Changing the way government loan programs programs assess risk may ease taxpayer burden and better prepare the country for extreme economic events, researchers argue.
“There’s a debate among policy makers and academics over what is the best method for the government to report and account for the credit risks that the government takes on with insurance programs—like FHA insurance programs, Fannie Mae and Freddie Mac, Social Security Insurance—all of these are insurance programs that are effectively being borne by the taxpayers,” says Brent Ambrose, professor of real estate at Penn Stte and an associate of the Institute for Cyber¾ÅÐãÖ±²¥.
“…the question is, are they accurately accounting for these risks?”
“When the government accounts for risk it is supposed to reflect the liabilities that the government is incurring—and the question is, are they accurately accounting for these risks?”
In a study, the researchers say that federal loan programs, such as the ones that provide loan guarantees and direct loans to make home ownership more affordable to low- and moderate-income borrowers, currently account for risk independently, but they may be vulnerable to correlated losses during an extreme economic event, such as a deep recession, which is a sharp decline in economic activity.
According to the researchers, instead of assessing the risk to the programs separately, the government should treat the government-backed loans as a portfolio and adjust the premiums that borrowers pay to better reflect that model.
“Let’s say there’s a recession and all of the sudden, you have lots of claims on unemployment at the same time that mortgages are defaulting and hitting the government on that side of the equation, too, so you have correlated defaults,” says Ambrose, who worked with Zhongyi Yuan, assistant professor of risk management.
“Everything is going bad at the same time and you’re getting exposed to all these losses at the same time. In our example, there are three government programs in the mortgage space that are doing essentially the same thing, all accounting for the risk independently, but we’re not recognizing that if the housing market collapses, they’re all going to get hit with defaults at the same rate, essentially.”
The amount of risk connected to government loan programs is significant, according to the researchers, who add that the federal government administers more than a hundred direct loan and guarantee programs, not including programs such as Social Security or federal civilian and military pension benefits.
The researchers estimate that the combined value of federal credit programs was about $18 trillion in 2013. Two of these programs, Fannie Mae and Freddie Mac, account for about half of the $10 trillion US mortgage market.
While the new estimation method would not necessarily help the country avoid a financial shock, more accurately pricing the premiums could ease the recovery process, especially for taxpayers.
“The hope is that this will stimulate debate in Washington among the policymakers…”
“This might help it so the government wouldn’t have to come to the taxpayers and increase the budget deficit to pay the claims,” says Ambrose. “As taxpayers, we’re on the hook for the losses, regardless of what happens, but this is just a way to better price the premiums that the homeowner would have to pay to reflect the risk.”
Whether the recommendation is implemented is up to policymakers.
“The hope is that this will stimulate debate in Washington among the policymakers who might decide that maybe we do need to change the way we account for these programs on the books of the government,” says Ambrose.
The researchers analyzed data on purchased 30-year fixed-rate mortgages, or those that government-sponsored entities, or GSEs, guarantee. Both Freddie Mac and Fannie Mae are examples of GSEs. The data, which are publicly available via the Federal Housing Finance Agency, cover GSE loans from 2000 to 2016.
The researchers presented their findings at a recent meeting of the Federal Reserve Bank of New York. The findings also appear in the .
Researchers performed computations for this work on the the Institute for Cyber¾ÅÐãÖ±²¥ Advanced CyberInfrastructure.
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