Introduction

Foreclosed housing refers to properties that have been taken over by a lender or a bank because the owner failed to make mortgage payments. In legal terms, foreclosed housing is defined as a property that has lost its right to redemption as collateral. When homeowners default on their mortgage loans, the lender can initiate foreclosure proceedings, a legal process that allows them to take possession of the property. These properties are then typically sold at auction to recover the outstanding mortgage debt. According to the regulations of the Supreme People’s Court of China, judicial auction is the mandatory procedure for handling debt-defaulted properties. Since 2016, the Supreme People’s Court mandated that all foreclosed properties must be auctioned online through officially authorized seven platforms open to the public (Zheng et al., 2023). The highest bidder with their bid exceeding the reserved price can conclude the transaction. The proceeds from the auction will be used to repay the mortgage loans on the property.

Recently, China’s sluggish real estate market has led to an increasing number of foreclosed properties because of defaults on mortgage loans. It was reported that a total of 533,607 foreclosed housing units were produced nationwide from January to December 2023, an increase of 20.89% year on year. Among these, 151,429 properties were transacted, with an average transaction rate of 28.38%. Table 1 shows the data on the transactions of foreclosed housing in major Chinese cities, which demonstrates a dramatic abortive rate, with some reaching as high as 80%, such as Lanzhou, Xining, Hohhot, Harbin, and Guiyang. This suggests that China’s property market is facing a severe debt crisis, with an increasing number of homeowners unable to repay their mortgages. Therefore, dealing with foreclosed properties has become a thorny issue for China’s financial sector today.

Table 1 2023 Foreclosure transactions in major cities of China.

Existing studies mainly focused on the pricing mechanism of foreclosed properties. Some researchers suggested that potential transaction risks of foreclosed properties may lead to a stigma effect, causing their prices to depreciate (Zheng et al., 2023), which requires buyers to bear potential risks (Zhou et al., 2015; Chinloy et al., 2016). The stigma effect associated with the ownership defect will reduce demands and cause the depreciation of foreclosed properties. It was estimated that the average price of foreclosed housing was approximately 21% lower than regular second-hand properties (Qian, 2024). The high transaction costs incurred in judicial auctions may magnify the perceived risks and psychological burdens on potential buyers, which will subjectively weaken their willingness to pay for the foreclosed property, or require greater discounts to offset these risks (Qu and Huang, 2024). Similarly, evidence from the U.S. also indicated that verdict documents issued by courts may amplify the stigma associated with foreclosed properties, which may weaken buyers’ willingness to pay (Clauretie and Daneshvary, 2009).

However, the high level of heterogeneity among foreclosed properties makes it challenging for buyers to fully comprehend a property through online information alone. In many cases, prospective buyers are unable to conduct an in-person inspection of the property and are unable to directly negotiate with the court. This challenge is further compounded by the high value of real estate assets and complex property rights issues, which intensifies the information asymmetry for potential homebuyers. To mitigate the negative impact of information asymmetry on foreclosure transactions, some scholars have proposed that improving online information disclosure can attract more prospective buyers, which will lead to a higher likelihood of successful auctions and lower discount rates of the transaction price (Xu et al., 2022; Qian, 2024).

Notably, existing studies overlooked potential risks associated with property rights in China’s foreclosure market which encompasses a diverse range of property rights, including commercial housing (CH), apartment housing (AH), marketized housing (MH), state-owned enterprise housing (SEH), relocation housing (RH), economic affordable housing (EAH), illegal housing (IH), and even mall-property-rights housing (SPRH). As these different categories of housing property rights are fragmented and held by various entities, buyers may find foreclosed properties available at a lower price than market value. However, thorough inspection is often required, and there are potential risks associated with property rights that may influence the outcome of judicial auctions. If these foreclosed properties are unable to be transacted through judicial auctions, it could lead to a debt crisis within the housing financial system of China.

However, current studies have not yet revealed the exact impact of these property rights risks on the results of judicial auctions (success or failure), nor have they clarified the mechanism behind these risks. This study aims to theoretically analyze how China’s fragmented housing property rights trigger property-rights risks through the cs-QCA method based on 136 cases of foreclosed housing. It seeks to address the following questions: How do various configurations of property-rights risks impact the outcomes of foreclosure auctions? And what is the role of credible commitments issued by local courts in judicial auctions?

Fragmented housing property rights and their risks

Theories of new institutional economics conceptualize property rights as a “bundle of rights”, which comprises the right to use, generate income from, and transfer or sell the property (Alchian and Demsetz, 1973). It is emphasized that clearly defined private property rights are crucial for transactions within a market economy (Coase, 2013; Williamson and Coase, 1989). De Soto further asserts that the formation of the real estate market is hindered in the absence of formal institutions to safeguard private property rights (Soto, 2001). It is noteworthy that housing property rights in China are occasionally incomplete, yet a well-operating market of small property rights housing (SPRH) can still thrive even in the absence of clearly defined property rights (He et al., 2019). Nevertheless, the uncertain nature of property rights typically results in increased transaction costs (Xu et al., 2022).

