In order to accurately measure corporate default risks of Chinese real estate enterprises and to solve the problem of insufficient adaptability of traditional KMV model, this paper takes 111 Shenwan real estate enterprises in 2020-2024 as samples and combines the “Three Redlines” regulatory framework. Firstly, the optimal correction logic is determined by using the ROC curve analysis method, which compares the multi-group measurement schemes of the expected asset growth rate and the default point, i.e. the expected asset growth rate is estimated by “the historical average asset growth rate of the previous two periods”, and the default point is set as “short-term debt (DS) + 0.45 × long-term debt (DL)”.At this time, the model prediction accuracy (AUC value) reaches 0.875, and the overall prediction accuracy is improved to 85.8%, which is significantly better than the traditional model and other schemes. Further comparing the effect of the model before and after the correction, it is found that the mean difference of the default distance between the “red orange grade (high risk)” and the “yellow green grade (low risk)” enterprises after the correction is expanded by 0.315, and the model’s discrimination of risk identification is significantly enhanced. The empirical results of 17 representative China Securities real estate enterprises show that the average default distance of the industry in 2020-2024 decreased from 0.515 to 0.432 year by year, which confirms the trend of rising credit risk in the real estate industry. The research conclusions show that the modified KMV model is more suitable for the characteristics of Chinese real estate enterprises, and can provide quantitative tools and practical references for risk management and control of regulatory authorities, credit decision-making of financial institutions and risk management of real estate enterprises.

Junzhe Yu, Qixuan Liu. The Debt Default Prediction of Real Estate Enterprises Based on the Modified KMV Model. Academic Journal of Business & Management (2025), Vol. 7, Issue 11: 1-10. https://doi.org/10.25236/AJBM.2025.071101.

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