LONG-RUN RATE OF RETURN FOR CANADIAN HOME PRICES Highlights
Bibliographic record
Abstract
With the slowdown in the Canadian housing market well entrenched, many are worried about the future value of their homes. This is not surprising as real estate is the largest financial asset most Canadians have in their possession. The housing market is prone to cyclical ups and downs and we should embark on a gradual, modest, downward adjustment over the next three years. We project a 3.5 % annual rate of return on real estate to prevail beyond 2015 – this is the long-run rate of increase for home prices in Canada. However, this pace will be moderately lower than they have been historically (5.4%). A string of lacklustre performances over the next few years will mean that the annual rate of return for real estate in nominal terms will be roughly 2 % over the next decade. In other words, home price gains should simply match the pace of inflation. The long-run rate of return for home prices is primarily driven by macroeconomic fundamentals, such as income and economic growth, and demographics (e.g., population and household formation). Structural changes, including an ageing populace and the number of immigrants as a share of total homebuyers, could influence real estate returns. However, the literature is mixed on whether these
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".