“Our third quarter performance reflects meaningful progress and growing momentum,” said Clint Stein, President and CEO. “We closed our strategic acquisition of Pacific Premier, which completes our Western footprint and enhances our ability to generate top-quartile returns. While reported results were impacted by acquisition-related items, core profitability remained strong. Customer deposit growth supported balance sheet optimization, as we organically reduced transactional loans and non-core funding. Underscoring confidence in our strategy and an outlook for continued excess capital generation, our Board of Directors authorized a $700 million share repurchase program. As we integrate new capabilities and deepen both new and existing customer relationships, we remain focused on delivering consistent, repeatable performance while positioning the company for sustainable, relationship-driven growth and capital return to our shareholders.”
– Clint Stein, President and CEO of Columbia Banking System, Inc.
3Q25 HIGHLIGHTS (COMPARED TO 2Q25)
Net Interest Income and NIM
• Net interest income increased by $59 million from the prior quarter, due to one month operating as a combined company and a favorable shift into lower-cost funding sources.
• Net interest margin was 3.84%, up 9 basis points from the prior quarter, due to an increase in customer deposits and corresponding reduction in higher-cost funding sources. The net interest margin was also impacted by one month operating as a combined company in the current period.
Non-Interest Income and Expense
• Non-interest income increased by $12 million. Excluding the impact of fair value and hedges,1 non-interest income increased by $6 million, due to one month operating as a combined company.
• Non-interest expense increased by $115 million, primarily due to merger and restructuring expense of $87 million and one month operating as a combined company.
Credit Quality
• Net charge-offs were 0.22% of average loans and leases (annualized), compared to 0.31% in the prior quarter.
• Provision expense was $70 million and driven by the acquisition of Pacific Premier.
• Non-performing assets to total assets was 0.29%, compared to 0.35% as of June 30, 2025.
Capital
• Estimated total risk-based capital ratio of 13.4% and estimated common equity tier 1 risk-based capital ratio of 11.6%.
• Declared a quarterly cash dividend of $0.36 per common share on August 15, 2025, which was paid September 15, 2025.
Notable Items
• Our third small business and retail campaign of 2026 is ongoing. Through mid-October, these campaigns have brought approximately $1.1 billion in new deposits to the bank.
• Our Board of Directors authorized the repurchase of up to $700 million of common stock under a new repurchase plan.
3Q25 KEY FINANCIAL DATA
PERFORMANCE METRICS
3Q25
2Q25
3Q24
Return on average assets
0.67 %
1.19 %
1.12 %
Return on average common equity
6.19 %
11.56 %
11.36 %
Return on average tangible common equity 1
8.58 %
16.03 %
16.34 %
Operating return on average assets 1
1.42 %
1.25 %
1.10 %
Operating return on average common equity 1
13.15 %
12.16 %
11.15 %
Operating return on average tangible common equity 1
18.24 %
16.85 %
16.04 %
Net interest margin
3.84 %
3.75 %
3.56 %
Efficiency ratio
67.29 %
54.29 %
54.56 %
Operating efficiency ratio, as adjusted 1
52.32 %
51.79 %
53.89 %
INCOME STATEMENT
($ in millions, excl. per share data)
3Q25
2Q25
3Q24
Net interest income
$505
$446
$430
Provision for credit losses
$70
$30
$29
Non-interest income
$77
$65
$66
Non-interest expense
$393
$278
$271
Pre-provision net revenue 1
$189
$233
$225
Operating pre-provision net revenue 1
$270
$242
$221
Earnings per common share – diluted
$0.40
$0.73
$0.70
Operating earnings per common share – diluted 1
$0.85
$0.76
$0.69
Dividends paid per share
$0.36
$0.36
$0.36
BALANCE SHEET
3Q25
2Q25
3Q24
Total assets
$67.5B
$51.9B
$51.9B
Loans and leases
$48.5B
$37.6B
$37.5B
Deposits
$55.8B
$41.7B
$41.5B
Book value per common share
$26.04
$25.41
$25.17
Tangible book value per common share 1
$18.57
$18.47
$17.81
Acquisition and Branding Update Columbia Banking System, Inc. (“Columbia,” the “Company,” “we,” or “our”) closed its acquisition of Pacific Premier Bancorp, Inc. (“Pacific Premier”) on August 31, 2025, elevating Columbia’s deposit market share to a top-10 position in Southern California. The acquisition completes our Western footprint and strengthens our presence as a leading financial institution in the western United States. Our integration efforts are progressing smoothly, and we remain on track to integrate systems in the first quarter of 2026.
Columbia Bank began serving customers under its unified name and brand effective September 1, 2025. The strategic transition streamlines our identity across all business lines, including Columbia Wealth Advisors, Columbia Trust Company, Columbia Private Bank, and Columbia Private Trust, making it easier for customers to recognize and engage with the full breadth of our services.
Share Repurchase Authorization Announcement Columbia’s Board of Directors has authorized the repurchase of up to $700 million of common stock under a new repurchase plan. COLB common share repurchases may be executed in the open market or through privately negotiated transactions, including under Rule 10b5-1 plans. The timing and exact amount of common share repurchases will be at the discretion of senior management and subject to various factors, including, without limitation, Columbia’s capital position, financial performance, market conditions, and regulatory considerations. The repurchase program does not obligate Columbia to purchase any particular number of shares. The authorization will expire on November 30, 2026, but may be suspended, terminated or modified by the Board at any time.
“Our excess capital position as of September 30, 2025 supports the return of additional capital to our shareholders through share repurchases,” commented Mr. Stein. “In addition, we expect to produce exceptional profitability, which will result in meaningful capital generation over the coming quarters. Even as we expand our capital return platform, we are continuing to drive organic growth as we optimize the balance sheet, in line with our commitment to enhancing long-term shareholder value.”
Net Interest Income Net interest income was $505 million for the third quarter of 2025, up $59 million from the prior quarter. The increase largely reflects the impact of one month operating as a combined company in the current period. Lower interest expense due to a favorable shift in Columbia’s funding mix also contributed to the increase.
Columbia’s net interest margin was 3.84% for the third quarter of 2025, up 9 basis points from the second quarter of 2025. Net interest margin benefited from lower funding costs, due to an increase in customer deposits and corresponding reduction in higher-cost funding sources. The net interest margin was also impacted by one month operating as a combined company in the current period.
The cost of interest-bearing deposits decreased 9 basis points from the prior quarter to 2.43% for the third quarter of 2025, compared to 2.29% for the month of September and 2.20% as of September 30, 2025, reflecting our proactive management of deposit rates ahead of and following the 25-basis point reduction in the federal funds rate in mid-September and a reduction in higher-cost brokered deposits during the month. The cost of interest-bearing deposits in September also benefited from the amortization of a premium related to Pacific Premier’s time deposits, which will continue through December 31, 2025 at an equivalent monthly amount. The amortization contributed $4 million to net interest income during September, and favorably impacted deposit rates. Excluding this impact, the cost of interest-bearing deposits was 2.41% for the month of September and 2.32% as of September 30, 2025.
Columbia’s cost of interest-bearing liabilities decreased 13 basis points from the prior quarter to 2.65% for the third quarter of 2025, compared to 2.47% for the month of September and 2.39% as of September 30, 2025. Excluding the previously discussed premium amortization, the cost of interest-bearing liabilities was 2.58% for the month of September and 2.50% as of September 30, 2025. We expect the premium to be fully amortized by December 31, 2025. Please refer to the Q3 2025 Earnings Presentation for additional net interest margin change details and interest rate sensitivity information.
Non-interest Income Non-interest income was $77 million for the third quarter of 2025, up $12 million from the prior quarter. The increase was driven by quarterly changes in fair value adjustments and mortgage servicing rights (“MSR”) hedging activity, due to interest rate fluctuations during the quarter, collectively resulting in a net fair value gain of $5 million in the third quarter compared to a net fair value loss of $1 million in the second quarter, as detailed in our non-GAAP disclosures. Excluding these items, non-interest income was up $6 million2 between periods, due to one month operating as a combined company.
Non-interest Expense Non-interest expense was $393 million for the third quarter of 2025, up $115 million from the prior quarter, due to higher merger expense and one month operating as a combined company. Excluding merger and restructuring expense and a $1 million reversal of prior FDIC assessment expense, non-interest expense was $307 million2, up $37 million from the prior quarter, as Pacific Premier contributed $34 million to the quarter’s run rate. Other miscellaneous expenses also trended higher as we reinvest prior cost savings into our franchise. Please refer to the Q3 2025 Earnings Presentation for additional expense details.
Balance Sheet Total consolidated assets were $67.5 billion as of September 30, 2025, up from $51.9 billion as of June 30, 2025, due to the addition of Pacific Premier, partially offset by balance sheet optimization activity in the quarter. Cash and cash equivalents were $2.3 billion as of September 30, 2025, up from $1.9 billion as of June 30, 2025. Including secured off-balance sheet lines of credit, total available liquidity was $26.7 billion as of September 30, 2025, representing 40% of total assets, 48% of total deposits, and 130% of uninsured deposits. Available-for-sale securities, which are held on balance sheet at fair value, were $11.0 billion as of September 30, 2025, an increase of $2.4 billion relative to June 30, 2025, as securities acquired from Pacific Premier and an increase in the fair value of the portfolio was partially offset by net sales during the quarter. Please refer to the Q3 2025 Earnings Presentation for additional details related to our securities portfolio and liquidity position.
Gross loans and leases were $48.5 billion as of September 30, 2025, an increase of $10.8 billion relative to June 30, 2025, due to the addition of Pacific Premier, partially offset by run-off in commercial development and transactional loans, as well as the transfer of $282 million in residential real estate loans to the held-for-sale portfolio. Excluding these factors, the loan portfolio was essentially unchanged between June 30, 2025 and September 30, 2025. “Our teams continue to focus on new client acquisition and relationship-building, contributing to the 19% increase in new loan originations for the current quarter compared to the prior quarter,” commented Chris Merrywell, President of Columbia Bank. “We continue to prioritize balance sheet optimization and profitability, as we reduce our exposure to non-relationship loans.” Please refer to the Q3 2025 Earnings Presentation for additional details related to our loan portfolio, which include underwriting characteristics, the composition of our commercial portfolios, and disclosure related to transactional loans.
Total deposits were $55.8 billion as of September 30, 2025, an increase of $14.0 billion relative to June 30, 2025, due to the addition of Pacific Premier and organic growth in customer deposits, partially offset by lower brokered deposits. “Customer deposit growth approached $800 million organically during the quarter, reflecting new customer activity and a seasonal lift in balances,” stated Mr. Merrywell. “Our focus on relationship banking directly contributed to new deposit generation in the quarter, which reduced our reliance on wholesale funding sources.” Brokered deposits and borrowings were $4.8 billion as of September 30, 2025, a decrease of $1.9 billion relative to June 30, 2025. Please refer to the Q3 2025 Earnings Presentation for additional details related to deposit characteristics and flows.
