A1210
Title: Mispricing in Tokyo Airbnb market: Expected revenue loss for professional versus casual hosts
Authors: Masaharu Yoshimoto - Hokkaido University (Japan) [presenting]
Abstract: The purpose is to conduct an empirical analysis of dynamic pricing in the Tokyo Airbnb market during the post-COVID surge in inbound tourism. It quantifies how deviations from the price that maximises revenue reduce host revenue and compares strategies between professional and casual hosts. Monthly data on bookings and prices are combined with property attributes, host features, and a log index of international arrivals. A logistic model of booking probability is estimated to obtain price sensitivities for each listing. For each listing in each month, the price that maximizes revenue is derived numerically, and the expected loss from charging the observed price is calculated. Aggregating losses by host type and month enables assessment of (i) the impact of demand shocks on price sensitivities and revenue loss and (ii) the extent to which professionalization mitigates missed revenue. The framework enables the platform to identify host segments that should be prioritized for smart-pricing tools and provides a basis to explore whether appropriate price adjustments during peak demand could help ease urban congestion.
