Journal Article

EGCN: Entropy-based graph convolutional network for anomalous pattern detection and forecasting in real estate markets

Nguyen, Quang, Le, Dat, Rajasegarar, Sutharshan, Angelova, Maia, Nguyen, Thanh Thi, Luo, Wei, Vo, Nhi

2025

Overview

Real estate markets are inherently dynamic, influenced by economic fluctuations, policy changes and socio-demographic shifts, often leading to emergence of anomalous—regions, where market behavior significantly deviates from expected trends. Traditional forecasting models struggle to handle such anomalies, resulting in higher errors and reduced prediction stability. In order to address this challenge, we propose EGCN, a novel cluster-specific forecasting framework that first detects and clusters anomalous regions separately from normal regions, and then applies forecasting models. This structured approach enables predictive models to treat normal and anomalous regions independently, leading to enhanced market insights and improved forecasting accuracy. Our evaluations on the UK, USA, and Australian real estate market datasets demonstrates that the EGCN achieves the lowest error both anomaly-free (baseline) methods and alternative anomaly detection methods, across all forecasting horizons (12, 24, and 48 months). In terms of anomalous region detection, our EGCN identifies 182 anomalous regions in Australia, 117 in the UK and 34 in the US, significantly more than the other competing methods, indicating superior sensitivity to market deviations. By clustering anomalies separately, forecasting errors are reduced across all tested forecasting models. For instance, when applying Neural Hierarchical Interpolation for Time Series Forecasting, the EGCN improves accuracy across forecasting horizons. In short-term forecasts (12 months), it reduces MSE from 1.3 to 1.0 in the US, 9.7 to 6.4 in the UK and 2.0 to 1.7 in Australia. For mid-term forecasts (24 months), EGCN achieves the lowest errors, lowering MSE from 3.1 to 2.3 (US), 14.2 to 9.0 (UK), and 4.5 to 4.0 (Australia). Even in long-term forecasts (48 months), where error accumulation is common, EGCN remains stable; decreasing MASE from 6.9 to 5.3 (US), 12.2 to 8.5 (UK), and 16.0 to 15.2 (Australia), highlighting its robustness over extended periods. These results highlight how separately clustering anomalies allows forecasting models to better capture distinct market behaviors, ensuring more precise and risk-adjusted predictions.

• Publisher: Public Library of Science
• Subject: Accuracy / Anomalies / Artificial neural networks / Australia / Business forecasting / Clustering / Computational linguistics / Economic growth / Entropy / Forecast accuracy / Forecast errors / Forecast improvement / Forecasting / Forecasting - methods / Forecasting models / Forecasts and trends / Geospatial data / Global economy / Graph representations / Humans / Industry forecasts / Infrastructure / Interpolation / Investors / Language processing / Machine learning / Markets / Models, Economic / Natural language interfaces / Neural networks / Prediction models / Prices / Real estate / Real estate industry / Real property / Social networks / Spatial data / Time series / Trends / United Kingdom / United States / Urban development