Research On Financial Risk Early Warning Of Real Estate Enterprises

In recent years,the urbanization rate has been increasing year by year.In the growth of China’s national economy,the rapidly developing real estate industry has played a role that cannot be ignored.However,due to its large capital investment,relatively long project cycle and weak liquidity ability,the real estate industry has a higher financial crisis than other industries.In order to achieve the steady development of the real estate market,since 2016,the state’s regulation and control of the real estate industry is expected to stabilize land prices,house prices,and expectations.Local government departments have also formulated more targeted measures for local housing prices.With the implementation of various regulatory policies,the possibility of financial crisis in real estate enterprises has increased.In addition,the domestic market environment is more complex than other countries,the financial crisis is often not paid enough attention.The real estate enterprises have the characteristics of capital intensity,long production cycle,high production costs,and a persistently high debt ratio,all of which may lead to financial crises.In this context,it is worthwhile to study in depth how real estate enterprises can early warning,prevent and control possible financial crises.In this paper,99 listed companies in the real estate industry in Shanghai and Shenzhen A-shares were selected as research samples,and the criteria for financial crisis were determined as whether they were included in the risk warning version from 2017 to 2021,and the early warning research of financial crisis of real estate enterprises was carried out based on the BP neural network model.Firstly,according to the characteristics of the industry,the variables are preliminarily selected to build a financial crisis early warning index system.Secondly,using factor analysis to reduce the variables that pass the significance test,the sample is divided into training group and test group,the BP neural network model is constructed based on the training group data,and then the prediction accuracy of the model is tested by using the test group data.Finally,combined with the early warning results,the key aspects of enterprise financial crisis need to be prevented and controlled,and corresponding suggestions are put forward.The following conclusions are obtained through empirical results:(1)Among the influencing factors of early warning of financial crisis,cost control ability,profitability and equity structure account for a relatively large proportion.(2)According to the empirical results,the misjudgment type of the early warning model in this paper mainly overestimates the crisis severity of the enterprise,indicating that the early warning model is more sensitive to the abnormal situation of the financial situation of the enterprise.(3)In this paper,BP neural networks were constructed using T-3 years and T-2 years data,and the prediction accuracy of the test group samples reached 88.89% and 92.59%,indicating that the prediction accuracy improved as the time approached.The research design of this paper can provide help for real estate enterprises to carry out early warning and prevention of financial crisis.