Spatial Autoregressive Analysis and Modeling of Housing Prices in City of Toronto
Publication: Journal of Urban Planning and Development
Volume 147, Issue 1
Abstract
Previous housing price studies based on hedonic price modeling have mainly focused on applying various factors, including built environment variables in the analysis, without establishing a comprehensive theoretical framework as a basis for the model formulation. To address this gap, this study introduces a more systematic framework for decomposing housing prices into land prices as determined by built form, neighborhood socioeconomic characteristics and individual dwellings' physical conditions. Following this logic, this study experiments with the related variables through regression analysis, including consideration of spatial lags, as well as develops a housing price model using a random forests (RF) algorithm. A comprehensive time-series database of housing transaction data for the City of Toronto is used. Modeling results show that neighborhood socioeconomic factors contribute the most to the explanation of housing prices, while housing characteristics and accessibility measures are also significantly influential. The RF model achieves an overall accuracy of 85%, a relatively good performance in reproducing observed prices. The findings provide insights for planners concerning factors influencing housing prices and, hence, residential location decision-making.
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Journal of Urban Planning and Development
Volume 147 • Issue 1 • March 2021
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© 2021 American Society of Civil Engineers.
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Received: May 15, 2020
Accepted: Sep 15, 2020
Published online: Jan 11, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 11, 2021
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