Boosting House Price Estimations with Multi-Head Gated Attention

TL;DR

This paper introduces a novel Multi-Head Gated Attention model for spatial interpolation, significantly improving house price prediction accuracy by capturing complex spatial dependencies more effectively than traditional methods.

Contribution

The paper presents a new attention-based spatial interpolation method with gating mechanisms that enhances feature representation and outperforms existing models in house price estimation.

Findings

Significant accuracy improvement over baseline methods

Embeddings enable simpler models to outperform complex ones

Model effectively captures complex spatial relationships

Abstract

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.