While Cohen admitted that he wasn’t initially a big fan of social media, his perspective changed approximately two to three years ago. He now recognizes the value of digital marketing channels, favoring them over traditional print methods. One of his notable marketing efforts includes a newsletter that features updates and commentary, which has received a … [Read more…]
A former employee of nonbank mortgage lender The Change Company, has filed a lawsuit alleging the company founded by former banker Steve Sugarman has mischaracterized home loans in certifications to the Treasury Department.
The lawsuit, filed Tuesday in Superior Court in Orange County, California, was brought by Adam Levine, CEO Sugarman’s former chief of staff. Levine is a former vice president at Goldman Sachs and former assistant White House press secretary in the George W. Bush administration. Before his stint in the White House, he had been a senior aide to Sen. Daniel Patrick Moynihan.
The lawsuit seeks damages for alleged wrongful termination, whistleblower retaliation and breach of contract. It also alleges that Change Co., a community development financial institution based in Anaheim, California that originates loans to minority and low-income communities, has made false representations to investors about the underlying characteristics of the mortgages it securitizes.
The lawsuit states that Levine reached out in March to Change Co. Chairman Antonio Villaraigosa, a former mayor of Los Angeles, and asked for an independent investigation into certain practices and issues at the company. When Levine “reported his concerns to government regulatory authorities,” he was terminated, the lawsuit states.
Alan Wayne Lindeke, Change Co.’s chief legal officer and general counsel, called the lawsuit “without merit.”
“Multiple third-party diligence firms have verified the accuracy of Change Lending’s Target Market data and the corresponding assessment methodology has been verified by outside counsel,” Lindeke said in an emailed statement.
David Lizerbram, a lawyer in San Diego who represents Levine, declined to comment.
Sugarman served as Chairman and CEO of Banc of California before resigning in 2017. He formed a new company focused on originating loans to borrowers with non-traditional credit needs.
In 2018, Change Co. was certified by the Treasury Department as a community financial development institution. CDFIs are government-certified lenders with a mission to provide financing to disadvantaged communities. Because they provide credit and financial services to underserved Black, Hispanic and low-income communities, they are exempt from certain mortgage regulations.
Specifically, CDFIs do not have to abide by the Consumer Financial Protection Bureau’s ability-to-repay rule, which requires that mortgage lenders document a borrower’s income, assets, employment and credit history. Those so-called qualified mortgage rules were put in place after the subprime mortgage crisis in an effort to prevent a reprise of the low-documentation and no-documentation loans that were rampant before 2008.
Change Co. states on its website: “Our regulatory certification enables us to serve prime, creditworthy borrowers who struggle with burdensome documentation requirements.”
In just five years, Change Co. has catapulted ahead of competitors largely because, as a CDFI, it is not bound by traditional underwriting requirements. This year, Scotsman Guide, which ranks mortgage lenders by size, ranked Change as the largest non-qualified mortgage lender in the U.S. with $4.2 billion in lending volume.
The company lends to people “with unpredictable or hard-to-document income,” but looks for compensating factors such as loan-to-value ratios below 80%, Change Co says on its website. Its borrowers have FICO scores above 640 and typically have more than a year’s worth of cash reserves to bridge gaps between paychecks, the website states.
All CDFIs have to demonstrate that they are serving at least one eligible target market — either a specific area or targeted population. They are required to provide annual certification and data collection reporting to the Treasury Department, attesting that 60% of their loans, both in number and dollar volume, are made to target markets.
In the lawsuit, Levine claims that he has documentation showing that the company is “mischaracterizing the race, ethnicity, and income level of borrowers,” and that it “falsifies information on its annual certification by mischaracterizing its loans.”
CDFIs represent a regulatory tradeoff. They are exempt from some government requirements to collect borrowers’ income documentation, which can be difficult for both the lender and the customer and can eliminate the ability to serve borrowers who don’t have a stable job or whose income comes from a business they own.
A company that wasn’t bound by these underwriting restrictions could shoot ahead of peers that were subject to the rules, so to compensate, CDFIs are required to restrict the majority of their lending to demographic groups considered underserved. If a CDFI was not following the rules and sticking to its target population, it would essentially operate as an untrammeled lender while its competitors were tied to stricter underwriting regulations.
Mortgage lenders typically bundle their loans and resell them to investors as residential mortgage-backed securities. The lawsuit alleges that Change Co. “makes false representations” to the buyers of its mortgage-backed securities “by mischaracterizing the underlying loans.”
