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

## TL;DR

This paper introduces EGCN, a new method for detecting and forecasting anomalies in real estate markets, improving prediction accuracy in the UK, USA, and Australia.

## Contribution

The novel EGCN framework clusters anomalous regions separately to improve forecasting accuracy and stability in dynamic real estate markets.

## Key findings

- EGCN achieves the lowest forecasting errors across all tested horizons in the UK, USA, and Australia.
- EGCN detects significantly more anomalous regions than competing methods in all three countries.
- Separate clustering of anomalies reduces forecasting errors for models like Neural Hierarchical Interpolation.

## Abstract

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.