Credit Quality The allowance for credit losses (“ACL”) was $492 million, or 1.01% of loans and leases, as of September 30, 2025, compared to $439 million, or 1.17% as of June 30, 2025. The $53 million increase in the ACL includes the addition of $5 million related to Pacific Premier purchased credit deteriorated (“PCD”) loans, which was booked at acquisition closing and did not affect the income statement. The provision for credit losses was $70 million for the third quarter of 2025 and includes an initial provision for acquired non-PCD loans and unfunded commitments and a recalibration of our models to incorporate historical Pacific Premier data into our ACL assumptions, where applicable. Excluding these items, our provision expense was $0 for the third quarter of 20252.
Net charge-offs were 0.22% of average loans and leases (annualized) for the third quarter of 2025, compared to 0.31% for the second quarter of 2025. Net charge-offs in the FinPac portfolio were $16 million in the third quarter, compared to $14 million in the second quarter. Net charge-offs excluding the FinPac portfolio were $6 million in the third quarter, compared to $15 million in the second quarter. Non-performing assets were $199 million, or 0.29% of total assets, as of September 30, 2025, compared to $180 million, or 0.35% of total assets, as of June 30, 2025. Please refer to the Q3 2025 Earnings Presentation for additional details related to the allowance for credit losses and other credit trends.
Capital Columbia’s book value per common share was $26.04 as of September 30, 2025, compared to $25.41 as of June 30, 2025. The increase reflects common shares issued and exchanged as a result of the acquisition, net capital generation from operations, and a favorable change in accumulated other comprehensive (loss) income (“AOCI”) to $(268) million as of September 30, 2025, compared to $(333) million as of the prior quarter-end. The change in AOCI is due primarily to a decrease in the tax-effected net unrealized loss on available-for-sale securities to $240 million as of September 30, 2025, compared to $311 million as of June 30, 2025. Tangible book value per common share3 was $18.57 as of September 30, 2025, compared to $18.47 as of June 30, 2025. The items discussed above offset 1.7% tangible book value dilution as a result of the Pacific Premier acquisition, resulting in net tangible book value expansion during the quarter.
Columbia’s estimated total risk-based capital ratio was 13.4% and its estimated common equity tier 1 risk-based capital ratio was 11.6% as of September 30, 2025, compared to 13.0% and 10.8%, respectively, as of June 30, 2025. Columbia remains above current “well-capitalized” regulatory minimums. The regulatory capital ratios as of September 30, 2025 are estimates, pending completion and filing of Columbia’s regulatory reports.
Earnings Presentation and Conference Call Information Columbia’s Q3 2025 Earnings Presentation provides additional disclosure. A copy will be available on our investor relations page: www.columbiabankingsystem.com.
Columbia will host its third quarter 2025 earnings conference call on October 30, 2025 at 2:00 p.m. PT (5:00 p.m. ET). During the call, Columbia’s management will provide an update on recent activities and discuss its third quarter 2025 financial results. Participants may join the audiocast or register for the call using the link below to receive dial-in details and their own unique PINs. It is recommended you join 10 minutes prior to the start time.
Join the audiocast: https://edge.media-server.com/mmc/p/i6z93t5w/ Register for the call: https://register-conf.media-server.com/register/BIde1295f868b04a969240d44867cade1a Access the replay through Columbia’s investor relations page: https://www.columbiabankingsystem.com/news-market-data/event-calendar/default.aspx
About Columbia Banking System, Inc. Columbia Banking System, Inc. (Nasdaq: COLB) is headquartered in Tacoma, Washington and is the parent company of Columbia Bank, an award-winning western U.S. regional bank. Columbia Bank is the largest bank headquartered in the Northwest and one of the largest banks headquartered in the West with locations in Arizona, California, Colorado, Idaho, Nevada, Oregon, Utah, and Washington. Columbia Bank combines the resources, sophistication, and expertise of a national bank with a commitment to deliver superior, personalized service. The bank supports consumers and businesses through a full suite of services, including retail and commercial banking, Small Business Administration lending, institutional and corporate banking, and equipment leasing. Columbia Bank customers also have access to comprehensive investment and wealth management expertise as well as healthcare and private banking through Columbia Wealth Management. Learn more at www.columbiabankingsystem.com.
1 “Non-GAAP” financial measure. See GAAP to Non-GAAP Reconciliation for additional information.
2 “Non-GAAP” financial measure. See GAAP to Non-GAAP Reconciliation for additional information.
3 “Non-GAAP” financial measure. See GAAP to Non-GAAP Reconciliation for additional information.
Forward-Looking Statements This press release includes forward-looking statements within the meaning of the “Safe-Harbor” provisions of the Private Securities Litigation Reform Act of 1995, which management believes are a benefit to shareholders. These statements are necessarily subject to risk and uncertainty and actual results could differ materially due to various risk factors, including those set forth from time to time in our filings with the Securities and Exchange Commission. You should not place undue reliance on forward-looking statements and we undertake no obligation to update any such statements. Forward-looking statements can be identified by words such as “anticipates,” “intends,” “plans,” “seeks,” “believes,” “estimates,” “expects,” “target,” “projects,” “outlook,” “forecast,” “will,” “may,” “could,” “should,” “can” and similar references to future periods. In this press release we make forward-looking statements about strategic and growth initiatives and the result of such activity. Risks and uncertainties that could cause results to differ from forward-looking statements we make include, without limitation: current and future economic and market conditions, including the effects of declines in housing and commercial real estate prices, high unemployment rates, continued or renewed inflation and any recession or slowdown in economic growth particularly in the western United States; economic forecast variables that are either materially worse or better than end of quarter projections and deterioration in the economy that could result in increased loan and lease losses, especially those risks associated with concentrations in real estate related loans; risks related to our acquisition of Pacific Premier (the “Transaction”), including, among others, (i) diversion of management’s attention from ongoing business operations and opportunities, (ii) cost savings and any revenue or expense synergies from the Transaction may not be fully realized or may take longer than anticipated to be realized, (iii) deposit attrition, customer or employee loss, and/or revenue loss as a result of the Transaction, and (iv) shareholder litigation that could negatively impact our business and operations; the impact of proposed or imposed tariffs by the U.S. government and retaliatory tariffs proposed or imposed by U.S. trading partners that could have an adverse impact on customers; our ability to effectively manage problem credits; the impact of bank failures or adverse developments at other banks on general investor sentiment regarding the liquidity and stability of banks; changes in interest rates that could significantly reduce net interest income and negatively affect asset yields and valuations and funding sources; changes in the scope and cost of FDIC insurance and other coverage; our ability to successfully implement efficiency and operational excellence initiatives; our ability to successfully develop and market new products and technology; changes in laws or regulations; potential adverse reactions or changes to business or employee relationships; the effect of geopolitical instability, including wars, conflicts and terrorist attacks; and natural disasters and other similar unexpected events outside of our control. We also caution that the amount and timing of any future common stock dividends or repurchases will depend on the earnings, cash requirements and financial condition of Columbia, market conditions, capital requirements, applicable law and regulations (including federal securities laws and federal banking regulations), and other factors deemed relevant by Columbia’s Board of Directors, and may be subject to regulatory approval or conditions.
TABLE INDEX
Page
Consolidated Statements of Income
8
Consolidated Balance Sheets
9
Financial Highlights
11
Loan & Lease Portfolio Balances and Mix
12
Deposit Portfolio Balances and Mix
14
Credit Quality – Non-performing Assets
15
Credit Quality – Allowance for Credit Losses
16
Consolidated Average Balance Sheets, Net Interest Income, and Yields/Rates
18
Residential Mortgage Banking Activity
20
Purchase Price Allocation
22
GAAP to Non-GAAP Reconciliation
23
Columbia Banking System, Inc.
Consolidated Statements of Income
(Unaudited)
Quarter Ended
% Change
($ in millions, shares in thousands)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq.
Quarter
Year over Year
Interest income:
Loans and leases
$ 619
$ 564
$ 553
$ 572
$ 589
10 %
5 %
Interest and dividends on investments:
Taxable
89
80
69
75
76
11 %
17 %
Exempt from federal income tax
8
7
7
7
7
14 %
14 %
Dividends
4
3
3
3
2
33 %
100 %
Temporary investments and interest bearing deposits
20
16
16
19
25
25 %
(20) %
Total interest income
740
670
648
676
699
10 %
6 %
Interest expense:
Deposits
195
180
177
189
208
8 %
(6) %
Securities sold under agreement to repurchase and federal funds purchased
1
1
1
1
1
— %
— %
Borrowings
30
35
36
40
50
(14) %
(40) %
Junior and other subordinated debentures
9
8
9
9
10
13 %
(10) %
Total interest expense
235
224
223
239
269
5 %
(13) %
Net interest income
505
446
425
437
430
13 %
17 %
Provision for credit losses
70
30
27
28
29
133 %
141 %
Non-interest income:
Service charges on deposits
21
20
19
18
18
5 %
17 %
Card-based fees
15
14
13
15
15
7 %
— %
Financial services and trust revenue
9
6
5
5
5
50 %
80 %
Residential mortgage banking revenue, net
7
8
9
7
7
(13) %
— %
Gain (loss) on investment securities, net
2
—
2
(1)
2
nm
— %
Loss on loan and lease sales, net
—
—
—
(2)
—
nm
nm
Gain (loss) on loans held for investment, at fair value
4
—
7
(7)
9
nm
(56) %
BOLI income
6
5
5
5
5
20 %
20 %
Other income
13
12
6
10
5
8 %
160 %
Total non-interest income
77
65
66
50
66
18 %
17 %
Non-interest expense:
Salaries and employee benefits
171
155
145
142
147
10 %
16 %
Occupancy and equipment, net
54
47
48
47
45
15 %
20 %
Intangible amortization
31
26
28
29
29
19 %
7 %
FDIC assessments
8
8
8
8
9
— %
(11) %
Merger and restructuring expense
87
8
14
2
2
nm
nm
Legal settlement
—
—
55
—
—
nm
nm
Other expenses
42
34
42
39
39
24 %
8 %
Total non-interest expense
393
278
340
267
271
41 %
45 %
Income before provision for income taxes
119
203
124
192
196
(41) %
(39) %
Provision for income taxes
23
51
37
49
50
(55) %
(54) %
Net income
$ 96
$ 152
$ 87
$ 143
$ 146
(37) %
(34) %
Weighted average basic shares outstanding (in thousands)
237,838
209,125
208,800
208,548
208,545
14 %
14 %
Weighted average diluted shares outstanding (in thousands)
238,925
209,975
210,023
209,889
209,454
14 %
14 %
Earnings per common share – basic
$ 0.40
$ 0.73
$ 0.41
$ 0.69
$ 0.70
(45) %
(43) %
Earnings per common share – diluted
$ 0.40
$ 0.73
$ 0.41
$ 0.68
$ 0.70
(45) %
(43) %
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Consolidated Statements of Income
(Unaudited)
Nine Months Ended
% Change
($ in millions, shares in thousands)
Sep 30, 2025
Sep 30, 2024
Year over Year
Interest income:
Loans and leases
$ 1,736
$ 1,748
(1) %
Interest and dividends on investments:
Taxable
238
230
3 %
Exempt from federal income tax
22
21
5 %
Dividends
10
9
11 %
Temporary investments and interest bearing deposits
52
71
(27) %
Total interest income
2,058
2,079
(1) %
Interest expense:
Deposits
552
614
(10) %
Securities sold under agreement to repurchase and federal funds purchased
3
4
(25) %
Borrowings
101
150
(33) %
Junior and other subordinated debentures
26
30
(13) %
Total interest expense
682
798
(15) %
Net interest income
1,376
1,281
7 %
Provision for credit losses
127
78
63 %
Non-interest income:
Service charges on deposits
60
53
13 %
Card-based fees
42
42
— %
Financial services and trust revenue
20
15
33 %
Residential mortgage banking revenue, net
24
17
41 %
Gain on investment securities, net
4
1
300 %
Loss on loan and lease sales, net
—
(1)
nm
Gain (loss) on loans held for investment, at fair value
11
(3)
nm
BOLI income
16
14
14 %
Other income
31
23
35 %
Total non-interest income
208
161
29 %
Non-interest expense:
Salaries and employee benefits
471
447
5 %
Occupancy and equipment, net
149
135
10 %
Intangible amortization
85
90
(6) %
FDIC assessments
24
33
(27) %
Merger and restructuring expense
109
21
419 %
Legal settlement
55
—
nm
Other expenses
118
112
5 %
Total non-interest expense
1,011
838
21 %
Income before provision for income taxes
446
526
(15) %
Provision for income taxes
111
136
(18) %
Net income
$ 335
$ 390
(14) %
Weighted average basic shares outstanding (in thousands)
218,694
208,435
5 %
Weighted average diluted shares outstanding (in thousands)
219,712
209,137
5 %
Earnings per common share – basic
$ 1.53
$ 1.87
(18) %
Earnings per common share – diluted
$ 1.53
$ 1.87
(18) %
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Consolidated Balance Sheets
(Unaudited)
% Change
($ in millions, shares in thousands)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq.
