Stuck in Subprime? Examining the Barriers to Refinancing Mortgage Debt

The views reflected in this article do not necessarily reflect those of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.

Abstract

Despite falling interest rates and federal policy intervention, many borrowers who could financially gain from refinancing have not done so. We investigate the rates at which, relative to prime borrowers, subprime borrowers seek and take out refinance loans, conditional on not experiencing mortgage default. We find that starting in 2009, subprime borrowers are about half as likely as prime borrowers to refinance, although they still shop for mortgage credit, indicating their interest in refinancing. This disparity is driven in part by the tightened credit environment postfinancial crisis, and the fact that many subprime borrowers were ineligible for the Home Affordable Refinance Program (HARP). In addition, we find that refinance rates have been significantly lower for black and Hispanic borrowers, even after controlling for borrower credit status. We argue that these barriers to refinancing for subprime borrowers have long-term implications for social stratification and wealth building.

Keywords:

• Mortgages
• credit
• minorities
• prepayment

Acknowledgments

The authors would like to thank the three anonymous reviewers and the editor for their comments. In addition, we would like to thank Larry Cordell, Steve Ross, and participants at UC Berkeley’s Fisher Center for Real Estate and Urban Economics seminar for providing constructive feedback that strengthened our data analysis and arguments.

Notes

1. Refinancing can lower payments through a variety of mechanisms, including rate reductions, refinancing out of private mortgage insurance payments, and/or spreading existing debt out over a longer term.

2. Although there is less evidence in the literature for this phenomenon, some researchers have shown that this is often driven by cash-out refinancing, in which borrowers are more concerned with consumption smoothing than with the long-term interest costs of the mortgage (Hurst & Stafford, 2004; Pennington-Cross & Chomsisengphet, 2007).

3. When the option to refinance is clearly a poor financial decision (out of the money) or clearly beneficial (in the money), then borrower and loan characteristics are less important in predicting refinance behavior (LaCour-Little, 1999).

4. The picture looks slightly different when the focus is on cash-out refinancing, particularly during the U.S. mortgage boom between 1998 and 2005. During this time period, households that were younger, nonwhite, noncollege graduates, or financially illiterate about portfolio risk were more likely to have actively withdrawn housing equity using cash-out mortgage refinancing or traditional second mortgages (Duca & Kumar, 2014). Green and LaCour-Little (1999) find that households headed by black consumers were more likely to prepay, after controlling for potential collateral-value constraints; however, their data do not allow them to distinguish between rate and cash-out refinancing or to distinguish prepayments because of homeowner mobility (selling the home).

5. Bennett, Peach, and Peristiani (2001) argue that both the financial and nonfinancial costs to refinancing have declined over time because the mortgage application-and-approval process has been streamlined. They also suggest that technological advances have enabled lenders to more easily identify borrowers with interest rates above prevailing market rates, thereby disseminating information about refinancing opportunities more quickly and broadly than had occurred in the past.

6. In addition to the burden of overcoming collateral constraints to refinancing (most borrowers with a second loan have a higher combined LTV ratio), there are also legal barriers specific to refinancing a second mortgage. As of December 2012, 22% of homes with a mortgage had more than one mortgage lien (Bond et al., 2013).

7. Representations and warranties are assurances that lenders make to investors (including Fannie Mae and Freddie Mac) about the quality of loans they originate; if the loan does not meet the criteria that the lender claimed it did, Fannie Mae or Freddie Mac may issue a request to the lender to repurchase the loan. (Although banks also make reps and warranties to private-label investors, there are meaningful distinctions between the capacity of the GSE and private-label investors to put back loans to the banks.)

8. One limiting factor in mortgage data collected under the HMDA is that many of the underwriting factors used by lenders, such as borrower credit score, are not included (Avery, Brevoort, & Canner, 2007).

9. We calculate this coverage rate by comparing the 8.7 million first-lien purchase mortgage originations in the McDash data with the corresponding 10.8 million originations reported in HMDA data for 2006.

10. We exclude mortgages that reset earlier than 5 years from our analysis because they were used by a relatively small number of borrowers, with credit scores over 620. By restricting the sample to conventional mortgages, we are excluding borrowers who purchased with Federal Housing Administration or Veterans Affairs mortgages. These agencies had streamlined refinance mortgage products with their own eligibility requirements. See DeFusco and Mondragon (2018) for research that specifically examines the barriers to refinancing for Federal Housing Administration mortgages.

