Comment Letter on Public Dissemination of HMDA Data
November 22, 2017
Docket No. CFPB-2017-0025
Office of the Executive Secretary
Consumer Financial Protection Bureau
1700 G. St. NW
Washington, DC 20552
Dear Ms. Jackson:
The undersigned organizations appreciate that the Consumer Financial Protection Bureau (CFPB) carefully applied the balancing test in determining which of the new Home Mortgage Disclosure Act (HMDA) data points to release publicly. The balancing test requires the CFPB to consider the benefits of HMDA disclosure and weigh those benefits against the privacy risk of applicants being re-identified via HMDA disclosure. We believe that the CFPB has thoughtfully considered the various types of privacy risks in making its decisions about public data disclosure. Nevertheless, we urge the CFPB to make public data on key variables that the CFPB proposes to not disclose. In addition for some of the excluded variables, the CFPB should be open to developing supplemental datasets that would not be part of HMDA data but would help answer critical fair lending, CRA, and consumer protection queries.
The CFPB has a deep understanding of the benefits of HMDA disclosure as evidenced by the preamble of the draft policy guidance. The CFPB clearly indicates that public disclosure of HMDA data is integral to and essential for the realization of HMDA’s three statutory purposes of determining whether lenders are serving the housing needs of their communities, assisting public officials to direct public investment to economically struggling areas in such a manner as to stimulate private sector investment, and identifying possible discriminatory lending patterns in order to enforce anti-discrimination statutes. The Bureau also states that community organizations and lenders use HMDA data to craft commitments for future lending programs to meet local credit needs, public agencies use the data to plan neighborhood-based strategies, and regulatory agencies employ HMDA data for fair lending enforcement.
After detailing HMDA’s benefits, the CFPB reviews possible risks of public data disclosure. The CFPB correctly concludes that HMDA is not useful for aiding identity theft since HMDA lacks Social Security numbers and other personally identifiable information. However, a few Dodd-Frank data elements such as the unique loan identifier could be used to link HMDA data to other publicly available datasets such as county real estate transaction records that are available electronically. Accordingly, the CFPB is not publicly disclosing the unique loan identifier.
The CFPB is primarily concerned about two possible harms that could be perpetrated by adversaries linking HMDA data to other publicly-available data or widely-available private sector data. First, the Bureau is concerned that harm to a person’s reputation or embarrassment could be caused by public disclosure of information like credit scores. Second, the CFPB seeks to deter predatory or abusive marketing that could arise if an adversary uses HMDA data to identify a vulnerable borrower who has a risky loan such as an adjustable rate mortgage with high levels of debt. An enormous amount of abusive marketing took place in the years leading up to the financial crisis without the enhanced Dodd-Frank HMDA data. Nevertheless, it is prudent to take steps to minimize the chances of HMDA being used in this manner.
Since HMDA is not effective for facilitating identity theft, the CFPB appropriately decided to disclose the majority of Dodd-Frank data elements so that HMDA’s statutory purposes can be better achieved. The CFPB made the correct judgment about the ineffectiveness of HMDA for identity theft as compared to other actions adversaries can take such as hacking Equifax and compromising the identities of 143 million people. In the more than forty years of HMDA’s existence, the groups signing this letter are not aware of any Congressional testimony or other reports from the federal regulatory agencies identifying HMDA as a source of compromising consumers’ identities or endangering their reputations. This record was at least implicitly taken into account by the CFPB.
In the final policy guidance, we urge the CFPB to hold the line on continuing full and complete disclosure of the pre-Dodd-Frank data elements. Some of the data points have been disclosed for more than forty years and many of them have been disclosed since the 1990s without evidence of privacy risk. It would be an unacceptable frustration of HMDA’s statutory purposes to either remove or substantially modify the disclosure of the pre-Dodd Frank data elements (see below for a discussion about loan amount which is a pre-Dodd Frank data point that will undergo some modification).
