This paper tests to what extent different types of variables (building factors, socio-demographics, appliance ownership and use, attitudes and self-reported behaviours) explain annualized electricity consumption in residential buildings with gas-fuelled space and water heating. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the study recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue. Using data from a sample of 845 English households collected in 2011/12, a comparison of four separate regression models showed that a model with the predictors of appliance ownership and use, including lighting, explained the largest share, 34%, of variability in electricity consumption. Socio-demographic variables on their own explained about 21% of the variability in electricity consumption with household size the most important predictor. Building variables only played a small role, presumably because heating energy consumption is not included, with only building size being a significant predictor. Self-reported energy-related behaviour, opinions about climate change and 'green lifestyle' were negligible. A combined model, encompassing all predictors, explained only 39% of all variability (adjusted R-2 = 34%), i.e. adding little above an appliance and lighting model only. Appliance variables together with household size, and dwelling size were consistently significant predictors of energy consumption. The study highlights that when attempting to explain English household non-heating electricity consumption that variables directly influenced by people in the household have the strongest predictive power when taken together. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license.