Before the reform and opening-up policy, socialist China didn’t entitle private housing property rights to citizens. All housing was owned by work units and was considered a form of state-owned property in urban areas. Under the planned economic system, state-owned land was directly allocated to work units for housing construction, and the housing was deemed as a welfare provision rather than a commodity (Chen and Han, 2014; Ping Wang and Murie, 1996). During the 1980s-1990s, to accommodate the market-oriented reform, Chinese authorities intentionally separated land ownership from land-use rights, as well as housing ownership from land-use rights through policy and legal arrangements. This institutionalized separation of property rights between land and housing led to the fragmentation of property rights in the housing market. Today, the property-rights risks arising from this fragmentation have become a significant barrier to foreclosed property transactions. Incomplete or uncertain property rights of foreclosed housing may diminish market demand, increase auction price discounts, and impede demand flow from the regular resale housing market. This section aims to theoretically elucidate how fragmented housing property rights trigger property-rights risks during judicial auctions.

The separation of land ownership and land-use rights

Under the socialist public ownership system, the 1982 Constitution of the People’s Republic of China explicitly prohibited the transfer and trading of state-owned landFootnote 1, which significantly impeded urban economic development in China (Qun et al., 2015). To explore the path of marketization reform on land, the central government initiated a policy pilot in the Shenzhen Special Economic Zone, where the first-ever public auction of “land use rights” was conducted in 1987, subsequently triggering a constitutional amendment. In April 1988, the National People’s Congress passed a constitutional amendment allowing the lease of use right of state-owned landFootnote 2. In December of the same year, the “Law of Land Administration” was also revised to permit the lawful transfer of state-owned land use rightsFootnote 3.

This reform fundamentally separated urban land ownership and land-use rights at the institutional level. Theories of property rights in new institutional economics regard property rights as an economic tool for individuals to assess their expected gains or losses in transactions (Demsetz, 1964, 1974). However, this reasoning does not apply to China’s foreclosed property market. The separation of land ownership and land-use rights in China makes property rights of foreclosed housing difficult to define clearly. This complexity hinders buyers from predicting transaction outcomes based on property rights. For example, small property rights housing lacks legal land-use rights, leaving its private ownership vulnerable without legal protection (Qiao, 2017). In the case of foreclosed small-property-right housing, what is auctioned is the right of use, not ownership. Buyers cannot acquire legal ownership of small-property-right housing through judicial auctions. By contrast, commercial housing comes with legal land-use rights and housing ownership, allowing buyers to legitimately exercise rights related to occupancy, use, benefits, and property disposal.

The separation of housing ownership and land-use rights

In the 1990s, welfare housing allocated to employees based on work units had been transformed into a market-oriented supply in urban China (Jiang and Qian, 2022; Qian et al., 2019a). It is notable that whether selling public-owned housing or purchasing private housing, the authorities faced the challenge of managing private property rights. During this period, China’s policy arrangements once again separated housing ownership from land-use rights. In October 1991, the policy document “Opinions on Advancing Urban Housing System Reform” drafted by the “Leading Group for Housing System Reform ” was officially issued by the State Council. This document allowed buyers purchasing public-owned housing at market prices to obtain full private ownership. This policy explicitly defined the status of “marketized housing” in terms of private ownership. The constitutional amendment of 2004 established legal protection for private property rights for the first timeFootnote 4, affirming private property rights as a fundamental citizen right in the Chinese constitution (Chen and Kielsgard, 2013; Xu, 2011). The 2007 enactment of the “Property Rights Law” further safeguarded the rights of disposal and transfer of private property. The 2020 Civil Code replaced the previous “Property Rights Law”, protecting residents’ right of habitation, and thus laying a legal foundation for property transactions of foreclosed housing.

Evidently, under the premise of public ownership of land in China, land ownership belongs to the state, while parcels of land designated for housing construction are granted land-use rights. The houses themselves, as a form of real estate, are endowed with private housing ownership. Hence, the property rights related to housing in China can be characterized as a combination of “housing ownership + land-use rights” (Qian, 2024). This combination can be analyzed from two primary perspectives. Firstly, the state is the legal owner of the land on which structures such as buildings or architecture are located. The utilization of land falls under the jurisdiction of local governments through processes of land acquisition or re-zoning (Liu et al., 2019). Meanwhile, developers can secure rights to use the land through competitive processes like bidding, auctions, and listings (Qian et al., 2019b; Yin and Qian, 2020).

Secondly, the property rights directly associated with individual houses are held by homeowners themselves, including rights such as possession, use, transfer, and inheritance. It is worth noting that the transfer of these two perspectives is separated in property transactions in China. Transactions typically revolve around the property rights related to the building, excluding the ownership of the land (Qian, 2024; Qian and Yin, 2018). Legally, the property rights associated with the dwelling secure the rights of homeowners to their private dwelling.

In general, during China’s transition towards land marketization and housing commercialization, a series of legal and policy arrangements have led to the separation of land ownership from land-use rights, followed by the separation of housing ownership from land-use rights. This has institutionally fragmented the housing property rights, allowing properties to be financialized as collaterals. In the event of default on housing mortgage loans, these properties are seized by the court and auctioned off judicially. The fragmentation of property rights means that different entities hold housing ownership and land-use rights separately, resulting in three categories of risks related to property rights which may significantly influence the judicial auction process.

