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Cascade: No. 76, Winter 2011

Spotlight on Research: What Determines Automobile Loan Defaults and Prepayment?

The recent financial meltdown has fostered a rash of loan defaults. Most of the discussion to date has centered on loans in the housing market. However, overlooked are the defaults on loans for motor vehicles or automobiles (cars and light trucks), which are among the largest nonfinancial assets held by Americans. While automobile loans — and automobile insurance when pricing for risk — have some of the same underlying characteristics as mortgage loans, there are differences that have important implications for lenders. In addition to defaults, lenders also incur an effective loss if purchasers prepay their loans. Sumit Agarwal, Brent W. Ambrose, and Souphala Chomsisengphet investigated these issues.1 The following is a summary of their findings.

Lending Behavior and Risks

Marvin M. Smith, Ph.D., Community Development Research AdvisorMarvin M. Smith, Ph.D., Community Development Research Advisor

According to the authors, “roughly three-quarters of automobile purchases are financed through credit, and loans for automobile purchases are one of the most common forms of household borrowing.” Previous studies have shown that “third party financing (direct loans) accounts for the largest portion of the automobile credit market, with dealer financing (indirect loans) second and leasing third.”

Lenders in the automobile market face two main risks. The foremost risk is “default — that is, the person who took out a loan to buy a car or truck fails to pay it back.” While a second important risk is prepayment, where the “car or truck purchaser pays off the loan early, reducing the lender’s stream of interest payments.”

Given the prominence of third-party financing in the automobile market and the pricing method used, the authors question whether the risks are adequately reflected in the price charged for the loan. This apprehension arises since this segment of the market “relies on a ‘house rate’ for pricing loans, such that all qualified borrowers with similar risk characteristics [e.g., credit score and down payment] pay the same rate.” The point of contention is that the lender does not take into account the automobile’s make and model when pricing the loan. This pricing scheme is in contrast with practices presently employed in the auto insurance and mortgage markets. The authors point out that “auto insurers have long recognized that automobile makes and models appeal to different clienteles and that these clienteles have heterogeneous risk profiles and accident rates.” Consequently, automobile insurers take into account the make and model that an applicant is insuring, when pricing the automobile policy. Similarly, mortgage lenders factor in “information on the underlying assets (for example, a house) as well as the borrowers’ personal characteristics” when originating a loan.

Given the importance of underlying assets in the pricing of products in the insurance and mortgage markets, “the question naturally arises as to whether incorporating information on automobile make and model would help third party lenders refine their loan pricing models.” Thus, the authors propose and attempt to answer the following question: “If we assume that the choice of auto make and model reveals individual financial (or credit) risk behavior of the borrower, what does this tell us about the borrower’s propensity to prepay or default on his loan?”

Data and Methodology

The authors used a proprietary data set from a large financial institution to conduct their analysis. The data contain information on automobile loans originated by the financial institution and offered directly to borrowers. The authors focused on direct loans, since this is the market in which lenders can compete, as opposed to indirect loans that are made available through the dealer. The sample for the analysis consisted of 20,466 direct auto loans with four-year and five-year maturities and fixed rates. The authors tracked the performance of the loans from January 1998 through March 2003, “such that a monthly record of each loan [was] maintained until the automobile loan [was] either paid in full (at loan maturity), prepaid, defaulted, or [stayed] current.” Of the total loans in their sample, 4,730 were prepayments and 534 were defaults.2

The authors included several variables that reflect the characteristics of the loan and the borrower. In the former, they included “automobile value, automobile age, loan amount, LTV [loan to value], monthly payments, contract rate, time of origination (year and month), and payoff year and month for prepayment and default.” For the latter, they included the borrower’s credit score, age, and monthly disposable income. They also included the unemployment rate in the county where the borrower resided and used the three-year Treasury note rate as the market rate in the analysis. In addition, the authors knew the automobile’s make, model, and year as well as whether the loan was for the purchase of a used or new automobile. The authors noted that their sample of loans had the following median values: $14,027 for the loan amount, 78 percent for the LTV, 8.99 percent for the annual percentage rate (APR), 723 for the credit score, $3,416 for the monthly disposable income, 40 years of age for the owner, 54 months for the age of the loan, and four years for the age of the car. Further, the car’s “blue book value (the car’s market value) at loan origination [ranged] from $4,625 to $108,000.”3

The authors used an estimation procedure that allowed them to “determine how borrower consumption decisions can affect loan performance,” with a particular focus on the prepayment or default on their loan.


The authors’ analysis produced several noteworthy findings,4 including the following:

  • a loan on a new car has a higher probability of prepayment, whereas a loan on a used car has a higher probability of default;
  • a decrease in the credit risk of a borrower, as measured by the credit score, lowers the probability of default and raises the probability of prepayment;
  • an increase in the LTV increases the probability of default and lowers the probability of prepayment;
  • an increase in income raises the probability of prepayment, while a rise in unemployment increases the probability of default;
  • a decrease in the market rate increases both the probabilities of prepayment and default; and
  • loans on most luxury cars have a higher probability of prepayment, whereas loans on most economy cars have a lower probability of default.

Concluding Observations

The authors hasten to note that their study has some limitations — it only considered direct auto loans that were originated primarily in northeastern states by a single lender. Nonetheless, their “results imply that lenders could improve the pricing of automobile loans by considering the type of car collateralizing the loan.”

  • 1 Sumit Agarwal, Brent W. Ambrose, and Souphala Chomsisengphet, “Determinants of Automobile Loan Default and Prepayment,” Federal Reserve Bank of Chicago Economic Perspectives, 32 (Third Quarter 2008), pp. 17–28.
  • 2 The authors defined prepayment as paying off a loan in full before maturity and default as a loan that is 60 days past due.
  • 3 The authors point out that “these statistics [were] comparable with the overall statistics for a typical auto loan portfolio.”
  • 4 See page 25 of Sumit Agarwal, Brent W. Ambrose, and Souphala Chomsisengphet’s study in Economic Perspectives.