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The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.

A systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.

Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.

The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.

Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.

The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

This paper aims to examine the heterogeneity of preferences of mortgage borrowers of Russian state-owned suppliers of residential housing mortgages.

Analysis takes into account the underwriting process and the choice of contract terms of all loans originated from 2008 to 2012. The data set contains demographic and financial characteristics for all applications, loan terms and the performance information for all issued loans by one regional bank which operates government mortgage programs. The paper uses a multistep semiparametric approach to estimate the determinants of bank and borrower choice controlling for possible heterogeneity of preferences, sample selection and endogeneity of contract terms.

The study found that the demand of low-income households who are unable to afford to improve the housing conditions by other instruments than government mortgage is less elastic according to the change both in interest rate and maturity compared with higher-income households.

Given lower elasticities of the demand, the low-income group of borrowers has higher potential cost of loan and is usually rejected by commercial banks. The presence of the Agency of Housing Mortgage Lending special programs with subsidized interest rate for special constrained categories (young families, teachers, researchers etc.) widens the access for housing conditions’ improvements as a part of housing affordability government program.

The main contribution to the literature is modeling choice of contract terms as interdependent by the structural system of simultaneous equations with heterogeneous marginal effects.

An established paradigm in small business lending is segmented by bank size with large banks more likely to lend to large informationally transparent firms while small banks are more likely to lend to small informationally opaque firms. In light of banking consolidation, this market segmentation can have implications for credit availability. Federal loan guarantees, such as those provided by USDA's Farm Service Agency (FSA) may reduce the risks of lending to informationally opaque firms thereby mitigating the impacts of the bank size lending paradigm. This paper aims to discuss these issues.

This analysis utilized a binomial logit procedure to determine if there was any empirical evidence that smaller community banks served a unique clientele of farmers when making FSA-guaranteed loans. The analysis relied on a unique data set which incorporated detailed data on farm businesses receiving FSA-guaranteed loans, loan characteristics, as well as information about the originating bank and characteristics of the local credit markets.

Results were consistent with the bank size lending paradigm with smaller banks being less likely to engage in fixed-asset lending, and more likely to serve a riskier and less established clientele when making guaranteed loans.

Data limitations did not permit detailed analysis of banks larger than $250 million in total assets nor for consideration of non-bank lenders. An expansion by these lender groups into serving more informationally opaque borrowers could mitigate any adverse impacts arising from fewer small community banks.

The results suggested that Federal guarantees do not completely eliminate the relative informational advantages of large and small size banks. And, continued bank consolidation, such that there are fewer small community banks, could result in less credit availability among smaller, less creditworthy farm businesses.

While FSA guarantees may not enhance a large banks propensity to serve informationally opaque farm borrowers, they may enhance the ability of smaller community banks to serve groups specifically targeted through FSA lending programs; the provision of credit to family farmers who, despite being creditworthy, are unable to obtain credit at reasonable rates and terms.

The analysis examines relationship between bank size and the use of FSA guarantees using a unique data set which incorporated information on FSA-guaranteed loans, farm financial characteristics, along with characteristics of commercial banks which participated in the FSA-guarantee program.

The purpose of this paper is to examine the drivers of default in the Malaysian housing market in the light of various policy interventions by the country’s central bank, and the government’s expressed concern to ensure balanced growth in the market. This paper assesses the importance of considering the endogeneity of loan-to-value (LTV) in predicting housing loan default and its implications.

In this paper, the author addresses the endogeneity problem in the LTV variable using two instrumental variables (IV) in this probit regression: national residential property gains tax and the statutory reserve ratio of Bank Negara Malaysia. This study uses the instrumental variable probit model to consider endogeneity bias. This study assumes a latent (unobservable) variable (Y*), representing a borrower’s tendency to default, which is associated linearly with the borrower’s and loan characteristics and other variables (Xi). This study uses individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia.

This study’s results confirm that endogeneity causes a substantial difference in the magnitude of the estimated effects of LTV on the default tendency. At the lower values of LTV, the probability of default is over-estimated, and at the higher values, the default probability is substantially underestimated. Endogeneity bias also affects the estimated coefficients of loan and borrower characteristics. The authors find that the interest rate is less relevant in predicting loan default. Other loan characteristics, such as loan age, tenure, payment amount and the built-up area, are relevant. This study’s result confirms that the borrower’s location matters, and an increase in state gross domestic product per capita and an increase in the supply of residential units reduce default probability.

The present study did not explore the applicability of the “equity theory of default” in the Malaysian housing market. This study did not assess “strategic default” issues and the effect of borrowers’ characteristics, personality traits and self-control of Malaysian housing loan borrowers in the mortgage decision-making process. The evolving dynamics of the Malaysian housing market microstructure in property valuation remained unexplored in the present study.

The findings have crucial relevance in the decision-making process of commercial banks, the central bank and the government to frame policies to foster balanced growth and development in the housing market. The authors argue that striking a subtle balance between the concerns of financial stability and productive risk-taking by commercial banks in Malaysia remains a continuing challenge for the country’s central bank. The authors also argue that designing suitable taxation policies by the government can deliver its cherished goal of balanced development in the housing market.

Empirical research on the Malaysian housing market based on micro-level data is scarce due to a paucity of relevant data. This study is based on the individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia. In this analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Malaysian housing market not addressed by earlier papers.

The purpose of this paper is to examine if credit rationing persists even in the era of financial liberalization, the extent to which individual, firm and loan characteristics influence the rationing behavior of commercial banks and whether the agricultural sector is discriminated against in the commercial bank credit market.

