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Dynamic pricing in Airbnb: Individual versus professional hosts

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  • Abrate, Graziano
  • Sainaghi, Ruggero
  • Mauri, Aurelio G.

Abstract

Dynamic pricing is a strategic revenue management tool used by various businesses to optimize profits, and sharing economy lodging has become an interesting context for its use. However, hosts managing multiple listings and nonprofessional hosts have structural and managerial differences. This paper explores the relevance of the degree of professionalization of Airbnb hosts and its relationships with pricing strategies and listing performance. The longitudinal (2016–2019) study analyzes two icons of Italy, Rome and Milan and includes more than 1.2 million observations. The findings show that both the listing performance and the average intensity of price variability tend to increase with the degree of professionalization (number of listings). At the same time, the impact of price variability on performance remains solid regardless of the number of listings. Thus, the study suggests that time-varying pricing is an important and relatively accessible strategy for both professional and nonprofessional hosts.

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  • Abrate, Graziano & Sainaghi, Ruggero & Mauri, Aurelio G., 2022. "Dynamic pricing in Airbnb: Individual versus professional hosts," Journal of Business Research, Elsevier, vol. 141(C), pages 191-199.
    • Handle: RePEc:eee:jbrese:v:141:y:2022:i:c:p:191-199
    DOI: 10.1016/j.jbusres.2021.12.012
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    2. 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.
    3. Xiang, Diandian & Li, Xia & Hampson, Daniel Peter, 2023. "Service exchange activities in the sharing economy: Professional versus amateur peer providers," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    4. Christen, Tatjana & Hess, Manuel & Grichnik, Dietmar & Wincent, Joakim, 2022. "Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings," Journal of Business Research, Elsevier, vol. 152(C), pages 82-92.
    5. Filieri, Raffaele & Alguezaui, Salma & Galati, Francesco & Raguseo, Elisabetta, 2023. "Customer experience with standard and premium Peer-To-Peer offerings: A mixed-method combining text analytics and qualitative analysis," Journal of Business Research, Elsevier, vol. 167(C).
    6. Benoit, Sabine & Wang, Yonggui & Teng, Lefa & Hampson, Daniel P. & Li, Xia, 2022. "Innovation in the sharing economy: A framework and future research agenda," Journal of Business Research, Elsevier, vol. 149(C), pages 207-216.
    7. Jorge V Pérez-Rodríguez & Juan M Hernández & Julián Andrada-Félix, 2024. "Modelling prices and volatilities in the sharing economy," Tourism Economics, , vol. 30(5), pages 1189-1215, August.
    8. Hongbo Tan & Tian Su & Xusheng Wu & Pengzhan Cheng & Tianxiang Zheng, 2024. "A Sustainable Rental Price Prediction Model Based on Multimodal Input and Deep Learning—Evidence from Airbnb," Sustainability, MDPI, vol. 16(15), pages 1-22, July.
    9. Lan-fei Gao & Hui Li, 2024. "Overcoming the pitfalls of economies-of-scale in shared accommodation: How can effective multi-listing management enhance the sustainability of homestay businesses?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    10. Zhang, Leyi & Mahmood, Rosli & Yasin, Ida Md & Ma, Yongpeng, 2024. "User perceptions and continuance intentions: An in-depth analysis of perceived values in amateur-hosted sharing accommodations," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).

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