Evictions, spatial inequality, and the financialization of rental housing in Toronto
Bibliographic record
Abstract
This paper examines the link between the financialization of rental housing and the rate of landlord eviction filings in Toronto, Canada. Our analysis is based on a novel database of all purpose-built rental properties (with 20 + units) in the city, linked to all eviction applications filed in the pre-pandemic decade (2010-2019), details on ownership and landlord-type, and socio-spatial data. Mirroring the extractive intensity of financial capitalism, we found that eviction filing behavior intensified along the spectrum of landlord types – from the least, to the most commodified and “financial” – with financial firms behaving most aggressively on all measures. Financial firms had the highest filing rates (eviction filings per 100 units annually), they increased filing rates the most after buying properties (by three times on average), and targeted properties in socially marginalized communities more aggressively than all other types of landlords. By contrast, non-market landlords of “decommodified” non-profit and public housing had the lowest filing rates, and similar rates across the city, regardless of neighborhood socio-economic status. Our findings suggest that expanded decommodification and definancialization of housing would protect tenants from eviction and residential instability and promote housing justice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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 teacher head, 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".