Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.
@article{arxiv.2311.11812,
title = {Improving Real Estate Appraisal with POI Integration and Areal Embedding},
author = {Sumin Han and Youngjun Park and Sonia Sabir and Jisun An and Dongman Lee},
journal= {arXiv preprint arXiv:2311.11812},
year = {2023}
}