Within the urban environment the building sector typically accounts for around half of the total energy used, with the majority of this demand driven by thermal services including hot water and space conditioning. Accurately quantifying and locating building energy demand is essential to aiding energy management and planning initiatives across the city. However, a shortage of techniques that enable the effective scaling of individual building energy models to larger areas is evident. To help meet this need, we present a novel approach that utilizes airborne Light Detection and Ranging (LiDAR) data to populate a range of residential building energy and urban form parameters, including envelope resistivity, air leakage and solar gains. These parameters are then integrated with additional spatial datasets and allow for the calculation of baseline estimates of energy demand for contiguous regions within urban areas. We illustrate the outcomes of the model by predicting energy demand across a mixed residential neighbourhood in the City of Vancouver, Canada. Results indicate that the annual estimates of thermal energy demand closely match those derived from building energy simulation software (R-2 = 0.93, p < 0.001), while monthly estimates of demand show no statistically significant differences between our results and those from the simulation software (t= -0.11, p = 0.91). (c) 2014 Published by Elsevier Ltd.