Construction of Real Estate Debt Crisis Early Warning Model Based on RBF Neural Network

Abstract

The current market economic environment is constantly changing, and real estate companies are constantly facing various risks in the course of their operations, which have created some obstacles to real estate companies’ normal financial activities, and the occurrence of a debt crisis may reduce the company’s expected benefits. If real estate companies can identify debt risks early on and take effective steps to avoid them, they will have a better chance of avoiding debt problems. Therefore, this study introduces RBF neural network technology to construct a new real estate debt crisis early warning model. This study selects 20 indicators, constructs the financial early warning index system of listed companies, collects the financial data of 86 real estate listed companies from 2016 to 2020, uses the principal component analysis method to reduce the dimension of the collected financial data, and uses the reduced dimension data to construct the real estate debt crisis early warning model of RBF neural network to realize the real estate debt crisis early warning. The empirical results show that the early warning model constructed in this study can effectively warn the real estate debt crisis, effectively analyze the development trend of real estate companies, help to better prevent the debt crisis of real estate enterprises, and improve the comprehensive benefits of real estate enterprises.