Bibliographic record
Abstract
Housing matters in modern strategies for economic success. It is a complex and important consumption good and asset, and the housing system is one of the key integrative systems in the society and economy, like the labour market or the financial system. Yet thinking on economic and housing policies is disconnected in Canada, Ontario, and Toronto. This study seeks to supply some of the missing connections between housing and the economy in the city of Toronto. It draws upon existing research and data to develop an overview of how the housing sector affects the development of the metropolitan economy. It is important to make these connections, because Canadian housing policymakers and advocates have eschewed economic arguments for housing and set the social consequences of inadequate housing provision at the centre of policy debates; they have failed to make the case for housing effects on economic and environmental outcomes. This neglect has atrophied the field of housing economics within Canadian universities. Canada lags countries such as the United States, Australia, and the United Kingdom in researching relevant issues. Globalization has changed metropolitan housing markets like that of Toronto. Deregulation of special housing finance circuits has brought housing and capital markets closer together in an era of lower inflation. Deregulation in trade has also created new opportunities for specialization and rising incomes, but also required new labour market flexibilities and supported rising inequalities in labour market incomes; in the housing sector this has meant that households with real income growth are expressing significant rising per capita demands for housing. At the same time, many in the Toronto labour force, usually the bottom half of earners, have experienced no or minimal gains in real incomes over the last decade. Older and younger workers have gained the least in the income distribution. This finding has great significance for the future of the city. When differences in wealth are assessed—and the ownership of housing assets lies at the core of these differences—there has been a more marked increase in inequality in Canada, Ontario, and, in all likelihood, Toronto. In many other countries, globalization has encouraged governments to assess tax, debt, and spending decisions more carefully and to root housing policies more firmly in economic decision-making. After making cutbacks in housing, several countries are reassessing the importance of housing policies in dealing with the dysfunctional inequalities and market failures that globalization has brought. Canada and Ontario, however, have not moved in this direction and Toronto seems, relative to most major OECD cities, to be starved of the resources, powers, and intergovernmental cooperation in housing policies that typify successful cities in the global economy. With its growing population, particularly a growing population of low-income households, the housing stock in Toronto is under pressure, while many middle- and upper-income households are leaving the city for new, single detached homes. Another worrying trend is seen in the housing careers of recent immigrants. When compared with earlier waves of new Canadians, recent immigrants are living longer in rental homes, making less progress up the income distribution and seeing their children lag in school and job market performance. The Toronto housing system must be effective in absorbing regional, national, and international moves to ensure the city’s long-term competitiveness. Much of the growth in low-income population in the city, until the early 1990s, was absorbed by social rental housing. Social renting now caters to less than half the expanding set of poor households and this share is falling rapidly. In the city’s private rental sector, rent increases have been modest relative to house prices, but have been increasing at rates faster than incomes in the lower end of the income distribution, raising the real burden of housing payments for Toronto’s poorer households. Vacancy rates were above average between 2002 and 2004, but since then have fallen significantly. Arguments that a high rental vacancy rate means that no policy stimulus for rental investment is needed no longer have any basis in market realities. Poor housing and neighbourhood outcomes have direct productivity effects and can raise costs of non-housing programs aimed at raising human capacities. Human capital levels related to child socialization and learning as well as teenage job and university readiness are significantly affected by housing and neighbourhood quality. Health effects, both physical and mental, which arise from poor housing and neighbourhoods also affect the economy through lowered levels of labour market participation, absenteeism, and reduced productivity. New housing output in the metropolitan area has doubled since 1996. Within the City of Toronto, much of the new stock is in the form of condominium apartments and townhouses; in the GTA as a whole, less than half of new homes are now single-family dwellings. This substantial supply has supported city cost competitiveness for now. But the longer-term prospect is a greater concern, as the environmental and commuting cost consequences of sprawling patterns of development are likely to be high. The Toronto housing market has not experienced the recent instabilities of many U.S. markets, although it may become more exposed in future business cycles. The major housing-toeconomy effects that are likely to affect the GTA in the next two years will flow through reduced exports to the United States and the rise in the Canadian dollar as home defaults in the United States erode consumer confidence and wealth there through 2008. The fundamental Toronto housing problems are not about a frothy cycle gone sour, but about the fundamentally reshaped geographies and inequalities that have emerged for poorer renters in the metropolitan areas over the last decade. Residential spread and poverty concentration have private, community, and wider social costs that have to be addressed to meet competitiveness, cohesion, and sustainability goals. But more work needs to be done to ascertain the scale of the spillover effects involved and their growth effects. The report concludes with recommendations for further study, and for measures to help make the connection between housing and the economy at the local, provincial, and federal levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".