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’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.
For the fourth time in five years, Ginnie Mae and its insuring and guaranteeing partners have financed homeownership for more than 900,000 first-time homebuyers. Fiscal year 2020 was the second highest total in five years at 965,115, coming just short of the 2017 high-point of 975,340 and significantly higher than the 888,437 initial buyers in 2019.
Ginnie Mae attracts capital for mortgage lending facilitated by four government programs: the Federal Housing Administration (FHA); the Veterans Administration (VA), the Rural Housing Service within the U.S. Department of Agriculture (USDA) and lending under the Public Indian Housing (PIH) program within the Department of Housing and Urban Development.
Measured by total loans within Ginnie Mae MBS, FHA was the most frequently used program by first-time buyers in FY 2020 with more than 636,000 mortgages. That is followed by the VA program at 228,148, USDA at 99,220 and PIH at 1,531.
However, as a percentage of each underlying agency’s program, 72 percent of all USDA loans went to first-time homeowners, followed by USDA and PIH each at 44 percent and VA at 19 percent.
Ginnie Mae recently launched a new podcast series – Capital Markets Live! The podcast explores issues that affect Ginnie Mae mortgage-backed securities (MBS), with insight from officials within the agency and from market participants. The first episode of Capital Markets Live, hosted by Alven Lam, managing director for International Markets at Ginnie Mae, examines the conditions influencing the performance of Ginnie Mae MBS during the COVID-19 pandemic, featuring analysis from Managing Director and Head of Structured Credit at State Street Global Advisors, Jim Palmieri. Please visit this page to hear the interview and see the presentation.