Real estate appraisal refers to the process of developing an unbiased opinion
for real property's market value, which plays a vital role in decision-making
for various players in the marketplace (e.g., real estate agents, appraisers,
lenders, and buyers). However, it is a nontrivial task for accurate real estate
appraisal because of three major challenges: (1) The complicated influencing
factors for property value; (2) The asynchronously spatiotemporal dependencies
among real estate transactions; (3) The diversified correlations between
residential communities. To this end, we propose a Multi-Task Hierarchical
Graph Representation Learning (MugRep) framework for accurate real estate
appraisal. Specifically, by acquiring and integrating multi-source urban data,
we first construct a rich feature set to comprehensively profile the real
estate from multiple perspectives (e.g., geographical distribution, human
mobility distribution, and resident demographics distribution). Then, an
evolving real estate transaction graph and a corresponding event graph
convolution module are proposed to incorporate asynchronously spatiotemporal
dependencies among real estate transactions. Moreover, to further incorporate
valuable knowledge from the view of residential communities, we devise a
hierarchical heterogeneous community graph convolution module to capture
diversified correlations between residential communities. Finally, an urban
district partitioned multi-task learning module is introduced to generate
differently distributed value opinions for real estate. Extensive experiments
on two real-world datasets demonstrate the effectiveness of MugRep and its
components and features.

该研究提出了一种基于多任务层次图表示学习（MugRep）的房地产精确估价框架，通过获取和集成多源城市数据，构建一个丰富的特征集，包括地理分布、人类流动分布和居民人口统计分布，并提出了一种异步时空依赖性的房地产交易图和相关事件图卷积模块，以及一种分层异质社区图卷积模块，以捕捉住宅社区之间的多样化关系，并引入了一种城市区域划分的多任务学习模块，用于为房地产生成具有不同分布的价值意见。