에어비앤비 숙소 성과에 영향을 미치는 요인: 신규 숙소와 기존 숙소의 비교

For Airbnb, one of the most successful examples of the sharing economy, to continuously grow, it needs a steady supply of attractive listings. To this end, this study draws on existing research on Airbnb and signaling theory to examine the factors that influence the performance of Airbnb listings. From a dynamic perspective, we expect the importance of factors affecting listings' performance to differ between new and existing listings. Analyzing Airbnb data from the 10 most active US cities using negative binomial regression, we find that dynamic attributes that require a time investment have a stronger impact on existing listings, while static attributes that require less of a time investment have a similar effect on both types. The host’s membership duration and number of listings were expected to have positive effects, but showed negative effects. While the instant booking policy increased users’ convenience, some hosts were reluctant to use it. The results of the analysis suggest that the importance of the factors varies depending on the type of Airbnb listing (new vs. existing), and the need for differentiated policies and complementary measures based on the type of listing is necessary in a practical perspective.