Asset Score is a standardized rating system and tool for assessing a building's energy-related systems in the United States. The web-based tool models building energy use under standard operating conditions to rate the energy efficiency of the as-built building systems and enable level comparisons of building assets. With basic characteristics entered by users, the tool creates simplified EnergyPlus building models to support the rating analysis. However, even with a reduced set of model inputs, data collection remains a challenge and the commercial building market demands a more simplified entry point to the rating system. This paper discusses a hybrid method that combines regression models with real-time simulations to allow users to enter as few as seven building parameters to quickly assess the building energy performance prior to a full-scale analysis. Built upon large-scale building stock simulations, a random forest approach was used to develop a set of regression models for various building use types. The majority of the Asset Score tool inputs were sampled extensively and fed into the regression models. These were combined with inferred inputs and user-defined uncertainty levels to create a distribution of possible energy use intensities for the building and its Preview score. With additional user inputs, the regression model can be transferred to an energy model for a full-scale energy simulation. The streamlined Asset Score Preview assessment provides an easy entry point to a full Asset Score assessment. It also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection. (C) 2018 Published by Elsevier B.V.