Risk Prediction Model of Real Estate Price Fluctuation Based on Neural Network Algorithm

The real estate business, being capital-intensive, has a strong interdependence with the economy. Consequently, the stability of the real estate sector has a direct impact on both the economy and finance. The real estate sector has emerged as a significant catalyst for the advancement of India's overall economic growth. Nevertheless, the inherent instability of the real estate sector renders it susceptible to fluctuations in the broader economic landscape, hence potentially engendering significant repercussions for the financial system and exposing it to various forms of risk. This research introduces a predictive model for assessing the risk of real estate price fluctuations, utilising a neural network method. The findings of this study indicate that, following numerous iterations, the predictive model for real estate price fluctuation risk presented in this research outperforms the conventional GA algorithm. The model achieves a remarkable accuracy rate of 95.71%, surpassing the GA method by 21.44% and reducing the error by 31.73%. Hence, this model possesses the capability to assess the volatility of real estate prices, enabling the implementation of appropriate measures to mitigate and manage economic and financial risks associated with real estate. These measures can be formulated based on the analysis outcomes, thereby proposing preventive actions to address financial risks.