Sizing Up Your Applicants Through Debt to Income
As the U.S. economy generally continues an upward tick, household debt has eclipsed that of levels in 2008 leading into the Great Depression. Total household debt has increased by $149 billion to $12.73 trillion.
Depending on who you talk to, that should cause alarm. But economists argue that the economic condition is now much healthier than when it teetered nine years ago. They cite about a 10 percent gain in the gross domestic product in the last several years.
U.S. credit card debt at all-time high
The fact is, after a few tight years of borrowing, the reins are loosening. Credit card debt is now at a U.S. all-time high after steady growth in the few years as more lenders are issuing more rope. Also, Fannie Mae increased its debt-to-income ratio for home loans from 45 percent to 50 percent in July.
Household debt-to-income percentage is the amount of debt compared to gross income. For example, a household that has $3,000 in debts (personal loans, car notes, credit cards, etc.) and generates $5,000 in monthly income has a debt-to-income percentage of 60 percent. That would flunk even today’s more generous Fannie Mae loan application.
For apartment operators, analysis of rent to income has been a staple in assessing risk of an applicant during the screening process. But the rise of America’s debt suggests that more emphasis should be placed on how much the applicant owes – and what makes up that debt.
Debt competes with resident’s ability to pay a lease
Rich Hughes, RealPage’s chief of data science, says multifamily operators can minimize risk by getting a better picture of applicants and the financial burdens they may bring to the door step instead of only relying on rent-to-income ratios. The concept is no different than how mortgage lenders cross the t’s and dot the i’s in the loan approval process (only borrowers have to sign their lives away amid dozens of documents).
“Suppose a person is carrying a bunch of debt,” he says. “That debt is competing with the lease you sign. The question is are they more likely to pay their rent or more likely to pay their debt. It becomes a very important question, whether you should let somebody live with you.”
The type of debt should factor into the equation, says RealPage Data Scientist Pavithra Ramesh. Long-term, low interest debt like a student loans or car payment is not the same as a revolving credit card debt or short-term loan at a much higher interest rate.
“An applicant just carrying debt by itself isn’t sufficient to make decisions,” she said. “You need to know what kinds of debt they are carrying and that will determine whether you want them as renters or not.”
Resident screening technology provides better insight in ability to pay
Resident screening products pour through a wealth of information, everything from criminal records to rental history, that applicants bring with them to sign a lease. The next wave of screening criteria to assess whether an applicant will pay rent on time – or at all – is how much money is going out for bills compared to paychecks coming in.
RealPage’s LeasingDesk has a proprietary credit-scoring model that, among other things, utilizes debt to income of applicants. The model goes beyond credit worthiness by looking at the ability of the applicant to pay rent on time and in getting the best residents into the properties, Hughes says.
Data scientists are discovering that greater analysis of debt type yields improved predictive analysis of an applicant’s ability to pay.
“We can model debt differently based on factors such as credit utilization and other debt,” Ramesh said. “Even among loans, the model differentiates between ‘malignant’ and ‘benign’ debt, since not all debt is bad or can be construed to lead to a bad lease outcome. By distinguishing the type of debt, we were able to achieve a lift in the predictive power of the component.”
No matter how much or how little Americans are borrowing, multifamily operators will have a better picture of who’s asking to rent their apartments.