Comment Letter on Advanced Notice of Proposed Rulemaking on HMDA Data Points

October 15, 2019
Comment on Advance Notice of Proposed Rulemaking (ANPR) Concerning HMDA Data Points
Docket No. CFPB-2019-0020

To Whom It May Concern:

The undersigned organizations (55 national and local organizations) oppose any dilution or diminishment of the new and enhanced Home Mortgage Disclosure Act (HMDA) variables added by the 2015 final rule issued by the Consumer Financial Protection Bureau (CFPB). The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 (Dodd Frank) required the CFPB to enhance HMDA data by adding a number of variables regarding loan terms and conditions and borrower demographics. The CFPB also used its discretionary authority under Dodd Frank to add more data points such as debt-to-income ratios, reverse mortgages, and characteristics of multifamily and manufactured home lending.

This letter will make the following major points:

• The updates to HMDA data mandated by Dodd Frank and those implemented by the CFPB per their discretionary authority were necessary so that HMDA could continue to achieve its statutory purpose of ensuring that lenders meet housing needs in communities and make responsible loans in a non-discriminatory manner.
• The lending industry has changed in profound ways since HMDA’s passage in 1975. Loan terms and conditions became more complex and thus subject to abuse. To guard against abusive lending, HMDA data needed to be updated to include information on loan terms and conditions.
• HMDA data also needed to be updated to ensure that vulnerable segments in the population were being served responsibly by the lending industry. In particular, HMDA data needed to be updated to contain the age of borrowers and to contain more information about multifamily and manufactured home loans. Disaggregated race and ethnicity data also became important as abusive lenders targeted segments of the Asian and Hispanic communities.
• The costs of collecting the Dodd Frank data are modest. The costs of not collecting this data are monumental. The financial crisis was caused in large part by abusive lending that was hidden from the public due to a dearth of data on loan terms and conditions.
• This ANPR is not consistent with the Administrative Procedure Act (APA). The CFPB is asking the general public to comment on the utility of the new Dodd Frank data without sufficient time and analysis tools. The CFPB will therefore not benefit from fully informed public comments and the rulemaking will be compromised.

The motivation behind the data improvements required by Dodd Frank was that more transparency in the form of publicly available data was needed to prevent widespread predatory and abusive lending that was largely responsible for the financial crisis. By providing information on loan terms and conditions, the data would help regulatory agencies, community groups, and other stakeholders spot increases in abusive lending and take steps to curb such lending before it caused another crisis.

In addition, stakeholders agreed that more information was needed to ensure that lenders were serving the housing and credit needs of vulnerable and/or underserved subgroups within the general population. The CFPB updated multifamily data reporting requirements to ascertain whether lenders were serving the needs of renters in a housing market that was posing challenges for increased homeownership and affordability for renters and homeowners alike. Likewise, lawmakers and the CFPB reacted to the abuses unscrupulous lenders inflicted upon older adults by requiring age to be a variable in HMDA data and information about reverse mortgage lending in HMDA.

Confronted by the financial crisis, the response of Congress in passing Dodd Frank and the CFPB in rigorously implementing Dodd Frank to update HMDA data was not only consistent with the 1975 HMDA law but enhanced the ability of HMDA to achieve its statutory purposes. HMDA needed to be updated to take into account changes in the lending industry and the demographic and housing market conditions of the country. Only with an update could the statutory purposes of HMDA be upheld. The statutory purposes are to assess whether lenders are meeting the housing needs of local communities, inform public sector investment decisions, and to detect and prevent discrimination. HMDA data was becoming less informative to assess whether sophisticated loan terms and conditions introduced in the late 1990s and 2000s were too complex or imposed too many costs on vulnerable borrowers. Since complex lending was becoming abusive and deceptive, lenders were not meeting credit and housing needs while the HMDA data, absent an update, could not accurately determine whether needs were being met in a legitimate and responsible manner.

If the CFPB now reverses its prior work on updating HMDA data, it will impede the ability of HMDA to achieve its statutory purposes in this rapidly changing housing and lending market. Indeed, the CFPB will be abdicating its responsibility to protect consumers and will invite a new round of abuses by shrouding lending in a veil of secrecy to the detriment of not only consumers but the economy as a whole.

This Proposal is not Consistent with the Administrative Procedure Act

The undersigned organizations maintain that this rulemaking violates the objectives of the Administrative Procedure Act (APA) to fully inform federal agencies of the impacts of proposed rules by providing the public with meaningful opportunities to comment. The CFPB initially issued this ANPR before the public had been able to access the complete HMDA data for the first year of the new data enhanced by Dodd Frank. The agency then extended the comment period to October 15 but released the first year of the Dodd Frank data on August 30, giving the public approximately an insufficient 45 days to comment.

The ANPR is sweeping in its scope of the questions it raises. Dodd Frank required that the CFPB enhance HMDA data by adding a number of mandatory data points on loan terms and conditions and also provided the CFPB with discretionary authority to add more data points. The CFPB asks the general public in the ANPR to opine on the utility of the mandatory and discretionary Dodd Frank data points in furthering the purposes of HMDA. Even though the CFPB is asking profound and difficult questions, it is not providing the public with sufficient time and analysis tools with which to answer these questions. The agency provided a 2018 analysis of national trends called Introducing New and Revised Data Points in HMDA: Initial Observations from New and Revised Data Points in the 2018 HMDA.[1] While useful and illuminating, the publication admits that as an introduction to HMDA, it cannot answer certain pressing questions. For example, in a section investigating racial disparities in denial rates for borrowers with similar credit scores, it suggests that a complete multivariate analysis beyond the scope of the publication needs to be conducted.[2] Only more sophisticated analyses of this sort will shed full light on the value of the data. These types of analyses are time consuming, labor intensive, and cannot be adequately performed in the 45 day comment window provided by the CFPB.

In addition, the analysis tools the CFPB has provided to the public are insufficient for members of the public to use and to provide informed comments on the utility of the new data. While advanced researchers can download the data, most members of the public depended on the previous tables and charts provided by the Federal Financial Institutions Examination Council (FFIEC). These tables and charts were pre-formatted, contained useful information such as action taken by loan type and demographic group of borrower, and were easy to download. In contrast, the new website has a feature called Data Browser that allows a limited set of cross-tabulations for geographical areas such as metropolitan areas.[3] A user can cross-tabulate only two variables at a time, which is quite limiting. For example, a user can cross-tabulate race and action on an application, but cannot filter by loan type. This makes the feature almost useless since actions including denials and loan approvals differ dramatically for different loan types and purposes. Moreover, a user can only cross-tabulate for eleven variable categories and cannot tabulate the new loan term and condition variables, which are major new Dodd Frank variables.

Overall, the analysis tools and data access the CFPB has elected to provide will guarantee that only researchers, sophisticated data users, and industry stakeholders will be able to comment in a meaningful manner. Members of the general public and practitioners such as local staff of human rights offices will not be able to provide meaningful commentary on the data since they cannot readily access data for their local area.

With the truncated time period and inaccessible data, the CFPB will receive comments that are substantially less useful for its rulemaking. The general public will be reduced to making educated guesses about these critical issues rather than using the data to provide specific and insightful comments. As a result, we believe that the ability of the CFPB to issue a well-informed and reasoned rule will be compromised.

The ANPR also leads respondents in a biased manner to overestimate the costs of HMDA data while overlooking its benefits. For example, Question 1 asks the respondents “to identify any new data point or any data point revised to require additional information…for which the cost of collecting and reporting the information does not justify the benefit that the information collected and reported provides in furthering the purposes of HMDA.” First, the question could have been phrased in a neutral fashion to simply ask respondents to assess the costs and benefits of the various data points. Second, the ANPR does not provide any information for the general public regarding costs and benefits of individual data points or the cumulative costs and benefits of the Dodd Frank data points. When an ANPR does not present well-grounded and comprehensible research into costs and benefits, it will not generate thoughtful responses and judgments regarding the balance between costs and benefits or which tradeoffs are acceptable to various stakeholders. Instead, these leading questions will generate ideological and self-serving responses from various stakeholders.

The entire ANPR is biased towards the industry. Lending institutions, because they have submitted the first year of Dodd Frank data, have more experience with the data, including the ease of reporting and the accuracy of the data. In contrast, members of the public must resort to looking at piecemeal and difficult-to-use data. In addition, members of the public will be at a disadvantage because they have less experience with the data since they did not compile and submit the data like lenders. The entire ANPR process is unfair as it is tilted to give lenders the advantage, arming them with data and knowledge using the data that the public has not had a chance to acquire.

Even lenders, however, will be disadvantaged in responding to the ANPR because they too will lack the entire 2018 database for a sufficient time period. Their answers will also be based on partial information, though they will have more information than the general public. The entire ANPR exercise is rushed and appears intended to generate disparaging comments about the Dodd Frank data instead of insights that will contribute to an effective rulemaking that will benefit society at large.

Analyses assessing the impacts of any changes to the Dodd Frank variables are complex and very difficult to undertake in this comment period. A forty five day period for access to the new data is inappropriate and not sufficient given the complexity of assessing possible changes and responding to the complicated ANPR questions.

A recent Congressional Research Service report on the APA states that the APA requires a “meaningful opportunity for public comment.” The report states, “although the APA sets the minimum degree of public participation the agency must permit, the legislative history of the APA suggests that matters of great importance, or those where the public submission of facts will be either useful to the agency or a protection to the public, should naturally be accorded more elaborate public procedures.”[4]

This rulemaking has not provided the public with a meaningful opportunity to comment as required by the APA. Therefore, the CFPB itself will not benefit from fully informed comments based on an analysis of the new HMDA data. The CFPB will lack critical information with which to make the fairest and most effective rule regarding the Dodd Frank data.

Costs of Dodd Frank Data Not as Much as Claimed by Some Stakeholders and Costs of Changing Data Now Would Be High

Large scale changes to HMDA data would pose a serious compliance burden for lenders and administrative burden for the CFPB. Lenders have spent considerable time and resources developing reporting systems to accommodate the new HMDA data. Hasty revisions to the data will cause significant interruptions and retooling costs for lenders. The CFPB itself will also need to invest considerable resources on its data collection system and updating instructions for lenders. When data is changed, the best approach is incremental and thoughtful change.

The costs of the Dodd Frank data will be overestimated by some stakeholders as a justification for deleting it. However, almost all of the Dodd Frank data would still need to be collected by the lenders in order to comply with other statutes like the Truth in Lending Act and/or to sell loans to Fannie Mae or Freddie Mac or acquire FHA insurance for loans. For example, Adam Levitin, professor of law at Georgetown University, states that loan costs and fees are required to be reported to borrowers under the Truth in Lending Act and Real Estate Settlements Procedures Act. Data points such as debt-to-income ratio are required for compliance with the Qualified Mortgage rule while other data points such as combined loan-to-value ratio are collected as part of securitization data.^[5]

Since lenders already collected much of the Dodd Frank data even before Dodd Frank, the costs of including this data in HMDA is modest. As described in our comments on the NPRM, the savings estimated by the CFPB for lenders exempted from reporting the new Dodd Frank data or HMDA data in any form were not significant. Even the higher cost estimates for the more sophisticated and larger volume lenders are not significant considering the asset sizes of these very large lenders. In its final rule in 2015 adopting the Dodd Frank HMDA data points, the CFPB estimated that the additional compliance cost of the rule in total to lenders would be $23 per closed end loan for most lenders.^[6]

While we do not deny that costs are incurred with data collection, the undersigned organizations maintain that the costs of not collecting this data are monumental. The trillions of dollars of lost wealth overall and the wealth loss in underserved communities due to the financial crisis was the result of opaque markets in which abusive lenders hid under a veil of secrecy about the extent of their unfair and deceptive lending. The benefits of additional data collection exponentially outweigh its costs.

The Need to Update HMDA Data: Surge in Abusive Lending

Abusive and high cost lending was the largest contributor to the worst recession since the Great Depression. Federal agencies, community organizations, and the public at large were disadvantaged in combating the widespread abusive lending because they lacked detailed data on lending practices in the industry as a whole and for individual lenders, including those violating fair lending and consumer protection laws.

In 2009, the Government Accountability Office (GAO) had to rely upon a private sector database instead of HMDA to document the pervasiveness of risky lending for leading members of Congress. This was a couple of years too late. Some notoriously risky and abusive lenders had already ceased operations after inflicting untold harm on consumers. For example, the FDIC placed Washington Mutual in receivership in 2008 as a result of the thrift’s unsustainable adjustable rate mortgage lending. Ameriquestand New Century, non-bank mortgage companies, offering high volumes of risky and high cost loans ceased operations around the same time. Although HMDA data was available for these lenders, federal agencies were unable to conduct sufficient fair lending and consumer compliance exams because the HMDA data lacked information on loan terms and conditions, which were necessary for the econometric analyses that could have identified discriminatory, unfair, and deceptive practices.

The GAO report documented a tremendous increase in nonprime (subprime and Alt A lending). Nonprime lending increased from 12 percent of all loans in 2000 to 34 percent in 2006.[7] A large share of subprime loans in these years had characteristics associated with default and foreclosure including adjustable interest rates, less than full documentation of borrower income, high debt-to-income ratios (DTI), and high loan-to-value ratios (LTV).[8] The percentage of subprime loans with DTIs over 41 percent increased from 47.1 percent in 2000 to 59.3 percent in 2007.[9] Over an eight year-time period, the GAO found that 60 percent or more of subprime loans had prepayment penalties.[10]

Subprime lending had multiple risks layered on top of each other. The loans usually did not just have one risky loan term or condition such as adjustable rates but rather several such as adjustable rates, high DTIs, prepayment penalties, and high fees which confronted borrowers with unaffordable and unsustainable loans. The best way to have discovered the extent of risk layering would have been through a publicly available loan level database such as HMDA, which unfortunately did not contain information on loan terms and conditions during those years. As a result of the abuses and risk layering being undetectable, about one quarter of the subprime loans originated between 2000 and 2007 and that were still outstanding in 2008 were in default or had started the foreclosure process.[11] In addition, the situation would get worse as adjustable rates were resetting to higher rates. The Wall Street Journal reported that two million of the adjustable rate loans originated in the 2000s would be resetting and adjusting upward from their initial rates in 2007 and 2008.^[12]

At the time, HMDA data provided community groups with information suggesting that problematic lending was pervasive in communities of color and even in middle-income and upper-income communities of color. HMDA data, however, was not comprehensive enough to paint a complete picture of the thoroughgoing nature of the abusive lending. As a result, community groups often felt that they were the proverbial canary in the coal mine that the federal agencies and members of Congress were not listening to with enough seriousness.

