Rebuilding the City: Why Some Neighborhoods Survived the Housing Bubble and Others Did Not
The Great Recession of the late 2000s impacted the lives of millions of people and caused upheaval in countless neighborhoods and cities. Nearly a decade since the recession began, we have seen some neighborhoods rebuild and recover, while others continue to flounder, plagued by scores of empty buildings still owned by banks. What explains this discrepancy? Brooklyn College Professor Emily Tumpson Molina, seeking to answer that question, studied the neighborhoods in the Los Angeles-Inland Empire area of California that were most affected by foreclosures.
News reports often argue that the rate of foreclosures in a given area is the strongest indicator of how long it took neighborhoods to recover following the Great Recession, but Molina has evidence rebutting that theory in her recent article. Using data on Real Estate Owned (REO) properties, specifically those foreclosed properties that remained unsold in the wake of the foreclosure crisis, Molina examines other factors that are more strongly correlated to slow recoveries than foreclosure rate: ethnicity, race, poverty rate, and school quality.
Housing experts point to a number of neighborhood characteristics that are seen as contributing to a neighborhood failing to overcome a glut of foreclosures, including the economic health of the neighborhood prior to the recession and its racial makeup. Molina examined over 150,000 properties that were vacant in 2008 and 2009 to see which of these characteristics were the most common. Molina debunks the notion that neighborhoods with the most foreclosures at the outset of the recession would take the longest to recover, instead finding that the strongest indicator of having longer durations of no occupancy was the level of poverty in the neighborhood before the crisis.
Molina’s data shows that a 10 percent increase in neighborhood poverty rate was associated with a 10 percent decrease in the likelihood of a sale. The percentage of owner-occupied units in a neighborhood before the crisis had a smaller but still significant correlation with the likelihood of the house being sold after it became the property of a lender. Analysis of the racial composition showed stark results, including a 10 percent increase in black population being correlated with a five percent decrease in likelihood of sale. The same increase in Latino population was correlated with a one percent decrease. This indicates that the recession magnified issues that already existed in high-poverty and majority-minority neighborhoods, rather than creating new problems in neighborhoods that were previously healthy and stable.
The concentration of foreclosures had a surprisingly small impact on recovery and was sometimes irrelevant. Molina notes that some areas with high foreclosure rates actually sold faster than average, particularly in wealthier neighborhoods just outside of Los Angeles.
There are many factors that need to be taken into account when thinking about the economic recovery in the wake of the Great Recession, but it is important to consider that not only were black and Latino residents more likely to have received sub-prime loans than whites, but these groups also found it harder to recover from the recession. In helping those most impacted by the collapse of the housing market, the underlying causes are more complicated than policymakers may realize, and the solutions will not be simple. Many neighborhoods are still floundering because high-poverty neighborhoods, as well as those with high black or Latino populations, were passed over by the real estate bubble but still bore the brunt of the bust. The problems of racial and economic inequity were not caused by the recession, but they have slowed the recovery. In order to create a more stable housing market, these underlying issues must be addressed.
Article source: Molina, Emily Thumpson. “Neighborhood Inequalities and the Long-Term Impact of Foreclosures: Evidence from the Los Angeles–Inland Empire Region.” City & Community 15:3 (2016): 315-337.
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