11. Bond et al. (2013) use similar procedures to match their data.

12. Mark-to-market LTVs are LTVs in which the denominator (value) is updated using a house price index to get an updated estimate of the borrower’s debt versus equity. (The numerator is also updated to reflect amortization and curtailment of the mortgage.) We use median home values at the zip code level from both Zillow (n.d.) and CoreLogic (n.d.). We find that, generally, CoreLogic offers a more complete panel of data for loans in particular geographies, but Zillow data can, in some instances, provide more comprehensive information for small geographies at particular points in time. We find that our results are robust to using either data source in our analysis.

13. The matched and unmatched samples appear similar on observable characteristics. For example, 65% of subprime, 62% of nonprime, 65% of prime, and 68% of superprime loans were uniquely matched. The mean origination amount for uniquely matched mortgages was $225,000, vs. $214,000 for other loans. Match rates were also very similar when cutting the data based on other measures, including combined LTV at origination, area house price levels, and last observed mortgage status. Additional details are available upon request.

14. For example, Elul, Souleles, Chomsisengphet, Glennon, and Hunt (2010) found that using only data from Loan Performance and Lender Processing Services (also known as McDash) significantly underestimates total combined LTV ratio.

15. The inquiry field in Equifax comes from two sources: (a) inquiries from a known mortgage lender, and (b) what Equifax refers to as potential mortgage inquiries, which are made by a credit service company that typically aggregates credit reports from the three main credit bureaus for the mortgage lender to review.

16. Subprime borrowers were about half as likely as nonprime, prime, or superprime borrowers to use a piggyback mortgage, coupled with a lower LTV ratio first-lien mortgage. Instead, they took out higher LTV ratio first-lien mortgages.

17. Measuring combined LTV in this way is not a perfect assessment of collateral constraints in any given period. In our model, we control for contemporaneous mark-to-market, combined LTV.

18. These refer to different types of ARM products and indicate the year in which the interest rate resets. A 5/1 mortgage, for example, is a mortgage that first resets 5 years after origination (the 5) and thereafter resets each year (the 1).

19. Subprime borrowers were also significantly less likely to prepay their mortgage; approximately 22% prepaid by May 2015, compared with 28% for borrowers with the highest FICO scores.

20. A significant share of subprime borrowers had their closing costs capitalized in their refinance mortgage, which could lead to erroneously coding an interest rate refinance as a cash-out mortgage. We address this by imposing the condition of a large increase between the old and new origination amounts to avoid falsely coding a refinance mortgage as a cash-out.

21. In the models displayed, we quantify these savings as the difference between the borrower’s contemporaneous interest rate and the prevailing 30-year mortgage rate, controlling also for the size of the loan. In alternative specifications, we instead control for the likely change in the borrower’s payments, and find very similar results.

22. Robustness checks include treating right-censored loans differently (coding transferred and vanished loans as a separate outcome, or omitting these loans from the analysis entirely), which we find still generates similar results. Results are also similar if we control for the expected change in monthly payments (in log dollars) instead of the change in interest rates, if we restrict the sample to loans in which the borrower would save at least $100 per month, or if we focus on high-balance loans (those with balances > $100,000). The results of these robustness checks are available from the authors upon request.

23. Most credit bureau data on credit/bank cards cannot distinguish between consumers carrying balances from one billing cycle to another or paying off the balance each cycle. One reason that having bank card debt of more than $10,000 may be a negative predictor of refinancing is that $10,000 is a large amount of money for most consumers to charge each month and pay off in full. Borrowers who have balances this high are probably more likely to be carrying over some of that debt from prior billing cycles.

24. We also estimated this same stratified model on the full sample and find no significant differences in the direction or value of the coefficients for control variables. This analysis is available from the authors upon request. Note that the combined number of observations in Table 3 is 55% of the number of observations in the final column of Table 2, rather than 67%, as our match rate would suggest. We exclude some matched observations because race, ethnicity, and sex of borrowers are not reported in HMDA for all loans.

25. In this discussion, we focus on conforming-loan borrowers who had combined LTV ratios greater than 80%, since this was the minimum LTV ratio requirement for HARP loans.

26. We exclude borrowers who refinanced into a 15-year loan term.