In July of 2007, NCRC testified before the House Financial Services Committee regarding the effectiveness of HMDA data in rooting out discrimination.[13] The testimony featured racial disparities in high cost lending. It stated that:

The lending disparities for African-Americans were large and increased significantly as income levels increased. In The Income is No Shield report, we found that African Americans of all income levels were twice as likely or more than twice as likely to receive high-cost loans as whites in 171 metropolitan statistical areas (MSAs) during 2005. Middle and upper-income (MUI) African-Americans were twice as likely or more than twice as likely to receive high-cost loans as MUI whites in 167 MSAs. In contrast, low- and moderate-income (LMI) African Americans were twice as likely or more than twice as likely to receive high-cost loans as LMI whites in 70 MSAs. Moreover, MUI African-Americans receive a large percentage of high-cost loans. In 159 metropolitan areas, more than 40% of the loans received by MUI African-American were high-cost loans.[14]

The NCRC testimony concluded:

Yet, discriminatory practices have shifted to more subtle forms. Instead of widespread redlining and outright rejections of applicants due to their protected status, a more subtle form of discrimination involves charging higher interest rates and fees than is warranted based on creditworthiness. The new pricing data (in HMDA) assists in uncovering discriminatory pricing, but the new pricing data by itself remains incomplete. Because HMDA data do not allow for the observation of fee gouging or dangerous risk layering involving high loan-to-value ratios and reduced documentation lending, unscrupulous lenders can continue to exploit financially vulnerable consumers. Until HMDA data includes more key underwriting variables and loan terms and conditions, the abusive parts of the industry will be one step ahead of the general public in inventing new methods for deceptive and usurious lending.[15]

In Congressional testimony during 2008, former Comptroller of the Currency John Dugan discussed how opaqueness in the secondary market was impeding the market and leading to a liquidity crunch. He states, “A lack of transparency has made it difficult to distinguish differences in risk among mortgage-related securities, and illiquid markets for many of these securities have made valuation difficult.” The Comptroller then discusses how fast the ratings for subprime securities fell. Part of the difficulty is that the lending in the primary market was opaque due to a lack of data concerning loan terms and conditions. This allowed ratings agencies to inflate ratings of securities, which then infected the secondary market with faulty and fraudulent information leading to an over-investment in subprime securities.[16]

The data points in HMDA data mandated by Dodd Frank and added by the CFPB per the discretionary authority are a direct response to the financial crisis. The data points include loan terms and conditions that were associated with widespread abuses. These include comprehensive pricing and fee information, DTI, LTV, prepayment penalties, and adjustable rates. These data points remain critical today in order to monitor and prevent abuses in the lending marketplace.

The Need to Update HMDA Data: Protect Subgroups within Communities of Color and Stop Abusive Lending in Underserved Communities

In addition, the HMDA data race and ethnicity categories required updating because community organizations and civil rights organizations had been reporting surges in foreclosures in Asian and Hispanic communities yet HMDA data was not detailed enough to reveal the extent of abusive lending targeted to subgroups within the Asian and Hispanic community. In response, the CFPB updated HMDA data to include disaggregated race and ethnicity categories for Asians and Hispanics. Combined with the enhancements associated with loan terms and conditions, the data will now help stakeholders protect vulnerable populations against a resurgence of predatory lending.

In general the enhanced HMDA data will be invaluable for fair lending enforcement. Just last year, journalists Emmanuel Martinez and Aaron Glantz, authored “Kept Out,” an expose highlighting continued mortgage redlining in 61 U.S. cities, using HMDA data, that resulted in city, state, and federal actions to address pervasive racial disparities in bank branching and mortgage lending patterns.^[17] Martinez and Glantz, however, did not have access to the enhanced Dodd Frank data so they were unable to fully document the extent in racial disparities in lending. For example, were disparities in access to loans also accompanied by more onerous terms and conditions offered to people and communities of color?

In the wake of the financial crisis, it is vital to identify and take swift action against racial disparities that include unfavorable terms and conditions. Communities of color are often among the first to experience abusive lending so curbing such lending in these communities is of paramount importance to prevent the wide-scale extraction of wealth from communities of color, older adults, and other groups subsequently targeted by predatory lenders. The 2018 data indicates that racial disparities in pricing remain that must be further investigated. The median interest rate for African-American and Hispanic borrowers are 12.5 basis points higher than that for White non-Hispanics.[18] The median rate spread is also higher for African-Americans and Hispanics.[19] It is also important to ascertain whether these pricing disparities are accompanied by more onerous loan terms and conditions for racial and ethnic minorities so the new Dodd Frank variables must be preserved for multi-year analysis.

The Need to Update HMDA Data: Targeting of Older Adults

Predatory and subprime lenders targeted older adults. The AARP approached NCRC in the years leading up to the financial crisis, asking for HMDA studies documenting the extent of high cost lending. Because HMDA data did not have an age variable, the best NCRC could do was identify census tracts with high concentrations of older adults. However, this approach was inadequate to document the extent of high cost and abusive lending targeted to older adults.

The National Consumer Law Center (NCLC) and numerous other legal aid societies represented scores of older adults victimized by predatory lenders in the years running up to the financial crisis. NCLC documented how serial refinancing, high costs, high fees, credit insurance added to the loan amount, balloon payments, home improvement scams, and other abuses often rendered older adults helpless in defending against foreclosure.[20]

Abusive lenders also exploited older adults with reverse mortgage scams. In a report to Congress, the CFPB documented increasingly risky practices associated with reverse mortgages. For example, the report states, “Reverse mortgage borrowers are withdrawing more of their money upfront than in the past. In FY2011, 73 percent of borrowers took all or almost all of their available funds upfront at closing. This proportion has increased by 30 percentage points since 2008. Borrowers who withdraw all of their available home equity upfront will have fewer resources to draw upon to pay for everyday and major expenses later in life. Borrowers who take all of their money upfront are also at greater risk of becoming delinquent on taxes and/or insurance and ultimately losing their homes to foreclosure.” The CFPB found that a high 9.4 percent of older adult reverse mortgage borrowers were at risk of foreclosure as of 2012 due to nonpayment of taxes and insurance. Finally, the CFPB stated that misleading industry advertising caused misperceptions about reverse mortgages, increasing the chances of “poor” consumer decision-making.[21]

Given the rampant abuses in the lending marketplace afflicting older adults, Congress required that age be added to the HMDA data and the CFPB further enhanced the data by adding a reverse mortgage flag. Any diminution of this data will encourage another round of abusive lending as predators will operate under a veil of secrecy.

The Need to Update HMDA Data: Affordable Housing Crisis, Rental Housing, and Manufactured Housing

Some stakeholders have criticized the multifamily data as unrelated to the statutory purposes of HMDA. The purpose of HMDA is to “is to provide the citizens and public officials of the United States with sufficient information to enable them to determine whether depository institutions are filling their obligations to serve the housing needs of the communities and neighborhoods in which they are located and to assist public officials in their determination of the distribution of public sector investments in a manner designed to improve the private investment environment.”[22] A pressing housing need in communities, given the high cost of housing, is affordable rental housing. Instead of being contrary to the statute, multifamily data in HMDA fulfills a central purpose of the statute. It is also vital in this time period to see if public sector investment in neighborhoods has leveraged multifamily housing lending. Thus, the enhancements to multifamily lending, including the data points on units affordable to lower income tenants, helps stakeholders determine if the public and private sectors, working together, have increased the stock of affordable housing in neighborhoods.

Rental housing has become more expensive to build, imposing housing cost burdens on tenants and lower income tenants in particular. In the State of the Nation’s Housing in 2018, the Joint Center for Housing Studies at Harvard University documents that the median rent has risen 20 percent faster than overall inflation from 1990 through 2016.[23] About one-third of all US households are cost burdened, devoting more than 30 percent of their incomes for housing in 2016. For renters alone, however, the cost-burdened share is much higher at 47 percent. Of the 20.8 million renter households with cost burdens, 11 million are severely cost burdened paying more than half their incomes for housing.[24] This housing cost squeeze is unlikely to be alleviated any time soon. According to the National Low Income Housing Coalition, for every 100 very low-income renters, only 56 rental units are affordable, and for every 100 extremely low-income renters, only 35 rental units are affordable.[25] In this light, HMDA data showing where multifamily units are being built and how many are affordable is critical.

Like HMDA data on multifamily lending, HMDA data on manufactured home lending is vital in order to assess whether lending institutions are responsibly serving the need for manufactured housing. In 2014, the CFPB released a report finding that manufactured housing is disproportionately located in rural areas and residents of manufactured housing are disproportionately older adults with lower incomes and net worth. While manufactured housing holds promise as affordable housing, lenders such as Clayton Homes have engaged in aggressive bait and switch tactics that saddled vulnerable buyers with high cost and unaffordable loans.[26] The CFPB revealed that most manufactured home lending is chattel lending with interest rates 50 to 500 basis points higher than real estate secured loans. Overall, 68 percent of all manufactured home loan purchase loans were higher cost in 2012.[27]

Why the Dodd Frank Data Points Must Remain in the HMDA Data and Suggestions for Improving the Reporting of the Data

Demographic Data Points

Age

The disclosure of age in bins or ranges is vital for fair lending enforcement and protection against unfair and deceptive lending. As discussed above, in the years leading up to the financial crisis, older adults, particularly older adults of color, were targeted by abusive lenders. These lenders would persuade seniors to take out unsustainable refinance loans, often targeting homeowners with substantial equity in their homes. Other abuses occurred with reverse mortgage lending for older adults 62 years or older.

The 2018 data reinforces the importance of collecting age information about applicants since it reveals disparities in denial rates by age that is inconsistent with the creditworthiness profile of older adults. The CFPB documents with the 2018 data that the median credit score for applicants aged 45 to 54 is 744, more than 20 points lower than median score of 767 for applicants aged 65-77.[28] At the same time however, older adults experience higher denial rates than their younger counterparts. When older adults over 62 apply for conventional conforming loans, they experience a denial rate of 20.8 percent as opposed to a denial rate of 14.5 percent for applicants under 62. The disparities for applicants of FHA loans are more pronounced at 44.6 percent for applicants over 62 compared to 21 percent for those under 62.[29] These disparities need to be carefully investigated with more complex statistical methods beyond the time allowed in the ANPR comment period. They also need to be investigated over a multi-year time period to see if progress is being made on narrowing them. Hence, it is imperative to retain robust data on age of applicants in the HMDA data.

The age data reveals additional disparities that need to be further investigated over the coming years. The CFPB found that the frequency of taking out adjustable rate mortgages (ARMs) increases with age. Just 4 percent of borrowers of closed end loans younger than 25 take our ARM loans; this increases to 9 percent of borrowers 75 years and older.[30] Moreover, home equity lines of credit are more likely to have prepayment penalties for older than younger borrowers.[31]

Problematic practices and pricing structures discussed above for reverse mortgages might still be present. Reverse mortgage loans have a higher median interest rate than most other loans types and are taken out by older adults. The median interest rate for reverse mortgages is 4.827 percent in contrast to 4.750 percent for conventional conforming loans.[32] The CFPB also found that almost 43 percent of the reverse mortgage borrowers are among the oldest adults aged 75 and older.[33] Given the higher pricing for reverse mortgages, this new data point must be preserved so that the fairness and affordability of the pricing structure for older adults, including the very oldest adults, can be assessed over the coming years.

The bins of 25 to 34; 35 to 44; 45 to 54; and 55 to 64 are useful for HMDA data analysis. Also, we appreciate that the data indicates whether the applicant is 62 years of age or older. Since older adults become eligible for reverse mortgage lending at age 62, it is imperative that the HMDA data enable users to identify if loan applicants qualify for reverse mortgages.

Instead of a top code for ages of 74 and higher, we urge the CFPB consider additional bins of 75 to 84 and 84 and older since older adults are living longer. Moreover, the public and federal agencies could assess if patterns of reverse mortgage lending and other types of home lending differ or are similar for the oldest age bins compared to other age bins; the lending patterns for the oldest age bins may also reveal whether there are particular fair lending or affordability concerns specific to the oldest seniors.

Disaggregated race and ethnicity data

The CFPB substantially furthered the statutory purposes of HMDA when it finalized its 2015 HMDA regulation which required banks to ask applicants more refined questions about their race and ethnicity and instituted reporting on disaggregated race and ethnicity data for “Asian” and “Latino” home loan applicants.

For years, community groups had bemoaned the failure of HMDA data to capture the diverse lending experiences of large segments of the mortgage market when lumping together and labelling millions of people as “Asian” and/or “Latino,” despite demographic, linguistic, and experiential differences within these broad groupings.^[34] HMDA data, instead of helping to identify potential discrimination as Congress intended, masked lending disparities and discrimination occurring within certain Asian and Latino communities. In contrast, existing reporting regimes under the Affordable Care Act and the Voting Rights Act support the continued collection of this critical data.

Reasons the data are needed. Prior to the inclusion of disaggregated data, the reported HMDA race data for Asian loan applicants, for example, has obscured the diversity of Asian-American and Pacific Islander (AAPI) communities and contributed to a relative dearth of HMDA analysis considering the experience of Asian borrowers. Many HMDA analyses do not even consider the experiences of Asian home loan applicants. In those analyses that have been conducted, Asian borrowers tend to receive loans at rates and costs comparable to white borrowers, though community groups working in AAPI communities know that these findings do not accurately reflect the diversity of Asian-American and Pacific Islander communities or the wide disparities in credit access within these communities. Similarly, analysis of lending to “Latino” borrowers obscures the experiences of populous and distinct communities within the Latino ethnicity category.

