Zhihua Zhang

Current research investigating the accommodation experience in the sharing economy in China is limited, especially from a cross-cultural perspective. To fill this gap, this study examined the accommodation experience of Airbnb guests using text-mining techniques and compared the accommodation experience perception between two culturally different groups: domestic Chinese and foreign English-speaking Airbnb guests. The results showed that the two groups shared eight common dimensions, including “Convenience/Location”, “Amenities”, “Feel at home”, “Check-in/out”, “Experience”, “Availability/Transportation”, “Host”, and “Style/Decoration”. However, there are differences in the relative importance of each dimension of accommodation experience between the domestic and foreign Airbnb guests. For example, the foreign guests more often mentioned homeliness, location/convenience, and availability/transportation, while the domestic guests showed greater interest in check-in procedures and style/decoration. Additionally, the two groups have several unique dimensions. The dimensions unique to foreign guests are “Recommendation” and “Booking flexibility”, while the dimensions unique to domestic guests are “Revisit” and “Cleanliness”. This study provides both theoretical and practical implications for peer-to-peer accommodation hosts and platforms. For example, Airbnb hosts can improve the satisfaction of Airbnb guests by improving several common extracted topics (e.g., amenities quality and host response) and the fact that foreign guests care more about homeliness, while domestic guests pay more attention to the check-in process and house design and decoration.

City managers and planners seek insights into Airbnb logistics in cities for the purposes of effective lodging management. This requires managers and planners to gain a holistic understanding of Airbnb geographic dynamics, which has drawn limited attention in the literature. To fill this gap, this paper explored Airbnb supply and logistics in three cities (New York City, Los Angeles, and Chicago) through the lenses of geographic clustering and location convenience. We explored the spatial allocations of Airbnb supply in cities and investigated Airbnb’s influencing factors at the census tract level, utilizing spatial regression models. The results showed that (1) the spatial distribution of Airbnb supply in all three cities has a clear center-peripheral pattern, indicating that Airbnb allocations predominate in the central area of the city; (2) the number of housing units and points of interest (POI) have an influential impact on Airbnb supply for three cities; (3) the proportion of youth population and employment has a positive effect on Airbnb supply in NYC and Chicago, but not in LA, while the distance to the city center negatively affects Airbnb supply in LA and Chicago, but not in NYC; (4) the income has a mixed effect on Airbnb supply in three cities, while the proportion of African Americans and education level has only a positive effect on Airbnb supply in NYC; and (5) rent is not associated with Airbnb supply for all three cities, which indicates that the Airbnb explosion may not contribute to rent increases in cities.

Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.