Political connection and credit risk of real estate enterprises: evidence from stock market

Since April 2007, the US subprime crisis has swept across the world as an unprecedented financial tsunami, which is mainly due to banks' underestimation of the credit risk of the real estate industry. Since then, how to correctly understand the credit risk of real estate enterprises and the possible influencing factors attracted researchers' attention (Davis and Zhu 2004; Kim 2013; Hu et al. 2018; Eichholtz 2021). Due to the impact of the epidemic, the real estate regulatory policy, the Federal Reserve's interest rate increase, and other internal and external events, China's real estate enterprises have been in crisis since 2021 (Li et al. 2021). Many highly leveraged real estate enterprises broke their capital chain and defaulted on their debts. For example, Evergrande Group has a debt of 200 billion yuan, and Blu-ray Group has a total capital gap of more than 150 billion yuan. Other famous real estate enterprises such as Taihe Group, Huaxia Happiness, and Sunshine 100 China have also successively defaulted on bonds. According to the statistics of IFIND database, by September 2022, 173 real estate enterprises had defaulted on their credit bonds, with a default amount of nearly 170 billion yuan, accounting for 23% of the total default amount of credit bonds. The credit risk of real estate enterprises is not only related to their own development but also affected by the entire financial system, which threatens the healthy development of the national economy. Guo Shuqing, secretary of the Party Committee of the People's Bank of China, pointed out in the magazine Seeking Truth that "real estate is the biggest gray rhino threatening financial security". Therefore, it is of great significance to study the influencing factors and mechanism of credit risk of Chinese real estate enterprises. The intricate relationship between the real estate industry and the government, shaped by policy and political ties, is particularly noteworthy. The influence of political connections on the credit risk of real estate enterprises is multifaceted. On the one hand, political connections, as a form of social capital, provide informational advantages and resources that can foster enterprise development. On the other hand, substantial political connections, often associated with investment opportunities, can also lead to over-investment. In particular, under China's current economic system, state-owned enterprises are inherently more closely connected with the government. In contrast, private enterprises are less controlled by the government. In order to obtain resource advantages, they often have a stronger motivation to "rent seeking" from the government, that is, to seek more political connections. Existing research on credit risk primarily focuses on factors such as policy adjustments and excessive expansion, yet tends to overlook the role of political connections. These connections represent an informal, special form of government-enterprise relationship that can increase leverage. Moreover, private real estate enterprises are often more motivated to seek political connections, a factor that should not be underestimated in understanding their credit risk dynamics. Examining the credit risk of real estate enterprises through their political correlation and considering the enterprise investment and financing efficiency are important supplements to the credit risk research of the real estate industry. Based on these, this paper mainly studies the research question that whether or not there is an impact of political correlation on the credit risk of private real estate enterprises. If there is, we further examine its impact mechanism through intermediary variables such as excessive debt. According to the industry classification of the China Securities Regulatory Commission in 2012, this paper selects the panel data of 123 real estate listed companies in Chinese stock markets (Shanghai and Shenzhen A shares) from 2008 to 2021 as our research sample. Through the annual financial report of the real estate company and collecting the records of 5157 former or current government officials, representatives of the People's Congress, and members of the Chinese People's Political Consultative Conference (CPPCC), we score the strength of political connection of the real estate listed companies. The default probability EDF of listed real estate companies is calculated by KMV model to measure their credit risk. We find that (1) there is a significant positive correlation between the political connection of private real estate listed companies and their credit risk, which suggests that the higher the political connection, the higher their credit risk, while the state-owned real estate listed companies do not have this phenomenon. (2) The political connection of private real estate listed companies will lead to excessive debt, which may increase the pressure of debt repayment and eventually lead to the accumulation of credit risk. We have replaced the measurement method of political connection and credit risk and the two-stage least squares method to test the robustness of our results. (3) We further find that political connections play different roles in different industries and the fact that political connections can increase credit risk is a unique phenomenon of real estate. In addition, considering that 2013 is the first year of China's Internet finance, it is interesting to find that the popularity of Internet finance and other decentralized lending channels, as well as the diversity of financing channels, make real estate enterprises more likely to enter the risk of excessive debt, leading the relative higher significance of the impact of political connections on real estate credit risk. The remainder of this paper is organized as follows: Section "Empirical model and data" outlines the empirical models and presents the data. Section "Results" presents the empirical results. Section "Influence channel and heterogeneous impact analysis" examines the influence mechanism and discusses the heterogeneous impact of political connections on real estate credit risk. Section "Conclusion" concludes and discusses the policy implications.

