The global contribution from residential buildings towards energy consumption has steadily increased reaching figures around 25% (IEA 2016). Surprisingly, the energy consumption for space cooling accounts for more than 70% of the overall electricity use in a typical building in the Gaza Strip (Muhaisen 2007). Recently, construction of residential buildings in Gaza Strip has significantly increased and consequently, the demand for space cooling has increased. This energy demand is significantly affected by “Building design variables”, such as building shape, glazing area, windows orientation and thermal characteristic of building envelope. Thus, it is essential to estimate the energy required for space cooling based on those variables at the early-stage building design in order to obtain less energy consuming buildings. Building simulation models can accurately quantify building energy loads but are not amenable to the early design phases. On this note, this study presents a new modeling approach to quantify building energy performance in early design stages through the development of multiple linear regression model. The resultant multiple linear regression model is based on a set of detailed simulations that consider the complex thermal interactions represented within a full-scale energy simulation engine, but once developed, can operate independently of the original, full scale model. This model was developed for the prediction of annual cooling loads in representative residential buildings across the climate of Gaza Strip, Palestine. A correlation analysis was conducted for ten different building envelope parameters: Thereby, two of these parameters have been identified as significant: the building Shape Factor (SF), and Window to Wall area ratio adjusted for orientation and fixed shading (). Subsequently, the results of the energy simulations were implemented into a regression equation to predict the energy consumption. The differences between regression-predicted and simulated annual cooling energy requirements were in the order of one to fifteen percent. The coefficient of determination (R2) exceeded 0.8, and thus indicating a good agreement between simulation results and the regression model. Based on the findings it can be said that the annual cooling energy requirements can be forecasted using the regression model with an acceptable accuracy. It is envisaged that the developed regression model can be used to estimate the total energy consumption in early stages of the design process when different building schemes and design concepts are being considered. In order to set a future target for building envelope upgrade, two more scenarios with different thermal characteristics of building envelopes were studied. Based on that, three regression equations were used to develop the prescriptive index. Such a streamlined method will hopefully encourage the decision makers to integrate the prescriptive approach, through developing regulations regarding building energy efficiency in Gaza Strip, Palestine.