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A Dynamic Model of Subprime Mortgage Default: Estimation and Policy Implications

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Listed:
  • Patrick Bajari
  • Chenghuan Sean Chu
  • Denis Nekipelov
  • Minjung Park

Abstract

The increase in defaults in the subprime mortgage market is widely held to be one of the causes behind the recent financial turmoil. Key issues of policy concern include quantifying the role of various factors, such as home price declines and loosened underwriting standards, in the recent increase in subprime defaults and predicting the effects of various policy instruments designed to mitigate default. To address these questions, we estimate a dynamic structural model of subprime borrowers' default behavior. We prove that borrowers' time preference is identified in our model and propose an easily implementable semiparametric plug-in estimator. Our results show that principal writedowns have a significant effect on borrowers' default behavior and welfare: a uniform 10% reduction in outstanding mortgage balance for the pool of borrowers in our sample would reduce the overall default probability by 22%, and borrowers' average willingness to pay for the principal writedown would be $16,643

Suggested Citation

  • Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2013. "A Dynamic Model of Subprime Mortgage Default: Estimation and Policy Implications," NBER Working Papers 18850, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:18850
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    Cited by:

    1. Mocetti, Sauro & Viviano, Eliana, 2017. "Looking behind mortgage delinquencies," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 53-63.
    2. You Suk Kim & Wenli Li & Hanming Fang, 2016. "The Dynamics of Subprime Adjustable-Rate Mortgage Default: A Structural Estimation," 2016 Meeting Papers 400, Society for Economic Dynamics.
    3. repec:hal:spmain:info:hdl:2441/4udtt6bh25946auar877ii1c1e is not listed on IDEAS
    4. Hanming Fang & You Suk Kim & Wenli Li, 2015. "The Dynamics of Adjustable-Rate Subprime Mortgage Default: A Structural Estimation," Finance and Economics Discussion Series 2015-114, Board of Governors of the Federal Reserve System (U.S.).
    5. Diego Avanzini & Juan Francisco Martínez & Víctor Pérez, 2016. "A micro-powered model of mortgage default risk for full recourse economies, with an application to the case of Chile," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Combining micro and macro data for financial stability analysis, volume 41, Bank for International Settlements.
    6. Thomas P. Boehm & Alan M. Schlottmann, 2017. "Mortgage Payment Problem Development and Recovery: A Joint Probability Model Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 55(4), pages 476-510, November.
    7. Wenli Li & Florian Oswald, 2014. "Recourse and residential mortgages: the case of Nevada," Working Papers 15-2, Federal Reserve Bank of Philadelphia.
    8. Seyed Morteza Emadi & Bradley R. Staats, 2020. "A Structural Estimation Approach to Study Agent Attrition," Management Science, INFORMS, vol. 66(9), pages 4071-4095, September.
    9. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    10. Wenli Li & Florian Oswald, 2014. "Recourse and the Residential Mortgage Market: the Case of Nevada," Sciences Po publications info:hdl:2441/4udtt6bh259, Sciences Po.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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