The study employed a probit model with marginal effects and a generalized Blinder-Oaxaca decomposition estimation on a randomly selected data of 1,239 entrepreneurs from eight commercial banks’ credit records about their individual, firm and loan characteristics.

The study revealed that credit rationing persists and that applying for a relatively longer payment period, providing collateral and guarantor, being illiterate, being relatively older and being in the agricultural sector increases the likelihood of being credit rationed, while having some relationship with the bank, having non-mandatory savings and applying from a bank with relatively high interest rates reduce the likelihood of being credit rationed. The study also revealed a credit gap of 17.77 percent and a positive discrimination against borrowers in the agricultural sector as the gap was largely being influenced by unexplained factors.

The research was intended to cover a large number of commercial banks in Ghana. However, most of the banks were unwilling to provide such information about their borrowers; hence, the research was limited to only eight commercial banks who provided the author with the information needed for the study.

The study concludes that policies that enhance human capital, women, and older access to credit and agricultural-oriented financial services and others, will go a long way to reduce rationing and increase access to credit, especially to the agricultural sector.

The research proposes the use of group lending as a form of collateral and monitoring to ease risks and default, and hence supports sustainable funding to increase access and outreach.

The paper looks at the comprehensive way about the various factors determining credit rationing in that it considers not only the individual, economic/firm and loan characteristics but also the extent to which discrimination toward the agricultural sector exists in the commercial banks credit market.

The purpose of this paper is to consider borrower-specific characteristics to understand the factors affecting both the probability and quantum of loan default by individual borrowers under Islamic and conventional banking.

Borrower-specific characteristics that explain the probability of default may not necessarily be similar factors that determine the quantum of default. The authors therefore apply a Box-Cox double hurdle model to treat both the probability and quantum of default in a two-step approach. The authors also explain the differences in default risk and quantum of default between Islamic and conventional banking borrowers from their behavioral perspectives following the Sharia principles in financial transactions between lenders and borrowers. The authors use borrower-specific information of two separate bank branches of the United Arab Emirates that solely deal with either Islamic or conventional banking products.

The paper demonstrates that the probability of default and the quantum of default appear to be influenced by different set of client-specific factors. The results suggest that the probability of default does not vary significantly between Islamic and conventional banking borrowers. The evidence also shows that Islamic banking defaulters, compared to those in conventional banking, repay a large quantum of overdue when their financial leverage improves. However, they do not tend to reduce their outstanding quantum of overdue faster than conventional banking defaulters.

Availability of data from only two bank branches may limit the explanatory power of empirical findings.

The study findings will enable the Islamic and conventional banks to appropriately address Basel Capital requirements based on the borrowers’ behavior.

The study findings have the potential for Islamic and conventional financing institutions to be more flexible with equity in their lending practices.

Religious beliefs are crucial in borrower’s default behavior in Islamic banking.

This study investigates factors contributing to residential mortgage loans default by utilizing a unique dataset of borrowers' default data from one of the pioneer lending institutions in Malaysia that provides home financing to the public. Studies on mortgage loan default have been extensively examined, but limited studies utilize the individual borrower's data, as financial institutions generally hesitant to reveal their customers' data due to confidentiality issue.

This study uses logistic regression model to analyze 47,158 housing loan borrowers' data for the year 2016.

The findings suggest that male borrowers, Malay and other type of ethnicity, guarantor availability, loan original balance, loan tenure, loan interest rate and loan-to-value (LTV) ratio are the significant factors that influence mortgage loans default in Malaysia.

Future studies may expand the sample by employing data from other types of financial institutions that would give greater insights as findings might vary due to differences in objectives, functions and regulations. In addition, the findings are subjected to the censoring bias where future studies could perform the survival analysis to control for censoring bias and re-validating the findings of the present study.

The findings provide valuable insights for lending institutions and the government to formulate housing loan policy in Malaysia.

To the best of the authors' knowledge, this is the first study in the context of emerging economies that uses financial institution's internal data to investigate factors of mortgage loan default.

This paper aims to investigate the determinants of default mortgage in China and the factors affecting the mortgage amount granted by Chinese banks.

This paper employs the credit scoring model to investigate the determinants of default mortgage in China and the factors affecting the mortgage amount granted by Chinese banks.

Using a proprietary dataset from branches of the Construction Bank of China containing information on all mortgages offered to borrowers from 2004 to 2009 1st quarter, the paper documents that borrower rating, mortgage rate and mortgage duration are significantly related to default rate and mortgage amount. These findings suggest that Chinese banks' mortgage lending are based on commercial basis. This helps to reduce the likelihood of a real estate bubble in China.

The findings in this paper argued that a good credit scoring model has the ability to detect bad loans; this could help the bank to reduce the loan losses from loan default. Consequently, it can improve the profitability and the financial stability of the bank.

This research would benefit both lender and borrowers. Lenders can apply an objective evaluation technique with a standard process and criteria to appraise their customer's credit risks and creditworthiness. A good credit risk management tool can effectively control risk selection, manage credit losses, evaluate new loan programs, improve loan approval processing time, and ensure that existing credit criteria are sound and consistently applied.

Prior research suggests that given the legal environment in the U.S., smaller syndicates with fewer lead banks should represent “best practices” to promote efficient monitoring and ease of renegotiation. Such syndicates should be associated with lower loan spreads. Controlling for other influences on loan pricing, we conduct tests of this proposition drawing on data from DealScan, Compustat and Federal Reserve Call Reports for U.S. loans between 1988 and 1999. Consistent with our hypothesis, the number of lead lenders is shown to have a significant positive influence on loan yield spreads.

The research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.

The research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.

The results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.

The results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.

This paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.