For example, the AAPI population was particularly impacted by this data limitation during the foreclosure crisis when a number of ethnic groups were overlooked by the aggregate HMDA data. National CAPACD and the Southeast Asia Resource Action Center (SEARAC) conducted research in the Central Valley in California in 2011 to document the impact of the foreclosure crisis on the Southeast Asian community. The research demonstrated that “because the data on the general population and the data on AAPIs are aggregated, the experience of Southeast Asian Americans in the Central Valley is often obscured and subsequently ignored.” The report also found that Southeast Asian American homeowners and renters in the Central Valley lived in higher concentrations in neighborhoods with high foreclosures, making them more likely to experience the impacts of foreclosures. In a four month period in 2010, nearly 850 Southeast Asian American homeowners received a Notice of Default, a higher rate of foreclosures than other Asian Americans in the county. Taken together with the fact that Southeast Asians are linguistically isolated, generally have lower median incomes, are highly concentrated in low-wage sectors, and have lower education levels than the AAPI community writ large, the community is vulnerable to foreclosure and its impact. Disaggregated data is essential to preventing another foreclosure crisis and holding banks accountable for their business practices.^[35]

Between 2008 and 2009, U.S. Census data shows that homeownership rates in California dropped from 38 percent to 28 percent for the Hmong community and 45 percent to 43 percent for the Korean community despite homeownership rates for Asian Americans in aggregate increasing slightly from 57 percent to 58 percent. Chhaya CDC’s analysis of 2008 foreclosure data by surname in Queens, New York, also found that South Asian households received a disproportionate share of risky mortgages – representing between 3.5 to 7 times more than their share of the population.^[36]

A 2007 report by the Census Bureau, entitled, “The American Community: Asians 2004,” part of the American Community Survey report series, analyzed experiences of several subcategories of Asian American families, including: Asian, Bangladeshi, Cambodian, Chinese, Filipino, Hmong, Indonesian, Japanese, Korean, Laotian, Malaysian, Pakistani, Sri Lankan, Taiwanese, Thai, Vietnamese, and other Asian. The report noted various differences in experiences for these groups, including differences in owner occupancy and home value.

The new disaggregated data requirements are critical to address past deficiencies in HMDA that have masked discrimination and frustrated Congressional goals.

Other federal programs and reporting regimes support disaggregated data collection. The data collection standards implemented by the Department of Health and Human Services (HHS) as required by the Affordable Care Act Section 4302 can serve as a model for HMDA. Section 4302 required HHS to establish data collection standards for race, ethnicity, sex, primary language, and disability status. The new standards include additional subcategories that roll-up to the Office of Management and Budget minimum standard categories – Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other Asian), Native Hawaiian and Other Pacific Islander (Native Hawaiian, Guamanian or Chamorro, Samoan, and Other Pacific Islander).

The Voting Rights Act provides another model to ensure a HMDA data collection and reporting regime that successfully captures the needs of additional subpopulations. Section 203 of the Voting Rights Act requires certain jurisdictions to provide bilingual voting materials in communities that meet a certain threshold of limited-English proficient residents. Congress, in various pieces of important legislation, is striving for a society that is inclusive and opens participation for all races and ethnicities. HMDA disaggregated race reporting furthers the purposes of HMDA to ensure housing needs are met for all groups of borrowers and communities, as well as promoting the purposes of the ACA and the Voting Rights Act.

Early analysis of 2018 data suggest disparities exist and continued reporting is necessary. Again, we are dismayed to see the CFPB open up for further consideration issues that have been long debated and considered. Despite years of public hearings and public comments submitted on HMDA, the CFPB’s ANPR shows it to be too eager to contemplate shielding disparities in lending from public view.

The CFPB reinforced the value of the disaggregated data in its recent analysis of the 2018 HMDA data. The CFPB reported a high incidence of disaggregated categories appearing in the data. For example, about half of applicants reporting Asian as their race in the first data field for race also reported a disaggregated category for Asian in the second data field. The CFPB found that of the approximately 700,000 Asian applicants, 13.9 percent reported that they were Asian Indian, 13 percent reported Chinese, 7.5 percent reported Filipino, 5.6 percent reported Vietnamese, 1.8 percent reported Japanese, 4.1 percent reported Korean, and 5.2 percent reported “other Asian” in the second field. Likewise the incidence of disaggregated Hispanic categories was high. Of the 1.3 million applicants who selected Hispanic or Latino in the first data field, 24.4 percent chose Mexican, 5.6 percent chose Puerto Rican, 2.5 percent chose Cuban, and 7.8 percent chose other Hispanic in the second field.[37] In other words, the disaggregated race and ethnicity data fields have more than sufficient number of observations which will yield statistically meaningful analyses when comparing the disaggregated categories against other variables such as loan action, price, and loan terms and conditions.

In many localities, Asian borrowers have been considered over-represented in 1-4 single family home lending, such that their share of lending exceeds their share of the population. Disaggregated data allows us to drill deeper, uncover disparities within that very broad category, and devise programmatic responses to the newly discovered disparities. Initial analysis of 2018 disaggregated data in New York City (NYC) show certain disparities, with Chinese Americans experiencing greater access to credit than other AAPI groups. Overall, 62% of home purchase loans to Asian borrowers were disaggregated. Of those, we found that 61% were to Chinese borrowers, who make up 48% of all Asians in the City. In contrast, Filipino borrowers make up 6% of the population and just 2.4% of home loans. Also, we found that “other Asian” borrowers are greatly underrepresented; 9.1% of home purchase loans versus their 17% of the population. In New York City, this includes Bangladeshi and Pakistani New Yorkers who make up significant percentages of the Asian population, particularly in the Bronx and Queens; 20% of the Asian population in the Bronx are Bangladeshi and 7.5% are in Queens. Similarly, 8% of the Asian population in Brooklyn and 10% in Staten Island are Pakistani.

Similar trends exist within the Hispanic populations, but the data is more limited. Just 32% of home purchase loans to Hispanic borrowers are disaggregated in 2018. Mexicans are quite underrepresented in total originations in comparison to their share of the population (7% vs 14%), but less so for Puerto Rican borrowers (25% of loans versus 30% of the population). The breakdown in New York City does not reflect the diverse Hispanic population, however, as Dominicans represent 28% of the Hispanic population, and Ecuadorans represent a large part of the 23% of Hispanics from Central and South America. This makes it difficult to draw too many conclusions from the “other Hispanic” category in HMDA data which make up 65% of loans to Hispanic borrowers versus their 54% of the population in New York City. Overall, this analysis suggests that lender reporting of disaggregated data is more robust for AAPI borrowers (over half of loans report disaggregated data for Asian borrowers), than it was for Hispanic borrowers (only a third of loans to Hispanic borrowers were disaggregated). It would be better if there were more Hispanic categories – at least adding populations on par with Cubans, such as Dominicans (3.5% of U.S. Hispanics), Salvadorans (3.9%), and Guatemalans (2.5%).^[38]  Dominicans make up nearly 30% of the New York Hispanic population.^[39]

An analysis of the 2018 data in the Los Angeles-Long Beach-Glendale metropolitan statistical area (MSA) likewise finds a high incidence of disaggregated race and ethnicity reporting. For applicants that indicated Hispanic in the first ethnicity data field, almost 40 percent indicated a disaggregated Hispanic category in the second data field; 32.4 percent identified themselves as Mexican, 1 percent as either Cuban or Puerto Rican, and about 6 percent as other Hispanic. Applicants that reported Asian in the first racial data field, identified an Asian disaggregated category 45 percent of the time; 17 percent identified themselves as Chinese, 9.6 percent as Filipino, 3.4 percent as Asian Indian, 3 percent as other Asian, 2.8 percent as Vietnamese, and 2.4 percent as Japanese.[40]

The reporting by the CFPB of the race and ethnic disaggregated race and ethnicity categories are likely under-estimates of the incidence of disaggregated reporting. This is because applicants or lending institutions are also reporting race and ethnic disaggregated categories in the first data field as well as the second data field (the CFPB’s analysis considers disaggregated reporting in just the second field). For example, when examining the first data field, NCRC observed that applicants reported Hispanic 93.5 percent of the time and the disaggregated categories 6.5 percent of the time. Of the disaggregated categories, Mexican was the most frequently reported at 4 percent of the time. When an applicant or lender reported Mexican in the first field, no entry appeared in the second data field 92.6 percent of the time and Hispanic appeared 6 percent of the time. Thus virtually all the Mexican entries in the first data field can be considered as entries for the second data field without duplication. Therefore, the 32.4 percent identified as Mexican in the second data field as discussed above is most likely just over 36 percent Mexican. Therefore the encouraging news above about the high incidence of disaggregated race and ethnicity entries as reported by the CFPB are underestimates. This further reinforces the value of the data.

The initial results suggest that while preliminary data are helpful to achieving the Congressionally articulated goals of HMDA, the CFPB needs to talk to lenders to understand their methods for collecting and reporting disaggregated race data, and to suggest improvements to those methods. Data collection and reporting appears to be inconsistent as suggested by analysis of data in the first and second racial and ethnic data fields.

The related issue of language access. A related issue is the language in which a loan was negotiated. Advocates have long reported, including at the Federal Reserve’s HOEPA hearings in 2006, the problem of Limited English Proficient borrowers being sold mortgages in their primary non-English languages, only to receive English-only documents with significantly worse terms than those promised.^[41] Such borrowers have been among the most vulnerable to predatory lending, abusive servicing, and loan modification scam practices. The quality of lending and servicing to Limited English Proficient borrowers is an important indicator of fair lending, and more granular data could help local jurisdictions direct resources to these at-risk communities.

With 40 percent of Asians and 15 percent of Native Hawaiians and Other Pacific Islanders speaking English “less than very well,” limited-English proficient (LEP) AAPI borrowers are particularly vulnerable to predatory lending and abusive servicing. While Census data show that 18 percent of Americans speak languages other than English in their homes, almost 40 percent of Californians fall into this category; more than half of this population speaks English less than “very well.” Spanish, Chinese, Tagalog, Vietnamese and Korean are spoken by approximately 83 percent of all Californians who speak a language other than English.

A 2014 Government Accountability Office report found statistically significant disparities in the rate of loan modification denials, cancellations, and re-defaults for LEP borrowers and other protected groups as compared to non-Hispanic white borrowers after analyzing certain loan modification data under the HAMP program.

Recommendations: To address these concerns, we urge CFPB continue to require the public reporting of disaggregated data, and further urge that CFPB:

• Explore with lenders and community organizations what groups are represented in the “other Asian” and “other Hispanic” categories and consider adding more categories to the disaggregated race and ethnic data fields;
• Work with HMDA reporters and the public to facilitate better data reporting and collection of disaggregated race and ethnicity data through the development of guidance and training for lenders, and public education campaigns for loan applicants;
• Create a clear expectation among lenders that they must properly seek and report disaggregated race and ethnicity data, and that CFPB will impose penalties for the failure to follow HMDA requirements; and
• Enhance existing reporting by requiring data collection relating to the primary language of the borrower, the language spoken to negotiate the loan and the language of the loan documents.

Data Points on Loan Purpose

Multifamily Lending

As stated above the purposes of HMDA include assessing if lenders are meeting housing needs and to assist public officials in directing public sector investments in a manner to leverage private lending and capital in disadvantaged communities. A pressing housing need in communities is affordable rental housing. Instead of being contrary to the statute, multifamily data in HMDA fulfills a central purpose of the statute. We would also argue that it is statutorily required. The HMDA statute refers to the recording of “mortgage loans.” According to the statute, “the term “mortgage loan” means a loan which is secured by residential real property or a home improvement loan.”^[42] Further, “The terms “residential real property”…. mean leaseholds, homes …and, combinations of homes or dwelling units and business property, involving only minor or incidental business use, or property to be improved by construction of such structures.”^[43] This statutory language indicates that the CFPB does not have the authority to exempt multifamily housing owned by non-person entities/corporations from HMDA reporting.

It is vital to assess whether public sector investment in neighborhoods has leveraged multifamily housing lending and if that lending is conducted responsibly. Thus, the enhancements to multifamily lending, including the data points on units affordable to lower income tenants, Combined Loan-to-Value (CLTV), and interest rates help stakeholders determine if the public and private sectors, working together, are investing in affordable housing so as to increase the stock of affordable housing in neighborhoods, or if they are putting it at risk.

Rents have been rising nationwide and rental housing has become increasingly more expensive to build, imposing housing cost burdens on lower income tenants. As discussed above, nearly half the renters nationally are cost burdened. In New York City (NYC) where nearly two-thirds of the city’s residents are renters, 44% are rent-burdened, paying more than 30% of their income on rent, and half of those are severely rent burdened, paying more than half their income on rent. A shocking 91% of severely rent-burdened households are low-income.^[44] This housing cost squeeze is unlikely to be alleviated any time soon as demand continues to outpace supply of affordable rental units.

Specific housing needs vary from place to place and HMDA enables all stakeholders to evaluate how lenders are meeting the needs of their local communities, be they manufactured housing, single family housing, multifamily housing, or some combination of all three. HMDA allows stakeholders to see what housing is being financed and where. With the additional variables, the public can better understand characteristics of that housing. For example, in New York City, over two million households (63%) are renters, making multifamily lending an important segment of the housing market. Of those, half are rent-stabilized and just 12% are under some sort of subsidy program. Rent-stabilized units are typically more affordable and provide more rights and protections for tenants than market rate units, including the right to a lease, the right to renew a lease, the right to organize, and limits on how much the rent can go up each year. Rent-stabilized tenants are more likely to be Hispanic, less likely to be White or Asian, and less likely to have a college degree than non-regulated tenants. Rent-stabilized tenants are also more likely to be low-income and on some form of public assistance.  We need lenders to lend, and lend responsibly, to build and preserve both affordable rent-stabilized and subsidized housing. Accurate HMDA data helps evaluate how well banks are meeting those needs, in addition to their lending on single family housing, which is also important to over a million households in NYC.

The Association for Neighborhood and Housing Development (ANHD) has been analyzing multifamily housing for many years, using a combination of HMDA, bank-reported data, and other public data to look at both the quantity and quality of multifamily lending. We analyze lending in low- and moderate-income (LMI) tracts and loans that count for community development credit in Community Reinvestment Act (CRA) exams, typically because of the number of affordable units.  For example, as reported in the latest State of Bank Reinvestment in NYC report, ANHD found a decline of lending in LMI tracts and community development loans in 2017.^[45] ANHD documented percentages of loans in LMI tracts ranging from 22% to 75%. Thus, HMDA data shows how well lenders are responding to housing needs on an annual basis. In addition, this analysis can be extended to assess lender success in responding to needs in the various boroughs in the city as well as smaller subdivisions.

