2024/05/10 by Enrique Riveros, Riveros, Enrique, Carla Vairetti +7
Decision Sciences · Engineering · #3D Modeling in Geospatial Applications #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2405.06553
openalex publication_date 2024/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.