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The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

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  • Yaseen Ghulam

    (Economics and Finance Subject Group, Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth PO1 3DE, UK
    Department of Management and Quantitative, College of Business Administration (COBA), Al Yamamah University 7010 King Fahd Road, Al Qirawan, Riyadh 13541, Saudi Arabia)

  • Kamini Dhruva

    (Department of Management and Quantitative, College of Business Administration (COBA), Al Yamamah University 7010 King Fahd Road, Al Qirawan, Riyadh 13541, Saudi Arabia)

  • Sana Naseem

    (Department of Management and Quantitative, College of Business Administration (COBA), Al Yamamah University 7010 King Fahd Road, Al Qirawan, Riyadh 13541, Saudi Arabia)

  • Sophie Hill

    (Inchcape Fleet Solutions, Haven House, Compass Road, Portsmouth PO6 4RP, UK)

Abstract

We utilize the data of a very large UK automobile loan firm to study the interaction of the characteristics of borrowers and loans in predicting the subsequent loan performance. Our broader findings confirm the earlier research on the issue of subprime auto loans. More importantly, unmarried borrowers living with furnished tenancy agreements who have relatively new jobs have a probability of defaulting of more than 60% compared to an average 7% default rate in overall subprime borrowers in the dataset. Also, in the above category are those who live in a less prosperous part of the UK such as the north-west, are full-time self-employed, have other large loan arrears, fall into the bottom 25% percentile of monthly income, secure loans with high loan to total value (LTV), purchase expensive automobiles with shorter loan duration payment plans, and have a high dependency on government support. This in fact is also true of those who go into arrears, except that the highest probability in this context is around 40% compared to 6% for an overall sample. These findings shall help in the understanding of subprime auto loans performance in relation to borrowers and loan features alongside helping auto finance firms improve predictive models and decision-making.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jrisks:v:6:y:2018:i:3:p:101-:d:169957
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    References listed on IDEAS

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    1. Rajan, Uday & Seru, Amit & Vig, Vikrant, 2015. "The failure of models that predict failure: Distance, incentives, and defaults," Journal of Financial Economics, Elsevier, vol. 115(2), pages 237-260.
    2. Gene Amromin & Anna L. Paulson, 2009. "Comparing patterns of default among prime and subprime mortgages," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 33(Q II), pages 18-37.
    3. William Adams & Liran Einav & Jonathan Levin, 2009. "Liquidity Constraints and Imperfect Information in Subprime Lending," American Economic Review, American Economic Association, vol. 99(1), pages 49-84, March.
    4. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    5. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    6. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    7. Marshall, Andrew & Tang, Leilei & Milne, Alistair, 2010. "Variable reduction, sample selection bias and bank retail credit scoring," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 501-512, June.
    8. Liran Einav & Mark Jenkins & Jonathan Levin, 2012. "Contract Pricing in Consumer Credit Markets," Econometrica, Econometric Society, vol. 80(4), pages 1387-1432, July.
    9. M Malik & L C Thomas, 2010. "Modelling credit risk of portfolio of consumer loans," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 411-420, March.
    10. Khaled Halteh & Kuldeep Kumar & Adrian Gepp, 2018. "Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk," Risks, MDPI, vol. 6(2), pages 1-13, May.
    11. George M. von Furstenberg & R. Jeffrey Green, 1974. "Estimation of Delinquency Risk for Home Mortgage Portfolios," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 2(1), pages 5-19, March.
    12. Danis, Michelle A. & Pennington-Cross, Anthony, 2008. "The delinquency of subprime mortgages," Journal of Economics and Business, Elsevier, vol. 60(1-2), pages 67-90.
    13. Kasper Roszbach, 2004. "Bank Lending Policy, Credit Scoring, and the Survival of Loans," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 946-958, November.
    14. Dennis Capozza & Thomas Thomson, 2006. "Subprime Transitions: Lingering or Malingering in Default?," The Journal of Real Estate Finance and Economics, Springer, vol. 33(3), pages 241-258, November.
    15. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    16. Peter Chinloy, 1995. "Privatized Default Risk and Real Estate Recessions: The U.K. Mortgage Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 23(4), pages 401-420, December.
    17. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    18. Daglish, Toby, 2009. "What motivates a subprime borrower to default?," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 681-693, April.
    19. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    20. 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.
    21. Ondřej Dvouletý, 2017. "Effects of Soft Loans and Credit Guarantees on Performance of Supported Firms: Evidence from the Czech Public Programme START," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    22. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    23. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    24. Christopher Mayer & Karen Pence & Shane M. Sherlund, 2009. "The Rise in Mortgage Defaults," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 27-50, Winter.
    25. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    26. Howard Lax & Michael Manti & Paul Raca & Peter Zorn, 2004. "Subprime lending: An investigation of economic efficiency," Housing Policy Debate, Taylor & Francis Journals, vol. 15(3), pages 533-571.
    27. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    28. T. Gregory Morton, 1975. "A Discriminant Function Analysis of Residential Mortgage Delinquency and Foreclosure," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 3(1), pages 73-88, March.
    29. Agarwal, Sumit & Chomsisengphet, Souphala & Liu, Chunlin, 2011. "Consumer bankruptcy and default: The role of individual social capital," Journal of Economic Psychology, Elsevier, vol. 32(4), pages 632-650, August.
    30. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
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