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Daniel Guttentag - One of the best experts on this subject based on the ideXlab platform.

  • progress on Airbnb a literature review
    Journal of Hospitality and Tourism Technology, 2019
    Co-Authors: Daniel Guttentag
    Abstract:

    The purpose of this paper is to review the extant literature on Airbnb – one of the most significant recent innovations in the tourism sector – to assess the research progress that has been accomplished to date.,Numerous journal databases were searched, and 132 peer-reviewed journal articles from various disciplines were reviewed. Key attributes of each paper were recorded, and a content analysis was undertaken.,A survey of the literature found that the majority of Airbnb research has been published quite recently, often in hospitality/tourism journals, and the research has been conducted primarily by researchers in the USA/Canada and Europe. Based on the content analysis, the papers were divided into six thematic categories – Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulation, Airbnb’s impacts on the tourism sector and the Airbnb company. Consistent findings have begun to emerge on several important topics, including guests’ motivations and the geographical dispersion of listings. However, many research gaps remain, so numerous suggestions for future research are provided.,By reviewing a large body of literature on a fairly novel and timely topic, this research provides a concise summary of Airbnb knowledge that will assist industry practitioners as they adapt to the recent rapid emergence of Airbnb.,This is the first paper to review the extant literature specifically about Airbnb.,本论文旨在审视过去文献对Airbnb的研究-旅游业中最显著发明之一-以衡量迄今为止的研究发展历程。,经过大量文献搜索，共132份同行评审型期刊文章，来自不同研究领域，被作者审阅。每个文章的关键词被摘抄出来，本论文采用内容分析方法来分析文本。,经过文献综述，作者发现大多数Airbnb研究都发表在近几年，往往发表在酒店/旅游期刊。期刊文章作者集中在美国/加拿大和欧洲。基于内容分析结果，发表的期刊文章被分类在六个主题-Airbnb顾客、Airbnb服务提供主、Airbnb供应商、以及其对旅游目的地的影响，Airbnb规范、Airbnb对旅游行业的影响、以及Airbnb公司。研究结果还归纳出几项重要的话题，包括顾客动机和民宿地理分布。然而，大多数研究空缺仍然存在，因此，本论文总结出多项未来研究方向。,本论文通过审阅大量较新和及时的文献，对Airbnb的相关知识进行了精准梳理，这个研究结果对从业者适应Airbnb较新较快发展的现象，有着实践意义。,本论文是首篇审阅有关Aribnb文献的文章。,Airbnb、文献综述、共享经济、P2P、短期出租

  • why tourists choose Airbnb a motivation based segmentation study
    Journal of Travel Research, 2018
    Co-Authors: Daniel Guttentag, Stephen L Smith, Luke R Potwarka, Mark E Havitz
    Abstract:

    Airbnb has grown very rapidly over the past several years, with millions of tourists having used the service. The purpose of this study was to investigate tourists’ motivations for using Airbnb and...

  • pricing in the sharing economy a hedonic pricing model applied to Airbnb listings
    Journal of Travel & Tourism Marketing, 2018
    Co-Authors: Chris Gibbs, Daniel Guttentag, Ulrike Gretzel, Jym Morton, Alasdair M. Goodwill
    Abstract:

    ABSTRACTThis paper examines the impact of a variety of variables on the rates published for Airbnb listings in five large metropolitan areas in Canada. The researchers applied a hedonic pricing model to 15,716 Airbnb listings. As expected, the results show that physical characteristics, location, and host characteristics significantly impact price. Interestingly, more reviews are associated with a drop in price. This information is useful to hosts who are forming a pricing strategy for their listings as well as for Airbnb, who needs to support them. The paper raises important questions about pricing in the sharing economy and suggests avenues for future research in this area.

  • Use of dynamic pricing strategies by Airbnb hosts
    International Journal of Contemporary Hospitality Management, 2017
    Co-Authors: Chris Gibbs, Daniel Guttentag, Ulrike Gretzel, Lan Yao, Jym Morton
    Abstract:

    The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts.,This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels.,Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most.,This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor.,This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.

  • assessing Airbnb as a disruptive innovation relative to hotels substitution and comparative performance expectations
    International Journal of Hospitality Management, 2017
    Co-Authors: Daniel Guttentag, Stephen L Smith
    Abstract:

    Abstract Millions of tourists have used Airbnb accommodations, and Airbnb is frequently discussed in terms of its current or future impacts on hotels. The purpose of this research was to investigate such impacts by determining the extent to which Airbnb is used as a hotel substitute and to examine how Airbnb guests expect their accommodations to perform relative to hotels. Together, these analyses were intended to provide empirical insight into Airbnb’s status as a disruptive innovation. The study involved an online survey of over 800 tourists who had used Airbnb within the previous year. Nearly two-thirds had used Airbnb as a hotel substitute. When considering traditional hotel attributes (e.g., cleanliness and comfort), Airbnb was generally expected to outperform budget hotels/motels, underperform upscale hotels, and have mixed outcomes versus mid-range hotels, signalling some – but not complete – consistency with the concept of disruptive innovation. Numerous practical and theoretical implications are discussed.

