National Mortgage Database

Technical Documentation

TECHNICAL

REPORT 1

2022

December 28

This document was prepared by Robert B. Avery, Craig Davis, Elizabeth Hoeffel, Ian H. Keith, Ismail E. Mohamed, Saty 

Patrabansh, Jay D. Schultz, and Rebecca Sullivan. It is updated from the version published on January 21, 2021.

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Table of Contents

1. Introduction ............................................................................................................... 1

2. The Experian Contract................................................................................................ 3

3. Selecting the Initial Sample ........................................................................................ 4

4. Processing the Initial Sample...................................................................................... 6

5. Updating the Sample.................................................................................................. 8

6. Merging with other Data Sources..............................................................................11

7. Production ...............................................................................................................14

8. Evaluating the NMDB Sample Frame....................................................................... 16

Appendix A. Origins of NMDB....................................................................................... A-1

Appendix B. Background on Mortgage Performance Reporting.......................................... B-1

Appendix C. Cleaning and Editing Data in the NMDB....................................................... C-1

Appendix D. Imputation of Missing Data in the NMDB....................................................D-1

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1. Introduction

The National Mortgage Database (NMDB®) program is jointly funded and managed by the 

Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection Bureau 

(CFPB). The program is designed to provide a rich source of information about the U.S. 

mortgage market based on a five percent sample of residential mortgages. It has three primary 

components:

(1) the National Mortgage Database (NMDB);

(2) the National Survey of Mortgage Originations (NSMO); and

(3) the American Survey of Mortgage Borrowers (ASMB).

The NMDB program enables FHFA to meet the statutory requirements of section 1324(c) of the 

Federal Housing Enterprises Financial Safety and Soundness Act of 1992, as amended by the 

Housing and Economic Recovery Act of 2008 (HERA).1 Specifically, FHFA must, through a 

monthly survey of the mortgage market, collect data on the characteristics of individual 

mortgages, including both those eligible and not eligible for purchase by Fannie Mae and 

Freddie Mac, and including subprime and nontraditional mortgages. In addition, FHFA must 

collect information on the creditworthiness of borrowers, including a determination of whether 

subprime and nontraditional borrowers would have qualified for prime lending. 2

For CFPB, the NMDB program supports policymaking and research efforts and helps identify 

and understand emerging mortgage and housing market trends. CFPB uses NMDB, among other 

purposes, in support of the market monitoring called for by the Dodd-Frank Wall Street Reform 

and Consumer Protection Act (Dodd-Frank Act), including understanding how mortgage debt 

affects consumers and for retrospective rule review required by this statute.3

In seeking to meet these objectives, FHFA and CFPB decided to create NMDB after determining 

that no existing database was sufficient.4 NMDB is a de-identified loan-level database of closedend first-lien residential mortgages. It has the following features:

(1) Represents the market as a whole; 

(2) Contains detailed, loan-level information on the terms and performance of mortgages, 

as well as characteristics of the associated borrowers and properties; 

(3) Continually updates; 

(4) Has a historical component dating back before the financial crisis of 2008; and 

(5) Provides a sampling frame for NSMO and ASMB. 5

The core data in NMDB represent a statistically valid 1-in-20 random sample of all closed-end 

first-lien mortgages in the files of Experian, one of the three national credit bureaus.6 When the 

NMDB program began, an initial sample was drawn from all mortgage files outstanding at any 

1 Housing and Economic Recovery Act of 2008, Pub. L. 110–289, 122 Stat. 2654 (2008). 2 FHFA interprets the NMDB program, including NSMO, as the “survey” required by the Safety and Soundness 

Act. The statutory requirement is for a monthly survey. Core inputs to NMDB, such as a regular refresh of creditrepository data, occur monthly, though NSMO is conducted quarterly. 3 Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. L. 111-203, 124 Stat. 1376 (2010). 4 Please see the Appendix A for a discussion of sources available at the genesis of the programand their limitations. 5 See NSMO Technical Documentation at http://www.fhfa.gov/nmdb. 6 Experian was chosen through a competitive procurement process to assist in creating NMDB.
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point from January 1998 through June 2012. The sample has since been updated quarterly with 

mortgages newly reported to Experian. Mortgages (and their borrowers) are tracked in NMDB 

from at least one year prior to origination to one year after termination of the mortgage, whether 

that termination is through prepayment, adverse termination, or maturity.

Using a sampling frame substantially reduces the privacy risk associated with any data 

collection. By contrast, a universal registry can present privacy challenges because it is known 

that a particular loan must be in the dataset. However, for a 1-in-20 sample, the odds are 95 out 

of 100 that a particular loan is not in the database. In addition, the sample used is large enough 

to support almost all types of statistically valid analyses but small enough to manage logistically, 

thus dramatically reducing both contract and personnel costs. Restricting the NMDB frame to 

closed-end loans was done for two reasons. First, it mimics the pre-2018 Home Mortgage 

Disclosure Act (HMDA) reporting requirements, and second, it reflects the practical fact that 

administrative data, which is a critical input for the NMDB data, is available for very few openended loans.

A random 1-in-20 sample of mortgages newly reported to Experian is added each quarter. 

Information from credit repository files on each borrower associated with the mortgages in the 

NMDB sample is collected from at least one year prior to origination to one year after 

termination. The information on borrowers and loans available to the FHFA, CFPB, or any other 

authorized user of the NMDB data is de-identified and does not include any directly identifying 

information such as borrower name, address, or Social Security number.

This technical report is designed to provide NMDB data users with background on the 

development of the database, as well as an assessment of the data quality. The remaining 

sections discuss the development of the contract with Experian, outline the process of selecting 

the initial historical sample, describe how the initial sample data were processed, discuss how the 

data are being updated, detail how administrative data are merged into the NMDB, and provide 

details of the current production version of the database (NMDB 20.0 as of this writing). The 

final section evaluates the NMDB sample frame.
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2. The Experian Contract

By interagency agreement between FHFA and CFPB, FHFA leads the production of the NMDB. 

Following a competitive procurement process, a five-year contract for the core data of the 

NMDB was signed between FHFA and Experian in September 2012. 7 Simultaneously, FHFA 

and CFPB signed an interagency agreement that codified the equal costsharing and 

administrative arrangement.

The Experian contract has several key elements designed to ensure compliance with the Fair 

Credit Reporting Act (FCRA) and to protect the privacy of both borrowers and lenders. 8 First, 

while Experian uses name, address, and Social Security number for matching purposes only, this 

information is never transmitted to FHFA or CFPB when constructing the NMDB. Second, any 

user of the database must sign a terms of use agreement stating that they will not attempt to learn 

the identity of any borrower. 9 Third, all access to the NMDB must be through a server at FHFA 

or CFPB and strictly controlled. Fourth, the NMDB – which is a sample and designed to 

describe the market as a whole – cannot be used for enforcement against any specific servicer or 

lender.

7 A 10-year extension of this contract was signed in September 2017. 8 The FCRA(Public Law No. 91-508)was enacted in 1970 and substantially amended since to promote accuracy, 

fairness, and the privacy of personal information assembled by credit reporting agencies (CRAs). The Act's primary 

protection requires that CRAs follow “reasonable procedures” to protect the confidentiality, accuracy, and relevance 

of credit information. To do so, the FCRA establishes a framework of requirements for credit report information to

include rights of data quality (right to access and correct), data security, use limitations, requirements for data 

destruction, notice, user participation (consent), and accountability.

9 The Experiancontract allows access to the NMDB to be extended to employees of other federal agencies–the 

Federal Reserve System, Fannie Mae, Freddie Mac, and Federal Home Loan Banks(FHLBs)– provided the 

employee has signed the terms of use agreement. At present, employees from Fannie Mae, Freddie Mac and 11

federal agencies have been granted access.
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3. Selecting the Initial Sample

The credit repository core of the NMDB was developed in two phases: 

(1) An initial 1-in-20 random sample of closed-end first-lien mortgages active at any time 

from January 1998 to June 2012 (as January 1998 was the earliest available date 

given Experian’s archive policies); and 

(2) Quarterly updates that add a 1-in-20 random sample of mortgages newly reported to 

Experian and updated information on existing loans still active in the database.

One virtue of the credit repository sampling frame is that the repositories maintain credit report

records not only of currently active mortgages (and other credit obligations), but also of closed

ones. However, because of FCRA, records with derogatory information are purged from the 

current credit report after seven years from their point of first continual delinquency, and 

Experian's policies dictate a purge of all closed accounts 10 years after their closing.

Since Experian retains archives of its data for 10 years or longer, data on mortgages that have 

been purged from Experian’s current files due to FCRA can be recovered. These archives, 

which are not used for credit granting decisions, contain snapshots of each credit record as it 

existed at the close of business on a given day of each month, but with suppressed personal 

information (such as name, address, and Social Security number).

The bulk of the initial NMDB sample was drawn from the June 2012 archive. This was 

supplemented by a sample from the December 2005 archive that captured loans that may have 

been purged by June 2012 and a sample from the July 2001 archive that captured loans that may 

have been purged by December 2005.

Trade lines, which are records that contain information about specific loans or debt obligations 

reported by loan servicers, account for most of the information contained in credit records. Loan 

servicers typically update trade line information monthly using a standardized format agreed 

upon by the servicers and the credit repositories (currently the Metro 2® format, introduced in 

1997 and made mandatory in 2018). The updates include information on the type of servicer on 

the loan, its opening date, current and original balance, term, type, payment amount, and 

repayment performance.

The format agreed upon by loan servicers and the credit repositories does not perfectly identify 

closed-end first-lien mortgages. Recognizing that some second liens would be sampled and have 

to be removed later, trade lines falling under the following categories were deemed eligible for 

the NMDB:

• Any trade line with a Metro 2 “Enhanced Account Type Code” of 08 (real estate loan, 

specific type unknown), 19 (Federal Housing Administration [FHA] real estate 

mortgage), 2C (Farmers Home Administration real estate mortgage), 25 (Department of 

Veterans Affairs [VA] real estate mortgage), 26 (conventional real estate mortgage), 27 

(real estate mortgage, with or without collateral, usually second mortgage), 85 (bimonthly mortgage payment), 87 (semi-monthly mortgage payment), 5A (real estate –

junior liens and non-purchase money first), 17 (manufactured home loan), and 05 (FHA 
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home-improvement loan); or

• Trade lines reported by servicers with “type of servicer” codes of: FB (mortgage

brokers), FM (mortgage companies), FR (mortgage reporters), RE (real estate sales and 

rentals), BM (bank-mortgage only), FL (savings and loan – mortgage department) and

Metro 2 “Enhanced Account Type Codes" of 02 (secured loan), 04 (home improvement 

loan), 66 (government-secured guaranteed loan), 7B (agriculture), and 9A (secured home 

improvement).

To be eligible for the initial sample, a trade line had to meet one of these two criteria. In 

addition, the loan had to be reported as close-ended and not revolving or with unknown terms. 

No other restrictions or criteria were imposed.

The first supplemental sample was a 1-in-20 random sample of trade lines drawn from the 

December 2005 archive that met the “Enhanced Account Type Code” and “Kind of Business 

Code” criteria used for the June 2012 archive and had information reported for some period in 

the past seven years (indicated by an “Account Balance Date” of January 1998 or later) and were 

opened in September 2005 or earlier. To exclude loans from the 2005 sample that should also be 

present in the June 2012 archive, loans were excluded if they were last reported after July 2002 

with a reported account status of “current.”

The second supplemental sample, drawn from the July 2001 archive, was a random 1-in-20 

sample of trade lines that met the “Enhanced Account Type Code” and “Kind of Business Code” 

criteria used for the June 2012 archive and had “Account Balance Dates” of January 1998 or 

later and were opened in April 1999 or earlier. Any trade line with an “Enhanced Status Code” 

of “current” was excluded from the sample. Again, these additional conditions were designed to 

exclude all trade lines from the 2001 sample that should also be present in the 2005 archive.
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4. Processing the Initial Sample

Once loans were selected for the initial NMDB sample, data from the July 2001, December 

2005, and June 2012 archives was assembled. For each archival pull, all available individual 

depersonalized credit records, including Trade lines, credit Inquiries, and Public records 

(collectively, TIPs) associated with all borrowers accompanying any initial sample trade line 

were provided regardless of the archive from which it was sampled. The data provided by 

Experian are de-identified and contain no directly identifying personal information such as name, 

address, or Social Security number. The credit records were tagged with de-identified servicer 

and loan identifiers as well as a de-identified borrower identification number called a personal 

identification number (PIN). 10 These could be used (imperfectly) to link TIP files to other 

account-level files both within an archive and over time.

