In this section, we discuss the results presented above, trying to offer an interpretation based on their societal context, and proposing recommendations concerning the design of sharing economy platforms. We break down the discussion into two parts: we first consider the Airbnb’s user base in each city under study, and reflect upon it relative to the city’s demographic, economic, and historical context; we then move our discussion to our findings on pairing dynamics.
6.1 City demographics vs. Airbnb community
Echoing the findings from other studies of the sharing economy, our investigation into the user base of Airbnb revealed a disparity between its communities and the city-level demographics surrounding them, both in terms of age, gender and race. As far as age is concerned, we found Airbnb hosts and guests to be overwhelmingly young (mid-twenties to mid-thirties). This can be interpreted as a reflection of the broader age-related digital divide phenomenon [34].
In terms of gender, we have found the Airbnb community to be predominantly female. In 2015, Airbnb reported that 54% [35] of their guests were female. Based on the data we have collected up to 2017 for the cities under study, such percentage seems to be substantially higher (60% and above), both for hosts and for guests, both for private and for shared rental properties. We do not know whether this is a signal of an evolutionary mechanism, whereby more female than male users join the platform attracted by homophily, as they already see more female users already being engaged with it.
Perhaps the most notable results were found in terms of race. In the following, we focus our attention only on hosts, so to compare our results with available census information on the demographics of the cities under study. In all cities, the majority of hosts were found to be racially White. In particular, Dublin was found to have the highest proportion of White hosts (at 96% for full property rental); this is expected for a city whose resident population was reported to be 90% racially White in the latest CensusFootnote 7 (2011).
Things are considerably different in the more diverse cities we analyzed, where we found well-known inequalities along racial lines to be largely replicated in Airbnb’s interactions, with systematic evidence of a divide between the Airbnb community and the local demographics. Indeed, we found up to 64% of Airbnb hosts in Hong Kong to be White (for full property rentals), even when 92% of the population is reported to be of Chinese (i.e., Asian) racial background as per the 2016 Census.Footnote 8 Hong Kong is well-known to be plagued by rising wealth inequality [36] and exorbitant property prices, that are known to be among the most unaffordable in major cities [37]. This is coupled with high income inequality, with Hong Kong’s racially White population amassing relatively high household income, while the same does not hold for the majority of the local Chinese population [38]. Owning a spare property to rent out might thus be a privilege mostly in the hands of the White population.
Similarly of interest is Amsterdam’s large majority of racially White hosts, resting at roughly 90% of the total Airbnb user pool. This too is substantially higher than expected, given that a third of Amsterdam’s population is composed of migrants recognized to be of non-western racial origins.Footnote 9 This finding is in line with research conducted by the Netherlands’ Central Commission for Statistics (CCS), which has highlighted the existence of societal integration issues [39] amongst the Netherland’s non-western population, which we speculate to be reflected into a decreased ability to secure properties to rent out.
Chicago and Nashville were found to have the highest proportion of Black hosts and guests recorded among the cities under study, averaging around 3–4% across the two property rental types. However, this result too is notable, given that the most recent census reports Chicago’s and Nashville’s total populations to be 32%Footnote 10 and 28%Footnote 11 racially Black, respectively—an order of magnitude more than what found on Airbnb. This resonates with recent research suggesting that Airbnb is a conduit for racial gentrification where the old, local community members of a neighborhood lose out in housing and in wealth [4].
6.2 Homophily and avoidance
Our statistical investigation on pairing dynamics detected evidence of homophily both in terms of gender and race. Gender homophily is well documented to be ‘built in’ even in young children [40, 41], so it is not surprising we could detect it in our results too. Conversely, it was interesting to see it supplanted by heterophilous behavior (i.e., a statistical over-expression of interactions between hosts and guests of different genders) in the case of Nashville, regardless of the property type, and both in Dublin and Hong Kong when switching from full property to shared property rentals. This is even more interesting when considering that full properties obviously do not imply any shared space between hosts and guests, and often allow to avoid any live interaction through automated checkin procedures. We speculate that a possible explanation behind this might lie in the different communities that naturally self-select based on property type, with those selecting shared accommodation most likely being more open-minded and prone to meeting different people (see [42] for similar findings in couchsurfing platforms).
