The Effect of State Anti-Predatory Lending Laws on the Mortgage Market
Bibliographic record
Abstract
I study the impact of state anti-predatory lending (APL) laws on the expansion of riskier loans. Banks were supplying low quality mortgages to risky borrowers via predatory practices, such as refinancing with higher fees, lending without regard for the ability to repay and inflating property values above the market price .In response to predatory lending practices, states began implementing APL laws between 1999 to 2006. However, this legislation was partially offset when the Office of the Comptroller Currency (OCC) exempted national banks from APL laws in 2004. I use the 2004 federal preemption rule, as an exogenous shock to assess the causal impact of APL laws on the mortgage market via national banks. I find that after the federal preemption rule, higher growing national banks increase loan origination by 10% relative to state banks. National banks increase the private share by 1.8% and the growth in marginal GSE by 3% in states with tougher APL laws.
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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 | 0.003 | 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 | 0.001 | 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".