Closing the Affordability Gap: An Archetype-Led Pathway to Retrofit Delivery

Applying an Archetype Approach to Identify the Actual Cost of Housing Retrofit

Dr Julio Bros-Williamson^a, PhD Researcher Parima Sharma^a, Professor Sean Smith^a

^a School of Engineering, Institute for Infrastructure and Environment, University of Edinburgh, King’s Buildings Campus, Colin Maclaurin Road, Edinburgh, Scotland, EH9 3DW.

j.broswilliamson@ed.ac.uk, P.Sharma-11@sms.ed.ac.uk, Sean.Smith@ed.ac.uk 

Summary 

This study was conducted using outputs generated from the NetZero Affordability microdata in the FINDS data catalogue alongside housing association data, EPC records and Home Analytics Scotland data. This dataset estimates peoples’ ability to afford the costs of retrofitting their home to meet government emissions targets by 2050.  

The study grouped homes into archetypes based on their construction and age, and then estimated energy savings and retrofit costs for different levels of retrofitting for each of these archetypes, using affordability data to identify where affordability could present a barrier to making energy efficient upgrades. 

Introduction 

The built environment is one of the main contributors to global energy demand and greenhouse gas emissions, with the domestic sector accounting for over a quarter of the UK’s energy use and around 20% of emissions. Without decarbonising existing dwellings, achieving the UK’s 2050 net zero target will be impossible. 

The challenge is stark. Much of the housing stock is ageing and inefficient, yet expected to remain in use for decades, particularly as alternative housing options remain scarce in many towns and cities. With construction and replacement rates at negligible levels, large-scale retrofit is not optional but essential. However, retrofit activity remains far below what is required, hindered by high capital costs, fragmented delivery models, and limited household affordability. 

An archetype-led methodology 

This project applied a building archetype-led approach that combines stock characterisation, retrofit intervention design, cost evidence, and socio-economic feasibility into a single methodology. 

Using Home Analytics Scotland, EPC records, and housing association data, dwellings were classified into archetypes based on wall construction method, property age, and type. Each archetype was then assigned to retrofit envelope interventions to model reductions in energy demand and carbon intensity. 

Cost estimates for interventions were grounded in as-delivered procurement data from a 2024 social housing pilot study, capturing both labour and capital costs. To assess affordability, anonymised disposable income distributions provided by Smart Data Foundry were overlaid, enabling household-level payback modelling and feasibility analysis. 

What the data shows 

The findings reveal a critical gap between the scale of retrofit interventions required, the availability of government funding, and household affordability. 

Deep retrofit interventions (aligned with AECB standards) deliver the largest reductions in energy demand and carbon intensity. Yet they require the highest upfront capital investment, placing them beyond reach for most homeowners without substantial government financial support. 

Medium-depth, fabric-first approaches emerge as the more practical route. They deliver significant demand reductions, align with policy benchmarks, and can be made feasible through grant aid or phased financing mechanisms. 

By linking technical typologies to socio-economic data, the framework identifies priority archetype categories, quantifies affordability gaps, and highlights where current grant funding is insufficient to secure uptake. 

Closing the affordability gap 

An archetype-based methodology offers a replicable and scalable pathway for accelerating retrofit by reducing cost variability, enabling action at scale, and supporting more consistent targeting of grant funding. Critically, the integration of household income data exposes where affordability is the barrier and ensures that vulnerable households and hard-to-treat archetypes are not excluded from the transition. 

Its effectiveness will depend on improved data resolution, validation across different housing tenures, and refinement of cost and funding assumptions. But the evidence strongly supports its adoption. 

Retrofitting homes at the pace demanded by net zero will require precisely this form of integration bringing together building physics, real-world costing, and socio-economic capacity. Closing the affordability gap is essential to ensure that retrofit delivery is technically robust, economically viable, and socially just. 

This research has been funded as one of ten pilot projects through a £120,000 research partnership with the Edinburgh Centre for Financial Innovations, an alliance supported with funding from the University’s Major Initiatives Fund. Under the MIF-funded project, the Centre is helping establish a sustainable research-driven relationship between Smart Data Foundry and University of Edinburgh researchers built on the use of SDF’s data assets.