In order to analyze the quality of lending, ANHD typically utilizes the Building Indicator Project database, which releases quarterly snapshots of multifamily buildings in NYC and reports the lender of record, violations, and liens. ANHD cross-references that list with the loss of rent-stabilized units, distressed building lists, and other indicators of bad-acting landlords who harass and displace tenants. Until recently, no one has had easily accessible data on total aggregate loans and underwriting criteria such as what is now available in HMDA.

Early analysis of a subset of lenders in NYC is helping community groups better understand their lending characteristics; revealing which lenders rely more heavily on interest-only loans and balloon payments, high/low CLTVs, and the ranges of interest rates charged. This will be incredibly valuable to evaluate lending in general, as well as lending just before and after the new rent laws passed in Albany recently, closing nearly all the loopholes landlords had to raise rents and deregulate rent-stabilized housing. Housing, civil rights, and community groups must be on the lookout now for new incidents of risky underwriting in all types of multifamily housing, and also for signs of redlining and disinvestment in low-income communities of color. HMDA can help us do that.

The CFPB went through a lengthy, thoughtful process in 2013 and 2015 to determine which additional and expanded fields would be included in HMDA, which would be disclosed, and how they would be disclosed, including numerous fields relevant to multifamily housing.  We believe that the existing fields must be retained and we maintain that we must disclose more granular details in some cases to allow for more comprehensive analysis.

As ANHD and our colleagues have reported in prior comments, multifamily lending has been woefully underreported in HMDA, particularly in states like New York where borrowers and lenders, including multifamily lenders, rely heavily upon MECA/CEMA loans that were not previously HMDA-reportable. Starting in 2018, NY CEMA loans are finally HMDA reportable and will capture a larger segment of the NYC lending market. It’s critical that we have this full data moving forward to accurately compare lending trends year-to-year.

Even with this expansion, however, HMDA will continue to miss data for multifamily depository lenders that do not originate any 1-4 family (“single family”) loans, as the reporting thresholds depend upon lenders originating at least one single family home purchase or refinance loan. This could potentially exclude some of the largest multifamily bank lenders that have announced their exit from single family lending, including BankUnited, New York Community Bank, and Capital One. New York Commercial Bank, formerly an affiliate of New York Community Bancorp, has never been a HMDA reporter as all of New York Community Bancorp’s 1-4 family lending was done through an affiliated savings bank, New York Community Bank. Now that the two affiliates have been combined and the bank has exited the single-family lending business, it’s possible they will never be a HMDA reporter despite being a major multifamily lender.

In order to report HMDA data, non-depository lending institutions must receive applications for, originate, or purchase at least five home purchase loans, home improvement loans, or refinancings for property within a metropolitan statistical area (MSA). They must also originate 25 or more loans, which is a high threshold for multifamily lenders that could potentially impact hundreds or thousands of people with just a few loans. In New York City, the average number of multifamily units in each building is 32; 3,370 buildings have 100 or more units; and 300 buildings have over 500 units.

The Urban Institute recently released a set of reports that highlight the importance of HMDA data, including two that featured multifamily lending.^[46] Using HMDA data, they documented the impact of multifamily lending as compared to single family lending in terms of the number of families housed. They also emphasized the importance of releasing the full set of data, especially actual number of units, in order to truly understand the scale of affordable housing being financed.

Those reports were written using older HMDA data that greatly under-reported multifamily lending in New York. With more reliable data in HMDA for New York in general, including New York City, that argument is even stronger as we now have a better picture of the totality of multifamily lending.  Among HMDA reporters in New York City during 2018, over 92% of closed-end loans are 1-4 family and 7.4% are multifamily loans. However, those 1-4 family homes total 68,743 units, whereas the multifamily housing has a minimum of 105,479 units and a maximum of well over 260,000 units.

In this context, we believe it is critical that the CFPB retain and expand upon all the multifamily data, in order to better capture and evaluate the multifamily lending market.

Recommendations

• Affordable Units.  Multifamily housing is a critical source of housing for many low- and moderate-income families. We very much appreciate the new HMDA data field that reports how many units are affordable under a city, state, or federal housing program.  The new data is limited to income-restricted housing and not housing that is “naturally” affordable or kept affordable due to rent-regulation, as is often the case in NYC and a few other regions in the country. That being said, banks are critical to financing income-restricted affordable housing and encouraged to do so through the Community Reinvestment Act (CRA). This data will provide valuable insight into who is financing this source of affordable housing. However, the reporting for the number of units needs to change to better understand the impact of that affordable housing.
• Number of units on all loans, 1-4 family homes and multifamily buildings. The new HMDA data now reports exact unit counts in 1-4 family loans, which is a huge improvement, but only reports multifamily units in ranges. This data is necessary for the public to understand how many families will be impacted by the loan. While the data is better than in prior years, when it simply reported 1-4 units vs 5+ units, it would be better if the data gave the precise count of units. The public would then have more accurate data on how many units a particular lender or all lenders in the locality were financing. Also, the public could better estimate if supply was rising to accommodate demand for rental units. In addition, the bin reporting does not allow the general public to generate estimates regarding the number of units that are affordable for lower income households. Only crude estimates are possible such as using the median value in a bin and multiplying it by the percentage of units that are affordable. For example, for the bin 25 to 49 units, does the analyst assume that a median number of units of around 37 should be multiplied by an affordable housing percent of say 10 percent to derive the number of units that are affordable? Or is the particular loan actually financing 26 units, which would result in a lower count of affordable units. Thus, inaccurate estimates of affordable housing units for alleviating cost burden will be generated. Additionally, the bin >149 units could be 150 units or 1000 or more, which renders this bin essentially useless for unit counts.

Estimating how many units are financed in the aggregate or how many units are affordable is frustrated by this bin reporting. Evaluating the extent to which lenders are responding to housing needs is quite difficult if not impossible. Furthermore, we do not see how bin reporting protects individual privacy since the vast majority of HMDA multifamily loans are for corporations. Bin reporting of units violates a statutory purpose of HMDA without any justification.

• Occupancy Type: Multifamily properties are almost always investment properties.   However, 1-4 single family homes also provide rental housing in many cases. We often consider non-owner occupied housing to be an investment property, but the new occupancy field, coupled with the number of units, will provide more clarity. As mentioned above in both the spirit and letter of the law, this is not only relevant to HMDA but important and legally required.
• Debt to Income Ratio is not reported for multifamily loans, but we urge the CFPB to reconsider similar indicators for multifamily housing, such as the Debt Service Coverage Ratio (DSCR), or the Net Operating Income that would allow us to calculate the DSCR and other indicators of overleveraging. ANHD finds that loans with a DSCR below 1.2, particularly on affordable rent-regulated housing, can provide significant incentives to harass and displace tenants – or neglect needed repairs – as the borrower must raise rents in order to pay off the mortgage. For enforcement purposes, it is important to know which lenders are complicit in this behavior, as it can have disastrous effects. One particularly egregious example is that of Raphael Toledano. The NY State Attorney General documented Madison Realty Capital loans to the now notorious landlord, Raphael Toledano, with DSCR well under 1.0X, meaning that the building’s net income was not sufficient to pay the debt. In order to raise the income, Toledano used all sorts of tactics to harass and displace rent-stabilized tenants and bring in higher-paying tenants.[47]
• Capturing More Bank and Nonbank lenders. This is not a new field, per se, but it must be noted that HMDA is valuable in distinguishing banks from non-bank lenders. The newly expanded data will help us get a better handle on who is lending on multifamily housing – CRA-regulated banks or private companies that are not regulated by the CRA. Trends in bank and non-bank lending will give stakeholders more insights into CRA and fair lending consequences and/or the need to adjust CRA and fair lending enforcement. We recommend eliminating the requirement that a depository institution must originate at least one single family loan in order to be required to report HMDA data as discussed above. Also, the 25 loan threshold for lenders is too high overall, and particularly for multifamily lenders that have lower lending volumes than 1-4 family lenders.
• Loan amount and property value.  Related to DTI and DSCR discussion above, these data points help us to see if loans are being made at an appropriate amount to support the existing tenants, or if they appear too large and could lead to harassment and displacement. Likewise, the CLTV data point will enable stakeholders to spot any increases in high CLTV lending which could also be a red flag for abusive practices.

Multifamily lending reporting will not impose a cost burden to lenders. Many New York lenders routinely report CEMA loans for CRA exam purposes. The additional information on multifamily loan terms is standard and collected on a regular basis. In fact, multifamily lending reporting involves fewer data fields than single family lending reporting. The additional multifamily data to be reported after the Dodd Frank enhancements is important to understanding how well lenders are meeting local rental housing needs.

Multifamily apartments are one of the most important sources of housing – and affordable housing in particular – in many regions of the country.  Responsible lending is critical to building, operating, and maintaining this source of housing. Robust data is needed to analyze this segment of the market so that stakeholders can hold lenders accountable and ensure they are supporting low-income tenants instead of destabilizing neighborhoods.

Manufactured Home Lending

An important part of the nation’s housing stock, manufactured housing accounts for 6.7 million homes in the nation, nearly half of which are in rural communities. Manufactured homes comprise 13 percent of all the occupied housing units in rural and small town communities and represent a quarter or more of the homes in 255 non-metropolitan area counties.^[48] In many of these sparsely populated counties, site-built homes are costly to construct relative to incomes, and manufactured homes provide a more affordable housing option. While extremely important to the rural housing stock, there are several areas of concern with this type of housing due primarily to financing terms and land ownership. Information about manufactured housing is limited, making it difficult to know, for example, how many manufactured home communities exist or what type of loan (personnel or real property) a borrower of a manufactured home received. The CFPB’s 2015 Home Mortgage Disclosure Act (HMDA) increased reporting requirements helped address this lack of information.^[49] The requirements also provide the public with much needed data to better understand the manufactured housing market and identify and address problems when they occur.

Manufactured homes are affordable relative to site-built homes in sales price. The average sales price for a new manufactured home in 2017 was $71,900 compared to $323,100 for a new single-family home built in that same year.^[50] Despite the relatively low-price, however, manufactured home loans often come with high interest rates. Loans on manufactured homes are usually treated as personnel property or chattel loans, like borrowing for a car instead of a home. This  financing structure helps explains why more than half of all HMDA reported loans involving a manufactured home in 2017 were considered high cost compared to just 6 percent for other home loans.^[51]Research notes that for manufactured home loans, the difference between a standard mortgage loan and a chattel loan can amount to thousands of dollars per year^[52]which is a considerable expense for lower income households. There are also challenges associated with ground rent which affect owners of manufactured homes who do not own the land on which their home sits. Ground rent escalation and the underlying risk of needing to move when the lease term expires create additional problems that do not exist for most single-family housing.^[53]

The CFPB’s 2015 final rule’s new reporting requirements can improve our understanding of these problems. This new information, which became available in full for the first time just a few weeks ago, can help identify when an applicant’s loan is secured with the manufactured home itself, not the land – which means it is most likely a chattel loan. A review of enhanced 2018 HMDA data identifies as secured by the home alone, 40 percent (43,298 originations) of all home purchase, first-lien loans involving a manufactured home. Using these data, along with other HMDA information, some of it newly available, researchers can ascertain a better understanding of how many borrowers might qualify for a standard, lower-cost, mortgage loan.^[54] The HMDA 2018 data includes 11,710 borrowers who owned the land under their manufactured home, yet secured the loan by the home alone. Ninety percent of those borrowers received a high-cost loan. Of those that received a loan secured by both the manufactured home and land, only 49% received a high cost loan.^[55] A closer inspection of these 11,710 borrowers, particularly with the enhanced applicant data, might help improve our understanding of whether any of these borrowers could have gotten less costly loans. The new data, along with closer scrutiny, could make this a possibility.

These new requirements also include identifying whether an applicant owns or leases the land on which his or her manufactured home will sit. Approximately 20 percent of 2018 home purchase loans involving a manufactured home dealt with directly leased land and another 9 percent involved unpaid leasehold land. This totals approximately 31,000 borrowers. A closer inspection of these data allow researchers and analysts to improve our understanding of ground rent, where it is occurring, and how it relates to other lending activity. It is important to remember that in many cases, particularly with regards to chattel loans, that the borrowers “are more likely to be older, to have lower incomes, and to pay higher prices for their loans.”^[56] The new data can help shed light on how well this market is working.

The new manufactured home specific data, along with additional reporting information on loan costs and borrower credit attributes, allow everyone to better understand the manufactured home market. An improved understanding of this market could make it possible to lower financing costs for many families and make manufactured housing an even more affordable and equitable housing option.

Data Points on Loan Type

Cash-Out Refinances

Details on refinance lending must remain in the HMDA data. In the subprime lending era, refinance lending became a method whereby abusive lenders extracted wealth by convincing borrowers to take out high cost, cash out refinance loans as a means of covering non-housing expenses. The cash outs often left vulnerable borrowers with few sources of wealth and savings after using the lump sum from the refinances. The Financial Crisis Inquiry Commission (FCIC) documented the high use of refinances and cash-out refinances during the subprime lending era. According to the FCIC, 75 percent of the subprime loans were first liens, and of these 82 percent were refinance loans; 59 percent of the refinance loans were cash out.[57]

A study conducted by the Center for Responsible Lending (CRL) and Coastal Enterprises, Inc. (CEI) revealed that borrowers in Maine “obtained a higher percentage of their subprime loans in the form of cash-out refinances than do borrowers in any other state. Between January 2004 and May 2005, 65 percent of the state’s subprime mortgage market represented cash-out refinances.  During the same period, only 28 percent of subprime mortgage loans in Maine were used for home purchases, the lowest percentage in the nation.”[58]

The CEI-CRL study had to rely on a private sector lending database. Since these databases are expensive, the data points distinguishing cash-out refinances from refinances that reduce interest rates must remain in the HMDA data in order to allow public sector agencies and community groups to continually monitor the volume of cash out refinances and to insure that they do not contain abusive terms and conditions.

