NexBank is a Dallas bank lending nationwide in the largest loans among similar lenders, with a typical loan of $405,000, and it also bought 1,431 loans from other lenders in 2025.
The largest loans among its peers
Its typical loan was $405,000, against $220,000 at similar lenders (state non-member banks making between half and twice as many loans), and 11.8% of loans were jumbo (larger than the conforming limit), against 2.2%; both were the highest among them. Its lending rose to $3.05 billion from $2.34 billion.
Dollars up, loan count flat
Originations rose 1% to 5,717, against 16% for peers and 10% for the whole market, slower than about four in five of them. A shift toward larger loans could explain why dollars rose faster than the count. It also bought 1,431 loans, against 1,175 the year before.
The highest share in majority-minority areas
It made 29.4% of its loans in majority-minority areas, against 14.8%, the highest among peers. Its biggest markets are large metros such as Los Angeles-Long Beach-Anaheim and Dallas-Fort Worth-Arlington, and California is its main state.
Few LMI borrowers
Only 19.2% of its loans went to LMI (low- and moderate-income) borrowers, against 28.7%, among the lowest tenth, down from 28.5% the year before. Its large loans, much of them in big metros, may mean fewer borrowers fall under the income limits. Its share in LMI areas, 15.1% against 17.4%, was lower than about two-thirds of peers.
Questions the data can answer next
Which metros drove the rise in its typical loan size, and how does its pricing differ for jumbo loans?
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
5,717 loans in 2025, against a peak of 6,179 in 2020.
Originated means the loan was made: applications denied, withdrawn or not accepted are not counted. NexBank took 7,805 applications in 2025.
69.0% of its 2025 loans were purchase loans; 96.7% were Conventional.
Purchase 69.0% · Refinance 26.9% · Other 4.0% Conventional 96.7% · FHA 1.9% · VA 1.4% · of 5,717 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, 269 metros, 722 counties
State
Loans
Share
California
1,262
22.1%
Texas
1,171
20.5%
Georgia
488
8.5%
Arizona
284
5.0%
Florida
275
4.8%
Washington
270
4.7%
Illinois
229
4.0%
Pennsylvania
169
3.0%
Colorado
168
2.9%
Minnesota
127
2.2%
Largest metro areas, 2025
Loans originated
Metro area
Loans
Dallas-Plano-Irving
466
Los Angeles-Long Beach-Glendale
271
Phoenix-Mesa-Chandler
245
Atlanta-Sandy Springs-Roswell
238
Houston-Pasadena-The Woodlands
230
Chicago-Naperville-Schaumburg
195
Austin-Round Rock-San Marcos
165
Oakland-Fremont-Berkeley
148
Anaheim-Santa Ana-Irvine
146
San Diego-Chula Vista-Carlsbad
146
Identifiers
LEI (Legal Entity Identifier, the global company code HMDA has used since 2018)
549300NWBS6MQJX15N44
2018–2025
RSSD (the Federal Reserve's identifier for banks and their parents)
652874
2004–2023
Respondent ID (the HMDA filer code before 2018)
43102
1990–1998
Respondent ID (the HMDA filer code before 2018)
329209
1999–2017
Established 30 November 1934. Reported under HMDA 1990-2025.
Mergers, name changes, data editions and corrections: NexBank's data.
The data and the software behind these pages
NexBank's data
$215
NexBank 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.