Drivers of Airbnb prices according to property/room type, season and location: A regression approach

Augusto Voltes-Dorta, Agustín Sánchez-Medina

Research output: Contribution to journalArticlepeer-review

Abstract

While past studies on Airbnb pricing highlight the importance of room features, host characteristics and location factors, little has been investigated about whether these factors are the same across different property/room types, locations and seasons. To fill that gap, this paper presents a study about the drivers of Airbnb prices in Bristol using ordinary least squares (OLS) and geographically-weighted regression (GWR) methods. The estimated models exhibit sharply different levels of goodness-of-fit, suggesting that the prices of different room types might not be explained by the same set of price factors. The results also uncover statistically significant differences between the price determinants of apartments and house listings and reveal spatial patterns in the price effects. These findings have implications for price setting and the assessment of competition. Future studies should account for potential differences across property/room types, as well as consider the spatial variability of the estimated coefficients.
Original languageEnglish
JournalJournal of Hospitality and Tourism Management
Volume45
Early online date11 Sep 2020
DOIs
Publication statusPublished - Dec 2020

Keywords

  • accommodation pricing
  • Airbnb
  • sharing economy
  • geographically-weighted regression

Fingerprint

Dive into the research topics of 'Drivers of Airbnb prices according to property/room type, season and location: A regression approach'. Together they form a unique fingerprint.

Cite this