Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation

Abstract

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.

Keywords

• geographically weighted regression
• local regression
• local model selection
• tourism
• price determinants
• Airbnb