Quarter
Year over Year
Assets:
Cash and due from banks
$ 535
$ 608
$ 591
$ 497
$ 591
(12) %
(9) %
Interest-bearing cash and temporary investments
1,808
1,334
1,481
1,382
1,520
36 %
19 %
Investment securities:
Equity and other, at fair value
112
93
92
78
80
20 %
40 %
Available for sale, at fair value
11,013
8,653
8,229
8,275
8,677
27 %
27 %
Held to maturity, at amortized cost
18
2
2
2
2
nm
nm
Loans held for sale
340
66
65
72
67
415 %
407 %
Loans and leases
48,462
37,637
37,616
37,681
37,503
29 %
29 %
Allowance for credit losses on loans and leases
(473)
(421)
(421)
(425)
(420)
12 %
13 %
Net loans and leases
47,989
37,216
37,195
37,256
37,083
29 %
29 %
Restricted equity securities
119
161
125
150
116
(26) %
3 %
Premises and equipment, net
416
357
345
349
338
17 %
23 %
Operating lease right-of-use assets
156
110
107
111
106
42 %
47 %
Goodwill
1,481
1,029
1,029
1,029
1,029
44 %
44 %
Other intangible assets, net
754
430
456
484
513
75 %
47 %
Residential mortgage servicing rights, at fair value
101
103
106
108
102
(2) %
(1) %
Bank-owned life insurance
1,199
705
701
694
691
70 %
74 %
Deferred tax asset, net
392
299
311
359
286
31 %
37 %
Other assets
1,063
735
684
730
708
45 %
50 %
Total assets
$ 67,496
$ 51,901
$ 51,519
$ 51,576
$ 51,909
30 %
30 %
Liabilities:
Deposits
Non-interest-bearing
$ 17,810
$ 13,220
$ 13,414
$ 13,308
$ 13,534
35 %
32 %
Interest-bearing
37,961
28,523
28,804
28,413
27,981
33 %
36 %
Total deposits
55,771
41,743
42,218
41,721
41,515
34 %
34 %
Securities sold under agreements to repurchase
167
191
192
237
184
(13) %
(9) %
Borrowings
2,300
3,350
2,550
3,100
3,650
(31) %
(37) %
Junior subordinated debentures, at fair value
331
323
321
331
312
2 %
6 %
Junior and other subordinated debentures, at amortized cost
107
108
108
108
108
(1) %
(1) %
Operating lease liabilities
168
125
121
126
121
34 %
39 %
Other liabilities
862
719
771
835
745
20 %
16 %
Total liabilities
59,706
46,559
46,281
46,458
46,635
28 %
28 %
Shareholders’ equity:
Common stock
8,189
5,826
5,823
5,817
5,812
41 %
41 %
Accumulated deficit
(131)
(151)
(227)
(237)
(304)
(13) %
(57) %
Accumulated other comprehensive loss
(268)
(333)
(358)
(462)
(234)
(20) %
15 %
Total shareholders’ equity
7,790
5,342
5,238
5,118
5,274
46 %
48 %
Total liabilities and shareholders’ equity
$ 67,496
$ 51,901
$ 51,519
$ 51,576
$ 51,909
30 %
30 %
Common shares outstanding at period end (in thousands)
299,147
210,213
210,112
209,536
209,532
42 %
43 %
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Financial Highlights
(Unaudited)
Quarter Ended
% Change
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Per Common Share Data:
Dividends
$ 0.36
$ 0.36
$ 0.36
$ 0.36
$ 0.36
— %
— %
Book value
$ 26.04
$ 25.41
$ 24.93
$ 24.43
$ 25.17
2 %
3 %
Tangible book value (1)
$ 18.57
$ 18.47
$ 17.86
$ 17.20
$ 17.81
1 %
4 %
Performance Ratios:
Efficiency ratio (2)
67.29 %
54.29 %
69.06 %
54.61 %
54.56 %
13.00
12.73
Non-interest expense to average assets (1)
2.74 %
2.16 %
2.68 %
2.06 %
2.08 %
0.58
0.66
Return on average assets (“ROAA”)
0.67 %
1.19 %
0.68 %
1.10 %
1.12 %
(0.52)
(0.45)
Pre-provision net revenue (“PPNR”) ROAA (1)
1.32 %
1.81 %
1.19 %
1.70 %
1.72 %
(0.49)
(0.40)
Return on average common equity
6.19 %
11.56 %
6.73 %
10.91 %
11.36 %
(5.37)
(5.17)
Return on average tangible common equity (1)
8.58 %
16.03 %
9.45 %
15.41 %
16.34 %
(7.45)
(7.76)
Performance Ratios – Operating: (1)
Operating efficiency ratio, as adjusted (1),(2)
52.32 %
51.79 %
55.11 %
52.51 %
53.89 %
0.53
(1.57)
Operating non-interest expense to average assets (1)
2.14 %
2.10 %
2.13 %
2.03 %
2.05 %
0.04
0.09
Operating ROAA (1)
1.42 %
1.25 %
1.10 %
1.15 %
1.10 %
0.17
0.32
Operating PPNR ROAA (1)
1.89 %
1.88 %
1.67 %
1.77 %
1.69 %
0.01
0.20
Operating return on average common equity (1)
13.15 %
12.16 %
10.87 %
11.40 %
11.15 %
0.99
2.00
Operating return on average tangible common equity (1)
18.24 %
16.85 %
15.26 %
16.11 %
16.04 %
1.39
2.20
Average Balance Sheet Yields, Rates, & Ratios:
Yield on loans and leases
5.96 %
6.00 %
5.92 %
6.05 %
6.22 %
(0.04)
(0.26)
Yield on earning assets (2)
5.62 %
5.62 %
5.49 %
5.63 %
5.78 %
—
(0.16)
Cost of interest bearing deposits
2.43 %
2.52 %
2.52 %
2.66 %
2.95 %
(0.09)
(0.52)
Cost of interest bearing liabilities
2.65 %
2.78 %
2.80 %
2.98 %
3.29 %
(0.13)
(0.64)
Cost of total deposits
1.66 %
1.73 %
1.72 %
1.80 %
1.99 %
(0.07)
(0.33)
Cost of total funding (3)
1.87 %
1.98 %
1.99 %
2.09 %
2.32 %
(0.11)
(0.45)
Net interest margin (2)
3.84 %
3.75 %
3.60 %
3.64 %
3.56 %
0.09
0.28
Average interest bearing cash / Average interest earning assets
3.41 %
2.97 %
3.13 %
3.29 %
3.74 %
0.44
(0.33)
Average loans and leases / Average interest earning assets
78.39 %
78.64 %
78.93 %
78.42 %
77.91 %
(0.25)
0.48
Average loans and leases / Average total deposits
88.39 %
90.07 %
90.36 %
89.77 %
90.42 %
(1.68)
(2.03)
Average non-interest bearing deposits / Average total deposits
31.41 %
31.39 %
31.75 %
32.45 %
32.52 %
0.02
(1.11)
Average total deposits / Average total funding (3)
93.47 %
91.92 %
91.86 %
91.88 %
90.25 %
1.55
3.22
Select Credit & Capital Ratios:
Non-performing loans and leases to total loans and leases
0.40 %
0.47 %
0.47 %
0.44 %
0.44 %
(0.07)
(0.04)
Non-performing assets to total assets
0.29 %
0.35 %
0.35 %
0.33 %
0.32 %
(0.06)
(0.03)
Allowance for credit losses to loans and leases
1.01 %
1.17 %
1.17 %
1.17 %
1.17 %
(0.16)
(0.16)
Total risk-based capital ratio (4)
13.4 %
13.0 %
12.9 %
12.8 %
12.5 %
0.40
0.90
Common equity tier 1 risk-based capital ratio (4)
11.6 %
10.8 %
10.6 %
10.5 %
10.3 %
0.80
1.30
(1) See GAAP to Non-GAAP Reconciliation.
(2) Tax-exempt interest was adjusted to a taxable equivalent basis using a 21% tax rate.
(3) Total funding = total deposits + total borrowings.
(4) Estimated holding company ratios.
Columbia Banking System, Inc.