T C Melewar - One of the best experts on this subject based on the ideXlab platform.

John W. Byers - One of the best experts on this subject based on the ideXlab platform.

  • A first look at online reputation on Airbnb, where every stay is above average
    Marketing Letters, 2021
    Co-Authors: Georgios Zervas, Davide Proserpio, John W. Byers
    Abstract:

    Judging by the millions of reviews left by guests on the Airbnb platform, this trusted community marketplace for accommodations is fulfilling its mission of matching travelers with hosts having room to spare remarkably well. Based on our analysis of ratings, we collected for millions of properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average star-rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star-rating. We contrast this with the ratings of roughly 700,000 hotels, B&Bs, and vacation rentals worldwide that we collected from TripAdvisor. We find that hotel and B&B average ratings are much lower—3.8 and 4.1 stars, respectively—with much more variance across reviews. TripAdvisor vacation rental ratings are more similar to Airbnb ratings, but only about 85% of properties have an average rating of 4.5 or 5 stars. We then consider properties cross-listed on both platforms. For these properties, we find that even though the average ratings on Airbnb and TripAdvisor are more similar than hotels and B&Bs, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only a weak correlation in the ratings of individual cross-listed properties across the two platforms. Finally, we show that these differences are consistent when considering data from two different time periods: 2015 and 2018.

  • the impact of the sharing economy on the hotel industry evidence from Airbnb s entry into the texas market
    Economics and Computation, 2015
    Co-Authors: Georgios Zervas, Davide Proserpio, John W. Byers
    Abstract:

    Spurred by technological advancement, a number of decentralized peer-to-peer markets, now colloquially known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question surrounding the sharing economy regards its long-term impact: will peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering short-term accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate the impact of Airbnb's entry into the Texas market on hotel room revenue, and study the market response of hotels. To identify Airbnb's causal impact on hotel room revenue, we use a difference-in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across citylevel markets. We estimate that each 10% increase in Airbnb supply results in a 0:37% decrease in monthly hotel room revenue. In Austin, where Airbnb supply is highest, the impact on hotel revenue exceeds 10%. We find that Airbnb's impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the most affected segments. Finally, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, and acquiring market share from, incumbent firms.

  • a first look at online reputation on Airbnb where every stay is above average
    Social Science Research Network, 2015
    Co-Authors: Georgios Zervas, Davide Proserpio, John W. Byers
    Abstract:

    Judging by the millions of reviews left by guests on the Airbnb platform, this "trusted community marketplace" is fulfilling its mission of matching travelers seeking accommodation with hosts who have room to spare remarkably well. Based on our analysis of ratings we collected for over 600,000 properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average user-generated rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star rating. We contrast this with the ratings of approximately half a million hotels worldwide that we collected on TripAdvisor, where there is a much lower average rating of 3.8 stars, and more variance across reviews. Considering properties by accommodation type and by location, we find considerable variability in ratings, and observe that vacation rental properties on TripAdvisor have ratings most similar to ratings of Airbnb properties. Last, we consider several thousand properties that are listed on both platforms. For these cross-listed properties, we find that even though the average ratings on Airbnb and TripAdvisor are similar, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only weak correlation in the ratings of individual cross-listed properties across the two platforms. Our work is a first step towards understanding and interpreting nuances of user-generated ratings in the context of the sharing economy.

Kannan Srinivasan - One of the best experts on this subject based on the ideXlab platform.

  • market shifts in the sharing economy the impact of Airbnb on housing rentals
    2019
    Co-Authors: Hui Li, Kannan Srinivasan
    Abstract:

    This paper examines the impact of Airbnb on the local rental housing market. Airbnb provides landlords an alternative opportunity to rent to short-term tourists, potentially causing some property owners to switch away from long-term rental to local residents, thereby affecting the rental housing supply and affordability. Despite recent government regulations to address this concern, it remains unclear whether and what type of properties are switching. Combining Airbnb listings data and American Housing Survey data, we estimate a structural model of property owners' decisions and conduct counterfactual analysis to generate policy implications. We find that Airbnb mildly cannibalizes the long-term rental supply. The reduction in rental supply is larger in metro areas where Airbnb is more popular, but the market expansion effect is also larger in these areas. Cannibalization is concentrated among lower priced, affordable units rather than among higher priced, luxurious ones, potentially causing concerns regarding housing affordability. The counterfactual results suggest that policies such as imposing a limit on the number of months a property can be listed outperform imposing a tax in terms of reducing the cannibalization effect while maintaining the market expansion effect. Finally, rent regulations on long-term rentals must be implemented with greater caution, as Airbnb can exacerbate the negative impacts of rent control.

