Are Mortgage Rates Bubbling Up Trouble for Canadas Metropolitan Housing Sector
Bibliographic record
Abstract
This paper determines how mortgage rate and income shocks affect new and resale housing prices, housing starts, and housing sales in Canadas metropolitan areas. We assess\nthe variance decompositions and impulse response results to mortgage rate and income shocks. An additional set of VARs is estimated to document whether the stock price, as\nan alternative source of investment, reduces the importance of the mortgage rate. Our results show that the importance of the mortgage rate and income varies significantly\nby metropolitan area and to a lesser degree, by the component of the housing market examined. More precisely, we find that: 1) two of BCs major metropolitan areas housing\nmarkets, Vancouver and Victoria, are vulnerable to interest rate bubble;. 2) Mortgage rates, and by extension the Bank of Canada monetary policy, seems to have little direct\nimpact on Albertas major metropolitan housing markets, Calgary and Edmonton, while income can be expected to have a drastic effect; and 3) The housing markets of Ontarios\nmajor metropolitan area and Canadas Capital Region are prone to mortgage rate bubbles, but the impact is dampened due to their connectedness to national financial markets. What these results mean in terms of policy-making decision is that close attention needs to be paid to housing markets in Canada that are vulnerable to spikes in mortgage rates as we are coming out of the recession provoked by the housing market meltdown in the United States. Although it is true that Banking regulations in Canada have helped weather the storm, with the massive fiscal stimulus implemented in both Canada and the United States,\neventually strong aggregate demand may build up pressure on prices to rise and interest rate will have to increase in order to maintain price stability, thereby causing troubles for mortgagees.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".