One major problem encountered with the NMDB sample frame is that a single mortgage can be 

associated with multiple trade lines. This can arise when the servicing of the loan is sold or 

transferred, and the trade line reported by the original servicer is not properly linked to the trade 

line reported by the new servicer. In such cases, borrowers may appear to have multiple 

mortgages, when, in fact, they have only one. Because of these duplicates, randomly sampling 

trade lines will result in mortgages with multiple records being overrepresented in the data. To 

correct for this, a processing methodology was developed to identify and combine multiple 

records that contain information about the same mortgage into one record.

The first step in the process of eliminating duplicate mortgage records (“de-duping”) was to find 

multiple trade lines for the same mortgage in the same archive. From these duplicates, sample 

loans were removed when the selected trade line was not the one with the latest “Account 

Balance Date” (correcting for the problem of having mortgages associated with multiple trade 

lines over-represented in the sample). The second step was de-duping across archives. The June 

2012, December 2005, and July 2001 samples were treated as sequential NMDB sample frames 

(in that order), whereby mortgages selected from a NMDB sample frame later in the order (e.g., 

July 2001) that can be found in a NMDB sample frame earlier in the order (June 2012 or 

December 2005) would be removed from the sample (again, correcting for the over-sampling of 

such mortgages resulting from overrepresentation in the raw frame).

The de-duping process also dealt with the problem of ambiguous lien status for the “Enhanced 

Account Type Codes” of 08 (real estate, specific type unknown), 27 (real estate mortgage, with 

or without collateral, usually second mortgage), and 5A (real estate – junior liens and nonpurchase money first). Sample trade lines associated with these codes were removed from the 

sample when they subsequently could be linked with trade lines that were unambiguously second 

liens.

10 The encrypted servicer identification and loan numbers are unique to the NMDB and are used by the development 

team primarily to update the database each quarter. They are not available to dataset users even in encrypted form. 

This is done to ensure compliance with the contract restriction that the database not be used for enforcement against 

servicers. The borrower PINs are also unique to the NMDB and are randomized. Experian, however, maintains the 

mapping between the borrower (and servicer and loan) identification numbers used in their system and the PINs 

supplied to the NMDB team so that records in the NMDB associated with the same PINwill be associated with the 

same borrower identification in the Experian records. 
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Once the initial samples were de-duped, it was necessary to link archival records over time to 

create a composite picture of each sample loan (this is particularly important for loan 

performance as described in Section 7 and Appendix B). Semi-annual archives were drawn for 

the period December 2001 to December 2011 for borrowers associated with the initial sample 

loans. Data from these archives were patched together to create a temporal picture of each loan. 

One issue that needed to be addressed was that PINs for a given borrower can change over time. 

There are times when a loan first reported to the credit repositories does not connect with 

existing credit records for the borrower(s). This can happen when lenders make errors in 

reporting names and addresses or because of changes to a borrower’s addresses or names. In this 

instance, Experian treats the loan as associated with a new borrower. In most cases, the records 

are ultimately reconciled with the correct existing borrower and a “PIN-merge” occurs. 

However, historical archives are stored with the PINs at the time of the archive. Thus, to 

properly connect borrowers (and mortgages) over time, it was necessary for Experian to provide 

a PIN-merge transformation table to map historical to current PINs.

As shown in Table 1, the de-duping process substantially reduced the size of the original NMDB

sample. About 16% of the mortgage trade lines originally sampled from the June 2012 archive, 

30% of the selections from the 2005 archive, and almost three-quarters of the selections from the 

2001 archive were dropped.11 The percentages were higher for the older archives because many 

of the loans from them were selected because they were not current at the date of the archive and 

thus subject to FCRA purge rules. However, many of these loans subsequently became current 

and could be found in later archives.

Table 1. Percent of Trade Lines Dropped in Initial Sample by Archive Date

Archive

Date

Sample Trade

Lines Final Loans

Final 

Borrowers

Percent of 

Trade Lines 

Dropped

Jul-2001 302,398 79,595 123,059 73.7

Dec-2005 2,955,675 2,057,571 3,362,278 30.4

Jun-2012 9,225,304 7,692,192 12,010,151 16.6

11 A small percentage of the trade lines (1-2%) were dropped for other reasons including(1) loans with no balance 

or terms information; (2) those in American territories other than Puerto Rico, Guam, or the Virgin Islands; (3) “frag 

files” missing information on all other consumer obligations; and (4) those with no information about the borrowers. 
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5. Updating the Sample

Under the NMDB sample design, credit records for borrowers associated with sampled 

mortgages are to be collected quarterly until one year after the mortgage is reported as closed. 

As of the date of the archive, approximately 3 million loans from the initial June 2012 sample 

were still active or had been closed for less than a year. In addition, to keep the NMDB up to 

date, it is necessary to add a representative sample of the new mortgages reported to Experian 

each quarter to the database from June 2012 onward.

The initial NMDB update from the June 2013 archive covered a full year of newly reported 

mortgages since June 2012. From that date until June 2020, updates took place quarterly 

drawing from the last archive (either Wednesday or Saturday, whichever is later) of the quarter 

(March, June, September, and December). In July 2020, the archive date was shifted about two 

weeks into the next quarter to ensure that all (or almost all) loans reported within the quarter are 

captured.12 Each quarterly update follows the same pattern. A 1-in-20 random sample of new 

closed-end first-lien mortgage trade lines is drawn. These loans, which are identified using the 

same criteria as used for the June 2012 archive, are selected from those that were newly reported 

to Experian since the date of the previous quarterly update archive. The new sample is de-duped 

using the same methodology as used for the initial sample. If multiple trade lines are identified 

for the mortgage, then only the line with the latest “Account Balance Date” is kept. In addition, 

checks are run to determine if the mortgage was already reported in an earlier archive period 

(perhaps as a different trade line). If so, the new loan is dropped.

Existing sample loans are also updated each quarter. Prior to the update, the PIN-merge 

transformation table is updated to account for “newly merged” PINs. To ensure that lagged 

information for all PINs newly added to the dataset is collected, the year-old archive is drawn 

each quarter for all active PINs for which this archive had not previously collected.

At present, approximately 200,000 new trade lines are selected for the NMDB each quarter (see 

Table 2). The number of mortgages ultimately added to the database is typically between 50,000 

and 70,000 less than the number of trade lines originally selected for the update sample.

Over time, several additional “sample-cleaning” metrics have been added. Each quarter, a small 

number of old but newly reported mortgages are included in the Experian update sample. This 

can occur when a previously missing servicer decides to report to Experian or when servicing for 

a seasoned loan is transferred. These loans are disproportionately delinquent so it may also 

occur when a servicer only reports negative information. Believing these to be nonrepresentative loans, it was decided to drop all loans more than 10 years old when first reported. 

In addition, a small number of new loans are identified as: (1) closed in the same month as they 

are opened; (2) having steadily increasing balances (probably reverse annuity mortgages); (3) 

associated with persons for whom there are no other trade lines except the sample mortgage

(frags); (4) associated with persons with no demographic or address data; or (5) added to 

Experian but removed in the next archive (probably an error in reporting). In each of these 

situations, the loans are only provisionally added to the NMDB and are removed if the problem 

12 Since the beginning of 2020, a partial update is done monthly for active sample mortgages. This allows the 

database to provide high-frequency information on mortgage performance. The monthly archive is drawn on the 

earliest Wednesday or Saturday between the 12th and 15th of the month. 
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is not corrected within a year.

Table 2. Percent of Trade Lines Dropped in Quarterly Updates by Archive Date

Archive 

Date

Sample Trade

Lines

Final 

Loans

Final 

Borrowers

Percent of 

Trade Lines 

Dropped

Jun-13 648,224 492,977 766,208 23.9

Sep-13 240,001 130,270 198,625 45.7

Dec-13 174,404 101,299 151,683 41.9

Mar-14 111,928 53,164 78,927 52.5

Jun-14 146,406 76,837 113,657 47.5

Sep-14 124,389 74,738 111,042 39.9

Dec-14 124,323 75,485 111,770 39.3

Mar-15 104,613 70,405 104,651 32.7

Jun-15 129,737 90,627 135,509 30.1

Sep-15 150,399 97,568 145,260 35.1

Dec-15 124,413 87,288 129,386 29.8

Mar-16 123,438 73,877 108,898 40.2

Jun-16 111,797 82,346 121,771 26.3

Sep-16 135,699 102,900 151,639 24.2

Dec-16 177,386 107,515 159,459 39.4

Mar-17 127,917 94,210 139,559 26.4

Jun-17 129,953 80,270 117,579 38.2

Sep-17 125,278 90,582 133,291 27.7

Dec-17 154,468 90,612 132,921 41.3

Mar-18 109,340 78,739 114,764 28.0

Jun-18 117,964 79,472 115,709 32.6

Sep-18 126,255 84,699 123,957 32.9

Dec-18 134,045 76,530 111,521 42.9

Mar-19 147,057 64,187 92,744 56.4

Jun-19 189,499 78,315 114,028 58.7

Sep-19 170,330 102,239 150,207 40.0

Dec-19 168,428 123,286 181,623 26.8

Mar-20 169,300 108,570 158,472 35.9

Jul-20 221,233 180,886 267,920 18.2

Oct-20 208,174 174,379 258,830 16.2
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Table 2. Percent of Trade Lines Dropped in Quarterly Updates by Archive Date 

(Cont.)

Archive 

Date

Sample Trade

Lines

Final 

Loans

Final 

Borrowers

Percent of 

Trade Lines 

Dropped

Jan-21 226,315 186,130 275,271 17.8

Apr-21 231,550 188,219 277,527 18.7

Jul-21 230,864 187,793 272,304 18.7

Oct-21 199,047 151,679 220,508 23.8

Jan-22 192,492 138,005 199,904 28.3

Apr-22 200,748 140,271 201,043 30.1

Jul-22 180,690 107,049 153,715 40.8
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6. Merging With Other Data Sources

Although Experian’s archive is extensive, it does not contain information on some key mortgage 

features, such as the loan’s purpose (home purchase or refinance), whether it had an adjustable or 

fixed interest rate, its securitization status, its origination channel (broker or retail lender), or 

whether it was for an owner-occupied property, vacation home, or investor property. Moreover, 

Experian’s archives contain no information on the property backing the mortgage, such as its 

location, purchase price, characteristics, or current value. Key information on borrowers 

associated with the loan, including income, is also missing. Consequently, values of these key 

variables need to be inferred indirectly or acquired from other data sources if they are to be 

included in the NMDB.

The NMDB obtains much of the missing information from matches to administrative file 

records. The core administrative files come from Fannie Mae and Freddie Mac (the Enterprises),

the FHA, VA, and the Rural Housing Service (RHS). Collectively, loans associated with these 

programs comprise about three-quarters of the loans in the NMDB.

The most accurate means of merging information from outside sources into the NMDB is to use 

information about the borrowers, such as their names, Social Security numbers, addresses, and 

dates of birth. Using personally identifying information (PII), however, heightens concerns 

about data security and borrower privacy. Consequently, FHFA contracted with an outside 

consultant to study how such concerns might be mitigated. The third-party-blind matching 

process that FHFA uses is consistent with the best practices and recommendations from that 

study.

The third-party-blind matching process adheres to three guiding principles. First, neither FHFA, 

FHA, VA, RHS, nor the Enterprises can receive PII from Experian. Second, Experian cannot 

access FHA, VA, RHS, or Enterprise administrative data and borrower PII in the same place. 

Third, FHFA must not be able to match loans in the NMDB records to the specific administrative 

records from FHA, VA, RHS, or the Enterprises.

In December 2014, a process was initiated to supplement the NMDB data with administrative 

data from Fannie Mae and Freddie Mac. Subsequently, the same process was repeated with 

FHA, VA, and RHS. The process for matching the data follows seven steps:

(1) The data partner (e.g., Fannie Mae) creates a unique anonymized identifier (AID) for 

each loan. This identifier, along with the borrower-level PII associated with each loan 

(including name, address, Social Security number, and date of birth), is transmitted 

directly to Experian using a secure portal. FHFA does not receive this information. 

Other administrative data on these loans is not sent to Experian.

(2) The data partner simultaneously sends the AID and the administrative data for each loan 

to an FHFA data processing unit that is separate from the NMDB development team. No 

borrower-level PII is included in the information sent to the FHFA data processing unit. 

The FHFA data processing unit transmits the administrative data and the associated AID 

to another unit within Experian which is separate from the one that receives the PII in 
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step (1). 

(3) Behind a secure firewall to protect FCRA-regulated data, Experian matches the PII it 

receives from the data partner to the PII maintained in its own files on the borrowers in 

the NMDB to determine potential matches. When a potential match is identified, 

Experian compiles the PIN for each matched borrower.

(4) For all potential matches, Experian transfers the partner-supplied AID and the matched 

NMDB borrower PINs to a separate unit within Experian that has no access to the credit 

repository data or any PII. This is the same unit that received the administrative data 

from FHFA in step (2).

