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

• URL: http://arxiv.org/abs/2107.05180v1
• Date: Mon, 12 Jul 2021 03:51:44 GMT
• Title: MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
• Authors: Weijia Zhang, Hao Liu, Lijun Zha, Hengshu Zhu, Ji Liu, Dejing Dou, Hui Xiong
• Abstract summary: We propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. By acquiring and integrating multi-trivial urban data, we first construct a rich feature set to comprehensively profile real estate from multiple perspectives. An evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronouslytemporal dependencies among real estate transactions.
• Score: 57.28018917017665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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