Data Disclosed Without Modification
In the preamble to the proposed policy guidance, the CFPB references the Dodd-Frank and current data points that will be publicly available without modification. These include location and characteristics of the properties securing loans, loan purpose and type, application channel, preapproval information, action taken, type of purchaser, lien status, prepayment penalty term, introductory rate period, interest rate, rate spread, loan costs, charges and discount points, HOEPA status, balloon payments, negative amortization status, and combined loan-to-value (CLTV). Each of these data points are essential to determine if lenders are responsibly meeting credit needs in a non-discriminatory manner. The pricing information, including interest rate, rate spread, and fees and costs will enable stakeholders to identify potentially discriminatory price disparities within the prime and subprime markets with more accuracy than with the current HMDA data. In addition, loan terms and conditions serve as an early warning system, enabling community organizations and government agencies to determine if unfair, deceptive, and unaffordable lending is increasing. Some of these early warning data points that were not available in the years before the financial crisis would have enabled stakeholders to take action ranging from public persuasion to enforcement to stop predatory lenders before they cause crises.
We are also pleased that the agency will disclose the new race and ethnicity subcategories for Asian and Hispanic loan applicants. As has been well-documented, Asian and Latino loan applicants are likely to have different experiences in the lending marketplace depending on their or their families’ countries of origin and whether or not they are immigrants, ranging from difficulties accessing credit to being targeted for risky and abusive lending. With these subcategories, stakeholders will be able to identify discrimination and targeting with more precision and will be better able to promote responsible lending in all communities.
The CFPB’s proposed guidance will support fair lending enforcement by making public reasons for denial for all lenders and publicly disclosing the name and version of the credit score model and the name of the automated underwriting system (see below for discussion of credit score and automated underwriting system (AUS) results). Analysts will be able to compare reasons for denial to credit score models and AUS systems to determine if there are consistent differences in reasons for denial based on the models and systems used. If, after controlling for other key variables, certain credit score models and AUS systems seem to be causing a disproportionate number of denials for reasons that do not seem justified, stakeholders can ask federal agencies to further investigate. These data elements will help remove a level of opacity in the lending marketplace and begin to hold the black box loan decision models more accountable for fair and responsible lending.
The CFPB should also ensure that the reasons for denial data, and all data fields, are reported accurately. In particular, the CFPB must specify that lending institutions cannot use the free text form fields to report pre-coded reasons for denial. Since the CFPB is not proposing to publicly report credit scores or Automated Underwriting System results, precise data for reasons for denial is critical for fair lending analysis.
The CFPB’s updated HMDA regulations will improve transparency in the multifamily and manufactured home lending markets. The undersigned organizations are pleased that the CFPB will publicly disclose the new data such as the number of units in multifamily buildings and the number of units that are affordable (units that are designated as income-restricted under local, state, or federal housing programs). In addition, the new data on manufactured home lending will provide important information about the manufactured home market and whether affordability, sustainability, or fair lending issues are of concern in the various segments of the manufactured home lending market.
In order to facilitate fair lending and other critical analysis, the CFPB will retain the name of the lending institution in the HMDA data and will also publicly disclose the legal entity identifier (LEI). It is our hope that the LEI will represent an improvement over the lender identification numbers used in the current HMDA data and will more readily accommodate grouping individual lenders together under their parent institutions. Fair lending and Community Reinvestment Act (CRA) analysis cannot be comprehensively conducted if analysts cannot readily assess subsidiary companies’ lending records separately as well as combining them under their parent for analysis. As the CFPB knows, this remains a difficult task. The CFPB should improve the lender identification functions on its website to allow users to easily conduct both parent-level and subsidiary-level analysis.
To ensure that HMDA users understand the full scope of the new data that will be publicly disclosed, the CFPB should publish a document that lists all HMDA fields and if and how they will be made public. For example, will interest rate and number of affordable units be publicly disclosed as they are reported by lending institutions to the federal agencies? Recently, the CFPB placed on its website a helpful chart called “Reportable HMDA Data: A Regulatory and Reporting Overview and Reference Chart.” This chart indicates how data is to be reported; it could be amended to also describe if and how the data will be disclosed to the public.
The undersigned groups appreciate that the CFPB’s data disclosure proposal seeks to preserve the benefits of HMDA data disclosure by modifying the form of certain data points that in unmodified form could increase privacy risk by facilitating the matching of HMDA data to county real estate transaction records or other databases. Overall, the CFPB succeeded in securing meaningful disclosure of the modified variables but we urge the CFPB to consider suggestions for improving some of their methods for modification to ensure the publicly available data are as useful and accurate as possible.
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 is currently.
If the Bureau proceeds with its proposed changes, we believe modifications to its proposal 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 proposes. 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 for 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. 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.
The CFPB asks if a parallel reporting should be made based on the FHA loan 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.
The CFPB’s proposal to disclose the midpoint of intervals of $10,000 for property value will result in inaccuracies similar to those for loan amount in the case of relatively low property values present in a large number of jurisdictions and in modest income and/or communities of color. In particular, the midpoint procedure applied to intervals of $10,000 could result in distortions of 10 percent or greater when the property value is $50,000 or less. The margin of error would likewise be too high for property values of less than $100,000. Therefore, either the interval should be smaller or the CFPB should report the value rounded to the nearest $1,000.
The disclosure of age in bins or ranges is vital for fair lending enforcement and protection against unfair and deceptive lending. 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.
We agree with the CFPB that the proposed bins of 25 to 34; 35 to 44; and 45 to 54 will be useful for HMDA data analysis. The CFPB should adopt one of its two proposed alternatives when disclosing age ranges for people 55 and older: 1) bins of 55 to 61 and 62 to 64 or 2) bins of 55 to 61 and 62 to 74. 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. The first alternative with the bin of 62 to 64 would provide more precise data with which to reveal the experiences of applicants first becoming eligible for reverse mortgages. This would be our preferred approach but we would also support the second alternative.
In addition, 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 can 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.
Debt-to-Income Ratio (DTI)
The CFPB’s proposal on how to disclose debt-to-income ratio (DTI) takes into account the critical importance of this data point for fair lending and consumer protection analyses in determining whether loans are affordable and sustainable. The proposed bins for DTI disclosure are a good start but should be refined. The undersigned groups urge the CFPB to adopt its alternative of more granular disclosure around the 43 percent DTI ratio since this ratio is a critical part of the Qualified Mortgage (QM) rule. Moreover, the disclosure should also be granular for values near 36 percent DTI since 36 percent is a common benchmark used for underwriting by lenders. One approach would be to disclose DTI ratios without modification that are within 2 percentage points of 36 percent DTI. The undersigned organizations support the CFPB’s proposal of disclosing DTI percentages between 40 and 50 without modification.
Data Excluded from Public Disclosure
The undersigned organizations urge the CFPB to reconsider exclusions from public disclosure for some of the proposed excluded data points and to provide data in other, non-HMDA reports for other data points the CFPB proposes excluding 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.
The undersigned organizations ask the CFPB to reconsider its proposal to exclude credit score data in all forms from the publicly available 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 or gender. 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. Regarding reputational harm, a normalized credit score reporting format would make it more difficult for adversaries seeking reputational harm to successfully embarrass applicants. 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 undersigned organizations urge the CFPB to 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:
- 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.
- The 25th, 50th and 75th 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 is excluding the AUS result from public 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 states 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, can aid significantly in fair lending analysis to determine the likelihood of similarly situated borrowers being treated differently due to race, 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 are in the best position to steer people to certain products and to 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 (where the lender allowed variations). Therefore, in many markets, the “lender” is no longer 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, as 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.
Universal Loan Identifier
For some of the excluded variables, the CFPB should consider producing data separate from HMDA data 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.
The universal loan identifier is also important for fair lending and consumer protection enforcement. If a group of loans have problematic loan terms and conditions, it can be important to know not only the entity that originated the loans but the entity that now owns the loans. A restricted access program to allow members of the public to use the unmodified HMDA data will be critical for these purposes (more below in restricted access program). Members of the public using the data would pledge to keep the data confidential and to use it only for non-commercial purposes.
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.
Census Data in HMDA Data
The CFPB’s decision to continue reporting demographic data for census tracts in its loan level HMDA release will facilitate public use of the data. Currently, each HMDA record includes total population in the tract, minority population percentage, median family income, tract to MSA/MD median family income percentage, number of owner occupied units, and number of 1- to 4-family units. This data is valuable for fair lending and CRA purposes in order to assess whether borrowers in census tracts with different demographic and housing stock characteristics have access to affordable and sustainable loans.
While the current census data are valuable in HMDA data, the minority percentage of a census tract can be incomplete as a demographic indicator. Adding the percentages of African-American and Hispanic residents separately would allow for a more accurate picture of the experience of geographic areas and neighborhoods in lending markets. Although neighborhoods with predominantly Asian residents are currently not as widespread as predominantly Hispanic and African-American neighborhoods, adding the percentage of Asians living in each census tract would be valuable in some major markets.
Restricted Access Program
In its proposed policy guidance, the CFPB states that it is still assessing the benefits and costs of a restricted access program. The costs would seem to be minimal since the CFPB is already preparing HMDA data annually. Moreover, the benefits are substantial since the unmodified HMDA data would provide interested members of the public (agreeing to use restrictions) with data valuable for fair lending and CRA analysis that will not be available in the publicly released data.
We ask the CFPB to expeditiously make a decision on a restricted access program and to make the program widely available to any member of the public that signs a non-disclosure and non-commercial use agreement. The CFPB hints that it is contemplating broad use of a restricted access program when it discusses that such a program would be available not only to academics but to industry and community researchers. Any prohibitions for subgroups of the public to a restricted access program would frustrate HMDA’s statutory purposes of holding lenders accountable for non-discriminatory access to credit.
The CFPB must investigate methods to make a restricted access program easily available. Instead of requiring members of the public to use the data at CFPB offices, the CFPB should determine if the data can be used in a secure portal on-line or if electronic data can be mailed to members of the public that sign confidentiality agreements. The CFPB should also encourage members of the public using the data to share research findings openly and with the CFPB, which can then produce a public archive of research highlighting the new Dodd-Frank data and how it can be used to shed light on whether or not lenders are meeting credit needs responsibly.
Related to the enhancements to HMDA data, Dodd-Frank required the CFPB and the Department of Housing and Urban Development (HUD) to develop and release data on foreclosure and loan performance on a census tract level. The CFPB, in conjunction with the Federal Housing Finance Agency, is using some of this data to develop a National Mortgage Database (NMD) in consultation with economists and industry and community stakeholders. We urge the CFPB to make as much of the NMD data publicly available as possible while protecting borrower privacy. Recently, the CFPB has added delinquency data to its website. We urge the Bureau to make the other required Dodd-Frank data on loan performance available as soon as possible.
The undersigned organizations appreciate that the CFPB has produced thoughtful proposed policy guidance that will release the great majority of the new Dodd-Frank HMDA data points. We believe that this data furthers HMDA’s purpose of promoting a fair, responsive, and responsible marketplace. The undersigned organizations also urge the CFPB to seriously consider our recommendations regarding credit score disclosure, changing some of the proposed modifications to improve the utility of modified data fields, and a creating an effective restricted access program.
If you have any questions, please contact Josh Silver, Senior Advisor, NCRC on 202-464-2733 or email@example.com. Thank you.
