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Abstract

In the automobile loan market, lenders face the risk that borrowers will default. In evaluating potential clients, lenders would benefit from the ability to answer three questions: Who will default? What is the potential loss exposure when the borrower defaults? When will the borrower default? This paper suggests that lenders consider the make of vehicle purchased by the borrower when attempting to answer these questions. We analyze applicant-level and loan-level data from an American credit union, employing logistic, tobit, and censored normal regression. Our results suggest that lenders could more efficiently price for risk by including a borrower’s choice of vehicle in predictive models specific to their market.

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