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

Yaseen Ghulam (), Kamini Dhruva (), Sana Naseem () and Sophie Hill ()
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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
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

Risks, 2018, vol. 6, issue 3, 1-21

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.

Keywords: defaults; arrears; subprime loans; credit risk; interaction (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2018
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