Courtesy of Mish.
Household debts climbed by $226 billion to $12.6 trillion in the fourth quarter.
Total debt is just under the housing bubble peak, but the composition is a bit different. Mortgage debt is down from $9.3 trillion to $8.5 trillion but student loans rose from $0.6 trillion to $1.3 trillion, and auto loans rose from $0.8 trillion to $1.4 trillion.
Delinquencies are near the recovery lows, but is this as good as it gets?
Please consider Household Debts Climbed in 2016 by Most in a Decade.
“Debt held by Americans is approaching its previous peak, yet its composition today is vastly different as the growth in balances has been driven by non-housing debt,” said Wilbert van der Klaauw, an economist at the New York Fed.
The biggest force driving household debts higher over the past decade has come from the rise of student loans and auto loans.
A decade ago, there were less than $500 billion in student loans, but as tuition rose and growing numbers of students borrowed for college, the sum surpassed $1 trillion for the first time in 2013 and stood at $1.3 trillion in the fourth quarter.
Auto lending fell during the recession but began to rise by 2010 and surpassed $1 trillion for the first time in 2015. Households had $1.2 trillion in outstanding auto loans at the end of 2016. Auto lending has been driven in part by low rates and in part by the fact that auto lending remains abundantly available even to the riskiest borrowers, to the point that some policy makers have become worried about the volume of subprime auto loans.
While student loans and subprime loans are particular sources of concern, especially for those with the heaviest burdens, overall delinquency rates have continued to decline. Just 4.8% of loan balances were in delinquency in the fourth quarter, the lowest level in more than a decade, and down from as high as 11.9% in 2009. Just 3.3% of that debt was seriously delinquent, down from a peak of 8.7%.
Debt Composition
Debt Delinquencies
The stack-style of chart is very difficult to read, so I took the data points from 2014 forward and plotted them.
