The determinants of Airbnb prices in New York City: a spatial quantile regression approach

Abstract

AbstractIn this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intrinsic heterogeneity of house prices. The results confirm that property and service attributes play a significant role in determining rental prices, while some variables exert a different impact on prices in magnitude and sign, depending on the quantile considered.