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Despite advancements in real estate appraisal methods, this study primarilyfocuses on two pivotal challenges. Firstly, we explore the often-underestimatedimpact of Points of Interest (POI) on property values, emphasizing thenecessity for a comprehensive, data-driven approach to feature selection.Secondly, we integrate road-network-based Areal Embedding to enhance spatialunderstanding for real estate appraisal. We first propose a revised method forPOI feature extraction, and discuss the impact of each POI for house priceappraisal. Then we present the Areal embedding-enabled Masked MultiheadAttention-based Spatial Interpolation for House Price Prediction (AMMASI)model, an improvement upon the existing ASI model, which leverages maskedmulti-head attention on geographic neighbor houses and similar-featured houses.Our model outperforms current baselines and also offers promising avenues forfuture optimization in real estate appraisal methodologies.

Improving Real Estate Appraisal with POI Integration and Areal Embedding