Boosting house price predictions using geo-spatial network embedding

• Published in: Data mining and knowledge discovery Vol. 35; no. 6; pp. 2221 - 2250
• Main Authors: Das, Sarkar Snigdha Sarathi, Ali, Mohammed Eunus, Li, Yuan-Fang, Kang, Yong-Bin, Sellis, Timos
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2021; Springer Nature B.V
• Subjects: Application Papers; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Context; Data Mining and Knowledge Discovery; Embedding; Graph neural networks; Houses; Housing prices; Information Storage and Retrieval; Performance enhancement; Physics; Prediction models; Railway stations; Real estate; Regression models; Shopping centers; Source code; Statistics for Engineering; House-price predictions; Graph neural networks; Geo-spatial network embedding; Real estate queries
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-021-00789-x

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users’ interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the geo-spatial network embedding (GSNE), that learns the embeddings of houses and various types of points of interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model. Relevant source code for GSNE is available at: https://github.com/sarathismg/gsne .