Explainable Graph Neural Networks on Linked Statistical Data for Predicting Scottish House Prices
Authors: 
Petros Brimos, Areti Karamanou, Evangelos Kalampokis, Marios Evangelos Mamalis, Konstantinos TarabanisAuthors Info & Claims
Petros Brimos, Areti Karamanou, Evangelos Kalampokis, Marios Evangelos Mamalis, Konstantinos TarabanisAuthors Info & ClaimsPages 36 - 41
Abstract
In the complex landscape of the real estate market, accurately estimating house prices is of paramount importance for a wide array of stakeholders. While traditional statistical methods have been employed in the past, machine learning and deep learning models are increasingly demonstrating their efficacy in predicting house prices by capturing intricate patterns and relationships between features of the housing market, such as neighborhood characteristics and geographical proximity to amenities. Recently, Graph Neural Networks have shown great performance in various predictive tasks, effectively capturing complex spatial dependencies in graph-strucured data. Towards this direction, this paper utilizes a Graph Convolutional Network and linked Open Government Data from the official Scottish data portal to predict house prices in Scotland. The results showed that the Graph Neural Network model achieved an accuracy of 0.84 on the test set. The GNN’s decisions are also interpreted using the GNNexplainer model, that identified important graph structures as well as node features that are most influential for the prediction. Comparative Illness Factor, an indicator of health conditions, is identified as the feature that primarily influences the model’s decisions for a particular data zone.
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November 2023
304 pages
ISBN:9798400716263
DOI:10.1145/3635059
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Published: 14 February 2024
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PCI 2023: 27th Pan-Hellenic Conference on Progress in Computing and Informatics
November 24 - 26, 2023
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