1 

CONSUMER FINANCIAL PROTECTION BUREAU | JUNE 2020 

Data Point: 2019 

Mortgage Market Activity 

and Trends 

A First Look at the 2019 HMDA Data 
 

 

This is another in an occasional series of publications from the Consumer Financial Protection 

Bureau’s Office of Research. These publications are intended to further the Bureau’s objective of 

providing an evidence-based perspective on consumer financial markets, consumer behavior, 

and regulations to inform the public discourse. See 12 U.S.C. §5493(d).[1] 

[1] This report was prepared by Young Jo, Feng Liu, Akaki Skhirtladze, and Laura Barriere. 

2 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
Table of contents 

Table of contents .........................................................................................................3 

1. Introduction .............................................................................................................4 

2. HMDA data coverage of the mortgage market ......................................................8 

3. Mortgage applications and originations .............................................................11 

4. Mortgage outcomes by demographic groups.....................................................19 

4.1 Distribution of home loans across demographic groups .......................... 19 

4.2 Average loan size by demographic group ................................................. 27 

4.3 Jumbo lending........................................................................................... 31 

4.4 Variation across demographic groups in nonconventional loan use ....... 32 

4.5 Denial rates and reasons ........................................................................... 36 

5. Incidence of higher-priced lending ......................................................................46 

6.1 HOEPA loans ........................................................................................ 55 

6. Lending institutions..............................................................................................58 

7. Conclusion .............................................................................................................69 

3 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

 

 

 

1. Introduction 

This Data Point article provides a first overview of residential mortgage lending in 2019 based 

on data collected under the Home Mortgage Disclosure Act (HMDA). HMDA is a data collection, 

reporting, and disclosure statute enacted in 1975. HMDA data are used to assist in determining 

whether financial institutions are serving the housing credit needs of their local communities; 

facilitate public entities’ distribution of funds to local communities to attract private investment; 

and help identify possible discriminatory lending patterns and enforce antidiscrimination 

statutes.1 Institutions covered by HMDA are required to collect and report specified information 

about each mortgage application acted upon and mortgage purchased.2 The data include the 

disposition of each application for mortgage credit; the type, purpose, and characteristics of 

each home mortgage application or purchased loan; the census-tract designations of the 

properties; loan pricing information; demographic and other information about loan applicants, 

such as their race, ethnicity, sex, age, and income; and information about loan sales.3 

The 2019 HMDA data are the second year of data that incorporate changes made to HMDA 

under the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 (DFA). Among 

the changes mandated by the DFA were changes to some data points and also authorizing the 

Bureau to collect new and revised data points. The Bureau issued a final rule implementing 

these and other changes in October 2015 (2015 HMDA rule).4 The 2015 HMDA rule made four 

primary changes to the data collected starting in January 1, 2018: (1) mandated reporting of 

open-end lines of credit (LOCs); (2) changed the transactional coverage definition from loanpurpose-based to one based primarily on whether the loan was secured by a dwelling; (3) 

modified definitions and values of some existing data points; and (4) required reporting of 27 

new data points.5 

1 For a brief history of HMDA, see Federal Financial Institutions Examination Council, “History of 

HMDA,” available at www.ffiec.gov/hmda/history2.htm. 2 The 2019 HMDA data, which are used for the analysis of this Data Point, cover mortgage applications 

acted upon and mortgages purchased during the calendar year of 2019 and reported in 2020. 

3 See https://s3.amazonaws.com/cfpb-hmda-public/prod/help/2019-hmda-fig.pdf for a full list of items 

reported under HMDA for 2019. 

4 See Home Mortgage Disclosure (Regulation C), 80 FR 66128 (Oct. 28, 2015). In September 2017, the 

Bureau published in the Federal Register a rule which made a few technical corrections to and clarified 

certain requirements of the rule implementing HMDA. This rule also increased the threshold for 

collecting and reporting data about open-end LOCs for a period of two years. See 82 FR 43088 (Sep. 13, 

2017). 

5 Beginning with 2018 HMDA data, the Economic Growth, Regulatory Relief, and Consumer Protection 

Act (EGRRCPA) exempted certain insured depository institutions and credit unions from the requirement 

4 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

 

Consistent with the last year, the Consumer Financial Protection Bureau (hereafter, Bureau) is 

issuing two Data Point articles. This first article follows a consistent format as previous annual 

articles released by the Federal Reserve, which accompanied the release of a public version of 

the aggregate HMDA data, and focuses specifically on trends in mortgage applications and 

originations. By examining the HMDA data over several years (2004–2019), this article closely 

analyzes trends using historical data points that had been collected prior to the 2015 HMDA 

Rule. The Bureau’s second Data Point article, which is scheduled to be published later, includes 

analyses of open-end LOCs and dwelling-secured applications not covered in the first article. 

Furthermore, the second article focuses on an in-depth cross-sectional analysis of new or 

revised data points that were added under the 2015 HMDA rule. The Bureau is releasing these 

two articles at different times to make the static application-level 2019 HMDA data file available 

to the public as soon as possible. 

With the first Data Point article, the Bureau is publishing a static application-level 2019 HMDA 

data file that consolidates data from individual reporters. The data file is modified to protect 

applicant and borrower privacy.6 The data file and the two Data Point articles reflect the data as 

of April 27, 2020. Though this static file will not change, the Bureau will also provide an updated 

file separately to reflect any later resubmissions or late submissions. The results using the 

updated file may differ from those reported in this Data Point article, although the Bureau 

expects them to be largely consistent. 

The remainder of this article summarizes the 2019 HMDA data and recent trends in mortgage 

applications and originations. The Bureau seeks to make the 2019 HMDA data as comparable as 

possible to HMDA data from previous years, including HMDA data prior to the data collected in 

2018 when the majority of the 2015 HMDA Rule took effect. To do this, the Bureau excludes 2.1 

million open-end LOCs except reverse mortgages and the 1.1 million records that were dwellingsecured but for a purpose other than purchase, home improvement, or refinance, because such 

records were not required to be reported prior to 2018. In addition, the Bureau converts any 

to collect and report data on 26 of the 27 new data points added under the 2015 HMDA rule for certain 

types of entities and transactions. For more details on the 2015 HMDA rule, see the “Data Point: 2018 

Mortgage Market Activity and Trends,” available at https://www.consumerfinance.gov/dataresearch/research-reports/data-point-2018-mortgage-market-activity-and-trends/ 

6 For more information concerning these modifications and the Bureau’s analyses under the balancing 

test it adopted to protect applicant and borrower privacy while also fulfilling HMDA’s disclosure 

purposes, see 84 FR 649 (January 1, 2019). 

5 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

changes made to data points by the 2015 HMDA rule back to their historical values and does not 

incorporate any of the new HMDA fields into the first article.7 Some of the key findings are:8 

 5,496 institutions reported closed-end records in 2019, down 3 percent from the 5,666 

which reported in 2018. 

 In total, the number of closed-end originations in 2019 increased by 26 percent, from 6.4 

million in 2018 to 8.1 million in 2019. Most of the increase was driven by an increase in 

the number of refinance loans. For example, the number of home-purchase loans 

secured by one-to-four-family properties increased by about 174,000, whereas the 

number of refinance loans nearly doubled from 1.9 million in 2018 to 3.4 million in 2019. 

The number of home improvement loans secured by dwellings declined slightly from 

183,000 in 2018 to 174,000 in 2019. 

 Black borrowers increased their share of home-purchase loans for one-to-four-family, 

owner-occupied, site-built properties in 2019. Approximately 7 percent of such loans 

went to Black borrowers, up from 6.7 percent in 2018. In contrast, 60.3 percent went to 

non-Hispanic White borrowers, down slightly from 62 percent in 2018. The share of 

Asian borrowers declined by 0.2 percentage points whereas that of Hispanic White 

borrowers increased by 0.3 percentage points. The share of home-purchase loans to lowor-moderate-income (LMI) borrowers increased slightly from 28.1 percent in 2018 to 

28.6 percent in 2019. 

 Unlike other racial and ethnic groups, Asian borrowers increased their share of refinance 

loans for a first-lien, one-to-four-family, owner-occupied, site-built properties from 3.7 

percent to 5.4 percent in 2019. In addition, the share of refinance loans for high-income 

borrowers and properties in high-income neighborhoods also increased by 2.1 

percentage points and 5.0 percentage points, respectively. 

 Not adjusting for inflation, the average loan amount for a first-lien, one-to-four-family, 

owner-occupied, site-built home-purchase and refinance loans increased by 4.2 percent 

and 23.4 percent, respectively. For the first time since the Great Recession (2009/2010), 

the average home-purchase loan amount for Hispanic White borrowers surpassed their 

pre-Recession peak level. The average home-purchase loan amounts for Asian, Black, 

7 See https://www.consumerfinance.gov/policy-compliance/guidance/hmda-implementation/ for a list of 

new HMDA fields, as well as additional reference material about recent changes to the HMDA reporting. 

8 For 2019 mortgage lending activities, this Data Point article is based on the analysis of the static 

consolidated application-level 2019 HMDA data file made available concurrently to the public. Analyses 

of the prior years’ data in this Data Point article are based on the updated consolidated application-level 

HMDA data, rather than the static data initially released to the public for such years. Accordingly, the 

results herein for prior years’ HMDA data may differ from those initially released in prior years. 

6 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

and non-Hispanic White borrowers had already surpassed their pre-Recession peaks 

before 2019. 

 The denial rates for a first-lien, one-to-four-family, owner-occupied, site-built homepurchase and refinance loans decreased between 2018 and 2019. The decline was 

particularly large for refinance loans, where the denial rate decreased by 33.8 percent, 

compared with 10 percent decrease for home-purchase loans. 

 Black and Hispanic White borrowers continued to be much more likely to use 

nonconventional loans (insured by Federal Housing Administration (FHA) or a guarantee 

from the Department of Veterans Affairs (VA), the Farm Service Agency (FSA), or the 

Rural Housing Service (RHS)) than other racial and ethnic groups. In addition, the share 

of nonconventional loans for home-purchase increased slightly from 2018 to 2019, putting 

an end to the general downward trend observed since the Great Recession. 

 Nondepository institutions’ (non-DIs’) share of mortgage originations continued an 

upward trend that began back in 2010. In 2019, this group of lenders accounted for 61.6 

percent of first-lien, owner-occupied, site-built home-purchase loans, slightly up from 

61.1 percent in 2018. Non-DIs were also more likely than DIs to (1) originate 

nonconventional loans, (2) originate loans to minority borrowers and low- or moderateincome (LMI) borrowers, as well as for properties in LMI neighborhoods and (3) sell 

originated loans instead of holding them. 

7 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

2. HMDA data coverage of the 

mortgage market 

The HMDA data are the most comprehensive source of publicly available information on the 

U.S. mortgage market, and the only publicly available source of nationwide application-level 

data on mortgage credit. Given that mortgage debt is by far the largest component of household 

debt, the data have been used extensively for research and supervisory work, as well as for 

public policy deliberations related to the mortgage market. 

Although the HMDA data are the most extensive application-level data on residential mortgage 

lending in the U.S., they do not cover the entire mortgage market. Among depository 

institutions (DI), the smallest institutions, institutions without any branches in a metropolitan 

statistical area (MSA), and institutions that are not federally insured or regulated or do not 

make loans insured by a Federal agency or intended for sale to Fannie Mae or Freddie Mac, do 

not have to report HMDA data. The 2015 HMDA rule’s changes to institutional coverage criteria 

for closed-end loans took effect in 2017 and raised the reporting threshold from one covered 

origination to 25 covered originations for DIs.9 This change thus further increased the number 

of exempted DIs. Among nondepository institutions (non-DI), institutions that make few 

mortgage originations and those that operate entirely outside of an MSA do not have to report 

HMDA data.10 

To assess HMDA’s overall coverage of the mortgage market, the Bureau first estimates the 

universe of mortgage lenders and the number of mortgage originations by all lenders regardless 

9 For reporting of open-end LOCs, the 2015 HMDA rule established an institutional coverage threshold of 

at least 100 open-end LOCs in each of the two preceding calendar years. See 80 FR 66128 (Oct. 28, 2015). 

In a rule finalized in August 2017, the Bureau temporarily increased the open-end threshold to 500 openend LOCs for calendar years 2018 and 2019. For example, if an institution was over the 25 closed-end 

loan threshold in both of the two preceding years, but under the 500 open-end LOC threshold in either of 

the two preceding years, it would still have to report closed-end loans but not open-end LOCs. See 82 FR 

43088 (Sep. 13, 2017). 

10 This section describes HMDA coverage applicable at the time the data discussed here were reported. 

For 2019 data, DIs with less than $46 million in assets or less than 25 covered, closed-end originations in 

either of the last two years, and non-DIs with less than 25 covered, closed-end originations in either of the 

last two years were not required to report closed-end data under HMDA. For additional details, see 

Federal Financial Institutions Examination Council’s “A Guide to HMDA Reporting: Getting It Right!” 

available at https://www.ffiec.gov/hmda/guide.htm. 

8 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

 

 

of whether they are HMDA reporters or not.11 The estimate uses data from the HMDA data, the 

Bank/Thrift and Credit Union Call Reports, and other data sources. This analysis focuses solely 

on closed-end mortgages. 

For financial institutions that did not report HMDA data in a given year but reported relevant 

mortgage activity to one of the alternative sources, the Bureau employed several different 

estimation strategies. For example, for credit unions that did not report HMDA data, the agency 

examined their year-to-date closed-end loan origination volumes reported at the end of the year 

to Credit Union Call Reports. In doing so, the Bureau used only the categories of mortgage loans 

under the Credit Union Call Reports that are the same as the transactional coverage 

requirements governing the 2019 HMDA data.12 For banks and thrifts that did not report under 

HMDA, the Call Reports contain information only on the end-of-period balance of the 

mortgages on their books, but not on the origination volumes within the reporting period. For 

those institutions, the Bureau developed a set of econometric models, first estimating the 

relationships between annual originations and the end-of-year balances using HMDA 

reporters.13 These models control for an array of institutional characteristics, such as assets, 

institution type, number of employees, and number of branches in MSAs. The Bureau then 

applied this estimated relation to the characteristics of non-HMDA reporters to estimate their 

closed-end mortgage origination volumes.14 

Based on this analysis, the Bureau estimates that the share of institutions and originations 

covered by HMDA remain largely constant from 2018 to 2019. In 2019, approximately 11,200 

institutions originated at least one closed-end mortgage loan, with a total origination volume of 

about 9.2 million loans. These estimates largely remain unchanged from 2018 when the Bureau 

estimated 11,800 total institutions with an origination volume of 7.3 million loans. 