“These misrepresentations are material, as many investors choose CDFI securitized products as part of a broader policy that promotes socially responsible investing,” the lawsuit states. “For example, investors who believe they were supporting loans to low-income members of the community would not choose to purchase a [Change Co.] security if they knew that the company falsely characterized its loans to wealthy individuals and even celebrities as low-income loans.”
Change Co. and its subsidiary Change Lending closed a $307 million securitization of home loans in June. The company said at the time that since becoming a CDFI five years ago, it has funded over $25 billion in loans to more than 75,000 families.
A potentially scary, or intriguing thought, depending on your worldview: Whether you are approved for a mortgage could hinge upon the type of yogurt you purchase.
Buying the more daring and worldly Siggi’s — a fancy imported Icelandic brand — could mean you achieve the American Dream while enjoying the more pedestrian choice of Yoplait’s whipped strawberry flavor could lead to another year of living in your parents’ basement.
Consumer habits and preferences can be used by machine learning or artificial intelligence-powered systems to build a financial profile of an applicant. In this evolving field, the data used to determine a person’s creditworthiness could include anything from subscriptions to certain streaming services to applying for a mortgage in an area with a higher rate of defaults to even a penchant for purchasing luxury products — the Siggi’s brand of yogurt, for instance.
Unlike the recent craze with AI-powered bots, such as ChatGPT, machine learning technology involved in the lending process has been around for at least half a decade. But a greater awareness of this technology in the cultural zeitgeist, and fresh scrutiny from regulators have many weighing both its potential benefits and the possible unintended — and negative — consequences.
AI-driven decision-making is advertised as a more holistic way of assessing a borrower than solely relying on traditional methods, such as credit reports, which can be disadvantageous for some socio-economic groups and result in more denials of loan applications or in higher interest rates being charged.
Companies in the financial services sector, including Churchill Mortgage, Planet Home Lending, Discover and Citibank, have started experimenting with using this technology during the underwriting process.
The AI tools could offer a fairer risk assessment of a borrower, according to Sean Kamar, vice president of data science at Zest AI, a technology company that builds software for lending.
“A more accurate risk score allows lenders to be more confident about the decision that they’re making,” he said. “This is also a solution that mitigates any kind of biases that are present.”
But despite the promise of more equitable outcomes, additional transparency about how these tools learn and make choices may be needed before broad adoption is seen across the mortgage industry. This is partially due to ongoing concerns about a proclivity for discriminatory lending practices.
AI-powered systems have been under the watchful eye of agencies responsible for enforcing consumer protection laws, such as the Consumer Financial Protection Bureau.
“Companies must take responsibility for the use of these tools,” Rohit Chopra, the CFPB’s director, warned during a recent interagency press briefing about automated systems. “Unchecked AI poses threats to fairness and our civil rights,” he added.
Stakeholders in the AI industry expect standards to be rolled out by regulators in the near future, which could require companies to disclose their secret sauce — what variables they use to make decisions.
Companies involved in building this type of technology welcome guardrails, seeing them as a necessary burden that can result in greater clarity and more future customers.
The world of automated systems
In the analog world, a handful of data points provided by one of the credit reporting agencies, such as Equifax, Experian or TransUnion, help to determine whether a borrower qualifies for a mortgage.
A summary report is issued by these agencies that outlines a borrower’s credit history, the number of credit accounts they’ve had, payment history and bankruptcies. From this information, a credit score is calculated and used in the lending decision.
Credit scores are “a two-edged sword,” explained David Dworkin, CEO of the National Housing Conference.
“On the one hand, the score is highly predictive of the likelihood of [default],” he said. “And, on the other hand, the scoring algorithm clearly skews in favor of a white traditional, upper middle class borrower.”
This pattern begins as early as young adulthood for borrowers. A report published by the Urban Institute in 2022 found that young minority groups experience “deteriorating credit scores” compared to white borrowers. From 2010 to 2021, almost 33% of Black 18-to-29-year-olds and about 26% of Hispanic people in that age group saw their credit score drop, compared with 21% of young adults in majority-white communities.
That points to “decades of systemic racism” when it comes to traditional credit scoring, the nonprofit’s analysis argues. The selling point of underwriting systems powered by machine learning is that they rely on a much broader swath of data and can analyze it in a more nuanced, nonlinear way, which can potentially minimize bias, industry stakeholders said.
