As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

One of the largest problems in the real estate market analysis, which includes valuation, is determining the significance of individual property attributes that may affect value or attractiveness perception. The study attempts to assess the significance of selected attributes of real estate based on the detection and analysis of the emotions of potential investors. Human facial expression is a carrier of information that can be recorded and interpreted effectively via the use of artificial intelligence methods, machine learning and computer vision. The development of a reliable algorithm requires, in this case, the identification and investigation of factors that may affect the final solution of the problem, from behavioural aspects through technological possibilities. In the presented experiment, an approach that correlates the emotional states of buyers with the visualization of selected attributes of properties is utilized. The objective of this study is to develop an original method for assessing the significance of property attributes based on emotion recognition technology as an alternative to the commonly used methods in the real estate analysis and valuation, which are usually based on surveys. The empirical analysis enabled determination of the mainstream property attributes significance from evoked emotions intensity within the group of property clients (represented by 156 respondents). The significance ranking determined on the basis of the unconscious expressed facial emotions was verified and compared to the answers given in a form of questionnaire. The results have shown that the conscious declaration of the attribute ranking differs from the emotion detection conclusions in several cases.