Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub). This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epochs.