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

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 esti-mated 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 dif-ferences 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 to consider the spatial variability of the estimated coefficients.