Pathways After Default: What Happens to Distressed Mortgage Borrowers and Their Homes?

Abstract

We use a detailed dataset of seriously delinquent mortgages to examine the dynamic process of mortgage default—from initial delinquency and default to final resolution of the loan and disposition of the property. We estimate a two-stage competing risk hazard model to assess the factors associated with post-default outcomes, including whether a borrower receives a legal notice of foreclosure. In particular, we focus on a borrower’s ability to avoid a foreclosure auction by getting a modification, by refinancing the loan, or by selling the property. We find that the outcomes of the foreclosure process are significantly related to: loan characteristics including the borrower’s credit history, current loan-to-value and the presence of a junior lien; the borrower’s post-default payment behavior, including the borrower’s participation in foreclosure counseling; neighborhood characteristics such as foreclosure rates, recent house price depreciation and median income; and the borrower’s race and ethnicity.

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Fig. 1

Notes

  1. 1.

    Further, borrowers typically are faced with mortgage distress on only a single or a very small number of occasions in their lifetimes, and therefore may have limited knowledge as to the best possible course of action.

  2. 2.

    For promising advances in modeling the modification decision, see, for example, Haughwout et al. (2009).

  3. 3.

    For example, Piskorski et al. (2010); Foote et al. (2009); Adelino et al. (2009, 2010).

  4. 4.

    Haughwout et al. (2009) find that 92 % of modified loans in the complete LoanPerformance modifications database were reported by the servicer and 8 % were inferred by LoanPerformance based on changes in the loan terms.

  5. 5.

    The hierarchical matching algorithm used is described in more detail in Chan et al. (2010). Of the loans in the LoanPerformance database, 93 % were matched back to the deed records. The deeds data do not include Staten Island, which therefore was dropped from the analyses.

  6. 6.

    The Center for New York City Neighborhoods (CNYCN) is a non-profit organization that coordinates foreclosure counseling from a variety of non-profit providers to homeowners at risk of losing their home to foreclosure. Distressed mortgage borrowers who call 311 (New York City’s widely known phone number for government information and non-emergency services) are directed to CNYCN. It is likely that the counseling reported by CNYCN represents the vast majority of foreclosure counseling taking place in New York City.

  7. 7.

    The deed records are categorized by the DOF as standard transactions, deeds-in-lieu, debt free gifts or divorce settlements, estate sales, other judgments, and referee sales (or auction sales). We identify auctioned properties that became REO by flagging deeds that transfer to the lender.

  8. 8.

    The New York legislature amended the process in 2008 to require that the process begin with a 90 day notice of intent to file a foreclosure action for certain “high-cost”, “subprime” and “non-traditional” home loans, effective September 1, 2008. That requirement was extended to all home loans in 2009, effective January 14, 2010. We found little difference in our results below when we compared the periods before and after these changes went into effect.

  9. 9.

    For descriptions of judicial and non-judicial foreclosure systems, see, e.g., Crews Cutts and Merrill (2008).

  10. 10.

    Lender Processing Services, 2010. This timeline is likely to have extended since the end of our study period.

  11. 11.

    Our findings can be roughly compared with several other studies: Agarwal et al. (2011) observed that 1 year after a loan becomes seriously delinquent, about half of borrowers in a national sample of the OCC-OTS Mortgage Metrics database are in liquidation, and about one quarter each were renegotiated or had no further action. Been et al. (2011) followed loans in a New York City subsample of the OCC-OTS database and found that 9 % received a modification, 8 % other workouts, about 15 % were cured, 5 % experienced liquidation, and almost two thirds remained in serious delinquency during the study period. Capozza and Thomson (2006) observed 6,000 mortgages from a single servicer, and found that of the mortgages that were delinquent but not in bankruptcy at the beginning of the study period, after 8 months 38 % remained in default but did not fall further behind on payments, 21 % remained in default with worsening delinquency, 6 % had cured, 11 % had entered bankruptcy proceedings and 24 % had become REO.

  12. 12.

    The widely used Fair Isaac Corporation (FICO) credit score depends on credit and payment history, credit use and recent searches for credit. According to FICO, 27 % of the general population have scores below 650.

  13. 13.

    The numerator of the combined LTV measure includes the balance for the first lien (the focus of our analysis) as well as any other liens in existence at the time of origination. While we know the size of any additional liens taken out afterwards, some are home equity lines of credit, and so would be misleading to include in our LTV measure.

  14. 14.

    Specifically, the number of foreclosure notices issued on 1–4 family buildings in a census tract during the preceding six-months, divided by the stock of 1–4 family buildings.

  15. 15.

    Incorporating a selection bias correction in the second stage model is difficult or impossible in the absence of a compelling variable that affects the receipt of a lis pendens, but that does not belong in the ultimate outcome model.

  16. 16.

