Studentification: Where? How many? So what?
Bibliographic record
Abstract
This report investigates the growing phenomenon of studentification—the clustering of postsecondary students within urban neighbourhoods—and its implications for housing markets in medium-sized Canadian cities. Focusing on five census metropolitan areas (London, Kitchener–Waterloo, Guelph, Hamilton, and Kingston), the study addresses three key questions: where students are concentrated, how many student dwellings exist, and how studentification is affecting local rents, particularly in marginalized neighbourhoods. Building on methods developed by Lauzon (2022) and Whitfield (2023), the analysis leverages a unique characteristic of Canadian census data, where dwellings fully occupied by domestic students living away from home are often classified as unoccupied. Using this proxy, alongside institutional enrolment figures, residence capacity data, and CMHC vacancy rates, the report maps studentification trends at the Census Tract level from 2006 to 2021. It also introduces new metrics to assess the rental impacts and spatial reach of studentification, including into lower-income areas, and contextualizes trends using data from Queen’s University dating back to 1971. Findings show that studentification has intensified across all five cities, largely due to rising university enrolment (up 49% from 2006–2021) far outpacing residence construction (up only 11%). The private rental market has absorbed much of the shortfall, with an estimated 27% increase in student and short-term rental dwellings. Institutional responses vary: while McMaster and Waterloo have expanded on- or near-campus housing options, Queen’s has lagged behind, contributing to pronounced neighbourhood encroachment. The COVID-19 pandemic temporarily distorted census data, but its effects are considered transient. Additionally, the report highlights that international students—who are increasingly concentrated near colleges—are undercounted in the data due to methodological limitations. The report concludes that studentification is a sustained and accelerating trend with significant implications for housing affordability and neighbourhood change. Policy recommendations include expanding Purpose-Built Student Accommodations (PBSAs), reinstating government-backed financing for student housing, and encouraging institutions to share anonymized student address data with planners to improve monitoring and response efforts.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".