Borrower Self-Selection,Underwriting Costs,and Subprime Mortgage Credit Supply

In the U.S., households participate in two very different types of credit markets. Personal lending is characterized by continuous risk-based pricing in which lenders offer households a continuous distribution of borrowing possibilities based on estimates of their creditworthiness. This contrasts sharply with mortgage markets where lenders specialize in specific risk categories of borrowers and mortgage supply is stepwise linear. The contrast between continuous lending for personal loans and discrete lending by specialized lenders for mortgage credit has led to concerns regarding the efficiency and equity of mortgage lending. This paper sheds both theoretical and empirical light on the differences in the two credit markets. The theory section demonstrates why, in a perfectly competitive credit market where all lenders have the same underwriting technology, mortgage credit supply curves are stepwise linear and lenders specialize in prime or subprime lending. The empirical section then provides evidence that borrowers are being effectively sorted based on risk characteristics by the market.     

Fast simulations in credit risk

We consider the problem of simulating tail loss probabilities and expected losses conditioned on exceeding a large threshold (expected shortfall) for credit portfolios. Our new idea, called the geometric shortcut, allows an efficient simulation for the case of independent obligors. It is even possible to show that, when the average default probability tends to zero, its asymptotic efficiency is higher than that of the naive algorithm. The geometric shortcut is also useful for models with dependent obligors and can be used for dependence structures modeled with arbitrary copulae. The paper contains the details for simulating the risk of the normal copula credit risk model by combining outer importance sampling with the geometric shortcut. Numerical results show that the new method is efficient in assessing tail loss probabilities and expected shortfall for credit risk portfolios. The new method outperforms all known methods, especially for credit portfolios consisting of weakly correlated obligors and for evaluating the tail loss probabilities at many thresholds in a single simulation run.     

Internal ratings systems,implied credit risk and the consistency of banks’ risk classification policies

This paper aims at improving our understanding of internal risk rating systems (IRS) at large banks, of the way in which they are implemented, and at verifying if IRS produce consistent estimates of banks’ loan portfolio credit risk. An important property of our work is that the size of our data set allows us to derive measures of credit risk without making any assumptions about correlations between loans, by applying Carey’s [Carey, Mark, 1998. Credit risk in private debt portfolios. Journal of Finance LIII (4), 1363–1387] non-parametric Monte Carlo re-sampling method.We find substantial differences between the implied loss distributions of two banks with equal “regulatory” risk profiles; both expected losses and the credit loss rates at a wide range of loss distribution percentiles vary considerably. Such variation will translate into different levels of required economic capital. Our results also confirm the quantitative importance of size for portfolio credit risk: for common parameter values, we find that tail risk can be reduced by up to 40% by doubling portfolio size.Our analysis makes clear that not only the formal design of a rating system, but also the way in which it is implemented (e.g. a rating grade composition; the degree of homogeneity within rating classes) can be quantitatively important for the shape of credit loss distributions and thus for banks’ required capital structure. The evidence of differences between lenders also hints at the presence of differentiated market equilibria, that are more complex than might otherwise be supposed: different lending or risk management “styles” may emerge and banks strike their own balance between risk-taking and (the cost of) monitoring (that risk).     

Portfolio credit-risk optimization

This paper evaluates several alternative formulations for minimizing the credit risk of a portfolio of financial contracts with different counterparties. Credit risk optimization is challenging because the portfolio loss distribution is typically unavailable in closed form. This makes it difficult to accurately compute Value-at-Risk (VaR) and expected shortfall (ES) at the extreme quantiles that are of practical interest to financial institutions. Our formulations all exploit the conditional independence of counterparties under a structural credit risk model. We consider various approximations to the conditional portfolio loss distribution and formulate VaR and ES minimization problems for each case. We use two realistic credit portfolios to assess the in- and out-of-sample performance for the resulting VaR- and ES-optimized portfolios, as well as for those which we obtain by minimizing the variance or the second moment of the portfolio losses. We find that a Normal approximation to the conditional loss distribution performs best from a practical standpoint.     

Default correlation: An empirical investigation of a subprime lender

In recent years, subprime lending has grown substantially as an important sector of the credit markets. This paper is concerned with the risk management of subprime loan portfolios and the importance of default correlation in measuring that risk. Using a large portfolio of residential subprime loans from an anonymous subprime lender, we show that default correlation is substantial for this lender. In particular, the significance of default correlation increases as the internal credit rating declines. Our results suggest that lenders and regulators would be well served investing in the understanding of default correlation in subprime portfolios.     

Residential Mortgage Lending and Borrower Risk: The Relationship Between Mortgage Spreads and Individual Characteristics

The mortgage banking environment in Hong Kong is quite different from that in the United States. For example, the secondary mortgage market and mortgage insurance only started after 1997. Using a large data set on mortgages, we examine empirically how mortgage rates in this market vary with various individual borrower, property, and loan characteristics. We find that mortgage rates in Hong Kong do vary with individual characteristics, which suggests credit sorting according to both prepayment risk and default risk, as a higher mortgage rate is found to be related to either higher collateral (a lower loan-to-value ratio) or slower prepayment. The empirical results suggest that lenders in Hong Kong can observe the risk type of individual borrowers to a certain extent and charge a corresponding mortgage spread. Overall, the evidence is consistent with the sorting-by-observed-risk paradigm as in Berger and Udell (1990).     

