Exploiting new forms of data to study the private rented sector: strengths and limitations of a database of rental listings

Livingston, M. , Pannullo, F. , Bowman, A. W. , Scott, E. M. and Bailey, N. (2021) Exploiting new forms of data to study the private rented sector: strengths and limitations of a database of rental listings. Journal of the Royal Statistical Society: Series A (Statistics in Society), (doi: 10.1111/rssa.12643) (Early Online Publication)

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Abstract

Reviews of official statistics for UK housing have noted that developments have not kept pace with real‐world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on‐line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it against external sources. The paper specifically examines what these data might add to our understanding of changing volumes and rents in the private rented sector. Fluctuations in market share across advertising platforms make assessment of volume problematic, while rental prices appear more robust through comparison with other reference information. Focussing on one urban area, we illustrate how the dataset can shed new light on local changes. Lastly, we discuss the issues involved in making more routine use of this kind of data.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Bowman, Prof Adrian and Pannullo, Miss Francesca and Livingston, Dr Mark and Bailey, Professor Nick and Scott, Professor Marian
Authors: Livingston, M., Pannullo, F., Bowman, A. W., Scott, E. M., and Bailey, N.
College/School:College of Science and Engineering > School of Mathematics and Statistics
College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Journal of the Royal Statistical Society: Series A (Statistics in Society)
Publisher:Wiley
ISSN:0964-1998
ISSN (Online):1467-985X
Published Online:09 January 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Journal of the Royal Statistical Society: Series A (Statistics in Society) 2021
Publisher Policy:Reproduced under a Creative Commons License

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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