  • competitive dynamics in the sharing economy an analysis in the context of Airbnb and hotels
    Social Science Research Network, 2018
    Co-Authors: Kannan Srinivasan
    Abstract:

    The entry of flexible-capacity sharing economy platforms (e.g., Airbnb and Uber) has potentially changed the competitive landscape in traditional industries with fixed- capacity incumbents and volatile demand. Leveraging panel data on hotels and Airbnb, we study how the sharing economy fundamentally changes the way the industry accommodates demand fluctuations and how incumbent firms should strategically respond. The demand estimates suggest that Airbnb’s flexible supply helps recover the lost underlying demand due to hotel seasonal pricing (i.e., higher prices during high-demand seasons) and even stimulates more demand in some cities. The counterfactual results suggest that some hotel types in some cities may benefit from conducting less seasonal pricing and even considering counter-seasonal pricing. Market conditions (e.g., seasonality patterns, hotel prices and quality, consumer composition, and Airbnb supply elasticity) play a crucial role in determining the impact of Airbnb on hotel sales and hotels’ strategic response. Finally, recent Airbnb and policy changes (e.g., higher Airbnb hosting costs due to hotel taxes or lower Airbnb hosting costs due to third-party services and the “professionalism” of hosts) affect the competitive dynamics. The profits of high-end hotels are the most sensitive to the changes in Airbnb hosting costs. Airbnb’s recent attempt to behave more like hotels can increase hotels’ vulnerability to lower Airbnb hosting costs.

  • how much is an image worth Airbnb property demand estimation leveraging large scale image analytics
    Social Science Research Network, 2017
    Co-Authors: Shunyuan Zhang, Dokyun Lee, Param Vir Singh, Kannan Srinivasan
    Abstract:

    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 dataset 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).

Davide Proserpio - One of the best experts on this subject based on the ideXlab platform.

  • A first look at online reputation on Airbnb, where every stay is above average
    Marketing Letters, 2021
    Co-Authors: Georgios Zervas, Davide Proserpio, John W. Byers
    Abstract:

    Judging by the millions of reviews left by guests on the Airbnb platform, this trusted community marketplace for accommodations is fulfilling its mission of matching travelers with hosts having room to spare remarkably well. Based on our analysis of ratings, we collected for millions of properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average star-rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star-rating. We contrast this with the ratings of roughly 700,000 hotels, B&Bs, and vacation rentals worldwide that we collected from TripAdvisor. We find that hotel and B&B average ratings are much lower—3.8 and 4.1 stars, respectively—with much more variance across reviews. TripAdvisor vacation rental ratings are more similar to Airbnb ratings, but only about 85% of properties have an average rating of 4.5 or 5 stars. We then consider properties cross-listed on both platforms. For these properties, we find that even though the average ratings on Airbnb and TripAdvisor are more similar than hotels and B&Bs, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only a weak correlation in the ratings of individual cross-listed properties across the two platforms. Finally, we show that these differences are consistent when considering data from two different time periods: 2015 and 2018.

  • the effect of home sharing on house prices and rents evidence from Airbnb
    Marketing Science, 2021
    Co-Authors: Kyle Barron, Edward Kung, Davide Proserpio
    Abstract:

    We assess the impact of Airbnb on residential house prices and rents: using a data set of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show th...

  • the effect of home sharing on house prices and rents evidence from Airbnb
    Social Science Research Network, 2018
    Co-Authors: Kyle Barron, Edward Kung, Davide Proserpio
    Abstract:

    We assess the impact of home-sharing on residential house prices and rents. Using a dataset of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents. This effect is stronger in zipcodes with a lower share of owner-occupiers, consistent with non-owner-occupiers being more likely to reallocate their homes from the long- to the short-term rental market. At the median owner-occupancy rate zipcode, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices. Considering the median annual Airbnb growth in each zipcode, these results translate to an annual increase of $9 in monthly rent and $1,800 in house prices for the median zipcode in our data, which accounts for about one fifth of actual rent growth and about one seventh of actual price growth. Finally, we formally test whether the Airbnb effect is due to the reallocation of the housing supply. Consistent with this hypothesis, we find that, while the total supply of housing is not affected by the entry of Airbnb, Airbnb listings increase the supply of short-term rental units and decrease the supply of long-term rental units.

  • who benefits from the sharing economy of Airbnb
    2016
    Co-Authors: Giovanni Quattrone, Davide Proserpio, Daniele Quercia, Licia Capra, Mirco Musolesi
    Abstract:

    Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of "algorithmic regulation".

  • the impact of the sharing economy on the hotel industry evidence from Airbnb s entry into the texas market
    Economics and Computation, 2015
    Co-Authors: Georgios Zervas, Davide Proserpio, John W. Byers
    Abstract:

    Spurred by technological advancement, a number of decentralized peer-to-peer markets, now colloquially known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question surrounding the sharing economy regards its long-term impact: will peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering short-term accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate the impact of Airbnb's entry into the Texas market on hotel room revenue, and study the market response of hotels. To identify Airbnb's causal impact on hotel room revenue, we use a difference-in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across citylevel markets. We estimate that each 10% increase in Airbnb supply results in a 0:37% decrease in monthly hotel room revenue. In Austin, where Airbnb supply is highest, the impact on hotel revenue exceeds 10%. We find that Airbnb's impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the most affected segments. Finally, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, and acquiring market share from, incumbent firms.