register 2004).

  21. Although HUD continued to use 1990 Census income data in 2003 and 2004 and therefore targeted the same tracts as in 2002 (except in cases due to tract boundary changes), 2000 Census income data was available and may have affected lending and business decisions in 2003 and 2004. As such, I use 2001/02 as the pre-period.

  22. I use the Census’ population-based relationship file to link 1990 census tracts to their 2000 counterpart. I link just over 85% of the census tracts from my 1997–2002 analysis to a specific 2000 census tract, where a link requires that at least 90% of the 2000 tract’s population resides within the 1990 tract boundary and vice versa. Most of the unlinked tracts were relatively large in 1990 and were split up for the 2000 Census.

  23. Loan growth measures are confounded by the inclusion of junior lien loans in the data in so far as such loans grew as a share of all loans during this period.

  24. HUD did not publish a subprime lender list for 2006. I identify subprime lenders in 2006 as those in the 2005 list (194 of the 210 lenders match in 2006), plus those highly likely to be subprime specialists, which I identify as lenders that made at least 500 site-built, owner-occupied home purchase or refinance originations (including junior liens), and that at least 75% of these originations were “higher-priced” (i.e. the Rate Spread exceeded the HMDA reporting threshold). In 2005, HUD classified 85% of lenders fitting this description as subprime.

  25. Changes in tracts’ relative income are highly correlated with changes in tracts’ median family income level. The correlation in the percent change in both measures between 1990 and 2000 is 0.68 for tracts in the h = 0.05 sample.

  26. I also ran the same set of regressions in Tables 5 and 7 using only GSE purchases of home-purchase loans since HUD introduced a home purchase mortgage subgoal in 2005. The results are similar, if not more modest. Second, I redid the regressions in Tables 5 and 7 after excluding junior liens in the 2005/06 period to test if junior liens are driving differential growth across the cutoff. These results are basically identical to those in Tables 5 and 7.

  27. Total GSE PLS purchases grew from an average of $26 billion per year between 1999 and 2001 to $204 billion per year between 2004 and 2006 (OFHEO 2008), while total issuance of subprime and Alt-A PLS grew somewhat more robustly over that stretch, from about $78 billion per year to about $711 billion per year (Inside Mortgage Finance 2008). Note that total GSE PLS purchases includes multifamily securities and “other” securities. Detailed data on the distribution of security types is available beginning only in 2006 from OFHEO. That year, nearly 85% of GSE PLS purchases were subprime or Alt-A MBS.

  28. For instance, see table C.5 in Bunce (2007). Bunce and Manchester both claim that loans acquired via PLS are reported under the “other seller” category in the data that the GSEs report to HUD.

  29. Some have argued that all these forces coalesced such that the GSEs played an important role in the credit “boom and bust” during the 2000s (e.g. Wallison and Calomiris 2008; White 2008).

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Acknowledgements

The views expressed in this paper are solely those of the author. This research draws partially from my economics dissertation at MIT. I thank Paul Calem, Glenn Canner, Michael Greenstone, Hui Shan and Christopher L. Smith for very helpful comments and suggestions. All mistakes are mine. Thanks to the MIT Dewey Library staff, the MIT Shultz fund, the MIT Department of Economics and National Science Foundation for support.

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    Neil Bhutta

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Bhutta, N. GSE Activity and Mortgage Supply in Lower-Income and Minority Neighborhoods: The Effect of the Affordable Housing Goals. J Real Estate Finan Econ 45, 238–261 (2012). https://doi.org/10.1007/s11146-010-9258-z

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