Lending to the Self-Employed
This automated solution, available through Loan Product Advisor, gives you time to underwrite more loans. Get your edge.
Source: The Pew Research Center
Back to top
THE FUTURE IS NOW
Lenders face rising interest rates and a shrinking borrower pool. But innovative technologies can help lenders broaden their borrower base by making it easier to lend to the self-employed homebuyer.
working Americans are either self-employed or working for the self-employed.
Lenders face a complex and time-consuming task when validating the income for self-employed borrowers.
Chief Credit Risk Officer, Single-Family,
Learn more about
The only AUS-integrated self-employed income assessment
How Lenders Can Turn More Self-Employed Americans Into Homeowners
Processing mortgage applications for self-employed borrowers is complex. Freddie Mac is teaming up with fintech company LoanBeam to make it easier for lenders to validate the income of self-employed borrowers. This way, lenders can spend more time cultivating relationships and delivering value to clients.
Sharpen your edge.
Learn how it works
Evolving U.S. Workforce Trends
The Challenge in Lending to the Self-Employed Borrower
How people earn money today is shaking up the mortgage industry. The U.S. workforce is made up of more nontraditional income earners—contractors, freelancers and on-demand workers—than it was a decade ago. And, many of them are looking to buy a home. But underwriting mortgages for these self-employed borrowers is complex and time consuming, a reality that discourages lenders from marketing to this group of prospective homebuyers.
With rising interest rates set to increase competition for borrowers, lenders cannot afford to overlook the self-employed customer. The good news is that technology has reached a point whereby it can automate and accelerate the income validation process for nontraditional wage earners, offering lenders an opportunity to innovate their approach to this customer segment.
So, what does higher work satisfaction, the burgeoning on-demand economy and the growing reliance of companies on contractors mean for lenders? The bottom line is that they’ll be confronted with more loan applications from self-employed homebuyers and, with them, the challenge to work smarter.
The Future Is Now: How Lenders Can Turn More Self-Employed Americans Into Homeowners
While lenders can’t change their practices overnight, they can incorporate new technology into them—in ways that strengthen their current capabilities. They can speed up mortgage application processing and, in this specific case, validate income more smoothly and quickly for the self-employed borrower.
The technology that stands to change the game is based on optical character recognition, or OCR. Built into software programs that integrates into lenders’ systems, OCR offers them a way to automate further and, in doing so, to compete more effectively for self-employed borrowers.
OCR programs extract and ingest information from tax returns and other mortgage application documents, and then they generate a total income figure. Software programs predicated on OCR then automatically populate relevant data into a pre-formatted workbook (see below). The workbook can be customized to incorporate government-sponsored enterprise (GSE) income guidelines and lender-specific requirements.
The reality of increasing interest rates only makes it more incumbent upon lenders to seek innovative solutions in casting a wider net for borrowers. The upside is that the necessary technology exists. Lenders can take advantage of it to free up their employees to cultivate client relationships and measure risk more accurately, leading to a faster turnaround time for borrower and lender. At the same time, they can leverage these solutions to scale their business and improve operational efficiency, giving them an edge against competitors.
For more information, visit LoanBeam and Freddie Mac Loan Advisor Suite®.
Automatically extracts and ingests data from the documents, searching for relevant data points that provides lenders with a complete income snapshot of the borrower.
Identifies missing documents into a single report, so lenders can quickly get what more they need from borrowers.
Outputs data into a pre-formatted workbook format—customized based on lenders’ needs and GSE guidelines—whereby lenders can quickly determine qualifying income.
Today, some 14 million borrowers are self-employed. And if experts prove correct, this number is bound to increase in coming years. It’s a trend that will exacerbate an existing problem for lenders—how to speedily and efficiently process mortgage applications for nontraditional wage earners.
To underwrite self-employed borrowers, lenders must dissect a customer’s tax returns—versus a W2—to validate his or her income. Complicating matters is the fact that the self-employed are incentivized to reduce their taxable income through deductions and write-offs. Loan officers and processors often must then manually reconcile dozens of pages of tax documents—including 1099s, Schedule C’s and other forms—to arrive at a reliable income total.
The income-validation process can take days to unfold. Loan processors often discover that essential information is missing from a file, requiring more back-and-forth between borrower and loan officer. Processing these mortgage applications translates into higher operational costs for lenders, who need to beef up staff and training to handle these customers.
Nontraditional income earners are becoming increasingly mainstream, particularly among Generation X and Millennials, who, compared to their parents and grandparents, move from job to job a lot more, and are more likely to be independent contractors.
By the year 2020, some 43% of U.S. workers will be freelancers, according to LinkedIn, compared to 6% in 1989. Other studies also predict major shifts in the U.S. workforce. For example:
Fintech company LoanBeam uses patented, highly refined OCR technology to extract and ingest information directly from a borrower’s paystubs, 1040 forms, w2 forms, and tax returns, and then it calculates an income total.
