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Spatial price dependencies between the traditional accommodation sector and the sharing economy

Author

Listed:
  • Irem Önder

    (MODUL University Vienna, Austria)

  • Christian Weismayer

    (MODUL University Vienna, Austria)

  • Ulrich Gunter

    (MODUL University Vienna, Austria)

Abstract

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative.

Suggested Citation

  • Irem Önder & Christian Weismayer & Ulrich Gunter, 2019. "Spatial price dependencies between the traditional accommodation sector and the sharing economy," Tourism Economics, , vol. 25(8), pages 1150-1166, December.
    • Handle: RePEc:sae:toueco:v:25:y:2019:i:8:p:1150-1166
    DOI: 10.1177/1354816618805860
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    References listed on IDEAS

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    Cited by:

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    2. Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2021. "The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data," Journal of Transport Geography, Elsevier, vol. 94(C).
    3. Lee, Yong-Jin Alex & Jang, Seongsoo & Kim, Jinwon, 2020. "Tourism clusters and peer-to-peer accommodation," Annals of Tourism Research, Elsevier, vol. 83(C).
    4. Giulia Contu & Luca Frigau & Claudio Conversano, 2023. "Price indicators for Airbnb accommodations," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4779-4802, October.
    5. Shohei Kurata & Yasuo Ohe, 2020. "Competitive Structure of Accommodations in a Traditional Japanese Hot Springs Tourism Area," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    6. Juan L Eugenio-Martin & José M Cazorla-Artiles & Christian González-Martel, 2019. "On the determinants of Airbnb location and its spatial distribution," Tourism Economics, , vol. 25(8), pages 1224-1244, December.
    7. Bozana Zekan & Ulrich Gunter, 2022. "Zooming into Airbnb listings of European cities: Further investigation of the sector’s competitiveness," Tourism Economics, , vol. 28(3), pages 772-794, May.
    8. Josep Lladós-Masllorens & Antoni Meseguer-Artola & Inma Rodríguez-Ardura, 2020. "Understanding Peer-to-Peer, Two-Sided Digital Marketplaces: Pricing Lessons from Airbnb in Barcelona," Sustainability, MDPI, vol. 12(13), pages 1-19, June.
    9. Sainaghi, Ruggero & Chica-Olmo, Jorge, 2022. "The effects of location before and during COVID-19," Annals of Tourism Research, Elsevier, vol. 96(C).
    10. Lim, Weng Marc & Yap, Sheau-Fen & Makkar, Marian, 2021. "Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading?," Journal of Business Research, Elsevier, vol. 122(C), pages 534-566.
    11. Yuting Chen & Rong Zhang & Bin Liu, 2021. "Fixed, flexible, and dynamics pricing decisions of Airbnb mode with social learning," Tourism Economics, , vol. 27(5), pages 893-914, August.

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