Meta-Transfer Learning Powered Temporal Graph Networks for Cross-City Real Estate Appraisal

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. However, the market value of real estate can be simultaneously influenced by various complicated intrinsic and extrinsic factors, which impose great challenges on precise valuation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. Nonetheless, deep learning is data-hungry, and thus it may not be trivially applicable to many small cities with limited data. To this end, we propose Meta-Transfer Learning Powered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance. Specifically, by modeling the ever-growing real estate transactions with associated residential communities as a temporal event heterogeneous graph, we first design an Event-Triggered Temporal Graph Network to model the irregular spatiotemporal correlations among evolving real estate transactions. Besides, we formulate the city-wide real estate appraisal as a multi-task dynamic graph link label prediction problem, where the valuation of each community in a city is regarded as an individual task. A Hypernetwork-Based Multi-Task Learning module is proposed to simultaneously facilitate intra-city knowledge sharing between multiple communities and task-specific parameters generation to accommodate the community-wise real estate price distribution. Furthermore, we propose a Tri-Level Optimization Based Meta-Learning framework to adaptively re-weight training transaction instances from multiple source cities to mitigate negative transfer, and thus improve the cross-city knowledge transfer effectiveness. Finally, extensive experiments based on six real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.
Funding Information
  • National Key Research and Development Program of China (2023YFF0725-001)
  • National Natural Science Foundation of China (92370204, 62572417)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2023B1515120057)
  • Key-Area Special Project of Guangdong Provincial Ordinary Universities (2024ZDZX1007)

This publication has 31 references indexed in Scilit: