ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal

2023/08/21 by Hojoon Lee, Hawon Jeong, Lee, Hojoon +7 · 1 citation
Computer Science · Engineering · #3D Modeling in Geospatial Applications #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2308.10609

openalex publication_date 2023/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural network to encapsulate temporal dynamics and spatial relationships simultaneously. Through comprehensive experiments on a large-scale real estate dataset, ST-RAP outperforms previous methods, demonstrating the significant benefits of integrating spatial and temporal aspects in real estate appraisal. Our code and dataset are available at https://github.com/dojeon-ai/STRAP.

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