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Hedonic pricing and the sharing economy: how profile characteristics affect Airbnb accommodation prices in Barcelona, Madrid, and Seville

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  • Baldwin Tong
  • Ulrich Gunter

Abstract

The sharing economy has allowed people from all over the world to more effectively utilize their assets. Owners or controllers of assets in the sharing economy are free to set any price they want subject to prevailing market demand as long as they operate in an imperfectly competitive market environment. This paper examines how various characteristics of an Airbnb listing (size, number of photos, ratings, host responsiveness, superhost status, distance from city centre, etc.) affect the prices of accommodation and determines which factors strongly affect price using weighted least squares (WLS) and quantile regression. A hedonic pricing model was developed and applied to data from the cities of Barcelona, Madrid, and Seville to determine how the different characteristics of an Airbnb listing affect the price of accommodation in these major three Spanish tourist cities. The estimation results, which are resilient to various robustness checks, indicate that overall rating as well as characteristics indicative of the size of the accommodation have the strongest positive influence on price, while the number of reviews and distance from the city centre have the strongest negative influence on price.

Suggested Citation

  • Baldwin Tong & Ulrich Gunter, 2022. "Hedonic pricing and the sharing economy: how profile characteristics affect Airbnb accommodation prices in Barcelona, Madrid, and Seville," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(20), pages 3309-3328, October.
    • Handle: RePEc:taf:rcitxx:v:25:y:2022:i:20:p:3309-3328
    DOI: 10.1080/13683500.2020.1718619
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    Cited by:

    1. Mohamed Amr Sultan & Tomaž Kramberger & Mahmoud Barakat & Ahmed Hussein Ali, 2023. "Barriers to Applying Last-Mile Logistics in the Egyptian Market: An Extension of the Technology Acceptance Model," Sustainability, MDPI, vol. 15(17), pages 1-25, August.
    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. Marius-Ionuț Gordan & Valentina Constanța Tudor & Cosmin Alin Popescu & Tabita Cornelia Adamov & Elena Peț & Ioana Anda Milin & Tiberiu Iancu, 2024. "Hedonic Pricing Models in Rural Tourism: Analyzing Factors Influencing Accommodation Pricing in Romania Using Geographically Weighted Regression," Agriculture, MDPI, vol. 14(8), pages 1-22, July.
    4. Chaang-Iuan Ho & Tzong-Shyuan Chen & Chin-Pei Li, 2023. "Airbnb’s Negative Externalities from the Consumer’s Perspective: How the Effects Influence the Booking Intention of Potential Guests," Sustainability, MDPI, vol. 15(11), pages 1-28, May.
    5. 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.

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