Accessing Homeownership With Credit Constraints

Abstract

The tightening of mortgage credit in the aftermath of the global financial crisis has been identified as a factor in the decline of homeownership in the United States to 50-year lows. In this article, we review findings about the role of borrowing constraints and tightened credit in lowering access to homeownership. We also discuss how institutional changes could hinder or support this access going forward.

Keywords:

• Borrowing constraints
• homeownership
• credit access

Acknowledgments

The authors thank Vincent Reina, Roberto Quercia, and two anonymous referees for their valuable comments. All errors remain our own.

Notes

1. Households’ permanent income (determined by education, age and training) is identified as a key factor in housing choice, but in the absence of access to credit, households remain constrained in their tenure by their current (transitory) income (Goodman, 1988).

2. Further work shows that younger households are particularly affected, including when using measures of permanent income (that is, even when excluding savings or current wealth, which arguably are endogenous) that indicated that owning would be a preferable option for them (Haurin, Hendershott, & Wachter, 1996).

3. The costs, including transaction costs, associated with owning also need to be taken into account. The high transaction costs associated with owning can decrease labor mobility and worsen labor outcomes for owners.

4. Changing preferences across generations, including an increased preference for living in central cities among some Millennials and retiring Baby Boomers, might contribute to a decline in homeownership although a recent analysis shows that the numbers because of this are small (Acolin, Goodman, & Wachter, 2016). The later age of marriage and parenthood might contribute to decreasing the demand for homeownership (JCHS, 2017; Myers, 2016), although these choices are to some extent endogenous with the tenure decision (Haurin et al., 1996).

5. At the same time, minority households were more affected by the decline in house prices during the GFC and fared worse in terms of equity accumulation over the entire cycle (Faber & Ellen, 2016).

6. All figures about the homeownership rate and number of homeowners come from the U.S. Census Current Population Survey/Housing Vacancy Survey available at: https://www.census.gov/housing/hvs/data/histtabs.html

7. In particular, loans to borrowers with FICO scores below 640, that represented about 10% of the market in the 2004–2007 period, have become virtually unavailable (Goodman, 2017a).

8. The moderate increase in DTI as reported prior to the GFC is biased downward by a lack of income verification for Alt-A products that represented a growing share during that period and enabled income overstatement (Mian & Sufi, 2017). The lack of increase in the median LTV points to the need to measure combined loan to value (CLTV) consistently to track the overall level of leverage in the system, as a constant median LTV can mask the increased use of second liens (piggyback loans) and home equity lines of credit (Levitin & Wachter, 2015), which have also declined since the GFC.

9. There is nonetheless a debate about whether conditions might be excessively loose because of the Federal Housing Administration accounting for a larger share of the market (Oliner, 2016).

10. The ex-ante default rate captures the expected default based on historical performance and observable characteristics at the time of origination, as opposed to ex-post default rates that capture the realized default rate.

11. As Goodman (2017b) points out, this likely represents a higher bound estimate of the credit tightening as the number of loans originated reflects both tightened supply by lenders and a potentially larger drop in demand by lower credit score borrowers (possibly because of close relatives and friends experiencing default and foreclosure, changes in expectation about housing price appreciation, or preference for owning).

12. Principal-agent problems emerge from information asymmetries between the lenders who originate the mortgage relative to the securitizers and the ultimate investors in the PLS because not all information on mortgage risk is available in the disclosure documents (Levitin & Wachter, 2011).

13. This exemption is included in the QM rule for a maximum period of 7 years.

14. In a follow-up piece, Seidman and Bai (2016) found a decline in the number of mortgages under $50,000, but were reluctant to attribute it primarily to QM.

15. The role of student debt in delaying or limiting access to homeownership has been a source of particular concern, accentuated by the rapid increase in student debt in recent years. Findings support a substantial negative impact of student debt on homeownership achievements (Bleemer, Brown, Lee, Strair, & Van der Klaauw, 2017; Mezza, Ringo, Sherland, & Sommer, 2016).

16. The GSE use the FICO 4 family of models (TransUnion FICO Classic 4, Equifax Beacon 5.0, and Experian/Fair Isaac Risk Model v2) for both screening (determining if the loan is eligible for purchase) and loan-level pricing adjustments. Fannie Mae’s automated underwriting system does not rely on credit score as an input, but pulls credit data directly instead. Freddie Mac’s automated valuation system relies on credit score as an input, but then supplements it with other credit data. In addition, FICO 4 credit scores influence lender decisions regarding which loans to originate for potential sale to the GSE.

17. In addition, as FHA importance increases, there have been calls to increase the transparency regarding FHA reporting by conducting annual stress tests similar to those mandated by the Dodd–Frank Act for Fannie Mae and Freddie Mac (Koss, 2017).

18. FHA does not engage in risk-based pricing along creditworthiness dimensions but enacts a FICO score cutoff. The lack of risk-based pricing enables FHA to spread the benefits of the government guarantee across borrowers and to provide standardized mortgages that can be effectively securitized by Ginnie Mae.

19. Features of these nontraditional products could address income, wealth and credit constraint by decreasing initial payment, requiring low downpayment, or extending repayment terms. In addition to somewhat higher interest rates (but not commensurate to their risk), these loans were offered with higher fees or prepayment penalties. An important origination channel for these loans was mortgage brokers, who sold them to larger financial institutions that aggregated them and sold PLS to investors.