Spendthrifts and Savers: Are Canadians Acting Like they are “House Poor” or “House Rich”?
Bibliographic record
Abstract
Are Canadians acting like they are “house poor” and scrimping on spending in other parts of their lives because of what they pay for homes? What is interesting about this question is the fact that Canadian monthly mortgage bills, measured by the mortgage debt-service ratio, are approximately the same in size as they have been historically. The issue with a flat mortgage debt-servicing ratio, however, is it masks debt composition between interest and principal. With interest rates near zero, it is more likely that rates will rise in the future, causing an increase in mortgage debt-servicing costs. Furthermore, as we are more than seven years removed from the last recession, and there are strong arguments to suggest we have a housing-market bubble in our largest cities, there is additional risk of a negative economic shock. A high-leverage environment would exacerbate this situation. Using Statistics Canada mortgage-debt data broken down between interest and principal, my results suggest that Canadian households, on aggregate, have not slowed non-housing consumption due to this riskier debt environment. Furthermore, households have spent out of accumulated housing wealth, suggesting the removal of a buffer, potentially worsening any negative economic shock should house values fall. While these results are concerning, I find a lack of consumption sensitivity to increases in total debtservicing costs. The implication is that the risk to Canadian households comes more from a negative economic shock than from rising interest rates that raise monthly mortgage payments. From a policymaking perspective, the Bank of Canada can use these results to help model the economy now and into the future given current debt dynamics. Being prepared for a potentially larger consumption impact from a negative economic shock is prudent. Governments at all levels should continue to monitor the effectiveness of their demand-side policies while considering what supply-side policies may be more appropriate in slowing down housing prices and cheap credit growth, thereby lowering debt loads. Options for the government to consider include the balancing of environmental concerns with housing supply growth, pricing the use of infrastructure, and making the application process for development more efficient and transparent.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".