This week the Labour Party has published its plan for improving the UK’s housing stock, cutting carbon emissions by 10%, preventing 1,500 early deaths and reducing energy bills for low income households.

This plan used research based on Parity Projects’ housing stock model. We use address-level housing data to research evidence-based and cost-optimised retrofit plans for our clients.   

Russell Smith, Managing Director of Parity Projects, said, “Our CROHM service gives clients control over their housing data to meet their carbon, SAP, and fuel poverty targets.

“Different clients have different targets, and choose different paths. We help all of them by testing their policy options against real-world data, at scale. Our software delivers cost-optimised whole house plans for each individual property, along with data visualisation tools that help users understand the cumulative impact of these plans at city, regional and national level.” 

We thought we would set out the broad approach used by our model in this research.

National data model

Most of our clients are landlords or local authorities, for whom we undertake analysis of every home in their remit. In this case, where a national model was required, we took a sampling approach due to the amount of data collation and cleansing required to prepare a set of homes for analysis.

Rather than reinvent the wheel, we used National Housing Survey data as our basis. We were able to combine these with our own large library of UK housing energy data to prepare complete energy models for a representative sample of homes, making use of scaling factors to allow conclusions to be drawn about the national picture. This aligns with the Government’s own approved methods for sampling homes and for assessing energy performance.

Scenario modelling

Our approach was to model the effect of a variety of policy interventions on the housing stock using different types of scenarios, such as minimum standards on different tenures by different dates, PV schemes, or various incentive schemes. We then stacked the scenarios to see the combined effect on the national housing stock. 

Our housing stock model tests up to 2400 energy efficiency interventions, varied in terms of technology, product performance and scale. This number can be restricted as appropriate, such as for listed buildings or those in conservation areas, or where clients have a preference for certain measures.

The model incorporates future carbon factors, to show the impact of the decarbonisation of the grid on their carbon footprint, which is starting to influence choices between gas and electric heating. It also incorporates real-life costs, which are continuously updated as the market evolves; and in-use factors, which adapts laboratory-tested performance to better reflect real-world performance once products are installed in homes.

Once the model is run to test various policy scenarios, our data analytics allow users to quickly navigate from strategic stock level reports to address-line detail, and report on the cost-optimal route for decarbonising the housing stock alongside the route for each individual home.

Your own evidence-base

If you are a landlord or local authority interested in a data model for your housing stock to develop evidence-based strategy, policies and programmes, please contact us for a no-obligation quote and demo.