Capital requirements and mortgage pricing: Evidence from Basel II

As a result of the Basel II reforms, capital requirements on UK mortgages fell substantially in coincidence with the financial crisis. We exploit a novel, loan-level dataset on within-lender variation in risk-weighted capital requirements and a triple-difference identification strategy to estimate the pass through of capital requirements to mortgage rates. We find that a 1pp lower risk-weighted capital requirement leads to a reduction in rates by 10–16bp on average, with stronger effects for less-capitalized lenders. The competitive advantage induced by multi-tier regulation also affects the composition of banks mortgage portfolios, with larger lenders specializing in lower risk loans. Finally, our results support the use of countercyclical capital requirements to sustain lending in a crisis.     

Breakeven Determination of Loan Limits for Reverse Mortgages under Information Asymmetry

Since the loan limit of a reverse mortgage is a major concern for the borrower as well as the lender, this paper attempts to develop an option-based model to evaluate the loan limits of reverse mortgages. Our model can identify several crucial determinants for reverse mortgage loan limits, such as initial housing price, expected housing price growth, house price volatility, mortality distribution, and interest rates. We also pay special attention to the important implication of mortgage lenders’ informational advantage over reverse mortgage borrowers concerning housing market risk. In reverse mortgage markets, the elderly borrowers typically hold far less, relative to the lenders, or no information about the lenders’ underlying mortgage pools. Such information asymmetry leads these two categories of market participants to generate different perspectives on the risk of the collateralized properties, which can be identified to be important in determining the maximum loan amounts of reverse mortgages. We further find that the maximum loan amount of a reverse mortgage decreases in the correlation between the returns on the pooled underlying housing properties but increases with the number of the pooled mortgages.     

Adverse selection in mortgage securitization

Using several large data sets of mortgage loans originated between 2004 and 2007, we find that in the prime mortgage market, banks generally sold low-default-risk loans into the secondary market while retaining higher-default-risk loans in their portfolios. In contrast, these lenders retained loans with lower prepayment risk relative to loans they sold. Securitization strategy of lenders changed dramatically in 2007 as the crisis set in with most unwilling to retain higher-default-risk loans in return for lower prepayment risk. Contrary to the prime market, the subprime market does not exhibit any clear pattern of adverse selection.     

The rise of risk-based pricing of mortgage interest rates in Italy

This paper assesses how much mortgage interest rates in Italy are priced on credit risk as proxied by the probability of household mortgage delinquency estimated using the EU-Silc database. Owing to data availability, we restrict the analysis of mortgage pricing to Italian households. Consistent with the more widespread use of credit scoring, estimates indicate that Italian lenders have increasingly priced mortgage interest rates on household credit risk. For mortgages granted between 2000 and 2007, we find that a 1% point increase in the probability of default is associated with a 21 basis point rise in mortgage interest rates, lower than the 38 basis point premium Edelberg (2006) estimated for the US at the end of the 1990s.     

Asymmetric Information about Collateral Values

I empirically analyze credit market outcomes when competing lenders are differentially informed about the expected return from making a loan. I study the residential mortgage market, where property developers often cooperate with vertically integrated mortgage lenders to offer financing to buyers of new homes. I show that these integrated lenders have superior information about the construction quality of individual homes and exploit this information to lend against higher quality collateral, decreasing foreclosures by up to 40%. To compensate for this adverse selection on collateral quality, nonintegrated lenders charge higher interest rates when competing against a better‐informed integrated lender.     

Pricing Effects of the Decision to Sell or Hold Adjustable Rate Mortgage Loans in a Portfolio

Residential mortgage originators can transfer loans to ultimate lenders quickly and efficiently using the secondary mortgage market. Some adjustable rate mortgage (ARM) lenders use this outlet consistently while others hold whole loans in their portfolios on a long-term basis. Selling and holding lenders should respond to different economic factors when setting yields on ARM loans originated because their long-term positions in the loans are so diverse. This paper develops and tests a model of differential pricing behavior for selling and holding strategies. Empirical results support the notion that lenders use different factors to price loans and that these factors are related to the risks faced by the originating lender given its origination strategy. Additional findings suggest that institutional and firm-specific pricing tendencies exist in the primary mortgage market for adjustable rate debt.     