The 2018 data reinforces the importance of maintaining HMDA data on cash out refinances since the volume of cash-out refinances is significant and these loans are higher cost and taken out by vulnerable borrowers. The CFPB found that a greater percentage of borrowers (15.6 percent vs. 14.1 percent) take out cash-out refinance loans as opposed to non-cash-out refinance loans.[59] In addition, the borrowers of cash-out refinance loans have lower median credit scores than non-cash-out borrowers.[60]Consistent with the credit score disparity is the higher costs for cash-out refinance loans. The median total costs for conventional confirming cash-out refinance loans is $3,430 compared to $2,810 for non-cash-out refinance loans.[61] The median rate spread is also higher for cash-out refinance loans than for non-cash-out loans at .56 compared to .42.[62]

The differences in the patterns of pricing and borrower demographics for the two types of refinance loans need to be monitored to assess whether voluntary or regulatory action in the future is needed to curb a rise of abusive pricing or loan terms and conditions. Therefore, the maintenance of this data point in the HMDA data is necessary.

Data Points on Loan Terms and Conditions

Price Variables and CLTV Ratio

In the years leading up to the housing crash, the nation experienced an unprecedented housing boom fueled in large part by unsustainable loans offered to borrowers who lacked the ability to repay. Although warning signs of this dangerous and irresponsible capital and lending flooding the housing market emerged, the lack of clear and public data about the terms and conditions of these loans blinded regulators and the public to the growing threat. In some cases, the federal agencies turned a blind eye to what was in front of them, but not in a public domain like HMDA data is.^[63] To rectify this lack of information, the Dodd Frank Act requires the collection of data on the total cost as well as points and fees charged to the consumer. In 2018 the CFPB began the prudent and reasonable practice of using their discretionary authority to collect data on origination charges, discount points, lender credits, interest rates, debt-to-income ratio (DTI), and the combined loan to value ratio (CLTV) of mortgage loans. This data is collected by lenders during their normal underwriting procedures. Therefore, its reporting represents no substantial additional burden according to the CFPB’s 2015 final rule.^[64]

The FCIC specifically noted that non-conforming loans experienced dangerous CLTV and DTI increases that directly contributed to the housing crash that followed.^[65]  In the wake of the housing collapse it became clear that lenders themselves recognized the threat indicated by the changes in their practices. According to the FCIC report:

Lenders made loans that they knew borrowers could not afford and that could cause massive losses to investors in mortgage securities. [Lender] executives recognized that many of the loans they were originating could result in “catastrophic consequences.” Less than a year later, they noted that certain high-risk loans they were making could result not only in foreclosures but also in “financial and reputational catastrophe” for the firm. But they did not stop.^[66]

In retrospect these loans and the inability of the industry to restrain itself would appear to represent what the economist David Friedman would call, “situations in markets where ‘individual rationality does not lead to group rationality.’”^[67]

In March 2019, the American Enterprise Institute released a report^[68] documenting the increased risk in the housing market posed by DTI and CLTV ratios. Rising DTI and CLTV ratios were correlated with both the increasing prices in the 16 markets they analyzed as well as greater risk layering in the secondary housing market.  Clearly, the collection of these variables in HMDA data is important to consumer advocates, the housing finance industry, and regulators.

The Bureau itself noted this in the original final rule on Regulation C:

The Bureau has considered this feedback and determined that CLTV ratio data would improve the HMDA data’s usefulness. CLTV ratio is a standard underwriting factor regularly calculated by financial institutions, both for a financial institution’s own underwriting purposes and to satisfy investor requirements…The Bureau believes that the CLTV ratio is an important factor both in the determination of whether to extend credit and for the pricing terms upon which credit would be extended.^[69]

Revealing the DTI and CLTV ratios alone still opens the door for a dangerous level of risk in the market as loan originators seek more creative ways to increase the profitability of loan originations.  It is tempting to think of mortgage lending as a simple process where the costs and fees are set by regulation or policy.  However, at the transactional level, the loan originator themselves have broad latitude about what fees to charge, how much those fees will be, and how they will be paid. Hence, data is needed to capture these fees and costs.

The FCIC report lays this out in clear detail:

The starting point for many mortgages was a mortgage broker. These independent brokers, with access to a variety of lenders, worked with borrowers to complete the application process….. For brokers, compensation generally came as up-front fees—from the borrower, from the lender, or both—so the loan’s performance mattered little. These fees were often paid without the borrower’s knowledge. Indeed, many borrowers mistakenly believed the mortgage brokers acted in borrowers’ best interest. One common fee paid by the lender to the broker was the “yield spread premium:” on higher-interest loans, the lending bank would pay the broker a higher premium, giving the incentive to sign the borrower to the highest possible rate.^[70]

HMDA data now includes not just the interest rate but detail on these other costs and fees that played such a large role in the crisis.  Loan brokers are the primary conduitfor many of the mortgages that are originated in the market, and their compensation structure motivates higher fees for the borrower. HMDA must capture this fee and cost data so that abuses in the future can be spotted and stopped before they become widespread.

“If the broker decides he’s going to try and make more money on the loan, then he’s going to raise the rate,” said Jay Jeffries, a former sales manager for Fremont Investment & Loan, to the FCIC. “We’ve got a higher rate loan, we’re paying the broker for that yield spread premium.”^[71]

Understanding the multiple costs associated with the loan origination process is critical in order to examine the risk in the housing finance sector. As the FCIC noted in their report, conditions in the non-conforming loan market were such “…that not only can the buyer put zero dollars down to purchase a new house but also that the mortgage can finance the closing costs….”^[72] Collecting and releasing data on the various forms of “closing costs” (origination charges, discount points, lender credits) as well as the interest rate of the loan allow regulators, the industry, and the public a way to assert “group rationality” over the lending market.

Clearly the CFPB understood this when they wrote in the 2015 final rule that,

…total loan costs may be more easily compared across borrowers because third-party charges are not included or excluded depending on various factors, such as whether they were paid to an affiliate of the creditor. This consistency enables users to better compare loan costs among borrowers and to understand the total upfront costs that borrowers face when obtaining mortgage loans. The amount of total loan costs may also be analyzed in combination with the other pricing data points more readily than the total points and fees. For example, the difference between the total loan costs and total origination charges provides the total amount the borrower paid for third-party services. Because of the improved utility of total loan costs, for covered loans subject to final § 1003.4(a)(17) for which total loan costs are available, the final rule requires financial institutions to report total loan costs.^[73]

The loan cost variables are necessary because they enable federal agencies and the public to determine where in the lending process the abuses are occurring. For example, the cost data combined with the data point on loan application channel will help determine whether abuses are concentrated among brokers or within retail lender institutions or among other third-party service providers. In addition, the presence of rate spread, interest rate and cost variables offer researchers the ability to compare the cost for mortgage credit among borrowers regardless of the specific date that the loan closes during the year and to monitor the market for growing risk.  By accounting for the rate spread variation from the average prime offer rate (APOR) and by calculating the effective Annual Percentage Rate (APR) by including the costs that the borrower pays at the closing table, it is possible to compare the cost that one borrower pays with that of other borrowers. Also, it is possible to compute total costs and fees charged by one group of lenders compared to another. NCRC recently published a paper using the 2018 data showing that independent mortgage companies charge significantly higher APRs and closing costs than banks. This finding suggests that robust competition among lenders must be encouraged, particularly if traditionally underserved groups in the population are disproportionately served by the higher cost lenders.[74]

NCRC has published a white paper to document a methodology for calculating loan price and its implications on lender transparency and accountability to the public.^[75]The presence of such detailed data on loan costs and interest rates presents an unprecedented opportunity compared to the previous HMDA data to identify patterns of disinvestment and predatory lending activity.

Other data points that are known to be related to price are equally important as explanatory variables. The lending industry has long asserted that variations in loan costs that were observed in other data sets were due to underwriting decisions based on safe and sound operation.  These factors often included the property value as well as the debt-to-income and loan-to-value ratios. By including this data in HMDA it is possible to confirm these claims and monitor the industry for bad actors that could destabilize the market.

Preliminary analysis by NCRC using the new pricing data confirms that variations in both the interest rate and closing fees paid by borrowers vary based on lender type, geography, CLTV, DTI, and other factors.  Statistical analysis of these variations will yield more information on structural barriers that exist in the housing finance system. This data is critical to the mission of shrinking the racial gap in homeownership.  The gap in black and white homeownership rates stands at its highest point in a century in the United States.^[76]  The impact of this will continue to be felt for generations, and data such as that included in HMDA offers the opportunity to better understand the barriers that perpetuate that gap.

These data cannot represent a significant regulatory burden to lenders, as all of these data points are collected as a part of the normal underwriting and securitization processes.  In their 2015 final rule, the CPFB itself concluded that the additional compliance cost of the rule in total to lenders would be $23 per closed end loan for most lenders.^[77] Combined with the clear public interest in the ability to predict future crises, the original decision of the CFPB to release these data points was prudent and must be sustained.

Debt-to-Income Ratio (DTI)

The CFPB’s current method for disclosing debt-to-income ratio (DTI) takes into account the critical importance of DTI ratios in determining whether loans are affordable and sustainable, a key component of fair lending and consumer protection analyses. The bins for DTI disclosure provide a clear measurement of debt burden and appropriately balance the utility of the data with protecting the privacy of loan applicants with unusually high or low DTIs. The granular disclosure of DTI ratios of 36 to less than 50 percent is particularly critical since the ratio of 43 percent is a key component of the Qualified Mortgage (QM) rule. In addition, granular disclosure of ratios between 36 and 50 percent provide key information about relatively high DTIs and allow regulators and the public to determine whether lenders making loans with high DTIs are engaging in predatory or high-cost lending.

Preliminary analysis of the recently released national HMDA data suggests that DTI ratios provide critical explanatory power when analyzing lending decisions and illuminate potential racial and ethnic disparities in mortgage lending. DTI ratios should therefore remain part of HMDA data collection and continue to be disclosed in the public HMDA data.

Application channel

Dodd Frank and its implementing regulations require that, except for purchased covered loans, institutions report the following information about the application channel of the covered loan or application: Whether the applicant or borrower submitted the application directly to the financial institution; and whether the loan was, or would have been, initially payable to the financial institution.^[78] This information is currently disclosed without modification in two separate fields.

As with most of the new data points required under Dodd Frank, application channel was added to HMDA as a result of the various predatory practices that contributed to the foreclosure crisis and often disproportionately affected borrowers and communities of color. When combined with many of the other new data elements (i.e. interest rate, points and fees, loan costs, origination charges, discount points, loan term and non-amortization features), application channel is critical to fair lending analyses. It allows regulators and the public to check for abusive or discriminatory conduct by loan channel.

The HMDA Data Points article includes a table to help understand how the two fields could work together to determine application channels.^[79]

Initially Payable
		Yes	No
DirectlySubmitted	Yes	The reporter madethe credit decisionand the loan wasclosed in thereporter’s name. Thereporter likelyoriginated the loan inits retail channel butcould participate inthe wholesale-correspondentchannel of anotherlender withdelegated underwritingauthority.	The reporter made thecredit decision pursuantto delegatedunderwriting authority.The loan closed in thename of another lender.The reporter belong towholesale channel ofthat lender.
	No	The reporter madethe credit decisionwithout delegating itsunderwritingauthority.  The loanwas closed in thereporter’s name.  Thereporter originatedthe loan in itswholesale-correspondent orwholesale-brokerchannel.	The reporter made thecredit decision withoutdelegating itsunderwriting authority.The loan was not closedin the reporter’s name.The reporter originatedthe loan in its wholesale-correspondent channel.

 

As seen in the Application Channel of the HMDA Data Points article and the related tables,^[80] over 86% of conventional conforming originations were both directly submitted and initially payable to the reporting institution (upper left box), while 8% were not directly submitted by but were initially payable to the reporting institution (lower left box), and 3.8% were neither directly submitted to nor initially payable to the reporting institution (lower right box). Only 2.2% of the loans were directly submitted to but not initially payable to the reporting institution (upper right box).

A preliminary review of the two application channel data point fields using New York State’s 2018 HMDA data shows their importance to the public more concretely. Of the 110,812 home purchase loan originations analyzed for this comment letter,^[81] 78% were both directly submitted and initially payable to the reporting institution, slightly lower than the national number. Fourteen percent were not directly submitted but were initially payable to the reporting institution; 5.9% were directly submitted but not initially payable to the reporting institution; and 2.6% were neither directly submitted nor initially payable to the reporting institution. In the years preceding the financial crisis, brokered loans had a high incidence of risky features. In Rochester currently, it is particularly important to monitor riskiness among the 22% of loans in 2018 that were not directly submitted and payable to the reporting institution since a significant number of these were brokered.

Directly Submitted and Initially Payable

Of the 86,404 loans originated by institutions in this box (upper left), 40% were loans from HUD regulated institutions and 34% were from CFPB regulated. The institutions with at least 1,000 originations (from most to least) in this box were:

• JPMorgan Chase Bank, National Association
• Wells Fargo Bank, National Association
• Homestead Funding Corp.
• Premium Mortgage Corp.
• Quicken Loans Inc.
• Manufacturers and Traders Trust Company
• Citizens Bank, National Association
• FREEDOM MORTGAGE CORPORATION
• 1ST PRIORITY MORTGAGE, INC.
• Bank of America, National Association
• Citibank, National Association
• PRIMELENDING, A PLAINSCAPITAL COMPANY
• HUNT MORTGAGE CORPORATION
• Bethpage Federal Credit Union
• KeyBank National Association
• Community Bank, National Association
• TrustCo Bank
• Loandepot.Com, LLC

Not Directly Submitted But Initially Payable

Of the 14,929 loans originated by institutions in this box (lower left), 80% were from HUD regulated institutions and 13% were from CFPB regulated. The institutions with at least 1,000 originations (from most to least) in this box were:

• UNITED SHORE FINANCIAL SERVICES, LLC
• PLAZA HOME MORTGAGE, INC.
• Quicken Loans Inc.
• Caliber Home Loans, Inc.
• Loandepot.Com, LLC

Directly Submitted But Not Initially Payable

Of the 6,536 loans originated by institutions in this box (upper right), 46% were from HUD regulated institutions and 42% were from NCUA regulated. The institutions with at least 100 originations[82] (from most to least) in this box were:

• SEFCU SERVICES, LLC
• CCFCU FUNDING, LLC
• HOME TOWN FUNDING, INC.
• InterContinental Capital Group, Inc
• UNITED NORTHERN MORTGAGE BANKERS LIMITED
• ASSOCIATED MORTGAGE BANKERS, INC.
• OWNERSCHOICE FUNDING, INCORPORATED
• CONTINENTAL MORTGAGE BANKERS, INC.
• Luxury Mortgage Corp.
• LYNX MORTGAGE BANK LLC
• QUIK FUND, INC.
• Northern Federal Credit Union
• Mortgage Access Corp dba Weichert Financial Services

Not Directly Submitted and Not Initially Payable

Of the 2,867 loans originated by institutions in this box (lower right), 51% were from HUD regulated institutions and 47% were from CFPB regulated. The institutions with at least 100 originations[83] (from most to least) in this box were:

• Wells Fargo Bank, National Association
• Flagstar Bank, FSB
• PLAZA HOME MORTGAGE, INC.
• Pacific Union Financial, LLC
• FREEDOM MORTGAGE CORPORATION
• JPMorgan Chase Bank, National Association
• WEI Mortgage LLC
• OWNERSCHOICE FUNDING, INCORPORATED
• GUIDANCE RESIDENTIAL, LLC

Note that JPMorgan Chase Bank and Wells Fargo Bank report loans here, when they did not directly close nor originate the loans. However, they made the bulk of their NYS loans in the upper left box, where each bank made its own credit decisions and closed the loans. Such information is important for the public and regulators to understand which lenders are participating more directly in the lending and origination process and which loans could be more risky.