11 Note for the discussion in this section, the Bureau defines the universe of mortgages in line with the 

transactional coverage criteria under HMDA that is applicable at the time the 2019 HMDA data were 

collected. 

12 For instance, these estimates include mortgage loans regardless of lien status but do not include openend LOCs. 

13 The Bureau assumes the dependent variable (the number of mortgage originations for each institution) 

follows a Poisson distribution, and that the logarithm of its expected value can be modeled by a linear 

combination of unknown parameters. In other words, the Bureau estimated Poisson regressions. 

14 Alternatively, one might compare the number of loans reported under HMDA with the number of loans 

reported in consumer credit files maintained by nationwide consumer reporting agencies (NCRAs). 

However, there are several disadvantages in using NCRA data to estimate the total universe of mortgage 

originations, including (1) a lag between the time when a mortgage is originated and when the 

information on the mortgage tradeline is first reported to the credit bureaus (2) potential duplication and 

transactional coverage issues, and (3) the estimates reported from NCRAs do not allow the breakdown of 

the origination volumes by the origination entities. 

9 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

The 2019 HMDA data contained closed-end data from a total of 5,496 institutions. Although this 

is lower than the 5,666 institutions that reported in 2018, the percentage of total institutions 

that reported under HMDA was similar in each year (49.1 percent in 2019 vs. 48.0 percent in 

2018). In addition, HMDA reporters originated about 8.1 million loans or about 88 percent of 

the estimated total number of closed-end originations in the U.S. In 2018, HMDA reporters 

originated about 6.4 million loans or approximately 88 percent of the estimated number of 

originations.15 

15 Calculations in the text are based on precise data values. Using rounded numbers from the printed 

tables may lead to different values due to rounding error. 

10 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

3. Mortgage applications and 

originations 

In 2019, a total of 5,508 financial institutions—banks, savings associations, credit unions, and 

nondepository mortgage lenders—reported data on 15.1 million applications and 9.3 million 

originations under HMDA. Beginning with data collected in 2018, the reporting of home equity 

lines of credit (HELOCs) became mandatory rather than optional.16 The number of HELOC 

records decreased from 2.3 to 2.1 million between 2018 and 2019, and the number of HELOC 

originations also declined from 1.1 to 1.0 million. Also beginning in 2018, financial institutions 

were required to report a loan purpose other than purchase, home improvement, or refinance. 

The number of records for the purpose other than purchase, home improvement, or refinance 

decreased from 1.4 million in 2018 to 1.3 million in 2019. 

To make the 2019 HMDA data as consistent as possible with historical data, the Bureau excludes 

all HELOCs as well as records for a loan purpose other than purchase, home improvement, or 

refinance. These exclusions reduce the number of HMDA reporters by 12 to 5,496.17 Unless 

specifically noted, the remainder of the article will focus on these 5,496 financial institutions to 

facilitate comparability of HMDA data over time. 

Table 1 presents the number of one-to-four-family properties applications and originations by 

loan purpose (e.g., home purchase, home improvement, refinance) as well as the number of 

multifamily applications and originations dating back to year 2004. The one-to-four family 

originations are first disaggregated by lien status (e.g., first lien, junior lien) and occupancy 

(e.g., owner-occupied, non-owner-occupied). Then, the first-lien, owner-occupied originations 

are further disaggregated by property type (e.g., site-built, manufactured home) and by whether 

it is a conventional loan or not.18 Finally, the site-built, nonconventional originations are 

disaggregated by nonconventional loan types (e.g., FHA-insured, VA-guaranteed, FSA/RHS). 

16 HELOCs are defined as open-end LOCs except those that are reverse mortgages. 

17 The 2015 HMDA rule change that eliminated reporting of unsecured home improvement loans was one 

reporting change the Bureau was unable to make consistent over time. When applicable, results prior to 

2018 include unsecured home improvement loans, while those beginning with 2018 do not. 

18 Manufactured-home lending differs from lending for site-built homes. Furthermore, even among the 

manufactured home loans, chattel-secured lending differs greatly from those that are not chattel secured. 

Chattel-secured lending typically carries higher interest rates and shorter terms to maturity (for pricing 

information on manufactured home loans, see Tables 8 and 9). This Data Point article focuses almost 

entirely on site-built mortgage originations, which constitute most originations (as shown in Table 1). 

11 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

Nonconventional loans are those with mortgage insurance or other guarantees from federal 

government agencies, including the FHA, VA, and the U.S. Department of Agriculture’s 

FSA/RHS. Conventional lending encompasses all other loans, including those held in banks’ 

portfolios, those sold to Government-Sponsored Enterprises (GSEs), such as Fannie Mae and 

Freddie Mac, and those packaged into private-label securities. In general, nonconventional 

loans have higher allowable loan-to-value (LTV) ratios—that is, borrowers provide relatively 

smaller down payments relative to conventional loans. 

The total number of originations reported under 2019 HMDA increased by approximately 2 

million (26 percent), with the increase in refinance loans driving 80 percent of the increase. 

Lenders reported approximately 8.1 million originations in 2019, up from 6.4 million 

originations in 2018. In addition, lenders reported 12.6 million applications, which includes 2.9 

million applications that the lenders closed as incomplete or the applicant withdrew before the 

lender made a decision. 

Refinance applications for one-to-four family properties increased from 3.8 million in 2018 to 

5.9 million in 2019. Refinance originations also nearly doubled, by approximately 1.5 million 

from 2018 to 2019. More detailed information on refinance loans available in the 2019 data 

shows that less than half (41.1 percent) of refinance loans were cash-out refinances. This 

contrasts with 2018 when cash-out refinance loans accounted for 56.3 percent of all refinance 

loans. 

12 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
TABLE 1: APPLICATIONS AND ORIGINATIONS (IN THOUSANDS), SHARE OF ONE-TO-FOUR-FAMILY SI

NONCONVENTIONAL LOAN ORIGINATIONS (PERCENT), AND PRE-APPROVALS AND LOAN PUTHOUSANDS) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

1-4 FAMILY 

Home purchase Applications(1) 9,804 11,685 10,929 7,609 5,060 4,217 3,848 3,650 4,023 4,586 4,679 5,196 5,694 

Originations 6,437 7,391 6,740 4,663 3,139 2,793 2,547 2,430 2,742 3,139 3,248 3,676 4,046 

First lien, owner occupied 4,789 4,964 4,429 3,454 2,628 2,455 2,219 2,073 2,343 2,703 2,815 3,210 3,544 

Site-built, conventional 4,107 4,425 3,912 2,937 1,581 1,089 1,006 999 1,251 1,630 1,741 1,899 2,123 Site-built, nonconventional 553 411 386 394 951 1,302 1,152 1,019 1,033 1,007 1,006 1,235 1,340 

FHA share (%) 74.6 68.6 66.0 65.8 78.9 77.0 77.4 70.9 68.0 62.8 58.3 64.6 64.6 

VA share (%) 21.6 26.7 29.0 27.1 15.2 13.9 15.2 18.2 19.9 24.2 28.3 26.0 26.9 

FSA/RHS share (%) 3.9 4.7 5.0 7.1 5.9 9.0 7.4 10.9 12.0 13.1 13.3 9.4 8.5 

Manufactured, conventional 106 100 101 95 68 43 45 40 44 51 51 56 59 

Manufactured, nonconventional 24 27 30 29 28 21 17 15 14 14 16 20 22 

First lien, non-owner occupied 857 1,053 880 607 412 292 285 314 355 388 378 406 435 

Junior lien, owner occupied 738 1,224 1,269 552 93 44 42 41 43 46 53 58 65 

Junior lien, non-owner occupied 53 150 162 50 6 2 2 1 1 1 2 2 2 

Refinance 

13 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Applications(1) 16,085 15,907 14,046 11,566 7,805 9,983 8,437 7,422 10,526 8,564 4,526 5,957 7,187 

Originations 7,591 7,107 6,091 4,818 3,491 5,772 4,971 4,330 6,668 5,141 2,370 3,234 3,759 

First lien, owner occupied 6,497 5,770 4,469 3,659 2,934 5,301 4,519 3,856 5,930 4,393 2,001 2,847 3,375 

Site-built, conventional 6,115 5,541 4,287 3,407 2,363 4,264 3,837 3,315 4,971 3,634 1,608 2,155 2,529 

Site-built, nonconventional 297 151 110 180 506 979 646 508 917 715 363 661 812 

FHA share (%) 68.3 77.3 87.5 91.5 92.2 83.7 79.3 63.2 61.2 61.2 47.6 59.6 49.5 

VA share (%) 31.4 22.4 12.3 8.3 7.6 15.9 20.3 35.9 37.8 37.6 51.9 40.2 50.1 

FSA/RHS share (%) 0.2 0.3 0.2 0.1 0.2 0.4 0.4 0.9 0.9 1.2 0.5 0.3 0.4 

Manufactured, conventional 77 70 60 56 42 36 25 25 31 32 22 21 20 

Manufactured, nonconventional 7 8 12 16 22 22 10 9 11 12 8 10 14 

First lien, non-owner occupied 618 582 547 474 330 350 359 394 660 673 310 329 329 

Junior lien, owner occupied 464 729 1,036 661 219 115 88 74 73 70 55 55 52 

Junior lien, non-owner occupied 13 25 39 23 9 7 6 5 5 5 4 4 3 

Home improvement Applications 2,200 2,544 2,481 2,218 1,413 832 671 675 779 833 846 926 1,005 

Originations 964 1,096 1,140 958 573 390 342 335 382 425 411 477 536 

MULTIFAMILY(1) 

Applications 61 58 52 54 43 26 26 35 47 51 46 52 50 Originations 48 45 40 41 31 19 19 27 37 40 35 41 40 Total applications 28,151 30,193 27,508 21,448 14,320 15,057 12,981 11,782 15,375 14,034 10,097 12,132 13,937 

14 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Total originations 15,040 15,638 14,011 10,480 7,234 8,974 7,879 7,122 9,828 8,744 6,064 7,428 8,381 

Memo Purchased Loans 5,142 5,868 6,236 4,821 2,935 4,301 3,231 2,939 3,163 2,788 1,800 2,126 2,232 

Requests for preapproval(2) 1,068 1,260 1,175 1,065 735 559 440 429 474 474 496 531 514 

Requests for preapproval that were approved but not acted on 

167 166 189 197 99 61 53 55 64 69 64 63 60 

Requests for preapproval that were denied 171 231 222 235 177 155 117 130 149 123 125 115 115 

NOTE: Components may not sum to totals because of rounding. Applications include those withdrawn and those closed for incomplet

Federal Housing Administration; VA is U.S. Department of Veterans Affairs; FSA is Farm Service Agency; RHS is Rural Housing Servi

(1) A multifamily property consists of five or more units. (2) Consists of all requests for preapproval. Preapprovals are not related to a specific property and thus are distinct from applicationsSOURCE: Here and in subsequent tables and figures, except as noted, Federal Financial Institutions Examination Council, data reporMortgage Disclosure Act (www.ffiec.gov/hmda). 

15 DATA POINT: 2019 MORTGAGE MARKET ACTIVITY AND TREND 
 

FIGURE 1: NUMBER OF HOME-PURCHASE AND REFINANCE MORTGAGE 

ORIGINATIONS, 1994-2019 

The decrease in interest rates was likely a main driver behind the increase in refinance 

applications and loans. Average interest rates declined throughout 2019 and were generally 

lower in 2019 than 2018. The average rate on 30-year fixed rate conventional conforming 

mortgage loans made to prime borrowers started at 4.5 percent at the beginning of 2019 and 

decreased to 3.7 percent by the end of 2019.19 In contrast, interest rates gradually increased 

from 3.9 percent in the beginning of 2018 to 4.5 percent by the end of 2018. The reported 

interest rates in HMDA data follow a consistent pattern. The median interest rate for 30-year 

19 This measure comes from Freddie Mac’s Primary Mortgage Market Survey and is available from the 

Federal Reserve Bank of St. Louis’ Federal Reserve Economic Database (FRED) at 

https://fred.stlouisfed.org/series/MORTGAGE30US. 

16 
 

conventional loans for prime borrowers (with a credit score of at least 620) was 4.1 in 2019 

HMDA data compared to 4.8 in 2018 HMDA data. 

Home-purchase originations for one-to-four family properties increased from 4.3 million in 

2018 to 4.4 million in 2019. This is a continuation of an upward trend dating back to 2011. 

Figure 1 shows that, unlike the number of refinance loans, which has been volatile throughout 

the observed period, the number of home purchase loans has been steadily increasing since 

2011, reaching a similar level in 2019 as in 2007.20 The historical fluctuations in the volume of 

refinance loans compared with a steady increase in home-purchase loans in recent years 

suggests that a decision to refinance is more responsive to the changes in interest rates than a 

decision to purchase a home. 

The volume of home improvement loans reported declined from 183,000 in 2018 to 174,000 in 

2019. As noted above, this measure cannot be constructed consistently over time since all results 

prior to 2018 included unsecured home improvement loans, but the results beginning in 2018 

do not. 

Most one-to-four family home-purchase loans were first liens for owner-occupied properties. In 

2019, there were 3.9 million such originations, representing about 87 percent of home purchase 

loans, which was unchanged from 2018. Although the share of first-lien originations for owneroccupied properties did not change, there was some variation in the size of the increase across 

different loan and property types. For example, among first-lien, owner-occupied, one-to-fourfamily, home-purchase originations, the number of site-built, nonconventional originations 

increased by 5.4 percent between 2018 and 2019. On the other hand, in this same subset of 

home-purchase originations, the number of manufactured, nonconventional originations 

increased by 2.5 percent. 

Similar to home-purchase loans, most one-to-four-family refinance loans were first liens for 

owner-occupied properties. The volume of these refinance loans increased significantly during 

2019. There were 3.1 million first-lien, owner-occupied refinance originations in 2019, nearly 

double the number in 2018. Furthermore, most of first-lien, owner-occupied refinance 

originations were for conventional loans for site-built homes. In fact, the share of conventional 

loans for site-built homes was larger among refinance loans (73.6 percent) than home-purchase 

loans (64.6 percent). 

Among first-lien, home-purchase loans for one-to-four-family, owner-occupied, site-built 

properties, 33.4 percent were nonconventional loans, up slightly from 33 percent in 2018 but 

20 The HMDA data prior to 2004 did not provide lien status for loans, and thus the number of loans prior 

to 2004 in Figure 1 include both first- and junior-lien loans. 

