Do Loan-to-Value Ratio Regulation Changes Affect Canadian Mortgage Credit?
Bibliographic record
Abstract
ABSTRACT: This paper investigates the relationship in the Canadian housing market between loan-to-value (“LTV”) ratios and residential mortgage credit over the 1981-2012 time period. More specifically, I look to determine whether LTV ratio regulation provides a mechanism with which to slow down the potentially overheated Canadian housing market. Due to the endogeneity of many macroeconomic variables, I use a structural vector autoregression (“SVAR”) to investigate this question. Results indicate that three of the four major LTV regulation changes that occurred during this timeframe either had insignificant effects on mortgage credit, or caused it to move contrary to expectations. Only the 2008 tightening of LTV was weakly significant. Therefore, regulation changes to LTV ratios are unlikely to be successful in slowing down the overheated housing market in Canada, which may force central bankers to use broader monetary policy or other forms of macroprudential regulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".