Bibliographic record
Abstract
Problem, research strategy, and findings The financialization of housing is a rapidly growing concern for planning researchers and policymakers, but the opacity of property ownership in most cities has hampered efforts to rigorously measure the phenomenon. Here we introduce a new approach based on big data methods. By combining web scraping of property assessment, business registry, and rental advertisement data, we reliably identified the networks of property ownership lurking behind anonymous numbered companies and established the extent of financialized rental housing ownership. We demonstrate the effectiveness of this approach with a quantitative case study of the financialization of rental housing in Montreal (Canada). Using spatial regression and clustering analyses, we found that there are two distinct types of financialized rental housing ownership in Montreal: one characterized by precarious and student tenants and another characterized by affluent tenants. In general, high proportions of financialized ownership are associated with higher levels of housing stress and dense housing typologies.Takeaway for practice By demonstrating meaningful differences in housing market outcomes across financialization status—which has not usually been readily accessible to either renters or planners—our findings show the importance of rental market information asymmetry. Planners should treat landlord data as one component of the information necessary to properly regulate a rental housing market. Municipalities should make property ownership information publicly accessible to facilitate public scrutiny of residential land use and more effective protection of tenant rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".