Empowering community and philanthropic funders: The case for investment in non-market housing
Bibliographic record
Abstract
Aotearoa New Zealand is in the middle of a prolonged housing crisis. This is marked by a chronic undersupply of private and non-market housing which has created social, health, and economic inequities. Māori communities are disproportionately affected, consistently experiencing the highest rates of housing insecurity, overcrowding, and unaffordability. Over 20,000 households remain on the social housing waitlist, while private rental costs continue to escalate. The reasons for the housing crisis are complex, structural and deeply entrenched. Housing access and affordability also remain poor across other developed countries, with particularly severe challenges in Commonwealth nations like Canada and Australia, where rising costs and stagnant wages have made homeownership increasingly unattainable for many. This research inquiry investigates how philanthropic and community funders can use impact investments to play their part in addressing the housing crisis. A pragmatism mixed-methodology model was used, including desktop research, namely reviewing literature and data analysis, semi-structured interviews and surveys. This employed purposive sampling and the participants were funders, third-party financiers and housing providers to give a sector wide overview. The exploration shows that philanthropic capital can increase non-market housing supply by providing adaptable, long-term funding unavailable through traditional markets. Collaborative financing, integrating public, private, and charitable resources, is essential to increase housing supply. Sector capacity requires strengthening to manage intricate financial and development arrangements. For Māori, there is a need for culturally responsive and flexible funding, particularly on whenua Māori, where standard financing is unavailable due to restrictive banking and regulatory conditions. Findings advocate for a centralised financing platform to consolidate diverse capital sources and bolster the sector’s financial and governance expertise. Regulatory adjustments are proposed to ease borrowing costs and align frameworks with social housing’s extended, low-profit timelines. Tailored funding solutions for Māori, crafted with iwi, government, and philanthropists, are deemed necessary to address systemic obstacles and advance self-determination. Measuring social outcomes via tools like Social Return on Investment (SROI) is recommended to demonstrate value and draw additional investment. Strong, bipartisan political resolve is urged to allocate substantial resources and coordinate efforts across parties. These measures position philanthropic and community funders as central to expanding non-market housing, offering a practical means to mitigate Aotearoa New Zealand’s housing crisis and foster equitable outcomes, especially for Māori.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".