(5) The second Experian unit matches the AIDs received from the first Experian unit in step 

(4) with the AIDs sent by FHFA in step (2). For all matches, the second Experian unit 

forwards the administrative data it received from the data processing unit at FHFA, plus 

the matched borrower PIN that it received from the first Experian unit, to the NMDB

development team at FHFA. The information sent to the NMDB development team 

includes neither the Enterprise-created AID nor any PII.

(6) The NMDB team compares the characteristics of the loans associated with the PINs 

received from the second Experian unit to the administrative information on the loans. If 

the information from both sources is consistent, the match is confirmed. The 

confirmation process also involves resolving issues when an NMDB loan is matched to 

multiple administrative loans or an administrative loan is matched to multiple NMDB 

loans. In these instances, the match with the closer fit is generally selected. The only 

instance where multiple matches are allowed are loans matched to either Freddie Mac or 

Fannie Mae and one of the government programs. These matches are allowed only if the 

codes for Freddie Mac or Fannie Mae indicate that their loan is government insured.

(7) A list of confirmed matches is sent to Experian. Upon confirmation, Experian stores the 

property address supplied as part of the PII file from the data partners, but otherwise 

permanently destroys all PII used in the match. 

The file matching process has been completed for all historical loans and is repeated quarterly

for loans which have been newly added to the NMDB.13 Generally, the matching process 

operates with a lag, such that it takes about 10 weeks after the end of a quarter for loans reported 

to Experian during that quarter to be flagged as confirmed matches. As noted below, the 

matching process has proved to be very accurate. Match rates for all five administrative partners 

are in the high 90-percent levels (see Section 8). The only potential significant shortfall is for 

13 The administrative file matching process is done with all mortgages tracked in the NMDB. Because the database 

contains the complete credit bureau records of all sample loan borrowers perforce,it will contain records on all 

mortgage loans associated with sample loan borrowers as long as they were closed within 11 years of the archive the 

sample loan was first reported from or opened within a year of the sample loan being closed. This includes home 

equity lines of credit (HELOCs) and second liens as well as other closed-end first liens. Information on these loans 

is subject to the same NMDB cleaning and de-duping process used for sample loans. At present,there are 76

million total mortgages (includingsecondliens and HELOCs) tracked at some point in the database, of which only 

14.2 million are sample loans.
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loans on non-owner-occupied properties, whose match rates are closer to 80 percent. One 

explanation is that borrowers on some of these loans are partnerships or limited liability 

companies (LLCs) and thus not in the Experian files.

Efforts are currently underway to merge property record information into the NMDB using 

similar third-party-blind matching techniques. At present, all loans in the NMDB database 

added through the end of 2020 have been matched to a property vendor’s property records, 

including both transaction records and assessment files, with plans to match the remaining ones 

shortly. 14 For loans where with complete property matching, data from the property vendor’s 

servicing and private-label mortgage-backed securities (PLMBS) databases have also been

matched, which providesmissing data elements for many of the non-government-affiliated loans 

in the NMDB. These match files are updated quarterly.

In addition, loans in the NMDB have been matched to loan-level information on loans reported 

under the Home Mortgage Disclosure Act (HMDA) from 1990 to 2021, loans included in a large 

national servicing vendor’s database, and to data files on the 2.1 million loans purchased by the 

FHLBs. These matches do not involve PII and thus rely on less accurate matching techniques.15 

The HMDA matching is conducted annually using a proprietary regulatory file which contains 

more granular loan information than the publicly released file, particularly with respect to dates 

and the original loan amount. The national vendor and FHLB servicing files are updated and 

matched to the NMDB quarterly.

Now in full production, the NMDB combines data from all these sources into a common file 

with one record per sampled loan. The record contains variables reflecting all the static 

characteristics of the loan collected from multiple sources, as well as vectors of dynamic data 

such as the monthly performance of the loan from origination to termination, the associated loan 

balances, and borrower credit scores and performance on other credit obligations. It should be 

noted that information from external databases is only used to supplement information about 

sample loans, not to add new loans to the sample. The NMDB sample frame continues to be that 

established in the Experian data files. Further, almost all dynamic information, such as loan 

performance, payments, loan balances, credit scores, and performance on other credit obligations 

is derived entirely from the Experian data files.

14 To facilitate property matching, the entire property database of one of the two largest U.S. property data vendors 

has been placed behind the secure firewall at Experian. This allows information on borrower name and address to 

be used in the matching process. Again, any PII used in the match is discarded once the matching process is 

completed.

15 Such merges use informationother than PII common to the NMDB and the external dataset to perform a match. 

Primarily the matches rely on the original loan balance, the opening date of the mortgage and the general location of 

the property (census tract, ZIP Code, or state/county). Unfortunately, mortgage servicers report the billing address 

of the mortgage borrowers to Experian, but this is not necessarily the property address, particularly for mortgages on 

non-owner-occupied properties. Additional address information maintained within Experian’s databases sometimes 

proves useful in supplementing the repository addresses, as does historical information on borrower location. 

Nevertheless,such merges areless accurate than those employing PII because the latter are less reliant on address.
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7. Production

On October 30, 2017, NMDB 1.0 was certified and released for internal production use at FHFA 

and CFPB. The NMDB 1.0 dataset featured payment performance data for the 11.9 million 

NMDB sample mortgage loans and 18.6 million borrowers associated with those mortgages as of

June 2017. 16 The initial release was used by the CFPB to support a public release of monthly 

county-level mortgage performance statistics. More detailed background information is 

provided on this component of NMDB 1.0 in Appendix B.

An updated production database, NMDB 2.0, was certified and released for internal production 

use at FHFA and CFPB on May 31, 2018. This process has been repeated quarterly through the 

present (NMDB 19.2 released on October 6, 2022).

NMDB 19.2 is a fully developed database consisting of information on 14.2 million sample 

mortgage loans and 21.9 million associated borrowers. The database is cumulative in that 

updated information is potentially added on all sample loans for each quarterly update. Only 

about 20 percent of the database sample loans are currently active, but information on closed 

loans can still be updated due to new administrative file matching.

The database fully reflects the results of the NMDB matching process. The database is 

“complete” with no missing values for key variablesfor any sample loans. Most values reflect 

“hard” information from administrative or Experian files. However, no administrative matches 

could be made in some instances, particularly with older loans or loans not associated with one 

of the NMDB data partners. In these instances, information that would have been available from 

an administrative match, such as loan-to-value (LTV) ratio or borrower income, is imputed using 

statistical techniques. These techniques rely heavily on census-tract-level variables created from 

HMDA data or other administrative sources that reflect summary statistics of all mortgage loans 

made in the borrower’s neighborhood during the same year that the loan in question was 

originated.

The dataset includes the state, county, and census tract where the property associated with the 

loan is estimated to be in 2021 (based on the 2010 census taxonomy) and in the year the loan was 

originated. Accompanying this is a set of tract-level information, which allows, for example, a 

user to determine if the loan would have qualified for Community Reinvestment Act or 

Enterprise-goals preference or would have been conformable.

The dataset also includes “static” loan variables based on origination values contained in the 

Experian files including original term (months), loan amount, balloon status, origination date,

and number of borrowers. Administrative matches are used to populate a number of other 

origination variables including property (collateral) value (used to compute LTV), income relied 

16 The production database excludes a small number of loan/borrower records because the borrower was not 

originally associated with the loan when it was sampled but added by Experian (or the servicer) later. This can only 

be picked up if the borrower happened to be in the NMDB because of another loan and thus will not be 

representative of all new borrowers added to loans. Consequently, they are excluded, and the production database 

should be considered a dataset of all loan/borrower combinations where the borrower was associated with the loan 

from the beginning.
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upon in underwriting, back-end debt-to-income (DTI) ratio, occupancy status, adjustable-rate 

mortgage (ARM) status, loan type (FHA, VA, RHS), ever purchased by a government-sponsored 

enterprise (GSE) (Fannie Mae, Freddie Mac, FHLB), in PLMBS, number of units in the 

property, manufactured housing and Home Affordability Refinance Program loan flags, and 

initial contract interest rate. The previous loan payoff balance (refinance only) and simultaneous 

new second lien (piggyback or HELOC) data are used to derive combined-loan-to-value (CLTV) 

ratio and “cash-out” status.

Demographic data on loan borrowers derived from a variety of sources are used to construct

variables for birth month and year (used to calculate age), gender, race, ethnicity, and 

VantageScore 3.0 at origination. Information on the date of the borrower’s first mortgage in the 

credit bureau and whether they have ever had a VA loan are used to infer veterans and first-time 

homebuyer status.

The dataset also contains several “dynamic” variables that vary by time. These include the 

monthly and quarterly unpaid loan balance and loan performance metric (e.g., 30 days past due), 

quarterly credit scores for each borrower, the quarterly FHFA house price index applicable to the 

each loan’s county (can be used to estimate the loans current LTV ratio), quarterly loan inquiry 

counts for borrowers associated with the loan (can be used to forecast prepayment), variables 

reflecting loan modifications, and the closing date (if applicable). Beginning in the first quarter 

of 2013, the dataset has included quarterly summary information on balances and performance of 

five different types of non-mortgage credit associated with the borrowers for each active sample 

loan (auto loans, credit cards, student loans, HELOCs and second liens, and all other loans).

The exact variables included in each new production release are described in the quarterlyupdated NMDB codebook. Details on the data editing, cleaning, and imputation process

employed in each quarterly data update are given in Appendices C and D.
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8. Evaluating the NMDB Sample Frame

The NMDB is intended to be a holistic representative of the mortgage market. One way of 

testing whether this goal was achieved is to compare loan totals implied by the NMDB with 

control totals obtained elsewhere. Comparisons were made of implied national quarterly 

mortgage originations derived from the NMDB with control totals obtained from the five 

NMDB partner agencies (FHA, VA, RHS, Fannie Mae, and Freddie Mac) for the period 1998 to 

2019. The comparisons for Fannie Mae and Freddie Mac were done separately for loans backed 

by owner-occupied and non-owner-occupied properties. Comparisons were also made with 

administrative loan control totals for loans acquired by the FHLBs and loans in PLMBS in the 

CoreLogic LP database (collected from PLMBS trustee filings and estimated to be over 90% of 

the PLMBS market). Finally, implied NMDB total market estimates for loans backed by owneroccupied and non-owner-occupied properties were compared with those derived from HMDA 

data.

The NMDB-derived estimates of loan originations for the five data partners tracks control totals 

remarkably well (Table 3). Almost all the quarterly ratios of NMDB estimates to control totals 

are over 90% with the vast majority over 95%. The average quarterly ratios are 96% for Fannie 

Mae backed by owner-occupied properties, 94% for Freddie Mac loans backed by the same, 

100% for FHA and VA, and 98% for RHS. RHS match ratios are shown for loans in the 

guaranteed program only. Much of the lending in the early years of the RHS program was 

direct, not guaranteed, so these loans are likely under-represented in the NMDB frame. In the 

control totals, 17% of the RHS loans are direct, yet in the NMDB only 6% are direct. The 

somewhat lower ratios for Fannie Mae and Freddie Mac reflect lower match rates for the early 

years when Social Security numbers were not used the in the matching process. The average 

quarterly ratios for the two GSEs are 91% and 90%,respectively,for non-owner-occupied

associated loans and 99% and 98% respectively for loans on owner-occupied properties when 

the analysis is restricted to loans originated since 2008.

The NMDB also compares well with control totals for the FHLBs and PLMBS. Average 

quarterly ratios are 101% for the FHLB and 114% for PMBS. The figures are somewhat 

misleading as both programs have diminished volumes in recent years contributing to small cell 

variance in the ratios. Over 70% of the FHLB loans and 98% of the PLMBS loans were 

originated in the 10-year period 1998-2007 preceding the Great Recession versus the 12-year 

period following it. For example, average PLMBS quarterly ratio for the earlier period was only

95%. Given that the Corelogic LP database likely missed some PLMBS loans, the NMDB 

database also likely does as well. 

NMDB also compares well with HMDA data. On average, quarterly NMDB estimated totals for

loans backed by owner-occupied properties are 104% of those reported in HMDA data. This 

may stem from known gaps in HMDA data’s coverage. Loan originators who are very small or 

operate exclusively in rural areas are exempt from HMDA reporting requirements, so their 

lending activity is not included in the HMDA data. Additionally, HMDA data excludes 

commercial loans and non-purchase loans backed by properties that were previously mortgagefree. Many of these loans, however, may not be reported to the credit repositories either. For 

example, loans to corporations, loans made as part of a seller-financed property sale, and loans 
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made by non-traditional lenders are unlikely to be in either database. Moreover, some types of 

loans may be missed by the NMDB though they are captured in the HMDA data. Some lenders 

that retain all their loans in portfolio, particularly credit unions, are known not to report their 

loans to the credit repositories but are nevertheless still subject to HMDA reporting 

requirements.

