Paper discussed: Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705
Peer-to-peer markets, also known as the sharing economy, has enabled people to collaboratively make use of underutilized inventory through fee-based sharing. The rapid growth of peer-to-peer platforms has arguably been enabled by two key factors: technology innovations and supply-side flexibility. This study analyzes Airbnb’s entry into the state of Texas and quantifies the impact on the Texas hotel industry between 2008 and 2014. The paper contributes to the growing literature on multi-sided platform competition, as Airbnb is an example of a two-sided platform. Besides, the work contributes to the existing literature by focusing on the impact of external shocks on the tourism and the hospitality industry. The researchers expect that some stays on the Airbnb platform will substitute certain hotel accommodations. This can significantly affect the hotel revenue. Though, the authors note that the impact differs per geographic region, hotel market segment and season.
In order to quantify the extent to which Airbnb’s entry has negatively affected the hotel room revenue, the researchers gathered data from various sources. The Airbnb platform was the main source of data for this study. Additionally, the monthly room revenue from 3,000 hotels in Texas together with several other datasets were included in this study to account for the information on control variables and in order to conduct robustness checks.
After collecting the necessary data, a difference in differences (DD) empirical strategy is conducted to identify the causal impact of Airbnb on hotel revenue. This strategy identifies the Airbnb treatment effect by comparing differences in revenue for hotels affected by Airbnb before and after Airbnb’s entry with a baseline of differences in revenue for hotels that were not affected by Airbnb in the same period. To perform the analysis, they regress against two measures of Airbnb supply, namely a cumulative measure of all Airbnb listings and an instantaneous measure that defines supply as those Airbnb listings active within a short period. In all their specifications, they included a set of control variables that vary over time. For example, control variables such as population, wages, unemployment, total hotel room supply, airport passengers counts and TripAdvisor ratings were taken into account for each hotel as a proxy for quality. Also, they included city-specific trends and city-month dummies to account for seasonal differences in demand across the different markets. Finally, they have conducted several robustness check in order to support the causal interpretation of the estimates.
The authors found that, in Texas, each additional 10% increase in the size of the Airbnb market resulted in a .39% decrease in hotel room revenue. These effects are primarily driven by Austin, where Airbnb inventory has grown extremely rapidly over the last years, resulting in an estimated revenue impact of 8%-10% for the most vulnerable hotels in Austin. Accordingly, the researchers found that the impact of Airbnb is bigger on cheaper hotels in comparison with expensive hotels. The impact of Airbnb also falls disproportionately on hotels lacking conference facilities. Another finding is related to type of hotel; chain hotels tend to be less affected by Airbnb than independent hotels. This can be explained by the fact that chain hotels have a larger marketing budget and can thus benefit from their stronger brand identity. To conclude, the research showed that Airbnb is flexible in terms of their ability to flexibly scale instantaneous supply in response to seasonal demand, whereas hotels lack the flexibility. This has significantly limited hotels’ pricing power during periods of peak demand.
4. Strengths & Weaknesses
The main limitation of this study is related to the representativeness of this study since the AirBnb effect on the hotel industry is only studied in Texas. The generalizability of the findings should be taken into account considering the volatility of the housing market and the sensitivity of the hotel industry towards economic differences and other dynamics influencing supply and demand for accommodation. Though, the research uses a diverse set of data sources and controls for various exogenous variables (e.g. population, wages, unemployment and total hotel room supply). The authors point a similar limitation In addition, the study investigates multiple cities in a large state and the data is collected in a time period of 6 years (2008-2014). On the one hand, the long time period adds to the level of reliability and consistency of the research. On the other hand, the timing of the data period (2008-2014) yields a point of discussion since it investigated the vacation rental platform before the explosion of peer-2-peer networks happened.
Another limitation of this paper is related to the analyzed properties, the authors only consider AirBnb as the main peer-to-peer platform, whereas other vacation rental platforms such as HomeAway and VRBO do gain traction as well and might influence the negative on the hotel industry as studied. Also, the authors of the research add that only short run implications are considered by including only two metrics; price and occupancy rate. A longer time scale is not included, this reasons that the authors did not include the longer time scale is arguable. Further research can take the findings of this research as a starting point to study possible ways to respond to peer-to-peer platforms such as AirBnb. For example, alterations of investment schedules can be analyzed or effect of government regulations can be taken into account.
Overall the paper considers the short-term effect of the peer-to-peer platform AirBnb on the revenue stream of the hotel industry in Texas. Strengths of this paper are mainly related to the comprehensive investigation of the AirBnb platform, economy and housing market in Texas including controlling for exogenous factors such as airport passengers counts and TripAdvisor ratings. Not to mention the wide time span of six years (2008-2014). All strengths of this research considered, generalizability is a main concern of the findings. Though, the research takes a first step in quantifying the effect on society by analyzing AirBnb which contributes to the recent development of peer-to-peer networks in the raising sharing economy. By quantifying the effect through including several reliable data sources (e.g. platform itself and the monthly room revenue from 3,000 hotels in Texas), control variables and other exogenous factors, the study does provide practical relevance in terms of showing the exact effect in percentages and how the researched variables account for differences in the effect. The study is therefore relevant for society and other countries as well as governmental bodies, consumers and the hotel industry itself.
Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705