Conceptual framework: three types of property-rights risks

The risks associated with foreclosure transactions arise from the segmented structure of housing property rights, which are identified as three types: cost risk, acquisition risk, and usufruct risk. However, these property-rights risks may not be immediately apparent to buyers before the auction. In cases of information asymmetry, homebuyers are forced to make decisions amidst uncertainty, relying on the limited information provided in auction announcements. These underlying property-rights risks have the potential to influence the outcomes of judicial auctions. Figure 1 presents the conceptual framework illustrating the relationship between property-rights risks and the judicial auction of foreclosures.

Fig. 1
figure 1

Conceptual framework.

The “cost risk” pertains to the possibility of homebuyers in the foreclosure market encountering high transaction costs such as various hidden taxes and miscellaneous fees because of information asymmetry. Typically, local courts do not conduct due diligence on foreclosures, and they require buyers to bear potential transaction costs themselves. The transactional costs are often implicit, making them challenging for homebuyers to ascertain. Before auctions, noetic homebuyers will evaluate the potential extra costs beyond the auction price, relying on available information to determine their participation in the auction. This influences the willingness of buyers to engage in bidding, thereby potentially affecting auction outcomes. The cost risk is linked to both land status (LS) and disclosure of transaction cost (DTC). LS for foreclosed properties falls into three categories: leased, allocated, and illegal land-use rights. Leased land-use rights, acquired through processes like bidding or auctioning, spare buyers from additional land leasing fees. Conversely, foreclosed properties on allocated land-use rights require buyers to pay extra post-auction land leasing fees, increasing transaction costs; unauthorized land-use rights, termed illegal land, bar buyers from attaining legitimate housing ownership. Moreover, buyers face various transaction costs due to fragmented property rights in the foreclosure market. Information asymmetry makes it difficult for homebuyers to anticipate foreclosure-related costs before auctions, potentially leading to increased transaction failures.

The “acquisition risk” refers to the chance of a buyer securing ownership after winning the bid, which depends on the combination of “housing ownership + land-use rights” (Qian, 2024). Properties with higher integrity of property right (IPR) face lower acquisition risks as they have clearer and less disputed ownership, making the acquisition process more straightforward. Conversely, properties with lower IPR encounter difficulties in acquiring ownership, and in certain instances, ownership may not be attainable. In the foreclosure property market, buyers evaluate various configurations of property rights to assess the likelihood of acquiring ownership, potentially influencing auction outcomes.

The “usufruct risk” refers to the opportunity for a homebuyer to exercise usufructs, such as habitation, disposal, and inheritance, based on their discretion of property rights (DPR) related to the property. Buyers in the auction market assess their ability to derive utility from foreclosures based on the available information. Evidence has shown that foreclosed properties with an unexpired lease depreciate by 2% compared to those with full property rights (Qu and Huang, 2024). Therefore, the usufruct risk may impact their willingness to bid, consequently influencing the outcomes of the auction.

The “Credible commitment (CC)” in new institutional economics is regarded as a way to reduce the uncertainty of various actors under scenarios of asymmetric information (North, 1993). If the court effectively upholds the commitment, it elevates the DPR for bidders, thereby ensuring the utility of their property rights. Unlike transactions in the secondary housing market, after successful judicial auctions, the court will issue three legal documents: Notice of Assistance in Execution, Confirmation of Auction Results, and Notice of EnforcementFootnote 5, which essentially become the endorsement of the transaction by the court. This aids in boosting the buyer’s confidence in handling property rights risks. In this study, these legal documents issued by the court are referred to as credible commitments that may reconcile property-rights risks.

Research design

Applicability of the QCA method

Qualitative Comparative Analysis (QCA) is a methodological approach based on Boolean algebra. It facilitates systematic case comparisons and the investigation of causal complexity by identifying diverse combinations of factors that result in the same outcome (He et al., 2023). QCA combines rich qualitative contextual information with structured quantitative data, enabling the simultaneous treatment of multiple cases. It applies to process data of small and medium-sized cases in social science research (He et al., 2023; Shephard et al., 2020). The advantage of QCA lies in revealing the complexity of certain social phenomena by identifying asymmetrical causal relationships, focusing on the direct consequences resulting from the necessity and sufficiency relationships among the various conditions within cases (Pappas and Woodside, 2021; Ragin, 2008). The QCA method doesn’t emphasize the isolated effects of single variables; instead, it identifies the impact of interactions among multiple variables on the outcome. In situations of asymmetric causal relationships, QCA can offer non-exclusive explanatory pathways for both the presence and absence of a phenomenon (Thomann and Ege, 2020). This study opts for the cs-QCA approach because the necessity of dichotomizing conditions, as mandated by cs-QCA, mirrors the practical decision-making processes in real-world foreclosure auction, which frequently adhere to a binary logic of “either this or that” (Caren and Panofsky, 2005; Grofman and Schneider, 2009; Longest and Vaisey, 2008; Vink and Van Vliet, 2009).

