Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This paper examines spatial patterns and socioeconomic influences on host participation in San Francisco’s short-term home rental markets. Spatial distributions of Airbnb property densities are mapped in a Geographic Information System and clusters and outliers are identified. Unlike major Airbnb markets, Airbnb hotspots in San Francisco are not located in the city’s core but are predominant in northeastern neighborhoods of the city located close to points of interest (POIs) frequented by visitors, have high proportions of hotel and lodging employment, and lower median household incomes. Regression results reveal that the dominant determinants of Airbnb property density are professional, scientific, and technical services, and hotel/lodging employment, Asian population, and POIs within located within 20 minutes of walking time from the centroid of the city’s census tracts. Implications of these findings are discussed to understand supply-side motivations of Airbnb hosts for participating in the shared accommodation economy.
Recommended Citation
Sarkar, Avijit; Pick, James; and Jabeen, Shaista, "Host Participation in Short-term Rental Markets: Geospatial and Socioeconomic Analysis of Airbnb in San Francisco" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/li/research/3
Host Participation in Short-term Rental Markets: Geospatial and Socioeconomic Analysis of Airbnb in San Francisco
Hilton Hawaiian Village, Honolulu, Hawaii
This paper examines spatial patterns and socioeconomic influences on host participation in San Francisco’s short-term home rental markets. Spatial distributions of Airbnb property densities are mapped in a Geographic Information System and clusters and outliers are identified. Unlike major Airbnb markets, Airbnb hotspots in San Francisco are not located in the city’s core but are predominant in northeastern neighborhoods of the city located close to points of interest (POIs) frequented by visitors, have high proportions of hotel and lodging employment, and lower median household incomes. Regression results reveal that the dominant determinants of Airbnb property density are professional, scientific, and technical services, and hotel/lodging employment, Asian population, and POIs within located within 20 minutes of walking time from the centroid of the city’s census tracts. Implications of these findings are discussed to understand supply-side motivations of Airbnb hosts for participating in the shared accommodation economy.
https://aisel.aisnet.org/hicss-57/li/research/3