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.

翻译：尽管房地产估价方法已取得进展，本研究主要聚焦于两大关键挑战。首先，我们探讨了兴趣点(POI)对房产价值往往被低估的影响，强调采用基于数据的综合性特征选择方法的必要性。其次，我们整合基于路网的区域嵌入以增强房地产估价的空间理解能力。我们首先提出一种改进的POI特征提取方法，并讨论各类POI对房价评估的影响；随后提出基于区域嵌入的掩码多头注意力空间插值房价预测模型(AMMASI)——该模型对现有ASI模型进行改进，通过掩码多头注意力机制对地理邻近房产及特征相似房产进行建模。本模型不仅优于现有基线方法，同时为房地产估价方法的未来优化提供了富有前景的研究方向。