Graph-Enhanced Multimodal Valuation: Integrating Climate Physical Risk into Green Mortgage Analytics with LLMs and Knowledge Graphs

作者：Ritu Gaur, Archana Jain, Deepak Gupta, Gaurav Kumar, Aditya Yadav, Rashmi Singh · 发表于：DMPedia Lecture Notes in Computer Science & Engineering · 年份：2026 · DOI：10.65890/dmp-lncse.iciccs26.185 · 研究领域：Advanced Graph Neural Networks、Sustainable Building Design and Assessment、Housing Market and Economics

Traditional property valuation and mortgage underwriting pipelines largely ignore explicit modelling of physical climate risk such as floods, heat stress, and extreme weather, despite growing evidence that these hazards materially affect asset values, default probabilities, and portfolio resilience. This omission creates a structural blind spot for banks and housing finance companies attempting to align with emerging green finance, ESG, and climate disclosure regimes. Green mortgages and sustainable housing finance products still tend to focus narrowly on energy efficiency metrics while underweighting location-specific climate hazards and resilience features. This paper proposes a climate-aware valuation framework that combines multimodal large language models, geospatial APIs, and Neo4j-based knowledge graphs to inject structured climate risk signals into property valuation workflows. The architecture comprises four layers: (i) a multimodal extraction layer using document understanding models such as LayoutLM/vision-enhanced LLMs to extract building attributes, materials, and layout features from loan and property documents; (ii) a geospatial integration layer that enriches each property with hazard and climate indicators from platforms such as ISRO Bhuvan and OpenWeatherMap; (iii) a knowledge graph construction layer that encodes properties, hazards, resilience features, and valuation events as nodes and relationships; and (iv) a GraphRAG-based risk scoring layer that perfo...