Foreign buyer taxes and housing affordability
Bibliographic record
Abstract
Abstract To improve housing affordability jurisdictions in different countries has introduced taxes on nonresident home buyers. We use the foreign buyer tax introduced in British Columbia, Canada, in August 2016 to investigate the extent to which such taxes improve housing affordability through their effect on local house prices. Our work uses direct transaction‐level identification of foreign buyers that resulted from policies prior to the announcement and subsequent introduction of the tax. Using a difference in differences methodology, we compare house price changes pre‐ and posttax between high and low foreign buyer concentration neighborhoods. We find that house prices decline by 6% in neighborhoods with above median concentrations of foreign buyers after the tax relative to prices in neighborhoods with below median concentrations of foreign buyers. The quantitative effects are also striking with overall foreign buyer share falling from 13.2% of single‐family transactions in the 6 weeks prior to the announcement of the tax to 1.7% for the 3 months following the tax. The unique contribution of this article is our use of transaction‐level data to explicitly identify the properties purchased by foreign buyers to create more accurate control and treatment groups than found in other analyses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".