Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/72157
Title: Selecting prices determinants and including spatial effects in peer-to-peer accommodation
Authors: Suárez Vega, Rafael Ricardo 
Hernández Guerra, Juan María 
UNESCO Clasification: 531290 Economía sectorial: turismo
Keywords: Airbnb
Geographically Weighted Regression
Heterogeneity
Local Model Selection
Local Regression, et al
Issue Date: 2020
Journal: ISPRS International Journal of Geo-Information 
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.
URI: http://hdl.handle.net/10553/72157
DOI: 10.3390/ijgi9040259
Source: ISPRS International Journal of Geo-Information [EISSN 2220-9964], v. 9 (4), 259 (Abril 2020)
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