改革开放以来，我国房地产行业快速发展，对国家经济的贡献度也显著上升，房地产企业的繁荣成为推动城市化和经济增长的关键力量。然而，伴随着市场的快速扩张和复杂性增加，自2014年我国债券市场发生第一笔实质性违约后，中国房地产行业经历了一系列信用风险事件，众多房地产企业，尤其是二、三线城市中小型房企因资金链断裂而申请破产或清算。近年来，政府相继实施的资管新规、“三道红线”政策和预售资金监管等措施也表明政府在政策层面对风险防控和市场的稳健运行给予了高度重视。在此背景下，加强对房地产行业信用风险的研究，不仅是应对当前困境的迫切需求，也是推动行业长远发展的必要条件。

本文旨在探索影响我国房地产上市企业信用风险的关键因素，并基于筛选出的影响因素通过主成分分析法搭建Logistic回归模型对企业的信用风险进行预测。本文首先概述了中国房地产行业的重要性，其在国民经济中的核心角色，以及该行业当前信用风险的状况与特点。接着，文章深入探讨了国内房地产市场的发展态势、所处的经济环境以及在发展过程中遭遇的挑战和问题。然后本文从我国房地产上市企业中选取327个样本作为研究对象，并筛选出21个财务与非财务指标，涵盖偿债能力、运营能力、盈利能力、发展能力及企业治理结构等多个维度。通过主成分分析法有效地提取了五个主成分因子，并依托这些因子建立Logistic回归模型。

研究结果显示，所建立的模型预测准确率达到了96.6%，在实际应用中对316个样本的信用风险进行了准确预测。最终模型对无风险企业的识别正确率高达98.7%，对风险企业的识别正确率为61.1%，显示出较高的预测能力。在信用风险的影响因素上，研究发现盈利能力和运营能力的提升能够显著降低房地产上市企业的信用风险。本文的方法对于金融机构在贷款审批、风险评估和资产管理过程中具有重要的参考价值。同时，本研究的成果也为政策制定者在制定相关政策时提供了数据支持，有助于引导房地产市场的健康发展，增强金融系统的稳健性。

Since the reform and opening up, China's real estate industry has developed rapidly, and its contribution to the national economy has significantly increased. The prosperity of real estate enterprises has become a key force in promoting urbanization and economic growth. However, with the rapid expansion and increasing complexity of the market, since the first substantive default occurred in China's bond market in 2014, China's real estate industry has experienced a series of credit risk events, and many real estate enterprises, especially the small and medium-sized real estate enterprises in the second and third tier cities, have applied for bankruptcy or liquidation due to the rupture of the capital chain. In recent years, the government has successively implemented measures such as new asset management regulations, "three red lines" policies, and pre-sale fund supervision, which also indicate that the government attaches great importance to risk prevention and control and the stable operation of the market at the policy level. In this context, strengthening research on credit risk in the real estate industry is not only an urgent need to address current difficulties, but also a necessary condition for promoting the long-term development of the industry.

This article aims to identify the critical determinants of credit risk in China's listed real estate firms and, using these determinants, constructs a predictive logistic regression model through principal component analysis. Initially, the paper highlights the significance of the real estate sector in China's economy, its pivotal economic role, and the prevailing conditions and traits of credit risk within this sector. It further examines the growth trends of the domestic real estate market, its operational economic environment, and the hurdles and issues faced during its growth. The research then focuses on 327 listed real estate companies in China, utilizing 21 selected financial and non-financial metrics that span various aspects such as debt repayment capacity, operational efficiency, profitability, growth potential, and governance structure. Through principal component analysis, five main component factors were distilled, serving as the foundation for the logistic regression model developed to forecast the credit risk of these enterprises.

The research results show that the established model has a prediction accuracy of 96.6%, and has accurately predicted credit risk for 316 samples in practical applications. The final model achieved an accuracy rate of 98.7% for identifying risk-free enterprises and 61.1% for identifying risky enterprises, demonstrating high predictive ability. In terms of the influencing factors of credit risk, research has found that the improvement of profitability and operational ability can significantly reduce the credit risk of real estate listed companies. The method presented in this article has important reference value for financial institutions in loan approval, risk assessment, and asset management processes. At the same time, the results of this study also provide data support for policymakers in formulating relevant policies, which helps guide the healthy development of the real estate market and enhance the robustness of the financial system.