Non-amortizing features

Non-amortizing features are another set of data points required by Dodd Frank. The law and its implementing regulations require institutions to report ‘‘the presence of contractual terms or proposed contractual terms that would allow the mortgagor or applicant to make payments other than fully amortizing payments during any portion of the loan term,” and includes whether there is a balloon payment, interest-only payments, negative amortization or any other non-amortizing features.^[84] These characteristics were essential elements of loans that drove the foreclosure crisis and future trends in this area are important to monitor, especially in how loans with these features impact vulnerable populations (low-moderate income, borrowers and communities of color, older borrowers) and/or disparately impact protected classes. All of these data elements need to continue to be reported and disclosed without modification so regulators and the public can monitor changes in their use.

The CFPB’s HMDA Data Points article shows that, except for balloon payments, the bulk of the non-amortizing features are attached to HELOC loans. Of the 243,000 originated loans that include a balloon payment, about 128,000 of them are closed-end loans, and 115,000 are HELOCs.^[85] Almost 50% of HELOCs have interest-only payments.

Given that the bulk of non-amortizing features are attached to HELOCs, we examined these loans via the New York State 2018 HMDA data^[86] to see how many features were attached to which types of HELOCs. Among the 19,738 HELOC loans originated in NY in 2018, the most common non-amortizing feature is the interest-only payment. Almost 54% of the HELOCs have this. Eight percent of the loans have some other non-amortizing feature. Only 23 of the HELOCs in NY have more than one non-amortizing feature, and only 33 have a balloon payment.

Refinance loans are the most common type of HELOC in NYS. Thirty-nine percent of HELOCs were refinance loans in 2018. Of these 7,704 loans, 57% (4,360) had the interest-only payment feature, the highest occurrence rate among all the loan purposes. Since HELOCs have the highest incidence of risky features, the HELOC data point must remain in HMDA data so that trends regarding risky features can be monitored. Also, the reporting threshold must not be permanently set at 200 HELOC loans.

Prepayment Penalties

In the decade before the foreclosure crisis, when subprime lending exploded, abusive prepayment penalties were another predatory feature; 80% of subprime loans had prepayment penalties compared to only 2% of conventional loans.^[87] Prepayment penalties kept many consumers in unaffordable, subprime loans, the costs of which often outweighed the interest rate savings.^[88] Due to this, Dodd Frank and its implementing regulations require that institutions report the term in months of any prepayment penalty for covered loans or applications, other than reverse mortgages or purchased covered loans.^[89]

While the use of prepayment penalties is much more limited after the passage of Dodd Frank, it is important to monitor any future increased use or increases in the term, especially by borrower race/ethnicity and in conjunction with other loan features.

The term of any prepayment penalty is already collected by lenders, so the burden of reporting is unlikely to outweigh the benefit of its usefulness to furthering the purposes of HMDA, particularly for helping identify possible discriminatory lending patterns and enforcing fair lending laws. One concern we have with how it is reported and disclosed is the overbroad use of “not applicable” (NA). NA is used for both loan transactions for which reporting is not applicable (i.e. reverse mortgages) as well as for covered loans which have no prepayment penalty. One possible way to distinguish between these two NAs might be to use NA for loan transactions not covered, and to use zero (0) for transactions with no prepayment penalty (for 0 months).

The combination of now knowing which loans are HELOCs with which loans have a prepayment penalty (and the term of that penalty) helps regulators and the public better understand the mortgage lending landscape. As seen in the HMDA Data Points article, less than 1% of conventional conforming originations had a prepayment penalty. However, over 28% of HELOC originations had prepayment penalties.^[90]

Given that the bulk of prepayment penalties are attached to HELOCs, we examined these loans via the New York State 2018 HMDA data^[91] to see which types of HELOCs were more likely to have prepayment penalties. Of the 19,738 HELOCs originated in NYS in 2018, 5,000 or 25% had a prepayment penalty. Only 200 or 4% of these 5,000 loans were home purchase loans; the rest were home improvement loans, refinance loans or loans for other purposes. Of the:

• 5,502 refinance loans, 29% had prepayment penalties
• 1,903 cash-out refinance loans, 26% had prepayment penalties
• 3,721 home improvement loans, 24% had prepayment penalties
• 3,070 other purpose loans, 20% had prepayment penalties

These new data points show that lenders are combining different terms to restrict the ability of borrowers to repay their loans. Of the 10,599 HELOCs with interest-only payments, almost one-third (3,414) also have a prepayment penalty of some kind. This is very revealing and demonstrates that combining these new HMDA data points can help regulators and the public better understand our ever-changing mortgage markets, and to better address any new predatory issues that arise.

Data Points on Loan Action

Reasons for Denial

The reasons for denial are important data points for fair lending enforcement and homeownership counseling. A lender reporting HMDA data is required to report principal reasons for denial from the following list: debt-to-income ratio, employment history, credit history, collateral, insufficient cash (downpayment, closing costs), unverifiable information, credit application incomplete, mortgage insurance declined, and other (specify in a free form field).

Differences in the incidence of a reason or reasons for denial by race or gender can be further investigated in fair lending inquiries to ensure that lenders are considering applications in a non-discriminatory manner. Public agencies, regulators, and community organizations can use the loan characteristics or loan term and conditions data points to see if reasons for denial are consistent with high DTIs, lower property values, or other data points for particular races or genders.

Housing counseling agencies can use the reasons for denial to assess which barriers are most common for residents in the census tracts that they serve. For example, a high incidence of credit history or DTIs may influence the housing counselor’s curriculum or work sessions with clients. If on the other hand, the primary reasons in the census tracts served by the counseling agency is incomplete application or unverifiable information, the curriculum would emphasize the loan application process.

Data Points on Loan Characteristics

Loan Amount – Midpoint reporting

Loan amount is a pre-Dodd Frank data element that has been disclosed for decades without any apparent risk or widespread instances of facilitating public re-identification of borrowers. Even though loan amount is in county real estate transaction records, there is no indication that adversaries have matched HMDA reported loan amounts to those in county records. Most likely, adversaries simply used county real estate transaction records to identify vulnerable consumers that they thought could be susceptible to abusive marketing. Therefore, our strong preference is that this variable continue to be reported as it was before the Bureau’s disclosure changes in 2018.

We believe modifications to the CFPB’s 2018 mid-point reporting is necessary for smaller loan amounts. For large loan amounts of $100,000 or higher, it does not appear that the accuracy of the data will be significantly diminished if the loan amounts are disclosed as the midpoint of a $10,000 range as the CFPB implemented. However, for home improvement lending, second liens, and other lending that consists of smaller dollar amounts, the CFPB’s proposal will result in loan amount data that will be misleading. In those cases, the CFPB should either report the loan amount to the nearest $1,000 as is done now or make its proposed interval smaller.

A few examples indicate the potential for misrepresentation of loan amounts due to the CFPB proposal. In the Cleveland metropolitan statistical area (MSA) in 2015, there were 1,810 home improvement loan applications with an amount less than $10,000. Of these, 28 percent had values less than $5,000. Similarly, in the Houston MSA in 2015, there were 2,258 home improvement loan applications with an amount less than $10,000. Of these, 44 percent had amounts less than $5,000.

In markets or communities where housing values are relatively low, the $10,000 midrange data would also be misleading for home purchase and refinance loans. Using the Cleveland example, there were 5,734 home purchase loan applications in 2015 with loan amounts of $50,000 or less.  Thus, a change of $5,000 (to pick the midpoint for a $50,000 loan) would be a 10 percent misrepresentation. When combined with a similar impact from using the midpoint in $10,000 ranges for the property value, this bias would be significantly increased when estimating the loan-to-value ratio. In addition, the number of refinance loans with amounts of $50,000 or less in the Cleveland market were 2,773. Moreover, 1,608 (58 percent) of these loans were equal to or less than $40,000, making the distortion even greater. In the Houston example, there were 2,247 home purchase loan applications for amounts equal to or less than $50,000, creating a similar issue in one of the nation’s larger housing markets. In Houston, there were 3,557 refinance loan applications for loan amounts equal to or less than $50,000, with more than half (55 percent) of these having values equal to or less than $40,000.

These are not the areas with the lowest property values in the nation, but they indicate the type of problem and misrepresentation that a uniform use of the $10,000 midpoints will create. While the release of the combined loan-to-value (CLTV) ratio may be helpful in general, the CLTV ratio would not deal effectively with these issues for lower loan amounts and property values.  Therefore, at a minimum, the CFPB should consider setting midpoints in smaller ranges for lower loan amounts and lower property values.

Loan amount data is critical for assessing if demand for loans of various amounts in neighborhoods are being met for borrowers of different races/ethnicities and income levels. It is therefore important to strive for as accurate reporting of this data as possible while also being sensitive to privacy risk. The CFPB can modify its proposal without endangering privacy since this piece of data has not been used to facilitate re-identification or predatory marketing to our knowledge.

Loan Amount – GSE and FHA Limits

An important component of fair lending analysis is assessing the patterns of lending and loan sales for loans within the Government Sponsored Enterprise (GSE) limits. A critical part of this analysis is examining the share of loans made within the GSE limits that are actually sold to the GSEs and to other investors compared to the market as a whole. Presently, this is quite difficult for HMDA users without significant technical and data processing skills and resources.

We are pleased that the CFPB chose to indicate in HMDA data if the loan exceeded the conforming loan limits and is therefore ineligible for sale to the GSEs. In reporting this data, the CFPB needs to ensure that the GSE limit threshold applied to each loan amount is based on the loan amount limits adjusted for the number of units in the subject property, a procedure the public could not do before even with significant data processing resources because data on the number of units in the property was not available. Since the new Dodd-Frank data has number of units, the CFPB will be in a position to adjust for number of units when calculating if the loan amount exceeds GSE limits.

We ask the CFPB to consider adding an indicator of whether the loan exceeds FHA limits.  As these limits are commonly quite different than the GSE limits, this would provide another valuable data point for HMDA analysis. The number of loans within the FHA limits that are FHA (especially when controlling for applicant income, applicant ethnicity or race, and census tract racial/ethnic composition) has been an important part of fair lending analysis. Indeed, the initial reason for enacting the HMDA was to reveal where conventional and FHA loans were being made in order to identify and combat redlining.

Property Value

Dodd Frank and its implementing regulations require institutions to report the value of the property securing the covered loan or, in the case of an application, proposed to secure the covered loan relied on in making the credit decision [deny, approve but not accepted, originate].^[92]

The property value data point is important because it allows HMDA users increased understanding of what went into a lender’s credit decision. On its own, property value helps regulators and the public assess whether financial institutions are serving communities with more modest housing values, one of the purposes of HMDA.

HMDA users can use property value along with the loan amount field to calculate the loan-to-value ratio with respect to the property securing the loan. As the National Consumer Law Center and other advocates noted in their 2014 comments, this information increases understanding of how much equity borrowers have upon origination and helps to identify disparities in how property values affect loan terms. While the expanded HMDA includes a combined loan-to-value ratio, that variable is based on the total amount of debt secured and property that “does not need to be the property identified in §1003.4(a)(9) [the location of the property securing the loan] and may include more than one property and non-real property.”^[93]

The usefulness of the property value field is reduced by how it is disclosed to the public. The value disclosed is “the midpoint for the $10,000 interval into which the reported value falls, e.g., for a reported value of $117,834, disclose $115,000 as the midpoint between values equal to $110,000 and less than $120,000.”^[94] This $10,000 midpoint disclosure significantly reduces the accuracy of any loan-to-value ratio.

Moreover, as with loan amount, the disclosure of property values as midpoints of intervals of $10,000 creates unacceptable margins of error for lower-value properties, such as those worth $100,000 or less and especially so for those worth $50,000 or less. For example, a property reported by a lender as valued at $60,500 would be disclosed as $65,000, almost 7.5% higher than its actual value. Many jurisdictions across the country have properties of $100,000 or less, including many small to mid-size cities, rural areas, lower income areas and/or communities of color. The Bureau should revisit the $10,000 interval and make it smaller for properties of $100,000 or less. This smaller interval will not add any burden to the HMDA reporters, and is simply an algorithm for the Bureau to add when disclosing the data.

As seen in the CFPB’s “HMDA Data Points” article, property values of mortgaged properties can vary depending upon the loan purpose and loan type. Taking jumbo loans out of the equation, the median property value for conforming conventional home purchase loans is $274,600, while the median value of properties securing FHA home purchase loans is $199,400, and for FHS/RHA loans it’s the lowest at $140,000.^[95]

A preliminary review of the property value data point using New York State’s 2018 HMDA data^[96] shows its value more concretely. In New York State as whole, the median property value securing the 110,000 originated home purchase loans was $295,000.^[97] There are large differences in secured property values on originated home purchase loans when looking across various NY metropolitan areas (MSAs). For example, the median property value in the:

• Albany-Schenectady-Troy MSA was $225,000
• Buffalo-Cheektowaga-Niagara MSA was $165,000
• Nassau-Suffolk Counties MSA was $435,000
• Rochester MSA was $155,000
• Non-MSA areas was $135,000

However, if we look at the home purchase loans made in census tracts where at least 50% of the residents are people of color, we can see differences in property values yet again. Note that, except for the Non-MSA areas, all the median property values are substantially lower that the overall median property values. The median property value in communities of color in the:

• Albany-Schenectady-Troy MSA was $125,000
• Buffalo-Cheektowaga-Niagara MSA was $105,000
• Nassau-Suffolk Counties MSA was $385,000
• Rochester MSA was $85,000
• Non-MSA areas was $155,000

Also note that, of the 110,000 home purchase loans reported to HMDA in New York State, almost 8,300 or 7.5% were secured by properties of less than $100,000. As noted earlier in this section, the reporting of property values as midpoints in intervals of $10,000 could create unacceptable inaccuracies in the value of these 8,300 lower value properties. This is especially true for the loans originated in the communities of color in the Rochester MSA, where the median property value was $85,000; so over half of the 9,850 loans originated here could have large errors in the reported property values.

