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What Drives Default and Prepayment on Subprime Auto Loans?

Author

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  • Erik Heitfield

    (Federal Reserve Board)

  • Tarun Sabarwal

    (University of Texas at Austin)

Abstract

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.

Suggested Citation

  • Erik Heitfield & Tarun Sabarwal, 2004. "What Drives Default and Prepayment on Subprime Auto Loans?," Finance 0405034, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0405034
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
    2. Dobromił Serwa, 2013. "Measuring Non-Performing Loans During (and After) Credit Booms," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 163-183, September.
    3. Wu, Deming & Fang, Ming & Wang, Qing, 2018. "An empirical study of bank stress testing for auto loans," Journal of Financial Stability, Elsevier, vol. 39(C), pages 79-89.
    4. Bruce L. Dixon & Bruce L. Ahrendsen & Brandon R. McFadden & Diana M. Danforth & Monica Foianini & Sandra J. Hamm, 2011. "Competing risks models of Farm Service Agency seven‐year direct operating loans," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 5-24, May.
    5. Bradley Katcher & Geng Li & Alvaro Mezza & Steve Ramos, 2024. "One Month Longer, One Month Later? Prepayments in the Auto Loan Market," Finance and Economics Discussion Series 2024-056, Board of Governors of the Federal Reserve System (U.S.).
    6. Michel Alexandre & Thiago Christiano Silva, 2023. "Labor Market and Systemic Risk: a network-based approach," Working Papers Series 584, Central Bank of Brazil, Research Department.
    7. Souphala Chomsisengphet & Anthony Pennington-Cross, 2006. "Subprime refinancing: equity extraction and mortgage termination," Working Papers 2006-023, Federal Reserve Bank of St. Louis.
    8. Yaseen Ghulam & Sophie Hill, 2017. "Distinguishing between Good and Bad Subprime Auto Loans Borrowers: The Role of Demographic, Region and Loan Characteristics," Review of Economics & Finance, Better Advances Press, Canada, vol. 10, pages 49-62, November.
    9. Dimuthu Ratnadiwakara, 2021. "Collateral Value and Strategic Default: Evidence from Auto Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 209-240, June.
    10. Stefan Jacewitz & Jonathan Pogach, 2018. "Deposit Rate Advantages at the Largest Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 53(1), pages 1-35, February.
    11. David Puskar & Aron A. Gottesman, 2012. "An Investigation of Underwriting Fees for Asset-Backed Securities," The American Economist, Sage Publications, vol. 57(2), pages 216-237, November.
    12. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    13. Bryan Stanhouse & Duane Stock, 2008. "Managing the risk of loan prepayments and the optimal structure of short term lending rates," Annals of Finance, Springer, vol. 4(2), pages 197-215, March.

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    More about this item

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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