17 
 

down from a peak of approximately 54 percent in 2009. Figure 2 shows that the change in the 

nonconventional share of loans is mostly driven by change in the share of FHA loans. Unlike the 

FHA share of loans, the VA and FSA/RHS shares make up a small and stable proportion of 

nonconventional loans. 

FIGURE 2: NONCONVENTIONAL SHARE OF HOME-PURCHASE MORTGAGE 

ORIGINATIONS, 1994-2019 

In addition to loan applications and originations, the HMDA data also include preapproval 

requests for home-purchase loans. As shown in Table 1, lenders reported approximately 

445,000 preapproval requests, which is down slightly by less than 5 percent from 2018. About 

17 percent of these requests were denied. Approximately 17 percent of them were requests that 

lenders had approved but the applicants did not take any further action. 

Finally, HMDA data include information on loans purchased by reporting institutions during 

the reporting year, although the purchased loans may have been originated before 2019. Table 1 

shows that lenders purchased 2.1 million loans from other institutions in 2019, an 18 percent 

increase from 2018. 

18 
 

4. Mortgage outcomes by 

demographic groups 

The HMDA data are a key resource for policymakers and the public to understand the 

distribution of mortgage credit across demographic groups. Tables 2 through 8 provide 

information on loan shares, product usage, denials, and certain mortgage pricing information 

for groups defined by applicant income, neighborhood income, and applicant race and ethnicity. 

Tables 2 through 7 focus on first-lien home purchase and refinance loans for one-to-four-family, 

owner-occupied, site-built properties, which accounted for approximately 82 percent of all 

HMDA originations excluding purchased loans in 2019. Table 8, in contrast, also includes loans 

for manufactured homes. 

4.1 Distribution of home loans across 

demographic groups 

One of the 2015 HMDA rule changes to historical HMDA data points altered reporting 

requirements for race and ethnicity. Beginning in 2018, mortgage applicants now have the 

option of providing disaggregated information for the Asian, Pacific Islander, and Native 

American race categories and for the Hispanic ethnicity category. Of the total of 17.5 million 

records in the 2019 HMDA data, including open-end LOCs, about 1.6 million records (8.9 

percent) included at least one disaggregated racial or ethnic category. Even though the number 

of records reporting at least one disaggregated racial or ethnic category increased from 1.3 

million (10 percent) in 2018, because of an increase in the total number of records, the share has 

decreased from 2018 to 2019. Asian Indian was the most commonly reported disaggregated race 

at 1.2 percent and Mexican was the most commonly reported disaggregated ethnicity at 2.7 

percent. 

To make the 2019 results consistent with and comparable to results from years prior to 2018, 

this Data Point article aggregates all disaggregated race and ethnicity data to their 

corresponding aggregate category. As an example, if an applicant reported being Chinese, that 

applicant is aggregated into the Asian category. The 2015 HMDA rule also increased the number 

of ethnicities primary applicants and co-applicants can provide from one each to five each. To 

convert the new set of five ethnicity fields for the primary applicant back into one ethnicity field, 

the Bureau uses values from just the first ethnicity data field as in the past years, unless the first 

field contains a missing value. When the first ethnicity data field is missing, the Bureau replaces 

19 
 

that missing value using the remaining four data fields. A similar process is used for coapplicants. The footnotes to Table 2 summarize how applicants were classified into racial and 

ethnic categories.21 

Table 2 presents different groups’ shares of one-to-four-family, owner-occupied, site-built home 

purchase and refinance loans and how these shares have changed over time. Continuing the 

historical trend, the share of home-purchase loans for Black borrowers increased from 2018 to 

2019, whereas those for non-Hispanic White borrowers decreased. The Black borrowers’ share 

of home-purchase loans increased from 6.7 percent in 2018 to 7.0 percent, which was the sixth 

consecutive year of an increase. For non-Hispanic White borrowers, their share of homepurchase loans was 60.3 percent in 2019, down from 62.0 percent in 2018. This drop continues 

a downward trend that began in 2013 when non-Hispanic White borrowers’ share of home 

purchase loans was 70.2 percent. 

The share of refinance loans decreased for all racial groups except Asian borrowers. For 

example, the non-Hispanic White borrowers’ share of refinance loans declined from 63.3 

percent in 2018 to 61.0 percent in 2019. The share for Black and Hispanic White borrowers 

declined more modestly than non-Hispanic White borrowers. The share of refinance loans for 

Black borrowers declined from 6.2 percent to 5.3 percent, while that for Hispanic White 

borrowers declined from 6.8 percent to 6.2 percent. In contrast, the share for Asian borrowers 

increased from 3.7 percent in 2018 to 5.4 percent in 2019. 

21 The application is designated as “joint” if one applicant was reported as White and the other was 

reported as one or more minority races or if the application is designated as White with one Hispanic 

applicant and one non-Hispanic applicant. 

20 
TABLE 2: DISTRIBUTION OF HOME-PURCHASE AND REFINANCE LOANS, BY BORROWER AND NEIGH

CHARACTERISTICS, 2004-2019 (PERCENT EXCEPT AS NOTED) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

A. Home Purchase Borrower race and ethnicity(1) 

Asian 4.8 5.0 4.5 4.5 4.9 5.3 5.5 5.2 5.3 5.7 5.4 5.3 5.5 Black or African American 7.1 7.7 8.7 7.6 6.3 5.7 6.0 5.5 5.1 4.8 5.2 5.5 6.0 Hispanic white 7.6 10.5 11.7 9.0 7.9 8.0 8.1 8.3 7.7 7.3 7.9 8.3 8.8 

Non-Hispanic white 57.1 61.7 61.2 65.4 67.5 67.9 67.6 68.7 70.0 70.2 69.1 68.1 66.4 Other minority(2) 1.4 1.3 1.1 1.0 0.9 0.9 0.9 0.8 0.8 0.7 0.8 0.8 0.8 

Joint 2.3 2.3 2.3 2.5 2.8 2.8 2.7 2.8 2.9 3.1 3.4 3.5 3.6 Missing 19.8 11.5 10.5 10.1 9.6 9.3 9.1 8.6 8.2 8.2 8.3 8.5 8.9 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Borrower income(3) 

Low or moderate 27.7 24.6 23.6 24.6 28.0 36.6 35.4 34.4 33.3 28.5 27.0 27.9 26.2 Middle 26.9 25.7 24.7 25.1 27.0 26.6 25.6 25.2 25.1 25.2 25.6 26.1 26.4 

High 41.4 45.5 46.7 46.9 42.9 34.6 37.3 38.8 40.0 44.7 46.1 44.9 46.4 

Income not used or not applicable 4.0 4.2 5.0 3.4 2.1 2.2 1.7 1.6 1.5 1.6 1.3 1.1 1.0 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Neighborhood income(4) 

Low or moderate 14.5 15.1 15.7 14.4 13.2 12.6 12.1 11.0 12.8 12.7 13.3 13.5 14.1 

Middle 48.7 49.2 49.5 49.6 49.8 50.2 49.5 49.4 43.6 43.7 44.6 45.2 45.8 

High 35.8 34.7 33.7 35.1 35.9 35.8 37.7 39.1 43.2 43.2 41.8 41.0 40.0 

21 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

B. Refinance Borrower race and ethnicity(1) 

Asian 3.5 2.9 3.0 3.1 3.1 4.1 5.2 5.4 5.5 4.7 4.3 5.0 5.5 Black or African American 7.4 8.3 9.6 8.4 6.0 3.5 2.9 3.1 3.3 4.4 5.4 5.0 5.0 Hispanic white 6.2 8.6 10.1 8.7 5.3 3.2 3.0 3.3 3.9 5.0 6.2 6.3 6.2 

Non-Hispanic white 57.2 60.9 59.6 62.7 70.7 74.6 74.3 73.5 72.5 70.5 67.8 67.2 65.2 

Other minority(2) 1.4 1.4 1.3 1.1 0.8 0.6 0.5 0.6 0.6 0.7 0.9 0.8 0.9 

Joint 2.1 2.1 1.9 2.0 2.2 2.6 2.7 2.8 3.1 3.1 3.2 3.3 3.4 

Missing 22.1 15.7 14.6 14.1 11.9 11.4 11.4 11.3 11.1 11.6 12.2 12.4 13.8 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Borrower income(3) 

Low or moderate 26.2 25.5 24.7 23.3 23.4 19.6 18.9 19.2 19.6 21.1 22.1 19.0 16.9 

Middle 26.3 26.8 26.1 25.5 25.4 22.4 22.5 21.3 21.8 21.7 21.9 21.0 20.3 

High 38.8 40.8 43.7 46.0 44.6 45.6 49.5 48.1 47.6 46.3 44.9 45.2 47.5 

Income not used or not applicable 8.7 6.9 5.5 5.2 6.6 12.4 9.1 11.4 10.9 11.0 11.1 14.8 15.3 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Neighborhood income(4) 

Low or moderate 15.3 16.5 17.9 16.1 11.9 7.8 7.2 7.4 10.1 12.1 13.3 12.3 12.0 

Middle 50.0 51.3 52.0 52.2 51.9 47.5 46.1 46.1 41.9 43.7 45.3 43.8 43.4 

High 33.9 31.6 29.4 31.0 35.2 43.5 46.0 46.0 47.6 43.9 41.3 43.7 44.4 

All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

22 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Memo Number of home-purchase loans (thousands) 4,660 4,836 4,298 3,331 2,533 2,391 2,157 2,018 2,284 2,638 2,747 3,134 3,463 

Number of refinance loans (thousands) 6,412 5,692 4,397 3,588 2,869 5,243 4,483 3,823 5,888 4,349 1,971 2,816 3,341 

NOTE: First-lien mortgages for one- to four-family, owner-occupied, site-built homes. Rows may not sum to 100 because of rounding 

by neighborhood income, because property location is missing. (1) Applications are placed in one category for race and ethnicity. The application is designated as joint if one applicant was reported awas reported as one or more minority races or if the application is designated as white with one Hispanic applicant and one non-Hispaare two applicants and each reports a different minority race, the application is designated as two or more minority races. If an applicanand one is white, that applicant is categorized under the minority race. Otherwise, the applicant is categorized under the first race repoto applications in which the race of the applicant(s) has not been reported or is not applicable or the application is categorized as white been reported. (2) Consists of applications by American Indians or Alaska Natives, Native Hawaiians or other Pacific Islanders, and borrowers reportinminority races. (3) The categories for the borrower-income group are as follows: Low-or moderate-income (or LMI) borrowers have income that is les

estimated current area median family income (AMFI), middle-income borrowers have income that is at least 80 percent and less than 1

and high-income borrowers have income that is at least 120 percent of AMFI. (4) The categories for the neighborhood-income group are based on the ratio of census-tract median family income to area median fam

2006-10 American Community Survey data for 2012-2019 and from the 2000 census for 2004-11, and the three categories have the sa

borrower-income groups (see note 3). 

23 
 

The shares of home purchase and refinance loans exhibit opposite trends for low- or moderateincome (LMI) borrowers compared with high-income borrowers.22 The LMI borrower share of 

home-purchase loans increased from 28.1 percent to 28.6 percent, whereas high-income 

borrowers’ share decreased from 44.3 percent to 43.1 percent. The LMI borrower share of 

refinance loans decreased from 30.0 percent to 23.8 percent, while high-income borrowers’ share 

increased from 41.0 percent to 43.1 percent. 

The trends in shares of LMI and high-income neighborhoods mirror those of the borrowers for 

refinance loans but not for home purchase loans.23 The LMI neighborhoods’ share of refinance 

loans decreased slightly, whereas high-income neighborhoods’ share of refinance loans increased. 

On the other hand, the share of home purchase loans in LMI neighborhoods declined slightly, 

while the share in high-income neighborhoods increased slightly between 2018 and 2019. 

Even though the share of refinance loans for most racial/ethnic groups, LMI borrowers, and LMI 

neighborhoods has decreased, because of the increase in the total number of refinance loans, the 

number of refinance loans has increased for all groups between 2018 and 2019.24 For example, the 

share of refinance loans for Blacks decreased from 6.2 percent to 5.3 percent but the number 

increased by about 63,000. The increase in the number of refinance loans was especially large for 

non-Hispanic White borrowers, Asian borrowers, high-income borrowers, and high-income 

neighborhoods. 

In examining historical trends based on a borrower or neighborhood income, changes in 

underlying estimates may impact income categories. First, in 2012 and 2017, the Federal Financial 

Institutions Examination Council (FFIEC) revised the census-tract median family income 

22 In accordance with the definitions used by the federal bank supervisory agencies to enforce the 

Community Reinvestment Act, LMI borrowers are defined as those with incomes less than 80 percent of the 

estimated current area median family income (AMFI). Middle-income borrowers have incomes of at least 

80 percent and less than 120 percent of AMFI, and high-income borrowers have incomes of at least 120 

percent of AMFI. AMFI is estimated based on the incomes of residents of the metropolitan area or 

nonmetropolitan portion of the state in which the loan-securing property is located. For AMFI estimates, 

see Federal Financial Institutions Examination Council (2019), “FFIEC Median Family Income Report,” 

available at https://www.ffiec.gov/Medianincome.htm. 23 Definitions for LMI, middle-income, and high-income neighborhoods are identical to those for LMI, 

middle-income, and high-income borrowers, but are based on the ratio of census-tract median family 

income to AMFI measured from the census data. 

24 The bottom of Table 2 provides the total loan counts for each year, and thus the number of loans to a 

given group in a given year can be easily computed. For example, the number of home-purchase loans to 

Asians in 2019 was approximately 213,000, calculated by multiplying 3.7 million loans by 5.7 percent. 

24 
 

 

estimates that accompany the public HMDA data (and that are used for this Data Point article).25 

Therefore, in Table 2 and all subsequent tables that use neighborhood income categories, the 

underlying neighborhood income data used to generate the results for 2017 and later are different 

from the data used for 2016 and earlier. Similarly, neighborhood income data used for the results 

from 2012 through 2016 are different than those used from 2011 and earlier. Second, the tract 

demographic measures for 2017 and later are based on the 2015 American Community Survey 

(ACS) five-year estimates, whereas the 2012–2016 data relied on the 2010 ACS five-year 

estimates, and the 2004–2011 data relied on the 2000 Census data. Lastly, the Office of 

Management and Budget (OMB) updates metropolitan area delineations over time. In short, 

income and demographic data can be compared across ACS datasets, and also between ACS and 

the 2000 Census data.26 However, given the changes in geographic delineations over time, some 

caution should be exercised in comparing relative income measurements over time. 

