Policy Forum: The Prevalence of Low Income Tax Payments Among Owners of Expensive Homes in Vancouver and Toronto
Bibliographic record
Abstract
In 2018, the top 5 percent of homes in Greater Vancouver, based on their property value, had a median value of $3.7 million, but the median owner of a home in this group paid income taxes of just $15,800. Using data from the Canadian Housing Statistics Program, the authors analyze the relationship between homeowners' income tax payments and the value of the homes they own. In metropolitan Toronto, the elasticity of non-corporate owners' income taxes paid with respect to property value appears to be in line with that in many US cities, at roughly 0.7. (A 10 percent increase in property value is on average associated with a 7 percent increase in income tax paid.) In metropolitan Vancouver, that same elasticity is only about 0.3 or 0.5, depending on whether the elasticity is calculated on the basis of medians or means, and would be at or near the bottom among US metropolitan areas. These results call into question the overall progressivity of taxation in Greater Vancouver. We provide mixed evidence concerning the role of foreign buyers in making Vancouver's income tax-property value relationship weak. In contrast to other Canadian and US metropolitan areas, Vancouver exhibited a particularly weak relationship between income tax and property value between 2011 and 2016. A modest minimum income tax based on property value could raise billions of dollars annually in both the Vancouver and Toronto metropolitan areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".