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

Land (Basel), 2021-11, Vol.10 (11), p.1148 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2073-445X ;EISSN: 2073-445X ;DOI: 10.3390/land10111148

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  • Title:
    Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
  • Author: Chai, Zhengyuan ; Yang, Yi ; Zhao, Yangyang ; Fu, Yonghu ; Hao, Ling
  • Subjects: Algorithms ; Central business districts ; Cities ; contextualized geographically and temporally weighted regression ; Economic development ; Empirical analysis ; GDP ; Gross Domestic Product ; Heterogeneity ; Housing ; Mathematical functions ; Neighborhoods ; Population ; Population density ; Prices ; Regression analysis ; Regression models ; Residential areas ; Residential density ; residential land prices ; School districts ; Shijiazhuang ; Spatial analysis ; spatial and temporal non-stationarity ; Urbanization ; Variables
  • Is Part Of: Land (Basel), 2021-11, Vol.10 (11), p.1148
  • Description: 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 R2 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2073-445X
    EISSN: 2073-445X
    DOI: 10.3390/land10111148
  • Source: Open Access: DOAJ Directory of Open Access Journals
    AUTh Library subscriptions: ProQuest Central
    ROAD: Directory of Open Access Scholarly Resources
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