The web of host–guest connections on Airbnb: a network perspective

The purpose of this paper is to explore Airbnb’s inherent network structure emerging from transactions between hosts and guests and provide comprehensive background information on the underlying data basis.

The analysis is based on actual Airbnb data from 16 major US cities (Asheville, Austin, Boston, Chicago, Denver, Los Angeles, Nashville, New Orleans, New York City, Oakland, Portland, San Diego, San Francisco, Santa Cruz, Seattle and Washington DC), available at InsideAirbnb.com, comprising a total of 135 thousand listings and 2.7 million transactions. The data are transformed into a graph and analyzed from a network perspective.

The web of host–guest connections on Airbnb represents a omniferous graph, that is, connecting virtually all users via relatively short distances. Hosts and guests differ markedly with regard to degree distribution. Overall, 98 per cent of all transactions represent first-time encounters.

This paper provides first insights into the very fabric of host–guest interactions on Airbnb from a macroscopic perspective. The platform’s network topology may be leveraged as a resource for trust-building between users. Moreover, platform operators may use network analyses to gain deeper insights into their user base. These may in turn be used to identify determinants of side-switching, deter users from platform circumvention or for churn prevention.

Platform ecosystems continue to expand and gain increasing economic, social and societal importance. For C2C platforms with two compartmentalized and decentral market sides (i.e. many individual providers and many individual consumers), the emerging transactional network structure has, thus, far experience almost no research attention. This analysis of Airbnb’s web of host–guest connections reveals a topology some archetypical social network properties (e.g. short distances). This structure and the knowledge about users’ positions therein yields viable cues for trust-building as well as a valuable resource for (platform) business analytics.