Who benefits from the "sharing" economy of Airbnb?

Conference paper


Quattrone, G., Proserpio, D., Quercia, D., Capra, L. and Musolesi, M. 2016. Who benefits from the "sharing" economy of Airbnb? WWW 2016: 25th International Conference on World Wide Web. Montreal, Canada 11 - 15 Apr 2016 Association for Computing Machinery (ACM). pp. 1385-1394 https://doi.org/10.1145/2872427.2874815
TypeConference paper
TitleWho benefits from the "sharing" economy of Airbnb?
AuthorsQuattrone, G., Proserpio, D., Quercia, D., Capra, L. and Musolesi, M.
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”.

ConferenceWWW 2016: 25th International Conference on World Wide Web
Page range1385-1394
ISBN
Hardcover9781450341431
PublisherAssociation for Computing Machinery (ACM)
Publication dates
Print11 Apr 2016
Publication process dates
Deposited06 Jul 2018
Accepted31 Jan 2016
Output statusPublished
Accepted author manuscript
Copyright Statement

© International World Wide Web Conference Committee (IW3C2), 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WWW '16: Proceedings of the 25th International Conference on World Wide Web, April 2016, Pages 1385–1394, http://dx.doi.org/10.1145/10.1145/2872427.2874815

Digital Object Identifier (DOI)https://doi.org/10.1145/2872427.2874815
LanguageEnglish
Book titleProceedings of the 25th International Conference on World Wide Web
Permalink -

https://repository.mdx.ac.uk/item/87v65

Download files


Accepted author manuscript
  • 33
    total views
  • 12
    total downloads
  • 4
    views this month
  • 2
    downloads this month