LoanBeam’s OCR technology has been developed over the past 14 years, with the company having tested it by scanning millions of tax documents. The result is a 99.7% accuracy score.
Freddie Mac is working with LoanBeam because it wants to provide lenders with a way to simplify the income validation process for the self-employed borrower. It will eventually integrate the calculation into Loan Product Advisor®, Freddie Mac’s automated underwriting system.
When lenders upload a self-employed borrower’s tax returns into LoanBeam, its software does the following:
Technology Solutions for Lenders
About 30% or 44 million working Americans are either self-employed or working for the self-employed, according to a study by The Pew Research Center. The McKinsey Global Institute (MGI) estimates the number of self-employed workers to be higher, between 54 million and 68 million.
Most self-employed Americans actively decide to work for themselves. A study by the Freelancers Union indicates that 60% of freelancers become independent contractors by choice. Answering the MGI survey, freelancers reported higher satisfaction than those with traditional jobs on 12 out of 14 aspects of their work life.
The “gig economy” has a lot of room to grow, and that means more people will make a living this way. While the JPMorgan Chase Institute estimates that 4% of the working-age population has earned income through “sharing economy” platforms, McKinsey says that some 15% of independent workers have used them to make money.
Working Together to Help Serve the Self-Employed Borrower
Made Easy: Lending to the Self-Employed
Spend hours - even days- to reconcile data from different sources and compute income.
Generate new business by easily underwriting more self-employed borrowers.
Lenders face a complex and time-consuming task when validating the income for self-employed borrowers.
Freddie Mac is working with fintech company LoanBeam to provide lenders with an easier way to validate the income of self-employed borrowers.
Save $200 per application*.
Now you can focus on analyzing other credit risks and making your underwriting decisions.
Populates into a formatted workbook with relevant data points, based on lender's specific requirements
Identifies missing documents.
Inputs the income calculation to meet Freddie Mac's requirements.
You upload a self-employed borrower’s tax documents...
Review and resolve potential errors to validate income calculation.
Extracts and ingests data from tax documents with 99.7% accuracy.
Keep pace with the growth of the self-employed population.
Manually capture data from complicated tax returns.
*If you process 2,500 loans annually for the self-employed using tax returns, that could translate into $500,000 in savings (*Source: LoanBeam)
As a result, lenders can:
The mortgage industry is riding high as we enter the ninth straight year of economic expansion. But
higher interest rates have sharply slowed home sales this year, and the industry is bound to feel the
ripple effects of a contracting housing market. As I digest the news, I find myself asking how lenders will adjust. The answer is that they’ll have to cast a wider net to secure more borrowers.
Enter the self-employed borrower. Lenders have to work extra hard to underwrite mortgages for
nontraditional wage earners because of the time it takes to validate their income. But I keep reading
studies about how this group is the fastest-growing component of the U.S. workforce:
Today, industry experts estimate that there are some 14 million self-employed borrowers in the U.S.,
and that this number will grow sharply. As for lenders, they’ll have to figure out a way to more
efficiently and speedily process mortgage applications for this group of prospective homebuyers.
Getting to Yes: A Better Way to Serve the Self-Employed Borrower
Preparing for What’s Around the Corner
At Freddie Mac, we strive to introduce solutions that lenders can use to grow their business. To this end, we’ve teamed up with fintech company LoanBeam to help our lenders reduce origination costs and deliver a better borrower experience. LoanBeam’s highly refined optical character recognition (OCR) technology extracts and ingests information from a borrower’s tax returns and other relevant data, and then calculates an income total based on what it’s read. Its software —tested over the last 14 years by having scanned millions of tax documents—has a 99.7% accuracy rate.
We believe technology like this will not only help reduce errors for lenders that stem from computing
income manually, but it will also help lenders achieve scale when working with nontraditional wage
earners. I think the technology’s value will grow exponentially, as the number and complexity of the
mortgage files increase. All of this should translate into higher productivity, lower origination costs and a competitive advantage in the growing self-employed borrower market.
We’re currently working with LoanBeam in an offline pilot and will soon integrate its technology with
Freddie Mac’s Loan Product Advisor®, our automated underwriting system. As the origination process
evolves, we’re committed to working with our lenders to meet the challenges that lie ahead. Contact
your Freddie Mac representative to learn more.
By Terri Merlino, Chief Credit Officer for Freddie Mac’s Single-Family Division
In a recent study, LinkedIn predicts that 43% of U.S. workers will be freelancers by 2020,
compared to 6% in 1989.
Three in 10 U.S. jobs are held by the self-employed or the workers they hire, according to a
report by the Pew Research Center.
The number of self-employed Americans increased to some 41 million in 2017, up almost 3%
from 2016, and they now represent 31% of the private workforce, per a study by MBO Partners.