The use of Blockchain technology is growing at Ginnie Mae and throughout the federal government. Here, at Ginnie Mae, we’re deploying blockchain and distributed ledger technology (DLT) to our processes, freeing finance staff to work on more value-added tasks. Ginnie Mae’s Innovation Lab is leading this internal effort and recently joined with U.S Federal Housing Blockchain Network (HBN), a community of solvers dedicated to exploring how blockchain and distributed ledger technologies may benefit U.S. housing finance and home ownership.
The current housing finance system remain paper intensive and reliant on processes that were developed decades ago. The Blockchain Network will serve as a central gathering point for Government innovators, technical and non-technical, interested in improving mortgage and mortgage securitization processes for Mortgage-backed Securities (MBS).
The Blockchain network intends to cultivate awareness and education through a number of mediums:
In fostering these government connections, while also staying in close communication with the private sector, Ginnie Mae is harnessing the wisdom-of-crowds to uncover innovative concepts, methods, technologies, and tools to rapidly evolve the mortgage lifecycle from origination through securitization across the entire U.S. Federal Housing ecosystem.
This effort is the latest on the part of Ginnie Mae to deploy technologies that modernize the mortgage process. In 2021, Ginnie Mae settled more than $2.1 billion of digital MBS, and anticipates more volume in 2022 as we open up participation to additional issuers.
Ginnie Mae is embracing technology throughout its business and is excited about the time ahead.
Ginnie Mae finances America. For more than 50 years Ginnie Mae has brought global
capital to the U.S. housing finance market at minimal risk to the U.S. taxpayer. We’ve
provided the liquidity and stability that helps millions of veterans and low- and moderate-income
households find affordable homes.
In just a little more than a year since Ginnie Mae launched its digital collateral program in late 2020, industry participation is ramping up. The first Ginnie Mae mortgage-backed security (MBS) backed by digital collateral settled in January 2021 — a $24-million deal from Quicken Mortgage — and nearly $2.1 billion of MBS have been issued since then.
Deployed as part of Ginnie Mae’s enterprise-wide modernization plan, the digital collateral program is an important segment of Ginnie Mae’s strategic effort to increase the flexibility and resonance of its platform on behalf of Issuers and the consumers they serve. Increasing eMortgage adoption is especially relevant as the industry develops and accelerates the use of virtual procedures to navigate the COVID-19 pandemic.
Many types of mortgage borrowers may need remote and virtual closing, but it could be argued that the most acute need is felt by deployed military servicemembers. Atlantic Bay Mortgage Group, an early participant in the Ginnie Mae digital collateral initiative, has its finger on the pulse of the veteran mortgage market and the impact digital collateral has on Department of Veterans Affairs borrowers stationed overseas. The company is headquartered in Hampton Roads, Va., which is home to one of the largest concentrations of active-duty military in the U.S.
Atlantic Bay closed its first eMortgage within the Ginnie Mae program in April 2021 and has since closed more than 300 mortgages, many to veterans. “There is a ton of value in digital collateral,” said Christina Brown, Atlantic Bay’s chief operations officer. “eMortgages are the way of the future, primarily because we want ease of service in every area of their lives, including the home-buying process.”
Many of Atlantic Bay’s military customers are purchasing a home in the United States while stationed overseas. That often requires a power of attorney (POA) for the servicemember who is unable to attend the closing in person. The flexibility enabled by the eMortgage/digital collateral process makes adjustments that may need to be made to the POA happen more quickly, reducing one level of stress.
“This allows the veteran to stay in control of their mortgage transactions, no matter where they are located,” said Brown.
Broadly, the issuance of securities backed by Digital Pools validates the flexibility and value of the Ginnie Mae securitization model, setting the foundation for more rapid adoption of eMortgages in the government market. In the year since launch, the initiative is the realization of efforts by numerous internal and external stakeholders in our digital initiatives, including Issuers, Document Custodians, warehouse lenders, technology providers and other industry partners.
With a dozen Issuers already on board, and more in the pipeline, there is no doubt that eMortgages are here to stay.
Ginnie Mae’s recent release of its latest Environmental, Social, and Governance (ESG) data is another step in enhancing the organization’s mortgage-backed securities (MBS) disclosure. The inclusion of this data provides Ginnie Mae MBS investors with better information to support their sustainable investing decisions, as well as attract a more diverse group of investors. With approximately $2.1 trillion in MBS outstanding, Ginnie Mae can maintain deep liquidity for its securities in order to attract the wide group of global investors.
As part of our focus on the “socially responsible” aspect of ESG investing, the new disclosure provides pool-level aggregate information on loans that are in low- and moderate-income (LMI) areas. LMI percentages of 51% and greater are eligible for Department of Housing and Urban Development (HUD) Community Block Grants. By now disclosing at the security level, the unpaid principal balance (UPB) percentage represents the aggregate of loan balances which are in a 51% or greater LMI Census Tract. Our disclosure compliments bank efforts to serve LMI Census Tract Communities by giving grants on loan fees to borrowers who are purchasing a home in a 51% or greater LMI Census Tract with an Agency Guaranteed Mortgage. The new dataset aggregates to the pool level the number of loans, percent of loans, UPB dollars and percent UPB dollars across LMI areas applicable to the pool.
