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Distinguishing between Good and Bad Subprime Auto Loans Borrowers: The Role of Demographic, Region and Loan Characteristics

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  • Yaseen Ghulam

    ((1)University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group; (2)Al Yamamah University, SAUDI ARABIA)

  • Sophie Hill

    (Inchcape Fleet Solutions, Haven House, U.K.)

Abstract

Research on subprime mortgages has recently been gaining momentum, but subprime auto loans have largely been ignored. By using a unique data set of a very large UK vehicle finance company, this study analyses secured loans extended to the subprime borrowers with impaired or limited credit history. It looks specifically at characteristics in relation to payment history, in order to determine what characteristics make a good or bad borrower. We conclude that married and divorced borrowers as well as borrowers living in low unemployment and relatively prosperous regions such as the South East and London are less likely to default compared to not married, furnished tenants or borrowers living in the North West of the UK who have a high probability of default. Similar to the prime loans, income of borrowers and defaults propensities are negatively associated. Loan and security characteristics with the most impact on default status are price and age of the automobile, effective interest rate measured by APR, loan-to-value (LTV) and term of the loan agreement. The results of this study will help in understanding subprime auto loans and borrowers as well as helping lenders to distinguish between good and bad subprime borrowers.

Suggested Citation

  • 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.
    • Handle: RePEc:bap:journl:170404
    Note: The authors thank valuable comments by two anonymous reviewers. Remaining errors are ours.
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    References listed on IDEAS

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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. 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.
    3. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.

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    More about this item

    Keywords

    Automobile loans; Defaults; Subprime; Credit risk; U.K.;
    All these keywords.

    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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