The “New Normal” in Mortgage Lending and Its Impact on Default Probabilities

The paper analyses the evolution of the use of subprime loans and the availability of credit to different classes of borrowers. It examines the time period from 1980 to 2008 as a whole, as well as the changes in credit profiles in five sub-periods. By tracking borrower characteristics and their impact on foreclosure probability over time it determines what went wrong and how policy can be developed that prevents a repeat of the housing crisis that began at the end of 2006. The findings suggest that over the sample period debt to income, FICO score and loan-to-value are significant determinants for the probability of foreclosure and their importance increases over time. Furthermore, some borrowers are three times more likely to default on a loan originated between 2001 and 2006 than a loan originated between 1980 and 1994 indicating a distinct difference in lending terms and the general lending environment over time.

GEOGRAPHY OF OPPORTUNITY AND RESIDENTIAL MORTGAGE FORECLOSURE: A SPATIAL ANALYSIS OF A U.S. HOUSING MARKET

South Florida has been among the top foreclosure markets in the United States, but little research has explored whether this market presents different dynamics compared to other metropolitan areas. This research chooses Broward County to explore whether socioeconomic characteristics and certain public policy instruments relate to subprime lending and mortgage foreclosure patterns. Results indicate areas bounded by linear highways and railroads have a concentration of low-income black population and subprime loans. The spatial distribution of subprime loans is mostly explained by a higher percentage of minority and/or Hispanic population in a neighborhood. Yet, racial minorities, instead of Hispanic origin, contributes mostly to the concentration of subprime loans. The spatial pattern of foreclosures is more complex, determined not only by subprime loans but also possibly other factors associated with the mortgage crisis. This suggests that disadvantaged neighborhoods are disproportionally lacking favorable opportunities due to institutional and sub- cultural forces shaping the geography of subprime and foreclosure.

Déjà Vu

Like the credit boom of the 1920s, the toxic credit bubble of the 2000s precipitated a devastating global financial crisis. The desire to earn quick profits from originating and securitizing subprime loans corrupted the risk management practices of large financial conglomerates and the credit review practices of credit ratings agencies that assigned investment ratings to mortgage-related securities. By the end of 2006, U.S. credit markets resembled an inverted pyramid of risk, in which multiple layers of financial bets depended on the performance of high-risk subprime loans held in securitized pools. When housing prices began to fall and subprime loans began to default in large numbers in 2007, the leveraged bets on top of that pyramid of risk blew up and inflicted devastating losses on financial institutions and investors in the U.S. and Europe. Officials on both sides of the Atlantic were slow to recognize and respond to the severity of the crisis. The Federal Reserve Board and the Treasury Department missed multiple warning signs that should have caused them to increase their oversight of major U.S. banks and other large financial institutions during 2007 and early 2008.

Determinants Of Mortgage Delinquency

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">This paper examines mortgage delinquency rates for loans in each state and Washington, DC from 2004 through 2009 in order to gain insight into the key factors that drive residential mortgage delinquency.<span style="mso-spacerun: yes;">&nbsp; </span>Models are estimated for 30-day, 60-day, 90-day, 90+ day, and all delinquency rates.<span style="mso-spacerun: yes;">&nbsp; </span>Prime and subprime loans are modeled separately in cross-sectional time series regressions.<span style="mso-spacerun: yes;">&nbsp; </span>The findings suggest that borrower income, type of loan, and the general health of the economy remain important in determining delinquency risk.<span style="mso-spacerun: yes;">&nbsp; </span>Also, factors that determine 30- and 60-day delinquency rates differ from those that determine 90-day and 90+ day delinquency rates.<span style="mso-spacerun: yes;">&nbsp; </span>In addition, factors that determine prime delinquency rates differ from those that determine subprime delinquency rates.<span style="mso-spacerun: yes;">&nbsp; </span>Finally, borrower race does not consistently explain delinquency rates.<span style="mso-spacerun: yes;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span></p>