 

NMDB loans backed by owner-occupied properties compare more favorably with HMDA data 

than those backed by investor and secondary homes (an average quarterly origination ratio of 

81%). Some of this difference may be accounted for by the fact that some investor loans are 

done through partnerships and LLCs and thus may not end up being reported to the consumer 

credit bureaus which form the basis of the NMDB.

Care should be exercised in placing too much weight on the specific quarterly numbers in Table 

3. There are issues with timing where a shortfall in one quarter is often offset by overshooting 

in the next. Missing information on opening date is common, particularly in the control files 

which generally reflect the current servicer, not necessarily the originator (NMDB cleaning rules 

use the original opening date when servicing is transferred). When this happens, servicers often 

assume that the loan is opened two months prior to the date of the first payment. Reflecting this, 

when the Experian date differs from the servicing file date for matched loans (this happens about 

20% of the time for Fannie Mae and Freddie Mac and 30% of the time for FHA, VA, and RHS), 

47% of the servicing file loans have an opening date of the 17th of the month— an implausible 

result. Also, the reported opening date falls on a Saturday or Sunday for 12% of the matched 

partner servicing files versus only 4% for the comparable Experian loans (mortgages almost 

always close on a business day).

The success of the NMDB coverage reflects the quality of the matching process. For every 

product-type except RHS Direct, 98% or more of the matches have a dollar-perfect match 

between the “original loan amount” reported in Experian and the amount shown in 

administrative records. For Freddie Mac and Fannie Mae, the rate is above 99%. As discussed 

above, however, “date opened” matches at a much lower rates ranging from two-thirds to 80%

(except for RHS Direct, which is 97%). This may occur, in part, due to a relatively high rate of 

servicing transfer for loans in the five partner programs. Loans “held in portfolio” and not 

placed in PLMBS securities or sold to the FHLB banks rarely have servicing transfers.

Despite the fact the Experian maintains the billing address for the mortgage and the 

administrative records maintain the property address, over 90% of the matches found an exact 

address match for all but the loans backed by non-owner-occupied properties. For non-owneroccupied properties, address matches were only about 60%, potentially contributing to lower 

overall match rates for these types of loan, particularly in earlier years. Social Security number 

(SSN) matching is used for all data partners except RHS in the current matching process. For 

example, over 99% of Fannie Mae, Freddie Mac, and FHA, and 97% of VA loans had an exact 

match with SSN in 2020. The use of SSN compensates for the lack of an address match. 

However, SSNs were not available for matching for any loans prior to 2000 and not available on 

a comprehensive basis until after the Great Recession.
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Table 3. Percent Ratio of NMDB Estimates of Originations to Control Totals, by Quarter

Fannie Mae Freddie Mac

FHA VA RHS FHLB PLMBS

HMDA

OwnerOccupied OtherOwnerOccupied OtherOwnerOccupied Other

1998Q1 87.9 78.5 85.7 75.6 101.1 101.0 58.3 82.6 75.9 - -

1998Q2 91.2 77.9 86.2 78.7 104.0 103.8 65.0 92.5 80.4 - -

1998Q3 90.6 80.1 88.1 78.7 100.2 102.1 58.5 99.5 73.7 - -

1998Q4 91.0 81.7 86.9 75.2 100.6 100.1 61.1 136.5 76.8 - -

1999Q1 90.7 79.2 88.0 74.4 102.4 101.5 64.2 107.5 76.6 - -

1999Q2 92.1 79.4 86.6 71.5 100.2 104.6 64.0 99.4 80.0 - -

1999Q3 91.4 77.8 83.2 72.0 106.0 105.9 61.7 116.1 78.1 - -

1999Q4 91.2 76.5 75.2 65.7 106.6 113.6 63.2 138.0 73.5 - -

2000Q1 85.9 71.8 79.5 70.2 107.8 115.1 70.0 106.2 79.8 - -

2000Q2 89.9 76.2 81.3 69.6 101.6 104.5 71.3 103.9 80.9 - -

2000Q3 88.9 77.4 87.8 76.1 104.9 103.4 72.8 114.5 82.9 - -

2000Q4 89.8 76.8 84.3 72.4 104.1 107.7 66.2 117.5 80.8 - -

2001Q1 91.4 77.3 85.7 73.7 99.8 100.5 64.5 138.5 84.6 - -

2001Q2 91.4 80.8 86.3 75.8 101.4 102.1 63.2 127.0 95.3 - -

2001Q3 93.4 79.5 90.0 74.0 103.0 104.3 66.9 99.6 93.9 - -

2001Q4 94.0 81.4 89.7 77.8 101.7 102.5 65.5 88.3 97.4 - -

2002Q1 93.9 78.4 89.2 74.5 105.3 102.1 74.0 120.8 100.7 - -

2002Q2 92.7 78.4 88.2 77.3 101.6 104.6 66.9 128.0 96.4 - -

2002Q3 92.5 81.6 89.6 76.0 103.3 103.6 75.0 121.3 105.7 - -

2002Q4 90.5 79.2 88.1 75.1 103.0 103.4 71.4 122.1 111.4 - -

2003Q1 93.8 82.7 94.9 84.8 102.0 101.8 76.9 100.9 108.8 - -

2003Q2 94.6 84.4 91.9 85.3 99.9 101.7 74.8 99.6 101.2 - -

2003Q3 94.7 83.9 92.7 88.6 101.3 100.7 79.4 97.5 107.7 - -

2003Q4 95.4 81.9 93.5 81.6 100.1 101.8 99.2 92.8 101.5 - -

2004Q1 95.5 80.9 94.5 78.3 100.5 99.1 109.1 91.1 99.8 104.6 90.2

2004Q2 95.7 82.3 92.8 78.8 100.6 99.4 102.6 78.0 102.7 99.7 96.8

2004Q3 96.9 82.5 92.6 76.5 98.9 95.2 116.3 82.3 99.6 102.1 95.7

2004Q4 96.0 84.5 91.8 74.8 99.1 97.9 105.6 102.6 100.4 101.5 92.9

2005Q1 97.2 84.7 93.3 78.6 98.4 96.5 117.9 91.6 98.7 104.4 92.0

2005Q2 95.5 83.8 92.3 80.6 97.9 100.2 129.2 88.6 98.6 104.0 96.6

2005Q3 95.4 81.9 94.7 80.0 98.5 99.4 117.4 88.0 100.2 104.1 98.3

2005Q4 92.9 82.0 90.7 73.7 102.6 98.1 119.1 96.4 96.2 102.6 95.7

2006Q1 93.1 82.2 92.7 78.9 99.3 99.1 133.6 92.9 90.8 105.9 91.7

2006Q2 94.3 81.2 92.8 80.9 103.0 101.7 116.1 76.5 94.2 106.9 95.4

2006Q3 93.7 81.9 92.6 81.6 101.7 97.4 129.2 89.1 93.6 107.7 96.3

2006Q4 92.1 79.6 93.1 82.4 99.4 101.9 124.8 110.7 92.4 107.3 93.4

2007Q1 92.8 82.8 90.8 78.9 99.2 99.6 122.9 111.7 87.1 113.9 95.8

2007Q2 91.7 83.8 89.0 76.9 98.9 98.6 114.5 120.6 92.6 111.0 93.7

2007Q3 92.0 81.4 90.7 81.8 97.4 101.3 115.8 118.4 82.8 108.8 83.3

2007Q4 93.3 83.6 92.4 82.1 97.4 99.0 118.7 90.5 66.2 106.8 79.3
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Table 3. Percent Ratio of NMDB Estimates of Originations to Control Totals, by Quarter (Cont.)

Fannie Mae Freddie Mac

FHA VA RHS FHLB PLMBS

HMDA

OwnerOccupied OtherOwnerOccupied OtherOwnerOccupied Other

2008Q1 93.6 84.2 95.5 83.6 98.9 102.7 114.5 94.3 59.0 110.5 76.8

2008Q2 92.8 82.5 95.6 80.5 99.5 98.9 112.6 90.2 59.2 108.8 67.7

2008Q3 92.3 87.4 94.3 81.3 98.7 100.9 105.2 89.4 59.4 108.1 65.2

2008Q4 91.0 87.6 101.0 84.1 98.5 100.4 99.1 85.1 71.9 109.7 66.3

2009Q1 95.8 85.9 98.7 88.5 100.9 99.2 112.9 94.0 84.1 108.5 69.8

2009Q2 96.8 91.5 97.6 92.6 100.7 99.3 99.2 89.6 64.7 106.2 74.9

2009Q3 97.6 84.7 97.4 90.0 98.9 95.7 106.1 100.0 84.2 104.3 70.4

2009Q4 99.7 87.0 100.7 87.7 99.9 98.3 103.8 106.7 92.2 106.2 73.8

2010Q1 99.9 91.0 100.2 89.1 99.0 99.9 103.3 82.4 107.8 104.2 73.1

2010Q2 100.7 95.3 98.2 95.5 99.9 99.7 123.1 80.7 105.8 104.8 75.6

2010Q3 99.6 94.0 98.5 91.1 99.3 98.0 102.1 82.9 129.2 105.7 79.0

2010Q4 100.2 90.6 97.9 89.5 99.2 99.4 109.6 98.9 206.7 103.3 79.5

2011Q1 100.2 92.4 98.7 88.4 100.9 100.3 100.1 90.6 252.7 102.0 77.9

2011Q2 99.9 91.5 98.4 90.4 101.1 99.7 103.4 83.6 136.4 103.6 77.6

2011Q3 98.9 91.1 99.9 93.0 100.1 100.7 103.8 80.2 155.9 104.2 79.0

2011Q4 99.5 90.9 95.9 90.0 99.5 100.2 105.4 91.9 208.1 102.0 79.3

2012Q1 99.0 96.7 95.7 93.2 100.3 99.6 98.7 91.7 188.4 103.1 88.7

2012Q2 99.5 96.1 97.1 95.3 101.9 100.5 103.1 87.3 146.7 101.3 92.5

2012Q3 100.2 99.6 99.0 96.7 100.2 99.9 105.5 84.8 149.6 102.4 95.5

2012Q4 100.7 93.5 98.8 91.6 99.4 101.2 101.6 101.2 167.4 102.9 87.0

2013Q1 100.8 93.5 96.9 92.1 101.6 100.4 97.8 97.2 146.9 102.3 87.3

2013Q2 100.2 95.3 96.3 90.5 100.3 101.3 99.1 91.3 150.2 101.1 92.6

2013Q3 100.6 98.0 94.3 87.1 100.3 99.3 106.1 96.4 203.0 100.0 91.0

2013Q4 100.2 93.7 94.7 88.2 96.5 95.9 98.5 83.6 257.0 100.2 83.6

2014Q1 96.3 92.6 95.6 87.9 99.7 97.8 101.6 86.7 232.1 97.9 78.3

2014Q2 102.9 96.6 99.8 92.4 100.1 101.1 106.3 92.1 185.5 102.2 85.2

2014Q3 101.6 93.6 95.3 91.9 98.3 97.1 104.3 83.6 181.0 99.7 84.7

2014Q4 100.7 93.6 94.0 84.7 98.6 96.6 103.4 97.0 116.2 99.6 81.5

2015Q1 99.6 90.6 93.2 87.1 100.3 98.0 106.3 110.1 105.5 101.9 82.8

2015Q2 101.4 93.7 96.2 87.1 100.1 96.9 110.8 92.0 117.2 100.9 85.5

2015Q3 101.8 92.1 97.7 85.1 97.9 97.6 102.4 95.5 75.9 100.1 83.4

2015Q4 100.1 90.2 96.2 85.7 96.7 95.9 106.0 92.7 241.3 100.3 78.5

2016Q1 97.7 87.8 97.7 86.3 98.9 100.3 110.6 98.2 250.9 100.8 78.9

2016Q2 98.1 90.7 96.3 87.9 98.4 97.8 105.8 101.6 228.3 99.6 88.2

2016Q3 97.6 87.9 98.0 89.4 97.8 98.2 106.5 102.8 198.2 100.3 86.1

2016Q4 97.1 90.6 97.3 89.8 98.5 97.1 103.3 106.0 171.3 100.0 82.9

2017Q1 98.1 88.4 97.7 82.9 97.7 96.9 106.5 104.9 133.3 100.2 74.9

2017Q2 98.0 87.0 98.2 92.1 97.9 96.8 101.9 113.8 126.1 100.5 79.4

2017Q3 97.3 86.8 97.8 91.1 98.4 96.0 102.7 105.7 126.1 99.9 78.5

2017Q4 98.1 84.7 99.1 83.2 99.6 97.9 101.4 102.8 101.4 101.0 72.7
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Table 3. Percent Ratio of NMDB Estimates of Originations to Control Totals, by Quarter (Cont.)

