A Comparison of US and Canadian Residential Mortgage Markets
Bibliographic record
Abstract
As financial markets move toward increased globalization, it becomes worth considering whether inherent differences in financial markets across different countries will diminish. For two countries more similar than different in terms of geography, location, government and culture, Canada and the U.S. remain strikingly different in terms of housing finance. Public policy objectives toward housing followed quite different paths over the past seventy years and fundamental differences in banking practices have led to considerably different outcomes in terms of mortgage finance instruments in the two countries. In light of that, it is particularly surprising that homeownership rates do not diverge by much, reaching 67% in the United States and 64% in Canada by year-end 2000. We examine some of the differences in policy and in competitive practices between Canada and the U.S. in an attempt to illuminate why differences in rates and terms across the two countries still exist. While a part of the difference remains due to legal constraints concerning the finance of the domestic housing sector, we do not attempt an analysis of the legal structure and focus, rather, on the economics and public policy choices that have led to the observed differences.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".