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Abstract
This article documents the growth and geographic distribution of nontraditional mortgages (NTMs) and subprime mortgages during 2000-2006, and examines the association between these products and homeownership at the county level between 2000 and 2012. It finds a significant relationship between the origination of NTM and subprime mortgages during the boom and changes in the number of homeowners (positive during the 2000-2006 period and negative during the 2006-2012 period) but no significant relationship with the change in the homeownership rate. Looking at specific categories of the population, the results indicate a positive relationship between the presence of NTMs and subprime mortgages and increased numbers of homeowners for young households as well as for low income and minority households, but the relationship is smaller than for the general population. Overall, the relationship between NTMs and homeownership is stronger than the relationship between subprime mortgages and homeownership during the boom and it is less negative during the bust.
Acknowledgments
The authors are grateful to Barry Cynamon and Neil Bhutta for helpful comments on an earlier version of this article. All errors are the responsibility of the authors alone. An gratefully acknowledges data access through the UCLA Ziman Center for Real Estate during his visit to the Center. Wachter gratefully acknowledges support from the Research Sponsors Program of the Zell/Lurie Real Estate Center at The Wharton School of the University of Pennsylvania.
Notes
1. These two developments are connected but separate. Nontraditional mortgages are mortgage products with characteristics that differ from the fully amortizable 30-year fixed-rate mortgages, “the American Mortgage” (Green and Wachter Citation2005, p. 93). Subprime mortgages are loans made to borrowers with low credit score (variously defined as below 680, 640, or 620) but because of data availability limitations, loans issued by lender on the U.S. Department of Housing and Urban Development’s (HUD) list of subprime lenders are used as a proxy for subprime mortgages. Many NTM were originated to subprime borrowers and many subprime borrowers used NTM (Bostic et al., Citation2012). The correlation between the share of NTM and the share of subprime mortgages at the county level is 0.58 based on the data and definition used in this article.
2. We restrict our sample to purchase mortgages here, but these products were also used for refinancing.
3. We provide more details about the data sets used to measure NTM and subprime in Section 2.
4. For both types of product, we restrict our sample to first-lien purchase mortgages.
5. The threshold used is actually 365 months since mortgages with terms between 360 and 365 are not different by nature and may reflect reporting error.
6. CLTV combines the balance on the first and second mortgage (piggyback) to capture the overall level of leverage.
7. This is a comprehensive definition of NTM that goes beyond the definition of alternative mortgage products used in LaCour-Little and Yang (Citation2010) and Brueckner et al. (Citation2016) or of complex mortgages in Amromin et al. (Citation2011) that restrict the definition to IO and option ARM, for example. We also tried alternative definitions by including hybrid ARM, mortgages with prepayment penalties, and changing the threshold for CLTV to strictly above 100% CLTV or decreasing it to 97%. The results are broadly similar and available upon request.
8. This definition of NTM is inclusive but heterogeneous, and the relationship with homeownership could vary across attributes. As we discuss below, the correlation between different attributes is always above 0.5 but substantially below 1. To test for the importance of this heterogeneity, we separated the attributes based on the type of constraint they are expected to contribute to overcoming: income (option ARM with negative amortization, IO loans, loans with balloon payments, low or no documentation, terms over 30 years, and teaser rates) or wealth (high CLTV). In both cases, the estimates are similar to those obtained with the overall NTM measure.
9. For example, we estimate that 31% of mortgages issued in 2006 were NTM, a figure close to the 30% reported in Sanders (Citation2008) using CoreLogic data and to the 32% reported in Inside Mortgage Finance (Citation2013). Further, there is no evidence suggesting that NTM kept on portfolio have a different spatial distribution than those securitized in PLS.
10. Avery, Bhutta, Brevoort, and Canner (Citation2011) estimate that HMDA data cover more than 80% of the total mortgage origination market.
11. The way subprime loans are identified from HMDA changes after 2006, from relying on a list of subprime originators identified by HUD to being based on a spread of the mortgage rate at origination relative to prime (3 percentage points). To remain consistent, and given our period of interest, the lender-based definition is the only one used in this study.
12. Our measure of the change in the number and share of homeowners comes from the U.S. Census and American Community Survey (ACS) and is therefore unlikely to be biased by the reporting of owner-occupy status by investors on mortgage applications, as discussed in Haughwout et al. (Citation2011).
13. These periods correspond roughly to the boom and bust periods. Whereas some view the end of the boom as occurring in late 2006, when house prices began to decline, other point to early 2007 when credit tightened and its availability became constrained. We selected an endpoint for the boom – the end of 2006 – that fell between these two while also being straightforward to implement. Based on data availability for the American Community Survey (ACS), it is not possible to measure the change in homeownership on an annual basis for the period prior to 2005. We also ran an annual regression at the state level with lagged annual NTM and subprime numbers for the period 2000–2006, and results are similar to those found over the entire 2000–2006 period. We also restricted the NTM and subprime measures to the 2004 to 2006 period with similar results. Future work can expand the study past 2012 – a period in which markets were still in recovery.
14. Both population-weighted and nonweighted regressions were run with broadly similar results (Appendix B). The results discussed in the analysis are not weighted by population. As suggested by a reviewer, we also combined the NTM and subprime measure into the same model. Because of the high level of collinearity between the two measures, we do not report the results. Those are available from the authors on demand.
15. We leave for further work to determine whether there are homogeneous effects across regions.
16. For non-MSA counties, we use the state-level index for non-MSA parts of the state produced by FHFA. House price volatility is calculated as “the variance of the five-year percentage change in the price index across 13 years of quarterly values” (Gabriel & Rosenthal, Citation2015, p. 11).
17. As defined by the Office of Management and Budget.
18. We present results using the aggregate number of NTM originated during 2001–2006 as the variable of interest. We also tested whether the effect changed by year, using annual lags for the number and share of NTM. We ran all the analyses for the 2006–2012 period using up to eight period lags. We further reestimated the relationships using the maximum NTM share in a county over the cycle as the independent variable. The results are robust to these alternative specifications.
19. In addition, as a robustness check we ran a specification that included the change in renters. The results are robust to that specification. This suggests that counties in which there was a higher prevalence of NTM and subprime mortgages experienced a higher increase during the boom and a higher decrease during the bust in the number of homeowners, but that it was proportional to their overall population gains.
20. We thank two anonymous referees for suggesting this additional specification.
21. We also conducted first-time homebuyer and racial group tests using two other approaches, with similar results. Specifically, we created interaction terms involving the NTM and subprime mortgage metrics and the share of the county population that is either young (for the first-time homebuyer analysis) or black or Hispanic (for the racial group analysis). We also stratified our sample based on their share of young, black or Hispanic households and compared the relationship in high- and low- young and minority counties. The results of these analyses, which yield qualitatively similar results to the analysis reported in the text, are available upon request.
22. We adopt the same approach for all subgroups.
23. We also looked at the relationships between NTM and subprime mortgages and changes in the young, Hispanic and black homeownership. As in the general population, the relationships are not significant, indicating that areas with a larger number or share of these products did not experience a larger increase or decrease in young and minority homeownership rate during the boom or bust.