Fannie Mae Freddie Mac

FHA VA RHS FHLB PLMBS

HMDA

OwnerOccupied OtherOwnerOccupied OtherOwnerOccupied Other

2018Q1 98.7 81.4 98.9 82.4 98.9 99.1 108.6 102.0 75.4 103.3 60.1

2018Q2 98.0 86.7 97.9 87.4 97.8 100.6 100.7 118.1 61.7 102.4 59.6

2018Q3 99.4 86.4 97.9 91.7 98.8 97.9 108.9 109.3 53.9 103.4 59.6

2018Q4 98.9 90.7 99.7 98.7 99.1 97.5 108.9 109.3 54.9 103.4 63.1

2019Q1 100.6 94.4 104.1 96.9 98.6 100.1 109.4 116.4 74.8 105.1 62.7

2019Q2 99.0 90.6 98.7 93.9 98.3 99.4 104.1 119.9 60.3 103.3 60.4

2019Q3 98.5 89.8 98.3 93.1 100.2 99.9 103.8 111.9 55.6 103.1 61.4

2019Q4 98.8 89.9 98.7 92.4 98.2 98.8 103.8 124.2 49.9 102.0 62.4

Another validation of the NMDB matching process is the high correlation between credit bureau 

codes indicating FHA, VA, RHS, Freddie Mac, and Fannie Mae loans and the match results 

(which do not depend upon these codes). Over 98% of FHA, 99% of VA, and 82% of RHS 

loans matched to administrative files were correctly marked by bureau account type codes. Only 

two-thirds of the Freddie Mac and Fannie Mae loans were correctly identified despite rules that 

servicers are supposed to report these loans to the bureaus. Some of this may be due to timing,

as the reporting rules were not put into effect until 2010. More than 80% of Freddie Mac and 

Fannie Mae loans originated since 2008, for example, are correctly flagged in credit records.

Overall, these results suggest that the NMDB should provide a very accurate representation of 

the mortgage market as a whole and for the market subprograms represented by FHA, VA, RHS, 

and FHLB, and loans backed by owner-occupied properties sold to Fannie Mae and Freddie 

Mac, and in PLMBS. There is a somewhat lower level of accuracy in earlier years, but the 

difference is not substantial. Loans backed by investor and secondary properties do appear to 

have somewhat lower representation, but coverage levels still should be above 80% even in 

earlier years.
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Appendix A. Origins of NMDB 

Prior to deciding to develop the NMDB, FHFA and CFPB considered many alternatives to meet 

their data requirements. The primary alternatives explored were HMDA data, the Federal 

Reserve Bank of New York's Equifax Consumer Credit Panel, the property and servicing 

databases owned by CoreLogic and Black Knight Financial Services, and data available from the 

three national credit repositories— Experian, Equifax, and TransUnion. Public survey databases, 

particularly the American Housing Survey (AHS), were also considered. All of these sources 

share several desirable features such as: (1) the databases are de-identified containing no directidentifying information such as borrower name, address, or Social Security number; (2) they are 

collected for other purposes, thus their use entails no new data collection from lenders, servicers, 

or borrowers; and (3) all of them have been collected for a period of time and are expected to 

continue into the future.

However, each was also found to be deficient in significant ways. 

The HMDA data include loan applications and underwriting outcomes for most mortgages with 

selected information about the loan, property, and borrower. The data are arguably the most 

representative publicly available existing data source about the mortgage market. However, the 

HMDA data contain no information on loan performance, little information on borrower 

creditworthiness until 2018, and have up to a 21-month delay in release. The CoreLogic and 

Black Knight property databases suffer from similar deficiencies. Although they have 

widespread coverage, the databases contain very limited information on mortgage characteristics 

or performance and none on the borrower. 

The Federal Reserve Bank of New York’s Equifax Consumer Credit Panel provides a nationally 

representative 1-in-20 sample of individuals with credit records, observed quarterly from 1999 

onward. However, mortgage loans are often represented by duplicate trade lines and miss

importance information, such as loan purpose, owner-occupancy, pricing, LTV ratio, income, 

and borrower demographics. Finally, these data are presently accessible only to the Federal 

Reserve System.

CoreLogic and Black Knight produce loan-level databases with performance information 

collected from mortgage servicers. The servicing fields available from CoreLogic and Black 

Knight are relatively comprehensive in both variables and coverage: the CoreLogic database 

claims about 32 million active mortgage loans, while the Black Knight database claims about 31 

million. However, these data offer no assurance of being representative, as data are only 

collected (currently) from about 55 servicers each. Moreover, mortgages cannot be tracked if 

servicing is transferred. Other drawbacks include minimal borrower demographics and no 

information on the borrower’s other obligations.

The biannual AHS contains comprehensive information on a nationally representative 1-in-2,000 

sample of mortgages of owner-occupied properties with very good information about the 

property and borrower demographics. However, the AHS has only limited information about the 

mortgage itself. As with the other nationally representative consumer survey data sources, AHS 

contains no information on mortgage performance, provides only a small number of 
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observations, and is released with a significant lag.

The credit repository data from Equifax, Experian, and TransUnion are rich in credit 

information. By construction, they incorporate data on credit card debt, installment loans, credit 

inquiries, and public records for the consumers they have in their respective databases. Their 

data can be linked to marketing datasets that provide borrower characteristics including age, 

gender, and marital status which, if validated, could be of potential use in a dataset. The credit 

repositories also maintain data on borrowers' changes of address and broader geographic 

classifications, such as the census tract. However, there are important areas that are not covered. 

They lack some information on borrowers (e.g., income), mortgages (e.g., loan product and 

contract rate), and the underlying property (e.g., location and value).

Given the foregoing, FHFA and CFPB, along with the Department of Housing and Urban 

Development, the Federal Reserve Board, Freddie Mac, and others, decided that a modified 

derivative of the credit repository data offered the best source from which to construct a 

nationally representative comprehensive mortgage database. The three credit repositories all 

actively pursue loan servicers as data providers. As a result, they obtain information on almost 

the entire population of non-private mortgage loans made in the United States. Furthermore, 

they archive their data, making it possible to “jump start” the data collection process by going 

back in time, collecting data in almost the same fashion as if it had taken place in real time.

As part of the exploratory process, using a competitive procurement process, Experian was 

engaged by Freddie Mac to construct a prototype to confirm the appropriateness of using credit 

repository data for the database. This effort confirmed the concept but suggested numerous steps 

needed to be taken to meet the design objectives.

First, it was recommended that the database should be a sample rather than a universal registry of 

loans. Second, the database should be restricted to closed-end first lien mortgages, mimicking 

the coverage of HMDA data and the availability of matching information. Third, while these 

data contain detailed information on loan performance and other borrower credit obligations, 

they are missing critical data items needed for the database, such as the location and features of 

the property, demographics, and loans characteristics like whether the loan had a fixed or 

adjustable interest rate and whether it was a refinance or for a home purchase. Thus, it would be 

necessary to access other data sources and merge information gleaned from them with the 

repository data to make the database comprehensive. Pilot testing also confirmed that the best 

method of merging data would rely on third-party blind matching conducted behind a firewall at 

the credit repositories.
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Appendix B. Background on Mortgage Performance Reporting

Almost all closed-end first-lien mortgages, such as those in NMDB, have a payment-in-arrears 

structure. That is, the mortgage payment for a month (e.g.,January) is generally due on the first 

day of the next month (e.g., February 1). Moreover, the first “ever” payment on a mortgage is 

generally due on the first day of the second full month after the mortgage closing date. For 

example, borrowers who close on their mortgage on January 15 will have their first payment due 

on March 1. These borrowers would have prepaid the interest for the period covering January 

15-31 at their mortgage closing. One component of each monthly mortgage payment is the 

interest of the previous month based on the balance at the beginning of the month. While the 

monthly mortgage payment is generally due on the first of the next month, most lenders allow a 

15-day grace period for borrowers to pay. However, if the payment is not received by the 15th, 

the mortgage loan is considered past due 15 days. Thereafter, a loan not paid by the X-th date 

after the due date is considered X days past due.

Each month, mortgage servicers report information to the credit bureaus for each mortgage loan 

they service as of a snapshot date (balance date). Generally, the bureaus will accept only one 

report per loan per month and will not accept a report unless it has performance information. 

Servicers report three measures of performance: (1) the account condition code that describes the 

condition of the mortgage, e.g., whether it is open, paid in full, closed, transferred, or inactive; 

(2) a special comment code that provides special information on the mortgage such as a loan 

modification, location in a disaster area county, or dispute by the borrower; and (3) a loan status 

code that provides information on how many days “past due” a loan is as determined by the 

oldest non-paid payment (loan payments are generally applied against the oldest non-paid 

payment). Industry and Metro 2 credit bureau reporting guidelines (available since 1997) 

differentiate between loans that are current or past due 29 or fewer days, 30 to 59 days past due,

60 to 89 days past due, 90 to 119 days past due, 120 to 149 days past due, 150 to 179 days past 

due, and 180 or more days past due. This is the classification used for most loans. If a loan 

becomes 90 days past due under many mortgage contracts, the lender can declare the loan “in 

default.” The borrower then typically has 90 days to become current. If not, the lender can file a 

foreclosure action, in which case the loan status is changed from “days past due” to some form of 

foreclosure or collection. If the borrower files for bankruptcy, the loan may be assigned a 

“bankruptcy” status even if the payments are current.

Under normal conditions, the bureaus will not accept a report unless it contains information on 

the loan’s status. However, a status code may be suppressed or not reported for some loans. 

This can occur for a variety of reasons— a borrower’s payments may have been suspended 

because of a natural disaster, reports may not be supplied for the period between a loan’s closing 

or the first due date, status is often not continually reported when loan servicing is sold from one 

lender to another when it takes time for the acquiring lender to set up reporting, status updates 

are often not reported for loans in foreclosure or other forms of serious delinquency (there are 

spikes in missing values for August 2015 and April 2016 because several larger servicers had 

problems with their servicing systems). The borrower may dispute a performance report, in 

which case it may be missing.

There are also loans which do not fit these circumstances— some loans do not have due dates of 
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the first, others have bi-weekly or quarterly payment requirements, some borrowers make partial 

payments (often to what is called a suspense account) which can leave them in a perpetually 

past-due status, and others can make extra payments (curtailment) to reduce their loan balance 

more rapidly.

Servicers reporting to the credit bureaus using Metro 2 guidelines are supposed to follow the 

guidelines described above. Thus, a loan with a due date of January 1 will be considered 30 days 

past due on January 31 if the payment has not been received by that date (the “days past due” 

standard). In the past, however, many lenders used a “billing cycle month” standard. Under the 

“billing cycle month” standard, a loan was not considered “30 days” past due until the due date 

of the next month (e.g., February 1 for a January 1 payment). The “billing cycle month” 

standard, associated with the older Metro 1 reporting format, was phased out over the 2000s for 

bureau reporting. However, this has not necessarily happened for other regulatory reporting. 

Mortgage delinquency metrics reported to the Federal Financial Institutions Examination 

Council (FFIEC) for banking institutions can be based on either “days past due” or “billing cycle 

month” standards at the reporter’s discretion. Credit unions used the “billing cycle month” 

standard until 2013 when they were required to report using both methods. Freddie Mac and 

Fannie Mae report delinquency statistics for loans in their security pools using the “billing cycle 

month” standard.

Within the Metro 2 reporting guidelines and for other delinquency reporting, there is also 

variation based on precisely when a loan’s status is measured. Under the Mortgage Bankers 

Association’s “MBA” method, a loan is considered past due X days if a payment is not received 

by close of business (COB) on the X-th day following its due date. That is, a loan with a due 

date of March 1 is considered 30 days past due at COB on March 31. Under the Office of Thrift 

Supervision’s “OTS” method, a loan is considered past due X days if the payment has not been 

received by COB on the X+1st day (e.g., April 1 for a March 1 due date).

The credit bureaus allow loan servicers to choose whichever reporting day within the month that 

they wish to use, and either the MBA or OTS method. Currently, about 90% of reporters use the 

same day of the month every month and the same day for all their loans. For NMDB loans 

active in 2014 and later, the modal report day (31%) was the last day of the month, 16% were on 

the 5th, 12% on the 7th, and 8% on the 21st. For “prime” first lien closed-end mortgages, which 

dominate the NMDB, lenders generally use the MBA method. Subprime servicers, however, 

who played a significant role in the 2003 to 2007 period, typically used the OTS method. 

These differences in reporting day and method can lead to significant variation in the incidence 

of delinquency for loans with identical payment patterns when comparisons are made month-tomonth or between lenders with different reporting patterns. These differences are shown in 

Table B-1. Servicers who report at the end of the month using the MBA method will maximize 

the 30-day delinquent count in the seven months with 31 days because the reporting day is the 

first day a loan can be 30 days delinquent. Lenders reporting in the latter half of the month— but 

not on the last day— will tend to systematically show lower delinquency rates.

