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    China Real Estate Market Crisis: Who's Next?

    November 2022

    China Real Estate Market Crisis: Who's Next?

    The Evergrande Group’s troubles are snowballing into a crisis that risks freezing the entire Chinese Real Estate sector. Since the introduction of the Three Red Lines policy in 2020, credit risk increased sharply as liquidity problems emerged. Today, many Chinese real estate developers are facing a funding crisis that threatens to spread beyond the property sector and China. Which company will become the next Evergrande? Identifying early warning signals of credit deterioration are paramount to understanding and evaluating today’s Chinese real estate market.

    The property sector has been a strong source of growth for the Chinese economy. For decades, China’s real estate developers have financed their expansion by issuing debt. While concerns about developers’ highly leveraged financial situation has been apparent for some time, the easy credit conditions in the real estate sector continued in the absence of timely and credible signals for investors, suppliers and lenders to minimize their exposure. In 2020 Q3, in an attempt to deleverage and improve the financial health of the sector, the Chinese government introduced the Three Red Lines policy, requiring developers, among other requirements, to reduce their outstanding debt. The implementation of the policy led to a liquidity crisis for the China Evergrande Group, one of the largest developers in China, which defaulted in 2021. Moody’s Analytics Early Warning System (EWS) in the EDF-X platform captured Evergrande’s surging default risk 20 months prior to its default.

    Evergrande is not the only Chinese real estate development group in trouble. Developers deemed safe a year ago are now facing liquidity problems due to sales contractions, house price reductions and weaker consumer confidence. To capture companies’ evolution of risk, the EDF-X EWS screens for material changes in credit risk, separates stable from deteriorating names, and highlights elevated risk to sharpen the subset of firms identified for intense monitoring or a watchlist. We found the share of Chinese real estate companies transitioning to excessive risk has increased since 2021 Q3, suggesting the need for actions to limit downside risks.


    Looking for credible and timely early warning signals is a complex exercise. Commonly used metrics, such as firm’s financials, the prices of traded financial assets or macroeconomic indicators, do not clearly indicate when and which exposures are at risk. Moreover, when setting early warning thresholds, warning systems may return excessive number of names at risk, which can be costly to monitor. In today’s uncertain economic environment, it is important to incorporate effective data and signals to anticipate and differentiate levels of risk and to manage portfolio exposure efficiently.


    Timely and forward-looking approaches to risk detection are key in today’s volatile economic environment. EDF-X EWS is a tool to define which exposures to focus on to take timely and appropriate action. Using the forward-looking risk measure Probability of Default (PD), which captures variations in the economic cycle, and two decision rules— the change in risk and risk compared to peers—we can identify the most vulnerable firms with sufficient advance notice. Based on PD value assessment and change in implied rating, the EDF-X EWS provides a gradient view by separating firms into severe, high, medium and low categories.

    » EDF-X EWS flagged China property giant Evergrande in the high-risk category 20 months before it failed to pay its debt in December 2021 and put Evergrande in the severe-risk category one year prior to the missed repayment deadline.

    » Because the real estate liquidity crisis is spreading, we need to spot other Chinese real estate developers likely in trouble, in order to take action and limit potential losses.

    » Of 148 developers in China as of 2022 Q3 our early warning system flagged more than 40% of companies in high- and severe-risk categories, which represents a 7.5% year-on-year increase.


    In this article, we demonstrate how EDF-X EWS helps to accurately flag credit deterioration in advance and segment portfolio subsets by different warning categories. The EDF-X EWS provides warning signals for firms likely to experience credit deterioration events. The system classifies firms into four warning categories: severe, high, medium and low, by observing a firm’s risk level as compared to its peers, using a trigger metric and the evolution of risk measured by the 12-month change in the implied rating. EDF-X EWS is powered by the most relevant and comprehensive data sources, to produce real-time, actionable, early warning signals for any company using alert thresholds; it is a system calibrated on over 400 million public and private firms globally.

    Figure 1 shows the 1-year PD of Evergrande and its respective PD trigger. Since 2018, Evergrande’s PD has exceeded its PD trigger, indicating high/severe risk. From 2020 Q3 to 2021 Q1, Evergrande’s probability of default remained around the level of 3.7%. After failing the Three Red Lines test in April 2021 the firm’s credit risk surged, reaching PD levels above 25% in the wake of a Chinese court order to freeze deposits.

