Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. 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 enormous 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 between 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 five real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.

翻译：房地产估价在房地产交易、投资分析及不动产税收等多种场景中具有重要意义。近年来，深度学习通过利用网络平台上的大量在线交易数据，在房地产估价领域展现出巨大潜力。然而，深度学习对数据具有高依赖性，因此难以直接应用于数据有限的大量小城市。为此，我们提出基于元迁移学习的时间图网络（MetaTransfer），将多个数据丰富的大都市中的有价值知识迁移至数据稀缺的城市，以提升估价性能。具体而言，通过将不断增长的房地产交易及其关联住宅社区建模为时间事件异构图，我们首先设计了一种事件触发的时间图网络，用于建模演化中房地产交易之间不规则的时空相关性。此外，我们将城市级房地产估价公式化为一个多任务动态图链接标签预测问题，将城市中每个社区的估价视为独立任务。我们提出一种基于超网络的多任务学习模块，以同时促进多个社区间的城市内知识共享，并生成任务特定参数以适应社区级房地产价格分布。进一步，我们提出一种基于三层优化的元学习框架，自适应地重新加权来自多个源城市的训练交易实例，以缓解负迁移，从而提升跨城市知识迁移的有效性。最后，基于五个真实数据集的广泛实验表明，MetaTransfer相较于十一种基线算法具有显著优越性。