These distinctions will matter when aggregated measures of delinquency are computed, 

particularly those for 30 days past due. For example, in 2016, lenders in the NMDB reporting on 
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the last day of the month showed an average 30-day delinquency rate 0.76 percentage points 

higher in the seven months with 31 days than they did for the five months with 30 days or less. 

For servicers reporting between the 16th and the second-to-last day of the month, there is only a 

0.03 percentage point difference. 

These reporting differences can cause systematic differences across states as well. For example, 

47% of the 2016 reporters in Mississippi were end-of-the-month reporters versus 17% in Alaska, 

almost surely influencing the number of 30-day delinquencies. Because loan status can only be 

reported once a month and it is impossible to know when a loan payment was received for many 

loans, this bias is difficult to correct for. If no adjustment is made, users of a bureau-based 

delinquency series need to be cautious in making comparisons across geographic units. Also, as 

just noted, numbers for the NMDB are likely to show persistent monthly patterns if results are 

not seasonally adjusted.

The mixture of reporting patterns in the credit bureau data is likely to lead to systematic 

differences in aggregate delinquency metrics constructed from the NMDB data when compared 

to other delinquency measures. For example, the MBA National Delinquency Survey asks 

respondents to classify loans by their status at COB on the last day of the quarter using the MBA 

method although it appears that the lender can use either the “billing cycle month” or “days past 

due” standard. If the “days past due” standard is used, it means that reports for March and 

December (which each have 31 days) will show persistently higher 30-day delinquency rates 

than those of June and September (which each have 30 days). Similarly, 90-day delinquency 

rates will be lower in the first quarter except for leap years. The degree of seasonality will 

depend on what percentage of the reporters use the “billing cycle month” versus the “days past 

due” standard. FFIEC call report statistics, which are also reported COB on the last day of the 

quarter, will also exhibit seasonality depending on the mix of lenders using different methods. 

FFIEC statistics are further clouded by the fact that lenders can use either the MBA or OTS 

accounting method.

Delinquency statistics reported by Freddie Mac and Fannie Mae for loans in their security pools 

should show the least month-to-month distortions. Both companies use an end-of-month 

measure computed using the MBA and “billing cycle month” standard, which should lead to 

stable monthly patterns. Given that the standard mortgage contract is based on a monthly 

payment standard, there is a compelling argument that the “billing cycle month” standard for 

measuring delinquency is the most appropriate. Nevertheless, that is not the standard used by 

most credit bureau reporters and thus is not the standard reflected in the NMDB data.

The delinquency data in the NMDB are built from the lender reports supplied by Experian but 

with some additional processing. The performance information supplied by lenders for loan 

status, account condition, and special comments is static; that is, each month when the servicers 

update the performance data for a loan, the previous values for these variables are overwritten 

with new information. The values supplied in the previous months can only be recovered from 

archives. However, all the credit bureaus maintain an abbreviated record of historical 

performance, known as a payment grid, which is not overwritten, but can be (and is) updated. 

Under FCRA rules, the payment grid can only go back 84 months. When an initial report is 

supplied for a month (say June 2016), the “June 2016” element of the payment grid is initially 
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populated. However, in subsequent filings the servicer can change the “June 2016” value. This 

can happen for a variety of reasons— the lender can catch an error, they may have inadvertently 

failed to report performance in the first filing, the consumer could dispute the report and get the 

record changed, or the report could subsequently be suppressed for reasons such as the borrower 

being impacted by a natural disaster.

In general, the monthly performance measure in the NMDB is constructed from the payment 

grid, using the most recently reported information for a given month. Payment grids retrieved 

from archival data— which were collected quarterly from June 2012 on and semi-annually 

before that— were used to piece together a full measure of performance and to get around the 

84-month limitation on current data. An additional problem is created when loans are transferred 

from one servicer to another. Here, payment grids need to be combined for two different 

reporters to create a continuous measure of performance. Often when this happens, the 

transferring servicer will initially report the loan as delinquent but then correct it when they 

receive the transfer notice. Transfers often create gaps in the payment grid when the new 

servicer is slow to report the loan. FCRA rules also place restrictions on how the new servicer 

reports performance under the assumption that some borrowers may have sent payments to the 

wrong place.

The effect of this process is that the initial performance report for a loan is often subsequently 

changed. On net, this tends to improve the overall measure of performance, but in recent years 

the change is small. For example, the initial NMDB report for June 2016 differed from the 

“final” report in June 2017 for 1.3% of the cases. The majority of these were blanks in the initial 

report but there were some real changes. Changes went both ways— 2.3% of the loans 

originally reported as 30-days past due were corrected to current. But an almost equivalent 

number were changed from current to delinquent.

On balance, the updating process reflected in the NMDB is likely to mean that for recent years,

delinquency measures in the NMDB will be slightly more positive than other indices, such as the 

Equifax index, which are compiled only from the initial report. However, during the mid-2000s,

when the private label subprime market was a significant part of the mortgage market, sale of 

servicing was more prevalent and more likely to have led to initially inaccurate delinquency 

reports. Here, the NMDB data show noticeable differences from indices based on initial reports.

Another difference arises when seriously delinquent loans are transferred within an organization 

(say from normal servicing to “work out” departments). It is not unusual for the loan to be 

reported as open and delinquent by both departments, which creates a double count if not 

corrected. In constructing the NMDB, these reports are combined but may not be in other 

indices which are based on open accounts with positive balances. Consequently, indices of 

serious delinquency constructed from the NMDB will likely be lower than those constructed 

from other sources.

There is some ambiguity as to how to define an open account. It is not unusual for lenders to 

initiate foreclosure actions on small mortgage loans but never complete the process, perhaps 

because they decide the property is not worth acquiring. In other cases, state law allows lenders 

to maintain a claim on the borrower, termed a deficiency judgement, after a foreclosure. These 
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loans can remain on the Experian files as open, with positive balances, for a long time until they 

are purged by FCRA rules. However, the borrower may well have lost title to the house or 

moved out much earlier in the process. Currently, the NMDB team arbitrarily treat these 

accounts as closed in the NMDB after 24 months, but others may have different rules.

Finally, there are two recent developments that impact performance metrics. Traditionally, when 

a consumer entered the bankruptcy process all their loans were tagged with performance

bankruptcy codes even in cases where the loan (often a mortgage) was not included in the 

bankruptcy and the consumer was continuing to pay on time. In 2019, the industry guidance was 

changed to recommend suppressing all credit reporting on these loans irrespective of whether the 

borrower was current. 

Another development is the passage of the Coronavirus Aid, Relief, and Economic Security 

(CARES) Act in March 2020. The CARES Act provided the option for homeowners with 

federally backed or funded mortgages to request forbearance (a pause) of mortgage payments for 

up to 180 days. During this period, loan servicers could not take any action, which had an

adverse impact on the credit of a borrower. Effectively, this action “froze” the status of the loan 

at its pre-forbearance level even in cases where the borrower was not making payments. 

Potentially, this could lead to artificially low levels of delinquency. Ultimately forbearance was 

phased out in the end of 2021, with credit reporting practices returning to “normal.” 

There are two monthly performance vectors contained in the NMDB. One is the “raw” data 

whereby months where a performance report for a loan was either suppressed or not given are 

tagged as “missing.” The second metric, and the one published by the FHFA, invokes a “stale 

account rule.” Here the most recent non-missing performance metric is substituted for all 

months in the raw data vector with missing data. The rule extends back three months for most 

loans but goes back up to two years for loans that are seriously past due (180 days or more or in 

bankruptcy, foreclosure, or collection). This rule emulates how performance would be treated in 

constructing most credit scores.
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Table B-1.

Impact of Reporting Cycle Standard and Reporting Method on Delinquency Measurement

Reporting of Days Past Due for a Mortgage Where Payments Were Stopped*

MBA Method** OTS Method**

Month 

Payments 

Stopped

Current 

Month

Month 

Plus 1

Month 

Plus 2

Month 

Plus 3

Month 

Plus 4

Month 

Plus 5

Month 

Plus 6

Current 

Month

Month 

Plus 1

Month 

Plus 2

Month 

Plus 3

Month 

Plus 4

Month 

Plus 5

Month 

Plus 6

End of Month Reporters - Days Past Due Standard

January D30 D30 D60 D90 D150 D180 D180 C D30 D60 D90 D120 D150 D180

February C D30 D60 D90 D120 D180 D180 C D30 D60 D90 D120 D150 D180

March D30 D60 D90 D120 D150 D180 D180 C D30 D90 D120 D150 D180 D180

April C D60 D90 D120 D150 D180 D180 C D30 D60 D120 D150 D180 D180

May D30 D60 D90 D120 D150 D180 D180 C D30 D90 D120 D150 D180 D180

June C D60 D90 D120 D150 D180 D180 C D30 D90 D120 D150 D180 D180

July D30 D60 D90 D120 D150 D180 D180 C D60 D90 D120 D150 D180 D180

August D30 D60 D90 D120 D150 D180 D180 C D30 D90 D120 D150 D180 D180

September C D60 D90 D120 D150 D180 D180 C D30 D60 D120 D150 D150 D180

October D30 D60 D90 D120 D150 D180 D180 C D30 D90 D120 D120 D180 D180

November C D60 D90 D90 D150 D180 D180 C D30 D90 D90 D120 D150 D180

December D30 D60 D60 D120 D150 D180 D180 C D60 D60 D90 D120 D180 D180

Middle of Month Reporters - Days Past Due Standard

All Months C D30 D60 D90 D120 D150 D180 C D30 D60 D90 D120 D150 D180

Billing Cycle Month Standard Reporters**

All Months D30 D60 D90 D120 D150 D180 D180 C D30 D60 D90 D120 D150 D180

Impact by Number of Days in the Month and Timing of Reporting Date

Reporting of Missed Payments and Subsequent Cure Using the Days Past Due Standard and MBA Method

March (31 Day Month) April (30 Day Month)

Performance Month Performance Month

Date 

Cured March April May June July August Date Cured April May June July August Sept.

End of Month Reporters

15-Apr D30 C C C C C 15-May C C C C C C

15-May D30 D60 C C C C 15-Jun C D60 C C C C

15-Jun D30 D60 D90 C C C 15-Jul C D60 D90 C C C

15-Jul D30 D60 D90 D120 C C 15-Aug C D60 D90 D120 C C

Report on 22nd of Month

15-Apr C C C C C C 15-May C C C C C C

15-May C D30 C C C C 15-Jun C D30 C C C C

15-Jun C D30 D60 C C C 15-Jul C D30 D60 C C C

15-Jul C D30 D60 D90 C C 15-Aug C D30 D60 D90 C C

Report on 7th of Month

15-Apr C D30 C C C C 15-May C D30 C C C C

15-May C D30 D60 C C C 15-Jun C D30 D60 C C C

15-Jun C D30 D60 D90 C C 15-Jul C D30 D60 D90 C C

15-Jul C D30 D60 D90 D120 C 15-Aug C D30 D60 D90 D120 C

Note: This illustration is for loans where the payment due date is on the first of the month.

C = Current, D30 = 30-59 days past due, D60 = 60-89 days past due, D90 = 90-119 days past due, 

D120 = 120-149 days past due, D150 = 150-179 days past due, D180 = 180 or more days past due.

*Bold indicates where performance reporting stays the same and italics indicates where reporting skips a reporting cycle. 

**See text for explanation of "MBA" and "OTS" methods; and "Days Past Due" and "Billing Cycle Month" standards. 
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Appendix C. Cleaning and Editing Data in the NMDB

Constructing the NMDB is a process fraught with errors of omission and commission (e.g., data 

entered incorrectly). It is built out of thousands of servicing databases maintained by individual 

servicers. Such databases are generally going to contain little inaccurate information but suffer 

from errors of omission whereby certain variables are simply not collected and others contain 

many missing values. The next step, when information from the 5,000 or so individual servicers 

is reported to and aggregated at Experian, creates an opportunity for more errors. Loans may be 

assigned to the wrong person. Servicers may apply Metro 2 reporting guidelines in an 

inconsistent manner. This may lead to loans thatshould be eligible for NMDB sampling being 

missed and to loans which should not have been eligible being included.

Further error is likely to be introduced when administrative loan information from the NMDB’s 

five data partners is merged into the NMDB. The administrative units themselves may receive 

incomplete or erroneous data from servicers. Matching these loans to the NMDB may be 

imperfect, potentially leading to inconsistencies between the administrative-data-based values 

and credit bureau-based values for the same loan.

The NMDB team has no control over data cleaning decisions taken by Experian or the NMDB’s 

data partners. However, many problems can emerge during the cleaning process at FHFA, 

which is in the NMDB team’s control. The production team executes over 50 programs with 

over 100,000 lines of code to process a normal NMDB update cycle. A significant portion of 

this code is used to determine whether new loans should be added to the NMDB (as described in 

Section 5) or whether loans are appropriately matched to administrative data (as described in 

Section 6). Most of the remaining processing is devoted to determining the dataset values of key 

variables.

