  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.
