Housing market dynamics and macroprudential policies
Bibliographic record
Abstract
Abstract In this paper, we analyze the implications of macroprudential and monetary policies for credit cycles, housing market stability and spillovers to consumption. We consider a countercyclical loan‐to‐value (LTV) policy that responds to a credit‐to‐income ratio, and we compare its effectiveness with a permanent tightening of the LTV ratio and a monetary policy rule that responds to credit. To this end, we construct a dynamic stochastic general equilibrium model with housing market, household debt and collateral constraints, and we estimate it with Canadian data using Bayesian methods. Our study suggests that a countercyclical LTV ratio is a useful policy to reduce spillovers from the housing market into consumption and to lean against housing market boom–bust cycles. It performs better than the permanent tightening of the LTV ratio—a policy that has been used in a number of countries—and the monetary policy rule, both in terms of the stabilization of household indebtedness and spillovers into consumption. Monetary policy that leans against the wind is the least desirable due to its large adverse consequences on the real economy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".