Do you behave better inside a sharing economy scenario—like a Lyft, Uber, or Airbnb—because you know the transparency goes both ways? Without a high score, your driver could see fewer ride requests, but a low rating from the owner of that vacation home means you might be taking fewer trips, too.
The old adage that “the customer is always right” only applies inside a service economy where one side must ensure smooth public relations. Now the love (or ire) goes both ways. Your digital reputation is tied to your phone number, headshot and social currency score, and—much like that Black Mirror episode—access to sharing economy services is mediated, monitored, and controlled accordingly.
PCMag went to University of Southern California (USC) to speak with Dr. Davide Proserpio, Assistant Professor of Marketing at the Marshall School of Business. He researches problems related to online markets and social media, including the sharing economy, competition between online and offline markets, online reviews and reputation, and advertising.
In a paper he co-wrote with Boston University’s Wendy Xu, Department of Economics, and Georgios Zervas Questrom, School of Business, Professor Proserpio details the socio-economic effects of the sharing economy on human behavior—specifically, Airbnb. Here are edited and condensed excerpts from our conversation with Dr. Proserpio.
Firstly, why did you pick Airbnb as your test case?
[DP] While the analytical model we developed applies to most of the sharing economy platforms, Airbnb was a natural choice because the interaction between buyers and sellers can be quite extensive, in terms of days. Moreover, I’ve done earlier studies on Airbnb, so had already collected extensive data about the platform.
What was your initial dataset?
Our dataset was a weekly panel of US Airbnb listings spanning a 17-month period from the beginning of July 2014 to the end of November 2015. During this timeframe, we collected information on all US listings and their hosts from the Airbnb website with a weekly frequency, to compile a novel dataset of Airbnb listing entry, exit, prices, supply, demand, and reviews.
What was the model you used that showed an economic impact of behavior?
In our model, buyers can induce sellers to exert more effort by behaving well themselves. We demonstrate that this joint increased effort can improve the utility of both parties and influence the market equilibrium. We also show that reciprocity is more salient in the sharing economy compared to traditional marketplaces for similar services, generating trust among strangers and informally regulating their behavior. We show that Airbnb hosts that are more reciprocal receive higher ratings, and that higher rated hosts can increase their prices. Therefore, reciprocity affects the Airbnb price equilibrium through its impact on ratings, as predicted by our analytical framework.
But you found that the more “professional” a host appeared, the less they were favored in the comments section?
Right. We show that Airbnb hosts who list their properties for rent on Airbnb frequently, whom, for simplicity, we refer to as professional hosts, have lower ratings than those with less frequent market participation, whom we call casual hosts. Our observations suggest that casual hosts offer higher levels of quality than professional hosts.
Because you get a (hopefully) warm and fuzzy global digital culture buzz of feeling at home wherever you roam?
Yes. As opposed to feeling as if you’re in someone’s short-term rental property.
Did Airbnb sponsor your research? And if not, how have they incorporated your findings to develop their product?
No, they did not, and they also did not provide any data. The paper was recently posted online as a working paper, and is currently under review, so I am not sure if Airbnb is even aware of the paper. If they are, I really hope that they find the results interesting, and perhaps our findings can be useful for improving their matching algorithm, of hosts and guests, in the future.
Did your main findings surprise you?
The pattern we observed in the data about casual and professional host wasn’t something we expected to see. However, our model could explain the pattern under the assumption that casual hosts are more reciprocal, as in a behavior in which a person reciprocates kind acts with kindness and unkind acts with spite, than professional hosts. We actually tested this assumption in the data, and showed that it did, in fact, hold.
What was your first experience of the sharing economy?
My first experience with the sharing economy was after I’d completed my Masters degree in engineering in Madrid and moved to the US. During my second year of PhD, at Boston University, I started using Airbnb, first as a guest, and then as a host. Of course, Uber followed.
Back to your academic pursuits, why the sharing economy as a focus?
I was fascinated by these markets because by taking a simple idea such as sharing, they were soon able to match and connect millions of people. Academically, such platforms are creating a host of open questions, not only for marketers, but also economist, computer scientist, and so on. My interest is in the effect that these platforms have on our society in general, from how they compete with standard service providers to how they could improve and stimulate the economy to how they change our own behavior.
Are you working in partnership with a data science team at USC?
No, because part of my background is in computer science, I’m able to cover the data science aspect myself, coding in Python and R, and building my own models.
Any machine learning?
Machine learning is hot right now in marketing, and I hope to be using it next to go deeper into the data as we further this research into investigating other economic impacts of the sharing economy.
Do you also see an issue where we tend to give everyone “5 stars” so we don’t offend anyone, especially as we’re no longer anonymous? Does that impact your model?
The issue you are describing could be defined as bias. Essentially, we feel bad giving a low rating to someone we just met, so we decide to inflate our evaluation. This is a bias present in many online platforms, and there is not an easy fix. However, in our paper, we show that the model predictions are robust to this type of bias in which users decide to not submit their true evaluation of the service. So, in other words, the higher ratings we observe on these platforms cannot be fully explained by this bias, but it does raise an interesting concern in general.
What’s next for your research?
I just finished a literature review paper with my colleague Gerry Tellis, where we reviewed over 30 papers investigating the sharing economy. We’re now looking at how Airbnb affects housing affordability rates; showing that Airbnb has an impact on the housing market in that it increases both rental rates and house prices. I presented this work at the 15th annual Product and Service Innovation Conference, in Midway, Utah, and I will next head to Philadelphia for the 40th Annual ISMS Marketing Science to share these findings with the marketing community.
Are you advising any tech startups or smart city initiatives as a result of your research?
No, I’m purely within academia at this time, but one day, I hope that my research can make a difference in the way we understand online marketplaces such as the sharing economy.