Affordable Homeownership Foundation, FL
American Federation State County Municipal Employees, NJ
Americans for Financial Reform
Anti-Poverty Network of New Jersey
Association for Neighborhood and Housing Development, NY
Baltimore Neighborhoods, Inc, MD
Birmingham Business Resource Center, AL
Bright Community Trust, FL
Building Alabama Reinvestment
California Coalition for Rural Housing
California Community Economic Development Association
California Reinvestment Coalition
Calvin Bradford & Associates, Ltd, IL
Casa of Oregon
Central Ohio Fair Housing Association, Inc., OH
Center for Responsible Lending
Charisma Community Connection, OH
Chicago Community Loan Fund
Chicago Rehab Network, IL
Civil Rights Enforcement Agency, MO
Community Action Committee of the Lehigh Valley, PA
Community Legal Aid Services, Inc, OH
Community Link, NC
Community Reinvestment Fund, USA, MN
Community Service Network Inc., MA
Connecticut Fair Housing Center
Consumer Federation of America
County Corp, OH
Delaware Community Reinvestment Action Council, Inc., DE
Durham Regional Financial Center, NC
Empire Justice Center, NY
Faith and Community Alliance of Greater Cincinnati, OH
Fair Finance Watch, NY
Fair Housing Center of Metropolitan Detroit, MI
Fair Housing Council of Northern New Jersey
Financial Justice Coalition, MI
Foundation Capital, AL
Friends of the African Union Chamber of Commerce, OH
Greater Cincinnati Microenterprise Initiative, OH
Greater New Orleans Housing Alliance, LA
Greenlining Institute, CA
Hamilton County Community Reinvestment Group, OH
Harlingen CDC, TX
Hawai’i Alliance for Community-Based Economic Development (HACBED)
Henderson and Company, NC
HigherSelf Lifestyle, NY
HOPE of Evansville, IN
Housing and Education Alliance, Inc., FL
Housing Action Illinois
Housing NOLA, LA
Jericho Road Episcopal Housing Initiative, LA
Jerusalem Economic Development Corporation (JEDC), LA
Justine PETERSEN, MO
Kingsley House Inc., LA
Legal Services NYC, NY
Long Island Housing Services, Inc., NY
Louisville Affordable Housing Trust Fund, KY
Lower 9th Ward Homeownership Association, LA
Maryland Consumer Rights Coalition
Massachusetts Affordable Housing Alliance
Massachusetts Communities Action Network
Metropolitan Milwaukee Fair Housing Council
Metropolitan St. Louis Equal Housing and Opportunity Council, MO
Miami Valley Fair Housing Center, Inc, OH
Michigan Community Reinvestment Coalition
Mobilization for Justice, Inc., NY
National Association of American Veterans, Inc.
National Business League of Alabama
National Community Reinvestment Coalition
National Consumer Law Center, on behalf of its low-income clients
National Fair Housing Alliance
National Housing Counseling Agency, GA
Nazareth Housing Dev. Corp., OH
NDF New Orleans Neighborhood Development, LA
Neighborhood Development Foundation, LA
Neighborhood Housing Services of Waterbury, CT
NeighborWorks Collaborative of Ohio
New Day Homeowner Services, LA
New Economy Project, NY
New Jersey Citizen Action
New Jersey Community Capital
New Jersey Education Association
New Orleans Neighborhood Development Foundation, LA
NJ Appleseed PILC, NJ
Northwest Fair Housing Alliance, WA
Northwest Indiana Reinvestment Alliance
Oak Park Regional Housing Center, IL
Ohio Fair Lending
PathStone Enterprise Center, NY
People for Change Coalition, MD
PHI Consulting, AL
Portland Community Reinvestment Initiatives, Inc., OR
Reinvestment Partners, NC
S J Adams Consulting, NC
Scott County Housing Council, IA
Southeast Houston CDC, TX
Southside Community Development and Housing Corporation, VA
The Fair Housing Center, OH
The Housing Partnership, Inc., KY
Titusville Development Corporation, AL
Top Agent Realty, Inc., LA
United Communities Southeast Philadelphia, PA
Universal Housing Solutions CDC, IL
Urban Economic Development Association of Wisconsin (UEDA)
Urban Coalition of Appraisal Professionals, KY
We Help Communities To Dev. Corp, FL
Western New York Law Center
White Wing Educational Community Development, Inc, NY
Wisconsin Partnership for Housing Development
Woodstock Institute, IL