Residential sector need quick and creative solutions since rising energy consumption poses serious risks to the economy and the environment. Information about residential houses is useful for promoting community well-being, protecting the environment, and fostering economic growth. By digging into the residential houses, we can accurately identify sudden spikes in energy consumption. Energy Performance Certificates (EPCs) play a critical role in reducing wasteful energy consumption by providing precise information about a home's energy efficiency. Unfortunately, inadequate EPC evaluations and suggestions contribute to the growing demand for energy. This research presents the creation of a smart web-based visual analytics platform that utilises data from cross-sectoral data to examine the effect of various variables on current house energy performance certificates (EPCs). In addition, our study illustrates a technique for mapping stakeholder assessments before offering substantial recommendations for refurbishments. To determine which smart home criteria are most important, we apply the Criterion Importance Through Intercriterion Correlation (CRITIC) method and weight the criteria based on their correlations. Finally, we sort smart house by their Energy Performance Certificate (EPC) ratings using the COmplex PRoportional ASsessment (COPRAS) technique.