WSFS Bank grew its own originations 20% in 2025 while almost entirely stopping loan purchases, which fell from 3,153 to 3, and it gained share in its Delaware and South Jersey home markets.
Loan buying all but stopped
It reported 3 purchased loans, against 3,153 a year earlier and 3,360 five years before. Its own originations rose 20% to 3,884, near the middle of similar lenders (savings institutions making between half and twice as many loans) and above the market's 10%. Possible reasons include ending a purchase programme or bringing that lending in house.
Gaining ground at home
It ranked 8 of 784 in Philadelphia-Camden-Wilmington. Its share rose from 1.2% to 2.2% in Atlantic City, ranking 14 of 423, and from 2.2% to 2.7% in Dover, ranking 9 of 274.
More LMI and majority-minority lending than peers
30.1% of its borrowers were LMI (low- and moderate-income), against 24.1% for similar lenders, higher than about four in five of them. Loans in majority-minority tracts rose to 22.6% from 8.3% five years earlier, against 13.1% for peers. Its mergers with Beneficial Bank in 2019 and The Bryn Mawr Trust Company in 2022 may have widened its reach.
Rates toward the top of its group
Its typical rate of 7.00% was higher than about nine in ten similar lenders, against 6.49% for peers, though its higher-priced share (a rate well above the average for similar loans), 1.1%, sat near the middle. Its mix could explain part of it: home improvement was 21.2% of its loans and other purposes 11.3%.
Questions the data can answer next
How does its pricing differ from peers by loan purpose?
Which counties in Delaware and New Jersey drove its share gains?
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
3,884 loans in 2025, against a peak of 5,014 in 2021.
Originated means the loan was made: applications denied, withdrawn or not accepted are not counted. WSFS Bank took 5,671 applications in 2025.
37.3% of its 2025 loans were purchase loans; 97.0% were Conventional.
Purchase 37.3% · Refinance 30.2% · Home improvement 21.2% · Other 11.3% Conventional 97.0% · FHA 2.4% · VA 0.5% · USDA (RHS or FSA) 0.1% · of 3,884 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
42 states, 141 metros, 270 counties
State
Loans
Share
Pennsylvania
1,700
43.8%
Delaware
1,210
31.2%
New Jersey
555
14.3%
Maryland
64
1.6%
Florida
52
1.3%
California
47
1.2%
North Carolina
28
0.7%
Georgia
26
0.7%
Virginia
25
0.6%
South Carolina
18
0.5%
Largest metro areas, 2025
Loans originated
Metro area
Loans
Philadelphia
924
Wilmington
799
Montgomery County-Bucks County-Chester County
716
Camden
371
Dover
194
Atlantic City-Hammonton
116
Lakewood-New Brunswick
31
New York-Jersey City-White Plains
16
Baltimore-Columbia-Towson
15
Charlotte-Concord-Gastonia
14
Identifiers
LEI (Legal Entity Identifier, the global company code HMDA has used since 2018)
5493005DKMV1IHIM8E20
2018–2025
RSSD (the Federal Reserve's identifier for banks and their parents)
437914
2004–2023
Respondent ID (the HMDA filer code before 2018)
47938
1990–2010
Respondent ID (the HMDA filer code before 2018)
1707938
2011–2017
Established 18 February 1832. Reported under HMDA 1990-2025.
Mergers, name changes, data editions and corrections: WSFS Bank's data.
The data and the software behind these pages
WSFS Bank's data
$215
WSFS Bank 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.