Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data

Hao Wu, Hongzan Jiao, Yang Yu, Zhigang Li, Zhenghong Peng, Lingbo Liu and Zheng Zeng
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Hao Wu: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Hongzan Jiao: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Yang Yu: Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
Zhigang Li: Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
Zhenghong Peng: Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China
Lingbo Liu: Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
Zheng Zeng: Department of School of Arts & Communication, China University of Geosciences, Wuhan 430074, China

Sustainability, 2018, vol. 10, issue 5, 1-17

Abstract: With the commercialization of housing and the deepening of urbanization in China, housing prices are having increasing influence on the land market, and thus indirectly affecting urban development. As various spatial features of an urban housing property directly affect its price, the study of this connection has significance for urban planning. The present study uses mainly open internet data of housing prices, supplemented by other data sources, to identify the spatial features of housing prices and the influence factors in a case study city, Wuhan. Methods employed in the study include the hedonic linear regression model, the geographically weighted regression (GWR) model and the artificial neural network (ANN) model, etc. Progress is made in the following two aspects: first, when calculating the influence factors, hierarchical values for accessibility variables of certain public facilities are used instead of simple Euclidean distance and the results shows a better model fit; second, the ANN model shows the best fit in the study, and while the three models all show respective strengths, the combined use of all models offers the possibility of a more comprehensive analysis of the influence factors of housing prices.

Keywords: regression model; housing prices; geographically weighted regression; influence factor; hedonic model; artificial neural network (ANN); geographically weighted regression (GWR); urban planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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