“The old way of underwriting loans is relying on FICO calculations,” said Subodha Kumar, data science professor at Temple University in Philadelphia. “But the newer technologies can look at [e-commerce and purchase data], such as the yogurt you buy to help in predicting whether you’ll pay your loan or not. These algorithms can give us the optimal value of each individual so you don’t put people in a bucket anymore and the decision becomes more personalized, which is supposedly much better.”
An example of how a consumer’s purchase decisions may be used by automated systems to determine creditworthiness are displayed in a research paper published in 2021 by the University of Pennsylvania, which found a correlation between products consumers buy at a grocery store and the financial habits that shape credit behaviors.
The paper concluded that applicants who buy things such as fresh yogurt or imported snacks fall into the category of low-risk applicants. In contrast, those who add canned food and deli meats and sausages to their carts land in the more likely to default category because their purchases are “less time-intensive…to transform into consumption.”
Though technology companies interviewed denied using such data points, most do rely on a more creative approach to determine whether a borrower qualifies for a loan. According to Kamar, Zest AI’s underwriting system can distinguish between a “safe borrower” who has high utilization and a consumer whose spending habits pose risk.
“[If you have a high utilization, but you are consistently paying off your debt] you’re probably a much safer borrower than somebody who has very high utilization and is constantly opening up new lines of credit,” Kamar said. “Those are two very different borrowers, but that difference is not seen by more simpler, linear models.”
Meanwhile, TurnKey Lender, a technology company that also has an automated underwriting system that pulls standard data, such as personal information, property information and employment, but can also analyze more “out-of-the-box” data to determine a borrower’s creditworthiness. Their web platform, which handles origination, underwriting, and credit reporting, can look at algorithms that predict the future behavior of the client, according to Vit Arnautov, chief product officer at TurnKey.
The company’s technology can analyze “spending transactions on an account and what the usual balance is,” added Arnautov. This helps to analyze income and potential liabilities for lending institutions. Additionally, TurnKey’s system can create a heatmap “to see how many delinquencies and how many bad loans are in an area where a borrower lives or is trying to buy a house.”
Bias concerns
Automated systems that pull alternative information could make lending more fair, or, some worry, they could do the exact opposite.
“The challenges that typically happen in systems like these [are] from the data used to train the system,” said Jayendran GS, CEO of Prudent AI, a lending decision platform built for non-qualified mortgage lenders. “The biases typically come from the data.
“If I need to teach you how to make a cup of coffee, I will give you a set of instructions and a recipe, but if I need to teach you how to ride a bicycle, I’m going to let you try it and eventually you’ll learn,” he added. “AI systems tend to work like the bicycle model.”
If the quality of the data is “not good,” the autonomous system could make biased, or discriminatory decisions. And the opportunities to ingest potentially biased data are ample, because “your input is the entire internet and there’s a lot of crazy stuff out there,” noted Dworkin.
“I think that when we look at the whole issue, it’s if we do it right, we could really remove bias from the system completely, but we can’t do that unless we have a lot of intentionality behind it,” Dworkin added. Fear of bias is why government agencies, specifically the CFPB, have been wary of AI-powered platforms making lending decisions without proper guardrails. The government watchdog has expressed skepticism about the use of predictive analytics, algorithms, and machine learning in underwriting, warning that it can also reinforce “historical biases that have excluded too many Americans from opportunities.”
Most recently, the CFPB along with the Civil Rights Division of the Department of Justice, Federal Trade Commission, and the Equal Employment Opportunity Commission warned that automated systems may perpetuate discrimination by relying on nonrepresentative datasets. They also criticized the lack of transparency around what variables are actually used to make a lending determination.
Though no guidelines have been set in stone, stakeholders in the AI space expect regulations to be implemented soon. Future rules could require companies to disclose exactly what data is being used and explain why they are using said variables to regulators and customers, said Kumar, the Temple professor.
“Going forward maybe these systems use 17 variables instead of the 20 they were relying on because they are not sure how these other three are playing a role,” said Kumar. “We may need to have a trade-off in accuracy for fairness and explainability.”
This notion is welcomed by players in the AI space who see regulations as something that could broaden adoption.
