RERA: A Multi-Agent Framework with Textual Gradient Optimization for Real Estate Enterprise Risk Attribution

Real estate markets are highly susceptible to policy shifts and macroeconomic fluctuations, making accurate risk prediction and attribution essential for enterprise decision-making. Although large language models have shown considerable promise in processing multi-source data, integrating disparate information such as company financial reports, policy documents, and macroeconomic indicators into coherent risk transmission chains remains a persistent challenge. To bridge this gap, we introduce RERA, a multi-agent framework with textual gradient optimization for real estate enterprise risk attribution through large language models (LLMs). RERA employs an analyst-attribution agent architecture, in which analysts review policy, macroeconomic, and financial information to provide structured semantic interpretations and summarized analyses. The attribution agent consolidates insights from multiple analyst agents to identify external factors relevant to the company, establish causal chains, and generate comprehensive risk attribution reports. Its attribution optimization component evaluates generated reports against human expert reports for logical consistency, factual alignment, and quantitative reasonableness. These evaluation results are treated as textual gradients, and the attribution prompt is optimized accordingly to guide the agent toward more expert-aligned and evidence-grounded reports. Experiments conducted on a dataset comprising data from multiple real estate enterprises show that RERA-generated reports outperform both general and finance LLMs in terms of semantic features, attribution accuracy, and readability.