This project leverages features associated with the geomasking algorithm to estimate the effect of public crime statistics on house prices.


Authors

Meng Le Zhang, Monsuru Adepeju, Rhiannon Thomas

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.

Publication link

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278463#sec008

Full reference

Zhang, M.L, Adepeju, M., 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. https://doi.org/10.1371/journal.pone.0278463

Linked Project

Estimating the effect of crime (maps) on house prices using a natural experiment
https://mmuperu.co.uk/blog/criminal-justice/estimating-the-effect-of-crime-maps-on-house-prices-using-a-natural-experiment/