Research On The Credit Rating System Of Listed Real Estate Companies Based On Data Analysi

Real estate has always been an important component of China’s economy.As the pillar industry of China’s economy,the development of the real estate industry affects the overall operation of China’s economy,but it has the characteristics of high risk and high leverage ratio,and is prone to credit risk.The real estate industry has relatively limited financing channels,with bank loans being its main source of funding.The credit risk of the real estate industry is transmitted to banks through funding channels,greatly enhancing their credit risk.Credit rating,as a service tool,can provide important basis for bank loan decisions,facilitate effective control of loan risks,and thus reduce bank credit risk at the source.It plays a decisive role in credit risk control.This study will establish a credit system model suitable for the real estate industry from the perspective of the real estate industry,providing specific data for real estate companies to improve their asset structure,providing certain reference value for banks,real estate investors,and homebuyers.At the same time,it also has important significance for the overall credit risk control system.This paper plans to build a credit rating system for the real estate industry from the indicator system,and use the literature review method,mathematical statistics analysis method,comparative analysis method and empirical research method to deeply explore and analyze the construction of the credit rating model for the real estate industry.Firstly,this article further summarizes the preference for selecting real estate indicators by revealing the formation mechanism of real estate credit risk.After data cleaning on 108 listed real estate enterprises collected,92 valid samples were obtained,and the data was standardized.Secondly,the principal component analysis method is used to construct an indicator system,comprehensively selecting 25 variable indicators that reflect the situation of the real estate industry.The principal component analysis method is used to extract and reduce the dimensionality of the evaluation indicators.Under the premise of losing a small amount of information,the25 indicators are transformed into 8 comprehensive indicators,maximizing the retention of information content.Then,the final enterprise credit score result is calculated through the scores of each principal component and the comprehensive scores.Thirdly,in the part of credit grading,K-means clustering analysis is used to grade the credit scores,and nine grades of credit grading results are obtained.Then the credit grading results are substituted into the discriminant model,and the samples are re classified and the clustering results are also tested.This article uses these two different methods for credit grading,considering the credit grading in two different situations,to make the grading results more scientific and accurate.At the same time,it also solves the problem of clustering analysis being unable to classify data outside of the sample,and improves the applicability of the model.Finally,a feasibility test was conducted on the overall model,and the results showed that the credit rating model can better demonstrate the overall credit situation of the company and provide certain reference value for investors such as banks in financing.