Boosting House Price Predictions using Geo-Spatial Network Embedding
- URL: http://arxiv.org/abs/2009.00254v1
- Date: Tue, 1 Sep 2020 06:17:21 GMT
- Title: Boosting House Price Predictions using Geo-Spatial Network Embedding
- Authors: Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali, Yuan-Fang Li, Yong-Bin
Kang, Timos Sellis
- Abstract summary: 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.
- Score: 16.877628778633905
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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