Events

• Ms. Shunyuan Zhang
  Ph.D Candidate in Marketing/Business Technology
  Tepper School of Business
  Carnegie Mellon University

We investigate how AirBnB hosts make decisions on the quality of property images to post. Prior literature has shown that the images play the role of advertisements and the quality of the images have a strong impact on the present demand of the property – as compared to lower quality amateur images, high quality professional images can increase the present demand by 14.3% on matched samples (Zhang et al. 2018). However, the reality is that there exist a large number (approximately two-thirds) of amateur (low-quality) images on AirBnB. One possible explanation is that these images are costly for the hosts, as most of them are amateur photographers. However, this does not completely explain the result – in 2011, AirBnB started offering highest quality professional images for free to all the hosts by sending their professional photographers to the property and shoot, process and post the photos for the hosts. To AirBnB’s surprise, only 30% of the hosts used the AirBnB professional photography program. We posit that the host’s decision on what quality of images to post depends not only on the advertising impact of images on the present demand and on the cost of images, but also on the impact of images on the future demand. Thus, some hosts would be hesitant to post high-quality images because they can create unrealistically high expectations for the guests, especially if the actual property is not as good as what the images portray and if the hosts are unable to provide a high level of service to match those expectations. This would result in the satisfaction level of guests to decrease, who would then leave a bad review or not write any review at all; and since the number/quality of reviews is one of the key drivers in generating new bookings, this will adversely affect the future demand.
In this paper, we attempt to disentangle the aforementioned factors that influence the host’s decision on the type of photographs to post, and explore policies that AirBnB can employ to improve the hosts’ adoption of professional photos and thereby improve the profitability of both the hosts and AirBnB. To do so, we build a structural model of demand and supply, where the demand side entails modeling of guests’ decisions on which property to stay, and the supply side entails modeling of hosts’ decisions on what quality of images to post and what level of service to provide in each period. We estimate our model on a unique oneyear panel data consisting of 2,421 AirBnB properties in New York where we observe hosts’ monthly choices of the quality of images posted and their and service that they provided. Our key findings are: First, guests who pay more attention to images tend to care more about reviews, revealing an interesting tradeoff problem for the hosts. Second, hosts incur considerable costs for posting above-average quality of images. Third, hosts are heterogenous in their abilities in investing service effort. In counterfactual analyses we simulate AirBnB properties assuming they all start with entry state and low-level images. We then compare the impact of the current policy (offering free high-level images to hosts) and of a proposed policy (offering free medium-level images to hosts) on the average property demand. We show that the proposed policy, though dominated by the current policy in the short-run (for the first four periods), outperformed the currently policy in the long-run (12.9 % vs 6.0%). The interpretation is that, medium-level images, compared to high-level images, despite forming a smaller expected utility for the consumers, has a greater effect on property demand in the long-run as they, with lower risks of creating a dissatisfactory gap, help hosts to obtain new reviews. Moreover, individual hosts who might end up using amateur (low-level) images to avoid the dissatisfactory gap under the current policy, now use free medium-level images to make more revenues under the proposed policy. In the second counterfactual, we explore an alternative policy in which AirBnB were to offer a menu of image quality choices for free. The menu includes both high- and medium- level of property images (images examples are provided) and allow the hosts to self-select which program they want. Comparing with the proposed policy in the first simulation, we find that this policy performance the best in the long-run by improving average property demand by 14.0%.