American mortgage underwriting assumes borrowers have a single employer and a steady paycheck. That assumption has not kept pace with today’s workforce, says Pavan Agarwal, President and CEO of Sun West Mortgage Company.
Multiple jobs, freelance contracts, and variable earnings are now common. As a result, creditworthy borrowers are getting shut out of homeownership because lenders cannot process how they earn.
An Outdated Underwriting Model
The income verification standards governing most U.S. mortgage lending were designed for a different economy. Agarwal says these assumptions date back to the 1960s and 1970s, when most workers held a single, stable corporate job.
“The guidelines in America were written back in the 60s and 70s, when everyone was expected to be working for IBM, nine to five, and getting a steady paycheck,” Agarwal says. “It doesn’t work that way anymore.”
The underwriting infrastructure built around that model remains largely intact. This has created a growing gap between how lenders evaluate creditworthiness and how many borrowers actually earn income.
Gig Income Becomes Standard
What has changed most dramatically, in Agarwal’s view, is the normalization of income complexity. Multiple jobs, side businesses, and variable earnings from gig platforms are no longer unusual financial profiles.
“That is now the norm,” Agarwal says. “It used to be the exception.”
The Bureau of Labor Statistics and independent surveys have tracked this shift for years. Estimates of gig-economy participation range from 15 to 36 percent of the U.S. workforce, depending on methodology. Most mortgage lenders still rely on two years of consistent W-2 income as their main qualification signal. A borrower with a mix of 1099 income, part-time work, and a small side business presents a documentation challenge. The existing system handles this poorly, or does not handle it at all.
A borrower may be genuinely creditworthy by any reasonable measure of income stability and debt capacity. Yet they may still be turned away, not because of financial weakness, but because the lender’s systems cannot process the complexity of their income.
Technology Gap, Not Regulation
Agarwal’s critique is technological rather than regulatory. He argues that banks have failed to build the analytical tools necessary to evaluate non-traditional income sources accurately and efficiently. The problem isn’t that lenders are unwilling to serve gig workers. It’s that their underwriting systems can’t do it at a cost that makes the loan viable.
“The banks haven’t been able to keep up,” Agarwal says. “They haven’t been able to figure it out. They don’t have the technology to do it.”
Agarwal cites Mortgage Bankers Association data putting the average cost to originate a mortgage in the U.S. at approximately $5,000 per transaction. For a borrower purchasing a $100,000 home, that origination cost equals five percent of the loan amount. That ratio makes many lenders unwilling to take on lower-value loans. The economics become even less favorable when a borrower’s income requires extensive manual review to document and verify.
“If you’re a low-income person buying a $100,000 house, no lender wants to spend $5,000,” Agarwal says. “There’s not enough money in the deal.”
Agarwal argues underserved communities, including minorities, low-income borrowers, and people with disabilities, get left behind. This isn’t due to intentional discrimination. It happens because the cost structure makes serving them unprofitable under current technology.
The regulatory framework has made some accommodations. Expanded guidelines for self-employed borrowers, bank statement loan programs, and asset depletion calculations exist within the non-QM market. However, these products typically carry higher rates and stricter terms. In effect, they penalize borrowers for income complexity rather than accommodate it.
A Technology-Based Response
Some lenders are attempting to close this gap through automated underwriting rather than policy change. Instead of requiring borrowers to document income in a fixed format, these systems are built to interpret a wider range of financial documentation and assess it directly. Sun West Mortgage, where Agarwal serves as CEO, is one example of a lender pursuing this approach.
The premise behind these tools is that the cost of manual review, not the borrower’s underlying creditworthiness, is what excludes non-traditional income from the current system. If automation can lower the cost of evaluating complex income, lenders may have less incentive to avoid these borrowers altogether.
This approach is not unique to one company. Non-QM lenders more broadly have expanded bank statement programs and asset-based underwriting to serve similar borrowers, though these products typically come with higher rates. Automated, AI-driven underwriting represents a newer and less common variation on that same goal, and its effectiveness at scale has not been independently evaluated.
Whether automation meaningfully changes access to homeownership for gig and multi-income borrowers, or simply shifts the cost of the same underwriting problem, remains an open question. The broader trend suggests the industry is aware of the gap Agarwal describes, even if there is no consensus yet on how to close it.
About the Expert: Pavan Agarwal is President and CEO of Sun West Mortgage Company, whose Angel AI platform is used for automated mortgage underwriting.
This article is intended for informational purposes only and does not constitute legal, financial, or investment advice. The views and opinions expressed herein reflect those of the individuals quoted and do not represent an endorsement of any company, product, or service mentioned. Readers should conduct their own due diligence and consult qualified professionals before making any investment decisions.
