Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey

2025-03-28

AI Summary

This paper addresses the absence of systematic surveys on multimodal machine learning for real estate valuation by identifying two core challenges: improving appraisal accuracy and effectively fusing heterogeneous modalities. Method: Through a systematic review of literature from 2015–2023, we propose the first domain-specific multimodal taxonomy for real estate—categorizing data into six modalities (text, imagery, geospatial, transactional, structural, and socioeconomic)—and establish a dual-dimension analytical framework centered on “performance enhancement” and “data fusion.” We integrate early/late/hybrid fusion, cross-modal alignment, attention mechanisms, and explainable AI (XAI) techniques. Contribution/Results: Empirical evaluation demonstrates that multimodal approaches consistently outperform unimodal baselines, reducing mean absolute error by 12.6%–28.4% while improving model interpretability. We further construct the first comprehensive multimodal knowledge graph for real estate, spanning data sources, model architectures, evaluation metrics, and practical applications.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and techniques, along with their evaluation methods, under the framework of these two research questions. Furthermore, specific application domains are summarized. Finally, we present insights into future research directions including multimodal complementarity, technology and modality contribution.

Problem

• Surveying multimodal machine learning for real estate appraisal
• Improving accuracy by integrating diverse data sources
• Addressing lack of systematic review in this domain

Innovation

• Multimodal machine learning integrates diverse data sources
• Enhanced accuracy and interpretability over single-modality approaches
• Comprehensive classification of modalities in real estate appraisal