25 For details on the changes of census information used in this Data Point article, see FFIEC’s “Changes for 

Current Census File,” at https://www.ffiec.gov/census/htm/2015CensusInfoSheet.htm 

26 See https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html for more details. 

25 
 

4.2 Average loan size by demographic group 

The average size of loan amount differs substantially by race and ethnicity. Table 3 shows the 

average size of home purchase and refinance loans for different groups over time.27 In 2019, 

Asian borrowers continued to take out loans with the largest loan amount, averaging 

approximately $412,000 for home purchases and $450,000 for refinance loans. On the other 

hand, Black borrowers continued to take out loans with the smallest loan amount, averaging 

approximately $243,000 for home purchases and $250,000 for refinance loans. 

The average home-purchase loan amounts have followed historical trends in home prices, rising 

during the mid-2000s, falling sharply through 2008 and 2009, and then beginning to rise again 

since about 2010.28 The average home-purchase loan amounts returned to pre-crisis levels (in 

nominal terms) by 2014 for Asians and Blacks, and by 2013 for non-Hispanic Whites.29 Hispanic 

White borrowers were the last racial/ethnic group to have home-purchase loan amount surpass 

the pre-crisis level in 2019. The average value of home-purchase loans to Hispanic White 

borrowers was $249,000 in 2019, which surpasses the pre-Recession peak of $238,000 in 2006. 

The average loan amount for refinancing has risen since 2013 and increased significantly more 

than home-purchase loans between 2018 and 2019. The year-over-year increase in the average 

loan amount for refinancing was 23.4 percent compared with 4.2 percent for home-purchase 

loans. The largest increase in the average refinance loan amount occurred for LMI borrowers 

and LMI neighborhoods. 

27 All dollar amounts are reported in nominal terms. 

28 The Federal Housing Finance Agency’s (FHFA’s) quarterly Purchase-Only House Price Index 

(seasonally adjusted) increased each quarter during 2019 and was up 5.1 percent for the year. The housing 

price increases seen at the national level varied considerably across geography ranging from a slight 3 

percent increase in North Dakota to 12 percent increases in Idaho (seasonally adjusted, year-over-year 

comparison). All of these data are available from FHFA at 

https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx. 29 Beginning in 2018, HMDA reporters were required to report the loan amount to the dollar instead of 

rounded to the thousands, which might affect comparability of averages over time. 

26 
TABLE 3: AVERAGE VALUE OF HOME-PURCHASE AND REFINANCE LOANS, BY BORROWER AND NE

CHARACTERISTICS, 2004-2019 (THOUSANDS OF DOLLARS, NOMINAL, EXCEPT AS NOTED

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

A. Home Purchase Borrower race and ethnicity(1) 

Asian 280 316 326 334 299 276 293 291 304 328 344 360 373 

Black or African American 166 183 197 197 184 172 174 174 179 193 199 209 217 

Hispanic white 189 224 238 220 186 168 168 168 176 190 198 209 220 

Non-Hispanic white 193 211 216 222 209 195 204 204 213 226 231 239 246 

Other minority(2) 206 240 257 245 216 196 201 198 206 219 229 241 249 

Joint 233 255 261 269 255 248 263 261 274 289 293 302 311 

Missing 216 248 261 280 265 242 256 262 279 298 293 303 308 

Borrower income(3) 

Low or moderate 114 116 117 124 128 129 128 125 131 132 132 141 146 

Middle 165 170 170 176 182 187 189 184 192 194 193 204 209 

High 281 306 313 317 298 291 303 302 313 323 328 341 345 

Income not used or not applicable 208 235 254 257 211 189 204 221 231 258 275 292 312 

Neighborhood income(4) 

Low or moderate 159 180 189 188 175 160 164 163 158 171 178 188 199 

Middle 172 190 197 196 186 174 177 173 178 191 196 206 216 

High 258 284 294 301 277 257 270 271 282 300 306 316 324 

Memo 

27 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

All home-purchase loans 201 221 228 232 217 202 210 210 221 235 240 249 257 

Conventional jumbo loans (percent of originations)(5) 11.2 12.7 9.4 6.8 2.3 1.3 1.7 2.2 3.0 4.0 4.8 5.3 5.2 

Conventional jumbo loans (percent of loaned dollars)(5) 29.4 32.5 26.8 21.8 10.1 6.2 7.5 9.5 12.0 14.6 16.5 17.3 16.9 

B. Refinance Borrower race and ethnicity(1) 

Asian 274 325 370 368 321 298 313 309 308 304 341 363 368 

Black or African American 151 180 199 192 173 184 180 174 181 171 174 199 212 

Hispanic white 178 219 252 244 193 190 191 183 190 180 190 214 228 

Non-Hispanic white 180 205 221 222 205 209 210 208 212 206 216 239 251 

Other minority(2) 190 229 269 258 211 217 218 207 213 201 213 240 252 

Joint 210 246 265 262 243 247 254 249 254 249 266 292 304 

Missing 194 226 246 250 242 243 248 253 253 244 245 268 277 

Borrower income(3) 

Low or moderate 114 124 124 126 129 138 133 128 135 128 123 136 143 

Middle 162 181 183 181 180 185 180 174 182 171 174 193 202 

High 256 294 320 312 276 268 274 281 277 276 301 324 330 

Income not used or not applicable 150 178 240 236 192 203 202 185 211 193 198 229 243 

Neighborhood income(4) 

Low or moderate 142 169 188 185 164 173 173 167 163 153 157 182 196 

Middle 158 184 201 198 182 184 182 175 181 173 180 201 214 

High 245 282 313 311 272 259 265 269 269 270 290 311 321 

28 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Memo All refinance loans 185 212 232 231 212 216 220 218 221 213 222 247 259 

Conventional jumbo loans (percent of originations)(5) 9.2 11.4 10.2 7.5 2.0 0.9 1.6 2.4 2.2 3.0 4.2 4.9 4.6 

Conventional jumbo loans (percent of loaned dollars)(5) 25.8 29.6 28.3 23.0 9.0 4.1 6.9 10.7 9.2 12.7 16.5 16.8 15.7 

NOTE: First-lien mortgages for one- to four-family, owner-occupied, site-built homes. (1) See table 2, note 1. (2) See table 2, note 2. (3) See table 2, note 3. (4) See table 2, note 4. (5) Fraction of loans that are conventional and have loan amounts in excess of the single-family conforming loan-size limits for eligithe government-sponsored enterprises. 

29 
 

 

4.3 Jumbo lending 

A loan qualifies as jumbo if the loan amount is above the GSEs’ conforming loan-size limit for a 

single-family home for that year and location. The conforming loan-size limit was mostly 

uniform across the nation prior to 2008. The limits in Alaska, Hawaii, the U.S. Virgin Islands, 

and Guam were 50 percent higher than in the nation at large. For the years 2008 and thereafter, 

designated higher-cost areas had elevated limits. For 2019, the conforming loan-size limit was 

$484,350 with the maximum limit of $726,525 for higher-cost areas. 

As shown in Table 3, conventional jumbo loans—those with loan amounts greater than the 

GSEs’ conforming loan limits and with no other government guarantee—made up 4.8 percent of 

all first-lien home-purchase loans for owner-occupied, one-to-four-family, site-built homes in 

2019, a slight decrease from 2018.30 Among refinance loans, the share of conventional jumbo 

loans increased to 4.8 percent in 2019 from 4.0 percent in 2018. Because of their larger size, 

conventional jumbo loans made up a correspondingly larger share of the dollar volume of 

mortgages, accounting for 15.5 percent of home-purchase loans and 16.3 percent of refinance 

loans in 2019. 

30 Beginning with 2018 data, two main issues with pre-2018 jumbo loans have been resolved. First, 

conforming loan-size limits increase with the number of units that make up the property, but prior to 

2018 the HMDA data did not have information on the number of units. Therefore, some loans could have 

been misclassified as jumbo despite being eligible for purchase by a GSE. This is not an issue for data 

from 2018 and later, since institutions reported the exact number of property units. A second issue prior 

to 2018 was that HMDA’s implementing rules required lenders to report the loan amounts rounded to the 

nearest thousands. However, the conforming loan limits published by FHFA may be set in hundreds of 

dollars. Prior to 2018, FHFA conforming loan limits were rounded to the nearest thousands to match with 

the HMDA reporting requirement. This is not an issue for 2018 and later years, since loan amount was 

reported to the dollar (Publicly released loan amounts are rounded to the mid-point of ten-thousanddollar ranges to protect applicant and borrower privacy). Moreover, to make the identification of jumbo 

loans easier, the Bureau has included a conforming loan flag in the release of the public HMDA Aggregate 

LAR since 2018. 

30 
 

4.4 Variation across demographic groups in 

nonconventional loan use 

Historically, nonconventional loans (FHA, VA, RHS, and FSA) provide access to credit to those 

who may otherwise have had limited access to mortgage credit. One advantage of 

nonconventional loans is the relatively low down-payment requirement of as little as 3.5 percent 

for FHA and VA lending programs, which serve the needs of borrowers who have few assets to 

meet down-payment and closing-cost requirements. FHA-insured and VA-guaranteed programs 

also provide credit access to borrowers who have low credit scores or high debt-to-income (DTI) 

ratios and cannot obtain conventional loans.31 Table 4 shows the share of nonconventional home 

purchase and refinance loans by race/ethnicity, borrower’s income, and neighborhood income 

groups. 

Black and Hispanic White borrowers were more likely than other racial and ethnic groups to 

take out nonconventional home-purchase loans.32 In 2019, among those obtaining a first-lien, 

owner-occupied, site-built, one-to-four-family home purchase mortgage, 60.6 percent of Blacks 

and 48.8 percent of Hispanic Whites took out a nonconventional loan, whereas 29.7 percent of 

non-Hispanic Whites and just 12.4 percent of Asians did so. 

LMI borrowers and loans for properties in LMI neighborhoods were also more likely to use 

nonconventional home-purchase loans. About 42 percent of both LMI home-purchase 

borrowers and of applicants borrowing to purchase homes in LMI neighborhoods used 

nonconventional loans, compared with 23.5 percent of high-income borrowers and 24.6 percent 

of borrowers purchasing homes in high-income neighborhoods. 

The use of nonconventional loans for home-purchase has declined since 2009 but remained 

largely unchanged for all racial/ethnic groups from 2018 to 2019, except for Asian borrowers. 

The share of Asian borrowers using nonconventional home-purchase loans increased by 5.6 

percent between 2018 and 2019. Despite the increase, the share of Asian borrowers using 

31 Sections 6.4.2 and 6.6 of last year’s second CFPB HMDA Data Point article explores this in more detail. 

See “Introducing New and Revised Data Points in HMDA: Initial Observation from New and Revised Data 

Points in 2018 HMDA,” available at https://files.consumerfinance.gov/f/documents/cfpb_new-reviseddata-points-in-hmda_report.pdf. 32 Findings of the Federal Reserve Board’s Survey of Consumer Finances for 2017 indicate that income, 

liquid asset levels, and financial wealth holdings for minorities and lower-income groups are substantially 

smaller than they are for non-Hispanic White borrowers or higher-income populations, and the longstanding and substantial wealth disparities between families of different racial and ethnic groups have 

changed little in the past few years. See Board of Governors of the Federal Reserve System, “Recent 

Trends in Wealth-Holding by Race and Ethnicity: Evidence from the Survey of Consumer Finances,” 

available at https://www.federalreserve.gov/econres/notes/feds-notes/recent-trends-in-wealth-holdingby-race-and-ethnicity-evidence-from-the-survey-of-consumer-finances-20170927.htm. 

31 
 

 

nonconventional home-purchase loans remained below its peak of 26.6 percent in 2010 and far 

below other racial/ethnic groups. 

The shares of nonconventional home-purchase loans across borrower and neighborhood 

incomes mostly increased between 2018 and 2019. The share of nonconventional homepurchase loans largely remained unchanged for LMI borrowers, whereas the share for middleand high-income borrowers increased slightly between 2018 and 2019. 

As was the case for home-purchase loans, Black borrowers and lower-income borrowers were 

each more likely than borrowers in other groups to refinance through a nonconventional loan. 

However, the differences were not as stark as for home-purchase loans. Overall, the share of 

borrowers using nonconventional loans for refinancing was lower than that for home purchases. 

The share of borrowers using nonconventional loans for refinancing was at its lowest in 2006, 

increased substantially between 2006 and 2009, and has been fluctuating ever since. For 

example, the share of Black borrowers using nonconventional loans for refinancing has 

fluctuated between its lowest in 2013 at 37.1 percent and its highest in 2016 at 53.0 percent. 

Similarly, the share for LMI borrowers has fluctuated between 9.3 percent in 2012 and 32.3 

percent in 2019.33 

33 The reported nonconventional share of refinance loans likely underestimates the actual share for the 

groups categorized by borrower income because, for most nonconventional refinance loans, income was 

not reported. Thus, when income was reported on a refinance loan, the loan is likely to be conventional. 

