Job Losses, Marriage Troubles and Rich Uncles: Foreclosure Prevention Policy when Borrowers Hold Private Information about their Financial Health

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Kytömaa, Lauri

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My dissertation studies foreclosure prevention in environments where borrowers have an incentive to appear distressed in order to receive mortgage reductions. Such behavior is possible when borrowers have knowledge about their abilities to repay debt that cannot be observed by their lenders. Using a sample of Fannie Mae loans originated in California between 2004 and 2007, I show that mortgage providers only offer debt relief when they are highly informed about borrower default probabilities. I then use the estimated model to explore the effects of the Federal Home Affordability Modification Program, which was launched in 2009 in response to the Great Recession. I find that subsidies offered to banks under the program were more effective at preventing foreclosures in loans originated earlier in the 2000’s, even though banks tended to be equally well informed about borrower financial health in all sample cohorts. The results suggest that government subsides decreased foreclosures by 7.2% for loans originated in 2004, but that this rate steadily declines to 1.0% for loans originated in 2007. I also find that the average subsidy expenditure per prevented foreclosure increased from $17,000 to $150,000 between my sample origination cohorts. Jointly, the results offer a comprehensive look at how borrower financial well- being and the behavior of financial institutions influence debt relief policy. This project benefits greatly from access to TACC resources. Estimation uses a maximum likelihood routine in which I solve for a high-dimensional grid that rationalizes bank behavior, and then match model-predicted loan outcome probabilities to data. Solving for bank behavior is conducive to parallel computing since the optimum can be computed independently for every set of inputs. Leveraging many nodes allows me to solve for optimal policy at 60.2 million input combinations in under an hour. All numerical maximization takes place using Python on the Stampede2 cluster.


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