Financial Highlights
(Unaudited)
Nine Months Ended
% Change
Sep 30, 2025
Sep 30, 2024
Year over Year
Per Common Share Data:
Dividends
$ 1.08
$ 1.08
— %
Performance Ratios:
Efficiency ratio (2)
63.66 %
57.99 %
5.67
Non-interest expense to average assets (1)
2.54 %
2.15 %
0.39
Return on average assets
0.84 %
1.00 %
(0.16)
PPNR ROAA (1)
1.44 %
1.55 %
(0.11)
Return on average common equity
8.06 %
10.42 %
(2.36)
Return on average tangible common equity (1)
11.22 %
15.27 %
(4.05)
Performance Ratios – Operating: (1)
Operating efficiency ratio, as adjusted (1),(2)
53.07 %
54.80 %
(1.73)
Operating non-interest expense to average assets (1)
2.12 %
2.07 %
0.05
Operating ROAA (1)
1.26 %
1.07 %
0.19
Operating PPNR ROAA (1)
1.81 %
1.65 %
0.16
Operating return on average common equity (1)
12.10 %
11.17 %
0.93
Operating return on average tangible common equity (1)
16.85 %
16.36 %
0.49
Average Balance Sheet Yields, Rates, & Ratios:
Yield on loans and leases
5.96 %
6.18 %
(0.22)
Yield on earning assets (2)
5.58 %
5.76 %
(0.18)
Cost of interest bearing deposits
2.49 %
2.93 %
(0.44)
Cost of interest bearing liabilities
2.74 %
3.28 %
(0.54)
Cost of total deposits
1.70 %
1.97 %
(0.27)
Cost of total funding (3)
1.94 %
2.31 %
(0.37)
Net interest margin (2)
3.73 %
3.55 %
0.18
Average interest bearing cash / Average interest earning assets
3.18 %
3.61 %
(0.43)
Average loans and leases / Average interest earning assets
78.64 %
78.02 %
0.62
Average loans and leases / Average total deposits
89.55 %
90.48 %
(0.93)
Average non-interest bearing deposits / Average total deposits
31.51 %
32.78 %
(1.27)
Average total deposits / Average total funding (3)
92.46 %
90.16 %
2.30
(1) See GAAP to Non-GAAP Reconciliation.
(2) Tax-exempt interest was adjusted to a taxable equivalent basis using a 21% tax rate.
(3) Total funding = Total deposits + Total borrowings.
Columbia Banking System, Inc.
Loan & Lease Portfolio Balances and Mix
(Unaudited)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
% Change
($ in millions)
Amount
Amount
Amount
Amount
Amount
Seq. Quarter
Year over Year
Loans and leases:
Commercial real estate:
Non-owner occupied term
$ 8,444
$ 6,190
$ 6,179
$ 6,278
$ 6,392
36 %
32 %
Owner occupied term
7,361
5,320
5,303
5,270
5,210
38 %
41 %
Multifamily
10,377
5,735
5,831
5,804
5,780
81 %
80 %
Construction & development
2,071
2,070
2,071
1,983
1,989
— %
4 %
Residential development
367
286
252
232
245
28 %
50 %
Commercial:
Term
6,590
5,353
5,490
5,538
5,429
23 %
21 %
Lines of credit & other
3,582
2,951
2,754
2,770
2,641
21 %
36 %
Leases & equipment finance
1,614
1,641
1,644
1,661
1,670
(2) %
(3) %
Residential:
Mortgage
5,722
5,830
5,878
5,933
5,945
(2) %
(4) %
Home equity loans & lines
2,153
2,083
2,039
2,032
2,017
3 %
7 %
Consumer & other
181
178
175
180
185
2 %
(2) %
Total loans and leases, net of deferred fees and costs
$ 48,462
$ 37,637
$ 37,616
$ 37,681
$ 37,503
29 %
29 %
Loans and leases mix:
Commercial real estate:
Non-owner occupied term
18 %
16 %
16 %
17 %
17 %
Owner occupied term
15 %
14 %
14 %
14 %
14 %
Multifamily
21 %
15 %
15 %
15 %
15 %
Construction & development
4 %
6 %
6 %
5 %
5 %
Residential development
1 %
1 %
1 %
1 %
1 %
Commercial:
Term
14 %
14 %
15 %
15 %
15 %
Lines of credit & other
7 %
8 %
7 %
7 %
7 %
Leases & equipment finance
3 %
4 %
4 %
4 %
4 %
Residential:
Mortgage
12 %
15 %
16 %
16 %
16 %
Home equity loans & lines
4 %
6 %
5 %
5 %
5 %
Consumer & other
1 %
1 %
1 %
1 %
1 %
Total
100 %
100 %
100 %
100 %
100 %
Columbia Banking System, Inc.
Deposit Portfolio Balances and Mix
(Unaudited)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
% Change
($ in millions)
Amount
Amount
Amount
Amount
Amount
Seq. Quarter
Year over Year
Deposits:
Demand, non-interest bearing
$ 17,810
$ 13,220
$ 13,414
$ 13,308
$ 13,534
35 %
32 %
Demand, interest bearing
11,675
8,335
8,494
8,476
8,445
40 %
38 %
Money market
16,816
11,694
11,971
11,475
11,351
44 %
48 %
Savings
2,504
2,276
2,337
2,360
2,451
10 %
2 %
Time
6,966
6,218
6,002
6,102
5,734
12 %
21 %
Total
$ 55,771
$ 41,743
$ 42,218
$ 41,721
$ 41,515
34 %
34 %
Total core deposits (1)
$ 51,535
$ 37,294
$ 38,079
$ 37,488
$ 37,775
38 %
36 %
Deposit mix:
Demand, non-interest bearing
32 %
32 %
32 %
32 %
33 %
Demand, interest bearing
21 %
20 %
20 %
20 %
20 %
Money market
30 %
28 %
28 %
27 %
27 %
Savings
5 %
5 %
6 %
6 %
6 %
Time
12 %
15 %
14 %
15 %
14 %
Total
100 %
100 %
100 %
100 %
100 %
(1) Core deposits are defined as total deposits less time deposits greater than $250,000 and all brokered deposits.
Columbia Banking System, Inc.
Credit Quality – Non-performing Assets
(Unaudited)
Quarter Ended
% Change
($ in millions)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Non-performing assets:(1)
Loans and leases on non-accrual status:
Commercial real estate
$ 53
$ 31
$ 42
$ 39
$ 37
71 %
43 %
Commercial
67
67
80
57
62
0 %
8 %
Total loans and leases on non-accrual status
120
98
122
96
99
22 %
21 %
Loans and leases past due 90+ days and accruing: (2)
Commercial
5
5
—
5
6
0 %
(17) %
Residential (2)
71
74
53
66
61
(4) %
16 %
Total loans and leases past due 90+ days and accruing (2)
76
79
53
71
67
(4) %
13 %
Total non-performing loans and leases (1), (2)
196
177
175
167
166
11 %
18 %
Other real estate owned
3
3
3
3
2
0 %
50 %
Total non-performing assets (1), (2)
$ 199
$ 180
$ 178
$ 170
$ 168
11 %
18 %
Loans and leases past due 31-89 days
$ 85
$ 142
$ 158
$ 105
$ 67
(40) %
27 %
Loans and leases past due 31-89 days to total loans and leases
0.18 %
0.38 %
0.42 %
0.28 %
0.18 %
(0.20)
—
Non-performing loans and leases to total loans and leases (1), (2)
0.40 %
0.47 %
0.47 %
0.44 %
0.44 %
(0.07)
(0.04)
Non-performing assets to total assets (1), (2)
0.29 %
0.35 %
0.35 %
0.33 %
0.32 %
(0.06)
(0.03)
Non-accrual loans and leases to total loan and leases (2)
0.25 %
0.26 %
0.33 %
0.26 %
0.26 %
(0.01)
(0.01)
(1)
Non-accrual and 90+ days past due loans include government guarantees of $70 million, $68 million, $67 million, $74 million, and $66 million at September 30, 2025, June 30, 2025, March 31, 2025, December 31, 2024, and September 30, 2024, respectively.
(2)
Excludes certain mortgage loans guaranteed by GNMA, which Columbia has the unilateral right to repurchase but has not done so, totaling $2 million, $2 million, $3 million, $2 million, and $4 million at September 30, 2025, June 30, 2025, March 31, 2025, December 31, 2024, and September 30, 2024, respectively.
Columbia Banking System, Inc.
