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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.

This paper aims to introduce mathematical models to capture the spreading of epidemics to explain the expansion of mortgage default events in the USA.

The authors use the state of infectiousness and death to represent the subsequent steps of payment elinquency and default, respectively. As the local economic structure influences regional unemployment, which is a strong driver of mortgage default, the authors model interdependencies of regional mortgage default rates through employment conditions and vicinity.

Based on a large sample between 2000 and 2014 of loan-level data, the estimation of key parameters of the model is proposed. The model’s forecast accuracy shows an above-average performance compared to well-known approaches such as linear regression or logit models.

The key findings may be useful in understanding the dynamics of mortgage defaults and its spatial spreading.

Credit market development requires appropriate credit assessment and default policies. This paper aims to examine the impact of household characteristics on mortgage default, using survey data collected from Ghanaian financial institutions.

Data were gathered using semi-structured questionnaires from customers of five universal banks in Ghana. A logistic regression was used to model the determinants of credit default propensity.

Contrary to established knowledge, the study shows that females are more likely to default on credit than their male counterparts. This is even more likely if the female is older, unmarried, divorced and financially illiterate and has lower educational attainments. These factors are associated with lower earning capacity, which increases default tendencies. The findings confirm that price instability (typified by excessive movements in inflation and exchange rates in addition to low national savings rate) are adversely linked to credit defaults. Borrower’s perception of constraints to credit access (such as collateral requirements, interest rate and loan size) influence credit default. Banks should be encouraged to invest in the financial literacy skills development of their customers to mitigate credit default tendencies.

The study is of practical value to credit officers and the development of the credit market in Ghana. A novel model is presented for assessing credit applications and developing credit default policies.

The research findings have not only expanded the frontiers of literature but also empirically examined the determinants of credit default propensity, which provides a basis for developing and improving credit default policy in the credit market.

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 study the data on losses on mortgage insurance in the Dutch housing market to find the key drivers of the probability of loss. In 2013, 25 per cent of all Dutch homeowners were “under water”: selling the property will not cover the outstanding mortgage debt. The double-trigger theory predicts that being under water is a necessary but not sufficient condition to predict mortgage default. A loss for the mortgage insurer is the result of a default where the proceedings of sale and the accumulated savings for postponed repayment of the principal associated to the loan are not sufficient to repay the loan.

For this study, the authors use a data set on losses on mortgage insurance at a national aggregate level covering the period from 1976 to 2012. They apply a discrete time hazard model with calendar time- and duration-varying covariates to analyze the relationship between year of issue of the insurance, duration, equity, unfortunate events like unemployment and divorce and affordability measures to identify the main drivers of the probability of loss.

Although the number of losses increases over time, the number of losses relative to the active insurance is still low, despite the fact that the Dutch housing market is the world’s most strongly leveraged housing market. On average, the peak in loss probability lies around a duration of four years. The average loss probability is virtually zero for durations larger than 10 years. Mortgages initiated just prior to the beginning of the financial crisis have an increased loss probability. The most important drivers of the loss probability are home equity, unemployment and divorce. Affordability measures are less important.

Mortgage insurance is available for the lower end of the market only and is intended to decrease the impact of risk selection by banks. The analysis is based on aggregate data; no information on individual households, like initial loan-to-value and price-to-income ratios; current home equity; and unfortunate events, like unemployment and divorce, is available. The research uses averages of these variables per calendar year and/or duration. Information on repayments of insured mortgages is missing.

This paper is the first to describe the main drivers of losses on insured mortgages in The Netherlands by using loss data covering two housing market crises, one in the early 1980s and the current crisis that started in 2008. Much has changed between the two crises. For instance, prices have risen steeply as has household indebtedness. Furthermore, alternative mortgage products have increased in popularity. Focusing a study on the drivers of mortgage losses exclusively on the current crisis could therefore be biased, given the time-specific circumstances on the housing market.

