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Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing

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  • Yiduo Chen

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China
    Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing 401147, China
    Chongqing Institute of Geology and Mineral Resources, Chongqing 401120, China
    China Chongqing Huadi Engineering Investigation Designing Institute, Chongqing 401120, China)

  • Qingyuan Yang

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China
    Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing 401147, China)

  • Li Geng

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China
    Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing 401147, China)

  • Wen Yin

    (School of Geographical Sciences, Southwest University, Chongqing 400715, China
    Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing 401147, China)

Abstract

By leveraging a multiscale geographically weighted regression (MGWR) model, this paper delves into the intricate factors that influence housing prices in the prototypical mountainous cityscape of Chongqing’s central urban area. The key findings are as follows: Firstly, the distribution of housing prices in the study region exhibits pronounced spatial heterogeneity, with the core area exhibiting a distinct “high-high” clustering pattern and manifesting characteristics of a multicenter group distribution. Secondly, the MGWR model effectively assigns an individual bandwidth to each feature quantity, allowing for a more nuanced portrayal of the varying influence scales exerted by diverse variables. Lastly, the study reveals that factors such as property cost, greening rate, building age, and proximity to rivers have a notable negative impact on housing prices, whereas, educational facilities exert a marked positive influence. Elevation, floor area ratio, and distance from the Central Business District (CBD) exhibit a more complex influence on housing prices.

Suggested Citation

  • Yiduo Chen & Qingyuan Yang & Li Geng & Wen Yin, 2024. "Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing," Land, MDPI, vol. 13(5), pages 1-15, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:602-:d:1386583
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    References listed on IDEAS

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    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
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    6. Wei Dong & Xinqi Fu & Yang Zhang & Guowei Qin & Tapan Senapati, 2022. "Research on the Policy Effect of Property Tax Reform: Take the Pilot Reforms in Chongqing and Shanghai as Examples," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-13, June.
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