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

Understand

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.