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.

Related papers

• A Multi-Modal Deep Learning Based Approach for House Price Prediction [19.02810406484948]
  We propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house. Results show that the text embedding of the house advertisement description and image embedding of the house pictures can significantly improve the house price prediction accuracy.
  arXiv  Detail & Related papers  (2024-09-09T05:26:33Z)
• Boosting House Price Estimations with Multi-Head Gated Attention [6.35565749560338]
  We have developed a new method called Multi-Head Gated Attention for spatial capture. Our model produces embeddings that reduce the dimensionality of the data. Results show a significant improvement in the accuracy of house price predictions.
  arXiv  Detail & Related papers  (2024-05-13T04:12:03Z)
• An Optimal House Price Prediction Algorithm: XGBoost [0.0]
  We use various machine learning techniques to predict house prices. We identify the key factors that influence housing costs. XGBoost is the best performing model for house price prediction.
  arXiv  Detail & Related papers  (2024-02-06T15:36:06Z)
• Improving Real Estate Appraisal with POI Integration and Areal Embedding [2.977678192707268]
  This study focuses on two pivotal challenges in real estate appraisal methods. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal.
  arXiv  Detail & Related papers  (2023-11-20T14:48:09Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China [6.023710971800604]
  A novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal. GNNWR captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.
  arXiv  Detail & Related papers  (2022-02-09T09:40:54Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• What Image Features Boost Housing Market Predictions? [81.32205133298254]
  We propose a set of techniques for the extraction of visual features for efficient numerical inclusion in predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors.
  arXiv  Detail & Related papers  (2021-07-15T06:32:10Z)
• Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market [75.28009817291752]
  We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market.
  arXiv  Detail & Related papers  (2020-08-12T07:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.