Enhancing our understanding of short-term rental activity: a daily scrape-based approach for Airbnb listings

Wang, Y. , Livingston, M. , Mcarthur, D. P. and Bailey, N. (2024) Enhancing our understanding of short-term rental activity: a daily scrape-based approach for Airbnb listings. PLoS ONE, 19(2), e0298131. (doi: 10.1371/journal.pone.0298131) (PMID:38324608) (PMCID:PMC10849255)

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Abstract

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.

Item Type:Articles
Additional Information:Funding support is from ESRC-funded Urban Big Data Centre (UBDC) [ES/L011921/1 and ES/S007105/1].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Livingston, Dr Mark and Mcarthur, Dr David and Bailey, Professor Nick and Wang, Dr Yang
Creator Roles:
Wang, Y.Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
Livingston, M.Funding acquisition, Validation, Writing – review and editing
Mcarthur, D.Funding acquisition, Writing – review and editing
Bailey, N.Funding acquisition, Methodology, Validation, Writing – review and editing
Authors: Wang, Y., Livingston, M., Mcarthur, D. P., and Bailey, N.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN:1932-6203
ISSN (Online):1932-6203
Copyright Holders:Copyright © 2024 Wang et al.
First Published:First published in PLoS ONE 19(2):e0298131
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data
304042UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration