Boosting House Price Predictions using Geo-Spatial Network Embedding

TL;DR

This paper introduces Geo-Spatial Network Embedding (GSNE), a novel graph neural network-based approach that captures neighborhood spatial context to significantly improve house price prediction accuracy.

Contribution

The paper presents a new method, GSNE, which learns embeddings of houses and Points of Interest in multipartite networks to incorporate geo-spatial context into house price prediction.

Findings

GSNE embeddings improve prediction accuracy across multiple regression models.

The method significantly outperforms existing techniques that lack spatial context.

Embedding quality is validated through extensive experiments.

Abstract

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.