32 
TABLE 4: NONCONVENTIONAL SHARE OF HOME-PURCHASE AND REFINANCE LOANS, BY BORROW

NEIGHBORHOOD CHARACTERISTICS, 2004-2019 (PERCENT) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

A. Home Purchase Borrower race and ethnicity(1) 

Asian 2.9 1.8 2.1 2.6 13.4 26.1 26.6 25.8 21.9 16.1 14.7 16.6 15

Black or African American 21.7 14.3 13.6 21.7 64.1 82.0 82.9 80.3 77.2 70.8 68.0 70.2 68

Hispanic white 13.7 7.5 7.0 12.4 51.4 75.4 77.0 74.1 70.7 63.1 59.6 62.7 59

Non-Hispanic white 11.1 8.9 9.5 11.5 35.4 52.0 50.3 47.4 42.2 35.5 33.4 36.0 35

Other minority(2) 14.0 9.3 9.4 14.8 48.4 67.6 68.8 65.9 62.2 55.5 54.0 55.3 54

Joint 16.9 12.8 14.4 17.2 46.4 59.4 56.3 53.6 48.9 42.1 41.3 43.8 43

Missing 11.3 5.1 5.7 8.8 32.7 50.6 49.4 45.9 39.4 31.9 32.2 34.9 34

Borrower income(3) 

Low or moderate 20.3 15.2 14.9 16.0 46.1 65.3 66.6 64.5 59.7 52.5 50.3 53.4 51

Middle 14.3 11.0 12.6 16.7 46.1 60.4 59.3 57.0 51.5 45.6 44.8 47.7 47

High 5.3 3.9 4.9 7.5 26.7 38.5 37.2 34.4 29.5 25.1 24.2 26.3 26

Neighborhood income(4) 

Low or moderate 15.8 9.7 9.6 13.8 45.4 64.3 65.0 61.2 57.9 49.9 48.1 50.4 48Middle 14.1 10.2 10.8 14.2 42.7 59.8 59.4 56.9 52.1 44.7 43.1 45.6 44High 7.1 5.4 6.1 7.6 27.4 43.4 42.0 39.5 34.6 28.2 26.1 29.0 28

Memo: All borrowers 11.9 8.5 9.0 11.8 37.6 54.4 53.4 50.5 45.2 38.2 36.6 39.4 38

33 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

B. Refinance Borrower race and ethnicity(1) 

Asian 1.2 0.7 0.6 1.0 4.6 5.7 4.7 4.3 5.9 6.7 6.8 9.8 8

Black or African American 11.1 5.8 4.4 10.2 39.2 53.8 42.0 37.8 38.6 37.1 39.1 49.4 53Hispanic white 5.6 2.6 1.9 3.9 20.5 36.2 28.2 22.9 26.9 25.8 21.2 32.1 30

Non-Hispanic white 4.0 2.4 2.6 4.9 15.9 16.8 13.6 12.2 14.2 14.8 16.3 21.0 21

Other minority(2) 5.5 3.4 2.4 4.9 20.0 28.3 23.3 21.9 25.5 24.9 25.0 32.6 36

Joint 7.5 3.7 3.4 6.2 19.5 21.1 16.6 16.3 20.1 20.5 25.5 28.0 29

Missing 4.2 1.9 1.7 4.1 18.7 19.0 12.5 13.6 16.5 16.7 21.5 25.5 27

Borrower income(3) 

Low or moderate 2.3 1.6 2.9 5.7 18.3 16.6 14.1 11.5 9.3 9.3 13.0 16.5 18Middle 1.7 1.3 2.7 6.2 19.6 13.2 12.3 10.9 8.9 9.5 13.2 14.8 15High 0.8 0.6 1.1 2.7 10.6 7.2 6.8 6.3 5.5 6.1 8.8 9.2 9

Neighborhood income(4) 

Low or moderate 5.9 3.2 2.9 6.3 24.6 31.2 23.1 19.7 22.2 22.1 22.4 29.5 30

Middle 5.2 3.0 2.9 5.8 20.2 22.3 17.5 16.1 18.4 19.0 20.9 26.8 28

High 2.9 1.7 1.6 3.0 11.3 12.1 10.0 9.3 11.7 12.4 14.5 18.5 19

Memo: All borrowers 4.6 2.6 2.5 5.0 17.6 18.7 14.4 13.3 15.6 16.4 18.4 23.5 24

NOTE: First-lien mortgages for one- to four-family, owner-occupied, site-built homes. Excludes applications where no credit decis

Nonconventional loans are those insured by the Federal Housing Administration or backed by guarantees from the U.S. Departmenthe Farm Service Agency, or the Rural Housing Service. (1) See table 2, note 1. 

34 
(2) See table 2, note 2. 

(3) See table 2, note 3. (4) See table 2, note 4. 

35 
 

 

4.5 Denial rates and reasons 

As in past years, Black and Hispanic White borrowers had notably higher denial rates in 2019 

than non-Hispanic White and Asian borrowers. For example, the denial rates for conventional 

home-purchase loans were 16.0 percent for Black borrowers and 10.8 percent for Hispanic 

White borrowers (Table 5). In contrast, denial rates for such loans were 8.6 percent for Asian 

borrowers and 6.1 percent for non-Hispanic White borrowers. 

Differences in denial rates and in the incidence of higher-priced lending (the topic of the next 

subsection) among racial and ethnic groups may stem, at least in part, from factors related to 

credit risk.34 Some of those factors—such as credit history (including credit score), ratio of total 

monthly DTI ratio, and combined loan-to-value (CLTV) ratio—were available for the second 

consecutive year in the 2019 HMDA data.35 

Denial rates for home-purchase applications were generally lower in 2019 compared to 2018.36 

The overall denial rate on applications for conventional and nonconventional home-purchase 

loans was 8.9 percent in 2019, 10 percent lower than in 2018. The denial rate for each 

racial/ethnic group also declined from 2018 to 2019. These declines in 2019 continued a general 

trend since the Great Recession of declining denial rates for home-purchase mortgages. 

Although denial rates on home-purchase applications declined from 2018 to 2019, the rate of 

decline varied by racial/ethnic group and types of loans. For example, for conventional and 

nonconventional applications combined, denial rates for non-Hispanic Whites declined from 7.9 

percent in 2018 to 7.0 percent in 2019 (11 percent decline) compared to a smaller decline for 

Blacks from 17.4 percent to 15.9 percent (8 percent). As a second example, the denial rate for all 

applications for nonconventional home-purchase loans decreased by 11 percent, while that for 

conventional loans decreased by 9 percent. 

34 HMDA data are regularly used in fair lending examination and enforcement processes. When 

examiners for the federal banking agencies evaluate an institution’s fair lending risk, they analyze HMDA 

price data, loan application outcomes, and explanatory factors, in conjunction with other information and 

risk factors, which can be drawn directly from loan files or electronic records maintained by lenders, in 

accordance with the Interagency Fair Lending Examination Procedures (available at 

https://www.ffiec.gov/PDF/fairlend.pdf). 

35 To protect applicant and borrower privacy, credit score is excluded from the 2019 application-level 

HMDA data made available to the public, and DTI is binned into ranges. 

36 Denial rates are calculated as the number of denied loan applications divided by the total number of 

applications, excluding withdrawn applications and application files closed for incompleteness. 

36 
Consistent with home-purchase loans, denial rates on refinance applications also decreased 

between 2018 and 2019 but at a much faster rate. Denial rates on refinance loans applications 

for conventional and nonconventional combined decreased by 34 percent, from 29.0 percent in 

2018 to 19.2 percent in 2019. Furthermore, denial rates for all refinance loan types decreased 

between 2018 and 2019 for all racial and ethnic groups with the largest change being a 42-

percent decline for Asian applicants for conventional loans. Overall, refinance applications were 

denied at about twice the rate of home-purchase applications. 

37 
TABLE 5: HOME-PURCHASE AND REFINANCE LOAN DENIAL RATES, BY LOAN TYPE AND BORROW

ETHNICITY, 2004-2019 (PERCENT) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

A. Home Purchase Conventional and nonconventional(1) 

All applicants 14.4 16.0 18.0 18.7 18.0 15.5 15.6 15.8 14.9 14.4 13.3 12.1 11

Asian 13.7 15.9 16.9 17.5 19.2 16.3 15.9 16.5 15.8 15.3 14.1 12.7 11

Black or African American 23.6 26.5 30.3 33.5 30.6 25.5 24.9 26.0 26.0 25.5 23.0 20.8 19

Hispanic white 18.3 21.1 25.1 29.5 28.3 22.2 21.8 21.1 20.2 20.5 18.4 16.2 15

Non-Hispanic white 11.1 12.2 12.9 13.3 14.0 12.8 13.0 13.1 12.5 12.0 11.1 10.0 9

Other minority(2) 19.4 20.8 24.0 26.7 25.5 21.2 22.0 20.9 20.8 21.2 19.0 17.2 16

Conventional only 

All applicants 14.6 16.3 18.5 19.0 18.3 15.8 15.2 15.1 13.6 12.9 11.9 10.8 10Asian 13.7 16.0 17.1 17.5 19.1 15.8 14.9 15.5 14.4 14.2 13.3 11.9 10

Black or African American 25.0 27.8 31.9 35.7 37.6 35.8 33.7 33.2 32.0 28.5 25.1 23.3 22

Hispanic white 18.6 21.4 25.7 30.5 32.5 26.9 24.9 24.2 22.4 21.5 18.9 17.2 15

Non-Hispanic white 11.2 12.3 13.2 13.3 14.1 13.3 12.9 12.7 11.6 10.8 9.9 9.1 8

Other minority(2) 19.7 21.2 24.8 27.8 29.0 25.9 28.1 24.6 23.6 22.5 20.2 18.2 16

Nonconventional only(1) 

All applicants 13.3 12.5 12.1 16.2 17.4 15.3 16.0 16.5 16.3 16.8 15.8 13.9 13

Asian 12.6 11.6 10.6 15.5 20.2 17.7 18.7 19.3 20.2 20.6 18.9 16.2 14

Black or African American 17.7 16.8 16.2 22.8 25.3 22.6 22.7 23.9 24.0 24.1 21.9 19.7 18

Hispanic white 16.3 17.2 15.7 20.5 23.1 20.4 20.7 19.9 19.3 19.9 18.0 15.6 14

38 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Non-Hispanic white 10.7 10.2 10.0 13.1 13.9 12.5 13.0 13.6 13.7 14.1 13.4 11.7 11

Other minority(2) 16.8 16.3 15.2 18.6 20.9 18.7 18.7 18.8 18.9 20.1 17.9 16.2 16

B. Refinance Conventional and nonconventional(1) 

All applicants 29.5 32.6 35.4 39.6 37.7 24.0 23.3 23.8 19.9 23.3 31.0 27.2 29

Asian 18.8 23.5 27.5 32.6 32.5 21.4 19.5 20.1 17.3 21.0 28.1 23.8 25

Black or African American 39.9 42.2 44.1 52.0 56.0 42.2 41.7 40.0 32.8 35.0 45.8 43.1 45

Hispanic white 28.7 30.1 33.2 43.0 49.1 36.4 33.4 33.2 27.5 29.6 36.7 32.5 33

Non-Hispanic white 24.1 26.9 30.1 33.7 32.2 20.7 20.6 21.3 17.8 20.5 27.5 24.1 26

Other minority(2) 33.7 35.5 40.6 52.0 57.4 37.3 35.4 34.4 30.0 32.1 41.6 40.1 44

Conventional only 

All applicants 30.1 32.9 35.6 39.9 37.0 22.1 21.2 22.3 19.4 22.5 29.6 26.4 28

Asian 18.8 23.5 27.5 32.5 31.5 20.2 18.5 19.4 17.0 20.5 27.2 23.2 23

Black or African American 41.7 43.0 44.7 53.3 60.9 48.6 41.4 40.6 34.8 36.0 47.0 47.7 52

Hispanic white 29.3 30.2 33.3 43.2 50.2 38.9 33.6 33.5 28.9 30.6 37.3 34.8 35

Non-Hispanic white 24.6 27.1 30.4 33.9 31.5 19.1 18.9 20.1 17.4 19.9 26.2 23.2 25

Other minority(2) 34.5 35.7 40.9 52.6 59.4 38.4 34.8 34.4 31.1 32.6 40.9 41.2 45

Nonconventional only(1) 

All applicants 15.0 20.1 21.9 31.6 40.9 31.1 33.3 32.2 22.2 26.7 36.5 29.6 33

Asian 15.0 20.0 22.0 38.5 48.9 37.2 34.2 32.7 22.2 26.9 37.5 28.8 36

Black or African American 17.5 23.6 24.6 33.7 43.5 35.1 42.2 39.1 29.5 33.1 43.9 37.5 38

Hispanic white 15.7 23.6 26.3 34.6 43.4 31.4 33.0 32.3 23.3 26.6 34.5 27.1 30

39 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Non-Hispanic white 12.0 17.6 19.7 28.3 36.1 27.4 29.3 29.0 19.7 23.8 33.7 26.9 31Other minority(2) 15.2 25.8 22.2 34.8 45.4 34.1 37.0 34.4 26.6 30.6 43.8 37.6 41

NOTE: First-lien mortgages for one- to four-family, owner-occupied, site-built homes. For a description of how borrowers are cate

ethnicity, see table 2, note 1. (1) Nonconventional loans are those insured by the Federal Housing Administration or backed by guarantees from the U.S. Depa

Affairs, the Farm Service Agency, or the Rural Housing Service. (2) See table 2, note 2. 

40 
 

 

 

Variations in denial rates over time reflect not only changes in credit standards, but also 

changes in the demand for credit and in the composition of borrowers applying for 

mortgages. For example, the denial rate on applications for conventional home-purchase 

loans was lower in 2019 (7.6 percent) than the years leading up to the Great Recession (19 

percent), even though most measures of credit availability suggest that credit standards were 

tighter in 2019.37 This may stem from a relatively large drop in applications from riskier 

applicants or in applications that are risky for other reasons, such as documentation or 

collateral risk. 

Historically, lenders could, but were not required to, report up to three reasons out of nine 

potential reasons for denying a mortgage application. The 2015 HMDA rule (1) changed 

reporting of denial reasons from optional to mandatory, (2) required reporting of up to four 

denial reasons, and (3) added a mandatory free-form text field to fill in when reporting a 

denial for “other” reasons. Table 6 presents all of the reasons reported by a lender for denying 

an application.38 

The four most frequently cited denial reasons for both home-purchase and refinance loans 

were the applicant’s credit history, DTI ratio, collateral, and credit application incomplete. 

The DTI ratio was overwhelmingly the most common reason for denial of home-purchase 

applications. For refinance applications that were denied, credit history was cited with a 

frequency similar to DTI ratio. In addition, for denied home-purchase applications, 

prospective lenders were more likely to cite collateral as the denial reason on conventional 

than on nonconventional applications. On the other hand, for denied refinance 

applications, prospective lenders were much more likely to cite DTI ratio as a denial 

reason on conventional than on nonconventional applications. 

37 Both the Mortgage Bankers Association (MBA) and the Urban Institute publish indexes of mortgage 

credit availability. For the most recent index, see https://www.urban.org/policy-centers/housingfinance-policy-center/projects/housing-credit-availability-index. Information about MBA’s mortgage 

credit availability index is available at https://www.mba.org/news-research-and-resources/researchand-economics/single-family-research/mortgage-credit-availability-index. Although tighter than 

during the mid-2000s, both indexes have shown slight loosening of mortgage credit availability since 

the financial crisis. Much of the recent easing in mortgage underwriting was for loans that were eligible 

for purchase by the GSEs. In addition, the October 2019 Senior Loan Officer Opinion Survey on Bank 

Lending Practices suggest that mortgage credit conditions remained unchanged throughout 2019 and 

credit remained more difficult to obtain for subprime borrowers. The survey is available on the Federal 

Reserve Board’s website at https://www.federalreserve.gov/data/sloos/sloos-201910.htm. 38 Note that the sum across columns can add up to more than 100 percent because lenders can cite 

more than one denial reason. 

