Electronic Thesis and Dissertation Repository


Master of Science




Diana Mok


Financial investment theory has concentrated on risk exposure and returns for decades. Many studies apply financial theory to the real estate market, and some of these studies control for its spatial structure. There is a deficiency, however, in studies that examine the spatial relationship of risk at varying spatial scales and even fewer that do so in a Canadian context. The current study addresses these deficiencies by examining housing returns in the Greater Toronto Area at varying spatial scales with rigours spatial regression techniques. Spatially Autoregressive Lag, Error, and Durbin models are estimated at the Toronto Real Estate Board and Enumeration Area spatial levels. Results of the current study provide insight the social perceptions of housing price risk as well as determining the spatial effects of data aggregation. The current study also provides individuals and institutional investors a more acute perspective of housing price risk in a spatial context.

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Real Estate Commons