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    Review of 'A new attribute-linked residential property price dataset for England and Wales, 2011 to 2019'

    A new attribute-linked residential property price dataset for England and Wales, 2011 to 2019Crossref
    A valuable new dataset on house prices and a sophisticated methodology for extending the analysis
    Average rating:
        Rated 5 of 5.
    Level of importance:
        Rated 5 of 5.
    Level of validity:
        Rated 5 of 5.
    Level of completeness:
        Rated 5 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
    Competing interests:

    Reviewed article

    • Record: found
    • Abstract: found
    • Article: found
    Is Open Access

    A new attribute-linked residential property price dataset for England and Wales, 2011 to 2019

    Current research on residential house price variation in the UK is limited by the lack of an open and comprehensive house price database that contains both transaction price alongside dwelling attributes such as size. This research outlines one approach which addresses this deficiency in England and Wales through combining transaction information from the official open Land Registry Price Paid Data (LR-PPD) and property size information from the official open Domestic Energy Performance Certificates (EPCs). A four-stage data linkage is created to generate a new linked dataset, representing 79% of the full market sales in the LR-PPD. This new linked dataset offers greater flexibility for the exploration of house price (/m 2 ) variation in England and Wales at different scales over postcode units between 2011 and 2019. Open access linkage codes will allow for future updates beyond 2019.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      data linkage,Land Registry Price Paid Data,Domestic Energy Performance Certificates,Built environment,Energy,England and Wales,Urban studies,Sustainable and resilient cities

      Review text

      The lack of official or rigorously derived data on house prices per square metre in England and Wales constitutes an important gap in the evidence base for housing policy-making and market monitoring. This gap makes it more difficult to discern spatial patterns in the housing market, as measures of house prices either ignore spatial variation in housing types or account for them using complex and opaque weighting procedures.

      Simple average price measures that do not take account of variation in housing types can lead to misunderstanding, for example by making prices in an area seem more expensive simply because it features larger properties. The lack of data on prices per square metre also make it difficult to compare costs in England with those in other countries.

      Prices per square metre are also valuable in operational terms, as they are a key input into the analysis of viability in housing development, which in turn affects the amounts of infrastructure and affordable housing that planning authorities are able to secure from new development. Finally, linking data on property prices and energy efficiency could enable valuable new analysis of willingness to pay for higher energy standards.

      The introduction of new open datasets on the sale prices and energy performance of dwellings in England and Wales has been very welcome, but the lack of unique property identifiers in in these datasets and the often messy nature of residential addresses makes linking these datasets much more difficult.

      The authors of this paper have developed a sophisticated linking method to overcome these challenges, and have achieved a high matching rate. The resulting data will very valuable in itself for a wide range of purposes, but by clearly explaining the method followed the researchers will hopefully also enable others to apply it to new data or to develop it further.


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