Previous research showed that buyers prefer information presented in a binary format to aid decision-making in market transactions (Wagemann et al., 2016). Although fs-QCA has its advantages, it lacks the clear-cut nature of cs-QCA with its binary conditions, especially when dealing with ordinal or continuous values (Blackman, 2013; He et al., 2023). When addressing foreclosed housing cases in China, utilizing cs-QCA provides two key benefits: first, the outcome variable offers only two possibilities—either a successful or failed transaction. Second, the conditional variables of cost risk (LS, DTC), acquisition risk (IPR), usufruct risk (DPR), and credible commitment (CC) can be distinctly dichotomized. By applying the cs-QCA method to analyze 136 foreclosed housing auction cases across 20 cities, this study aims to explore how different combinations of property-right risks influence the outcomes of foreclosure auctions and uncover the significance of credible commitment in judicial auctions in China.

Case selection and data collection

The Cs-QCA method requires adherence to the “most-similar-case design”, organizing information into a set of conditional variables to control confounding factors and identify causal effects (Thomann and Ege, 2020). This study compiled 136 relevant foreclosure cases from online judicial auction platforms, spanning 20 cities in China. To ensure data reliability and validity, the study adhered to four key principles when selecting cases: (1) properties located in provincial capitals or municipalities, specifically in main urban areas excluding suburbs; (2) transaction prices are comparable to average prices of second-hand properties during that period, excluding villas or high-end properties; (3) cases with clear outcomes after the initial auction, categorized as either “successful” or “failed”, excluding cases of postponements, withdrawals, or suspensions; (4) cases with adequate information to cover all conditional and outcome variables.

To gather comprehensive qualitative data, this study collected Auction Notices, Property Evaluation Reports, Notices of Assistance in Execution, Confirmations of Auction Results, and Notices of Enforcement for 136 cases from Jingdong and Ali online judicial auction platforms. Additionally, the author obtained 152 foreclosure reports of due diligence through personal connections from a real estate agency. These data and documents encompass historical transaction records of auctioned properties, leasing status, and instances of unauthorized occupation, providing abundant information for conditional variables of the QCA method.

Outcome and explanatory variables

Outcome variable

The outcome variable is defined as whether a foreclosed property is sold at the initial auction. Auction outcomes are observable and objectively identifiable on online auction platforms. Typically, there are two post-auction results: successful or failed auctionFootnote 6. To present the auction outcome clearly, this study sets the result as a binary variable: a successful auction is assigned the value of 1, and a failed auction is assigned the value of 0.

Explanatory variables

The cost risk determined by LS and DTC refers to the possibility of homebuyers in the foreclosure market encountering high transaction costs such as various hidden taxes and miscellaneous fees because of information asymmetry. According to China’s “Law of Land Administration”, the lawful acquisition of land-use rights is limited to two methods: leasing and allocation. Under the system of reimbursable use of state-owned land, local governments offer fixed-term land-use rights to developers through bidding, auctioning, or listing. For instance, residential land-use rights typically span 70 years, while land for apartment housing is granted for 40 years. On the other hand, allocated land is gratuitously allocated by local authorities for certain public-interest construction projects such as relocation housing and economic affordable housing. These properties often require additional payment of land leasing fees during second transactions. In the foreclosed housing market, a considerable number of properties fall under the allocated land-use rights, resulting in relatively higher property transaction costs. Additionally, auction notices might not always specify the land status of foreclosed properties, potentially implying significant land leasing fees for buyers. This study categorizes the land status of foreclosed properties as a binary variable: allocated or illegal = 0, leased = 1.

On the other hand, the potential cost risk associated with transaction costs primarily involves undisclosed taxes and unpaid miscellaneous fees. The taxation on foreclosed properties is contingent on their prior transaction history. Buyers may be unaware of any outstanding fees. However, this crucial tax information is often implicit as local courts typically focus on procedural rather than factual aspects when handling property forfeiture. Consequently, detailed tax payment amounts are not typically disclosed by local courts, leaving buyers to estimate tax obligations according to restricted information from online auction announcements. This information asymmetry can lead to substantial transaction costs, potentially impacting buyers’ willingness to participate in auctions. Some interviews with buyers revealed cases where successful bidders had to settle significant fees for property management owed by former owners, amounting to tens of thousands of RMB. Failure to address these fees may result in the buyer being unable to obtain property rights and forfeiting their deposit. The less implicit cost information is disclosed, the higher the uncertain cost for the buyer, potentially leading to transaction failures. In this study, DTC is a binary variable: no disclosure = 0; disclosure of one or more items = 1.

The acquisition risk determined by IPR pertains to the potential of a buyer obtaining ownership after achieving the bid, contingent on the combination of “housing ownership + land-use rights”. This combination is a composite property-rights structure, where the higher the IPR, the lower the risk of acquiring property rights, thereby favoring transactions of foreclosures. Previous research indicates that there exist eight primary categories of properties with varying degrees of property rights integrity in China’s foreclosure market (Qian, 2024). IPR is set as a binary variable: illegal housing/small-property-right housing (IH/SPRH), state-owned enterprise housing/economic affordable housing (SHE/EAH), relocation housing (RH), marketized housing (MH) = 0; apartment housing/commercial housing (AH/CH) = 1.

