Neighbourhood immigration, displacement of native-born, and local housing price in Toronto, Vancouver, and Montréal, 2001-2011
Bibliographic record
Abstract
This paper examines the impact of immigration on local housing price – house valuation in particular – at the neighbourhood level, and the role of displacement of native-born by immigrants in the dynamics. Using Canadian census data for Toronto, Vancouver, and Montréal in 2001, 2006, and 2011, I adopt a three-step procedure to identify the effect of net inflows of immigrants on neighbourhood house values, the effect on native mobility, and the link between these two effects accordingly. The empirical evidence shows that net inflows of immigrants to a neighbourhood: 1) escalate the growth of average house value of the neighbourhood (after controlling for the impact of housing supply); and 2) cause displacement of previous native residents. Further analyses reveal that house values appreciate more in areas that were initially immigrant neighbourhoods – namely those with a high concentration and a large share of immigrant residents initially – than in other locations for a similar increase in the immigrant population; besides, increases in the immigrant population also cause house values to grow faster than city average in immigrant neighbourhoods. This is likely because of a weaker displacement effect on native residents in immigrant neighbourhoods than in other areas. Further evidence indicates that the relocation of native-born in response to increasing immigrant population is partially attributed to the inconvenience they experience in social interactions. The paper concludes with policy implications for the provision of affordable housing and the integration of immigrants in Toronto, Vancouver, and Montréal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".