Credit Quality – Allowance for Credit Losses
(Unaudited)
Quarter Ended
% Change
($ in millions)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Allowance for credit losses on loans and leases (ACLLL)
Balance, beginning of period
$ 421
$ 421
$ 425
$ 420
$ 419
0 %
0 %
Initial ACL recorded for PCD loans acquired during the period
5
—
—
—
—
nm
nm
Provision for credit losses on loans and leases
69
29
26
30
31
138 %
123 %
Charge-offs
Commercial real estate
(3)
—
—
(3)
—
nm
nm
Commercial
(22)
(33)
(33)
(26)
(33)
(33) %
(33) %
Residential
—
—
(1)
—
(1)
nm
nm
Consumer & other
(2)
(1)
(1)
(1)
(1)
100 %
100 %
Total charge-offs
(27)
(34)
(35)
(30)
(35)
(21) %
(23) %
Recoveries
Commercial
4
5
4
4
5
(20) %
(20) %
Consumer & other
1
—
1
1
—
nm
nm
Total recoveries
5
5
5
5
5
0 %
0 %
Net (charge-offs) recoveries
Commercial real estate
(3)
—
—
(3)
—
nm
nm
Commercial
(18)
(28)
(29)
(22)
(28)
(36) %
(36) %
Residential
—
—
(1)
—
(1)
nm
nm
Consumer & other
(1)
(1)
—
—
(1)
0 %
0 %
Total net charge-offs
(22)
(29)
(30)
(25)
(30)
(24) %
(27) %
Balance, end of period
$ 473
$ 421
$ 421
$ 425
$ 420
12 %
13 %
Reserve for unfunded commitments
Balance, beginning of period
$ 18
$ 17
$ 16
$ 18
$ 20
6 %
(10) %
Provision (recapture) for credit losses on unfunded commitments
1
1
1
(2)
(2)
0 %
nm
Balance, end of period
19
18
17
16
18
6 %
6 %
Total Allowance for credit losses (ACL)
$ 492
$ 439
$ 438
$ 441
$ 438
12 %
12 %
Net charge-offs to average loans and leases (annualized)
0.22 %
0.31 %
0.32 %
0.27 %
0.31 %
(0.09)
(0.09)
Recoveries to gross charge-offs
18.52 %
15.19 %
14.05 %
15.23 %
16.76 %
3.33
1.76
ACLLL to loans and leases
0.98 %
1.12 %
1.12 %
1.13 %
1.12 %
(0.14)
(0.14)
ACL to loans and leases
1.01 %
1.17 %
1.17 %
1.17 %
1.17 %
(0.16)
(0.16)
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Credit Quality – Allowance for Credit Losses
(Unaudited)
Nine Months Ended
% Change
($ in millions)
Sep 30, 2025
Sep 30, 2024
Year over Year
Allowance for credit losses on loans and leases (ACLLL)
Balance, beginning of period
$ 425
$ 441
(4) %
Initial ACL recorded for PCD loans acquired during the period
5
—
nm
Provision for credit losses on loans and leases
124
83
49 %
Charge-offs
Commercial real estate
(3)
(1)
200 %
Commercial
(88)
(113)
(22) %
Residential
(1)
(2)
(50) %
Consumer & other
(4)
(5)
(20) %
Total charge-offs
(96)
(121)
(21) %
Recoveries
Commercial real estate
—
1
(100) %
Commercial
13
14
(7) %
Residential
—
1
(100) %
Consumer & other
2
1
100 %
Total recoveries
15
17
(12) %
Net (charge-offs) recoveries
Commercial real estate
(3)
—
nm
Commercial
(75)
(99)
(24) %
Residential
(1)
(1)
0 %
Consumer & other
(2)
(4)
(50) %
Total net charge-offs
(81)
(104)
(22) %
Balance, end of period
$ 473
$ 420
13 %
Reserve for unfunded commitments
Balance, beginning of period
$ 16
$ 23
(30) %
Provision (recapture) for credit losses on unfunded commitments
3
(5)
nm
Balance, end of period
19
18
6 %
Total Allowance for credit losses (ACL)
$ 492
$ 438
12 %
Net charge-offs to average loans and leases (annualized)
0.28 %
0.37 %
(0.09)
Recoveries to gross charge-offs
15.63 %
14.37 %
1.26
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Consolidated Average Balance Sheets, Net Interest Income, and Yields/Rates
(Unaudited)
Quarter Ended
September 30, 2025
June 30, 2025
September 30, 2024
($ in millions)
Average Balance
Interest Income or Expense
Average Yields or Rates
Average Balance
Interest Income or
Expense
Average
Yields or Rates
Average
Balance
Interest
Income or
Expense
Average Yields or Rates
INTEREST-EARNING ASSETS:
Loans held for sale
$ 80
$ 1
7.14 %
$ 67
$ 1
6.66 %
$ 68
$ 1
6.62 %
Loans and leases (1)
41,164
618
5.96 %
37,648
563
6.00 %
37,544
588
6.22 %
Taxable securities
8,523
93
4.35 %
7,937
83
4.22 %
7,943
78
3.97 %
Non-taxable securities (2)
950
10
4.26 %
798
8
3.95 %
828
8
3.78 %
Temporary investments and interest-bearing cash
1,793
20
4.40 %
1,421
16
4.46 %
1,802
25
5.45 %
Total interest-earning assets (1), (2)
52,510
$ 742
5.62 %
47,871
$ 671
5.62 %
48,185
$ 700
5.78 %
Goodwill and other intangible assets
1,719
1,472
1,560
Other assets
2,594
2,209
2,264
Total assets
$ 56,823
$ 51,552
$ 52,009
INTEREST-BEARING LIABILITIES:
Interest-bearing demand deposits
$ 9,630
$ 53
2.17 %
$ 8,480
$ 48
2.28 %
$ 8,313
$ 57
2.74 %
Money market deposits
13,476
83
2.46 %
11,783
72
2.46 %
11,085
78
2.80 %
Savings deposits
2,358
1
0.16 %
2,287
1
0.13 %
2,480
1
0.17 %
Time deposits
6,481
58
3.57 %
6,126
59
3.85 %
6,141
72
4.65 %
Total interest-bearing deposits
31,945
195
2.43 %
28,676
180
2.52 %
28,019
208
2.95 %
Repurchase agreements and federal funds purchased
176
1
2.15 %
186
1
2.06 %
195
1
2.29 %
Borrowings
2,648
30
4.54 %
3,058
35
4.53 %
3,874
50
5.10 %
Junior and other subordinated debentures
430
9
7.99 %
428
8
8.05 %
417
10
9.43 %
Total interest-bearing liabilities
35,199
$ 235
2.65 %
32,348
$ 224
2.78 %
32,505
$ 269
3.29 %
Non-interest-bearing deposits
14,627
13,123
13,500
Other liabilities
840
794
885
Total liabilities
50,666
46,265
46,890
Common equity
6,157
5,287
5,119
Total liabilities and shareholders’ equity
$ 56,823
$ 51,552
$ 52,009
NET INTEREST INCOME (2)
$ 507
$ 447
$ 431
NET INTEREST SPREAD (2)
2.97 %
2.84 %
2.49 %
NET INTEREST INCOME TO EARNING ASSETS OR NET INTEREST MARGIN (1), (2)
3.84 %
3.75 %
3.56 %
(1)
Non-accrual loans and leases are included in the average balance.
(2)
Tax-exempt income was adjusted to a tax equivalent basis at a 21% tax rate. The amount of such adjustment was an addition to recorded income of approximately $2 million for the three months ended September 30, 2025, as compared to $1 million for the three months ended June 30, 2025 and $1 million for the three months ended September 30, 2024.
Columbia Banking System, Inc.
Consolidated Average Balance Sheets, Net Interest Income, and Yields/Rates
(Unaudited)
Nine Months Ended
September 30, 2025
September 30, 2024
($ in millions)
Average Balance
Interest Income or Expense
Average Yields or Rates
Average Balance
Interest Income or Expense
Average Yields or Rates
INTEREST-EARNING ASSETS:
Loans held for sale
$ 69
$ 3
6.74 %
$ 67
$ 3
6.56 %
Loans and leases (1)
38,843
1,733
5.96 %
37,601
1,745
6.18 %
Taxable securities
8,053
248
4.11 %
7,954
239
4.01 %
Non-taxable securities (2)
856
26
4.04 %
835
24
3.77 %
Temporary investments and interest-bearing cash
1,570
52
4.44 %
1,738
71
5.48 %
Total interest-earning assets (1), (2)
49,391
$ 2,062
5.58 %
48,195
$ 2,082
5.76 %
Goodwill and other intangible assets
1,565
1,589
Other assets
2,340
2,241
Total assets
$ 53,296
$ 52,025
INTEREST-BEARING LIABILITIES:
Interest-bearing demand deposits
$ 8,832
$ 147
2.23 %
$ 8,166
$ 162
2.66 %
Money market deposits
12,295
225
2.44 %
10,850
227
2.79 %
Savings deposits
2,332
2
0.13 %
2,574
3
0.14 %
Time deposits
6,249
178
3.81 %
6,345
222
4.67 %
Total interest-bearing deposits
29,708
552
2.49 %
27,935
614
2.93 %
Repurchase agreements and federal funds purchased
192
3
2.09 %
217
4
2.40 %
Borrowings
2,913
101
4.63 %
3,898
150
5.15 %
Junior and other subordinated debentures
432
26
7.99 %
419
30
9.44 %
Total interest-bearing liabilities
33,245
$ 682
2.74 %
32,469
$ 798
3.28 %
Non-interest-bearing deposits
13,668
13,622
Other liabilities
826
929
Total liabilities
47,739
47,020
Common equity
5,557
5,005
Total liabilities and shareholders’ equity
$ 53,296
$ 52,025
NET INTEREST INCOME (2)
$ 1,380
$ 1,284
NET INTEREST SPREAD (2)
2.84 %
2.48 %
NET INTEREST INCOME TO EARNING ASSETS OR NET INTEREST MARGIN (1), (2)
3.73 %
3.55 %
(1)
Non-accrual loans and leases are included in the average balance.
(2)
Tax-exempt income was adjusted to a tax equivalent basis at a 21% tax rate. The amount of such adjustment was an addition to recorded income of approximately $4 million for the nine months ended September 30, 2025, as compared to $3 million for the same period in 2024.
Columbia Banking System, Inc.
Residential Mortgage Banking Activity
(Unaudited)
Quarter Ended
%
($ in millions)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Residential mortgage banking revenue:
Origination and sale
$ 5
$ 5
$ 4
$ 5
$ 5
— %
— %
Servicing
5
6
6
6
6
(17) %
(17) %
Change in fair value of MSR asset:
Changes due to collection/realization of expected cash flows over time
(3)
(3)
(3)
(3)
(3)
— %
— %
Changes due to valuation inputs or assumptions
—
(2)
(1)
7
(6)
nm
nm
MSR hedge gain (loss)
—
2
3
(8)
5
(100) %
(100) %
Total
$ 7
$ 8
$ 9
$ 7
$ 7
(13) %
— %
Closed loan volume for sale
$ 166
$ 164
$ 136
$ 175
$ 161
1 %
3 %
Gain on sale margin
3.01 %
2.77 %
3.23 %
2.58 %
3.24 %
0.24
-0.23
Residential mortgage servicing rights:
Balance, beginning of period
$ 103
$ 106
$ 108
$ 102
$ 110
(3) %
(6) %
Additions for new MSR capitalized
1
2
2
2
1
(50) %
— %
Change in fair value of MSR asset:
Changes due to collection/realization of expected cash flows over time
(3)
(3)
(3)
(3)
(3)
— %
— %
Changes due to valuation inputs or assumptions
—
(2)
(1)
7
(6)
nm
nm
Balance, end of period
$ 101
$ 103
$ 106
$ 108
$ 102
(2) %
(1) %
Residential mortgage loans serviced for others
$ 7,797
$ 7,852
$ 7,888
$ 7,939
$ 7,966
(1) %
(2) %
MSR as % of serviced portfolio
1.30 %
1.31 %
1.34 %
1.36 %
1.28 %
(0.01)
0.02
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Residential Mortgage Banking Activity
(Unaudited)
Nine Months Ended
% Change
($ in millions)
Sep 30, 2025
Sep 30, 2024
Year over Year
Residential mortgage banking revenue:
Origination and sale
$ 14
$ 11
27 %
Servicing
17
18
(6) %
Change in fair value of MSR asset:
Changes due to collection/realization of expected cash flows over time
(9)
(9)
0 %
Changes due to valuation inputs or assumptions
(3)
(2)
50 %
MSR hedge gain (loss)
5
(1)
nm
Total
$ 24
$ 17
41 %
Closed loan volume for sale
$ 466
$ 389
20 %
Gain on sale margin
3.00 %
2.98 %
0.02
Residential mortgage servicing rights:
Balance, beginning of period
$ 108
$ 109
(1) %
Additions for new MSR capitalized
5
4
25 %
Change in fair value of MSR asset:
Changes due to collection/realization of expected cash flows over time
(9)
(9)
0 %
Changes due to valuation inputs or assumptions
(3)
(2)
50 %
Balance, end of period
$ 101
$ 102
(1) %
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
Purchase Price Allocation (1)
(Unaudited)
($ in millions)
August 31, 2025
Purchase price consideration
Fair value of common shares issued and exchanged
$ 2,355
Total consideration
$ 2,355
Fair value of assets acquired:
Cash and due from banks
$ 874
Investment securities
2,828
Loans held for sale
1
Loans and leases
11,382
Restricted equity securities
98
Premises and equipment
53
Other intangible assets
355
Deferred tax assets
132
Other assets
889
Total assets acquired
$ 16,612
Fair value of liabilities assumed:
Deposits
$ 14,542
Other liabilities
167
Total liabilities assumed
$ 14,709
Net assets acquired
$ 1,903
Goodwill
$ 452
(1)
The estimates of fair value were recorded based on initial valuations available at August 31, 2025 and these estimates, including initial accounting for deferred taxes, were considered preliminary as of September 30, 2025 and subject to adjustment for up to one year after the acquisition date.