    The relative risk ratio is the exponentiated value of the multinomial logit coefficient.

  17. 17.

    A relative risk ratio greater than one in the MNL framework implies a greater probability of that particular outcome as compared to the reference outcome. It does not necessarily imply a greater absolute probability of that particular outcome, because the absolute probability will also depend on the baseline probabilities of the other outcomes.

  18. 18.

    The relative interest rate at origination for FRMs is calculated as the loan’s interest rate minus the Freddie Mac average rate for prime 30-year FRMs during the month of origination. For ARMs, it is the loan’s initial interest rate minus the six-month London Interbank Offered Rate (LIBOR) at origination.

  19. 19.

    Our results are contrary to Capozza and Thomson (2006) who find that loans with high interest rate premia are less likely to be foreclosed, and that FRMs, loans with standard documentation and high LTVs are more likely to be foreclosed. The difference may reflect the different time periods studied (2001–2002 originations for Capozza and Thomson versus 2003–2008 here), the different populations studied (6,000 subprime loans from one lender, versus most non-prime loans originated in New York City here), or the fact that we look at the full range of post-default outcomes that may occur (rather than the more restricted outcomes they study), during a time when the federal, state and local governments were incentivizing modifications.

  20. 20.

    We also tried specifications that included the current rate premium (the current rate on the loan minus the Freddie Mac average rate on 30 year FRMs), because it is usually an important predictor of prepayment behavior. However, this was not significant in explaining refinances and sales, probably because it is highly correlated with the relative rates at origination, and because we also have calendar year dummies in our model.

  21. 21.

    This finding is in contrast to Pennington-Cross (2010), who found that the relative risk of REO decreases as the unpaid balance increases.

  22. 22.

    The adverse selection of higher FICO score borrowers in default is also consistent with Brevoort and Cooper (2010) who find that post-foreclosure borrowers with originally higher FICO scores take longer to recover their scores than those with originally lower scores.

  23. 23.

    This is consistent with Haughwout et al. (2009) who observed, based upon a national sample, that borrowers who eventually modify had lower credit scores at origination.

  24. 24.

    Been et al. (2011), who are able to observe contemporaneous FICO scores, find that borrowers experiencing greater declines in FICO since origination are less likely to receive modifications. As noted above, since FICO scores tend to fall following default, our results are also consistent with their findings.

  25. 25.

    Some have argued that some banks have delayed foreclosure sales to avoid realizing loan losses. Banks might equally well delay even beginning the foreclosure process on underwater properties for the same reasons.

  26. 26.

    Note that we already control for origination year and calendar year.

  27. 27.

    Foreclosures could be causing diminished house price appreciation by increasing the housing supply on the market and driving down prices. And, they may generate negative externalities such as the visible deterioration of properties that lead to lower property values in high foreclosure tracts relative to others in the same community district.

References

  1. Adelino, M., Gerardi, K., & Willen, P. S. (2009). Why don’t lenders renegotiate more home mortgages? Redefaults, self-cures, and securitization. Federal Reserve Bank of Boston Public Policy Discussion Papers, No. 09-4.

  2. Adelino, M., Gerardi, K., & Willen, P. (2010). What explains differences in foreclosure rates? A response to Piskorski, Seru, and Vig. Federal Reserve Bank of Boston Working Paper, No. 10-2.

  3. Agarwal, S., Amromin, G., Ben-David, I., Chomsisengphet, S., & Evanoff, D.D. (2011). The role of securitization in mortgage renegotiation. Federal Reserve Bank of Chicago Working Paper, No. 2011-02.

  4. Ambrose, B. W., & Capone, C. A., Jr. (1996). Do lenders discriminate in processing defaults? Cityscape: A Journal of Policy Development and Research, 2(1), 89–98.

    Google Scholar 

  5. Ambrose, B. W., & Capone, C. A., Jr. (1998). Modeling the conditional probability of foreclosure in the context of single-family mortgage default resolutions. Real Estate Economics, 26(3), 391–429.

    Article  Google Scholar 

  6. Been, V., Weselcouch, M., Voicu, I., & Murff, S. (2011). Determinants of the incidence of loan modifications. http://ssrn.com/abstract=1941915. Accessed 1 July 2012.

  7. Brevoort, K. P., & Cooper, C.R. (2010). Foreclosure’s wake: the credit experiences of individuals following foreclosure. Board of Governors of the Federal Reserve System Finance and Economics Discussion Series, No. 2010-59.

  8. Capozza, D., & Thomson, T. (2006). Subprime transitions: lingering or malingering in default? Journal of Real Estate Finance and Economics, 33(3), 241–258.

    Article  Google Scholar 

  9. Chan, S., Gedal, M., Been, V., & Haughwout, A. (2010). The role of neighborhood characteristics in mortgage default risk: Evidence from New York City. http://ssrn.com/abstract=1631944. Accessed 1 July 2012.

