A homeowner in New York was about three times more likely to have a forbearance mortgage, according to an analysis by Kroll. Being self-employed doubles the probability. (iStock)

The number of homeowners asking for forbearance on their mortgages has increased dramatically due to the coronavirus crisis, with the Mortgage Bankers Association finding more than 8 percent home loans withheld from mid-May. But what types of homeowners have applied for loan relief?

A recent analysis by the Kroll Bond rating agency provides some clues. The company performed logistic regression analysis on approximately 22,000 mortgages across 47 residential mortgage-backed securities transactions. He took into account attributes such as geography, interest rate, credit rating, and employment status to determine which factors had the most impact on the likelihood of forbearance requests.

The analysis found that geography plays a key role in determining which homeowners are most likely to apply for and accept a forbearance plan. It turned out that a homeowner in New York was three times more likely to have a forbearance mortgage compared to an average “benchmark” borrower. This would be an employee in owner-occupied property with a decent but not exceptional credit score of 750, paying 4.25% fixed interest and living outside of California, New York, Florida, New Jersey, Texas or Nevada.

New Jersey residents also had significantly higher abstention rates, at 2.2 times the benchmark rate. Another key factor was self-employment, which doubles the likelihood of applying for and accepting a forbearance plan. Higher interest rates, incomplete income documents, or living in California also increased the likelihood of forbearance, while credit rating and cash reserves played a more minor role.

Due to the relatively small size of the sample of loans collected in a single month – April – the Kroll report notes that “we believe these results are best viewed as informative indications of rank and direction (positive or negative ) effects and not as precise predictions. measures.”


Source link

Leave a Reply

Your email address will not be published.