Automated appraisal approaches incorporating hedonic and spatial analysis have become an established feature of Computer Assisted Mass Appraisal (CAMA) of residential property for taxation purposes. Large datasets of property attribute data are routinely gathered, verified and analysed to enable this, with scrutiny of both transacted data and the larger unsold population. This is justified given the established relationship between property attributes, sale prices and the value of similar properties in the general population. More recently, residential property has been seen as not only a durable, valuable commodity but also as a persistent and considerable polluter, due its carbon footprint both in production and use. The very durability that contributes to value, in this context, locks in inefficiency for considerable time periods, particularly in locations such as the UK where housing may be used for in excess of 100 years, with largely cosmetic upgrade and alteration. Many jurisdictions have introduced energy rating schemes which typically are compulsory at the point of transaction, with the aim of highlighting the energy efficiency of current and new stock and highlighting where improvement can be made. Given the low turnover of stock and the relatively low weighting of such issues in the house buying decision process, the impact of such schemes in affecting market behaviour is necessarily limited. Many jurisdictions have also endeavoured to improve knowledge of their housing stock, to facilitate upgrade and renewal activities and better target awareness campaigns and funding. Many of the key attributes which affect value can be seen to be significant contributors to energy efficiency, such as total floor area, property type and era of construction. The aim of this project is to apply CAMA techniques to several large property related data sets, in order to model energy performance. It is expected that the work will allow a better understanding of the likely nature, scale and dispersal of the perceived problem of inefficient housing. This can have benefits both in terms of the climate change agenda and also the issue of fuel poverty. This has the potential to provide a valuable insight for policy framing and programme targeting at a local, regional and national level, such as targeted awareness campaigns and grant provision for issues such as upgrading of properties, transfer to greener fuels and ìgreeningî of the tax system. Given the capacity of modern property tax authorities to gather high quality data whilst carrying out their core business, a powerful GIS based assessment tool could be established to better manage housing stock and the urban environment. The research builds upon earlier work by introducing official Energy Performance Certificate data to the modelling process.