BD-GNN: Integrating Spatial and Administrative Boundaries in Property Valuation Using Graph Neural Networks

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware Dual-Path Graph Neural Network (BD-GNN), a heterophily-oriented GNN specifically designed for continuous regression tasks. The model uses a dual and adaptive message passing design, separating inter- and intra-boundary pathways and combining them through a learnable gating parameter $\alpha$. This allows it to capture boundary effects while preserving spatial continuity. Experiments conducted on three structurally contrasting housing datasets, namely Bangkok, King County (USA), and Singapore, demonstrate consistent performance improvements over strong baselines. The proposed BD-GNN reduces MAPE by 7.9%, 4.4%, and 4.5% and increases $R^2$ by 3.2%, 0.7%, and 5.0% for the respective datasets. Beyond predictive performance, $\alpha$ provides a clear picture of how spatial and administrative factors interact across urban scales. GNN Explainer provides local interpretability by showing which neighbors and features shape each prediction. BD-GNN bridges predictive accuracy and structural insight, offering a practical, interpretable framework for applications such as property valuation, taxation, mortgage risk assessment, and urban planning.