Shunyuan Zhang’s research while affiliated with Harvard Medical School and other places

We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel data set spanning 7,423 properties over 16 months, we find that properties with verified images had 8.98% higher occupancy than properties without verified images (images taken by the host). To explore what constitutes a good image for an Airbnb property, we quantify 12 human-interpretable image attributes that pertain to three artistic aspects—composition, color, and the figure-ground relationship—and we find systematic differences between the verified and unverified images. We also predict the relationship between each of the 12 attributes and property demand, and we find that most of the correlations are significant and in the theorized direction. Our results provide actionable insights for both Airbnb photographers and amateur host photographers who wish to optimize their images. Our findings contribute to and bridge the literature on photography and marketing (e.g., staging), which often either ignores the demand side (photography) or does not systematically characterize the images (marketing). This paper was accepted by Juanjuan Zhang, marketing.

We examine whether and how ride-sharing services influence the demand for home-sharing services. Our identification strategy hinges on a natural experiment in which Uber/Lyft exited Austin, Texas, in May 2016 due to local regulation. Using a 12-month longitudinal dataset of 11,536 Airbnb properties, we find that Uber/Lyft’s exit led to a 14% decrease in Airbnb occupancy in Austin. In response, hosts decreased the nightly rate by $9.3 and the supply by 4.5%. We argue that when Uber/Lyft exited Austin, the transportation costs for most Airbnb guests increased significantly because most Airbnb properties (unlike hotels) have poor access to public transportation. We report three key findings: First, demand became less geographically dispersed, falling (increasing) for Airbnb properties with poor (excellent) access to public transportation. Second, demand decreased significantly for low-end properties, whose guests may be more price-sensitive, but not for high-end properties. Third, the occupancy of Austin hotels increased after Uber/Lyft’s exit; the increase occurred primarily among low-end hotels, which can substitute for low-end Airbnb properties. The results indicate that access to affordable, convenient transportation is critical for the success of home-sharing services in residential areas. Regulations that negatively affect ride-sharing services may also negatively affect the demand for home-sharing services.

We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasinatural experiment. Among those who adopted the algorithm, the average nightly rate decreased by 5.7%, but average daily revenue increased by 8.6%. Before Airbnb introduced the algorithm, White hosts earned $12.16 more in daily revenue than Black hosts, controlling for observed characteristics of the hosts, properties, and locations. Conditional on its adoption, the revenue gap between White and Black hosts decreased by 71.3%. However, Black hosts were significantly less likely than White hosts to adopt the algorithm, so at the population level, the revenue gap increased after the introduction of the algorithm. We show that the algorithm’s price recommendations are not affected by the host’s race—but we argue that the algorithm’s race blindness may lead to pricing that is suboptimal and more so for Black hosts than for White hosts. We also show that the algorithm’s effectiveness at mitigating the Airbnb revenue gap is limited by the low rate of algorithm adoption among Black hosts. We offer recommendations with which policy makers and Airbnb may advance smart-pricing algorithms in mitigating racial economic disparities.

We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve their skills (learn) more when competing against a superstar than otherwise. We show that an individual’s probability of winning in subsequent contests increases significantly more after she has participated in a contest with a superstar coder than otherwise. We build a dynamic structural model with individual heterogeneity where individuals choose contests to participate in and where learning in a contest happens through an information theory-based Bayesian learning framework. We find that individuals with lower ability to learn tend to value monetary reward highly, and vice versa. The results indicate that individuals who greatly prefer monetary reward tend to win fewer contests, as they rarely achieve the high skills needed to win a contest. Counterfactual analysis suggests that instead of avoiding superstars, individuals should be encouraged to participate in contests with superstars early on, as it can significantly push them up the learning curve, leading to higher quality and a higher number of submissions per contest. Overall, our study shows that individuals who are willing to forego short-term monetary rewards by participating in contests with superstars have much to gain in the long term. The online appendix is available at https://doi.org/10.1287/isre.2017.0767 .