Property Risk, Foreclosure Costs and Rational Lending Discrimination
Bibliographic record
Abstract
Observations of significant differences in access to credit, loan terms and the volume of lending between demographically distinct groups of borrowers are often interpreted as evidence of potential ethnic, racial or gender discrimination by lenders. The competitive structure of credit markets and the accuracy of measuring individual credit risk render extant models of lending discrimination based on assumptions of credit market inefficiencies, such as adverse selection, increasingly implausible. In stark contrast to existing models of demographic discrimination, we consider a model of mortgage lending in an economy having complete markets, common knowledge and arbitrage-free pricing. Market equilibria in this classical environment may exhibit discrimination even when borrowers, who are distinguished only by observable demographic traits, share an identical measure of individual credit risk. Relatively costlier loan terms, a higher frequency of loan denials, or a complete rationing of credit to a particular demographic class of borrowers may be a value-maximizing strategy when rational lenders perceive that one or more such traits are directly related to adverse features of the representative property securing the loan to a borrower in this class. Omitted from standard statistical underwriting and regulatory review procedures, these features reduce the value of the collateral available to the lender in the event of future default. When loans are secured by such properties and both lenders and borrowers act strategically, discrimination on this basis will be a property of all market equilibria and can be consistent with an efficient allocation of credit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".