Industry Update
Opinion Article 3 October 2017

Research reported positive effects of Airbnb’s price positioning on hotel performance

By Linchi Kwok , Associate Professor at The Collins College of Hospitality Management

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There is an on-going debate among academic researchers and professional analysts on Airbnb's impacts on the traditional lodging business. Some found Airbnb had significant negative impact on the hotel industry; others claimed that the impact remained negligible.


Some of those findings are challenged by industry professionals on several grounds, including conflicts of interest involving room-sharing platforms in data analysis, methodology rigor as most of them were descriptive studies, and the oversight of some confounding factors, such as price comparison among Airbnb and hotels, which could cause bias in their estimations.

Price is a very important influential factor when consumers choose between a hotel room and an Airbnb listing. Meanwhile, price is a critical variable in analyzing supplies and demands in economics. Hence, Airbnb's price positioning in a market should not be ignored when Airbnb's impacts on the hotel industry are mesasured.

Accordingly, I worked with Karen Xie, a professor at University of Denver on an empirical study, which was published in International Journal of Hospitality Management [1]. Our particular interest in this study include:

Purposes of the study

  1. To (re)assess the impact of Airbnb supply on the performance of its hotel counterparts in a market.
  2. To investigate if Airbnb's price positioning in the market, as measured in price difference between a hotel property and the hotel's nearby Airbnb listings in the same zip code as well as price dispersion among these Airbnb listings, moderates the main effects of Airbnb supply on the performance of its hotel counterparts in the market.
  3. To examine if hotels' quality attributes, including class category (e.g., budget traveler hotels vs. luxury hotels) and average ratings on online review websites, moderate the main effects of Airbnb supply on the performance of its hotel counterparts in the market. In other words, we tested if the main effects vary according to a hotel's class category and/or online review ratings.

The data and analysis

The dataset used in our analysis was built from three different sources, including (a) for hotel-related information (i.e., class category and review ratings of the hotels), (b) for listing-related information (e.g., listing price and characteristics), and (c) Texas Comptroller of Public Accounts for hotels' quarterly performance records in the Austin, Texas market. The dataset included 1,482 observations of 86 hotels located in 20 zip codes of the city over a period of 12 quarters from Quarter 3, 2008 to Quarter 2, 2011, covering all hotels in the Austin market during the period of observations as long as they reported financial performance data to the Texas Comptroller and received reviews on TripAdvisor. Then, we used a blend of econometrics models to perform the estimations for hypothesis testing.

The results

  • Our analysis confirmed Airbnb's negative impact on hotel performance. As the supply of Airbnb listings in a market increases, hotels' RevPAR (Revenue per Available Room) performance will go down.
  • Interestingly, depending on Airbnb's price position, hotels may gain benefits from the Airbnb in the neighborhood. Our analysis reveals that price difference between a hotel and the Airbnb listings, as well as price dispersion within Airbnb listings are positively related to a hotel's RevPAR performance. Such results can be explained with the "agglomeration effects" of a product's strategic orientation, in which low-cost hotels can possibly yield higher RevPAR if they are strategically located in an area with many high-priced competitors (in our sample, the average price of Airbnb listings were much higher than their hotel counterparts).
  • Furthermore, as the gaps in price difference and price dispersion increase, the negative impact of Airbnb supply on a hotel's RevPAR performance decreases significantly, supporting the moderations effects from price difference and price dispersion.
  • A hotel's class category and online review ratings have no impacts on Airbnb supply's negative effect on the hotel's RevPAR performance. In other words, hotels of different types cannot immune from the negative impacts from Airbnb.


Theoretically, we added "price" as an important variable into the analysis of Airbnb's impacts on hotels in a market. Practically,

  • We suggest managers should also pay attention to the price positioning of the Airbnb listings in the same neighborhood. When Airbnb listings are charging for a much higher or lower price than the hotel, chances are they are not competing in the same market. Yet, special attention should be given to those Airbnb listings in the same price range.
  • If a hotel is located in a nice neighborhood with a lot of high-end short-term residential rentals, the hotel may be able to leverage the "agglomeration effects." It turns out having some pricy Airbnb listings in the neighborhood is good for hotels.
  • Because many OTAs have already added residential rentals into their room inventory, OTAs now have the business intelligence of both hotels and short-term residential rentals. We encourage hotels to reconsider their relationships with OTAs.
  • We also suggest hotels to consider adding Airbnb listings as part of their competitive sets what they have been doing with their competitors.

In conclusion, it is no doubt the supply of Airbnb negatively affects a hotel's performance, but depending on Airbnb's price positioning in a market, such impact might not be as bad as what we thought. What other factors should researchers look into when they measure Airbnb's impacts on the hotel industry? What are your suggestions?

[1] This journal article is available for free access on the publisher's website until mid November 2017.

Linchi Kwok

Linchi Kwok is an associate professor in The Collins College of Hospitality Management at California State Polytechnic University Pomona (Cal Poly Pomona). He came to Cal Poly Pomona by way of Syracuse University and Rochester Institute of Technology. He is a blogger and publishes refereed journal articles on service operations, information technology and social media.

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