Non-GAAP Financial Measures In addition to results presented in accordance with generally accepted accounting principles in the United States of America (“GAAP”), this press release contains certain non-GAAP financial measures. The Company believes presenting certain non-GAAP financial measures provides investors with information useful in understanding our financial performance, our performance trends, and our financial position. We utilize these measures for internal planning and forecasting purposes, and operating pre-provision net revenue and operating return on tangible common equity are also used as part of our incentive compensation program for our executive officers. We, as well as securities analysts, investors, and other interested parties, also use these measures to compare peer company operating performance. We believe that our presentation and discussion, together with the accompanying reconciliations, provides a complete understanding of factors and trends affecting our business and allows investors to view performance in a manner similar to management. These non-GAAP measures should not be considered a substitution for GAAP basis measures and results, and we strongly encourage investors to review our consolidated financial statements in their entirety and not to rely on any single financial measure. Because non-GAAP financial measures are not standardized, it may not be possible to compare these financial measures with other companies’ non-GAAP financial measures having the same or similar names.
Columbia Banking System, Inc.
GAAP to Non-GAAP Reconciliation
Tangible Capital, as adjusted
(Unaudited)
Quarter Ended
% Change
($ in millions, shares in thousands)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Total shareholders’ equity
a
$ 7,790
$ 5,342
$ 5,238
$ 5,118
$ 5,274
46 %
48 %
Less: Goodwill
1,481
1,029
1,029
1,029
1,029
44 %
44 %
Less: Other intangible assets, net
754
430
456
484
513
75 %
47 %
Tangible common shareholders’ equity
b
$ 5,555
$ 3,883
$ 3,753
$ 3,605
$ 3,732
43 %
49 %
Total assets
c
$ 67,496
$ 51,901
$ 51,519
$ 51,576
$ 51,909
30 %
30 %
Less: Goodwill
1,481
1,029
1,029
1,029
1,029
44 %
44 %
Less: Other intangible assets, net
754
430
456
484
513
75 %
47 %
Tangible assets
d
$ 65,261
$ 50,442
$ 50,034
$ 50,063
$ 50,367
29 %
30 %
Common shares outstanding at period end (in thousands)
e
299,147
210,213
210,112
209,536
209,532
42 %
43 %
Total shareholders’ equity to total assets ratio
a / c
11.54 %
10.29 %
10.17 %
9.92 %
10.16 %
1.25
1.38
Tangible common equity to tangible assets ratio
b / d
8.51 %
7.70 %
7.50 %
7.20 %
7.41 %
0.81
1.10
Book value per common share
a / e
$ 26.04
$ 25.41
$ 24.93
$ 24.43
$ 25.17
2 %
3 %
Tangible book value per common share
b / e
$ 18.57
$ 18.47
$ 17.86
$ 17.20
$ 17.81
1 %
4 %
Columbia Banking System, Inc.
GAAP to Non-GAAP Reconciliation – Continued
Income Statements, as adjusted
(Unaudited)
Quarter Ended
% Change
($ in millions)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Non-Interest Income Adjustments
Gain (loss) on investment securities, net
$ 2
$ —
$ 2
$ (1)
$ 2
nm
— %
(Loss) gain on swap derivatives
(1)
(1)
(1)
3
(3)
— %
(67) %
Gain (loss) on loans held for investment, at fair value
4
—
7
(7)
9
nm
(56) %
Change in fair value of MSR due to valuation inputs or assumptions
—
(2)
(1)
7
(6)
nm
nm
MSR hedge gain (loss)
—
2
3
(8)
5
(100) %
(100) %
Total non-interest income adjustments
a
$ 5
$ (1)
$ 10
$ (6)
$ 7
nm
(29) %
Non-Interest Expense Adjustments
Merger and restructuring expense
$ 87
$ 8
$ 14
$ 2
$ 2
nm
nm
Exit and disposal costs
—
—
1
1
1
nm
(100) %
FDIC special assessment
(1)
—
—
—
—
nm
nm
Legal settlement
—
—
55
—
—
nm
nm
Total non-interest expense adjustments
b
$ 86
$ 8
$ 70
$ 3
$ 3
nm
nm
Net interest income
c
$ 505
$ 446
$ 425
$ 437
$ 430
13 %
17 %
Non-interest income (GAAP)
d
$ 77
$ 65
$ 66
$ 50
$ 66
18 %
17 %
Less: Non-interest income adjustments
a
(5)
1
(10)
6
(7)
nm
(29) %
Operating non-interest income (non-GAAP)
e
$ 72
$ 66
$ 56
$ 56
$ 59
9 %
22 %
Revenue (GAAP)
f=c+d
$ 582
$ 511
$ 491
$ 487
$ 496
14 %
17 %
Operating revenue (non-GAAP)
g=c+e
$ 577
$ 512
$ 481
$ 493
$ 489
13 %
18 %
Non-interest expense (GAAP)
h
$ 393
$ 278
$ 340
$ 267
$ 271
41 %
45 %
Less: Non-interest expense adjustments
b
(86)
(8)
(70)
(3)
(3)
nm
nm
Operating non-interest expense (non-GAAP)
i
$ 307
$ 270
$ 270
$ 264
$ 268
14 %
15 %
Net income (GAAP)
j
$ 96
$ 152
$ 87
$ 143
$ 146
(37) %
(34) %
Provision for income taxes
23
51
37
49
50
(55) %
(54) %
Income before provision for income taxes
119
203
124
192
196
(41) %
(39) %
Provision for credit losses
70
30
27
28
29
133 %
141 %
Pre-provision net revenue (PPNR) (non-GAAP)
k
189
233
151
220
225
(19) %
(16) %
Less: Non-interest income adjustments
a
(5)
1
(10)
6
(7)
nm
(29) %
Add: Non-interest expense adjustments
b
86
8
70
3
3
nm
nm
Operating PPNR (non-GAAP)
l
$ 270
$ 242
$ 211
$ 229
$ 221
12 %
22 %
Net income (GAAP)
j
$ 96
$ 152
$ 87
$ 143
$ 146
(37) %
(34) %
Day 1 acquisition provision expense
70
—
—
—
—
nm
nm
Less: Non-interest income adjustments
a
(5)
1
(10)
6
(7)
nm
(29) %
Add: Non-interest expense adjustments
b
86
8
70
3
3
nm
nm
Tax effect of adjustments
(43)
(1)
(8)
(2)
1
nm
nm
Operating net income (non-GAAP)
m
$ 204
$ 160
$ 139
$ 150
$ 143
28 %
43 %
nm = Percentage changes greater than +/-500% are considered not meaningful and are presented as “nm.”
Columbia Banking System, Inc.
GAAP to Non-GAAP Reconciliation – Continued
Average Balances, Earnings Per Share, and Performance Metrics, as adjusted
(Unaudited)
Quarter Ended
% Change
($ in millions, shares in thousands)
Sep 30, 2025
Jun 30, 2025
Mar 31, 2025
Dec 31, 2024
Sep 30, 2024
Seq. Quarter
Year over Year
Average assets
n
$ 56,823
$ 51,552
$ 51,453
$ 51,588
$ 52,009
10 %
9 %
Less: Average goodwill and other intangible assets, net
1,719
1,472
1,502
1,528
1,560
17 %
10 %
Average tangible assets
o
$ 55,104
$ 50,080
$ 49,951
$ 50,060
$ 50,449
10 %
9 %
Average common shareholders’ equity
p
$ 6,157
$ 5,287
$ 5,217
$ 5,226
$ 5,119
16 %
20 %
Less: Average goodwill and other intangible assets, net
1,719
1,472
1,502
1,528
1,560
17 %
10 %
Average tangible common equity
q
$ 4,438
$ 3,815
$ 3,715
$ 3,698
$ 3,559
16 %
25 %
Weighted average basic shares outstanding (in thousands)
r
237,838
209,125
208,800
208,548
208,545
14 %
14 %
Weighted average diluted shares outstanding (in thousands)
s
238,925
209,975
210,023
209,889
209,454
14 %
14 %
Select Per-Share & Performance Metrics
Earnings per share – basic
j / r
$ 0.40
$ 0.73
$ 0.41
$ 0.69
$ 0.70
(45) %
(43) %
Earnings per share – diluted
j / s
$ 0.40
$ 0.73
$ 0.41
$ 0.68
$ 0.70
(45) %
(43) %
Efficiency ratio (1)
h / f
67.29 %
54.29 %
69.06 %
54.61 %
54.56 %
13.00
12.73
Non-interest expense to average assets
h / n
2.74 %
2.16 %
2.68 %
2.06 %
2.08 %
0.58
0.66
Return on average assets
j / n
0.67 %
1.19 %
0.68 %
1.10 %
1.12 %
(0.52)
(0.45)
Return on average tangible assets
j / o
0.69 %
1.22 %
0.70 %
1.14 %
1.15 %
(0.53)
(0.46)
PPNR return on average assets
k / n
1.32 %
1.81 %
1.19 %
1.70 %
1.72 %
(0.49)
(0.40)
Return on average common equity
j / p
6.19 %
11.56 %
6.73 %
10.91 %
11.36 %
(5.37)
(5.17)
Return on average tangible common equity
j / q
8.58 %
16.03 %
9.45 %
15.41 %
16.34 %
(7.45)
(7.76)
Operating Per-Share & Performance Metrics
Operating earnings per share – basic
m / r
$ 0.86
$ 0.77
$ 0.67
$ 0.72
$ 0.69
12 %
25 %
Operating earnings per share – diluted
m / s
$ 0.85
$ 0.76
$ 0.67
$ 0.71
$ 0.69
12 %
23 %
Operating efficiency ratio, as adjusted (1)
u / y
52.32 %
51.79 %
55.11 %
52.51 %
53.89 %
0.53
(1.57)
Operating non-interest expense to average assets
i / n
2.14 %
2.10 %
2.13 %
2.03 %
2.05 %
0.04
0.09
Operating return on average assets
m / n
1.42 %
1.25 %
1.10 %
1.15 %
1.10 %
0.17
0.32
Operating return on average tangible assets
m / o
1.47 %
1.28 %
1.13 %
1.19 %
1.13 %
0.19
0.34
Operating PPNR return on average assets
l / n
1.89 %
1.88 %
1.67 %
1.77 %
1.69 %
0.01
0.20
Operating return on average common equity
m / p
13.15 %
12.16 %
10.87 %
11.40 %
11.15 %
0.99
2.00
Operating return on average tangible common equity
m / q
18.24 %
16.85 %
15.26 %
16.11 %
16.04 %
1.39
2.20
(1)
Tax-exempt interest was adjusted to a taxable equivalent basis using a 21% tax rate and added to stated revenue for this calculation.
