Bibliographic record
Abstract
This paper estimates a reduced form model of the Canadian mortgage demand from 1971 to 2010. Three equations are estimated, one for the average real value of new mortgage loans originated, another one for the number of new loans and a third for the flow of real repayment of existing loans. The results show that the nominal interest rate is the main source of change in the number of new loans while real housing price is the main determinant of the value of new loans. Two other variables, the real per-capita disposable income and the inflation rate, are also significant in changing the flow of new loans originated. A fall in the inflation rate accompanied by a concomitant reduction in the interest rate is in average the main source of increase in households' mortgage debt, because it increase the flow of new loans and at the same time reduces the rate of repayment of existing loans. Between 2000 and 2007, the unprecedented increase in real housing price while inflation was stable became the main factor behind the rise in mortgage debt, mostly because the average mortgage debt increased significantly. After that, the reduction in the interest rate sustained an increase in the number of new loans. The model does not find indications that a change in the supply side of the mortgage market played a significant role in the increased level of mortgage debt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".