Citibank is a large-loan lender: its typical loan of $805,000 was about four times the $200,000 at similar national banks, and it bought more loans (20,818) than it made (15,812).
The largest loans in its peer group
Its typical loan size was the highest among similar lenders (national banks making between half and twice as many loans), and jumbo loans (larger than the conforming limit) were 37.0% of its lending against 10.6%. It ranks 3 of 459 in San Jose, where its share rose from 5.9% to 6.6%.
A buyer of loans as well as a maker
It bought 20,818 loans, against 133 for the typical peer, the most in the group, about the same level as a year and five years before. Its own originations therefore understate its role in the market.
Low LMI share, high majority-minority share
Low- and moderate-income (LMI) borrowers took 9.5% of its loans, against 25.0% for peers, among the lowest tenth; its focus on large loans could explain much of this. Its majority-minority share of 40.8% was the highest among peers, which lending in metros such as New York and Los Angeles may account for.
Growing again, far below 2020
Originations rose 12%, against 1% for peers and 10% for the market, but remain well under the 36,612 of five years before. Its typical rate of 6.00% was lower than nine in ten peers, and higher-priced loans (a rate well above the average for similar loans) were 0.0%; its large conventional loans could be one reason.
Questions the data can answer next
How does its share of jumbo lending in San Jose and San Francisco compare with Wells Fargo and Bank of America?
Similar lenders: same lender type, originations between half and twice the lender's in the data year.
Written from the 2025 HMDA data and checked figure by figure against it.
FIG. 1 — LOANS ORIGINATED, 2018–2025
15,812 loans in 2025, against a peak of 36,612 in 2020.
Originated means the loan was made: applications denied, withdrawn or not accepted are not counted. Citibank took 33,966 applications in 2025.
58.1% of its 2025 loans were purchase loans; 99.6% were Conventional.
Purchase 58.1% · Refinance 27.7% · Home improvement 7.4% · Other 6.8% Conventional 99.6% · FHA 0.3% · VA 0.1% · of 15,812 loans. Purchase: buying a home. Refinance: replacing an existing mortgage, including cash-out. FHA: insured by the Federal Housing Administration. VA: guaranteed by the Department of Veterans Affairs. Conventional: neither.
SOURCE: FFIEC HMDA 2025, LOANS ORIGINATED
Where it lends, 2025
49 states, 264 metros, 667 counties
State
Loans
Share
California
5,786
36.6%
New York
3,687
23.3%
Illinois
1,251
7.9%
New Jersey
707
4.5%
Florida
565
3.6%
Texas
506
3.2%
Maryland
400
2.5%
Washington
317
2.0%
Virginia
311
2.0%
District of Columbia
281
1.8%
Largest metro areas, 2025
Loans originated
Metro area
Loans
New York-Jersey City-White Plains
2,937
Los Angeles-Long Beach-Glendale
1,532
San Jose-Sunnyvale-Santa Clara
1,326
Chicago-Naperville-Schaumburg
1,075
Oakland-Fremont-Berkeley
1,072
Nassau County-Suffolk County
879
San Francisco-San Mateo-Redwood City
583
Anaheim-Santa Ana-Irvine
438
Washington
433
Arlington-Alexandria-Reston
270
Identifiers
LEI (Legal Entity Identifier, the global company code HMDA has used since 2018)
E57ODZWZ7FF32TWEFA76
2018–2025
RSSD (the Federal Reserve's identifier for banks and their parents)
476810
2004–2023
Respondent ID (the HMDA filer code before 2018)
11461
1990–2010
Respondent ID (the HMDA filer code before 2018)
9476810
2011–2017
Established 16 June 1812. Reported under HMDA 1990-2025.
Mergers, name changes, data editions and corrections: Citibank's data.
The data and the software behind these pages
Citibank's data
$215
Citibank and its peers, 2018–2025: every application in the HMDA data, cleaned and corrected, as Parquet files, with the Jenner programs that made these pages.
Every mortgage application reported under HMDA from 1990 to 2025, cleaned and corrected, every lender followed across name changes and mergers, with the market-share and fair-lending tables behind these pages and the Jenner programs that built them: the warehouse and the datamart, as Parquet files.
Jenner runs programs written in the SAS language. It drew every chart on these pages and fitted the fair-lending model, PROC GLIMMIX on 3.5 million applications. Run the programs that come with the data, or your own.