Utilities are often interested in understanding residential HVAC equipment penetration at national-, regional- and state-level, as well as understand how the penetration may change in future. EPRI has end-use models that often forecast future trends in equipment stock and resultant energy impact, but the scenario assumptions can be better informed by historical sales information. This research is an attempt to leverage past trends of historical sales data of residential equipment and use them to predict future trends in electric equipment penetration. The project utilizes national-, regional- and state-level data sources such as AHRI, Energy Star, HARDI to obtain current and historical data of residential HVAC and water heating equipment shipments. Baseline estimates of data are then fitted into statistical and machine learning algorithms to predict trends in future equipment penetration. These forecasting algorithms predict future sales up to 5 years with 10-15% accuracy. The results of the project, both baseline data and future predictions can help utilities directly or through the improvement of forecasting assumptions in the diffusion curves for equipment penetration.