The usufruct risk determined by DPR refers potential of buyers to utilize usufructuary rights, such as habitation, disposal, and inheritance. Upon entering the foreclosure market, buyers typically evaluate the probability of realizing these usufructs according to restricted information from auction announcements issued by courts. In this regard, the usufruct risk may discourage buyers from bidding (Qian, 2024). The DPR is set as a binary variable to indicate the usufruct risk, which is restricted by incompetent property rights/shared ownership, fraudulent contracts, illegal occupation=0; negotiable tenancy/full DPR = 1.

Property-rights risks are moderated by credible commitments. Existing research indicates that if the executing court provides explicit credible commitments, it may mitigate those risks related to property rights encountered by buyers to a certain extent, thereby enhancing their confidence in auctions and facilitating transactions (Qian, 2024). However, not all courts can offer credible commitments. It is foreseeable that if a court does fulfill its commitments as promised, it can significantly enhance the protection of individual property rights. Credible commitment plays a binary variable in this study: no commitment = 0; one or more commitments = 1.

In summary, Table 2 demonstrates all variables and their description and dichotomization for the cs-QCA method. Table 3 shows descriptive statistics of variables based on 136 cases of foreclosure in China.

Table 2 Operationalization of outcome and explanatory variables.
Table 3 Descriptive statistics of variables.

Data processing and interpretation

The cs-QCA analysis comprises two primary phases: first, a necessity analysis aims to identify necessary conditions, as well as sufficiency analysis to identify sufficient configurations; second, a truth table analysis for all possible configurations with corresponding cases and their outcomes. The assessment of necessity analysis seeks to establish whether any chosen condition is imperative for the outcome to be materialized, which involves gauging consistency and coverage (Grofman and Schneider, 2009). The consistency measures the occurrence of a specific condition within cases resulting in the outcome (Pappas and Woodside, 2021). Following this assessment, the condition undergoes scrutiny based on its coverage. The coverage determines the practical significance of a consistent condition and calculates the ratio of cases displaying the outcome among those with the given condition (Thiem, 2013).

The necessity of a single conditional variable does not guarantee the occurrence of outcomes. Therefore, further configuration analysis is required for those variables that cannot individually act as necessary conditions. This aims to identify the best explanatory combinations for the outcome variable (Cotte Poveda and Pardo Martínez, 2011). Based on the Boolean algebraic rules, QCA analysis requires determining the membership among variable sets. Subsequently, it further analyzes the necessity of each configuration contributing to the outcome variable (Roig-Tierno et al., 2017). The measure of necessity for variables is evaluated using a consistency value, calculated by the formula:

$${Consistency}\left({X}_{i}\le {Y}_{i}\right)=\sum [\min ({X}_{i},{Y}_{i})]/\sum {(X}_{i})$$
(4-1)

In equation (4-1), min(X) is employed to signify the intersection (“AND”) of all X, while ∑(X) denotes the union (“OR”) of all X. If the membership in outcome Y is lower than the membership in causal configuration X, the numerator becomes smaller than the denominator, resulting in a decrease in the consistency score (Olsen and Nomura, 2009). The consistency scores range from 0 to 1, where “0” implies no subset relationship, while “1” signifies a complete subset relationship (Pappas and Woodside, 2021).

After examining the consistency of conditions, the indicator of coverage is introduced to assess the strength of each explanatory configuration on the outcome. The equation to calculate coverage is:

$${Coverage}\left({X}_{i}\ge {Y}_{i}\right)=\sum [\min ({X}_{i},{Y}_{i})]/\sum {(Y}_{i})$$
(4-2)

In equation (4-2), the coverage indicates the proportion of the total membership scores in an outcome that a specific configuration accounts for (Olsen and Nomura, 2009). It essentially denotes the number of cases included in the sufficient configuration for outcome Y. The value of coverage varies from 0 to 1: a high coverage value implies alignment with the outcome, encompassing many cases with the configured outcome “in”; while a low coverage value implies that although the causal configuration aligns with the outcome, its substantive significance is limited (Olsen and Nomura, 2009).

Using the fs-QCA 3.0 software, this study performed calculations on 5 explanatory variables across 136 cases of foreclosed housing auctions, yielding consistency and coverage values for each explanatory variable (see Table 4). The results show that none of a single variable exhibits a consistency equal to or exceeding 0.8, indicating that no variable alone leads to a successful auction. That is to say, the success of foreclosure auctions is determined by the combination of multiple explanatory variables.

Table 4 Results of necessity analysis for single variable.

After the necessity analysis for single variables, this study needs to build a matrix of truth table representing all possible combinations of conditions and the corresponding observed outcomes. In the truth table, each row indicates a configuration of conditions, while columns indicate the presence (1) or absence (0) of these conditions alongside the observed outcome for each configuration (Marx et al., 2013). Given N conditional variables, there are 2n configurations in the truth table, each of which represents a causal explanatory pathway (Fiss, 2011). This study established 5 explanatory variables, theoretically encompassing 32 causal pathway combinations. Following the standard conventions of QCA, the case frequency threshold is set at 1, while the consistency threshold is 0.8 (Pappas and Woodside, 2021). Utilizing the fs/QCA 3.0 software, cases with combinations resulting in 0 pathways are excluded, and then the truth table is generated (see Table 5). In this way, it can identify patterns, analyze relationships between conditions and outcomes, and determine which combinations of conditions are associated with successful auctions.

