This paper studies the application of LLM (large language models) to a European listed real estate analysis framework. The researchers map a 16-dimensional analysis framework to eight specialist LLM agents and compare the performance of a single LLM model with that of multiple LLM agents. Across 19 firms and seven regulatory wrappers, the study finds that decomposing tasks into multiple LLM agents can improve numerical task performance but does not necessarily improve judgment task performance. Further optimization using reinforcement learning and structured rewards improves judgment task performance and generalizes to unseen firms and regulatory wrappers. The results suggest that LLM agent decomposition can improve numerical task execution efficiency, while targeted parameter adaptation can improve overall financial judgment quality. Blogger's Review: This paper demonstrates the potential of LLM in financial analysis, particularly in numerical and judgment tasks. By decomposing tasks into multiple LLM agents and using reinforcement learning, the researchers are able to improve model performance and generalizability, providing new insights and methods for financial analysis.