Bibliographic record
Abstract
This paper provides background information that the public can use to assess the merits and consequences of introducing a land transfer tax in Alberta. Land transfer taxes are levied when real property is transferred from one owner to another. Five provincial governments levy land transfer taxes and in Ontario and British Columbia, they raise substantial amounts of revenue for the provincial governments. What is also clear is that land transfer taxes are very volatile sources of tax revenue that increase rapidly during housing market booms, but then decline sharply when housing markets crash. The econometric evidence on the impact of land transfer taxes on housing prices and sales volumes, based on the experiences in different countries, is somewhat mixed, but most studies indicate that a substantial share of the burden is borne by current homeowners through reductions in housing sales prices and many studies find that land transfer taxes significantly reduce the volume of residential real estate transactions. The authors of many of the studies that we review conclude that residential property tax is a better source tax revenue than a land transfer tax because it causes few distortions in the housing market. We estimate that a one per cent land transfer tax in Alberta would have yielded between $480 and $500 million in 2017. The value land transfers in Alberta can vary substantially from year to year, making a land transfer tax in Alberta would be a highly volatile source of tax revenue. A land transfer tax would likely exacerbate the volatility of total provincial revenues, making budgeting and fiscal decisions even more difficult than currently. A one percent land transfer tax on an average land transfer in 2015 would represent six to seven percent of median household income in Edmonton and Calgary. This would be significantly higher than the four to five percent land transfer tax burden on the residents of other Alberta cities because of the higher housing prices in Alberta’s two largest cities. We conclude that a land transfer tax is an inferior source of tax revenue and we are not in favour of the introduction of a land transfer tax in Alberta.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".