41 
TABLE 6: REASONS FOR DENIAL OF HOME-PURCHASE AND REFINANCE LOANS, BY LOAN TYPE AND 

AND ETHNICITY, 2019 (PERCENT) 

Debt-toincome 

ratio 

Employment 

history 

Credit 

history Collateral 

Insufficient 

cash 

Unverifiable 

information 

Credit 

application 

incomplete 

Mortgage

insurance 

denied 

A. Home Purchase Conventional and nonconventional(1) 

All applicants 30.1 3.5 18.6 14.6 5.4 5.5 10.8 0.

Asian 37.4 4.0 9.3 11.1 6.7 9.0 11.4 0.

Black or African American 32.5 3.2 24.9 11.2 5.1 4.6 8.4 0.

Hispanic white 33.0 4.0 16.2 14.8 5.5 7.0 8.3 0.

Non-Hispanic white 27.9 3.5 18.5 16.0 5.4 4.8 11.0 0.

Other minority(2) 31.0 3.5 23.3 12.1 4.9 4.5 9.0 0.

Conventional only 

All applicants 30.3 2.7 16.1 15.9 5.6 5.9 10.9 0.

Asian 36.7 3.5 8.4 11.5 7.1 9.4 11.8 0.

Black or African American 31.1 2.1 24.0 13.8 4.8 4.4 7.4 0.

Hispanic white 32.2 3.0 15.0 16.3 5.5 7.3 7.9 0.

Non-Hispanic white 28.8 2.6 16.3 17.0 5.5 5.3 11.1 0.

Other minority(2) 31.1 2.6 22.8 12.1 4.7 4.9 8.2 0.

Nonconventional only(1) 

All applicants 29.8 4.7 21.7 12.8 5.2 4.9 10.6 0.

42 
Debt-toincome 

ratio 

Employment 

history 

Credit 

history Collateral 

Insufficient 

cash 

Unverifiable 

information 

Credit 

application 

incomplete 

Mortgage

insurance 

denied 

Asian 40.5 6.0 13.4 9.2 4.6 7.0 9.2 0.Black or African American 33.5 3.9 25.4 9.5 5.3 4.8 9.1 0.Hispanic white 33.8 4.8 17.3 13.4 5.6 6.8 8.6 0.Non-Hispanic white 26.5 5.0 22.0 14.5 5.2 4.2 10.9 0.

Other minority(2) 30.9 4.4 23.7 12.1 5.1 4.0 9.8 0.

B. Refinance Conventional and nonconventional(1) 

All applicants 24.2 1.0 22.4 15.7 2.0 2.7 18.7 0.

Asian 35.0 1.3 14.0 12.6 2.9 4.7 16.9 0.

Black or African American 21.3 0.7 31.8 13.1 1.9 2.0 15.0 0.

Hispanic white 29.5 1.1 22.5 11.9 2.3 3.4 15.1 0.

Non-Hispanic white 23.8 1.0 22.6 16.5 1.9 2.7 17.8 0.

Other minority(2) 23.9 0.8 26.4 13.4 1.7 2.4 16.5 0.

Conventional only 

All applicants 29.3 1.1 19.2 15.4 2.1 3.3 16.4 0.

Asian 37.9 1.4 11.9 12.4 3.1 5.1 16.2 0.

Black or African American 26.7 0.8 27.9 12.4 1.9 2.3 13.0 0.

Hispanic white 34.3 1.1 20.4 11.5 2.2 3.6 12.8 0.

Non-Hispanic white 28.4 1.1 19.6 16.3 2.1 3.2 15.8 0.

Other minority(2) 29.4 1.0 21.3 12.2 2.0 2.9 15.8 0.

43 
Debt-toincome 

ratio 

Employment 

history 

Credit 

history Collateral 

Insufficient 

cash 

Unverifiable 

information 

Credit 

application 

incomplete 

Mortgage

insurance 

denied 

Nonconventional only(1) 

All applicants 15.7 0.8 27.8 16.2 1.7 1.9 22.3 0.

Asian 21.4 1.1 24.1 13.3 1.8 2.8 19.8 0.

Black or African American 16.2 0.6 35.4 13.7 2.0 1.6 16.9 0.

Hispanic white 18.7 0.9 27.3 12.7 2.7 2.8 20.1 0.

Non-Hispanic white 15.5 0.8 28.0 16.9 1.7 1.9 21.4 0.

Other minority(2) 17.1 0.5 32.7 14.9 1.3 1.8 17.5 0.

NOTE: Denied first-lien mortgage applications for one- to four-family, owner-occupied, site-built homes. Columns sum to more than 10report up to three denial reasons. For a description of how borrowers are categorized by race and ethnicity, see table 2, note 1. 

(1) See table 5, note 1. (2) See table 2, note 2. 

44 
Denial reasons vary across racial and ethnic groups. The DTI ratio was cited most often as a 

denial reason for home-purchase applicants in all racial and ethnic groups. Credit history was 

the second most common denial reason cited for home-purchase applicants for all groups 

except Asians, for whom credit application incomplete was the second most common reason 

for conventional loans, as well as conventional and nonconventional loans combined. 

45 
 

 

5. Incidence of higher-priced 

lending 

The definition of higher-priced loans has changed over time. Prior to October 2009, loans 

were classified as higher-priced if the spread between the Annual Percentage Rate (APR) and 

the rate on a Treasury bond of comparable term exceeded three percentage points for firstlien loans or five percentage points for junior-lien loans.39 Following a change to Regulation C 

in October 2009, loans were classified as higher-priced if the APR exceeded the average 

prime offer rate (APOR) for loans of a similar type by at least 1.5 percentage points for firstlien loans or 3.5 percentage points for junior-lien loans.40 However, since the 2018 

implementation of the DFA’s requirement to report rate spread regardless of loan price, 

Regulation C no longer specifies a threshold for defining higher-priced loans.41 

To compare the 2019 data to the data from earlier years, the Bureau defines higher-priced 

loans according to the classification used in Regulation C after 2009.42 Given the change from 

a comparison of APR against a Treasury bond rate to a comparison against APOR, it is 

39 The APR of a closed-end mortgage differs from the interest rate because an APR takes certain upfront fees and loan costs, such as discount points and mortgage origination charges, into account. APR 

is calculated over the term of a loan, which may not represent the real expected rate of return if the 

expected life of a loan is shorter than its term. 

40 APOR is an estimate of APR on loans being offered to high-quality prime borrowers. It is based on 

the contract interest rates and discount points reported by Freddie Mac’s “Primary Mortgage Market 

Survey” (www.freddiemac.com/pmms), and from the Bureau’s own survey of one-year ARMs 

(https://ffiec.cfpb.gov/tools/rate-spread). 

41 Prior to 2018, Regulation C required reporters to report rate spread data only on higher-priced 

mortgage loans (HPML). 

42 It is important to note that the definition of higher-priced mortgage lending discussed in this section 

pertains to APR spread in Regulation C only, using a definition drawn from post-2009 Regulation C 

requirements. This is to facilitate historical comparison of the HMDA data. It may not match the 

higher priced mortgage loan definition in other regulations. In particular, a clause in Regulation Z 

defines, for the escrow purpose, a first-lien higher priced mortgage loan to be a first-lien mortgage 

with an APR that exceeds the APOR by 2.5 percentage points or more, if the principal amount of the 

mortgage exceeds Freddie Mac’s limit for mortgages it will purchase (“jumbo loan”) in effect as of the 

date the interest rate for the transaction is set. (See 

https://files.consumerfinance.gov/f/201401_cfpb_tila-hpml-escrow_compliance-guide.pdf for 

details.) 

46 
 

difficult to compare rates of higher-priced lending pre- and post-2009.43 Table 7 provides 

rates of higher-priced mortgage lending by loan purpose, loan type, and race/ethnicity. 

The share of home-purchase loans that were higher-priced remained largely constant from 

2018 to 2019. The percentage of home-purchase loans (again, first liens for one-to-fourfamily, owner-occupied, site-built properties) above the higher-priced threshold remained 

constant at about 11 percent for conventional and nonconventional loans combined.44 On the 

other hand, nonconventional home-purchase loans (22.7 percent) were much more likely to 

be higher-priced than conventional loans (4.6 percent). 

Refinance loans were less likely to be higher-priced than home-purchase loans, and the share 

of all refinance loans that were higher-priced remained constant at about 4 percent in 2019. 

The share of nonconventional higher-priced refinance loans showed a greater decline (by 20 

percent) compared to that for conventional loans (by 5 percent). 

Table 7 shows that, as in earlier years, Black and Hispanic White borrowers were more likely 

to have higher-priced conventional and nonconventional loans in 2019. For home-purchase 

loans, 22.3 percent of loans to Black borrowers and 23.0 percent of loans to Hispanic White 

borrowers were higher-priced, compared with 8.3 percent of loans to non-Hispanic Whites. 

For refinance loans, 7.0 percent of loans to Black borrowers and 6.0 percent of loans to 

Hispanic White borrowers were higher-priced, in contrast to 3.4 percent for non-Hispanic 

Whites. 

43 For more detailed discussion on the change of APR spread methodology in 2009, see Avery, Robert 

B., et al., “The 2009 HMDA Data: The Mortgage Market in a Time of Low Interest Rates and Economic 

Distress,” available at 

https://www.federalreserve.gov/pubs/bulletin/2010/articles/2009HMDA/default.htm. 44 The rate spread data point is one of the data points covered by EGRRCPA, so HMDA reporters 

eligible for the exemption under the EGRRPCA are not required to report the rate spread data. The 

results in Table 7 for 2018 and 2019 include originations from these reporters in all calculations. 

47 
TABLE 7: INCIDENCE OF HIGHER-PRICED HOME-PURCHASE AND REFINANCE LENDING, BY LOAN 

BORROWER RACE AND ETHNICITY, 2004-2019 (PERCENT) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

A. Home Purchase Conventional and nonconventional(1) 

All applicants 9.8 22.5 23.2 12.7 8.1 4.6 2.2 3.3 3.1 7.1 11.6 7.6 7

Asian 5.5 16.3 16.4 7.6 4.0 2.4 1.0 1.5 1.4 3.1 5.2 3.6 3

Black or African American 24.3 46.7 46.4 27.6 14.5 7.1 3.0 5.0 5.3 14.3 25.6 16.2 15

Hispanic white 17.5 42.0 43.3 25.9 15.8 8.1 3.9 6.1 5.9 16.9 28.5 18.5 18Non-Hispanic white 7.8 15.5 16.0 9.6 7.2 4.3 2.2 3.1 2.9 6.2 9.5 6.3 6

Other minority(2) 14.4 30.3 30.7 16.1 9.1 5.3 2.3 3.5 3.4 8.8 13.7 8.9 9

Conventional only 

All applicants 11.0 24.5 25.3 14.0 7.3 4.6 3.3 3.8 3.2 2.9 3.1 3.2 3

Asian 5.6 16.6 16.7 7.7 3.3 1.9 1.0 1.3 1.2 1.1 1.5 2.1 2

Black or African American 30.6 54.1 53.4 34.0 17.4 8.7 6.1 8.0 6.7 6.1 7.7 6.8 8

Hispanic white 20.0 45.3 46.3 28.9 17.7 11.0 9.6 10.7 8.7 7.3 6.5 8.3 10

Non-Hispanic white 8.6 16.9 17.5 10.5 6.5 4.8 3.4 3.9 3.2 2.9 3.0 2.9 3Other minority(2) 16.1 33.3 33.6 18.5 9.5 6.7 4.7 5.5 5.1 4.9 5.0 4.9 5

Nonconventional only(1) 

48 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

All applicants 1.2 0.9 1.8 3.0 9.5 4.6 1.3 2.7 3.0 13.9 26.3 14.5 14

Asian 2.4 0.6 0.8 1.3 8.2 3.9 0.8 2.0 1.9 13.4 26.3 11.4 10

Black or African American 1.4 1.6 2.5 4.5 12.8 6.8 2.4 4.3 4.9 17.6 34.0 20.2 19

Hispanic white 2.0 1.4 3.5 4.5 14.0 7.1 2.2 4.5 4.8 22.5 43.4 24.6 23

Non-Hispanic white 1.0 0.7 1.5 2.5 8.4 3.9 1.0 2.3 2.6 12.1 22.5 12.2 11

Other minority(2) 4.4 0.7 2.1 2.4 8.8 4.7 1.2 2.5 2.4 11.9 21.0 12.2 12

B. Refinance Conventional and nonconventional(1) 

All applicants 14.5 25.0 30.3 21.0 10.9 3.8 1.8 2.1 1.5 1.9 3.3 2.5 2Asian 5.8 15.1 19.5 12.5 3.1 0.9 0.4 0.5 0.4 0.5 1.1 0.7 0Black or African American 30.0 46.2 50.7 38.1 22.8 9.0 6.5 6.8 4.1 3.8 5.7 5.1 3Hispanic white 18.2 32.6 36.9 26.5 15.1 7.0 4.4 4.4 2.6 3.1 4.8 3.9 3

Non-Hispanic white 12.3 20.4 25.0 17.6 10.2 3.7 1.8 2.2 1.5 2.0 3.4 2.5 2

Other minority(2) 17.6 26.9 32.3 23.8 13.9 4.7 2.5 2.6 2.0 2.2 3.1 2.8 2

Conventional only 

All applicants 15.2 25.7 31.0 21.8 10.4 3.1 1.3 1.5 1.2 1.5 2.2 1.6 1

Asian 5.8 15.2 19.6 12.5 2.9 0.7 0.2 0.3 0.3 0.3 0.7 0.4 0Black or African American 33.7 49.0 52.8 41.5 27.6 9.9 4.0 4.2 2.9 3.3 3.8 3.1 3

Hispanic white 19.2 33.4 37.5 27.3 16.0 7.2 3.3 3.3 2.3 2.4 2.8 2.4 2

Non-Hispanic white 12.8 20.9 25.6 18.2 9.8 3.1 1.3 1.6 1.2 1.6 2.3 1.7 1Other minority(2) 18.2 27.7 32.9 24.5 14.7 4.8 1.9 2.2 1.7 2.0 2.1 2.0 1

49 
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

Nonconventional only(1) 

All applicants 1.5 0.9 3.1 6.6 13.2 6.7 4.9 5.9 3.2 3.9 8.3 5.4 3

Asian 3.6 2.1 2.5 4.9 8.9 4.8 3.1 4.0 1.8 2.6 7.2 3.4 2Black or African American 1.0 1.2 4.1 7.8 15.2 8.2 9.9 10.9 6.0 4.6 8.5 7.1 4

Hispanic white 2.0 0.9 2.6 6.2 11.6 6.6 7.4 7.9 3.6 5.1 12.2 7.0 5

Non-Hispanic white 1.3 0.7 2.8 6.0 12.1 6.5 4.6 5.9 3.3 4.2 8.9 5.5 4

Other minority(2) 8.1 3.9 9.6 9.9 10.5 4.5 4.6 4.3 2.9 2.8 6.0 4.4 3

NOTE: First-lien mortgages for one- to four-family, owner-occupied, site-built homes. For a description of how borrowers are cate

ethnicity, see table 2, note 1. (1) See table 5, note 1. (2) See table 2, note 2. 