    To further refine our watchlist, we included in our analysis the 12-month change in the PD-implied rating.1 Figure 2 shows the history of Evergrande’s PD-implied rating. Over the past couple of years, the PD-implied rating was within Caa-C territory without any major upgrades. Since 2021 Q2, the implied rating gradually deteriorated by 1 notch each successive quarter in 2021.

    Figure 3 provides a visualization of Evergrande’s risk migration in the quadrant design of the EDF-X EWS. The x-axis captures the distance-to-trigger metric, while the y-axis shows the 12-month change in the PD-implied rating.2 The analysis starts from 2015 with Evergrande already being classified as severe risk. Throughout 2015-2018, Evergrande’s evaluation was jumping between low, high, and severe, making it a strong candidate for watchlist inclusion. The company’s deteriorating condition was evident from 2018 to 2020, with PD rising from 3.5% in Q4 2018 to 8.3% in Q1 2020.

    In Q2 2020, Evergrande migrated to the low risk level for the last time due to a large PD drop. Yet the company was not able to recover going forward; it was flagged as severe risk, given its two-notch deterioration in implied rating and an increased distance to trigger. As Evergrande failed to pass the Three Red Lines test and the Chinese court froze its deposits, its PD values skyrocketed to values above 20% and the breached point of no return. Finally, Evergrande failed to pay its offshore debt in December 2021.

    Evergrande is a telling example of the power of the EDF-X EWS. Not only did the EWS signal flag Evergrande in the severe bucket 20 months prior to its default, the EWS quadrant design also provided an informative visualization of its migration across different warning buckets over time to help interpret changes in risk.

    Who's Next?

    As stalled projects accumulate in the Chinese housing market, we evaluate the entire China Real Estate peer group in 2021 and 2022 in our EWS to understand who is at risk. Our group covers 148 listed developers (Figure 4 and 5, respectively), and tracks the risk migration of the sector over time using the EWS quadrant. The circles represent the firm’s size measured by total book assets in USD. Comparing 2022 Q3 to 2021 Q3, more firms have clearly tended toward the higher risk EWS categories, with more than a third of the developers assigned to the severe risk group as of 2022 Q3, a 17% increase from 2021.

    From a transition perspective, all highly endangered firms mostly moved to the severe risk category with a slight chance to remain within the high-risk bracket (Table 1). Similarly, we observe a 21.4% movement from low to severe, indicating a rapid quality fall within the industry. At the same time, 30% of medium-risk companies moved to the low-risk category. This divergence stems from some firms experiencing liquidity problems while others worked to decrease debt under the more pronounced regulatory supervision.

    In another critical signal, several large groups (highlighted in grey) are visibly deteriorating in quality. Besides Evergrande, we focused on three of the largest corporates that demonstrate severe risk and that are located considerably above from the trigger, showing extremely high risk of default: Greenland Holdings, Sunac China Holdings, and Country Garden Holdings.

    Greenland Holdings has been above the PD trigger since mid-2020 and is also known to have failed one of the Three Red Lines tests. Sunac has been in the severe group since 2021 Q2 and its huge debt repayments for 2022 might not be met either. Country Garden Holdings’ profits keep falling and its credit risk intensity resembles that of Evergrande. Beyond that, 50 other companies within the peer group demonstrated the severe status as of 2022 Q3, which is more than third of the reviewed peer group. From the asset perspective, this totals more than half of book assets from the China Real Estate peer group. Moreover, the average 1-year PD of the severe group in 2022 Q3 is 14.47%, which means that it has more than doubled from 6.87% as of 2021 Q3.

    By identifying the riskiest firms in the sector, we can potentially mitigate downside risk by refining our watchlist, allocating resources to better scrutinize a firm, hedging our position, and reducing or eliminating the exposure. Despite real estate being a crucial sector for the Chinese economy and having the potential to be supported by the state, Evergrande’s example shows that no firm is “too-big-to-fail” in this sector.


    The PD-implied rating is obtained by mapping the firm’s PD to the Moody’s Investors Service rating scale using a static table of median PD measures for each rating class.

    The trigger metric is a fixed percentile PD for a peer group adjusted for the credit cycle that allows us to compare across different industries. The distance-to-trigger metric is the log difference between the point-in-time (PiT) PD and the trigger.

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