Per the new data disclosure, more than 1.7 million government loans outstanding were originated in LMI areas, comprising 15.9% of the roughly 10.7 million loans. The UPB outstanding for LMI area loans is about $259 billion, representing 13.5% of the $2 trillion outstanding. Socially responsible investors could leverage this new data to better target their investment dollars into pools with higher concentrations of LMI area loans.
Pools with LMI share of 50% or greater have a lower average loan amount at $101,647 compared to $134,737 for pools with LMI share of less than 50%.
Majority LMI pools also have a much higher Federal Housing Administration (FHA) share, 87.7%, compared to 73.9% FHA share for pools that are less than 50% LMI. Conversely, the Department of Veterans Affairs (VA) share, while much smaller than FHA share on average, is lower in majority LMI pools. These findings are consistent with FHA’s mission of enabling homeownership for LMI households. Further insights can be obtained by studying credit characteristics for the two LMI buckets.
For example, pools with at least 50% LMI area UPB have slightly lower average credit scores at 674 compared to pools with a lower LMI share at 678. The average debt-to-income (DTI) ratio of 40% for majority LMI area pools is slightly higher than the 39.3% share for non-majority LMI pools. Loan-to-value (LTV) ratio for LMI-heavy pools was slightly lower, at 93.9%, compared to 94.2% for the other group. Overall, credit characteristics for majority LMI pools are slightly worse compared to those for non-majority LMI pools.
This can be a value add for investors as loans to LMI borrowers are less likely to prepay when rates fall. Even when these loans refinance, lower balances further mitigate the impact of prepayment risk on investment portfolios. The average note rate for pools with over 50% LMI share was 1% higher, at 6.1%, compared to 5.1% for pools with under 50% LMI share. This data can be sliced and diced other ways to focus on specific vintages, coupons, loan balances and other relevant attributes. Further analysis of this data can yield valuable insights for investors looking to enhance their ESG investments.
(This article was excerpted from the June Global Markets Analysis Report prepared for Ginnie Mae by State Street Global Advisors, Urban Institute and Housing Finance Policy Center.)
Artificial intelligence (AI) — or the use of computer algorithms to perform tasks such as learning and problem solving that have traditionally required human intelligence — is no longer the stuff of science fiction. From supporting customer service requests via chatbots to enabling virtual assistants to respond to voice commands, AI is transforming virtually every element of how we live, work and interact with one another, including the home lending industry.
More than half of senior mortgage industry executives in a Forbes study agreed that AI is revolutionizing the business. AI reaches into all aspects of the lending process, from loan application and origination to underwriting and servicing, and it may disrupt aspects of these processes that have been in place for years. AI is enabling new players to enter the market, sparking greater competition, and revealing a potential need for new regulations. In 2017, over half of financial services firms had made investments in AI technology, and analysts project that most mortgage lenders will use AI to transform some parts of their operations by the end of 2021.
Organizations in the home lending space are already leveraging AI and machine learning in a variety of ways to speed up processing and automate tasks that used to be manual and time intensive.
For example, Freddie Mac is integrating AI and machine learning into their Automated Collateral Evaluation to determine if a mortgage is eligible for sale, bypassing traditional requirements for an appraisal report.
At Ginnie Mae, our forays into AI and cognitive technologies have largely centered on Robotic Process Automation (RPA). One bot that we’ve put in place, called DABO, streamlines the previously manual process of determining adjustable mortgage rates. Another bot tackles the task of managing and reporting commitment authority reconciliation data to the general ledger, a task that previously took three hours and at least three employees to accomplish. With RPA, Ginnie Mae was able to reduce that time to one hour for a single staff person. By automating these processes, Ginnie Mae has reduced the time employees spend performing manual tasks and has also significantly reduced errors, freeing up employees to focus on other, more valuable activities and projects.
Over the last three years, Ginnie Mae’s Office of Enterprise Risk (OER) has launched a series of machine learning and AI model pilots, utilizing different approaches to explore new ways of measuring and analyzing data. One AI algorithm similar to the one used in genomic sciences is being deployed to reduce the probability of false negatives and false positives when identifying Issuers that may pose enhanced risk to the program but may slip through the cracks of traditional methods of risk identification. We plan to use this algorithm in our issuer risk management processes by fall 2021.
OER has also explored cutting-edge graph technology to create a digital replica of the mortgage financing markets — mapping out the complex network of issuers, their creditors, parental institutions, and other players in the mortgage finance ecosystem. This simulation can identify cascading effects on the greater ecosystem, drive proactive policy analysis, and perform Black Swan simulations. In another nod towards efficiency, OER is putting in place a machine learning tool to automatically review Issuers’ credit by translating financial metrics into qualitative assessments. The tool, which also will be live in fall 2021, will reduce the workload of credit analysts significantly.
AI can also automatically flag missing or inconsistent loan data. Usually, it is the job of the underwriter to verify the data, and many underwriters are still manually inputting data by transcribing it from paper documents to spreadsheets. With AI, underwriters can focus on dealing with automatically flagged errors and exceptions, rather than searching for the error with their naked eyes.
The bottom line with AI is that it soon will be ubiquitous throughout our industry and embedded more deeply into Ginnie Mae’s processes, becoming an ongoing force for broader access to low-cost mortgage financing — just what Ginnie Mae has been working to do since our founding more than 50 years ago.