Post-Pandemic Rental Housing Affordability Economics in the U.S., U.K., & Canada
Bibliographic record
Abstract
Rental unaffordability is defined as spending more than 30% of a household’s gross income on rent. Post-pandemic inflation and interest rate increases have intensified rental unaffordability. This research examines rental affordability in the U.S., the U.K., and Canada. It also explores the effect of renters’ “affordability knowledge” – defined as the expertise tenants have and use to make economical rental housing choices – on rent expenditure and affordability positioning and compares personal finances, economic perspectives, and demographics based on renters in affordable and unaffordable situations. The results show that nearly two-thirds of the renters studied are in unaffordable rental situations. Interestingly, affordability knowledge was found to reduce rent spent and increase affordability situations. Significant demographic differences were found between those in affordable and unaffordable rental situations, including rent spending, food spending, transportation spending, savings, perceived homeownership likelihood, and age. The research offers important insight into current rental affordability economics, recommendations for policymakers, and opportunities for real estate organizations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".