Lessons from U.S. securitized auto loans for consumer lending in African markets. A loan-level empirical study of what actually predicts repayment when formal credit histories are thin or absent.
This paper examines how consumer credit can be efficiently extended in environments where formal credit histories are limited or incomplete. Using loan-level data on U.S. securitized auto loans disclosed under Regulation AB II and accessed via Wharton Research Data Services, it studies repayment performance, delinquency transitions, and default outcomes in a market where lenders combine collateral values, borrower characteristics, and contract design rather than relying solely on credit scores. The analytic sample comprises 239,374 unique loans drawn from 154 trusts issued by 18 sponsors, yielding just over 4 million loan-month observations between November 2016 and December 2024. Estimating discrete-time hazard, logit, and linear-probability models with trust-clustered standard errors, the paper finds that loan-to-value ratio, vehicle age, and contract maturity retain economically and statistically significant predictive power after conditioning on credit scores, and that removing score information from the model reduces explanatory power only modestly. The results are then calibrated to African consumer-lending conditions, showing that conservative loan-to-value limits and faster amortization can materially reduce projected default. The paper positions collateral-backed consumer credit as a distinct informational architecture for extending responsible credit where histories are thin.
The estimated hazard applied to alternative covariate profiles. These are calibrations, not forecasts, the policy-relevant signal is the gradient across design choices, not the absolute level.
Monthly asset-level performance data on securitized auto loans, accessed through Wharton Research Data Services, deduplicated to one observation per loan-month across amended filings.
A complementary log-log hazard for delinquency onset, a logit for the delinquent-to-default transition, and a two-way fixed-effects linear-probability model to benchmark explained variance, all with trust-clustered standard errors.
Performance is estimated with the continuous score, with score collapsed into three coarse bins, and with score excluded entirely, isolating exactly how much predictive power sits outside formal credit histories.
Results hold after treating prepayment as a competing exit rather than censoring it away, and the stress-period interaction isolates how the same underwriting signals behave under the 2020 and 2022–2023 macro shocks.