Private credit, especially direct lending, has experienced rapid growth and transformation in recent years. As the asset class becomes a mainstream allocation, investors are shifting their attention to optimizing portfolio construction, risk management, and value creation. At the same time, rising interest rates and market volatility have further broadened the appeal of direct lending, attracting a diverse investor base ranging from individuals to large capital allocators and sophisticated issuers.
Amidst this growth and precarious backdrop, market participants will need to continue to refine their approach to underwriting and portfolio construction. Disciplined risk management and thoughtful diversification are essential for navigating today’s evolving landscape. Managers must maintain vigilance in underwriting, employing dynamic, bottom-up due diligence that evolves as markets shift. Specifically, managers need a strong understanding of complex geopolitical developments (such as tariff and trade policy) and new transformative technologies (such as Generative AI) to anticipate and avoid credit mistakes. In addition, a disciplined, top-down approach to portfolio construction remains just as crucial. Diversification across industries and issuers and limiting exposure to sectors prone to cycles or regulatory risk helps reduce overall portfolio risk.
An often-overlooked consideration is the importance of structure in direct lending transactions. Given current market dynamics, maintaining a risk-first mindset is essential, and thoughtful structuring can be a powerful driver of value in direct lending. Features such as faster capital calls, more efficient deployment, and potentially lower fees can enhance net returns while reinforcing the robust risk management highlighted above.
Investor Challenges with Traditional Drawdown Structures
Historically, direct lending was only offered via traditional drawdown structures. However, the rise of business development companies and other liquid vehicles in recent years has provided credit investors with additional options. While each of these structures has pros and cons, we believe a hybrid solution, the Institutional Open-Ended Structure, can provide investors with the best of both worlds.
Key Considerations for Investors When Evaluating Direct Lending Structures:
Familiarity:
For investors accustomed to traditional drawdown structures, vehicles with defined investment and harvest periods offer a familiar experience.
Simplicity:
A well-designed structure can provide pure-play direct lending exposure. This approach avoids accelerated liquidity options (reducing cash drag), gates, and the need to hold liquid, non-direct lending investments.
Optionality:
Investors should be able to retain control over key decisions, including: (i) whether to receive distributions or recycle income, and (ii) whether to remain invested or begin harvesting exposures. Each investor’s choices should operate independently without affecting others.
Addressing the Challenges Investors Face with Traditional Closed-End Structures
Challenge 1: Slower deployment and the J-Curve
In traditional drawdown structures, capital is called in a pro-rata fashion, whether it was committed months ago or the day before the final close. As a result, it can often take an investor 3-4 years to reach specific deployment targets. While this has been the industry standard for locked-up capital, many investors today are looking for faster ways to put capital to work to generate income and manage asset allocation targets.
Potential Solution:
We believe it is preferable to use a structure where capital is called from next quarter’s tranche only after the previous quarter’s tranche is fully called. By calling capital in this fashion, investors who committed earlier get their capital called first and deployed faster. Importantly, new investors enter the existing portfolio at the latest quarterly NAV and avoid blind pool risk by underwriting an existing portfolio during their diligence process.
EXHIBIT 1: Visual Representation of Tranche and Queue System
Apple is set to report its first quarterly earnings since the release of its new lineup of iPhones on Thursday. After the company hit a $4tn market value this week for the first time, analysts are expecting it to demonstrate steady financial growth and a strong bottom line despite slow progress on artificial intelligence.
The slate of new iPhones, in particular the iPhone 17 and 17 Pro, have reinvigorated demand for Apple’s products, especially in China, where sales had been lagging behind projections. Demand for the ultra-thin iPhone Air remains the subject of speculation, with some analysts saying that the company has lowered production of the device and others asserting it has not.
Wall Street is anticipating Apple to report $102bn in revenue and earnings of $2.53 per share for the fourth quarter of 2025, according to analyst group LSEG.
John Belton, a portfolio manager at Gabelli Funds, said the positive estimates are due to increasing iPhone sales along with a price increase with the device’s newest model. “The most bullish data point coming out of Apple’s last earnings report was the iPhone revenue number,” Belton said. “The double-digit growth represented the best iPhone growth in at least three years.”
The strong iPhone revenue comes even as Apple has lagged behind other tech companies in releasing artificial intelligence products. The company has yet to fully roll out an AI product to compete with companies like Meta, Google and Microsoft. Apple has also struggled with the up-and-down tariffs that Donald Trump has levied on China and India, where the vast majority of the company’s manufacturing takes place.
Nevertheless, Apple’s stock has risen over the past few weeks, inflating the company’s market cap, one of only three companies worldwide worth more than $4tn. Both Nvidia and Microsoft have also hit that milestone.
Apple’s share price has increased by more than 50% since a low point in April, which analysts credit to the debut of its latest products. Along with the iPhone 17, the company also launched new AirPod earbuds with live translation tools and upgrades to its Apple Watch lineup.
skip past newsletter promotion
after newsletter promotion
Apple is reporting earnings this week, along with other top tech behemoths, including Microsoft, Meta, Amazon and Alphabet, as the wider US stock market hits record highs. Microsoft and Alphabet posted strong results Wednesday, while Meta’s were more mixed, leading to a slump in share price.
NEW YORK (Reuters) – Oil prices held steady on Thursday as investors assessed a potential trade truce between the United States and China after U.S. President Donald Trump lowered tariffs on China following a meeting with Chinese leader Xi Jinping in South Korea. Brent futures rose 8 cents, or 0.1%, to settle at $65.00 a barrel, while U.S. West Texas Intermediate (WTI) crude rose 9 cents, or 0.1%, to settle at $60.57. Trump agreed to reduce tariffs on China to 47% from 57% in a one-year deal in exchange for Beijing resuming U.S. soybean purchases, keeping rare earths exports flowing and cracking down on the illicit fentanyl trade. PVM analyst Tamas Varga said investors see the announced agreement between China and the U.S. as more of a de-escalation of tension than a structural change in the relationship.
Oil majors Shell and TotalEnergies posted quarterly profit falls of 10% and 2% respectively on Thursday, dragged down by lower oil prices, though Shell beat expectations, helped by better trading results in its huge gas division. FED RATE CUT LIFTS ECONOMIC OUTLOOK Also helping to boost the economic outlook, the U.S. Federal Reserve lowered interest rates on Wednesday, in line with market expectations. However, it signalled that it might be the last cut of the year as the ongoing government shutdown threatens data availability. Lower interest rates reduce consumer borrowing costs and could boost economic growth and oil demand. “The Fed’s decision underscores a broader turn in its policy cycle – one that favours gradual reflation and support over restraint, providing a tailwind to commodities sensitive to economic activity,” Rystad Energy’s chief economist Claudio Galimberti said in a note. In Europe and Asia, meanwhile, the European Central Bank and the Bank of Japan kept interest rates unchanged. The euro zone economy grew a touch more quickly than expected in the third quarter, lifted by buoyant growth in France and Spain that more than offset faltering exports and persistent struggles in Germany’s oversized industrial sector. In Germany, however, gross domestic product stagnated in the third quarter, data showed on Thursday, highlighting the struggle Europe’s biggest economy faces in regaining momentum as exports dwindle. OVERSUPPLY CONCERNS Both crude benchmarks were on track to decline by around 3% in October, which would be their third consecutive month of losses following concerns about oversupply. In the U.S., crude output hit a weekly record high of around 13.6 million barrels per day (bpd) last week. Investors said they were looking ahead to an OPEC+ meeting scheduled for November 2, where the alliance will likely announce another 137,000 bpd supply hike for December. OPEC+ includes the Organization of the Petroleum Exporting Countries (OPEC) and allies like Russia. In a series of monthly increases, eight OPEC+ members had boosted output targets by a total of more than 2.7 million bpd – or about 2.5% of global supply. In Saudi Arabia, the world’s top oil exporter, the budget deficit widened to 88.5 billion riyals ($23.60 billion) in the third quarter, a 160% rise from the previous quarter as spending increased and revenues fell, the finance ministry said on Thursday.
($1 = 3.7504 riyals)
(Reporting by Scott DiSavino in New York, Enes Tunagur in London, Mohi Narayan in New Delhi and Colleen Howe in Beijing; Editing by Edwina Gibbs, Jamie Freed, Bernadette Baum, Jane Merriman, Joe Bavier and Diane Craft)
North American Retail Growth: 9% increase, led by strong Off-Road performance.
Dealer Inventory: Down 21% year-over-year.
Adjusted EBITDA Margin: Under pressure due to increased tariffs and normalized incentive compensation.
Adjusted EPS: $0.41, driven by strong mix and operational efficiencies.
Operating Cash Flow: $159 million for the quarter.
Free Cash Flow: Approximately $485 million year-to-date.
Off-Road Sales Growth: 8%, supported by a richer mix of vehicles.
Marine Sales Growth: 20%, driven by positive shipments of new boats.
Gross Profit Margin: Impacted by $35 million in new tariffs.
Full Year Sales Guidance: $6.9 billion to $7.1 billion.
Full Year Adjusted EPS Guidance: Expected to be a loss of approximately $0.05.
Release Date: October 28, 2025
For the complete transcript of the earnings call, please refer to the full earnings call transcript.
Polaris Inc (NYSE:PII) reported strong third quarter results with sales reaching $1.8 billion, driven by improved retail and a solid mix of Off-Road vehicles.
The company gained approximately 3 points of market share in the Off-Road Vehicle (ORV) segment, led by strong performance in the Polaris RANGER and crossover vehicles.
Dealer inventory levels have improved significantly, with a 21% year-over-year reduction, leading to healthier inventory and lower flooring expenses for dealers.
Polaris Inc (NYSE:PII) successfully executed operational efficiencies, exceeding their goal of $40 million in structural operational efficiencies for the year.
