The Loan Store, a mostly conventional purchase lender, grew its originations 70% to 40,038 in 2025, faster than any similar lender, and gained share in Sacramento-Roseville-Folsom, where it ranked 3 of 574.
The fastest growth among similar lenders
Similar lenders are independent non-bank lenders making between half and twice as many loans. Its originations rose 70%, against 20% at similar lenders and 10% for the whole market. It made 4,091 loans in 2023 and 23,548 in 2024, so almost all of its present size was built in two years.
Gaining share in California and Phoenix
Its share rose to 3.2% from 2.0% in Sacramento-Roseville-Folsom, behind United Wholesale Mortgage and Rocket Mortgage. It rose to 1.7% from 1.0% in Riverside-San Bernardino-Ontario, where it ranked 11 of 707, and to 1.2% from 0.8% in Phoenix-Mesa-Chandler.
Conventional lending, fewer LMI borrowers
Purchases were 63.1% of its loans and conventional loans 90.3% against 62.9%. FHA (insured by the Federal Housing Administration) loans were 7.2% against 21.3%. 18.7% of its loans went to LMI (low- and moderate-income) borrowers against 29.5%, lower than more than nine in ten similar lenders; its small FHA share could account for part of this. 34.4% were in majority-minority areas against 25.9%, higher than most.
From buying loans to making them
Five years before it bought 16,819 loans while originating 2,341. It bought none in 2025 or the year before, against 298 at the typical similar lender, so its reported lending is now all loans it made itself.
Questions the data can answer next
Which counties in Sacramento-Roseville-Folsom and Riverside-San Bernardino-Ontario drove its gains, and from which competitors?
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
40,038 loans in 2025, its most in any year since 2018.
Originated means the loan was made: applications denied, withdrawn or not accepted are not counted. The Loan Store took 58,895 applications in 2025.
63.1% of its 2025 loans were purchase loans; 90.3% were Conventional.
Purchase 63.1% · Refinance 19.6% · Other 10.9% · Home improvement 6.4% Conventional 90.3% · FHA 7.2% · VA 2.3% · USDA (RHS or FSA) 0.2% · of 40,038 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
51 states, 376 metros, 1,537 counties
State
Loans
Share
California
8,432
21.1%
Florida
4,627
11.6%
Texas
3,502
8.7%
Arizona
2,579
6.4%
Utah
1,917
4.8%
Illinois
1,746
4.4%
Colorado
1,646
4.1%
Michigan
1,407
3.5%
Washington
1,253
3.1%
Georgia
1,055
2.6%
Largest metro areas, 2025
Loans originated
Metro area
Loans
Phoenix-Mesa-Chandler
1,762
Sacramento-Roseville-Folsom
1,582
Riverside-San Bernardino-Ontario
1,432
Chicago-Naperville-Schaumburg
1,333
Los Angeles-Long Beach-Glendale
1,202
Houston-Pasadena-The Woodlands
1,104
Denver-Aurora-Centennial
922
Dallas-Plano-Irving
846
Miami-Miami Beach-Kendall
812
San Diego-Chula Vista-Carlsbad
736
Identifiers
LEI (Legal Entity Identifier, the global company code HMDA has used since 2018)
254900DTLHVWQ7NP7R34
2018–2025
RSSD (the Federal Reserve's identifier for banks and their parents)
5023684
2016–2023
Respondent ID (the HMDA filer code before 2018)
7463676810
Established 17 September 2013. Reported under HMDA 2015-2025.
The Loan Store 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.