Bibliographic record
Abstract
The city of Vancouver has seen record high home prices in recent months. The housing price inflation has led to the city becoming one of the least affordable cities in the world in terms of housing. The rapid price increase has been blamed on various supply and demand side issues. This paper argues that foreign capital is playing a significant role in driving up home prices in Vancouver and proposes a series of policy options to mitigate the impact of foreign capital. Using secondary data analysis, the paper demonstrates that factors such as income, population growth, and supply of homes have not influenced home prices at a significant level. The paper provides evidence that influx of large sums of foreign capital and investment activity of High Net Worth Individuals are the primary drivers of housing prices in the city. Keeping this in mind, the policy options in the paper have been developed through substantive background research, expert interviews, jurisdictional scan and secondary data analysis. Policy options are evaluated using a criteria and measures matrix, which reflects the societal and government management objectives of effectiveness, budget impact, and stakeholder acceptance. The paper recommends using a Progressive Property Tax in conjunction with a Speculation Tax to mitigate some of the effects of foreign capital.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".