Commercial real estate decision making is a complex process influenced by various factors. Factors such as supply and demand, economic conditions, sustainability objectives, government regulations, infrastructure, socioeconomic and demographic trends, and market sentiment all play a role in shaping the dynamics of the market. To optimize real estate portfolios, companies need cost-effective and efficient space utilization through data-informed decision frameworks supported by AI-backed intelligence. Extracting insights from diverse data with complex causal dynamics requires contextual knowledge. Additionally, as economic and market conditions evolve, synoptic evaluations may lose value. In this talk, we explore the potential of combining knowledge graphs and insights from large language models to enhance real estate decision making.