“We’ve had very large customers that have gotten very close to a partnership deal [with us] but at the end of the day it got canceled because they didn’t want to stick their neck out because they were concerned with what might happen, not knowing how future rulings may impact this space,” said Zest AI’s Kamar. “We appreciate and invite government regulators to make even stronger positions with regard to how much is absolutely critical for credit underwriting decisioning systems to be fully transparent and fair.”
Some technology companies, such as Prudent AI, have also been cautious about including alternative data because of a lack of regulatory guidance. But once guidelines are developed around AI in lending, GS noted that he would consider expanding the capabilities of Prudent AI’s underwriting system.
“The lending decision is a complicated decision and bank statements are only a part of the decision,” said GS. “We are happy to look at extending our capabilities to solve problems, with other documents as well, but there has to be a level of data quality and we feel that until you have reliable data quality, autonomy is dangerous.”
As potential developments surrounding AI-lending evolve, one point is clear: it is better to live with these systems than without them.
“Automated underwriting, for all of its faults, is almost always going to be better than the manual underwriting of the old days when you had Betty in the back room, with her calculator and whatever biases Betty might have had,” said Dworkin, the head of NHC. “I think at the end of the day, common sense really dictates a lot of how [the future landscape of automated systems will play out] but anybody who thinks they’re going to be successful in defeating the Moore’s Law of technology is fooling themselves.”
There has been a seismic shift in the mortgage industry, and it has redefined how mortgage originators must approach the new market, as mortgage rates have more than doubled from their 2022 lows. Over the past few years, 90% of new business has come from refinances, but the landscape has flipped, and we are in a purchase product-driven market, forcing the entire industry to be agile and adjust to a new normal.
In a rising rate environment, we often see the industry hone in on the ultra-competitive business of trying to get the lowest rate possible. While this may work in the agency space, the low number of borrowers in the market means many originators are stuck with navigating half, or less than half, of the volume they were doing last year. Because of this, more and more originators are turning to the non-qualified mortgage (non-QM) sector to grow their pipeline and revenue.
The challenge is that the rate-chasing game doesn’t apply in the non-QM space. Rather, relationships and referrals are the keys to success. In order to foster these relationships and better serve borrowers, originators must work with trusted lenders with expertise in this niche sector.
Non-QM loans can lead to more referrals
The current housing market has necessitated a new approach for mortgage professionals who want to increase the number of loans on their desks. Non-QMs could present a new revenue channel for the originators facing reduced loan volume.
In recent surveys, about 75% of originators claimed not to have originated a non-QM loan in the past 12 months — or ever. Yet non-QM loans continue to grow as an overall component of the purchase market.
Non-QM loans allow originators to serve a largely untapped market and expand their offerings to realtors and borrowers. In times when overall industry transactions are dwindling, non-QM loans can drive new business and open the door to a new subset of potential clients.
For example, self-employed borrowers often struggle to show substantial income and don’t qualify for agency products, and are a great niche to complement your existing business. An originator who’s well-educated in non-QM products can utilize their expertise to stand out among industry peers, connect with new agents who are key referral sources, and build on their network.
What matters in non-QMs is not the lowest rate — it is the best service and the ability to deliver quickly. Given the individuality of each non-QM loan, working with a trusted originator who understands this space is critical. Surety of execution is critical to building relationships, and unlike in the agency space, it isn’t black and white. That’s why, if you’re going to add non-QMs to build new relationships and grow your referral base, you need to work with a trusted lender and not go for the lowest rate.
Many novice non-QM lenders have popped up in recent years, and one mistake could cost you an important referral partner or ruin a relationship with a long-standing contact.
The right technology isn’t a perk; it’s a necessity
Differentiation requires a combination of exceptional service, unique value propositions, and effective communicating of your expertise. Excellent client service and surety of execution are pillars of a successful originator’s business, but technology has increasingly become a necessity in the mortgage industry.
In the non-QM space, technology has advanced rapidly since the beginning of the pandemic— nearly to the point where leaders in this space could help process and qualify non-QM borrowers as fast as qualified borrowers. Document management systems and automated prequalifying tools can streamline your processes and make them more efficient, potentially allowing you to get back to borrowers in hours, not weeks.
This, in turn, improves the overall customer experience by reducing paperwork, speeding up approvals, and providing a seamless digital interface. Clients appreciate a smooth and hassle-free mortgage process, which can lead to positive reviews, referrals, and repeat business.
