# Boosting House Price Predictions using Geo-Spatial Network Embedding

arXiv: https://arxiv.org/abs/2009.00254 · HTML: https://mlsanity.com/paper/2009.00254/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Generating Faithful Text From a Knowledge Graph with Noisy Reference Text](https://mlsanity.com/paper/2308.06488/) (2023)
- [Boosting House Price Estimations with Multi-Head Gated Attention](https://mlsanity.com/paper/2405.07456/) (2024)
- [A Multi-Modal Deep Learning Based Approach for House Price Prediction](https://mlsanity.com/paper/2409.05335/) (2024)
- [Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes](https://mlsanity.com/paper/2209.09448/) (2022)
- [Generating Faithful and Salient Text from Multimodal Data](https://mlsanity.com/paper/2409.03961/) (2024)
- [A Multi-Source Information Learning Framework for Airbnb Price Prediction](https://mlsanity.com/paper/2301.01222/) (2023)
- [Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises](https://mlsanity.com/paper/2505.01591/) (2025)

## Abstract (arXiv)

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.