Foreign buyer taxes and housing affordability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 | 0.001 |
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 it