arXiv cs.CL· Khandokar Md. Rahat Hossain, Souvik Das, Tahsina Hashem, Mohammed Eunus Ali, Tanzima Hashem·· 12 小时前AI 评分18
LLM-Enhanced 多模态房价预测：融合结构化、文本与视觉特征
LLM-Enhanced Multimodal House Price Prediction using Structured, Textual, and Visual Features
AI 导读
研究提出一个多模态房价预测框架，用大语言模型从房源文本中提取领域相关的时空与上下文特征，并用无监督视觉-语言方法将房源照片自动聚类为室内、室外、混合或户型图等类别，无需人工标注。所有特征融合为统一表示后经优化回归器预测价格，在大型真实房源数据集上优于传统方法和现有多模态基线，消融实验证实文本特征提取与分类视觉嵌入贡献显著。
正文 · 原文
Abstract:Accurate house price prediction is essential for real estate buyers, sellers, agents, and financial institutions. Traditional methods rely primarily on structured data, e.g., number of bedrooms, bathrooms, parkings, but they overlook rich contextual information embedded in textual descriptions and property photos. Previous deep learning approaches for house price prediction have fused multimodal data but they fall short to extract structured spatio-temporal and contextual features from texts and semantic visual information from images. In this paper, we introduce a novel multimodal framework that addresses these limitations by systematically integrating structured attributes, textual descriptions, and visual images of properties to achieve higher prediction accuracy while maintaining interpretability. We use a large language model to derive domain-specific spatio-temporal and contextual features from text, and an unsupervised vision-language approach to automatically group photos into meaningful classes like indoor, outdoor, mixed, or floorplan, that are commonly present in real-world house listings. These categories are created without manual labeling by aligning image clusters with carefully designed pseudo text prompts. We fuse all features into a unified representation and use an optimized regressor to predict prices. Experiments on a large real-world property dataset demonstrate that our method outperforms traditional methods and existing multimodal baselines. Ablation studies confirm that the novel textual feature extraction and categorized visual embeddings substantially contribute to more accurate and reliable price predictions.
| Comments: | 35 pages, 7 figures, 5 tables. Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2611.01615 [cs.LG] |
| (or arXiv:2611.01615v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.01615 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Souvik Das [view email]
[v1]
Sun, 1 Nov 2026 06:07:04 UTC (18,509 KB)
来源：arXiv cs.CL · arxiv.org