Evaluating Affordable Housing Outcomes in Toronto: An Analysis of Density Bonusing Agreements
Why this work is in the frame
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Bibliographic record
Abstract
Owing to limited public-sector funding, municipalities have increasingly relied on the private sector to help build affordable housing. Some cities have employed value capture tools – such as incentive zoning (which may involve density bonusing and other incentives) – to address housing affordability problems. These tools use the increase in land value that results from public actions (such as rezoning) to pay for affordable housing. In Toronto, the City has secured such affordable housing contributions largely through the development approvals process and individual negotiations with developers. This process has been facilitated through Section 37 of Ontario’s Planning Act, which permits the City to approve increases in height or density or both above the limits allowed by current zoning in exchange for community benefits. Very little research has examined how effective this density bonusing approach has been in producing affordable housing in Toronto. This paper examines Section 37 agreements from 1988 to 2018 that contain affordable housing benefits to show the housing outcomes achieved through Toronto’s approach. In November 2021, the City of Toronto adopted a new inclusionary zoning policy that requires developers to set aside a percentage of new housing units as affordable housing. So it is important to analyze Section 37 data and map where, how many, and what type of affordable units were produced under the previous affordable housing governance structure to create a baseline against which a future approach could be evaluated. The results of the analysis show that while Section 37 has managed to generate some physical affordable units, the tool has been more successful at securing funding (more than $65 million) for affordable housing. Unfortunately, these cash contributions translate into relatively few units. Moreover, the funds have been received in many small amounts over the years, further reducing the effectiveness of this approach to creating new affordable housing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 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 it