Spatial Analysis of Hotel Room Rate: Evidence from Star-Rated Hotels in Beijing

• Activity Number: 411
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Chuan Wang*+
• Companies: University of Florida
• Keywords: Spatial Analysis ; SVCP ; MCMC ; INLA

Abstract

Insufficient attention has been given to hotel-room-price attributions and its mechanism in the lodging research field till now. This research examines how site and situation factors differently affect lodging industry and room prices. Although in most applications, the objective is to build regression models to explain a response variable under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to vary at the local or sub regional level. Geographically Weighted Regression (GWR) and Spatial Varying Coefficients Process (SVCP) are two most frequently used approach to implement the analysis. The results of SVCP are compared with that of GWR, showing that SVCP has an apparent advantage over GWR in both statistical interpret-ability and goodness of fit. However, SVCP also demonstrates an apparent tendency of over fitting, but since Markov Chain Monte Carlo (MCMC) is used in the fitting of SVCP, the validation of the model becomes a challenging question. So alternative approach such as Integrated Nested Laplace Approximatio