Assignee Liability for Predatory Lending

Undoubtedly, any debate over a predatory lending bill in Congress will have to address the question: who should bear the costs of abusive lending?  Right now, borrowers, their neighbors and their towns bear most of these costs.  In contrast, the secondary market, which finances abusive loans through securitization, successfully insulates investors from most of the risks of predatory lending.  This imbalance needs to change. 

There are a number of rationales for imposing assignee liability on the trusts that hold securitized predatory loans.  We mention a few here.  It costs less for trusts and investment banks -- as part of securitizations -- to screen loans for predatory terms than it collectively costs borrowers to hire attorneys to review their loan transactions.  One study by the Center for Responsible Lending (see www.responsiblelending.org) estimated that automated review of loan files cost less than one dollar per file and manual review cost $43, or about three percent of origination costs. 

Suitability, Now More Than Ever

In the old days, when lenders had to hold their home mortgages in portfolio, they cared about whether loan applicants could repay their loans.   True, there were serious problems, including credit rationing, redlining, and race discrimination, but one thing was for sure:  traditional underwriters took default risks seriously.  As a result, more borrowers were able to pay their loans.

Over the past two decades, securitization and outsourcing have revolutionized the lending industry.     Investment banks can slice and dice even the wildest credit risks and diversify those risks among investors worldwide.  Increasingly, lenders can even move their unrated or “B” tranches off their balance sheets by re-securitizing these tranches as collateralized debt obligations.  When lenders can collect upfront fees and pass off lemon loans and their retained risk to the secondary market, why should sound underwriting take top priority?

Automated Underwriting: Ten Years Later

Last year marked the tenth anniversary of Freddie Mac’s Loan Prospector, the first statistically-based automated underwriting (AU) system for home mortgages.  Shortly after Loan Prospector made its debut, Fannie Mae rolled out its own AU system, Desktop Underwriter.  By 2002, about two-thirds of all home mortgages were originated through Loan Prospector, Desktop Underwriter, or proprietary AU systems.

Automated underwriting has many benefits, not the least of which is faster loan decisions and lower origination costs.  Loan Prospector and Desktop Underwriter have also spurred greater flexibility in mortgage terms and underwriting standards.  Previously, under manual underwriting, any major negative often resulted in a “knock-out,” regardless of the applicant’s other strengths.  In contrast, the GSEs’ AU systems weight negative and compensating factors for risk and then offset the negative factors with the compensating factors as appropriate. The resulting flexibility has been especially important for minorities and lower-income borrowers, many of whom could not qualify for home loans under older, stricter manual underwriting standards.

HMDA Reporting of Credit Scores

Since last year, reporters and researchers have been publishing reports on the new Home Mortgage Disclosure Act (HMDA) pricing data that find that blacks and Hispanics pay more than whites for subprime home loans.  Industry dismisses these reports because the findings do not establish causation.  While this is a legitimate criticism, the real problem is the mortgage industry’s refusal to provide -- and the government’s refusal to require -- the reporting of credit score data that would permit more nuanced analyses, including whether race, credit risk, or something else drives the observed differences in the price that people of color pay for mortgage loans.

Why don’t lenders want to make credit score data available?  We asked a representative from a reputable lender that question and his response was, “Why don’t I just lie down in the middle of the road and make it easier for you?”  He was concerned that the data would open the door to fair lending lawsuits, which, as he rightly pointed out, have to be defended even if the lawsuits are not substantiated.  In addition, he expressed concern about borrowers’ privacy.  Arguably, it would be possible to identify property using HMDA data and then learn the borrowers’ credit scores. 

Why Comparison-Shopping is Impossible in Subprime

With last month’s elections and the Democrats’ upcoming control in Congress, predatory home mortgages are back in the spotlight.  Congressman Barney Frank, the incoming chair of the House Financial Services Committee, has made clear that a federal anti-predatory lending law is high on his agenda, and industry representatives and consumer activists are scurrying to draft bills.  Given the recent attention on the Hill, we decided to devote our guest entries this week to residential mortgages.  Our heartfelt thanks go to Bob Lawless and his colleagues at Credit Slips for inviting us to make a guest appearance.

Today, we focus on a persistent myth: that if subprime customers just comparison-shopped, they would not end up with predatory loans.  In our humble view, no matter how smart customers are, it is impossible – totally impossible – for them to engage in informed comparison-shopping in the subprime market.  Policymakers have a hard time grasping this fact because it is so easy to comparison-shop in the prime market.  However, price revelation works differently in the subprime market, making meaningful comparison-shopping impossible.