Price Competition and Overtourism: A Stochastic Approach Leveraging Complex Data

This study explores the pricing tactics and strategic decision-making processes within the hospi-tality industry. The availability of complex data from online travel agencies has transformed pricing into a dynamic process similar to stock market dynamics by providing continuous information on competitors' pricing decisions. The study highlights the importance of a stochastic approach to capturing price trajectories. By utilizing a Structural Vector Autoregressive (SVAR) model, we analyze how the interplay between demand seasonality, hotel features, and customers' willingness to pay affects price competition throughout the booking window. The suggested multi-variate stochastic approach aligns better with modern dynamic pricing algorithms based on stochastic demand functions. The findings reveal the dominance of seasonality-based pricing strategies set by revenue managers in Venice, highlighting the potential for more sophisticated pricing policies that leverage factors such as room availability, quality, and rates fences within the booking window. However, only few hotels consider these factors to modulate price competition, and mostly in the last minute. This result underscores the importance of investing in revenue management algorithms and skilled professionals who can accurately forecast the pick-up curve and optimize pricing strategies, even in destinations facing challenges related to overtourism.