Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

Zhengyuan Chai, Yi Yang, Yangyang Zhao, Yonghu Fu and Ling Hao
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Zhengyuan Chai: School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China
Yi Yang: School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China
Yangyang Zhao: Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Yonghu Fu: School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China
Ling Hao: Meteorological Observation Centre, Lianyungang Meteorological Bureau, Lianyungang 222000, China

Land, 2021, vol. 10, issue 11, 1-20

Abstract: A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R 2 value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China.

Keywords: residential land prices; spatial and temporal non-stationarity; contextualized geographically and temporally weighted regression; Shijiazhuang (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:11:p:1148-:d:666677

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