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Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach

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  • Insu Hong

    (Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA)

  • Changsok Yoo

    (Department of Culture, Tourism & Content, Kyung Hee University, Seoul 02447, Korea)

Abstract

A sharing economy accommodation service like Airbnb, which provides trust between strangers to connect them for profiting from underutilized assets, was born and has thrived thanks to the innovations in the platform technology. Due to the unique structure of Airbnb, the pricing strategies of hosts are very different from the conventional hospitality industry. However, existing Airbnb pricing studies have limitations considering the varying scale of operation among hosts, spatial variances in pricing strategies, and crucial geographic information for estimating the influence of the pricing variables, as well as ignoring inter-city variances. In this research, we explored the spatially heterogeneous relationship between price and pricing variables using an innovative spatial approach, Multiscale Geographically Weighted Regression (MGWR). Analysis results for Airbnb listing in Log Angeles and New York in the US showed the effectiveness of MGWR regarding estimating the influence of pricing variables spatially. By revealing spatially heterogeneous and dependent relationships, this research fills gaps in Airbnb pricing research and deepens the understanding of the pricing strategies of the hosts.

Suggested Citation

  • Insu Hong & Changsok Yoo, 2020. "Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach," Sustainability, MDPI, vol. 12(11), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4710-:d:369190
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    1. Bobrovskaya, EV. & Polbin, A., 2023. "Econometric modeling of the demand for short-term rental housing: The case of Airbnb in Moscow," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 64-84.
    2. 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.
    3. Bobrovskaya, Ekaterina & Polbin, Andrey, 2022. "Determinants of short-term rental prices in the sharing economy: The case of Airbnb in Moscow," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 5-28.
    4. Yifei Jiang & Honglei Zhang & Xianting Cao & Ge Wei & Yang Yang, 2023. "How to better incorporate geographic variation in Airbnb price modeling?," Tourism Economics, , vol. 29(5), pages 1181-1203, August.
    5. Trinath Sai Subhash Reddy Pittala & Uma Maheswara R Meleti & Hemanth Vasireddy, 2024. "Unveiling Patterns in European Airbnb Prices: A Comprehensive Analytical Study Using Machine Learning Techniques," Papers 2407.01555, arXiv.org.
    6. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    7. Meijian Yang & Enjun Xia, 2021. "A Systematic Literature Review on Pricing Strategies in the Sharing Economy," Sustainability, MDPI, vol. 13(17), pages 1-28, August.
    8. Lyu, Tao & Wang, Yuanqing & Ji, Shujuan & Feng, Tao & Wu, Zhouhao, 2023. "A multiscale spatial analysis of taxi ridership," Journal of Transport Geography, Elsevier, vol. 113(C).

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