How to better incorporate geographic variation in Airbnb price modeling?

• Yifei Jiang ^[1] ; Honglei Zhang ^[1] ; Xianting Cao ^[1] ; Ge Wei ^[1] ; Yang Yang ^[2]
  1. [1] Nanjing University

     Nanjing University

     China

     
  2. [2] Temple University

     Temple University

     City of Philadelphia, Estados Unidos

     
• Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 29, Nº. 5, 2023, págs. 1181-1203
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Since entering the Chinese market in 2015, Airbnb has become a major player in the Chinese home-sharing arena. This article uses data from 8012 active Airbnb listings in Shanghai and presents three models (linear regression, geographically weighted regression, and random forest) to study the determinants of Airbnb listing prices and incorporate geographic variation in price modeling. Results show that property quality plays a key role in shaping listing prices. Due to Airbnb’s distinctions from traditional lodging in both features and business models, Airbnb pricing determinants differ accordingly. For example, location conditions were found to have a limited impact in regions with established transportation networks. Among the three models, random forest performed best in terms of prediction accuracy. Lastly, practical implications are discussed.