Rural Housing Affordability: a Location-Based Investigation of the Characteristics of Those Experiencing Housing Affordability Problems in Ontario
Bibliographic record
Abstract
This report explores locational differences in the incidence of those experiencing housing affordability problems between 1991 and 2001 in Ontario. Three variables were created for this purpose using publicly available census data (PUMF) for: Toronto, CMAs and Other/Rural. The following key research questions were considered: \n1. Are there differences between affordability rates in rural and urban locations? \n2. What are the identified characteristics of low-income households experiencing affordability problems? (e.g. income, education, age, etc.) \n3. Do differences in the identified characteristics between rural and urban locations help to explain differences in the intensity of low-income and housing affordability problems in these locations? \n4. What are the planning implications of any identified differences between rural and urban locations? \nIn considering these questions, this report takes the following structure: \nSummary of general trends in each of the locales (including in population, incidence of low-income, and affordability) Household trends in each of the locales (including household size, income and employment) Primary maintainer characteristic trends in each of the locales (including employment income, labour-force participation, monthly payments, education, gender trends, age, immigrant status). Comparison of immigrant and non-immigrant primary maintainer trends (to consider attraction and impact of immigrant populations to rural areas) \nRecommendations for future planning directions lastly consider loosening restrictive zoning regulations, promoting local control and economic development, and further development of immigrant oriented programming. The investigation revealed that Other/Rural locales experienced comparatively dramatic increases in the proportion of households with affordability problems, and an increasing divide between those above and below the low income cut off (LICO). Several factors were found to have contributed to the change in affordability, including: low relative incomes and rates of full-time work, low relative household size, increase in the proportion renters, increasing proportions university educated low-income, increase in senior aged populations, and increase in immigrant populations.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".