In this paper, a network-based hedonic model (NLHM) is proposed based on geographically weighted regression (GWR) and extended space syntax (sDNA), which improves the traditional hedonic price model. Using NLHM, we focus on the spatio-temporal effects of street layout on the housing price in Guangzhou from 2014-2015. Results provide the following conclusions. (1) If we don’t consider the effect of road network, variables like subway, property management fees and whether fine decoration or not will exert a significant positive effect on housing price. (2) The central area of Guangzhou is mainly located in the foreground network core of closeness and betweenness, while the suburban area is located in the background network core. (3) There is a positive correlation between closeness and hosing price, whereas a negative correlation is shown between betweenness and hosing price. The influence of closeness on hosing price is greater than that of betweenness, and the two parameters’ influence coefficients will decrease if analysis radius increases. (4) The spatial patterns of closeness in different analysis radius illustrate a similar characteristic that closeness is high in suburban area and low in central area, while the spatial patterns of betweenness depict distinctive characteristics in different radius, for instance, the central area demonstrates high value and low value in local scale radius and global scale radius respectively. (5) The coefficient of subway variable decreases with the increase of analysis radius, while the variation of other variables’ coefficients (e.g. property management fees, the ratio of parking, whether fine decoration or not) is smaller.
GU Hengyu
,
SHEN Tiyan
,
ZHOU Lin
,
CHEN Huiling
,
XIAO Fan
. Measuring Street Layout’s Spatio-Temporal Effects on Housing Price Based on GWR and sDNA Model: The Case Study of Guangzhou[J]. Economic geography, 2018
, 38(3)
: 82
-91
.
DOI: 10.15957/j.cnki.jjdl.2018.03.010
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