Peak-Season Price Adjustments in Shared Accommodation: The Role of Platform-Certified Signals and User-Generated Signals

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Abstract
This study investigates the factors influencing landlords’ price adjustments during peak seasons on accommodation-sharing platforms, focusing on the role of platform-certified and user-generated signals. Utilizing a comprehensive dataset of 11,795 observations from a leading Chinese accommodation-sharing platform, we employ binary logit models to investigate how these signals affect landlords’ pricing strategies during “festival” and “weekend” peak times. Our analysis reveals that both platform-certified signals (such as “Preferred House” badges) and user-generated signals (such as customer satisfaction ratings) significantly increase the probability and magnitude of price adjustments during both festival and weekend peak seasons. Specifically, houses with a “Preferred” status are up to 28 times more likely to have price hikes during weekends compared to non-preferred ones. Further analysis reveals that higher levels of landlord professionalism, measured by the number of properties managed, amplifies the impact of user-generated signals on both the probability and magnitude of price adjustments. However, as the level of professionalism increases, this effect diminishes, indicating that highly professional landlords may have less flexibility to adjust prices due to already-high baseline rates. Interestingly, landlord professionalism did not significantly influence the impact of platform-certified signals on price adjustments, suggesting that the influence of such signals remains consistent across different levels of landlord professionalism. These results underscore the significant roles that both types of signals and landlord professionalism play in shaping pricing strategies, offering valuable insights for platform management and policy formulation aimed at enhancing consumer trust and competitive dynamics in the sharing economy.
Funding Information
  • National Natural Science Foundation of China (71972004)

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