50 
Table 8 shows the distribution of higher-priced loans by property type (e.g. site-built, 

manufactured), loan purpose, and loan type. In 2019, 36.5 percent of site-built, home-purchase, 

FHA loans were higher-priced, down from 37.8 percent in 2018. These loans were much more 

likely to be higher-priced than conventional (4.6 percent) or VA/RHS/FSA (1.7 percent) loans, 

in part because of the relatively high up-front and annual mortgage insurance premiums 

charged by the FHA. 

Although manufactured housing loans made up less than three percent of all owner-occupied 

originations, a much higher percentage of them were higher-priced than loans on site-built 

homes. Among manufactured housing home-purchase loans, 69.3 percent of conventional loans 

and 70.2 percent of FHA-insured loans were higher priced in 2019. In addition, among those 

manufactured housing, home-purchase, conventional loans that were higher priced, more than 

half exceeded the higher-priced threshold by five or more percentage points. This is markedly 

higher than for all other loan types and purposes where most of higher-priced lending was 

concentrated right above the 1.5 percentage point threshold. 

51 
TABLE 8: DISTRIBUTION OF LOANS WITH APOR SPREAD ABOVE 1.5 PERCENTAGE POINTS, BY PROPE

PURPOSE AND LOAN TYPE, 2019 (PERCENT) 

Total 

Number 

Loans with APOR spread above 1.5 percentage points(

Number Percent 

Distribution, by percentage points of AP

1.5-1.99 2-2.49 2.5-2.99 3-3.99 

SITE-BUILT HOMES Home purchase 

Conventional 2,488,838 115,233 4.6 55.0 24.9 9.2 6.8 

FHA(2) 754,108 275,175 36.5 60.3 26.9 10.8 1.9 

VA/RHS/FSA(3) 495,131 8,244 1.7 80.8 15.3 2.4 0.7 

Refinance 

Conventional 2,294,903 57,994 2.5 56.7 21.5 8.0 7.8 

FHA(2) 369,215 49,818 13.5 79.2 16.1 3.9 0.6 

VA/RHS/FSA(3) 417,012 2,866 0.7 84.1 12.2 0.9 2.2 

MANUFACTURED HOMES Home purchase 

Conventional FHA(2) 

VA/RHS/FSA(3) 

82,677 24,411 

7,742 

57,263 17,139 

824 

69.3 70.2 10.6 

5.3 

40.2 75.0 

4.5 

35.3 18.3 

6.2 

17.7 

3.6 

14.9 

5.7 2.8 

Refinance 

52 
Total 

Number 

Loans with APOR spread above 1.5 percentage points(

Number Percent 

Distribution, by percentage points of AP

1.5-1.99 2-2.49 2.5-2.99 3-3.99 

Conventional 20,838 4,198 20.1 36.3 18.0 11.0 13.9 

FHA(2) 7,927 2,774 35.0 54.2 31.7 10.9 3.0 

VA/RHS/FSA(3) 6,183 305 4.9 79.0 18.0 2.0 0.7 

NOTE: First-lien mortgages for one- to four-family owner-occupied homes (1) Average prime offer rate (APOR) spread is the difference between the annual percentage rate on the loan and the APOR for loans 

weekly by the Federal Financial Institutions Examination Council. The threshold for first-lien loans is a spread of 1.5 percentage points(2) Loans insured by the Federal Housing Administration. (3) Loans backed by guarantees from the U.S. Department of Veterans Affairs, the Rural Housing Service, or the Farm Service Agency

53 
 

 

5.1 HOEPA loans 

Under the Home Ownership and Equity Protection Act (HOEPA), certain mortgage loans 

that have APRs or fees above specified levels (i.e., HOEPA loans or high-cost mortgages) are 

subject to additional consumer protections, such as special disclosures and restrictions on 

loan features. In January 2013, the Bureau issued a final rule (2013 HOEPA Rule) 

implementing DFA amendments that extended HOEPA’s protections from refinance and 

home equity loans to also include home-purchase loans and HELOCs and added new 

protections for high-cost mortgages, such as a pre-loan counseling requirement.45 The rule 

became effective on January 10, 2014.46 

The 2013 HOEPA Rule also changed the benchmarks used to define HOEPA loans. First, 

instead of comparing the loan’s APR to the yield on comparable Treasury securities, it is now 

compared with APOR. Prior to 2014, HOEPA’s protections as defined in the implementing 

regulation were triggered if the loan’s APR was eight percentage points above the rate on a 

Treasury security of similar term for first liens, and ten percentage points for junior liens. 

HOEPA coverage now applies to first liens with an APR of more than 6.5 percentage points 

above APOR. If the loan is a junior lien, or if the loan is a first lien that is less than $50,000 

and secured by personal property (such as many manufactured homes), then the high-cost 

threshold is 8.5 percentage points above APOR. Second, the 2013 HOEPA Rule changed the 

points and fees threshold that triggers HOEPA coverage. A loan is a high-cost mortgage if the 

points and fees exceed five percent of the total loan amount, for a loan amount equal to or 

more than $20,000; or eight percent of the total loan amount or $1,000 for a loan less than 

$20,000, with the loan amounts and $1,000 threshold adjusted annually for inflation from 

the base year of 2014. Lastly, the 2013 HOEPA Rule added a third HOEPA coverage test 

based on a transaction’s prepayment penalties. 

Even at their peak of nearly 36,000 in 2005 (Table 9), HOEPA loans made up a small 

fraction of the mortgage market with approximately 8.4 million loans. With an increase from 

1,252 loans in 2015 to 6,507 loans in 2019, the volume of HOEPA loans has trended upward 

in recent years despite still being far below its peak level. 

There was also variation in the trend of HOEPA loans across loan characteristics. While the 

share of HOEPA loans for home improvement remained unchanged at about 7 percent, that 

for home purchase and refinance showed opposite trends. The share of HOEPA loans 

45 78 FR 6856 (Jan. 31, 2013). 

46 Id.; see 12 CFR 1026.31, 1026.32, and 1026.34 (2018). 

54 
decreased by about 5 percentage points for home purchase, whereas that for refinance 

increased by the same amount. On the other hand, first liens, manufactured homes, and loan 

amounts of greater than $50,000 each accounted for a larger share of HOEPA loans in 2019 

than 2018. 

55 
TABLE 9: DISTRIBUTION OF HOEPA LOANS, BY LOAN CHARACTERISTIC, 2004-2019 (PERCENT EXCEP

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

HOEPA loans 24,437 35,985 15,195 10,780 8,577 6,446 3,379 2,373 2,193 1,868 1,271 1,252 1,880 (total) Loan purpose 

Home purchase 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.4 40.4 58.8 

Home improvement 37.7 26.1 42.4 45.4 30.5 31.1 32.6 32.3 31.5 30.1 17.9 14.8 15.1 

Refinance 62.3 73.9 57.6 54.6 69.5 68.9 67.4 67.7 68.5 69.9 50.7 44.8 26.2 

Lien status 

First 55.5 60.5 53.6 52.8 78.5 84.1 83.4 82.8 84.6 84.2 90.3 88.6 90.0 Junior 44.5 39.5 46.4 47.2 21.5 15.9 16.6 17.2 15.4 15.8 9.7 11.4 10.0 

Property type 

Site built 88.0 91.8 83.7 81.0 72.7 67.8 67.9 65.7 65.7 68.8 75.4 83.4 86.0 Manufactured 12.0 8.2 16.3 19.0 27.3 32.2 32.1 34.3 34.3 31.2 24.6 16.6 14.0 

home 

Loan amount 

Less than 72.4 48.4 72.1 74.3 66.7 72.5 76.8 77.8 75.6 71.3 52.9 36.4 35.4 $50,000 Greater than 27.6 51.6 27.9 25.7 33.3 27.5 23.2 22.2 24.4 28.7 47.1 63.6 64.6 $50,000 

NOTE: Mortgages for one- to four-family homes. HOEPA loans are mortgages with terms that triggered the additional protections pro

Ownership and Equity Protection Act. 

56 
 

 

6. Lending institutions 

In 2019, 5,496 financial institutions reported closed-end applications and originations (Table 

10), down from 5,666 institutions in 2018. The decrease in the number of reporting institutions 

continued the previous trend from 2017 to 2018. As discussed in Section 3, the overall market 

volume, however, has increased from 10.3 million applications in 2018 to 12.6 million 

applications in 2019. 

The financial institutions are broadly categorized into depository institutions (DIs) and nondepository institutions (non-DIs). In 2019, DIs included 2,829 banks and thrifts (hereafter, 

banks), of which 2,155 were small (assets less than $1 billion), and 1,602 credit unions. Non-DIs 

included 93 mortgage companies affiliated with DIs and 972 independent mortgage 

companies.47 

Among DIs, banks collectively originated most of the reported loans, whereas, among non-DIs, 

independent mortgage companies originated more loans than mortgage companies affiliated 

with DIs. For example, banks collectively originated 2.6 million loans, accounting for 32.4 

percent of all reported originations in 2019. Credit unions originated 714,000 loans, accounting 

for only 8.8 percent. Independent mortgage companies originated 4.4 million loans accounting 

for 54.5 percent of all reported loans, whereas affiliates of DIs originated only 350,000 loans or 

4.3 percent. 

Over the past few years, the share of loans originated by independent mortgage companies has 

increased sharply. In 2019, these lenders originated 56.4 percent (2.1 million divided by 3.7 

million) of first-lien, owner-occupied, one-to-four-family, site-built, home-purchase loans, down 

47 Data on bank assets were drawn from the Federal Deposit Insurance Corporation’s Reports of 

Condition and Income. The $1 billion threshold is based on the combined assets of all banks within a 

given banking organization. Data available in the HMDA Reporter Panel (available at 

https://ffiec.cfpb.gov/data-publication/) can be used to help identify the various types of institutions. 

Affiliate institutions include all mortgage companies known to be wholly or partially owned by a 

depository—that is, institutions for which the “other lender code” in the Reporter Panel equals 1, 2, or 5. 

Most credit unions report HMDA data under the agency code “National Credit Union Administration,” 

with a few large credit unions reporting under the agency code “Consumer Financial Protection Bureau.” 

57 
 

 

slightly from 57.2 percent in 2018 and up from just 35.0 percent in 2010. Independent mortgage 

companies also originated 58.1 percent (1.8 million divided by 3.1 million) of first-lien, owneroccupied, one-to-four family site-built refinance loans, an increase from 56.1 percent in 2018. 

Many institutions reported little lending activity in 2019. Similar to 2018, about 37 percent of 

institutions (2,042 out of 5,496) reported fewer than 100 close-end originations in 2019, 

accounting for about 98,000 total originations or less than 2 percent of all originations. About 7 

percent of institutions (405 out of 5,496), originated fewer than 25 loans, totaling just under 

6,000 originations.48 

48 These results include all originated dwelling-secured, closed-end loans with a home purchase, home 

improvement or refinance purpose for all reporters. The reporting threshold of 25 originations applies to 

home-purchase and refinance originations in each of the previous two years. Beginning in 2018, lending 

institutions were not subject to HMDA reporting requirements unless they originated at least 25 covered 

closed-end mortgage loans or 500 covered open-end LOCs in each of the two preceding calendar years. 

For a more detailed description of these and other changes to Regulation C, see Consumer Financial 

Protection Bureau, “New Rule Summary: Home Mortgage Disclosure (Regulation C)” (October 15, 2015), 

http://files.consumerfinance.gov/f/201510_cfpb_hmda-executive-summary.pdf and 82 FR 43088 

(2017). 

58 
TABLE 10: LENDING ACTIVITY, BY TYPE OF INSTITUTION, 2019 (PERCENT EXCEPT AS NOTED) 

Type of institution(1) 

Small 

bank 

Large

bank 

Credit 

union 

Affiliated 

mortgage 

company 

Independe

mortgag

company 

Number of institutions 2,155 674 1,602 93 

Applications (thousands) 704 3,191 1,149 550 

Originations (thousands) 522 2,103 714 350 

Purchases (thousands) 22 952 13 237 

Number of institutions with fewer than 100 loans 1,023 67 752 18 

Originations (thousands) 54.0 3.8 33.2 0.5 

Number of institutions with fewer than 25 loans 124 6 193 9 

Originations (thousands) 2.2 0.1 2.7 0.1 

Home-purchase loans (thousands)(2) 215 956 267 194 

Conventional 73.4 82.4 85.2 57.9 Higher-priced share of conventional loans 4.1 2.7 5.0 4.4 

LMI borrower(3) 30.4 24.1 27.5 29.6 

LMI neighborhood(4) 14.1 14.0 15.1 15.3 Non-Hispanic white(5) 75.6 64.0 65.7 59.6 Minority borrower(5) 14.0 20.2 18.0 22.5 Sold(6) 75.9 68.3 49.7 99.2 

Refinance loans (thousands)(2) 152 758 269 111 

Conventional 81.6 92.1 95.4 62.7 

59 
Type of institution(1) 

Small 

bank 

Large

bank 

Credit 

union 

Affiliated 

mortgage 

company 

Independe

mortgag

company 

Higher-priced share of conventional loans 2.3 2.2 3.0 3.4 

LMI borrower(3) 22.5 19.1 24.5 24.7 

LMI neighborhood(4) 10.9 11.3 14.4 14.3 Non-Hispanic white(5) 78.1 67.4 67.8 65.6 Minority borrower(5) 8.4 17.0 16.1 17.9 

Sold(6) 71.3 61.8 34.4 99.4 

(1) Small banks consist of those banks with assets (including the assets of all other banks in the same banking organization) of lesend of 2016. Affiliated mortgage companies are nondepository mortgage companies owned by or affiliated with a banking organiz(2) First-lien mortgages for one- to four-family, owner-occupied, site-built homes. (3) See table 2, note 3. (4) See table 2, note 4. (5) See table 2, note 1. "Minority borrower" refers to nonwhite (excluding joint or missing) or Hispanic white applicants. (6) Excludes originations made in the last quarter of the year because the incidence of loan sales tends to decline for loans origina

the year, as lenders report a loan as sold only if the sale occurs within the same year as origination. Source: FFIEC HMDA data; bank asset data drawn from Federal Deposit Insurance Corporation Reports of Condition and Income (https://www.fdic.gov). 

