Our paper “Reverse engineering the last-minute on-line pricing practices: an application to hotels“, has been published on Statistical Methods & Applications. In this paper, with Andrea Guizzardi (Department of Statistical Sciences, University of Bologna) and Luca Vincenzo Ballestra (Department of Statistical Sciences, University of Bologna), we propose a nonlinear time series methodology aimed at capturing the (last-minute) price adjustments implemented by hotels operating in the online market.
These adjustments are made to tailor early-booking rates in response to unpredictable demand fluctuations. Utilizing this methodology, we analyze the pricing strategies of six hotels in Milan, Italy, each characterized by unique features and services. Our findings indicate that the ability of hotels to synchronize last-minute adjustments with early-booking decisions and accommodate stochastic demand seasonality varies based on factors such as size, star rating, and brand affiliation. Notably, our primary empirical finding demonstrates that autocorrelations of the first four moments of last-minute price adjustments offer valuable insights into hoteliers’ pricing strategies. The potential for scaling up this approach is significant, as it could provide policymakers in smart destinations with a dependable and transparent tool for real-time monitoring of demand dynamics.
Please feel free to read the paper, which if fully available in Open Access here.
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