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Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China

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  • Chunfang Zhao

    (Department of Electronic Business, South China University of Technology, Guangzhou 510006, China
    International Business College, Guangzhou City Polytechnic, Guangzhou 510405, China)

  • Yingliang Wu

    (Department of Electronic Business, South China University of Technology, Guangzhou 510006, China)

  • Yunfeng Chen

    (General Education College, Jinan Engineering Polytechnic, Jinan 250200, China)

  • Guohua Chen

    (Network Center, South China Normal University, Guangzhou 510006, China
    Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou 510335, China)

Abstract

An investigation into the pricing mechanism of Airbnb is crucial for achieving the sustainable development of sharing economy accommodations and has great academic and practical significance. The existing pricing studies on sharing economy accommodation have identified a set of important factors impacting prices based on the hedonic price model. However, the spatial scale of the impact of various hedonic attributes on Airbnb listing prices is not yet clear. This study takes Beijing, China, as a case study; develops a conceptual framework that incorporates four categories of hedonic attributes; and uses a spatial heterogeneity perspective to investigate the multiscale spatial effects of various attributes on the prices of Airbnb listings. Our findings revealed the following: (1) The explanatory power of different categories of attributes towards listing prices varies from high to low, as follows: functional attributes, locational attributes, reputational attributes, and host status attributes, among which the functional attributes are the most important determinants of Airbnb listing prices. (2) There are multiscale, spatially heterogeneous relationships between Airbnb listing attributes and prices. Specifically, the functional attribute variables have local influencing scales, the reputation attribute variables have regional scales, and the variables of host status and locational attributes have global scales. (3) Compared with ordinary least squares (OLS) regression and geographically weighted regression (GWR), multiscale geographic weighted regression (MGWR) improves overall modelling ability by introducing multiple scales and is better suited to illuminating the hedonic pricing of sharing economy accommodations. This study provides new insights into the spatially varied relationships between listing attributes and Airbnb listing prices, which can deepen our understanding of sharing economy accommodation and help hosts formulate location-based pricing strategies.

Suggested Citation

  • Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1703-:d:1037449
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    References listed on IDEAS

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    2. Zahyah H. Alharbi, 2023. "A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 15(17), pages 1-19, September.

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