Abstract
Proximity to transportation infrastructure (highways and public transit) influences residential real estate values. Housing values also are influenced by propinquity to a shopping facility or a recreational amenity. Spatial autoregressive (SAR) models were used to estimate the impact of locational elements on the price of residential properties sold during 1995 in the Greater Toronto Area. A large data set consisting of 27,400 freehold sales was used in the study. Moran’s I was estimated to determine the effects of spatial autocorrelation that existed in housing values. SAR models, using a combination of locational influences, neighborhood characteristics, and structural attributes, explained 83 percent variance in housing values. Using the “comparable sales approach,” a spatiotemporal lag variable was estimated for every property in the database. This research discovered that SAR models offered a better fit than nonspatial models. This study also discovered that in the presence of other explanatory variables, locational and transportation factors were not strong determinants of housing values. On the other hand, the number of washrooms and the average household income in a neighborhood were found to be significant determinants of housing values. Stepwise regression techniques were used to determine reduced spatial hedonic models.
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