The Financing & Economics of Affordable Housing Development: Incentives and Disincentives to Private-Sector Participation
Bibliographic record
Abstract
The development of multi-unit residential housing is a complex, costly, capital-intensive, and risky business, particularly for the major players: real estate developers, owners of rental buildings, and financers of development projects and long-term mortgages. All expect their financial returns to be commensurate with the risks they assume, and all need to cover their investment of time, money, and expertise. The purpose of this paper is to help a broader audience unfamiliar with real estate finance to understand the economics of the major for-profit players, or “how they make money.” Better understanding of the for-profit real estate business and the issues faced by for-profit players in rental development should help generate ideas for incentives (or ways to overcome disincentives) to stimulate greater private-sector involvement in creating affordable multi-unit rental housing. The paper uses simplified financial models to explain and compare the economics of for-profit condo development, for-profit apartment development, and affordable rental development. The models show that a for-profit developer would need to charge luxury rents of more than double an affordable rent level to reach a minimum acceptable profit margin. Charging lower rents means insufficient income to cover interest costs – that is, bankruptcy. This is why it is not economically attractive for the private sector to participate in the creation of multi-unit rental housing, particularly in large urban centres like Toronto. Toronto’s high land prices and construction costs, difficulty obtaining financing on favourable terms, and lack of incentives to create rental apartments make rental development riskier and less profitable than condominium development. This is true even for luxury apartments demanding high rents, and even more so for affordable rental development, which is not economically feasible without significant government subsidies. Even when subsidies are available, private-sector involvement in creating affordable rental is hampered by uncertainty about government commitments to programs that support the creation of affordable rental housing (such programs have sometimes been cancelled with little notice); government requirements that result in higher construction and operating costs for affordable rental buildings; and other irritants that make it difficult and time-consuming to obtain building permits, zoning approval, and construction and mortgage loan insurance. What would it take to increase private-sector participation in creating or helping to preserve affordable rental housing? The people interviewed for this paper had many ideas that would improve the economics by reducing costs and risks and streamlining approval processes. Reducing land costs, potentially by freeing up surplus government land, was considered most important in combination with government grants or tax incentives. There were also ideas for reducing construction costs by lowering soft costs (such as those for environmental assessments or development charges) and by changing building codes to allow less expensive wood frame construction for low-rise rental buildings. Every development is different and many would like to see a “menu” of incentives that could be applied as appropriate for the situation. Improved access to financing at favourable terms was also considered essential. Loan guarantees by government would help remove lenders’ risk in the event of default. Ideas for bringing in new investors included reinstating an updated and more targeted version of the Multi-Unit Rental Building (MURB) tax incentive programs of the 1970s and 1980s and developing new financial vehicles, potentially similar to those in the U.S. or U.K., to attract private investment. Measures to ensure that owners of aging affordable rental stock maintain their buildings appropriately are also needed. Interviewees felt that rehabilitating aging, poorly maintained apartment buildings would not only benefit the tenants, but would also attract a broader mix of incomes to rental housing, reducing the concentration and isolation of low-income tenants. They favoured a combination of “carrots and sticks” for owners who fail to maintain their rental buildings. “Carrots” included tax incentives to free up funds for rehabilitation and “sticks” included stronger enforcement and larger financial penalties for non-compliance. Finally, the paper includes proposals to encourage the sale of rental buildings to non-profit groups to ensure that the units remain affordable – the suggestions included tax incentives, such as deferring tax on capital gains, and new financing vehicles that would enable non-profits to compete with for-profit Real Estate Investment Trusts for properties in good condition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".