Improving Real Estate Appraisal with POI Integration and Areal Embedding

• URL: http://arxiv.org/abs/2311.11812v1
• Date: Mon, 20 Nov 2023 14:48:09 GMT
• Title: Improving Real Estate Appraisal with POI Integration and Areal Embedding
• Authors: Sumin Han, Youngjun Park, Sonia Sabir, Jisun An, Dongman Lee
• Abstract summary: This study focuses on two pivotal challenges in real estate appraisal methods. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal.
• Score: 2.977678192707268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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