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

Yiduo Chen, Qingyuan Yang (), Li Geng and Wen Yin
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Yiduo Chen: School of Geographical Sciences, Southwest University, Chongqing 400715, China
Qingyuan Yang: School of Geographical Sciences, Southwest University, Chongqing 400715, China
Li Geng: School of Geographical Sciences, Southwest University, Chongqing 400715, China
Wen Yin: School of Geographical Sciences, Southwest University, Chongqing 400715, China

Land, 2024, vol. 13, issue 5, 1-15

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.

Keywords: MGWR model; mountain city; housing price; influencing factors (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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