Table 5 Truth table.

To further identify configurations of explanatory variables affecting the results of foreclosure auction, this study utilized fs/QCA 3.0 software to generate three types of solutions based on the truth table: complex, parsimonious, and intermediate solutions. The complex solution encompasses all possible combinations of conditions. In contrast, the parsimonious solution simplifies the complex solution, emphasizing the core conditions necessary for any solution (Fiss, 2011). The intermediate solution results from counterfactual analysis of both complex and parsimonious solutions, which comprises a part of complex solutions along with parsimonious solutions (Ragin, 2008). In this study, the intermediate solution was adopted as it presents both core and peripheral conditions (Pappas and Woodside, 2021; Marx and Dusa, 2011).

Fiss (2011) suggested 0.8 as the consistency threshold for sufficient conditions, and it is also currently followed by many QCA studies (Misangyi and Acharya, 2014; Bell et al., 2014; Campbell et al., 2016; Greckhamer et al., 2018). Therefore, this study considers a consistency threshold of 0.8 or higher as an effective solution. By fs/QCA 3.0, all solutions have reached this threshold, and their overall coverage and consistency are the same at 0.439 and 0.879 respectively. This indicates that all solutions are sufficient to explain the result of the foreclosure auction.

Each solution identified by fs/QCA 3.0 includes both “raw coverage” and “unique coverage”. The raw coverage measures the proportion of cases that can be explained by this solution, and the unique coverage refers to the proportion of cases that can only be explained by this specific solution. In general, the QCA method does not require a specific threshold for the coverage, but a lower coverage indicates that this conditional configuration occurs less frequently in reality (Rihoux, 2006). Notably, the complex and intermediate solutions for successful auctions include eight effective configurations, while the unique coverage rates of two configurations “LS*DTC*DPR*CC” and “DTC*IPR*DPR*CC” are both 0, so they are excluded from the solutions (see Appendix 1). Table 6 reports six configurations leading to successful auctions of foreclosed housing, in which the consistency value of each configuration reached or exceeded 0.8, indicating that they can significantly constitute sufficient conditions for the success of the auction.

Table 6 Overall solution for successful auctions.

Results of configuration analysis

Configuration 1:~LS*DTC*IPR*~DPR

Configuration 1 suggests that a successful auction is more likely when the land status of a foreclosed property is non-leasing with relatively lower DPR, but clearer DTC and higher IPR. An illustrative example of this scenario is Case 22, a state-owned enterprise property in Fengtai District, Beijing. The original owner of the property was an employee of a state-owned enterprise who purchased full ownership of the house at a cost in 2003 (high IPR). However, the house was occupied by the mother of the original owner at that time (low DPR). To facilitate the auction, the court of Fengtai district disclosed the property’s transaction costs on the auction platform transparently and timely, including property management fees, and utilities, and clarified the absence of any outstanding or late charges (high DTC). This indicates that clearer DTC can bridge the information gap between the buyer and the property, which will reduce transaction risks and contribute to a successful auction.

Configuration 2/3: LS*DTC*~IPR*DPR/LS*DTC*~IPR*CC

Configurations 2 and 3 indicate that if the land status of a foreclosed property is based on leased land-use rights with higher DTC and lower IPR, its auction is determined by the higher DPR or CC. Cases 25 and 42 are consistent with these two solutions.

Case 25 was a marketized housing located in Putuo District, Shanghai. The original owner encountered bankruptcy due to mismanagement of business, resulting in his house being seized by the court for auction and sold in October 2021. This property was a marketized housing from a state-owned enterprise allocated to its employees. In 2002, the enterprise initiated a housing marketization reform. After paying the land leasing fees, the previously allocated land-use right was converted into the leased land-use rights. All housing units were sold to individual employees at preferential prices, but the enterprise retained partial property rights (low IPR). Such marketized housing faces restrictions during transactions, typically involving the original property-holding unit retaining the right of first refusal. This means that reselling this property requires the original unit’s consent and payment of the difference at market price. This circumstance indicates a lower IPR for this type of property. Notably, the property was vacant at the time (high DPR), and the auction notice explicitly stated no outstanding fees from any third party (high DTC), easing the transaction process.

Case 42 was a relocation housingFootnote 7 located in Luohu District, Shenzhen, transacted in March 2021. The original owner engaged in construction contracting but mortgaged this property in a bank for project funding. However, due to the first party’s arrears in project payments, the original owner couldn’t repay the bank loan, resulting in the forced auction of the property. Although the original owner signed an “Agreement of Compensation for Relocation and Resettlement” with the developer, the certificate of property ownership had not yet been obtained (low IPR). The court meticulously disclosed transaction costs in the auction notice (high DTC) and committed to taking responsibility for property defects and assisting the buyer in obtaining legitimate ownership (high CC). Ultimately, the property was successfully transacted.

