Building energy assessment is essential to accomplish the sustainable energy targets of new and present buildings. Retrofitting of the existing buildings by assessing them through energy models is the most prominent method. Studies revealed that there is still blank information about the building stocks, and these affect the valuation of building energy efficiency policies. Literature also recommends that the existing energy models are too complex and unreliable to predict the energy use. Reliability of such energy models would improve through a better alignment of the input parameters and the model assumptions. The authors hypothesized that the reliability of models would be improved through identification of the most relevant energy use parameters for the building stocks in different regions and models. One of the most commonly accepted methods for detecting the most dominant input parameters is sensitivity analysis, though its shortcomings include the need for a large number of data samples and long computing time. In this research, the Energy, Carbon, and Cost Assessment for Buildings Stocks (ECCABS) model is adopted to identify the most important parameters of the presented model. The research team uses the model that has been validated by studies conducted for the UK building stock. Moreover, by assessing the feasibility study with the stepwise regression to identify the significant input parameters have been discussed. Results show that stepwise regression method could produce the same results compared to sensitivity analysis. This paper also indicates that stepwise regression is considerably faster and less computationally intensive compared to common sensitivity analysis methods.