Zhang, M. orcid.org/0000-0002-7609-7457, Adepeju, M. and Thomas, R. (2022) Estimating the effects of crime maps on house prices using an (un)natural experiment: a study protocol. PLoS ONE, 17 (12). e0278463. ISSN 1932-6203
Abstract
Street-level crime maps are publicly available online in England and Wales. However, there was initial resistance to the publication of such fine-grained crime statistics, which can lower house prices and increase insurance premiums in high crime neighbourhoods. Identifying the causal effect of public crime statistics is difficult since crime statistics generally mirror actual crime. To address this question empirically, we would ideally experiment and introduce a source of random variation in the crime statistics. For instance, we could randomly increase or decrease the number of offences displayed in crime statistics and measure their effects on local house prices. For obvious reasons, we cannot pursue this research design. However, street-level crime maps contain intentional errors, which are the product of a geomasking algorithm designed to mask the location of crimes and protect the identity of victims. This project leverages features associated with the geomasking algorithm to estimate the effect of public crime statistics on house prices.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Funding Information: | Funder Grant number BRITISH ACADEMY (THE) SRG21\210192 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2022 16:42 |
Last Modified: | 05 Dec 2022 13:52 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0278463 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193665 |