Increasing the Affordability of Rental Housing in Canada: An Assessment of Alternative Supply-Side Measures
Bibliographic record
Abstract
Homelessness is a serious social problem that is unlikely to be solved by grand proclamations or a single policy initiative. It is, more likely, to be solved by the introduction of innumerable changes both in how we understand the problem and how we approach its solution. In this paper we examine the costs and benefits of tax measures that would promote greater involvement of the private sector in the provision of affordable housing. We also examine the costs and benefits of a variety of regulatory initiatives. In an earlier era, centrally directed federal-provincial grant programs for housing run by governments and non-profit organizations were the means of providing affordable housing. Most of the proposals in this paper, in contrast, aim to harness the energy and the efficiency-promoting competition of the private sector. The focus is on decentralized decision-making. Some measures would depend heavily on individual entrepreneurs and non-profit organizations. Others would depend on municipal governments, whose program capacities have grown greatly in recent decades and whose closeness to their constituencies makes them well-placed both to develop and to deliver supportive measures. Our assessment of possibilities suggests that a low-income housing tax credit best balances effectiveness with the need to minimize costs on strained government budgets. Tax measures aimed at investors in multi-unit rental buildings are also likely to meet these criteria.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".