Analysing Determinants of Foreclosure among High-income African-American and Hispanic Borrowers in the Washington, DC Metropolitan Area

Abstract

Foreclosures have disproportionately affected minority borrowers and communities. Many academic studies have focused either on the nation as a whole or on specific metropolitan areas, but few have concentrated on the nation's capital. Using a merged dataset consisting of Home Mortgage Disclosure Act (HMDA), US Census, and Lender Processing Services (LPS) data and utilizing a logistic regression model, we analyse the likelihood of foreclosure in the Washington, DC metropolitan area. We find that high-income African-American borrowers are 36 per cent and Hispanic borrowers 79 per cent more likely to go into foreclosure, controlling for key financial variables. Moreover, we find that exotic mortgage products, such as adjustable rate mortgages (ARMs), high-cost mortgages, balloon mortgages and interest-only mortgages, have a higher likelihood of foreclosure than standard 30-year fixed rate mortgages.Foreclosure, minorities, housing equity, US, Washington, DC metropolitan area, logistic regression,

Race, Space, and Cumulative Disadvantage: A Case Study of the Subprime Lending Collapse

“…The gap is also quite large at 9.2 points for privately securitized loans (13.8% predicted versus an observed likelihood of 23.0% or 75 extra filings). As before, higher socioeconomic status carries little or no protective effect against foreclosure within the black community (Anacker and Carr 2011; Lacy 2012), and once again private mortgage securitization accentuates the risk (Bocian et al 2011). The top panel reveals that the average black borrower who entered foreclosure had accumulated nearly $42,000 in equity at the time of filing, putting a total of some $3.7 million in home equity at risk of potential loss through foreclosure.…”

“…Yet the remaining sample statistics reveal other important differences between black and white borrowers that might plausibly account for racial disparities in lending terms and foreclosure rates. For example, blacks evince a greater likelihood of borrowing at the housing bubble's peak in 2006 as well as lower incomes, credit scores, and rates of neighborhood educational attainment, all factors that prior studies have shown to undermine loan performance and raise the odds of default and foreclosure (e.g., Anacker and Carr 2011; Chan et al 2013; Ding et al 2011; Gilderbloom et al 2012; Pennington-Cross and Ho 2010). …”

“…Predatory lenders target borrowers with established home equity who have historically been underserved on the basis of age, gender, race, and neighborhood composition (Fisher 2009; Goldstein 1999; Howell 2006; Renuart 2004). Because subprime loans are more likely to yield adverse outcomes than standard loans (Anacker and Carr 2011; Gilderbloom et al 2011), during the subprime lending boom the principal mechanism of discrimination was not a denial of credit to minorities but the systematic seeking out of minority borrowers for loan packages that carried excessive costs and risks (Lipsitz and Oliver 2010; Oliver 2008). …”

“…In his multinomial model of loan denial and high cost subprime loan receipt, for example, Faber (2013) reports that rates of subprime lending decline as incomes increase among white borrowers but increase with income among borrowers of color, net of other factors (see also Immergluck and Wiles 1999; Bocian et al 2011). As a result of these unexplained racial differences in lending, racial disparities in foreclosure are greater among high income minority than poor minority borrowers (Anacker and Carr 2011; Anacker, Carr, and Pradhan 2012). …”

“…First, we develop a theoretical model that links segregation, cumulative disadvantage, and racial stratification and apply it to the case of subprime lending and foreclosure (Anacker and Carr 2011; Bocian et al 2011; Lichtenstein and Weber 2014). Second, we take into account omitted variables and multiple stages in the lending and foreclosure process not considered by prior work (e.g., Barr, Dokko, and Keys 2011, Faber 2013, Rugh and Massey 2010; Sen 2012; Williams, Nesiba, and McConnell 2005).…”