  10. Christie, L. (2010). How foreclosure impacts your credit score. CNNMoney.com. http://money.cnn.com/2010/04/22/real_estate/foreclosure_credit_score/index.htm. Accessed 1 July 2012.

  11. Collins, J., & Reid, C. (2010). Who receives a mortgage modification? Race and income differentials in loan workouts. Federal Reserve Bank of San Francisco Working Paper.

  12. Collins, J. M., & O’Rourke, C. (2011). Homeownership education and counseling: Do we know what works? Research Institute for Housing America. http://www.housingamerica.org/RIHA/RIHA/Publications/76378_10554_Research_RIHA_Collins_Report.pdf. Accessed 1 July 2012.

  13. Collins, J. & Reid, C. (2010). Who Receives a Mortgage Modification? Race and Income Differentials in Loan Workouts. Federal Reserve Bank of San Francisco Working Paper 2010-07.

  14. Crews Cutts, A., & Merrill, W. A. (2008). Interventions in mortgage default: Policies and practices to prevent home loss and lower costs. In N. P. Retsinas & E. S. Belsky (Eds.), Borrowing to live: Consumer and mortgage credit revisited (pp. 203–254). Washington DC: Brookings Institution.

    Google Scholar 

  15. Danis, M. A., & Pennington-Cross, A. N. (2005). A dynamic look at subprime loan performance. Journal of Fixed Income, 15(1), 28–39.

    Article  Google Scholar 

  16. Danis, M. A., & Pennington-Cross, A. N. (2008). The delinquency of subprime mortgages. Journal of Economics and Business, 60(1–2), 67–90.

    Article  Google Scholar 

  17. Foote, C. L., Gerardi, K. S., Goette, L., & Willen, P. S. (2009). Reducing foreclosures: No easy answers. NBER Macroeconomics Annual, 24, 89–138.

    Google Scholar 

  18. Gerardi, K., Shapiro, A. H., & Willen, P. S. (2007). Subprime outcomes: Risky mortgages, homeownership experiences, and foreclosures. Federal Reserve Bank of Boston Working Paper, No. 07-15.

  19. Gerardi, K. S., & Willen, P. S. (2009). Subprime mortgages, foreclosures, and urban neighborhoods. The B.E. Journal of Economic Analysis & Policy, 9(3), Article 12.

    Google Scholar 

  20. Haughwout, A., Okah, E., & Tracy, J. (2009). Second chances: Subprime mortgage modification and re-default. Federal Reserve Bank of New York Staff Reports, No. 417.

  21. Lauria, M., Baxter, V., & Bordelon, B. (2004). An investigation of the time between mortgage default and foreclosure. Housing Studies, 19(4), 581–600.

    Article  Google Scholar 

  22. Lender Processing Services. (2010). LPS Mortgage Monitor Report, October 2010. Jacksonville: Lender Processing Services, Inc.

    Google Scholar 

  23. Pennington-Cross, A. N. (2010). The duration of foreclosures in the subprime mortgage market: A competing risks model with mixing. Journal of Real Estate Finance and Economics, 40(2), 109–129.

    Article  Google Scholar 

  24. Pennington-Cross, A. N., & Ho, G. (2010). The termination of subprime hybrid and fixed-rate mortgages. Real Estate Economics, 38(3), 399–426.

    Article  Google Scholar 

  25. Piskorski, T., Seru, A., & Vig, V. (2010). Securitization and distressed loan renegotiation: Evidence from the subprime mortgage crisis. Journal of Financial Economics, 97(3), 369–397.

    Article  Google Scholar 

  26. Voicu, I., Jacob, M., Rengert, K., & Fang, I. (2011). Subprime loan default resolutions: Do they vary across mortgage products and borrower demographic groups? Journal of Real Estate Finance and Economics. doi:10.1007/s11146-011-9305-4.

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Acknowledgments

We thank Amy Crews Cutts, Michael Gedal, Josiah Madar, Mary Weselcouch, and the participants in the Association for Public Policy Analysis and Management Fall 2010 and American Real Estate and Urban Economics Mid-Year 2011 conferences for their thoughtful suggestions. We are also grateful to the staff of the Federal Reserve Bank of New York for their help with the LoanPerformance data, as well as to the Public Data Corporation and the New York City Department of Finance for their assistance in providing the additional data needed for this project.

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Correspondence to Sewin Chan.

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The views represented here are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System.

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Chan, S., Sharygin, C., Been, V. et al. Pathways After Default: What Happens to Distressed Mortgage Borrowers and Their Homes?. J Real Estate Finan Econ 48, 342–379 (2014). https://doi.org/10.1007/s11146-012-9400-1

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Keywords

  • Mortgage
  • Default
  • Modification
  • Foreclosure
  • REO