Unregulated Lending, Mortgage Regulations and Monetary Policy
Bibliographic record
Abstract
Policy-makers have supported the resilience of the housing market by adopting rules to encourage prudent lending practices by mortgage lenders. These measures are often aimed at the traditional banking sector, while non-depository financial institutions or shadow banks have limited or no prudential regulations. Could this shift credit intermediation toward unregulated lenders? This paper evaluates the effectiveness of macroprudential policies when regulations are uneven across mortgage lender types. We look at credit tightening that results from macroprudential regulations and examine how much of it is counteracted by credit shifting to unregulated lenders. We also study the impact of monetary policy tightening when some lenders are unregulated. Our results show that the presence of unregulated lenders weakens the impact of the policies on house prices, household debt and output. We also find that leakage to unregulated lenders increases when monetary policy is tightened.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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 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".