  Evaluating house prices is crucial for various stakeholders, including
homeowners, investors, and policymakers. However, traditional spatial
interpolation methods have limitations in capturing the complex spatial
relationships that affect property values. To address these challenges, we have
developed a new method called Multi-Head Gated Attention for spatial
interpolation. Our approach builds upon attention-based interpolation models
and incorporates multiple attention heads and gating mechanisms to capture
spatial dependencies and contextual information better. Importantly, our model
produces embeddings that reduce the dimensionality of the data, enabling
simpler models like linear regression to outperform complex ensembling models.
We conducted extensive experiments to compare our model with baseline methods
and the original attention-based interpolation model. The results show a
significant improvement in the accuracy of house price predictions, validating
the effectiveness of our approach. This research advances the field of spatial
interpolation and provides a robust tool for more precise house price
evaluation. Our GitHub repository.contains the data and code for all datasets,
which are available for researchers and practitioners interested in replicating
or building upon our work.
