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Determinants of the Price of Airbnb Accommodations Through a Weighted Spatial Regression Model: A Case of the Autonomous City of Buenos Aires

Author

Listed:
  • Agustín Álvarez-Herranz

    (Faculty of Social Sciences, University of Castilla-La Mancha, Avda. de los Alfares 44, 16071 Cuenca, Spain)

  • Edith Macedo-Ruíz

    (European University of Madrid (Alcobendas Campus), Avda. Fernando Alonso 8, 28018 Alcobendas, Spain)

  • Eduardo Quiroga

    (Faculty of Economics, National University of La Plata, Calle 6 No. 777, Ciudad de La Plata 1900, Buenos Aires, Argentina)

Abstract

In the context of the global growth of the collaborative economy, Airbnb has established itself as one of the most influential players in the transformation of the tourist accommodation market, especially in the reconfiguration of urban tourist accommodation. This article examines empirically and critically how this platform operates in Buenos Aires, the most visited city in Argentina and one of the main tourist hubs in South America. Based on a database of 17,249 active listings, the price formation of accommodations is analyzed using a comparative methodological approach between a general linear model (GLM) and a geographically weighted regression (GWR) model. While the GLM allows for capturing general patterns, the GWR reveals significant territorial differences, offering a detailed reading of the spatial behavior of prices in the city. The results show that variables such as the capacity of the accommodation, its type (full house), the host’s condition, the users’ ratings and the proximity to strategic points such as the subway or Plaza de Mayo have a significant influence on prices. In addition, it is shown that the influence of these variables varies by neighborhood, confirming that the pricing logic in Airbnb is deeply territorialized. This study not only provides novel empirical evidence for a Latin American city that has been little explored in the international literature, but also offers useful tools for hosts, urban planners and public decision makers. Its main contribution lies in showing that prices respond not only to accommodation attributes, but also to broader spatial inequalities, opening the debate on the effects of Airbnb on housing access and urban management in cities with strained real estate markets. By shedding light on these territorial asymmetries, the study offers valuable insights for public policy and urban governance and contributes directly to the achievement of Sustainable Cities and Communities (SDG 11), while also supporting Industry, Innovation and Infrastructure (SDG 9) and Reduced Inequalities (SDG 10), by providing practical knowledge that fosters more equitable and sustainable urban development.

Suggested Citation

  • Agustín Álvarez-Herranz & Edith Macedo-Ruíz & Eduardo Quiroga, 2025. "Determinants of the Price of Airbnb Accommodations Through a Weighted Spatial Regression Model: A Case of the Autonomous City of Buenos Aires," Sustainability, MDPI, vol. 17(21), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:21:p:9364-:d:1776748
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    References listed on IDEAS

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    1. 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.
    2. Daniel Guttentag, 2015. "Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(12), pages 1192-1217, December.
    3. Agustín Cócola Gant, 2016. "Holiday Rentals: The New Gentrification Battlefront," Sociological Research Online, , vol. 21(3), pages 112-120, August.
    4. Zhihua Zhang & Rachel J. C. Chen & Lee D. Han & Lu Yang, 2017. "Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach," Sustainability, MDPI, vol. 9(9), pages 1-13, September.
    5. Lorde, Troy & Jacob, Jadon & Weekes, Quinn, 2018. "Price-Setting Behavior in a Tourism Sharing Economy Accommodation Market: A Hedonic Price Analysis of AirBnB Hosts in the Caribbean," MPRA Paper 95475, University Library of Munich, Germany.
    6. Fang, Bin & Ye, Qiang & Law, Rob, 2016. "Effect of sharing economy on tourism industry employment," Annals of Tourism Research, Elsevier, vol. 57(C), pages 264-267.
    7. Davide Proserpio & Wendy Xu & Georgios Zervas, 2018. "You get what you give: theory and evidence of reciprocity in the sharing economy," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 371-407, December.
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