Agent Behavior in the Sharing Economy: Evidence from Airbnb

Li, Jun; Moreno, Antonio; Zhang, Dennis

doi:10.2139/ssrn.2708279

Cited by 85 publications

(92 citation statements)

References 33 publications

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“…The levels were at the listing itself, the census tract in which the listing was located, and the borough in which the listing was located. This model differs from previous hedonic analyses of Airbnb listings which rely on ordinary least squares (OLS) regression using the listing's reported location (Gibbs et al, 2018;Li et al, 2015;Teubner et al, 2017;Wang & Nicolau, 2017). The core difference is a multilevel model 'explicitly models spatial autocorrelation and heterogeneity' (Orford, 2000(Orford, , p. 1649.…”

Section: Methodsmentioning

confidence: 97%

“…With the rapid growth of short-term rentals over the last several years, scholars have begun to examine price determinants of Airbnb listings (Gibbs et al, 2018;Hill, 2015;Lee et al, 2015;Li, Moreno, & Zhang, 2015;Teubner et al, 2017;Wang & Nicolau, 2017). Airbnb has its own hedonic pricing algorithm, the development of which is described by Hill (2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regarding the impact of location, these studies only examined the Euclidian distance from either the city centre or city hall (Gibbs et al, 2018;Teubner et al, 2017;Wang & Nicolau, 2017), and thus were unable to take account of heterogeneity of neighbourhoods and neighbourhood amenities. Li et al (2015) examined the impact of professional hoststhose who manage more than one Airbnb listingin Chicago. These professional hosts were found to have 16.9% higher daily revenue on the properties they manage, and a 15.5% higher occupancy rate.…”

Section: Literature Reviewmentioning

confidence: 99%

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Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue

Deboosere

Kerrigan

Wachsmuth

et al. 2019

Regional Studies, Regional Science

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Hedonic modelling techniques have frequently been used to examine real estate valuation, and they have recently started to be applied to short-term rental valuation. Relying on a web-scraped data set of all Airbnb transactions in New York City (NYC) between August 2014 and September 2016, this paper presents the first hedonic regression model of Airbnb to take into account neighbourhood effects and to predict both average price per night and revenue generated by each listing. The model demonstrates that locational factorsabove all, transit accessibility to jobsand neighbourhood variation have a large impact on both price per night and monthly revenue, and further reveals how professionalization of the short-term rental market is driving more revenue to a narrower segment of hosts. Further, the findings suggest that Airbnb hosts earn a significant premium by converting long-term housing in accessible residential neighbourhoods into de facto Airbnb hotels. This premium incentivizes landlords and hosts with properties in accessible neighbourhoods to replace long-term tenants with short-term guests, forcing those in search of housing to less accessible neighbourhoods. ARTICLE HISTORY

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Section: Methodsmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue

Deboosere

Kerrigan

Wachsmuth

et al. 2019

Regional Studies, Regional Science

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“…Discussions about regulations of Airbnb have also generated much attention [5,7]. Li et al investigated differences in performances and behaviors between professional hosts-those who own two or more listings-and nonprofessional hosts on Airbnb [23]. Our analysis, however, reveals the lack of threshold that allows us to separate professional and nonprofessional hosts.…”

Section: Related Workmentioning

confidence: 96%

“…To further characterize multi-listers and their listings, we need to answer a key question-which threshold of number of owned 10 https://www.yelp.com/dataset_challenge/ [23]. Here, we argue that a typical threshold value may not be well-defined, as Fig.…”

Section: Existence Of Multi-listersmentioning

confidence: 99%

Sharing Means Renting?: An Entire-Marketplace Analysis of Airbnb

2017

SSRN Journal

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Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue-commercial hosts who own multiple listings on Airbnb-repeatedly discussed in the current debate. We find that their existence is prevalent, they are early-movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.

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Take me on a ride: The role of environmentalist identity for carpooling

Hartl

Kamleitner

Holub

2020

Psychology and Marketing

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Sharing does not need to involve corporate providers but can also happen on a peerto-peer (P2P) basis. P2P sharing platforms who match private providers and users are thus dealing with two different customer segments. An example of this is carpooling, the sharing of a car journey. Recent years have seen considerable research on why people use sharing services. In contrast, there is little knowledge of why people may offer a good for sharing purposes. Drawing on identity theory, this paper suggests that users and providers of carpooling need to be addressed differently. A pilot study and two studies, including both actual car owners and nonowners confirm that the extent to which one identifies as an environmentalist predicts car owners' willingness to offer carpooling, but does not affect nonowners' willingness to use carpooling services.These findings remain robust when controlling for various potential confounds. Furthermore, Study 2 suggests that an environmentalist identity plays an important role for car owners' actual decision to offer a ride via an online platform. These results suggest that marketers of P2P platforms need to pursue different strategies when addressing potential users and providers on the same platform. K E Y W O R D Scarpooling, environmentalist, identity, peer-to-peer platform, sharing economy

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Agent Behavior in the Sharing Economy: Evidence from Airbnb

Cited by 85 publications

References 33 publications

Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue

Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue

Sharing Means Renting?: An Entire-Marketplace Analysis of Airbnb

Take me on a ride: The role of environmentalist identity for carpooling

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