详情

原文传递 Spatial Autoregressive Analysis and Modeling of Housing Prices in City of Toronto

题名：	Spatial Autoregressive Analysis and Modeling of Housing Prices in City of Toronto
正文语种：	eng
作者：	Zhang, Yu;Zhang, Dachuan;Miller, Eric J.
作者单位：	Univ Toronto Dept Civil & Mineral Engn Toronto ON M5V 1B1 Canada;Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Peoples R China;Univ Toronto Dept Civil & Mineral Engn Toronto ON M5J 1T1 Canada
关键词：	Housing price modeling;Geographical weighted regression (GWR);Random forests (RF) model
摘要：	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.
出版年：	2021
期刊名称：	Journal of Urban Planning and Development
卷：	147
期：	1