The final NMDB dataset consists primarily of data provided through the Experian credit files 

supplemented by administrative data for variables that are not available from Experian. When 

there is overlap between the two sources, generally the Experian value is used in the dataset. 

This is done under the belief that the credit bureau data are generally cleaner and more 

consistently collected than raw servicing data. With small exceptions described below, values of 

the key variables including original loan amount and term, date opened and closed, monthly loan 

performance, balance, and payments are all derived entirely from Experian.

Complete information on original loan amount and term and opening date are conditions for a 

loan being eligible for the NMDB. Nevertheless, in a few cases there is some ambiguity in these 

variables that needs to be addressed. This can happen when there is an inconsistency between 

Experian or administrative data or within the Experian data. Typically, these inconsistencies are 

resolved by choosing the oldest opening date or term or loan amount most aligned with balance 

information.

More serious is the problem of ambiguity on loan closing. About three percent of all NMDB 

loans end with an uncertain disposition— a servicing transfer, death of a borrower, foreclosure 

started but not finished, or just a stop in reporting. Several rules are employed to resolve these 

cases. If a new mortgage loan is opened immediately following the loan’s last report, the loan’s 
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borrowers move, or the loan was very near the end of its term, the loan is assumed to have closed 

at the date of last report. In the remaining cases, the loan is treated as provisionally open for two 

years. If at that point no update has been received (as should be the case with a servicing 

transfer) the loan is treated as closed, with a closing date one month after the date of last report.

Experian data are also used to construct monthly values of loan performance, balance, and 

payments. Construction of loan performance is discussed separately in Appendix B. Unlike 

performance (in which the final dataset contains missing values and nothing is imputed), it was 

decided to include in the final dataset complete loan balances and required payments for every 

month for every loan from its origination (or January 1998) to termination.

Historical monthly data on balance plus required and actual payments (going back 24 months) 

are included in the core credit bureau files and thus subject to the dispute process, which can lead 

to cleaner data. They have been included only since 2010 though and are not present for every 

loan. Moreover, unlike performance, historical balance and performance data are constructed 

and maintained by Experian and not reported each month by servicers. NMDB data were 

supplied by Experian semi-annually prior to 2012 and quarterly until 2020 when the NMDB 

moved to a monthly schedule. Consequently, in constructing complete monthly vectors of 

balance and payments it is necessary to deal with significant gaps, particularly for loans 

originated prior to 2010.

When possible, monthly balance gaps were dealt with by interpolating from the two end points 

using the inferred appropriate amortization rate. This process works most of the time, but breaks 

down when loans are in dispute, forbearance, or default or buyers make extra or non-standard 

payments (e.g., curtailment). Here it was necessary to use linear or other forms of interpolation. 

Loans missing balance information in the beginning were generally assumed to have prepaid 

interest. Non-delinquent loans that had missing data at the end point were extrapolated using 

inferred amortization rates. In a few rare cases, the entire monthly balance vector had to be 

constructed using assumptions about amortization. 

Only the required payment vector was filled, not the actual payment, which is left unedited. 

Here, it was generally assumed that missing required payments were the same as the last known 

value. For ARMs with required payments changing once a year, that information is accounted 

for in constructing the infill. Again, in a few cases the entire payment vector needed to be 

constructed. Here, contract interest rate and balance information were used to infer the likely 

required payment.

The remaining editing and cleaning dealt with variables added with the matching process. Most 

prominent of these is loan purpose (e.g., home purchase, refinance) which is not directly 

available in the Experian data. It is, however provided in almost all cases when there is an 

administrative or HMDA match. In about 9% of these cases, though, HMDA reporting is 

inconsistent with the administrative data and even within the same source, purpose data is 

inconsistent almost 1% of the time. This is a particular problem with loans which do not fall 

neatly into either the purchase category or the refinance category, such as when a borrower buys 

out their co-borrowers. To deal with this problem, the 11% of overall loans that involve a 

change in borrowers, but not property, are classified as a separate purpose category (change of 
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borrowers) to let users decide how to treat them.

Loans with missing purpose are coded by examining the borrowers’ other loans and potential 

moves. If there is no preceding loan or the timing of the termination of the previous loan is 

inconsistent with a refinance, the loan is classified as home purchase. When the timing is 

consistent with both a purchase or refinance, it is treated as a purchase if all borrowers moved at 

the time of origination, a refinance when there is no change of borrowers and at least one 

borrower did not move, and a “change of borrowers” in all other cases. Finally, in about three 

percent of cases, a loan was taken out so long ago (prior to July 1991) that no information on the 

previous loan is available in the credit files. In these instances, the loan purpose is imputed using 

statistical methods with the outcome variable constrained to be home purchase or refinance.

Property location is also not supplied directly in the Experian data. The final dataset includes the 

state, county, and census tract that the property associated with the loan is estimated to be in 

2021 (using the 2010 census tract taxonomy) and in the year the loan was originated (using the 

taxonomy in place at the time the loan was made). For loans with an administrative match, the 

address supplied by the administrative partner is geocoded by Experian and tract information 

supplied to the NMDB. For other loans, Experian maintains addresses for each person in their 

files compiled from the billing addresses supplied by loan servicers augmented by addresses 

from marketing sources. Going back to archival files, Experian was able to identify and supply 

to the NMDB team information on up to 61 addresses associated with each borrower. For each 

address, Experian provided the ZIP Code and 2000 and 2010 census tracts associated with the 

address, as well as the date the address was first linked to the borrower.

Tract assignments for mortgages not matched to administrative files are determined from these 

data. For example, for all active mortgages, the tract of the most recently reported address is 

used. For closed loans, addresses are given preference when the borrower moves (and the new 

address is reported to Experian) around the time the loan was originated. Respondents 

participating in the NSMO or ASMB are asked if the property is located at the address used in 

the mailing. If so, then that address is prioritized in assigning the tract; if not, then it is not 

allowed to be assigned.

Assignment of both the original and 2021 tract was straightforward for almost all NMDB loans 

originated in 2003 or later because Experian provided both 2000 and 2010 taxonomy tracts for 

each address in the database. From 1992 to 2002, however, the Census Bureau relied on the 

1990 Census tract taxonomy. Prior to 1992, Census Bureau applied the 1980 census taxonomy, 

which left out much of un-tracted rural America. Assigning origination year tracts to NMDB 

loans originated during these periods represented a challenge. Administrative data and tract 

assignments available from HMDA provided 1990 or 1980 origination-year tract numbers for 

some of these older NMDB loans. However, for most others, it was necessary to first identify 

the 2000 census tracts associated with the property backing the loan from information available 

in the Experian address file and then use Census Bureau crosswalk files to assign the 1990 tract 

and a NMDB-constructed crosswalk based on tract centroids to assign 1980 tracts. In a small 

number of cases, only the 1980 or 1990 tract was available, so the crosswalk files needed to be 

used to determine the appropriate 2021 tract to assign to the loan. 

Starting in 2022, HMDA loans will be reported using the 2020 Census tract taxonomy 
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necessitating a change in the NMDB taxonomy as well. Thus, all new NMDB loans from 2022 

on will use 2020 for their originating year taxonomy. However, converting the “current year” 

tract from a the current 2010 to 2020 taxonomy will represent a challenge. Experian will regeocode all actively tracked borrowers and their addresses using the 2020 taxonomy, however, 

older closed loans will have to be converted to the 2020 taxonomy using a crosswalk file. 

Another variable requiring some editing/cleaning is loan type (FHA, VA, or RHS) and GSE type 

(Fannie Mae, Freddie Mac, or FHLB). For the most part, both variables are determined by the 

administrative loan matching process. For example, loans confirmed as matches to VA 

administrative data are designated as VA loans. These designations do need to be supplemented 

for loans newly added to the database, which have not yet gone through the matching process, 

and for older loans, where the matching process is less accurate. In these cases, Experian 

“account-type” codes are used to identify FHA, VA, and RHS loans and “secondary loan type” 

codes are used to identify Fannie Mae and Freddie Mac loans. For older loans, identifiers 

derived from HMDA data matches are also used. It should be noted that loan type is a 

characteristic of the loan and does not change during its lifetime. GSE acquisition though is time 

dependent. NMDB flags a loan as GSE if it is ever owned by a GSE. This means that the 

classification of a loan can change in the NMDB if it is not sold to the GSEs until well after it is 

originated. 

Most remaining editing and cleaning is devoted to implementing rules to determine values for 

four key borrower variables— age, gender, race, and ethnicity— and nine key loan variables—

borrower income, property value, LTV/CLTV, contract interest rate, debt-to-income ratio, 

occupancy status (owner-occupied or not), property type (manufactured housing or not), number 

of units in the property (1 to 4), and ARM status.

Values for most of these variables are derived from administrative or HMDA data matches. In 

most instances, determining the variable value is straightforward. In some cases, however, 

administrative sources may have conflicting values, or the values may be implausible. 

Sometimes these issues are obvious and easy to resolve— for example, when LTVs are reported 

in ratios instead of percentages, or when monthly instead of annual income is reported. In other 

cases, the best resolution is less clear cut. In general, the waterfalls developed for the NMDB to 

resolve these data issues prioritize plausible values over implausible and data collected from high 

quality administrative matches (those done by Experian with data partners and matched by SSN) 

over data from fuzzy logic matches (those done with HMDA, FHLB and servicing vendor

databases). These rules also consider internal consistency. For example, an administrative data 

match implying both a very high borrower income and a very high debt-to-income ratio might be 

given a low weight. Contract interest rates inconsistent with the implied amortization rate 

derived from balance patterns also might be deprioritized or disallowed.

Because borrower characteristics are not loan dependent, values of these variables are dealt with 

in a different way. NMDB includes birth month and year, gender, race, and ethnicity for every 

NMDB borrower. Birth month and year are populated for almost all NMDB borrowers because 

they are carried and collected as part of a credit report. Gender is not included as a credit 

variable but is widely and accurately available through Experian’s marketing database. Gender 

is also available from HMDA data, as is borrower age beginning with the 2018 data. Both 
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variables are also available for survey respondents and, in some cases, from administrative data. 

When consistent with the timing of mortgage loan originations (for example, borrower need to 

be at least 18 when their earliest mortgage loan was taken out), the Experian-based values were 

used. Otherwise, when values for age or gender were inconsistent or, in the case of age, 

implausible, they were imputed (see Appendix D) rather than assigned through waterfall 

priorities.

High quality data on race and ethnicity are not available from Experian. Fortunately, such 

information is available from HMDA data and administrative sources. Moreover, because all 

NMDB borrower’s loans, sample or not, are potentially matched to both HMDA data and the 

administrative data, only one of their loans needs to have matched and have race and ethnicity 

data populated. The best and least ambiguous case is when the matched loan has a single 

borrower. However, even with multiple borrowers, race and ethnicity are most often reported as 

the same for all, so the assignment is still straightforward. In other cases, the borrower’s gender 

can be used to determine which HMDA data field or administrative data field to use. In the 

relatively small number of cases where external matches provide inconsistent or ambiguous 

values, the values are imputed rather than subjected to a waterfall.

One issue that arises in determining race and ethnicity is the change in collection rules for

HMDA data beginning in 2004. Prior to that date, HMDA data treated ethnicity as a race, 

effectively forcing reporters to report either race or ethnicity, but not both. Moreover, reporters 

were allowed only one racial classification (that is still the case with some administrative data). 

In these instances, the variable not reported (e.g., race if the HMDA variable were reported as 

“Hispanic”) needs to be imputed (see Appendix D). Ultimately, NMDB provides age, gender, 

race, and ethnicity for each borrower associated with every sample mortgage.
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Appendix D. Imputation of Missing Data in the NMDB

Early in the development of the NMDB, it was decided that missing values of key variables 

would be imputed. This is a common practice for many statistical databases, including, for 

example, most Census products. Obviously, this makes the dataset easier to use, but more 

importantly it also likely makes the statistical analysis using the dataset more accurate. Unless 

missing data occurs completely at random, analysis using loans only with no missing data is 

likely to produce bias. Statistical imputation, as used in the NMDB and described in this 

appendix, requires the weaker assumption that missing data occurs randomly conditioned on the 

predictor variables used in the imputations. Recognizing that some analysts may prefer to deal 

with missing information themselves, indicator variables are included in the NMDB that identify 

which specific variable values were imputed. 

In Appendix C above, methods of dealing with missing information, such as monthly 

information on loan balances and payments, through interpolation and editing procedures are 

addressed. Here, methods of dealing with missing values that cannot be addressed that way are 

presented.

Five continuous variables (borrower income, property value, LTV/CLTV, contract interest rate, 

and debt-to-income ratio) and nine categorical variables (borrower age, gender, ethnicity, and 

race, and mortgage purpose, occupancy status (owner-occupied or not), property type

(manufactured housing or not), number of units in the property (one to four), and ARM indicator 

(yes if ARM, no otherwise) are subject to imputation. Different methods are used for each 

variable.

