Is it possible to shorten the fallout of an economic crisis? Researchers at the University of Maryland believe it’s possible. Preventative measures can mitigate the impact of economic downturns by creating neighborhoods that are naturally resilient to variances in economic performance. Diverse neighborhoods, researchers propose, can decrease the rate of foreclosures and sales before and after economic crises strike. What puts homogeneous neighborhoods at risk?  After World War II, planners developed neighborhoods with a single income bracket in mind. Like their urban counterparts, these planned communities segregated individuals and families based on their earning power and, occasionally, place of employment. During difficult economic times, neighborhoods that lack diversity are prone to clusters of foreclosures and sales before and after the peak of the recessionary activity. Neighborhoods that relied heavily on one employer also suffered severe spikes in financial difficulties. The homogeneous nature of these neighborhoods left them vulnerable. If one company or industry suffered, the entire neighborhood suffered as well. Neighborhoods with high concentrations of bank-owned and for-sale homes take longer to recover from economic downturns. Residents that struggled with mortgages may have postponed home maintenance in an effort to conserve resources for mortgage payments. As a result, many foreclosed homes show signs of physical deterioration. Additional damages may occur during the eviction process. Neighborhoods with high concentrations of foreclosures and sales experience the devaluation of nearby homes. Additionally, as lender confidence decreases, so will investment in the area. To create neighborhoods that are more resilient in the face of economic trouble, University of Maryland researchers suggest that developers shift to mixed-income housing models. Uncovering the economic disadvantages of homogeneous neighborhoods The study began by creating a data set that logged zoning and foreclosures across 14 metropolitan statistical areas (MSA). Researchers selected MSAs from throughout the...