Bidirectional Multi-Scale Graph Learning

Abstract

Accurate residential property valuation is essential for mortgage lending, taxation, and urban planning, yet remains challenging due to complex spatial and temporal dynamics. Traditional econometric models are interpretable but rely on restrictive assumptions, while machine learning–based automated valuation models (AVMs) improve predictive accuracy but treat transactions as independent, overlooking spatial spillovers and evolving trends. Recent graph-based approaches partially address spatial autocorrelation, but often rely on static or unidirectional structures that limit their expressiveness.

We introduce a Multi-Scale Bidirectional Spatio-Temporal Graph Neural Network (MBSTGNN) that models transactions and neighbourhoods as dynamic graphs linked through bidirectional message passing. A temporal memory mechanism maintains consistency across time, enabling the model to capture evolving market conditions. Evaluated on Rotterdam housing transactions, MBSTGNN outperforms strong baselines, particularly in sparse-data settings, and produces embeddings that reveal domain-consistent socio-spatial and temporal patterns. These results demonstrate its potential for advancing automated valuation and related spatio-temporal prediction tasks.