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What Drives Default and Prepayment on Subprime Auto Loans?

  • Erik Heitfield

    (Federal Reserve Board)

  • Tarun Sabarwal

    (University of Texas at Austin)

This paper uses novel data on the performance of pools underlying asset- backed securities to estimate a competing risks model of default and prepayment on subprime automobile loans. We find that prepayment rates increase rapidly with loan age but are not affected by prevailing market interest rates. Default rates are much more sensitive to aggregate shocks than are prepayment rates. Increases in unemployment precede increases in default rates, suggesting that defaults on subprime automobile loans are driven largely by shocks to household liquidity. There are significant differences in the default and prepayment rates faced by different subprime lenders. Those lenders that charge the highest interest rates experience the highest default rates, but also experience somewhat lower prepayment rates. We conjecture that there is substantial heterogeneity among subprime borrowers, and that different enders target different segments of the subprime market. Because of their higher default rates, loans that carry the highest interest rates do not appear to yield the highest expected returns.

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File URL: http://128.118.178.162/eps/fin/papers/0405/0405034.pdf
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Paper provided by EconWPA in its series Finance with number 0405034.

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Date of creation: 31 May 2004
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Handle: RePEc:wpa:wuwpfi:0405034
Note: Type of Document - pdf
Contact details of provider: Web page: http://128.118.178.162

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  1. Yongheng Deng & John M. Quigley & Robert Van Order, 2000. "Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options," Econometrica, Econometric Society, vol. 68(2), pages 275-308, March.
  2. Ambrose, Brent W & Sanders, Anthony B, 2003. "Commercial Mortgage-Backed Securities: Prepayment and Default," The Journal of Real Estate Finance and Economics, Springer, vol. 26(2-3), pages 179-96, March-May.
  3. Han, Aaron & Hausman, Jerry A, 1990. "Flexible Parametric Estimation of Duration and Competing Risk Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(1), pages 1-28, January-M.
  4. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
  5. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
  6. Fred Phillips-Patrick & David Malmquist & Clifford Rossi, 1997. "The Economics of Low-Income Mortgage Lending," Journal of Financial Services Research, Springer, vol. 11(1), pages 169-188, February.
  7. Pavlov, Andrey D, 2001. "Competing Risks of Mortgage Termination: Who Refinances, Who Moves, and Who Defaults?," The Journal of Real Estate Finance and Economics, Springer, vol. 23(2), pages 185-211, September.
  8. Calhoun, Charles A & Deng, Yongheng, 2002. "A Dynamic Analysis of Fixed- and Adjustable-Rate Mortgage Terminations," The Journal of Real Estate Finance and Economics, Springer, vol. 24(1-2), pages 9-33, Jan.-Marc.
  9. Sueyoshi, Glenn T., 1992. "Semiparametric proportional hazards estimation of competing risks models with time-varying covariates," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 25-58.
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