Credit risk of real estate enterprises. The real estate industry played an extremely important role in the Southeast Asian financial crisis in 1997 and the US subprime crisis in 2007. Therefore, the research on the credit risk of real estate enterprises has also attracted much attention. For instance, Davis and Zhu (2004) studied the relationship between the asset value of real estate enterprises and their credit risk. The study found that due to the profit-seeking nature of capital, real estate enterprises will expand their financing needs when house prices rise, while commercial banks also tend to increase loans for high-capital real estate enterprises, eventually increasing the foam and increasing credit risk. Kim (2013) deduced the probability of default and expected loss of commercial real estate mortgage loans under the Morton framework and believed that only when the net operating income and property value were lower than the threshold level the real estate enterprises would default. When studying the credit risk of the real estate industry in the United States, Eichholtz (2021) found that the credit cycle of the real estate industry significantly affects its default risk. Identifying the credit level of the real estate industry through indicators such as the overall debt water can effectively identify and prevent the credit risk of the real estate industry. Manz et al. (2021) analyzed the credit risk of real estate enterprises from the perspective of regional economics, believed that the economic development level of the region where the enterprise headquarters is located is an important indicator to study its credit risk, and proposed that the enterprise financing channel has a significant impact on its credit risk. China's real estate industry began to rise and gradually become market-oriented in the 1990s, and thus, the research on its credit risk started late. Early studies, such as Jin (2007), used the Credit Portfolio View (CPV) model to study the credit default risk of China's real estate enterprises and found that the high debt ratio brought serious financial risks. The credit risk of China's real estate enterprises is not sensitive to changes in house prices but is very sensitive to the credit policy of the real estate industry. Recently, Hu et al. (2018) studied the real estate credit risk measurement and its key indicators in China from five aspects: solvency index, profitability index, operational ability index, development ability index, and macroeconomic index. Following that, this paper studies and finds that the profitability and development ability of enterprises plays a more important role in the credit risk of real estate enterprises. The impact of political connections on real estate enterprises. Political connection, that is, the informal relationship between enterprises and government, can provide enterprises with advantages in resource allocation. This paper introduces the political correlation of real estate enterprises for the first time and studies its impact on the credit risk of real estate enterprises. The real estate industry is extremely vulnerable to the guidance of national macro policies. In addition, land, a scarce resource controlled by the government, is the key to real estate development and operation. There are often various types of political connections between real estate enterprises and the government in order to obtain the advantages of information resources and other scarce resources. For real estate enterprises, the impact of political connection can run through the main process of project development, including early project planning and selection, land purchase and financing, and pre-sale approval. Although there is no evidence found on the role of political connections on real estate credit risk, previous studies have also pointed out that political connections can affect the investment and financing process of enterprises. For example, Faccio (2006) found through a comparative study of more than 20000 enterprise samples from 27 countries that enterprises with higher political affiliation have higher asset-liability ratios and find it easier to obtain bank loans but also have higher default rates. Political connections could bring more financing channels for enterprises but also increase their credit risk. Yu and Pan (2008) found that political connections can reduce the tax burden for enterprises, and enterprises in higher tax areas will have a stronger tendency to establish political connections. Yu et al. (2012) found that political connection can indeed alleviate the financing constraints of enterprises, and its core mechanism is information effect and resource effect, of which resource effect plays a leading role. Based on the perspective of political connection and technological innovation, Yuan et al. (2015) examined whether Chinese enterprises had the curse effect of political resources and found that corporate political connection hindered the innovation activities of enterprises and reduced the efficiency of innovation. The latest research, such as Li and Jin (2021), Ding et al. (2023), Liu and Zhao (2023), and Brahma et al. (2023) further investigated the impact of quantitative political correlation on corporate performance, M&A performance, risk resistance, and other aspects. Different countries and regions adapt to different methods of defining political connections. Combined with China's actual condition, political connection is defined as whether the chairman of the board, board members, or other senior executives have served in the government or as a representative of the National People's Congress and the CPPCC. Corresponding to this definition, we assign the level of political connection. To sum up, there has been a long line of literature on real estate credit risk, mainly focusing on its influencing factors and formation mechanism. Existing literature shows that real estate credit risk mainly arises from investment and financing efficiency. It is found that asset value, operating income, financing channels, financial hidden dangers, financing capacity, and other factors will have an impact on enterprise credit risk. Still, few articles discuss the impact of political connections on real estate credit risk. Therefore, this paper aims to fill this research gap and further explore whether the political connection affects credit risk through excessive debt.