This paper presents the first known non‐proprietary empirical examination of the relationship between Commercial Mortgage Backed Security (CMBS) pricing. CMBS prices are examined as a function of the “moneyness” of the default option, the age of the security, the interest rate, interest rate volatility, property price volatility, amortization features and yield curve slope utilizing a proprietary data set of monthly prices on 40 CMBS securities. We find that though the senior tranche CMBS in the sample are effectively immune from default loss per se, they are not immune from early return of principal and resulting duration shift implied by increasing default probabilities. Thus, they behave very much like residential mortgage backed securities in that discount security prices are positively related to explanatory variables associated with potential shifts in duration. As a result, senior tranche CMBS prices increase with explanatoryd factors that raise the likelihood of default such as property volatility and loan to value ratio whereas CMBS prices decrease with variables that lower default probability such as amortization. These empirical results fit well with existing theoretical models of multi‐tranche CMBS pricing and models of commercial mortgage default and suggest that senior tranche CMBS may embody elements of risk that justify their seemingly rich spreads to similar duration corporate securities.

Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters.

The purpose of this paper is to examine the recent subprime mortgage market meltdown from a theoretical and practical perspective.

The authors apply the principles of transaction costs economics to critically evaluate the roles of lenders, borrowers, institutions, and investors.

It is found that a combination of need, greed, perverse incentives, inadequate risk controls, lax regulation, and lax oversight caused a bubble in the subprime mortgage market which has inevitably burst. The principles of transaction cost economics provide a template for analysis and corrective action.

The subprime mortgage market provides a useful example of where theory can provide helpful insights. The example has implications for future research in other financial market settings.

The results provide insight and guidance to lenders, borrowers, institutions, investors, regulators, and central bankers in how to identify and handle potentially toxic financial scenarios.

The theoretical perspective has not been applied to the subprime market or other similar financial settings. It offers both academic and practical contributions.

The purpose of this paper is to investigate linkages between households’ expectations and credit markets in the housing crisis.

In the Markov-switching framework, the sample period is classified into high- and low-impact regimes based on impacts of expectations on default rates, and the good-time-to-buy (GTTB) index is chosen to proxy for expectations toward the housing-market dynamics.

The results suggest that in high-impact regimes, optimistic expectations are substantially associated with lower defaults for all default rates analyzed, and second mortgage defaults are more sensitive to households’ expectations than first mortgage defaults. In low-impact regimes, the GTTB index significantly influences composite and first-mortgage default rates, but its impact is insignificant for second mortgage and bankcard default rates.

The results provide compelling evidence that households’ expectations play more important roles in credit markets in turmoil periods.

In October 2008, in the midst of a financial crisis, Anthony Keating, investment manager at the Boston private bank Billingsley, Blaylock, and Montgomery, was searching for an investment strategy to recommend to his high-net-worth clients. Traditional investments in the equity markets were being decimated, and Keating’s clients would be looking to him for ideas. Inspired by the success of Paulson and Co., Keating began to explore the possibility of entering a trade that would profit as homeowners defaulted on their mortgages. The more Keating learned about the trade, the more he realized that he needed to know about mortgage-backed securities and credit default swaps. The case provides instructors with a chance to introduce these financial instruments, while at the same time providing lessons applicable to students interested in value investing or real estate finance.

After reading and analyzing the case, students will be able to:

• Explain how home mortgages are securitized into financial instruments that are traded in public markets

• Describe how credit default swaps can be used to speculate on the value of an underlying financial instrument

• Identify potential mispricing across related financial instruments

• Understand the potential risks and rewards of various financial investment strategies that look to capitalize on defaults on subprime mortgages

Explain how home mortgages are securitized into financial instruments that are traded in public markets

Describe how credit default swaps can be used to speculate on the value of an underlying financial instrument

Identify potential mispricing across related financial instruments

Understand the potential risks and rewards of various financial investment strategies that look to capitalize on defaults on subprime mortgages