The financialization of rental housing in Montreal: Spatiality, local specificity and empirical methodology
Bibliographic record
Abstract
"During the 1990s, new financial innovations made it easier for individuals, landlords, investors, developers and trusts to invest in Canada’s rental sector. These innovations allowed housing to be treated as an asset capable of producing financial returns, a process known as assetization (Sassen 2014). Securitization also enabled the value of housing to be segmented into multiple portfolios, and their values traded on markets, similar to trading (August 2020a). Housing, through assetization and securitization, was now rendered ‘liquid’, dissociated from its physical entity and more easily capitalized on (Gotham 2009). During the 1990s, government deregulation enabled real estate investment trusts (REITs) to operate on Canadian territory. Financial innovations and the legalization of REITs caused the rental sector to become financially appealing: REITs and other firms thus started acquiring existing rental units en masse. The emergence of financialized landlords within Canada’s multi-family rental housing sector gained momentum following the 2007-2008 Global Financial Crisis (GFC), partly due to the increased rental demand (August and Walks 2018). The introduction of financialized landlords in Canadian geographies has come with rent increases, displacement, cuts in services, submetering, and firing of facilities employees (August 2020a; August and Walks 2018). Additionally, takeovers were sometimes paired with intimidation and threats (Crosby 2020). These companies push for higher turnover rates to increase rents, thereby satisfying their greater financial expectations and achieving the frequent goal of ‘double digit returns’ (Fields 2017; Fields 2019). Such acquisitions have also caused overleveraged buildings, with investors having too much debt to be adequately serviced by the property’s rental income (Fields, see also Schultz 2009). [...] "@eng
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".