Distinguishing between good and bad subprime auto loans borrowers: the role of demographic, region and loan characteristics
Bibliographic record
Abstract
Research on subprime mortgages has recently been gaining momentum, but subprime auto loans have largely been ignored. By using a unique data set of a very large UK vehicle finance company, this study analyses secured loans extended to the subprime borrowers with impaired or limited credit history. It looks specifically at characteristics in relation to payment history, in order to determine what characteristics make a good or bad borrower. We conclude that married and divorced borrowers as well as borrowers living in low unemployment and relatively prosperous regions such as the South East and London are less likely to default compared to not married, furnished tenants or borrowers living in the North West of the UK who have a high probability of default. Similar to the prime loans, income of borrowers and defaults propensities are negatively associated. Loan and security characteristics with the most impact on default status are price and age of the automobile, effective interest rate measured by APR, loan-to-value (LTV) and term of the loan agreement. The results of this study will help in understanding subprime auto loans and borrowers as well as helping lenders to distinguish between good and bad subprime borrowers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".