The development of smart communities has provided additional sources of data for innovative new services. In residential areas, Home Energy Management Systems (HEMS) provide a data stream from the home, including room temperature and the status of heating, ventilation, and air conditioning (HVAC) appliances. The data is of interests to both residents and utilities for energy saving purposes. The Green New Deal is a scheme that subsides building improvement to increase energy efficiency by insulating buildings and introducing energy-saving electric appliances. However, the scheme is often criticized due to high installation costs. Therefore, a simple and low cost method to assess building thermal performance is important to select those homes, that can benefit the most from the scheme. In this study, a novel method for assessing building thermal performance based on HEMS data is proposed. The thermal equilibrium of a house is modeled to state the system identification problem to estimate the unknown parameters, such as heat transfer coefficient and heat capacity of a house. The unknown parameters are estimated by using Kalman Filter. In this way, the thermal performance of building can be assessed remotely while residents live their daily lives. Estimated parameters are then validated by considering those elements affected by the behavior of residents. The proposed method was found to estimate the heat transfer coefficient well and thus aid in effectively targeting Green New Deal projects.