Real estate for social purpose: varieties of entrepreneurialism in Toronto’s non-profit housing sector
Bibliographic record
Abstract
Non-profit housing providers have faced ongoing pressure from neoliberal restructuring policies since the late twentieth century. In reaction to funding cuts and policies requiring them to become more business-like, housing organisations have become more hybrid, incorporating entrepreneurial logics and practices from the real estate sector. We expand on the concept of hybridity to argue that under certain institutional contexts, non-profits can apply real estate entrepreneurialism towards their social housing missions. Analysing development and acquisition practices of 13 non-profit housing providers in the Greater Toronto Area, we explore how non-profits balance entrepreneurial practices with their commitment to de-commodified housing. Three types of hybrid organisations are identified: large economy-of-scale organisations that prioritise growth and real estate professionalisation; service-focused organisations whose mission statement limits their growth aspirations; and newcomers, whose forays into housing development face both internal capacity limits and criticism from veteran organisations. The variances in hybridisation processes across and within institutional contexts, we find, require a more nuanced theorisation of the longer-term implications of neoliberalisation on social housing. Learning from Toronto’s budding social purpose real estate sector, we identify key resources for entrepreneurial housing non-profits: building sectoral assets, knowledge sharing, risk management, and a balance between organisational diversity and scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".