In addition, we also detected less strong statistical signals of homophily when analysing pairing dynamics based on racial background. Once again, this is somewhat to be expected, as racial homophily has been detected in a broad variety of social environments [7], ranging from labour markets [43] to online social networks [19]. Symmetrical to this, we also detected phenomena of racial avoidance (still with quite weak statistical signals in most cases), i.e., under-expressions of relationships between guests and hosts belonging to different racial groups. This, again, resonates with pre-existing literature. For example, racial avoidance has been found to partially explain relocation patterns within countries [44]. These results were accompanied by a few exceptions where we detected under-expressed homophily (White-to-White relationships in Dublin’s and Nashville’s full properties) and heterophily (e.g., Asian-to-White relationships in Dublin).
Since our results are of a purely statistical nature, we can only highlight what relationships are over/under-represented, without making any claims on causality. In particular, we are in no position to distinguish between avoidance and outright discrimination. Yet, some of the trends we observe are worrying and raise questions about potential countermeasures that platforms might adopt in order to monitor their progress and possibly control them. For example, following research on unconscious biases [45], platforms could design interventions aimed at providing users with detailed information about the peers they chose to interact with (or not) in the past, possibly highlighting systematic preferences or deviations from the outcomes that would be obtained under an unbiased selection process. Interventions could also go a step beyond raising awareness of individual behaviours. For example, they could encourage behaviours to enhance heterophily (which we already detected in some cities) by means of incentive systems similar to those that are already in place to promote service excellence (e.g., rewarding outstanding Airbnb hosts with a ‘superhost’ status). In this fashion, users with a history of interactions with peers from different racial backgrounds could be rewarded with badges or statuses highlighting their role as diversity champions. Last but not least, platforms could incentivise users to give up potentially unnecessary steps in the interaction process where additional, and potentially biasing, information about other peers is usually acquired; for example, Airbnb’s ‘instant booking’ option, where a guest’s request is automatically accepted by the platform, without an explicit consent action from the host, has been an exemplary step in this direction.
6.3 Limitations
We ought to acknowledge three main limitations of this work: first, in the generation of the null network models, we have not enforced the preservation of temporal constraints (i.e., it is possible for a stay that occurred between guest \(g_{1}\) and host \(h_{1}\) in year \(y_{1}\) to be swapped with a stay between \(g_{1}\) and \(h_{2}\), despite host \(h_{2}\) joining Airbnb only in year \(y_{2} > y_{1}\)). We chose to adopt this simplified approach under the assumption that Airbnb demographics composition has not changed significantly between 2008 and 2016 (e.g., women have consistently made up the majority of the Airbnb host community [46]). In the future, we will consider generating null network models that preserve timing constraints (see, e.g., [47]).
Second, our findings rely on the accuracy of several image processing tools, to automatically annotate profile pictures in terms of gender, age and race. If the accuracy of these annotations is low, then the findings are void. In this paper, we have tried to reduce this risk by cross validating annotations across several image processing tools, and by verifying the robustness of our findings with respect to variations in their accuracy. Even so, we had to disregard any user whose profile picture did not present a (recognisable) human face, or where the estimated confidence of the annotation was low. Platform owners are most likely in possession of more accurate demographic information, explicitly provided at the time of user registration; they could thus skip the image annotation step (Airbnb Data section) and directly use this information to annotate nodes in the bipartite graph, then proceeding with the application of the statistical network analysis method we proposed (Method section) to extract more robust results.
Finally, we ought to acknowledge that our analysis of Airbnb pairing dynamics was limited to what the platform makes externally visible (i.e., reviews that hosts and guests leave to one another after a stay); results might differ if one had the opportunity to apply our method to the whole history of interactions (including stays that resulted in no reviews, and reservation requests that were cancelled/refused). Once again, platform owners do possess the whole interaction history, and might thus want to repeat this study so to validate our findings on a complete network of host/guest stays. Yet, we have reason to believe our results would hold regardless. Indeed, the large samples our analysis relies on are such that only major differences in the tendency to leave reviews between groups would affect the significance of the findings reported in this paper.