Mediating Real-Estate: Understanding Property Platforms in Toronto’s Residential Real-Estate Markets
Bibliographic record
Abstract
Property platforms are promising to reduce the risks, time, and costs associated with finding and securing a home; they are claiming to improve existing real-estate processes and operations using insights derived from spatial and user-data. In light of these claims, this research examines the impacts of these new property platforms on real-estate markets in Toronto, Canada. Though the material implications of these technologies have yet to fully manifest, I argue that property platforms are covertly impacting real-estate markets in three main ways: they are allowing new entrants and intermediaries into the field of real-estate, they are leveraging societal crises to rationalize the development and adoption of their technologies, and they are merging and consolidating out of economic necessity paving the way for monopoly power over spatial data. As these new entrants gain access to real-estate and authority over its processes, I contend that property platforms should be understood as a conduit through which financial and technological actors can assert unilateral power over our spatial and behavioural information. Platforms’ newfound dominion over how we find, valuate, buy, sell, manage, develop, and build real-estate prefigures their potential to exert unprecedented influence and control over physical space.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".