If you think non-QMs are still a paper-and-pen business, think again. You are an originator and technology is your friend. If you are going to turn to non-QMs to help drive business, work with a lender that has the tech stack that makes your life — and ultimately your client’s life — easier.
The next steps for success
In order to excel in the transforming mortgage industry, originators must shift their focus back to the basic business principle of customer service. Ongoing volatility in the market will continue to reshape the needs and demands of buyers, resulting in a call for mortgage professionals to rise to new challenges and deliver better service.
Overall, utilizing good technology and establishing partnerships with trusted originators can be significantly beneficial in expanding networks and winning new business. By leveraging efficient processes, accessing a wider range of products, and enhancing credibility, originators can position themselves as reliable, tech-savvy professionals in the mortgage space, attracting more clients and fostering long-term success.
Tom Hutchens is the executive vice president of production for Angel Oak Mortgage Solutions. He has more than 25 years of experience in leading sales for a wholesale and correspondent lending platform and with proven success in expanding a lending footprint nationwide.
Quontic Bank, which bills itself as an “adaptive digital bank,” has launched what they refer to as a “first of its kind” no doc home loan with loan amounts as high as $3 million.
The non-Qualified Mortgage (non-QM), which is available nationwide, doesn’t require income or asset verification, similar to the loans offered during the lead up to the mortgage crisis over a decade ago.
Quontic Streamline Refinance Highlights
No income or asset verification
Minimum credit score of 660
Must have clean 24-month mortgage payment history
Max LTV of 80%
Loan amounts up to $3 million
Mortgage rates start at 4.875%
Closing costs may be rolled into loan
Can close in less than 30 days
All that is required is a 660+ FICO score and a clean 24-month mortgage payment history.
The bank doesn’t ask for tax returns, W2s, bank statements, or any other documents typically required to qualify for a home loan.
Additionally, a home appraisal may not be required if the loan amount is $400,000 or less.
In terms of interest rates, Quontic says they start at 4.875%.
While well above current market rates for a 30-year fixed, they may be much lower than what these homeowners would otherwise qualify for.
They note a borrower with a $400,000 loan amount and a 7% mortgage rate could save over $500 per month if refinancing to a rate of 4.875%.
The loan can be closed in less than 30 days thanks to the reduced documentation requirements, and while standard closing costs apply, they can be rolled into the loan amount.
Quontic believes the product is a perfect fit for borrowers who’ve had difficulty qualifying for a mortgage in the past, or who financed their home at a higher-than-market rate.
Of course, you should always shop around to see what else you might qualify for, including streamline refinance options associated with the FHA and VA.
You may also think you don’t qualify for a traditional mortgage when in fact you do.
Quontic Is a Community Development Financial Institution (CDFI)
The NYC-based bank is able to offer these types of loans because it’s a Community Development Financial Institution (CDFI), whose mission is to provide mortgages to low income and underserved households.
This includes immigrants, seniors, Millennials, self-employed borrowers, and those who have traditionally had trouble securing home loan financing.
Per Quontic, CDFIs are required to lend at least 60% (in both units and dollars) to home buyers in their target low-income markets.
Apparently only 2% of banks in the United States are considered CDFIs, and Quontic was able to claim this status because roughly 70% of their home loans go to low income populations.
In late 2019, the company was awarded an expanded national low-income target market by the CDFI, which is a department of the U.S. Treasury.
They had originally been limited to the boroughs of New York City, along with Nassau and Westchester counties in New York state.
The bank also offers an owner-occupied Community Development Loan (CDL) that requires little or no income verification for home purchases.
It is geared toward self-employed individuals who may not qualify for a mortgage based on their reported income.
Additionally, they’ve got an investment property loan program that is no ratio, meaning no DTI ratio is calculated because they don’t verify income.
While it sounds like 2006 all over again, my assumption is they have rules in place to ensure the loans are underwritten properly, like ensuring a borrower’s ability to repay.
Quontic has lent nearly $3 billion to “those in need” to date.
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Despite curtailing its non-qualified mortgage volumes in 2022, Impac expects to originate more non-QM products in the broker channel. Additionally, the non-agency originator has decided to wind down its operations in the third-party originations (TPO) channel, citing significant volume and margin deterioration in 2022. Impac said it will continue to “honor its pipeline and related … [Read more…]
A regulator in Illinois has suspended the MLO license of Michael Strauss and the license for his mortgage company, Smart Rate Mortgage, essentially blocking the company from operating in the state.