The use of credit scoring in the mortgage industry

In times of increased focus on risk management, acquiring or growing comparatively low risk mortgage portfolios has become an attractive value proposition. Banks that pursue an aggressive growth strategy in this sector, do, however, require risk control mechanisms that enable them to make a clear judgment on how great a growth appetite they can afford to have in order to still grow profitably. Moreover, under Basel II, the proper quantification of mortgage portfolio risk tends to help the release of own capital, because the mortgage portfolio is one of those portfolios where the relative benefits of internal ratings-based approaches compared with the standardised approach are greatest. Credit scoring models in general, and credit scorecards in particular, are suitable methods for quantifying the risk of an individual mortgage applicant or mortgage customer. In addition to score card development, this paper reviews alternative scoring model types that could be used for mortgage scoring. It presents reasons why it is beneficial to build such models in-house, before focusing on the steps necessary for building a mortgage scorecard. Finally, it discusses the important topics of creating segments, deploying models and eventually monitoring models.     

The Price of UK Commercial Credit Lines: A Research Note

Abstract:  The UK Cruickshank Report (2000) highlighted the potential for market power issues where bank concentration could lead to more expensive credit. Because of the many risk factors influencing the cost of credit, it is difficult to establish whether differences in pricing is a consequence of risk or is due to differential pricing practices among lenders. In this study, we find that even when risk factors are taken into account, considerable variation in pricing among UK lenders remains. Moreover, the lenders with the largest market share in SME finance over the 8 years surveyed, charged significantly higher interest premia.     

Mortgage Redlining: Race,Risk, and Demand

Charges that geographical redlining is widely practiced by mortgage lenders and is associated with racial discrimination have received much attention. However, empirical research in this area has yet to document a convincing answer to the question of whether redlining even exists. Much of the previous research in this area has suffered from failure to account for variations in risk, and/or failure to adequately control for geographical differences in demand. This study addresses these problems in an effort to determine whether the disparity in the flow of mortgage credit can be explained by differences in risk and demand.     

Embedded Options in the Mortgage Contract

Loss mitigation is the process by which lenders attempt to minimize losses associated with foreclosure. As competition increases in the mortgage industry, lenders and servicers are under great pressure to adopt loss mitigation tactics rather than simply use foreclosure as the means of dealing with borrowers in default. This study presents a mortgage-pricing model that fully specifies all borrower options with respect to default, including the ability to reinstate the mortgage out of default. We document the impact of various loss mitigation programs, including forbearance and antideficiency judgments, as well as the value of credit on borrower default behavior.     

The orbit of consumer credit choices

Resulting from instability in the UK financial climate in recent times, consumers have increasingly turned to alternative credit sources such as payday loans, logbook loans (car title loans) and pawning. Recognising this increasingly important trend in UK society, this study explores how UK consumers manage and select alternative credit sources, through a Consumer Culture Theory lens. Primary data were sourced through a multi-stage interview process with ten consumers of alternative credit providers. Findings were subjected to a rigorous six-stage thematic analysis, which enabled generation of a three-ring orbit model showing how the consumers migrated between categories of credit sources. Furthermore, it was found that other concepts could be traced on to the orbit model, such as access to other credit sources, time pressures, perceived risk and emotional state. It is expected that the findings from this study will benefit lenders, policy makers and regulatory bodies from greater insights into understanding of the emotional state of their customers and the particularly the pressures they may be experiencing when taking last resort credit sources.     

Asset pricing models: implications for expected returns and portfolio selection

When a risk factor is missing from an asset pricing model, theresulting mispricing is embedded within the residual covariancematrix. Exploiting this phenomenon leads to expected returnestimates that are more stable and precise than estimates deliveredby standard methods. Portfolio selection can also be improved.At an extreme, optimal portfolio weights are proportional toexpected returns when no factors are observable. We find thatsuch portfolios perform well in simulations and in out-of-samplecomparisons.     

Employment in the Great Recession: How Important Were Household Credit Supply Shocks?

I pool data from all large multimarket lenders in the United States to estimate how many of the over 7 million jobs lost in the Great Recession can be explained by reductions in the supply of mortgage credit. I construct a mortgage credit supply instrument at the county level, the weighted average (by prerecession mortgage market shares) of liquidity-driven lender shocks during the recession. The reduction in mortgage supply explains about 15% of the employment decline. The job losses are concentrated in construction and finance.     

Checking for asymmetric default dependence in a credit card portfolio: A copula approach

Traditional credit risk models adopt the linear correlation as a measure of dependence and assume that credit losses are normally-distributed. However some studies have shown that credit losses are seldom normal and the linear correlation does not give accurate assessment for asymmetric data. Therefore it is possible that many credit models tend to misestimate the probability of joint extreme defaults.This paper employs Copula Theory to model the dependence across default rates in a credit card portfolio of a large UK bank and to estimate the likelihood of joint high default rates. Ten copula families are used as candidates to represent the dependence structure. The empirical analysis shows that, when compared to traditional models, estimations based on asymmetric copulas usually yield results closer to the ratio of simultaneous extreme losses observed in the credit card portfolio.Copulas have been applied to evaluate the dependence among corporate debts but this research is the first paper to give evidence of the outperformance of copula estimations in portfolios of consumer loans. Moreover we test some families of copulas that are not typically considered in credit risk studies and find out that three of them are suitable for representing dependence across credit card defaults.