Research design and sample. The study uses panel data for 123 A-share listed real estate companies in Shanghai and Shenzhen from 2008 to 2021. Financial data are from the IFind database and the People's Bank of China statistical database. Political connections are hand-collected from annual financial reports and cross-checked using Oriental Fortune (https://www.eastmoney.com/). Samples with ST, *ST, PT status or trading suspensions in the accounting year are excluded. Outcome variable (credit risk). Credit risk is measured by the Expected Default Frequency (EDF) computed via the KMV model: (1) compute equity value E and volatility σε using net assets per share × number of non-tradable shares and daily closing price × number of outstanding shares; (2) infer asset volatility from equity volatility; (3) define default point DP = short-term liabilities + 0.5 × long-term liabilities; (4) compute default distance DD as the standardized distance between asset value and DP and obtain EDF = N(−DD), bounded between 0 and 1. Key explanatory variable (political connection, PC). PC reflects the degree of senior executives' political ties. If the chairman or general manager has served/is serving in government, Party organs (including Discipline Inspection), the NPC or CPPCC Standing Committee, procuratorate, or court, levels are scored 1–4 for county/district, municipal, provincial, ministerial/national; otherwise 0. Similarly, if serving as Party representative, NPC deputy, or CPPCC member, levels 1–4 correspond to county to national; 0 otherwise. If multiple positions apply, the higher level is used. PC is aggregated across relevant senior executives excluding independent directors and supervisors, consistent with Table 1. Controls. Firm-level: management shareholding (Manger), proportion of independent directors (Director), number of senior executives (Num), growth rate of total operating revenue (Growth). Macro-level: M2 (broad money), and tertiary-industry GDP. Baseline model. A panel regression is estimated: EDF_it = β0 + β1 PC_it + β2 Controls_it + ε_it A Hausman test (p = 0.0021) supports fixed effects for subsequent analyses. Robustness checks. Alternative measures include: (i) dropping firms without political relevance; (ii) binary PC indicator; (iii) alternative credit risk using a reduced-form Merton distance-to-default (DD*) where debt market value D ≈ book value F, asset value V = D + equity market value, debt volatility σ_D = 0.05 + 0.25 σ_y, asset return r equal to the one-year PBoC deposit rate, maturity = 1, DD* = ln(V/F) + (−1/2)σ_D^2, EDF = N(−DD*). Endogeneity mitigation. Two-stage least squares (2SLS) uses executive education background (ordinal: 1–7, including MBA/EMBA) as instrument for PC. First-stage F = 17.53 (>10); Wald χ² = 130.32, p = 0.000 indicates model validity. Propensity score matching (PSM) addresses selection bias using covariates: Size (log market value), Solvency (equity ratio), Investment Opportunity (Tobin’s Q), Growth Ability (AGR), Profitability (ROA), Development Ability (OGR). Post-matching diagnostics show standardized bias <10% and good common support and kernel density overlap. Mechanism analysis (excessive debt). Following Lu et al. (2015), target leverage is estimated via Tobit: LEVB*i = α + α1 SOE_i^−1 + α2 ROA_i^−1 + α3 IND_i^−1 + α4 GROWTH_i^−1 + α5 FATA_i^−1 + α6 SIZE_i^−1 + α7 FIRST_i^−1 + ε Excessive debt LEVB is the target minus actual leverage (total debt/total assets). Mediation regressions evaluate channels via LEV and LEVB on EDF. Heterogeneity analyses. Subsamples include state-owned vs private real estate, construction, and manufacturing sectors. A temporal split examines pre-2013 vs post-2013 (the inaugural year of Internet finance in China) to assess changes in the PC–credit risk relationship.

