Integrating customer behaviors and data on social networks for personalized real estate decision support

While traditional real estate recommendation systems rely on objective metrics, they frequently fail to capture the complex, often irrational psychological landscapes and cognitive drivers governing high-stakes investment decisions. This study proposes an innovative framework based on the DIKWP (Data – Information – Knowledge – Wisdom – Purpose) process to comprehensively analyze user behaviors and latent psychological constructs in the context of real estate investment advisory. Beyond objective financial capacity and property attributes, the framework explicitly incorporates behavioral heuristics and psychological factors, such as cognitive biases, risk perception, and emotional decision-making tendencies, which play a critical role in real estate purchasing behavior. The proposed approach integrates customer profiles with heterogeneous real estate market data to quantify and model both observable actions and the underlying psychological drivers influencing investment intentions. Addressing key challenges in personalized recommendation and property selection, a decision-support system is designed and implemented to align real estate suggestions with users’ unique psychological profiles and cognitive characteristics. Experimental results demonstrate the effectiveness of the proposed system in delivering highly relevant and psychologically resonant recommendations, thereby enhancing decision confidence and supporting informed investment choices in a competitive real estate environment.