English

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

Machine Learning 2023-08-22 v1

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.

Keywords

Cite

@article{arxiv.2308.10609,
  title  = {ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal},
  author = {Hojoon Lee and Hawon Jeong and Byungkun Lee and Kyungyup Lee and Jaegul Choo},
  journal= {arXiv preprint arXiv:2308.10609},
  year   = {2023}
}

Comments

Accepted to CIKM'23

R2 v1 2026-06-28T12:00:17.327Z