- Descriptive and correlations: Political connection (PC) and credit risk (EDF) have a Pearson correlation of 0.118 (p<0.01). Default probabilities are widely dispersed (mean EDF 0.534, SD 0.400) across 2008–2021. - Baseline regressions (private real estate): PC is positively and significantly associated with EDF across specifications. Table 4 shows β_PC ≈ 0.019–0.021 (SE ≈ 0.006–0.007), significant at 1%. Fixed-effects preferred by Hausman test (p = 0.0021). - Robustness: Results hold when (i) excluding observations without political ties (β_PC = 0.015, SE 0.009), (ii) using binary PC (β = 0.058, SE 0.033, p<0.1), and (iii) replacing EDF with reduced Merton DD* (β_PC = 0.014, SE 0.007, p<0.05). 2SLS yields strong first-stage (F = 17.53) and significant second-stage effect of PC on EDF (β = 0.104, SE 0.037, p<0.01). - Mechanism (leverage and excessive debt): PC increases leverage (LEV) (β = 0.009, SE 0.003, p<0.01). LEV strongly increases EDF (β = 1.22, SE 0.074, p<0.01). PC is positively related to excessive debt (LEVB) (β = 0.006, SE 0.003, p<0.1). Including LEVB in EDF regression shows LEVB raises EDF (β = 1.17, SE 0.074, p<0.01) and PC remains significant (β = 0.012, SE 0.006, p<0.05), supporting a channel via excessive debt. - Heterogeneity across sectors: The positive PC–EDF relationship is significant for private real estate but not for state-owned real estate firms. In construction, PC is positively associated with EDF (β = 0.022, SE 0.009, p<0.05) but does not operate via excessive debt (PC→LEVB not significant). In manufacturing, PC has no impact on EDF; instead, it correlates with higher technological qualification counts (Tech) (β = 0.065, SE 0.012, p<0.01) and lower excessive debt (β = −0.001, SE 0.001, p<0.1). - Internet finance era effect: The PC–EDF relationship is insignificant before 2013 (β = 0.005, SE 0.013) but becomes stronger and significant after 2013 (β = 0.02, SE 0.011), suggesting that the rise of Internet finance and diversified lending channels amplify the risk effect of political connections. - Macroeconomic controls: M2 is negatively and tertiary GDP positively associated with EDF across multiple specifications (e.g., Table 4: M2 ≈ −1.6, GDP ≈ +1.7, both p<0.01).

The study set out to test whether political connections elevate credit risk among private real estate firms in China and to uncover the mechanisms behind any such effect. The results consistently show that stronger political ties among senior executives are associated with higher default risk (EDF), even after controlling for firm-level and macroeconomic factors, addressing endogeneity via 2SLS, and mitigating selection bias with PSM. The findings suggest that political connections facilitate easier and cheaper access to debt, which, combined with incentives for short-term performance and local government growth objectives, leads to higher leverage and excessive indebtedness. This leverage channel is empirically supported: political connections raise leverage and excessive debt, both of which significantly increase default risk. The amplification of the PC–credit risk link after 2013 indicates that the expansion of Internet finance and decentralized lending broadened financing options for politically connected firms, further encouraging debt accumulation and increasing credit risk. Importantly, the effect is not universal across ownership types or industries. State-owned real estate firms do not exhibit increased credit risk from political ties, likely due to different governance structures and implicit support mechanisms. In construction, political connections raise credit risk but not via excessive debt, suggesting alternative channels (e.g., project risk or procurement-driven expansion). In manufacturing, political connections are associated with innovation-related certifications and reduced excessive debt, indicating that such ties may be leveraged for capability building rather than financial risk-taking. Overall, the results highlight that political connections can be double-edged, with their impact contingent on sectoral context and financing environments.

This paper examines the impact of political connections on the credit risk of China's private real estate firms. It documents a robust positive association between executive political ties and default risk, operating primarily through increased leverage and excessive debt that heighten repayment pressure. The effect is concentrated in private real estate firms, with no analogous amplification among state-owned real estate companies. In related sectors, construction shows a positive PC–risk link without the excessive-debt channel, while manufacturing shows no risk increase and indications of innovation-related benefits. These results carry policy implications. Firms should strengthen risk management around politically enabled financing, particularly monitoring leverage and liquidity. Regulators might improve disclosure and oversight of politically connected governance and debt accumulation, and tailor credit and bond market surveillance in sectors prone to leverage buildups. As Internet finance expands, guardrails on non-bank lending to highly leveraged, politically connected firms may help contain systemic risk. Future research could broaden the scope across regions and sectors to compare institutional contexts and explore additional channels (e.g., governance quality, project approval dynamics).

The study defines political connections narrowly, focusing on formal links (current/former officials, NPC deputies, CPPCC members) and may omit informal relationships (e.g., personal networks, ties to government departments or major financial institutions). The sample is confined to Chinese A-share listed firms in the real estate sector (and comparative sectors), limiting generalizability across regions and industries. Future work could expand the scope to other countries and sectors and incorporate broader measures of political ties.