Configuration 4:~LS*IPR*DPR*CC

Configuration 4 indicates that in the case of a foreclosed property with non-leased land status, typically leading to increased transaction costs as buyers may need to pay extra land leasing fees, higher IPR, DPR), and CC can facilitate a successful auction. This solution involves many cases of economic affordable housing and marketized housing. A prime example that aligns with this pattern is Case 79, located in Chengdu, sold in June 2021. This property was an economic affordable housing whose land status was allocated land-use rights (low LS). Before the auction, this property finished a five-year restricted trading period, which implied that it could be sold in the real estate market after paying the land leasing fees (high IPR). Moreover, this property had neither leasing contracts with anyone else nor illegal occupants, allowing the buyer to freely utilize the property (high DPR). Importantly, the local court pledged to help the buyer acquire valid property rights (high CC). Consequently, these favorable conditions led to the successful auction of this property.

Configuration 5/6: LS*~DTC*IPR*DPR*~CC/LS*~DTC*IPR*~DPR*CC

Configurations 5 and 6 suggest that if a foreclosed property is based on leased land-use rights with lower DTC and higher IPR, its transaction is determined by DPR or CC. These two solutions involve a majority of commercial housing and apartment housing. Case 81 and Case 116 are well aligned with these two solutions respectively.

Case 81 is situated in Jiangbei District, Chongqing, transacted in December 2022. The former owner of this property had mortgaged it to the bank to purchase multiple properties for speculative purposes. Due to the impact of the COVID-19 pandemic, the former owner became unemployed and unable to continue repaying the mortgage, leading to the forced auction of this property. However, the local court explicitly stated a “disclaimer” in the auction notice, refused to take any potential post-auction risk (low CC), and the court didn’t disclose any potential transaction costs (low DTC). This was a typical commercial housing unit with 70-year land-use rights (high IPR). Furthermore, there was no one living in this property and it also had no leasing contract (high DPR), which suggests that the buyer would face relatively lower acquisition risk and usufruct risk. Finally, it was auctioned successfully.

Case 116 is located in Chaoyang District, Beijing. The property was seized and auctioned by the court due to the former owner’s involvement in debt disputes, and it was transacted in January 2018. This was an apartment housing with a 40-year land-use rights (high IPR). Importantly, according to a private due diligence report obtained from a real estate agency, it was revealed that the former owner had rented out the property with a long-term leasing contract, implying that the buyer wouldn’t be able to occupy it after the transaction (low DPR). Although the auction notice didn’t disclose any transaction costs (low DTC), the local court explicitly committed to assisting the buyer in vacating and clearing the illegal occupancy, facilitating the buyer to move in (high CC). Ultimately, the property was successfully sold.

Heterogeneity analysis based on land status

As analyzed previously, Chinese authorities institutionally separated land ownership from land-use rights and housing ownership from land-use rights to facilitate market-oriented housing reforms in the 1980s and 1990s. The formal separation of property rights between land and housing led to the fragmentation of property rights in the housing market. This exhibits different types of property rights among foreclosed properties, which vary with their land status: leased, allocated, and illegal. Presently, the risks associated with property rights stemming from this fragmentation pose a substantial obstacle to foreclosed property transactions. To further differentiate the impact of different land statuses on transaction outcomes of foreclosed properties, this study intends to conduct a heterogeneity analysis based on land status.

The heterogeneity analysis is divided into two groups: Group 1 with higher LS, including apartment and commercial housing (N = 64); Group 2 with lower LS, including marketized housing, state-owned enterprise housing, relocation housing, economic affordable housing, illegal housing, and small-property-rights housing (N = 72). The LS was employed as a binary variable for grouping, but it exhibited limited variability between the two groups, leading to its exclusion from the heterogeneity model. The output of heterogeneity analysis based on two groups by fs/QCA 3.0 can be found in Appendix 2. Table 7 shows the results of heterogeneity analysis based on two groups.

Table 7 Heterogeneity analysis based on land status.

Foreclosed housing with higher LS

There are five configurations for the group of higher LS with an overall consistency of 0.864, demonstrating a sufficient explanation. For those commercial housing or apartment housing with lower IPR, Configurations 1a, 2a and 3a indicate that clearer DTC is significant to a successful auction while enhancing DPR or CC also aids in completing the transactions. Besides configuration 4a suggests that for foreclosures with lower DTC and without CC, higher IPR and DPR are key to closing the deal.

Particularly, configurations 2a, 3a, and 5a emphasize the importance of CC for those foreclosed properties with lower DPR, DTC, or IPR. In many cases, the original owner has signed long-term leases with tenants, which means the buyer has to wait until the lease expires to move in; in other cases, the property is illegally occupied, preventing the buyer from moving in. In such cases, a strong commitment from local courts to help buyers remove barriers to occupancy and safeguard their residential rights will facilitate the success of the auction because it can mitigate property-rights risks stemming from weaker DTC, DPR, and IPR.