 Data Excluded from Public Disclosure

The undersigned organizations urge the CFPB to reconsider exclusions from public disclosure for some of the data points excluded from public reporting and to provide data in other, non-HMDA reports for other data points the CFPB excluded from the public HMDA data. In certain cases, the undersigned organizations believe it is possible to provide useful information derived from the excluded data points so that HMDA’s statutory purposes can be better realized.

Credit Score

The CFPB should reconsider its exclusion of credit score data from the publicly available HMDA data. Credit score data is essential for fair lending analysis in order to determine whether similarly situated applicants are treated differently solely due to their race, ethnicity, gender, or age. In its review of the 2018 data, the CFPB found that African-Americans and Hispanics have higher denial rates for whites, even for those applicants within similar credit score ranges. The CFPB adds that people of color have higher Combined Loan to Value (CLTV) ratios than whites, which might explain some of the differential in denial rates.[98] The overall point is that public availability of credit scores would allow stakeholders to conduct their own analyses to further probe these disparities and assess whether the differences in denial rates are justified after taking credit scores, CLTV, and other factors into consideration.

Although the CFPB states that credit score data is not useful to identify applicants, the Bureau suggests that credit score data of applicants identified via non-credit score data fields could be a source of embarrassment or help adversaries engage in abusive marketing. To substantially reduce the risk of reputational harm, the CFPB could use a normalized credit score reporting format. It would be harder for the general public to understand, for example, what someone’s credit score expressed as a z-score means than a precise reporting of a FICO score or other credit score.

The CFPB should normalize the credit score data reported each year and report loan applicants’ credit scores either as z-scores, a measure of a credit score’s place in the overall distribution of credit scores for loan applicants that year, or in percentile ranges based on the distribution of loan applicants’ credit scores. Z-scores have the advantage of being useful for statistical analysis.

If the CFPB opts to retain its proposed exclusion of credit scores, it should consider summary reporting of credit scores by census tract for the aggregate (all lenders) and for each lender. For each census tract, the CFPB could report in one of two ways:

1)    The number and percentage of applicants denied loans and the number and percentage of applicants approved for loans in each quintile of normalized credit scores.

2)    The 25^th, 50^th and 75^th percentiles of normalized credit scores for applicants denied loans and the same percentiles of normalized scores for applicants approved for loans.

Although not as comprehensive as loan-level credit score data, summary data at the census tract level would be nevertheless valuable for fair lending analysis to assess if the industry as a whole or individual lenders are treating similarly situated neighborhoods differently due to the racial, ethnic, income or age composition of the neighborhood.

Automated Underwriting System (AUS) Result

The CFPB has excluded AUS result from public HMDA data disclosure because the Bureau believes that the AUS result could damage the reputation of the applicant and may subject a borrower to targeted marketing. The CFPB has stated that a “negative” AUS result would “likely be perceived as reflecting negatively on the applicant or borrower’s willingness or ability to pay.” The AUS result, however, could aid significantly in fair lending analysis to determine the likelihood of similarly situated borrowers being treated differently due to race, ethnicity gender, or age. In addition, we do not believe that coded results like approve/ineligible or ineligible or incomplete will reflect any more negatively on applicants than a loan application denial. These are relatively obscure technical terms that could indicate that any of a number of factors could have resulted in a denial. Since the benefits of disclosure outweigh the costs or risks, the CFPB should reconsider its proposal to exclude AUS result from the publicly available HMDA data.

National Mortgage Licensing System and Registry (NMLSR)

The NMLSR data field opens a whole new and important level of analysis for the HMDA data.  Today, many lenders, especially national banks that make loans across the country operate through mortgage brokers. Even though the lender is liable for the final loan decision, a borrower’s contact is through the broker – and that broker is also liable. Brokers often steer people to certain products and/or work with real estate sales agents that may steer people to particular properties or areas (as brokers often work in a relatively small geographic area). The broker decides which lenders to work with and to which ones to send a particular loan application. In instances where legal teams in fair housing cases had access to broker information, it was clear that different brokers favored different types of loans, different areas, different racial and ethnic groups, and had different fees (when the lender allowed variations). Therefore, in many markets, the “lender” is not the most important actor in the loan process.

Including some form of the loan originator’s ID in the HMDA data represents a critical opportunity to make transparent a previously hidden part of the mortgage lending process – one that is particularly important for issues of discrimination and reinvestment. After all, a lender’s decision to work with particular brokers can open up critical markets as well as close off opportunities.

Finally, one of the issues with HMDA data is that while brokers may send loan applications to several lenders, the public has no way of analyzing these patterns and relationships. A loan originated may also be represented in the HMDA data as a loan rejected by another lender. Having a form of the NMLS ID on the application data would represent a fundamental change in the transparency of this part of the lending market.

The CFPB is correct that releasing the NMLSR ID for a particular “individual loan originator” (as defined in the Mortgage Licensing Act – 12 CFR § 1026.36) might make it possible to link legal documents (where that ID number is required) with an individual’s HMDA data. On the other hand, the Resource Center for the NMLS Website states that:

The NMLS Unique Identifier is the number permanently assigned by the Nationwide Mortgage Licensing System & Registry (NMLS) for each company, branch, and individual that maintains a single account on NMLS. The NMLS Unique Identifier (“NMLS ID”) improves supervision and transparency in the residential mortgage markets by providing regulators, the industry and the public with a tool that tracks companies and individuals across state lines and over time (emphasis added).

It continues explaining the NMLS Unique Identification Number Specifications:

NMLS assigns a unique identifier (NMLS ID) to each entity that has a record in the system. An NMLS ID is assigned to each company (Form MU1), branch (Form MU3), and natural person (Form MU2 or Form MU4) when the entity first creates its record in NMLS.

Using the NMLS ID for a company or branch rather than each individual would eliminate the ease of re-identification (as the individual ID required on several legal documents would not be disclosed). Having the ID for the mortgage company – and branch – would provide valuable information that would give the public access to this previously hidden and critical part of the mortgage market. Therefore, the CFPB must reconsider the weight of the public benefit – especially as it is included in Dodd-Frank and noted as an important factor by the NMLS itself – and consider the option of using the company and branch ID. Indeed, because mortgage brokers and individual banks may have uniform policies or common practices, using the company and branch IDs might be even more valuable than the individual originator ID as it would identify instances of variation from the uniform policy that is problematic from a fair lending perspective.

Universal Loan Identifier

For some of the excluded variables, the CFPB should consider producing data separate from the HMDA Snapshot Loan Level Dataset that achieves key purposes of the excluded variables. In the case of universal loan identifier, one such purpose is CRA evaluation. In particular, banks purchase loans made to low- and moderate-income (LMI) borrowers from each other in order to boost their CRA ratings. Purchasing loans is a permissible activity for CRA evaluations but the agencies have warned banks to avoid gaming CRA exams by purchasing large numbers of loans shortly before CRA exams in order to improve their rating. Data that would be useful to detect gaming would be number of purchases of loans by income level that include recently originated loans as well as loans originated in previous years. It is possible that banks purchasing large volumes of loans to boost their ratings may be more likely to purchase “seasoned” loans sitting on other banks’ portfolios as well as current loans. Data on purchases by income level and vintage could be important for CRA purposes. The CFPB should consider providing this data in a separate data set.

Property Address

While property address cannot be disclosed in the publicly available HMDA data, we encourage the CFPB to develop a hashed value to include in the publically available data. One purpose of a location variable for a unique residential unit is to determine whether loan flipping is occurring. Loan flipping is a predatory tactic in which abusive lenders target borrowers for a series of refinancings that only increase debt and strip equity. Since no data is proposed to be disclosed that will assist the public in tracing loan flipping, the CFPB should consider reporting an indicator of loan flipping on a census tract level. Perhaps the Bureau could calculate the median for the number of times loans are secured by a given property over a multiple year time period and then indicate census tracts with a threshold of properties above and below the median. This would identify those tracts with potential flipping as well as other tracts that may be underserved by lenders. In general for excluded variables, the CFPB should be open to developing supplemental data that answer critical fair lending, CRA, and consumer protection queries.

Conclusion

The undersigned organizations oppose any diminution of the enhancements the CFPB made to HMDA data as it was implementing Dodd Frank. The CFPB enhanced the data carefully over several years after multiple requests for public comment and in response to widespread lending abuses. Moreover, as the lending industry, housing markets, and the nation’s population underwent significant changes, it was necessary to augment HMDA data so that the data could be used to assess whether housing and credit needs associated with older adults, subgroups within the Asian and Hispanic communities, and residents of multifamily and manufactured homes were being met responsibly and in a non-discriminatory manner.

If the CFPB proceeds with a rulemaking that dramatically alters the publicly available HMDA data, the agency will be acting contrary to the requirements of the APA, which requires meaningful opportunity for public comment. The CFPB initially issued the ANPR before the first year of the new Dodd Frank HMDA data became publicly available. The CFPB then re-issued the ANPR but by time the first year of Dodd-Frank data became available, only an insufficient 45 days remained available in the comment period. Also, the data was inaccessible to a wide variety of practitioners and stakeholders because the CFPB disseminated the data in a manner that is usable for only the most sophisticated researchers. Therefore, the quality and objectivity of any hastily constructed proposed rule based on this ANPR will be compromised.

Thank you for the opportunity to comment on this important manner. If you have any questions, please contact Josh Silver, Senior Advisor at NCRC, on 202-628-8866 or jsilver@ncrc.org.

Sincerely,

National

African Diaspora Directorate

Americans for Financial Reform Education Fund

Center for Responsible Lending

Consumer Action

Consumer Federation of America

NAACP

National Community Reinvestment Coalition

National Fair Housing Alliance

Prosperity Now

 

Alabama

Titusville Development Corp.

 

California

CAARMA Consumer Advocates Against Reverse Mortgage Abuse

California Reinvestment Coalition

CASH Community Development

City of San Jose Department of Housing

Coachella Valley Housing Coalition

East Bay Housing Organizations

Law Foundation of Silicon Valley

Non-Profit Housing Association of Northern California

Renaissance Entrepreneurship Center

 

Delaware

Delaware Community Reinvestment Action Council, Inc.

Edgemoor Revitalization Cooperative, Inc.

 

District of Columbia

Latino Economic Development Center-LEDC

Illinois

Illinois People’s Action

Woodstock Institute

 

Indiana

Continuum of Care Network NWI, Inc.

 

Kentucky

Comprehensive Valuation Services LLC

LHOME

 

Louisiana

Multi-Cultural Development Center

 

Maine

Coastal Enterprises, Inc.

 

Maryland

Maryland Consumer Rights Coalition

 

Massachusetts

Community Service Network Inc.

Ecumenical Social Action Committee, Inc.

 

New Mexico

Rural Housing Inc.

 

New York

Association for Neighborhood and Housing Development

Empire Justice Center

Fair Finance Watch

Greater Rochester Community Reinvestment Coalition

Legal Services NYC

New Economy Project

 

North Carolina

S J Adams Consulting

 

Ohio

County Corp.

Fair Housing Center for Rights & Research

Fair Housing Resource Center, Inc.

Friends Of the African Union Chamber of Commerce

Home Repair Resource Center

Ohio Fair lending

 

Oregon

CASA of Oregon

 

Pennsylvania

Philadelphia Association of Community Development Corporations

 

Rhode Island

HousingWorks RI

 

Texas

Harlingen CDC

Texas Appleseed

 

Wisconsin

CAP Services, Inc./Community Assets for People

Dominican Center

Havenwoods EDC

Metropolitan Milwaukee Fair Housing Council

Milwaukee Urban League

---

 

[1] CFPB, Introducing New and Revised Data Points in HMDA: Initial Observations from New and Revised Data Points in 2018 HMDA, August 2019, https://files.consumerfinance.gov/f/documents/cfpb_new-revised-data-points-in-hmda_report.pdf

[2] CFPB, New and Revised, Data Points, pp. 54-55.

[3] FFIEC, HMDA Data Browser, https://ffiec.cfpb.gov/data-browser/

[4] Todd Garvey, A Brief Overview of Rulemaking and Judicial Review, Congressional Research Service, March 2017, p. 2, https://fas.org/sgp/crs/misc/R41546.pdf

[5] Adam Levitin, New HMDA Regs Require Banks to Collect Lots of Data…That They Already Have, in Credit Slips, June 23017, https://www.creditslips.org/creditslips/2017/06/new-hmda-regs-require-banks-to-collect-data-they-already-have.html

[6] Final Rule, CFPB, https://www.federalregister.gov/d/2015-26607/p-1530

[7] Government Accountability Office (GAO), Characteristics and Performance of Nonprime Mortgages, p. 1, https://www.gao.gov/assets/100/96332.pdf

[8] GAO, Characteristics and Performance, pp. 4 and 9.

[9] Ibid., p. 10.

[10] Ibid., p. 13.

[11] Ibid, p. 4.

[12] “Regulators are Pressed to Take Tougher Stand on Mortgages,” by Gregg Hitt and James R. Hagerty, Wall Street Journal, March 23, 2007

[13] Testimony of John Taylor, President and CEO of NCRC, before the Oversight and Investigations Subcommittee of the House Financial Services Committee, Rooting Out Discrimination in Mortgage Lending: Using HMDA as a Tool for Fair Lending Enforcement, July 25, 2007, https://ncrc.org/wp-content/uploads/2007/07/ncrc_test_reg_oversight_hearing_finsvs_july_07(2).pdf

[14] Testimony of John Taylor, p. 4.

