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Location advantages of lodging properties: A comparison between hotels and Airbnb units in an urban environment

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  • Yang, Yang
  • Mao, Zhenxing

Abstract

This study aims to investigate various types of location advantages that contribute to lodging property performance. Using monthly revenue data for individual urban hotels and Airbnb units in Houston, Texas from 2014 to 2016, we apply the Hausman-Taylor model to estimate the effects of location factors. Several factors are confirmed, including accessibility to points of interest, transport convenience, the surrounding environment, and market conditions. The overall effect of location advantage is more substantial for urban hotels than Airbnb units. Findings do not reveal a sizable competition effect between urban hotels and Airbnb units. Furthermore, we unveil factors associated with location advantages for different hotel classes and Airbnb types by estimating the model using different sub-samples.

Suggested Citation

  • Yang, Yang & Mao, Zhenxing, 2020. "Location advantages of lodging properties: A comparison between hotels and Airbnb units in an urban environment," Annals of Tourism Research, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:anture:v:81:y:2020:i:c:s0160738320300050
    DOI: 10.1016/j.annals.2020.102861
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    Cited by:

    1. Alejandro Norton & Marcos Herrera-Gomez, 2020. "Una propuesta de medición de daño del delito y su impacto sobre los precios de Airbnb en la Ciudad de Buenos Aires," Asociación Argentina de Economía Política: Working Papers 4358, Asociación Argentina de Economía Política.
    2. Lee, Yong-Jin Alex & Jang, Seongsoo & Kim, Jinwon, 2020. "Tourism clusters and peer-to-peer accommodation," Annals of Tourism Research, Elsevier, vol. 83(C).
    3. 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.
    4. Jianzhuang Zheng & Lingyan Huang, 2022. "Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    5. Zheng, Zuolong & Li, Ziying & Zhang, Xuwen & Liang, Sai & Law, Rob & Lei, Jiasu, 2023. "Substitution or complementary effects between hosts and neighbors’ information disclosure: Evidence from Airbnb," Journal of Business Research, Elsevier, vol. 161(C).
    6. Jang, Seongsoo & Kim, Jinwon, 2022. "Remedying Airbnb COVID-19 disruption through tourism clusters and community resilience," Journal of Business Research, Elsevier, vol. 139(C), pages 529-542.
    7. Sainaghi, Ruggero & Chica-Olmo, Jorge, 2022. "The effects of location before and during COVID-19," Annals of Tourism Research, Elsevier, vol. 96(C).
    8. 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.
    9. Emeka Ndaguba & Cina Van Zyl, 2023. "Professionalizing Sharing Platforms for Sustainable Growth in the Hospitality Sector: Insights Gained through Hierarchical Linear Modeling," Sustainability, MDPI, vol. 15(10), pages 1-24, May.

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