Providing neighbourhood insights

The CDRC is working to deliver insight on a range of topics that have an impact on local communities. The size and composition of the population that comprises neighbourhoods and who access local services has been investigated in collaboration with Leeds City Council. Housing is a key consideration at the local level, and research has provided insight into rental prices and the energy efficiency of the housing stock. CDRC researchers have also been working on improving estimates of local area crime, which is a key concern for residents.

Generation Rent and Supermarkets

Channel 5 programme “Inside Waitrose” and previous research has highlighted the “Waitrose effect” – the impact of retail brands on UK house prices, particularly in relation to the luxury brands.

However, this effect had only been considered in relation to house sales and purchase prices, not on the private rental market. Research done by CDRC researcher, Dr Stephen Clark, using Zoopla and GEOLYTIX data, has now addressed that market as well.

In order to hone in on a true retailer association, the research controlled for other aspects that make properties more desirable (confounders), such as the size of the property, the affluence and character of the neighborhood, access to services and the quality of local schools.

The model produced reassuring insights. Confounding variables all estimated the correct magnitude and influence on rental prices, e.g. higher rents for larger properties, in more affluent areas that are closer to good schools and railway stations. But the retail findings were also insightful. Waitrose did indeed have the strongest effect on rental prices (5.6% increase, for nearby convenience stores), with Marks & Spencer’s Simply Food close behind (5.1%). For medium to large stores, this effect was also strong – 11.1% for Waitrose and 8.7% for M&S.

For statistical reasons, it is only possible to claim an association between the retail brands in a neighbourhood and the rent for local private rental properties. To claim an actual causation requires more sophisticated statistical techniques. Initial investigations are proving positive. Further research considering the effect of the post-COVID-19 living, working, leisure and retail landscape would also be important.

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Creating a road map for retrofitting social housing stock

Domestic energy use accounts for a quarter of all greenhouse gas emissions produced by the UK. In order to meet the 2050 net zero target, retrofitting the existing housing stock is a priority. This is a complex and costly undertaking and there are few guidelines, especially when it comes to large-scale implementation.

Leeds City Council, who recently announced a £100m investment in this area, has been working in partnership with the University of Leeds to identify the best way to target improvements to maximise the benefits for residents and the environment.

As part of this partnership, former CDRC Interns, Natalie Nelissen and Claire Shadbolt, worked with a team at the Faculty of Environment to produce an interactive model to help decide on the best retrofit strategy for their housing stock and available resources.

Making crime estimates more accurate

The consequences of unchecked measurement error in crime statistics are grave. At its simplest, inaccurate crime counts can inhibit the effective allocation of resources for crime control. Inaccuracies can also impact on public trust, undermining the relationship between the police and public. Beyond this, failure to effectively recognise and adjust for measurement error can also have severe implications for the conclusions from academic research, with even mild forms of random measurement error affecting a single variable used in regression models leading to severely biased estimates, often in ways that are difficult to anticipate.

Two sources of crime data form the basis for these counts: police recorded crime figures and victim data from the annual Crime Survey of England and Wales (CSEW). CDRC researchers undertook a Re-counting Crime project to better understand the nature of the gaps in coverage of the two sources of crime data, explore the implications of relying on crime estimates prone to measurement error, develop adjustment methods and estimate new ‘corrected’ crime counts at the local area level.

Research papers: “The impact of measurement error in models using police recorded crime rates” (2021) and “Estimating crime in place: Moving beyond residence location” (2021)

Investigating population estimate discrepancies in GP Registers in Leeds

In 2017, GP Registers for Leeds counted 60,000 more people than the Office for National Statistics estimated to be living in Leeds. Researchers at the CDRC undertook a project to assess how these discrepancies occur and provide further understanding as to why they are occurring. The importance of large discrepancies across particular areas in Leeds has implications for city planning, including health planning, transport planning and election preparation. A single agreed version is therefore highly desirable.

This project used Geographical Information Systems and classification methods to assess where the discrepancies exist within Leeds and give further understanding as to why these discrepancies are occurring, indicating potential recent changes in the population composition of Leeds which are unaccounted for by the Mid-Year Estimates (MYEs).

A classification was conducted of the UK, using variables derived from the 2011 Census outputs, to recognise demographic patterns across the UK and how these influence the disparity between population estimates from MYEs and GP registers. This classification highlighted a reoccurring pattern of higher GP counts occurring across the whole country, which appear to be more pronounced in diverse clusters. This indicates that differences between population estimates is a wider problem.

Leeds, however, is unique to the rest of the UK as it displays a higher frequency of LSOAs which contain demographics that could be driving the disparity between MYEs and GP registration counts, suggesting that ONS methods of collecting population estimates in certain areas require reviewing. As cluster distribution across Leeds reflects patterns of discrepancies of population estimates, this gives some indications that particular groups may have a larger influence over population estimates.

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