Borrower birth month and year are populated for almost all NMDB borrowers because they are 

carried and collected as part of a credit report. Gender is not included as a credit variable but is 

widely available through Experian’s marketing database. Both variables are partially available 

from some of the administrative sources as well as HMDA data. Nevertheless, values need to be 

imputed in a relatively small number of cases. “Hot deck” methods are used for these 

imputations. Borrowers are matched to cells of borrowers with complete gender and age 

information based on the relative age and gender of co-borrowers, loan size, age of oldest 

tradeline, and date of origination. A random match within the cell is selected to assign values. 

Age and gender are assigned iteratively: age first, then gender, then age is re-estimated based on 

the assigned gender.

Values for the remaining 12 variables are assigned using logistic or linear regression models. 

The imputation models are constructed from NMDB data as well as demographic (tract or county 

based) data from HMDA data and other sources. The tract-level data, particularly the median 

borrower income level in HMDA data of all borrowers in that tract in the same year the NMDB 

loan was originated, are powerful predictors. The models are defined by time period and 

mortgage characteristic group. The intent of the models is to provide the statistically best 

unbiased estimated value of the imputed variable.

Mortgage purpose (i.e., home purchase or refinance) is imputed separately from the other 

variables. It is imputed only for older loans (originated pre-1992) when HMDA data are not 
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available. The property type variable is available in the NMDB beginning with 2004 

originations, thus is not included in the early year models. Otherwise, the imputation estimation 

is performed recursively in a specific order, such that the assigned imputed values from earlier 

models in the scheme can be used as predictor variables in models later in the scheme. Of the 

categorical variables, ethnicity and race are imputed on the borrower level data. The rest of the 

variables are imputed on the loan level.

Imputation models are based on different time periods, which is a tradeoff between flexibility of 

the modelling process and the availability of explanatory variables. Starting in 1992, all loan 

level imputation models are estimated and applied separately for each year based on the 

origination year of the loan through 2021. Loans originated prior to 1992 are imputed using 

single models for each variable with “time effects” as intercept shifts rather than completely 

different models. Values for loans originated in 2022 are based on estimates from the 2021 

models. When new HMDA data are available for 2022, a separate model will be created for that 

year and used to impute 2022. 

With each quarterly update of the NMDB, the imputation models for the most recent three 

origination years are re-estimated. This action allows the size of the dataset used to estimate the 

models, which rely on loans with “hard data,” to expand naturally as more NMDB loans are 

matched to administrative loan files with such data. Similarly, missing values of NMDB loans 

originated in these three years are re-imputed each quarter to capitalize on the improved models. 

Otherwise, the predictive models are “frozen,” but still can be used to impute values for missing 

variables with loans newly added to the NMDB.

The two borrower level imputations are estimated based on three models each. These models 

reflect different periods defined by the availability of HMDA data. The periods are 2004 to the 

present, 1992 to 2003, and pre-1992. Prior to 2004, originators were able to report either race or 

ethnicity, but not both, in HMDA data. Thus, if only pre-2004 HMDA data were available for a 

borrower (many borrowers have multiple HMDA data matches in the NMDB) special imputation 

models needed to be used to predict ethnicity given race or vice-versa. These models 

supplemented models used when all information was missing. 

Imputation follows a generic process for each variable. First, models are estimated using loans 

or borrowers with complete information. Then, values for loans or borrowers with missing 

information are assigned. For continuous variables, the assignment is based on the predicted “yhat” from a standard regression model plus a random error term drawn from the actual error 

distribution divided into deciles based on the values of “y-hat” (thus the error assigned to a loan 

with a predicted “y-hat” in the top decile would be restricted to actual errors of loans used in the 

model estimation that also had “y-hats” in the top decile). Final assignments are bounded by the 

1st and 99th percentiles of the distribution for known values. Binary and multinomial logistic 

models are used to impute categorical variables with specific assignments based on random 

draws conditioned on the probabilities assigned by the logistic models. 

Once estimates for missing values were developed, both for the overall dataset and various loan 

subsets, the distribution of imputed values was compared to existing values to ensure that they 

are consistent. The selection of the specific regressors and model forms was based on 
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econometric goodness-of-fit tests and the application of various robustness checks.

The imputation process follows the following recursive order: borrower income, property value, 

LTV/CLTV, contract interest rate, and debt-to-income (all continuous). Then occupancy status 

and property type (both dichotomous), number of units (one to four), and ARM indicator. For 

borrowers, ethnicity is estimated first (dichotomous) and race second (multinomial).

The percentage of each variable that is imputed in the NMDB is given in Table D-1. Statistics 

are presented separately for three periods defined by different HMDA data rules and for loans 

associated with NMDB administrative data from Fannie Mae, Freddie Mac, FHLB, FHA, VA, 

and RHS (data partners) and those that are not.

Table D-1. Percent Imputed

Before 2004 2004 to 2011 2012 to 2019

Loan Level

Data 

Partner Other

Data 

Partner Other

Data 

Partner Other

Borrower Income 14.3 44.5 5.6 42.8 7.0 52.8

Property Value 13.1 78.3 3.1 46.4 4.5 72.0

LTV/CLTV 13.1 78.3 3.1 46.4 4.5 72.0

Contract Interest Rate 6.9 36.4 1.3 24.8 2.7 32.7

Debt-to-Income Ratio 23.7 94.6 7.7 78.1 11.3 86.2

Occupancy Status 7.2 37.1 1.4 24.5 3.7 48.8

Property Type - - 12.3 40.1 32.8 58.8

Number of Units 10.8 81.2 3.1 51.5 6.6 74.2

ARM Indicator 44.2 78.0 21.0 46.1 18.3 72.7

Borrower Level All All All

Birth month 0.2 0.1 0.1

Gender 2.7 1.4 1.9

Race 14.7 8.9 16.5

Ethnicity 42.6 12.3 16.7

The specific models and sample groupings used in this process and summary statistics from the 

estimation are given in Tables D-2 and D-3.
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Table D-2. Imputation Models, 1992 - Present 

Variable Allowable Values Estimation Model Grouping

Adjusted R2

Range

Min Max

Borrower 

Income

Continuous Purchase, FTHB* 37.7% 63.7%

Purchase, Non-FTHB 38.8% 50.5%

Refinance 37.0% 52.7%

Property 

Value

Continuous Purchase, FTHB 75.8% 94.4%

Purchase, Non-FTHB 77.8% 90.6%

Refinance 74.0% 87.8%

LTV / CLTV Continuous Conventional Purchase, FTHB 36.2% 63.5%

Conventional Purchase, Non-FTHB 35.6% 55.7%

Conventional Refinance 35.3% 55.2%

Government Purchase, FTHB 6.6% 46.9%

Government Purchase, Non-FTHB 12.7% 68.0%

Government Refinance 22.6% 64.5%

Contract 

Interest 

Rate

Continuous Purchase, FTHB 9.8% 44.1%

Purchase, Non-FTHB 11.6% 39.6%

Refinance 9.6% 23.4%

Debt-toIncome 

Ratio

Continuous Purchase, FTHB 34.2% 48.4%

Purchase, Non-FTHB 27.2% 38.0%

Refinance 28.9% 48.8%

Ethnicity 0 = Non-Hispanic Purchase, FTHB 57.7% 67.4%

1 = Hispanic Purchase, Non-FTHB 49.6% 58.8%

Refinance 47.4% 58.6%

Race 1 = White Not Applicable NA NA

2 = Black 86.3% 89.8%

3 = American Indian 43.8% 47.1%

4 = Asian 81.8% 88.4%

5 = Hawaiian 48.0% 57.5%

6 = More than one race, Other Black 48.3% 67.5%

7 = More than one race, Other Non-Black 53.0% 57.2%

Occupancy 

Status

0 = Not Owner Occupied Purchase 39.7% 42.2%

1 = Owner Occupied Refinance 29.4% 31.3%

Property 

Type

1 = Site Built Purchase 51.2% 54.2%

2 = Manufactured Housing Refinance 37.6% 39.4%

Number of 

Units

1 - 4 Units Owner Occupied 29.5% 31.3%

Not Owner Occupied 24.1% 25.7%

ARM 

Indicator

0 = Not ARM Purchase 17.1% 18.5%

1 = ARM Refinance 19.2% 25.7%

Note: *FTHB is first-time homebuyer
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Table D-3. Imputation Models

Loan 

Purpose

Borrower 

Income

Property 

Value

LTV / 

CLTV

Contract 

Interest 

Rate

Debt-toIncome 

Ratio

Ethnicity Race Occupancy 

Status

Property 

Type

Number 

of Units

ARM 

Indicator

Model Type Logistic OLS

(Log-Log)

OLS

(LogLog)

OLS OLS

(LogLog)

OLS

(LogLog)

Logistic MultiNomial

Logistic

Logistic Logistic Logistic Logistic

Explanatory Variables

Loan Purpose - Purchase/Refinance X(c)

Loan Amount X X X X X X X X X X

Borrower Income X X X X X X X X X

Income Ratio X

Property Value X

LTV X X X X X X

Rate Spread X X X X X

DTI X X X X

Ethnicity X(c)

Occupancy Status X(c) X(c)

Primary Market Interest Rate X X X X X X

Credit Score at Origination X X X X X X X X X X

Number/Gender of Borrower(s) X(a) X(a) X(a) X(a) X(a) X(a) X(a)

Number of Borrowers X(c) X(c)

Gender of Borrower X(c) X(c)

Age of Borrower X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a)

Hispanic Borrower (marketing data) X(c)

Hispanic Borrower (country of 

origin) X(c) X(c)

Minority Borrower X(c) X(c) X(c) X(c) X(c)

Ethnic Category Based on Surname 

(marketing data) X(a)

Education Level of Borrower X(a) X(a) X(a) X(a) X(a) X(a) X(a)

Tract Median Income X X X X X

Tract Median Loan Amount X X X X X

Tract Median Property Value X(b) X X X X X(b) X(b) X(b) X(b)
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Table D-3. Imputation Models (Cont.)

Loan 

Purpose

Borrower 

Income

Property 

Value

LTV / 

CLTV

Contract 

Interest 

Rate

Debt-toIncome 

Ratio

Ethnicity Race Occupancy 

Status

Property 

Type

Number 

of Units

ARM 

Indicator

Model Type Logistic OLS

(Log-Log)

OLS

(LogLog)

OLS OLS

(LogLog)

OLS

(LogLog)

Logistic MultiNomial

Logistic

Logistic Logistic Logistic Logistic

Explanatory Variables

Tract Median Debt-to-Income Ratio X X X X X

Tract Median Monthly Loan 

Payment X 

Tract Median LTV X X X X

Tract Percent Owner-Occupied X(d) X(d) X(d) X(d)

Tract Percent Refinance Originations X

Tract Percent Minority X X X X

Tract Percent Racial Category X(d)

Tract Percent Hispanic X X

Ratio: Tract Median Loan Amount to 

Median Property value X 

Loan Made at Beginning of Month X(c) X(c) X(c)

Loan Made at End of Month X(c) X(c) X(c)

Non-Conforming Loan X(c) X(c) X(c) X(c)

FHA Eligible Loan X(c) X(c) X(c) X(c) X(c)

Borrower Has Taken Out VA Loan 

Before X(c) X(c) X(c) X(c) X(c) X(c)

Small Loan X(c) X(c) X(c) X(c) X(c) X(c)

Tract Median Income Associated 

with Small Loans X X 

Tract Median Loan Amount of Small 

Loans X 

Tract Median Debt-to-Income Ratio 

of Small Loans X 
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Table D-3. Imputation Models (Cont.)

Loan 

Purpose

Borrower 

Income

Property 

Value

LTV / 

CLTV

Contract 

Interest 

Rate

Debt-toIncome 

Ratio

Ethnicity Race Occupancy 

Status

Property 

Type

Number 

of Units

ARM 

Indicator

Model Type Logistic OLS

(Log-Log)

OLS

(LogLog)

OLS OLS

(LogLog)

OLS

(LogLog)

Logistic MultiNomial

Logistic

Logistic Logistic Logistic Logistic

Explanatory Variables

Year of Origination X(a)

Origination Month of the Year X(a) X(a) X(a) X(a)

Location - State X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a) X(a)

Location - Metro Area X(c) X(c) X(c) X(c) X(c)

Location - Rural Area X(c) X(c)

Term of Loan X X X X X

Short-Term Loan X(c) X(c) X(c) X(c)

Loan Closed During current year X

FHA, VA, RHS Insured/Guaranteed X(c)

Fannie Mae, Freddie Mac or FHLB X(c)

Note: (a) Multi-Categorical Binary Variables (b) Post 2017 (c) Binary Variable (d) Tract Share by Multiple Categories

(Words in parentheses) indicate where that data came from/how it was derived