Dynamics of affordability and immigration in the Canadian housing market
Bibliographic record
Abstract
Purpose This study aims to make two major contributions. First, given the literature gap in housing unaffordability for different immigrant groups in Canada, it makes an essential contribution to the literature. To the best of the knowledge, this study is the first study of its kind to examine housing unaffordability by examining different immigrant groups. Second, differences in unaffordability can help understand the decline in welfare, as it can have financial implications and a negative impact on health outcomes. Third, this study’s findings are valuable for policy formulation to improve immigrant integration and ease the housing unaffordability crisis. Design/methodology/approach This study examines the determinants of housing affordability to investigate differences among various immigrant groups in Canada. A bivariate logit model using public microdata from the Canadian census estimates the determinants of moderate and severe unaffordability. Additionally, the separation of tenants and owners provides insights into the dynamics of unaffordability. The results show significant differences between immigrant groups with higher levels of unaffordability among Asian immigrants. The insights can help devise and implement housing assistance programs to address the challenges arising from the post-COVID-19 pandemic phase. Findings The results indicate that unaffordability declines with increasing age, education and full-time employment. Gender dynamics are evident, with women faring worse than men regarding the likelihood of extreme housing unaffordability. Households face a greater likelihood of unaffordability in more populous provinces and larger census metropolitan areas that struggle with the high cost of living, racial disparities and low income. Immigrants, especially from Asia, Africa and the Middle East, continue to struggle with chronic and severe unaffordability issues. The impact is much more severe for those renting, exemplifying the strain it is taking on the financial health of recent immigrants. Originality/value Given the literature gap in housing unaffordability for different immigrant groups in Canada, it makes an essential contribution to the literature. To the best of the knowledge, this study is the first study of its kind to examine housing unaffordability by examining different immigrant groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".