Abstract
Despite abundant research on modeling hotel room prices, traditional hedonic pricing models (HPMs) have failed to consider spatial variations in the relationships among hotel room price and attribute variables. This study demonstrates the utility of a spatial HPM (s-HPM) using a geographically weighted regression analysis of 387 hotels in the Chicago area. Specifically, this study explored spatial variations in modeling hotel room prices and further identified spatial clustering patterns of relationships between room price and hotel attributes across market segments. The findings reveal that the s-HPM successfully identified spatially varying relationships between room price and hotel attributes, such as site attributes – size, age, class and service quality – and situation attributes – distances to airports, highways and tourist attractions – across the study area. This study contributes to a better understanding of local patterns of modeling room prices, ultimately providing guidelines for effective location-based hotel room pricing strategies.
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