%0 Conference Paper %A Lee, Timothy %A He, Candy %A Taylor, Simon %A Firth, Steven %A Lomas, Kevin %D 2014 %T Developing a geographically detailed housing stock model for the North East of England %U https://repository.lboro.ac.uk/articles/conference_contribution/Developing_a_geographically_detailed_housing_stock_model_for_the_North_East_of_England/9428222 %2 https://repository.lboro.ac.uk/ndownloader/files/17049209 %K Domestic stock model %K Geographic variations %K Energy modelling %K Built Environment and Design not elsewhere classified %X Housing stock models predict long term changes in the stock to inform national policy. They operate with a set of reference dwellings representing the national stock, which are changed in response to different scenarios. However, national level models do not consider geographical variations (urban location/rural surroundings, index of multiple deprivation score, etc.), so cannot aid in targeting improvement measures (eg: insulation, microgeneration, etc.) locally. A geographically varying model can identify which measures are most appropriate in a particular location. In this paper a method has been designed and implemented using information at LSOA level (c. 700 dwellings each) to introduce geographical variation for a model of the North East of England. It has been tested against DECC meter data and over 80% of LSOAs are predicted to within ±25% of DECC’s data. The model allows localised policies and interventions to be tested, and is principally of interest to local government and energy efficiency initiatives. %I Loughborough University