Modeling hotel room price with geographically weighted regression

Insufficient attention has been given to hotel-room-price attributions and its mechanism in the lodging research field till now. This article examines how site and situation factors differently affect lodging industry and room prices. Comparative analysis of four hedonic price models has been conducted to investigate how these attributions influence room price of Beijing's hotels above star three. Spatial autocorrelation in hotel prices and in hedonic room price equation residuals were analyzed in this research too. Some conclusions can be found and summarized: according to the estimated results, for specific locales, the results expressed in a global model might be inaccurate. The fitting coefficient of geographically weighted regression demonstrates the importance of going beyond the global modeling framework when incorporating geographically weighted regression into hedonic price model. At last, an innovative method for determining the influence of a hotel's attributes at market rates on its’ values, or implicit prices was put forward by the authors.

► Four hedonic price models are conducted which include global models and local models.
► We examine spatial autocorrelation in hotel prices and in hedonic room price equation residuals.
► The estimated results of global models might be inaccurate.
► Geographically weighted model provides furthered research by means of local regression analysis of room prices.