[15] Testimony of John Taylor, p. 14.

[16] Testimony of Comptroller of the Currency John Dugan before the United States Senate Committee on Banking, Housing and Urban Affairs, March 2008, p. 12, https://www.occ.gov/news-issuances/congressional-testimony/2008/pub-test-2008-28-written.pdf

 

[17] Emmanuel Martinez and Aaron Glantz, Kept Out, For people of color, banks are shutting the door to homeownership, February 2018, https://www.revealnews.org/article/for-people-of-color-banks-are-shutting-the-door-to-homeownership/

[18] CFPB, New and Revised Data Points, p. 68.

[19] Ibid, p. 73.

[20] NCLC, Consumer Concerns: Information for Advocates Representing Older Adults, Helping Elderly Homeowners Victimized by Predatory Mortgage Loans, https://www.nclc.org/images/pdf/older_consumers/consumer_concerns/cc_elderly_victimized_predatory_mortgage.pdf

[21] Consumer Financial Protection Bureau, Reverse Mortgages: Report to Congress, June 28, 2012, https://files.consumerfinance.gov/a/assets/documents/201206_cfpb_Reverse_Mortgage_Report.pdf

[22] HMDA purpose, 12 USC 29 Section 2901, https://www.law.cornell.edu/uscode/text/12/2801

[23] Joint Center for Housing Studies at Harvard University, State of the Nation’s Housing in 2018, p. 1, https://www.jchs.harvard.edu/state-nations-housing-2018

[24] Joint Center for Housing Studies, p. 30.

[25] Joint Center for Housing Studies, p. 28.

[26] Seattle Times, The mobile-home trap: How a Warren Buffett empire preys on the poor, February 2016,  https://www.seattletimes.com/business/real-estate/the-mobile-home-trap-how-a-warren-buffett-empire-preys-on-the-poor/

[27] CFPB, Manufactured housing consumer finance in the United States, September 2014, pp. 5-6, https://files.consumerfinance.gov/f/201409_cfpb_report_manufactured-housing.pdf

[28] CFPB, New and Revised Data Points, Table 6.4.4, p. 195; the statistic is for conventional conforming loans.

[29] CFPB, New and Revised Data Points, Table 3.1.4, p. 102.

[30] CFPB, New and Revised Data Points, p. 33.

[31] CFPB, New and Revised Data Points, p. 37.

[32] CFPB, New and Revised Data Points, Table 7.1.1, p. 219.

[33] CFPB, Ibid., p. 16.

[34] In 2010, 73 organizations signed onto a letter to the Federal Reserve Board, calling for 5 specific enhancements to HMDA reporting, including the disaggregation of certain race data.

[35] National CAPACD and SEARAC (2011). Untold Stories of the Foreclosure Crisis: Southeast Asian Americans in the Central Valley. Washington, D.C.

[36] “Queens Neighborhoods with High Percentages of South Asian Owners in Default”, Chhaya CDC, January 12, 2009 (analysis of June-December 2008 Notice of Default).

[37] CFPB, New and Revised Data Points, pp. 20 and 21, and Table 3.2.2, pp. 106-111.

[38] Louis Noe-Bustamante, Key facts about U.S. Hispanics and their diverse heritage, Pew Research Center, September 16, 2019, https://www.pewresearch.org/fact-tank/2019/09/16/key-facts-about-u-s-hispanics/

[39] Greer S, Naidoo M, Hinterland K, Archer A, Lundy De La Cruz N, Crossa A, Gould LH.

Health of Latinos in NYC. 2017; https://www1.nyc.gov/assets/doh/downloads/pdf/episrv/2017-latino-health.pdf

[40] NCRC analysis of the 2018 data for the Los Angeles MSA.

[41] See Building Sustainable Homeownership: Responsible Lending and Informed Consumer Choice—Public Hearing on the Home Equity Lending Market, Federal Reserve Board (June 16, 2006), Tr. at 85, 12 238-39, 13 238-40, 14 242-45, 15 250-5216 (available at http://www.federalreserve.gov/events/publichearings/hoepa/2006/20060616/transcript.pdf).

[42] https://www.law.cornell.edu/uscode/text/12/2802

[43] https://www.law.cornell.edu/uscode/text/12/1464#c_6_A

[44] Citizens Budget Commission, Think Your Rent is Too High? Documenting New York’s Severest Rent Burdens, October 2018, https://cbcny.org/research/think-your-rent-high

[45] ANHD, State of Bank Reinvestment in NYC Annual Report, https://anhd.org/project/state-bank-reinvestment-nyc-annual-report

[46] Urban Institute, Concentration in Multifamily Lending Argues for Full Public Release of More HMDA Data, April 2019, https://www.urban.org/urban-wire/concentration-multifamily-lending-argues-full-public-release-more-hmda-data https://www.urban.org/urban-wire/while-less-plentiful-multifamily-loans-pack-bigger-cra-punch-single-family-loans

[47] Jaime Weisberg, The “Bad Boy” Carveout, ANHD, May 2017, https://anhd.org/blog/bad-boy-carveout

[48] Housing Assistance Council (HAC) tabulation of American Community Survey 2017, five-year estimates. Rural and small town refers to HAC’s census tract classification based on housing density and commuting patterns. For specifics on this definition see the following report (page 113): http://www.ruralhome.org/storage/documents/ts2010/ts_full_report.pdf and https://www.minnpost.com/politics-policy/2018/05/trailer-parks-may-be-twin-cities-most-endangered-form-affordable-housing/

[49] Bureau of Consumer Financial Protection. Home Mortgage Disclosure Act (Regulation C) – Final Rule, 12 C.F.R. Part 1003 [Docket No. CFPB-2014-0019] RIN 3170-AA10, Federal Registry, Vol. 80:208 (October 28, 2015):66,128 – 66,340.

[50] Average sale price for manufactured home source: Census Bureau’s Manufactured Housing Survey 2017: https://www.census.gov/data/tables/2017/econ/mhs/2017-manufactured-housing-survey-annual-data.html. Average price for newly constructed single-family home source: Census Bureau’s Characteristics of New Housing: https://www.census.gov/construction/chars/highlights.html

[51] High costs loans are defined as first-lien loans with interest rates that exceed the interest rate on a similar prime rate loan by 1.5 percent or more. Subordinate-lien mortgages with interest rates that exceed the interest rate on a similar prime rate loan by 3.5 percent or more is also considered high cost.

[52] Laurie Goodman and Bhargavi Ganesh. 2018. Challenges to Obtaining Manufactured Home Financing. Urban Institute Brief. This brief, as of 6/4/19, can be found at the following url:https://www.urban.org/research/publication/challenges-obtaining-manufactured-home-financing

[53]Multiple news stories have discussed ground rent issues. Here are two such examples: https://www.npr.org/2019/01/23/687085941/mobile-home-owners-are-upset-about-rising-costs-to-rent-land

[54] See the work done in these brief and note how improved HMDA data might expand upon such work. Laurie Goodman and Bhargavi Ganesh. 2018. Challenges to Obtaining Manufactured Home Financing. Urban Institute Brief. This brief, as of 6/4/19, can be found at the following url: https://www.urban.org/research/publication/challenges-obtaining-manufactured-home-financing

[55] This data refers to home purchase, first lien loans. The threshold for first lien loans to be considered high costs is 1.5 percentage points over the interest rate charged on a similar prime rate loan.

[56] Bureau of Consumer Financial Protection. Home Mortgage Disclosure Act (Regulation C) – Final Rule, 12 C.F.R. Part 1003 [Docket No. CFPB-2014-0019] RIN 3170-AA10, Federal Registry, Vol. 80:208 (October 28, 2015):66,128 – 66,340, p. 66208.

 

[57] The Financial Crisis Inquiry Report, Final Report of the National Commission on the Causes of the Financial and Economic Crisis in the United States, 2011, PublicAffairs, New York, p. 80.

[58] Coastal Enterprises, Inc. and Center for Responsible Lending, Predatory Mortgages in Maine: Recent Trends and the Persistence of Abusive Lending in the Subprime, p. 10,  http://www.responsiblelending.org/mortgage-lending/policy-legislation/states/rr009-Predatory_Lending_Maine-0206.pdf

[59] CFPB, New and Revised Data Points, p. 27.

[60] CFPB, New and Revised Data Points, p. 49.

[61] CFPB, New and Revised Data Points, Table 7.3.2, p. 75.

[62] CFPB, New and Revised Data Points, Table 7.2.2, p. 72.

[63] 10 Years After The Financial Crisis: Early Warning Signs, NPR, All Things Considered, September 2018, https://www.npr.org/2018/09/15/648318771/10-years-after-the-financial-crisis-early-warning-signs

[64] Final Rule, CFPB, https://www.federalregister.gov/d/2015-26607/p-1530

[65] United States. (2010). The financial crisis inquiry report: Final report of the National Commission on the Causes of the Financial and Economic Crisis in the United States. Washington, DC: Financial Crisis Inquiry Commission. http://fcic-static.law.stanford.edu/cdn_media/fcic-reports/fcic_final_report_full.pdf#page=22

[66] Ibid.

[67] David Friedman, “Market Failure: An Argument For and Against Government,” http://www.daviddfriedman.com/Machinery_3d_Edition/Market%20Failure.htm.

[68] Lynn M. Fisher, Norbert Michel, Tobias Peter, Edward J. Pinto Analysis of the CFPB’s Temporary Qualified category announced in January 2013, commonly known as the patch, March 2019, http://www.aei.org/publication/analysis-of-the-bcfps-cfpbs-temporary-qualified-mortgage-category-announced-in-january-2013-commonly-known-as-the-patch/

[69] Final Rule, CFPB, 12 CFR Part 1003, Docket No. CFPB-2014-0019, pp. 344-345, https://files.consumerfinance.gov/f/201510_cfpb_final-rule_home-mortgage-disclosure_regulation-c.pdf

[70] United States. (2010). The financial crisis inquiry report: Final report of the National Commission on the Causes of the Financial and Economic Crisis in the United States. Washington, DC: Financial Crisis Inquiry Commission. https://files.consumerfinance.gov/f/201510_cfpb_final-rule_home-mortgage-disclosure_regulation-c.pdf#page=295

[71] Ibid.

[72] United States. (2010). The financial crisis inquiry report: Final report of the National Commission on the Causes of the Financial and Economic Crisis in the United States. Washington, DC: Financial Crisis Inquiry Commission. http://fcic-static.law.stanford.edu/cdn_media/fcic-reports/fcic_final_report_full.pdf#page=526

 

[73] Final Rule, CFPB, 12 CFR Part 1003, Docket No. CFPB-2014-0019, p. 295 https://files.consumerfinance.gov/f/201510_cfpb_final-rule_home-mortgage-disclosure_regulation-c.pdf#page=295

[74] National Community Reinvestment Coalition, The $90 Billion Bill We Pay Each Year For Non-Bank Mortgage Lenders, Jason Richardson, September 2019, https://ncrc.org/the-90-billion-bill-we-pay-each-year-for-non-bank-mortgage-lenders/

[75] National Community Reinvestment Coalition, NCRC’s HMDA 2018 Methodology: How To Calculate Loan Price

Jason Richardson – https://ncrc.org/ncrcs-hmda-2018-methodology-how-to-calculate-loan-price/

[76] National Community Reinvestment Coalition, 2019, A Review Of The State Of Barriers To Minority Homeownership https://ncrc.org/a-review-of-the-state-of-barriers-to-minority-homeownership/

[77] Final Rule, CFPB, https://www.federalregister.gov/d/2015-26607/p-1530

[78] Dodd Frank, p. 724, as found at: https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf and CFPB Interpretation, as found at: https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1003/4/#a-33.

[79] CFPB, New and Revised Data Points, pp. 39-40.

[80] CFPB, New and Revised Data Points, pp. 38-41, and Tables 5.7.1-5.7.3.

[81] Includes all originated first-lien closed-ended home purchase conforming conventional and gov’t-backed (FHA, VA, FSA/RHS) loans on 1-4 family site-built principle residences that had an MSA identifier (including 9999, not in an MSA) and application submission and initially payable data.

[82] Due to much lower numbers here, the cutoff point is 100 loans rather than 1,000.

[83] Ibid.

[84] Dodd Frank, p. 723, as found at: https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf and CFPB Interpretation, as found at: https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1003/4/#a-27.

[85] CFPB, New and Revised Data Points, p. 34.

[86] Includes all originated first-lien open-ended conforming conventional loans on 1-4 family site-built principle residences that had an MSA identifier (including 9999, not in an MSA).

[87] https://www.responsiblelending.org/sites/default/files/nodes/files/research-publication/ib008-PPP_in_Subprime_Loans-0604.pdf

[88] Ibid.

[89] Dodd Frank, p. 723, as found at: https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf and CFPB Interpretation, as found at: https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1003/4/#a-20.

[90] CFPB, New and Revised Data Points, p. 37.

[91] Includes all originated first-lien open-ended conforming conventional loans on 1-4 family site-built principle residences that had an MSA identifier (including 9999, not in an MSA).

[92] From PUBLIC LAW 111–203—JULY 21, 2010, DODD-FRANK WALL STREET REFORM AND CONSUMER PROTECTION ACT, 124 STAT. 1376, p. 723, as found at: https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf and 12 CFR Part 1003 (Regulation C), and CFPB Interpretation, as found at: https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1003/4/#a-28.

[93] https://www.consumerfinance.gov/policy-compliance/rulemaking/regulations/1003/4/#a-24.

[94] CFPB, “Executive Summary of the HMDA Data Disclosure Policy Guidance,” December 21, 2018, as found at: https://files.consumerfinance.gov/f/documents/HMDA_Data_Disclosure_Policy_Guidance.Executive_Summary.FINAL.12212018.pdf.

[95] CFPB, New and Revised Data Points, Table 6.2.2.

[96] New York State data obtained by downloading by state via the HMDA Data Filtering tool at: https://ffiec.cfpb.gov/data-browser/data/2018.

[97] Includes all originated first-lien closed-ended home purchase conforming conventional and gov’t-backed (FHA, VA, FSA/RHS) loans on 1-4 family site-built principle residences that had an MSA identifier (including 9999, not in an MSA) and a property value (not Exempt or NA).

[98] CFPB, New and Revised Data Points, pp. 53-54.