Attention-Based Spatial Interpolation for House Price Prediction

Estimating the market price of a house is important for many businesses such as real estate and mortgage lending companies. The price of a house depends not only on its structural features (e.g. area and number of bedrooms) but also on the spatial context where it is located. In this work we estimate the price of a house based solely on its structural features and the characteristics and price of its neighbors. For that, we propose a hybrid attention mechanism that weights neighbors based on their similarity to the house in terms of structural features and geographic location. For the structural features, we apply an euclidean-based attention and, for the geographic location, we propose an attention layer based on a radial basis function kernel. Those attention mechanisms are then used by a neural network regressor to predict the price of a house and to generate a vector representation of the house based on its implicit context: the house embedding, which can be used as a feature set by any regressor to perform house price prediction. We have performed an extensive experimental evaluation on real-world datasets that shows that: (1) regressors using house embedding obtained the best results on all 4 datasets, outperforming baseline models; (2) the learned house embedding improves the performance of the evaluated regressors in almost all scenarios in comparison to raw features; and (3) simple regressor models such as Linear Regression using house embedding achieved comparable results to more competitive algorithms (e.g. Random Forest and Xgboost).