MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

TL;DR

MugRep is a comprehensive multi-task hierarchical graph learning framework that integrates multi-source urban data and models complex spatiotemporal and community correlations for more accurate real estate appraisal.

Contribution

It introduces a novel multi-task hierarchical graph learning framework that captures diverse factors and dependencies for real estate valuation.

Findings

Outperforms existing methods on real-world datasets

Effectively models asynchronously spatiotemporal dependencies

Captures diversified community correlations

Abstract

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.