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.

本研究主要关注两个关键挑战：一是探索被低估的兴趣点对房地产价值的影响，强调了数据驱动的特征选择方法的必要性；二是整合基于道路网络的面积嵌入以增强房地产评估的空间理解。我们提出了一种改进的兴趣点特征提取方法，并讨论了每个兴趣点对于房价评估的影响。然后，我们提出了 AMMASI 模型，它是对现有 ASI 模型的改进，利用地理邻居房屋和相似特征房屋上的遮蔽多头注意力。我们的模型优于当前的基准线，同时也为未来房地产评估方法的优化提供了有希望的途径。