Financialization, housing rents and affordability in Toronto
Bibliographic record
Abstract
This article examines the links between financialization, rent increases and spatial inequality in Toronto, Canada. By drawing on qualitative data from the grey literature, corporate records and real estate events, we first find that financial landlords (REITs, REOCs, asset managers, private equity and institutions) attest to rent price increases as a strategy core to their financial structure, leading to a systematic undermining of affordability. Drawing on a Toronto-wide database of property rent levels, we then quantitatively demonstrate that financial firms charge higher rents, charge higher premiums to neighbourhood average rents and post the highest same-property quarterly rent increases, compared to other types of landlords. We analyzed the geography of financialization and rent, finding that financial firms charge the highest premiums to average rents in lower-income and racially marginalized 'Neighbourhood Improvement Area', (NIAs), capitalizing on the higher rent gap potential in devalued areas with lower rent levels. We conclude by stressing the importance of reining in on financial landlords, especially as they have become the largest acquirers of suites in Toronto in the past two decades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".