Abstract: The The sharing economy utilizes internet platforms to optimize resource allocation,and foster innovation in consumption and employment models,and driving efficient supply-demand matching as well.Online short-term rental platforms like Airbnb have emerged as a new choice for travelers.However,with increasing market competition,lower retention rates of listings,and squeezed profit margins for hosts have become pressing issues.Therefore,how to enhance the attractiveness of listings has become a critical concern for hosts.This article utilized web scraping techniques to obtain data from Airbnb,focusing on the 20 cities in China with the most Airbnb listings,selected and categorized factors influencing listing popularity,with a specific focus on handicap signals where hosts have greater room for proactive adjustment based on the signal theory,and explored how these handicap signals impact listing popularity.The Ordinary Least Squares（OLS） regression results indicate that among handicap signals,price signals have a significant negative impact on listing popularity,while host information signals have a positive effect,and flexible booking policies and strict cancellation policies also contribute to higher listing popularity.The Quantile regression results demonstrate that,apart from price,other handicap signal factors have a more pronounced impact on listings with lower popularity.Furthermore,the study did a moderating effect analysis involving city types,the number of nearby hotels,and the number of nearby Airbnb listings.The results reveal that the impact of handicap signals on listing popularity in tourist cities is attenuated compared to comprehensive cities;in environments with a high density of hotels,the influence of handicap signals is weakened,whereas in environments with a high density of listings,the impact of handicap signals is amplified.

Key words: online short-term rentals, airbnb, handicap signals, listing popularity, quantile regression