Bibliographic record
Abstract
We investigate how macroprudential policies intended to dampen rises in debt and house prices are influenced by segmentation in the housing and mortgage market. We develop a modeling framework with two mortgage submarkets: a government-insured sector with loose LTV limits and tight PTI limits, and an uninsured sector displaying the reverse pattern. This form of heterogeneity is modeled after the Canadian mortgage system, but is common in countries around the world. We find that this segmentation has important consequences for the effectiveness of macroprudential policy. While tightening payment-to-income (PTI) limits is highly effective at dampening a housing boom in a one-sector system, tightening these limits in the insured sector only is much weaker, due to substitutions into the uninsured sector. In contrast, the effect of tightening loan-to-value (LTV) limits in the uninsured sector is strengthened by market segmentation, causing price-rent ratios to fall, while the same tightening in the insured sector would counterproductively cause price-rent ratios to rise.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".