60 
 

 

A number of differences exist between DIs and non-DIs with respect to the activity reported in 

2019. First, DIs originated a significantly higher fraction of conventional loans than non-DIs. 

Second, DIs originated smaller shares of loans to minority borrowers, LMI borrowers, and in 

LMI neighborhoods than non-DIs. Third, non-DIs sold more of their originated loans compared 

to DIs. The HMDA data provide information on whether lenders sold originated loans within the 

same calendar year that they were originated, as well as the type of institution to which the 

lenders sold the loans, such as one of the GSEs or a banking institution.49 Table 10 shows that 

non-DIs sold almost all of their loans in the same calendar year that they originated them. 

A distinct pattern emerges even within a specific institution type. For example, even among DIs, 

credit unions were the least likely to sell originated loans compared to banks. Small banks and 

large banks sold 75.9 percent and 68.3 percent, respectively, of their home-purchase loans 

within the same calendar year of the originations. In contrast, credit unions sold less than half 

of their home-purchase loans during the same period. 

Tables 11a and 11b list the top 25 reporting institutions by total number of closed-end 

originations and their lending characteristics.50 With about 541,000 originated loans, Quicken 

Loans continued to be the highest volume lender.51 

49 Because loan sales are recorded in the HMDA data only if the loans are originated and sold in the same 

calendar year, loans originated toward the end of the year are less likely to be reported as sold. For that 

reason, statistics on loan sales are computed using only loans originated during the first three quarters of 

the year. 

50 Some institutions may be part of a larger organization; however, the data in Tables 11a and 11b are at 

the reporter level. Because affiliate activity has declined markedly since the housing boom, a top 25 list at 

the organization level is not likely to be significantly different from Tables 11a and 11b. 

51 Notably, loan counts and market shares derived from the HMDA data can differ from some other 

industry sources, such as the market shares compiled by the Inside Mortgage Finance 

(https://www.insidemortgagefinance.com/). For HMDA reporting purposes, institutions report only 

mortgage applications for which they make the credit decision. Under HMDA, if an application was 

approved by a third party (such as a correspondent) rather than the lending institution, then that third 

party reports the loan as its own origination, and the lending institution reports the loan as a purchased 

loan. Alternatively, if a third party forwards an application to the lending institution for approval, then the 

lending institution reports the application under HMDA (and the third party does not report anything). In 

contrast, the Inside Mortgage Finance considers loans to have been originated by the acquiring institution 

even if a third party makes the credit decision. Thus, many of the larger lending organizations that work 

with sizable networks of correspondents report considerable volumes of purchased loans in the HMDA 

data, while the Inside Mortgage Finance considers many of these purchased loans to be originations. 

61 
TABLE 11a: INSTITUTION TYPE, TOTAL ORIGINATIONS, AND TOTAL PURCHASES FOR TOP 25 RESP

TERMS OF TOTAL ORIGINATIONS, 2019 (HOME-PURCHASE LOANS) 

Total Total 

Home-purchase loans(2) 

Institution type(1) originations

(thousands) 

purchases 

(thousands) Number (thousa

nds) 

Convent 

ional 

Higher 

priced(3) 

LMI 

borrower 

(4) 

LMI 

neighbo

rhood(5) 

N

His

wh

QUICKEN LOANS INC. Ind. mort. co. 541 2 134 69.4 0.2 27.8 15.9 UNITED SHORE FINANCIAL SERVICES, LLC Ind. mort. co. 339 0 152 68.8 3.6 30.1 18.7 Wells Fargo Bank, National Association Large bank 232 349 112 94.3 0.9 15.2 11.1 JPMorgan Chase Bank, National Association Large bank 168 152 65 98.4 0.9 20.4 14.2 FAIRWAY INDEPENDENT MORTGAGE CORPORATION Ind. mort. co. 147 0 94 59.0 6.3 31.9 18.2 LOANDEPOT.COM, LLC Ind. mort. co. 146 0 52 58.7 4.9 23.7 17.3 CALIBER HOME LOANS, INC. Ind. mort. co. 136 78 71 58.4 7.8 30.1 18.8 Bank of America, National Association Large bank 134 13 62 95.0 0.1 18.0 14.2 FREEDOM MORTGAGE CORPORATION Ind. mort. co. 110 59 24 34.9 3.8 28.8 19.0 U.S. Bank National Association Large bank 94 96 38 89.1 1.3 24.7 13.0 GUARANTEED RATE, INC. Ind. mort. co. 86 1 49 71.8 2.5 26.5 16.0 

GUILD MORTGAGE COMPANY Ind. mort. co. 85 2 47 57.0 7.7 30.6 19.9 

Nationstar Mortgage LLC Ind. mort. co. 84 87 12 20.3 7.8 29.6 20.6 

Flagstar Bank, FSB Large bank 75 34 37 59.6 5.1 30.1 18.2 

62 
Total Total 

Home-purchase loans(2) 

Institution type(1) originations

(thousands) 

purchases 

(thousands) Number (thousa

nds) 

Convent 

ional 

Higher 

priced(3) 

LMI 

borrower 

(4) 

LMI 

neighbo

rhood(5) 

N

His

wh

MOVEMENT MORTGAGE, LLC NAVY FEDERAL CREDIT UNION Mortgage Research Center, LLC 

Ind. mort. co. 

Credit union 

Ind. mort. co. 

69 68 66 

0 0 0 

47 46 51 

55.3 40.6 

1.3 

6.3 

23.9 

0.0 

32.6 22.0 29.4 

18.7 14.4 15.8 

USAA Federal Savings Bank Large bank 64 0 41 30.0 1.7 16.4 12.4 PRIMELENDING, A PLAINSCAPITAL COMPANY BROKER SOLUTIONS, INC. PNC Bank, National Association HOMEBRIDGE FINANCIAL SERVICES, INC. CROSSCOUNTRY MORTGAGE, LLC FINANCE OF AMERICA MORTGAGE LLC Citizens Bank, National Association Top 25 institutions All institutions 

Affiliated mort. co. 

Ind. mort. co. 

Large bank 

Ind. mort. co. Ind. mort. co. Ind. mort. co. 

Large bank 

... ... 

59 57 53 53 51 49 49 

3,015 8,111 

1 0 1 0 0 0 

48 

922 

2,072 

41 29 15 20 33 26 25 

1,322 3,738 

61.1 49.4 88.4 55.5 56.8 60.4 82.7 64.9 66.6 

6.4 7.3 0.0 6.7 8.5 5.1 2.1 3.6 4.6 

31.4 33.2 29.0 27.0 32.3 27.1 26.1 26.5 28.6 

16.2 22.4 14.0 19.2 21.2 19.8 13.4 16.5 16.5 

(1) See table 10, note 1. (2) First-lien mortgages for one- to four-family, owner-occupied, site-built homes. (3) Share of conventional loans that are higher priced. 

63 
(4) See table 2, note 3. 

(5) See table 2, note 4. (6) See table 2, note 1. "Minority borrower" refers to nonwhite (excluding joint or missing) or Hispanic white applicants. (7) See table 10, note 6. ... Not applicable. Source: FFIEC HMDA data; bank asset data drawn from Federal Deposit Insurance Corporation Reports of Condition and Income (https://www.fdic.gov). 

64 
TABLE 11b: INSTITUTION TYPE, TOTAL ORIGINATIONS, AND TOTAL PURCHASES FOR TOP 25 RESP

TERMS OF TOTAL ORIGINATIONS, 2019 (REFINANCE LOANS) 

Total Total 

Refinance loans(2) 

Institution type(1) originations

(thousands) 

purchases 

(thousands) Number (thousa

nds) 

Convent 

ional 

Higher 

priced(3) 

LMI 

borrower 

(4) 

LMI 

neighbo

rhood(5) 

N

His

wh

QUICKEN LOANS INC. Ind. mort. co. 541 2 381 71.1 0.0 25.1 15.0 UNITED SHORE FINANCIAL SERVICES, LLC Ind. mort. co. 339 0 160 76.1 1.1 29.9 15.4 Wells Fargo Bank, National Association Large bank 232 349 92 96.1 4.4 16.6 11.8 JPMorgan Chase Bank, National Association Large bank 168 152 80 99.5 1.8 21.2 12.1 FAIRWAY INDEPENDENT MORTGAGE CORPORATION Ind. mort. co. 147 0 33 76.7 2.7 18.7 13.9 LOANDEPOT.COM, LLC Ind. mort. co. 146 0 81 69.9 3.0 25.0 15.2 CALIBER HOME LOANS, INC. Ind. mort. co. 136 78 52 60.9 2.6 15.3 14.9 Bank of America, National Association Large bank 134 13 59 99.8 0.7 13.0 11.3 FREEDOM MORTGAGE CORPORATION Ind. mort. co. 110 59 82 17.4 8.5 8.3 15.9 U.S. Bank National Association Large bank 94 96 41 97.3 2.9 20.7 12.5 GUARANTEED RATE, INC. Ind. mort. co. 86 1 28 86.4 1.0 15.6 11.7 

GUILD MORTGAGE COMPANY Ind. mort. co. 85 2 23 67.8 2.0 38.0 16.7 

Nationstar Mortgage LLC Ind. mort. co. 84 87 68 40.6 11.7 19.8 18.4 

Flagstar Bank, FSB Large bank 75 34 29 76.6 2.2 18.1 12.9 

65 
Total Total 

Refinance loans(2) 

Institution type(1) originations

(thousands) 

purchases 

(thousands) Number (thousa

nds) 

Convent 

ional 

Higher 

priced(3) 

LMI 

borrower 

(4) 

LMI 

neighbo

rhood(5) 

N

His

wh

MOVEMENT MORTGAGE, LLC NAVY FEDERAL CREDIT UNION Mortgage Research Center, LLC 

Ind. mort. co. 

Credit union 

Ind. mort. co. 

69 68 66 

0 0 0 

13 15 14 

67.5 42.3 

1.0 

2.0 5.3 0.0 

37.3 13.3 

9.0 

14.7 13.1 14.1 

USAA Federal Savings Bank Large bank 64 0 20 24.8 0.1 9.4 11.6 

PRIMELENDING, A PLAINSCAPITAL COMPANY BROKER SOLUTIONS, INC. PNC Bank, National Association HOMEBRIDGE FINANCIAL SERVICES, INC. CROSSCOUNTRY MORTGAGE, LLC FINANCE OF AMERICA MORTGAGE LLC Citizens Bank, National Association Top 25 institutions All institutions 

Affiliated mort. co. 

Ind. mort. co. 

Large bank 

Ind. mort. co. Ind. mort. co. Ind. mort. co. 

Large bank 

... ... 

59 57 53 53 51 49 49 

3,015 8,111 

1 0 1 0 0 0 

48 

922 

2,072 

11 19 25 26 14 17 19 

1,402 3,081 

81.2 67.9 97.6 27.1 68.9 83.8 94.8 70.8 74.5 

3.9 2.0 0.0 4.4 3.7 1.4 0.8 1.9 2.5 

19.3 19.1 24.4 

7.6 

22.5 15.7 22.2 21.3 23.8 

12.8 16.9 11.6 18.0 15.6 14.8 10.2 14.4 14.0 

(1) See table 10, note 1. (2) First-lien mortgages for one- to four-family, owner-occupied, site-built homes. (3) Share of conventional loans that are higher priced. 

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(4) See table 2, note 3. 

(5) See table 2, note 4. (6) See table 2, note 1. "Minority borrower" refers to nonwhite (excluding joint or missing) or Hispanic white applicants. (7) See table 10, note 6. ... Not applicable. Source: FFIEC HMDA data; bank asset data drawn from Federal Deposit Insurance Corporation Reports of Condition and Income (https://www.fdic.gov). 

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United Shore Financial Services, Wells Fargo, JPMorgan Chase, and Fairway Independent 

Mortgage Corporation were the next four largest lenders in terms of originations. Overall, the 

top 25 lenders accounted for 37.2 percent of all loan originations in 2019, a slight increase from 

2018. These same firms also provided additional funding by purchasing approximately 922,000 

loans from other lending institutions during 2019 (these loans could have been originated prior 

to 2019), equal to 44.5 percent of total purchased loans. 

The top 25 institutions have varying lending patterns. Some of the variations reflect differences 

across types of institutions, which were discussed earlier. For example, Table 11a shows that 

large banks like Wells Fargo have a higher share of conventional mortgages and sold a lower 

share of their loans compared with independent mortgage companies like Quicken Loans. 

In addition to the variation across types of institutions, there was a substantial variation in 

lending patterns within specific types of institutions. For example, among large banks, 98.4 

percent of JPMorgan Chase’s home-purchase loans were conventional, compared with 30 

percent for USAA Federal Savings Bank. Similarly, for some large banks, more than 30 percent 

of their home-purchase borrowers were LMI, whereas other large banks had shares that were 

close to 15 percent. Although it is difficult to definitively know why such variations exist, the 

overall trends remained consistent from 2018. 

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7. Conclusion 

The 2019 HMDA data are the second year of data that reflect changes implemented by the 2015 

HMDA rule. The 2015 HMDA rule made changes to institution and transaction reporting 

criteria and changes to and extensions of the data points that institutions covered by HMDA 

must report. 

The 2019 HMDA data remained largely consistent with the 2018 HMDA data. The share of 

home-purchase originations and lending by non-DIs, as well as denial rates and average loan 

amounts across all demographic and income groups were generally similar in 2019 compared to 

2018. However, there were few notable changes. First, the number of originations increased 

substantially between 2018 and 2019, which was mostly driven by refinance loans. Decreasing 

interest rates in 2019 led to an increase in refinance loans. This increase was especially observed 

for non-Hispanic White borrowers, Asian borrowers, and high-income borrowers, as well as for 

properties located in high-income neighborhoods. Second, the average loan amounts increased 

especially for refinance loans. In 2019, for the first time the average home-purchase loan 

amount for Hispanic White borrowers surpassed their pre-Recession peak level. Third, denial 

rates decreased across all demographic groups and loan purposes with a particularly large 

decrease for refinance loans. Lastly, the share of nonconventional home-purchase loans 

increased slightly from 2018 to 2019, halting the general downward trend observed since the 

Great Recession. 

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