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.
Ginnie Mae is among the organizations across the public and private sectors that are finding a growing number of applications for blockchain that increase the security, efficiency, and speed of digital transactions. Ginnie Mae is in the process of establishing a blockchain workgroup consisting of Ginnie Mae technology and business members and participants across U.S. Federal Housing-related agencies.
At its simplest, blockchain is a way to authenticate, record and track decisions across a network of blockchain participants’ computers. While organizations have recorded transactions in ledgers for centuries, those ledgers have traditionally been isolated to protect their accuracy, with each entity involved in the transaction maintaining their own separate record. Blockchain allows all entities to access a single digital ledger that is updated in real-time and is irreversible, decentralized, and transparent. By eliminating redundancy and compliance burdens and increasing trust across parties, blockchain offers the potential to accelerate and lower the costs associated with transactions across sectors. Reducing costs in the mortgage finance system while ensuring safety and liquidity is at the center of Ginnie Mae’s mission.
As blockchain has matured, Ginnie Mae has come to view the technology as a critical opportunity for innovation. In the home lending, private sector financial service companies are using blockchain-based platforms to encrypt and protect data exchanges between lenders and borrowers in real-time. Importantly, innovations involving blockchain are taking aim at increasing transparency, accelerating capital deployment, and overcoming geographical barriers to lending.
Bringing Blockchain to Ginnie Mae
Ginnie Mae’s Innovation Lab, which explores emerging technology applications to Ginnie Mae’s mission and operations and develops proofs of concept for testing within the organization, is conducting a preliminary analysis of how blockchain might be used to reduce cost, reduce risk, enhance stakeholder’s experience, improve liquidity and meet the needs of the evolving mortgage industry.
Smart Contracts Evolve Legacy Technology Concepts
Traditionally, government regulations and requirements for loan pooling across U.S. agencies have been enforced by computers using batch processes to validate that loans in a set of pools meet the specified criteria for the government guarantor loan program. This standalone, multi-step process mimics the even older process of reviewing each lender’s submitted loan documents for acceptance into a government approved loan pool. Blockchain capabilities provide opportunities to develop and examine multiple variants and modeled scenarios to meet various home buyer, seller, guarantor and investment needs. When we look at the life cycle of a mortgage through a new technology lens, there is potential to be able to embed the guarantor loan acceptance criteria further up the lending process with digital technologies that connect core elements of property borrower and lender information. This provides the ability to examine and identify eligible combinations of borrowers, properties, lenders and pools.
Blockchain to Reduce Work, Paper, Time, and Risk
Looking at the U.S. Federal Housing information life cycle , it’s estimated that a permissioned, distributed ledger using blockchain could significantly reduce data transactions timelines. This is due to the efficiencies created by decreasing the number of times the same information is captured, transferred, stored and reconciled across the mortgage origination-to-securitization process.
Blockchain may provide Ginnie Mae greater information security controls through a permissioned, role-based network. In addition, a distributed ledger design will improve process efficiencies by a reduction in data handoffs from stakeholder to stakeholder in the Ginnie Mae loan pooling process.
For example, a distributed ledger design will reduce the current data movement from stakeholder to stakeholder with a double benefit of; 1) reducing the cost for each stakeholder to store, verify, and transit data, and 2) reduce information latency by each stakeholder accessing shared, updated data immediately once entered or altered within the government guarantor mortgage ecosystem.
Ginnie Mae is only at the beginning of its Blockchain exploration and looks forward to its research and findings to ensure that this technology will bring significant improvements over the long term for participants in and beneficiaries of the government backed MBS market.
Since the first Ginnie Mae mortgage-backed security (MBS) was issued more than 50 years ago, Ginnie Mae has maintained a laser-like focus on guaranteeing securities that attract a diverse group of fixed-income investors. The breadth and depth of this investor pool helps keep liquidity flowing to the U.S. government-backed mortgage market. With more than $2.1 trillion of MBS outstanding and investors from five continents holding our securities, the market for Ginnie Mae MBS clearly is deep and liquid. Nevertheless, the agency chose not to rest on its past success and decided to press ahead with program enhancements that could attract more investors and help Ginnie Mae better finance affordable housing for America’s families.
Earlier this month, Ginnie Mae announced that it is implementing a new Environmental, Social, and Governance (ESG) data point in the Single-Family Supplemental File investors use to analyze the agency’s securities. The objective is to give Ginnie Mae MBS investors information that supports their sustainable investing decisions and solutions.
The ESG record will provide pool level aggregate information about the extent of loans and unpaid principal balance (UPB) dollars that are in low- and moderate-income areas.
ESG is growing in importance as a lens through which investors in America and around the world measure investment suitability. By enhancing visibility into the Ginnie Mae pools that contain mortgage loans on homes located in low- and moderate-income areas, Ginnie Mae gives investors another way to gauge the agency’s focus on an aspect of its mission.
The low- and moderate-income areas used in formulating this new disclosure are defined by the Department of Housing and Urban Development (HUD). The new disclosure aggregates to the pool level the number of loans, percent of loans, UPB dollars, and percent UPB dollars across low- and moderate-income areas applicable to the pool.
The new dataset compliments the existing information Ginnie Mae currently provides on the number of first-time homebuyers who are financed with Ginnie Mae MBS. A test file of the enhanced disclosure will be provided in mid-April 2021 and the first production MBS SF PORTFOLIO – POOL SUPPLEMENTAL file containing Record Type 25 will be provided on the Disclosure Data Download page on May 10th.
Ginnie Mae’s role in the fixed-income markets is stronger than it has ever been, with record-breaking MBS issuance in several of the past few months. Our outstanding mortgage-backed securities (MBS) have grown steadily, in parallel with the demand for affordable home financing as 30-year fixed-rate mortgage costs fell to levels never before seen. The Ginnie Mae MBS program is here for mortgage borrowers and investors through all market conditions, whether led by purchase mortgage activity or refinance mortgage volume. Consider the numbers: Over the past decade, the value of Ginnie Mae’s outstanding MBS doubled from $1.05 trillion at the end of fiscal year 2010 to $2.12 trillion at the end of fiscal year 2020.
The volume increase in outstanding MBS reflects an expansion of the portion guaranteed by the Department of Veterans Affairs (VA). The share of VA mortgages in new Ginnie Mae MBS has increased sharply over the past ten years, from 23 percent in 2011, to nearly 44% in 2020.
Ginnie Mae is committed to maintaining a strong MBS program built on a foundation of flexibility and reliability in order to meet the secondary market needs of the Issuers responsible for loans to veterans under the VA program, while also minimizing risks to taxpayers.