Export as

Related outputs

A global-scale analysis of the sharing economy model – an AirBnB case study
Quattrone, G., Kusek, N. and Capra, L. 2022. A global-scale analysis of the sharing economy model – an AirBnB case study. EPJ Data Science. 11 (1), pp. 1-29. https://doi.org/10.1140/epjds/s13688-022-00349-3
Nowcasting gentrification using Airbnb data
Jain, S., Proserpio, D., Quattrone, G. and Quercia, D. 2021. Nowcasting gentrification using Airbnb data. CSCW 2021: The 24th ACM conference on Computer-Supported Cooperative Work and Social Computing. Virtual conference 23 - 27 Oct 2021 Association for Computing Machinery (ACM). https://doi.org/10.1145/3449112
Work always in progress: analysing maintenance practices in spatial crowd-sourced datasets
Quattrone, G., Dittus, M. and Capra, L. 2017. Work always in progress: analysing maintenance practices in spatial crowd-sourced datasets. CSCW 2017. Portland, Oregon, United States 25 Feb - 01 Mar 2017 Association for Computing Machinery (ACM). pp. 1876-1889 https://doi.org/10.1145/2998181.2998267
Mass participation during emergency response: event-centric crowd-sourcing in humanitarian mapping
Dittus, M., Quattrone, G. and Capra, L. 2017. Mass participation during emergency response: event-centric crowd-sourcing in humanitarian mapping. Lee, C. and Poltrock, S. (ed.) CSCW 2017. Portland, Oregon, United States 25 Feb - 01 Mar 2017 Association for Computing Machinery (ACM). pp. 1290-1303 https://doi.org/10.1145/2998181.2998216
Measuring urban deprivation from user generated content
Venerandi, A., Quattrone, G., Capra, L., Quercia, D. and Saez-Trumper, D. 2015. Measuring urban deprivation from user generated content. Fussel, S., Lutters, W., Ringel Morris, M. and Reddy, M. (ed.) CSCW 2015. Vancouver, Canada 14 - 18 Mar 2015 Association for Computing Machinery (ACM). pp. 254-264
There’s no such thing as the perfect map: quantifying bias in spatial crowd-sourcing datasets
Quattrone, G., Capra, L. and De Meo, P. 2015. There’s no such thing as the perfect map: quantifying bias in spatial crowd-sourcing datasets. Cosley, D., Forte, A., Ciolfi, L. and McDonalld, D. (ed.) CSCW 2015. Vancouver, Canada 14 - 18 May 2015 Association for Computing Machinery (ACM). pp. 1021-1032 https://doi.org/10.1145/2675133.2675235
Is the sharing economy about sharing at all? A linguistic analysis of Airbnb reviews over time
Quattrone, G., Nicolazzo, S., Nocera, A., Quercia, D. and Capra, L. 2018. Is the sharing economy about sharing at all? A linguistic analysis of Airbnb reviews over time. ICWSM 2018. Stanford, California, United States 25 - 28 Jun 2018 The AAAI Press, Palo Alto, California USA. pp. 668
Social interactions or business transactions? What customer reviews disclose about Airbnb marketplace
Quattrone, G., Nocera, A., Capra, L. and Quercia, D. 2020. Social interactions or business transactions? What customer reviews disclose about Airbnb marketplace. Huang, Y., King, I., Liu, T. and van Steen, M. (ed.) WWW'20. Taipei, Taiwan 20 - 24 Apr 2020 Association for Computing Machinery (ACM). pp. 1526-1536 https://doi.org/10.1145/3366423.3380225
Analysing volunteer engagement in humanitarian mapping: building contributor communities at large scale
Dittus, M., Quattrone, G. and Capra, L. 2016. Analysing volunteer engagement in humanitarian mapping: building contributor communities at large scale. 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16. San Francisco, CA, USA 27 Feb - 02 Mar 2016 Association for Computing Machinery (ACM). pp. 108-118 https://doi.org/10.1145/2818048.2819939
Guns of Brixton: which London neighborhoods host gang activity?
Venerandi, A., Quattrone, G. and Capra, L. 2016. Guns of Brixton: which London neighborhoods host gang activity? Urb Iot 2016: 2nd International Conference on IoT in Urban Space. Tokyo, Japan 24 - 25 May 2016 Association for Computing Machinery (ACM). pp. 22-28 https://doi.org/10.1145/2962735.2962750
Exploring maintenance practices in crowd-mapping
Quattrone, G., Dittus, M. and Capra, L. 2016. Exploring maintenance practices in crowd-mapping. Hypertext 2016: 27th ACM Conference on Hypertext and Social Media. Halifax, Nova Scotia, Canada 10 - 13 Jul 2016 Association for Computing Machinery (ACM). pp. 285-290 https://doi.org/10.1145/2914586.2914621
Social contribution settings and newcomer retention in humanitarian crowd mapping
Dittus, M., Quattrone, G. and Capra, L. 2016. Social contribution settings and newcomer retention in humanitarian crowd mapping. 8th International Conference Social Informatics (SocInfo 2016). Bellevue, WA, USA 11 - 14 Nov 2016 Springer. pp. 179-193 https://doi.org/10.1007/978-3-319-47874-6_13
City form and well-being: what makes London neighborhoods good places to live?
Venerandi, A., Quattrone, G. and Capra, L. 2016. City form and well-being: what makes London neighborhoods good places to live? 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016). Burlingame, CA, USA 31 Oct - 03 Nov 2016 Association for Computing Machinery (ACM). https://doi.org/10.1145/2996913.2997011
A scalable method to quantify the relationship between urban form and socio-economic indexes
Venerandi, A., Quattrone, G. and Capra, L. 2018. A scalable method to quantify the relationship between urban form and socio-economic indexes. EPJ Data Science. 7 (1). https://doi.org/10.1140/epjds/s13688-018-0132-1
Analyzing and predicting the spatial penetration of Airbnb in U.S. cities
Quattrone, G., Greatorex, A., Quercia, D., Capra, L. and Musolesi, M. 2018. Analyzing and predicting the spatial penetration of Airbnb in U.S. cities. EPJ Data Science. 7 (1), pp. 1-24. https://doi.org/10.1140/epjds/s13688-018-0156-6