Credit Cycle and Adverse Selection Effects in Consumer Credit Markets – Evidence from the HELOC Market

We empirically study how the underlying riskiness of the pool of home equity line of credit originations is affected over the credit cycle. Drawing from the largest existing database of U.S. home equity lines of credit, we use county-level aggregates of these loans to estimate panel regressions on the characteristics of the borrowers and their loans, and competing risk hazard regressions on the outcomes of the loans. We show that when the expected unemployment risk of households increases, riskier households tend to borrow more. As a consequence, the pool of households that borrow on home equity lines of credit worsens along both observable and unobservable dimensions. This is an interesting example of a type of dynamic adverse selection that can worsen the risk characteristics of new lending, and suggests another avenue by which the precautionary demand for liquidity may affect borrowing.Home equity loan;adverse selection;liquidity;consumption;housing finance

Credit cycle and adverse selection effects in consumer credit markets -- evidence from the HELOC market

The authors empirically study how the underlying riskiness of the pool of home equity line of credit originations is affected over the credit cycle. Drawing from the largest existing database of U.S. home equity lines of credit, they use county-level aggregates of these loans to estimate panel regressions on the characteristics of the borrowers and their loans, and competing risk hazard regressions on the outcomes of the loans. The authors show that when the expected unemployment risk of households increases, riskier households tend to borrow more. As a consequence, the pool of households that borrow on home equity lines of credit worsens along both observable and unobservable dimensions. This is an interesting example of a type of dynamic adverse selection that can worsen the risk characteristics of new lending, and suggests another avenue by which the precautionary demand for liquidity may affect borrowing.Home equity loans ; Risk

Subprime mortgages and the housing bubble

This paper explores the link between the house-price expectations of mortgage lenders and the extent of subprime lending. It argues that bubble conditions in the housing market are likely to spur subprime lending, with favorable price expectations easing the default concerns of lenders and thus increasing their willingness to extend loans to risky borrowers. Since the demand created by subprime lending feeds back onto house prices, such lending also helps to fuel an emerging housing bubble. The paper, however, focuses on the reverse causal linkage, where subprime lending is a consequence rather than a cause of bubble conditions. These ideas are illustrated in a theoretical model, and empirical work tests for a connection between price expectations and the extent of subprime lending.Subprime mortgage ; Global financial crisis

The asset-correlation parameter in Basel II for mortgages on single-family residences

Bank capital ; Risk management ; Basel capital accord ; Mortgages

An overview of consumer data and credit reporting

For some time, the Board of Governors of the Federal Reserve System has sought to obtain more detailed and timely information on the debt status, loan payment behavior, and overall credit quality of U.S. consumers. For decades, information of this type has been gathered by credit reporting companies primarily to assist creditors in evaluating the credit quality of current and prospective customers. To evaluate the potential usefulness of these data, the Federal Reserve Board engaged one of the three national consumer reporting companies to supply the credit records, without personal identifying information, of a nationally representative sample of individuals. This article describes the way the credit reporting companies compile and report their data and gives background on the regulatory structure governing these activities. This description is followed by a detailed look at the information collected in credit reports. Key aspects of the data that may be incomplete, duplicative, or ambiguous as they apply to credit evaluation are highlighted in the analysis. The article concludes with a discussion of steps that might be taken to address some of the issues identified. ; Also identified as FRB Philadelphia Payment Cards Center Discussion Paper 03-03Credit cards ; Consumer behavior

Credit report accuracy and access to credit

Data that credit-reporting agencies maintain on consumers' credit-related experiences play a central role in U.S. credit markets. Analysts widely agree that the data enable these markets to function more efficiently and at lower cost than would otherwise be possible. Despite the great benefits of the current system, however, some analysts have raised concerns about the accuracy, timeliness, completeness, and consistency of consumer credit records and about the effects of data problems on the availability and cost of credit. ; In this article, the authors expand on the available research by quantifying the effects of credit record limitations on the access to credit. Using the credit records of a nationally representative sample of individuals, the authors examine the possible effects of data problems on consumers by estimating the changes in consumers' credit history scores that would result from "correcting" the problems in their credit records. Moreover, the authors report results for consumer groups segmented by strength of credit history (credit history score range), depth of credit history (number of credit accounts in a credit record), and selected demographic characteristics.Credit cards

Switching costs and adverse selection in the market for credit cards: new evidence

To explain persistence of credit card interest rates at relatively high levels, Calem and Mester (AER, 1995) argued that informational barriers create switching costs for high-balance customers. As evidence, using data from the 1989 Survey of Consumer Finances, they showed that these households were more likely to be rejected when applying for new credit. In this paper, they revisit the question using the 1998 and 2001 SCF. Further, they use new information on card interest rates to test for pricing effects consistent with information-based switching costs. The authors find that informational barriers to competition persist, although their role may have declined. ; Also issued as Payment Cards Center Discussion Paper No. 05-09Credit cards

Changes in the distribution of banking offices

The past twenty years have been marked by major structural and regulatory changes in the banking industry. This article explores the relationships between these changes and the distribution of "brick and mortar" banking offices between 1975 and 1995. The analysis explores how population shifts, deregulation, and mergers, acquisitions, and failures may have influenced changes in the number and location of banking offices. Special attention is given to changes in banking office distributions across neighborhoods grouped by the median income of their residents and their central city, suburban, or rural location.Banks and banking ; Banking structure

Quality and location choices under price regulation

In a model of spatial competition, we analyze the equilibrium outcomes in markets where the product price is exogenous. Using an extended version of the Hotelling model, we assume that firms choose their locations and the quality of the product they supply. We derive the optimal price set by a welfarist regulator. If the regulator can commit to a price prior to the choice of locations, the optimal (second-best) price causes overinvestment in quality and an insufficient degree of horizontal differentiation (compared with the first-best solution) if the transportation cost of consumers is sufficiently high. Under partial commitment, where the regulator is not able to commit prior to location choices, the optimal price induces first-best quality, but horizontal differentiation is inefficiently high

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

BACKGROUND: Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. RESULTS: The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. CONCLUSION: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general