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Purpose This study aims to examine the effect of hotels’ star ratings and customer ratings on online hotel prices from both supply- and demand-side perspectives. Design/methodology/approach To compile the supply-side data, a Web crawler was designed and implemented to read online prices and characteristics of available hotels from Trivago. Demand-side data were compiled from surveys conducted using the Amazon Mechanical Turk portal. Data were analyzed with an array of advanced machine learning regression models. Findings The results show that while a star rating is the most important predictor of price from both supply- and demand-side perspectives, customer rating influences the price much more significantly on the demand-side. Customers showed a tendency to overestimate the room price of three- and four-star hotels and underestimate the price of five-star hotels. Customers placed a heavier weight on customer ratings when estimating prices particularly when the average rating was above 7.5 (out of 10). The study also confirms the strong effect of price adjustment for customers when they were exposed to the prices of other similar hotels. Finally, the study examines the impact of demographics on the perceived hotel value. Age, ethnicity, education and income are shown to be the most significant demographic characteristics. Originality/value The results are valuable from a research perspective because they demonstrate how to price rooms more effectively based on their perceived value from consumers’ perspectives. From a practical standpoint, the findings provide useful managerial tools for pricing in competitive environments.
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