Foreclosed housing with lower LS

The group with lower LS generated two configurations with an overall consistency of 0.909, which remarkably performs a strong explanation. Configuration 1b suggests that for those cases with non-leased land-use rights, the presence of both DTC and IPR can significantly contribute to successful auctions. More importantly, configuration 2b pertains to scenarios involving many small-property-rights housing or illegal housing with unauthorized land-use rights, where successful transactions rely heavily on high IPR and DPR, along with the pivotal role of the CC. Despite lacking legal land-use rights, these properties still hold use value by generating economic returns through rentals or meeting occupants’ living needs. Acquiring these properties through judicial auctions is a legal way to endorse their questionable property rights as the local court will issue three legal certificates: Confirmation of Auction Results, Notice of Enforcement, and Notice of Assistance in Execution. These certificates serve as endorsements from authorities, confirming the legitimacy of the transaction, thereby facilitating successful auctions by reducing post-auction risks.

Conclusion and policy Implications

In the 1980s and 1990s, amid China’s market-oriented land and housing reforms, a sequence of legal and policy measures ensued, resulting in the dissociation of land ownership from land-use rights and the disconnection of housing ownership from land-use rights. This dissociation gave rise to the fragmented housing property rights in China. The separation of housing ownership from land-use rights enabled properties to be commodified, particularly financialized as collateral for housing mortgage loans. Once the mortgage on these properties defaulted, they will be seized by local courts and auctioned for repaying debts. Fragmented property rights imply that housing ownership and land-use rights are held by different entities, forming three categories of property risks (cost risk, acquisition risk, and usufruct risk) during the process of foreclosure transaction. Through the application of cs-QCA to analyze 136 cases of foreclosure transactions across 20 Chinese cities, this study has further elucidated how different configurations of property-rights risks influence the outcome of foreclosure auctions, along with the moderating effect of credible commitments on these risks.

Firstly, the leased land-use rights and a higher level of transparency regarding property transaction costs (high DTC) effectively facilitate successful auctions by reducing transaction costs. Particularly, when DTC, IPR, or DPR are weaker, foreclosed properties with leased land-use rights are more likely to achieve successful transactions. Leased land refers to land acquired by developers from local governments through bidding, auctioning, or listing, without requiring buyers to pay additional land premiums. Foreclosed properties on leased land entail lower transaction costs and fewer cost risks, echoing Coase’s theory of transaction cost (Coase, 2013; Deng, 2021). On the other hand, due to asymmetric information between the parties involved in foreclosure transactions, buyers face complex tax issues and various outstanding payments related to the property before the auction. These fees are intricately linked to the status of the former owner’s property rights. Any oversight might lead buyers to bear substantial outstanding payments. Therefore, enhancing DTC helps mitigate the information asymmetry between the buyer and the foreclosure.

Secondly, the combination of IPR and DPR significantly influences the willingness of bidders to participate in the auction. The IPR associated with potential acquisition risk is determined by the combination of “housing ownership + land-use rights”. Properties like commercial and apartment housing boast the highest IPR and are favored by buyers due to less acquisition risk. While small-property-rights housing cannot obtain legitimate property rights and can only be entitled to use rights, making them challenging to transact. Simultaneously, higher DPR signifies foreclosures without illegal occupation or leasing contracts, indicating a higher likelihood for buyers to manage usufructs of residence, income, transfer, and inheritance after auction. As a result, foreclosed properties with higher IPR and DPR tend to be favored by homebuyers, exhibiting the greatest likelihood of successful auction.

Thirdly, the credible commitment (CC) can effectively mitigate the property-rights risks. In cases of foreclosed properties with lower DTC, lower IPR, or lower DPR, when local courts are willing to provide explicit credible commitments, homebuyers’ confidence will be enhanced significantly, which facilitates transactions. Moreover, configurations 3, 4, and 6 in the overall solution indicate the critical role of the court’s credible commitments in the successful auction, which functions to reconcile three types of property- rights risks associated with weaker DTC, IPR, and DPR.

Fourth, the results of heterogeneity analysis based on land status indicate that: for those foreclosures with leased land-use rights (mainly consisting of apartment and commercial housing), the impact of whether the court provides credible commitments on the auction results has become less significant. Increasing the transparency of their transaction costs (higher DTC) facilitates successful transactions of these foreclosures. For foreclosed properties with non-leased land-use rights, higher DTC and IPR contribute to successful auctions. Particularly, the auction of small-property-rights housing highly depends on credible commitments offered by local courts.

Last but not least, the current real estate sector in China is grappling with a severe debt crisis, with a noticeably increasing number of foreclosed properties indicating that more and more homeowners are unable to meet their housing mortgage payments. To improve the success rate of judicial auctions and facilitate the repayment of mortgages, thereby ensuring a stable debt structure in China’s housing system, this study provides the following policy implications for China’s courts and financial institutions to handle foreclosed properties reasonably (see Table 8). These policy recommendations are expected to enhance the efficiency and fairness of the foreclosure auction process in China. By addressing the legal, financial, and informational challenges, courts and financial institutions can foster a more transparent, secure, and attractive market for foreclosed properties, ultimately contributing to the stability of the real estate sector and the broader economy.

Table 8 Policy implications for improving judicial auctions.