The sale of a majority stake in Indian Motorcycle is expected to be accretive to adjusted EBITDA by approximately $50 million and to adjusted EPS by approximately $1, allowing Polaris to focus on high-margin growth opportunities.
Adjusted EBITDA margin was under pressure due to increased tariffs and normalized incentive compensation.
The company faced headwinds in the Youth segment due to a shift in production out of China, which is expected to continue into early Q4.
Tariffs are expected to have a significant impact, with an anticipated $90 million hit in 2025, increasing to over $200 million in 2026.
The On-Road segment experienced a decline in sales, driven by softness in the broader motorcycle market and within the Slingshot business.
Fourth quarter adjusted EPS is expected to be lower than the third quarter due to increased tariffs, negative mix, and higher operating expenses.
Q: What drove the 9% growth in ORV retail and share gains in the quarter? A: Michael Speetzen, CEO, explained that the growth was due to right-sized inventory, a broad RANGER lineup, and significant quality improvements. The RANGER 500 attracted new customers, and the NorthStar rewards program enhanced dealer performance, contributing to the share gains.
Q: What are the expectations for fiscal 2026, considering the Indian Motorcycle deal and tariffs? A: Michael Speetzen, CEO, noted that the Indian Motorcycle deal will significantly impact revenue but add $50 million in EBITDA and $1 in EPS. Tariffs are expected to be just over $200 million, but mitigation efforts are underway. The company anticipates a flat industry with potential growth from shipping aligning with retail demand.
Q: Can you provide insights into the consumer profile for the RANGER 500? A: Michael Speetzen, CEO, stated that the RANGER 500 attracts new customers who previously couldn’t find an entry point into Polaris products. These customers often have small properties and are new to powersports, transitioning from alternatives like golf carts.
Q: How did the Factory Authorized Clearance (FAC) program impact demand, and what are the expectations for Q4? A: Michael Speetzen, CEO, mentioned that the FAC program successfully generated excitement without significant cost increases. It helped reduce noncurrent inventory, and October trends indicate continued strength in key areas like RANGER XD and XPEDITION, with Q4 retail expected to rise in low single digits.
Q: What are the key takeaways from 2025, excluding tariffs, and what lessons have been learned? A: Robert Mack, CFO, highlighted that promotions were heavier than expected, but mix and plant performance exceeded expectations. Operational execution improved significantly, with plants outperforming targets, indicating strong future potential for operational improvements.
For the complete transcript of the earnings call, please refer to the full earnings call transcript.
Jerome Powell, chairman of the US Federal Reserve, during a news conference following a Federal Open Market Committee (FOMC) meeting in Washington, DC, US, on Wednesday, Oct. 29, 2025.
Al Drago | Bloomberg | Getty Images
Federal Reserve Chair Jerome Powell faces if not the most difficult challenge of his time in office at least the trickiest in his final months as head of the all-powerful U.S. central bank.
Fresh off his surprisingly tough talk Wednesday on the potential for another interest rate cut in December, Powell will have to steer his way through a suddenly contentious atmosphere among policymakers that will make whichever direction the Fed chooses divisive.
While it’s not the existential economic threat posed by the Covid pandemic in 2020, it nevertheless indicates a level of peril uncommon for the institution.
“December could get messy,” Bank of America economist Aditya Bhave said in a client note. “We still think the Fed won’t cut rates again under Chair Powell. But barring a clear signal in either direction from the data, the December decision will likely be even more contentious than October.”
The Fed on Wednesday approved a widely anticipated quarter percentage point rate reduction that took its benchmark rate down to 3.75%-4%. However, Powell warned that another cut in December “is not a foregone conclusion,” something the market was not expecting.
While Wall Street economists and strategists were split over whether the committee will in fact approve another reduction at the Dec. 9-10 meeting, they were in agreement that this is a pivotal moment for Powell and the legacy he ultimately will leave when his term runs out in May.
“Even in a situation without much additional data due to the shutdown, it can actually make sense to push against market pricing to keep optionality going forward,” wrote Michael Gapen, chief U.S. economist at Morgan Stanley. “A 95% probability assigned to a December cut does not seem consistent with a data-dependent Fed.”
Markets react
For their part, traders weren’t buying the hawkish rhetoric. Fed fund futures pricing Thursday still indicated a 75% probability of a cut in December, though that was down from around 90% the day before, according to the CME Group’s FedWatch.
But Powell went to great lengths in his post-meeting news conference Wednesday to dispel the notion that the reduction, which would be the third since September, is a slam dunk.
The thrust of his argument was multi-pronged: What data there is available during the government shutdown blackout has largely showed a stable economy though the labor market is a risk; inflation is still above target; and, in an unusual development, there are “strongly differing” views on the FOMC for where policy should move.
Markets were clearly caught off guard by the move, with stocks slipping and Treasury yields surging. The 10-year Treasury yield was solidly above 4% Thursday while the policy-sensitive 2-year note climbed over 3.6% to its highest level in about a month.
“The reaction of the bond market should certainly give Fed officials pause,” wrote Ed Yardeni, head of Yardeni Research and coiner of the term “bond vigilantes” to describe buyers’ strikes in the fixed income markets. “The bond market isn’t buying the Fed’s cover story that interest rates were too restrictive.”
For Powell, the statement regarding December was an unusual step considering markets had been expecting a more neutral tone. Asked whether he was bothered by the strong anticipation of another cut, Powell said markets should take his statement that a reduction “is not a foregone conclusion” should be “taken on board.”
“You’ve got get right in front of that, because you don’t want to surprise the market a couple weeks down the road. Now is the time to do it,” said Dan North, senior economist for North America at Allianz Trade. “He doesn’t usually use words quite so forcefully. So that was interesting, and he’s clearly trying to squash speculation about December. We feel the same way, December is going to be a pause.”
Political overhang
The developments come at a ticklish time for the Fed.
Powell, a favorite target for President Donald Trump’s criticism, has only seven months or so left in his term. Treasury Secretary Scott Bessent has been busy interviewing potential successors — among them current Governors Christopher Waller and Michelle Bowman, both of whom voted in favor of the cut.
In addition, Governor Stephen Miran, a hand-picked Trump appointee who will only serve through January, again dissented from the vote in favor of a half-point.
At the other end of the spectrum, Kansas City Fed President Jeffrey Schmid voted “no” as well, but because he wanted to not cut. Between them run a range of views on the normally consensus-driven FOMC.
Whether Powell’s tip of the hat to the doves reflects merely a courtesy or deeper misgivings about cuts will be central to Fed analysis in the coming weeks.
“While the press conference played out somewhat differently than we expected, we have not changed our Fed forecast and continue to see a December cut as quite likely,” Goldman Sachs economist David Mericle wrote. “We suspect that there is substantial opposition on the FOMC to the risk management cuts and that Powell thought it was important to voice other participants’ concerns today in his press conference. But we still think that the arguments for a December cut remain intact.”
OpenAI is reportedly gearing up for a stock market listing valuing the company at $1tn (£76bn) as soon as next year, in what would be one of the biggest ever initial public offerings.
The developer behind the hit AI chatbot ChatGPT is considering whether to file for an IPO as soon as the second half of 2026, according to Reuters, which cited people familiar with the matter. The company is thought to be looking to raise at least $60bn.
A stock market float would give OpenAI another route to raising cash, supporting ambitions by the chief executive, Sam Altman, to splash trillions of dollars on building datacentres and other forms of infrastructure needed for the rapid buildout of its chatbots.
During a staff livestream on Tuesday, Altman was reported to have said: “I think it’s fair to say it [an IPO] is the most likely path for us, given the capital needs that we’ll have.”
An OpenAI spokesperson said: “An IPO is not our focus, so we could not possibly have set a date. We are building a durable business and advancing our mission so everyone benefits from AGI.”
AGI stands for artificial general intelligence, which OpenAI defines as “highly autonomous systems that outperform humans at most economically valuable work”.
skip past newsletter promotion
after newsletter promotion
OpenAI was founded as a nonprofit in 2015 with a mission to safely build AGI for humanity’s benefit. However, it completed a lengthy restructuring process this week that turned its main business into a for-profit corporation. While it is still technically controlled by the non-profit entity, the move makes it easier for OpenAI to raise capital while also laying the groundwork for an IPO.
The deal also gave Microsoft a stake of about 27% in the for-profit company, with OpenAI valued at $500bn under the terms of the deal. News of the restructuring helped push Microsoft’s valuation above $4tn for the first time.
OpenAI reportedly posted revenue of $4.3bn in the first half of this year, with an operating loss of $7.8bn, according to the tech news site the Information.
The massive valuations will do nothing to allay fears that the AI industry is operating in a bubble. Officials at the Bank of England this month flagged the growing risk that tech stock prices pumped up by the AI boom could burst, saying equity markets were “particularly exposed should expectations around the impact of AI become less optimistic”.
OpenAI’s chief financial officer, Sarah Friar, reportedly told colleagues that the company was aiming for a 2027 listing, according to the sources cited by Reuters, while some advisers said it could come the year before.
The World Health Organization emphasizes that digital technologies are essential components and enablers of sustainable health systems and universal health coverage []. Despite robust support and the rapid expansion of eHealth services in many countries [], a significant digital divide remains [].
Marginalized and vulnerable populations—such as older adults, individuals with limited education, low-income groups, and rural residents—face substantial challenges in accessing and using eHealth services [,]. These challenges include acquiring necessary internet-enabled devices, developing sufficient internet usage skills, and improving digital literacy [,]. Such barriers increase their risks of inadequate internet access and poor eHealth literacy, further exacerbating the digital divide they experience [-]. This divide in eHealth utilization between users and nonusers undermines the potential benefits of eHealth services, limiting their capacity to contribute effectively to improved health outcomes [].
On the basis of consumer purchasing decision model or new techniques of adoption decision-making [,], individuals’ engagements with eHealth services follow a hierarchical progression through 3 stages: awareness, want, and adoption []. Adequate knowledge of emerging technologies fosters a more rational want for eHealth services []. For example, a study conducted in a Spanish border community revealed that eHealth literacy positively influences the intention to use telehealth services []. However, it is important to note that a need for eHealth does not always lead to actual adoption. As health status deteriorates and physical functionality declines, older adults often exhibit a heightened demand for eHealth services, particularly those that help overcome geographical constraints []. However, because of vision impairments and reduced learning capacities [], their actual utilization of eHealth remains significantly lower than that of younger individuals []. Nevertheless, once older adults begin using eHealth services, they tend to show greater persistence in using health management apps compared to their younger counterparts [].
According to their functions of eHealth services, eHealth services’ typical classifications include health information services [], e-consulting services [], online appointment booking [], and eHealth commerce []. Health information services, which involve activities such as health information seeking, health risk assessments, and personal health records, are prevalently used globally [-]. E-Consulting, introduced in the early 2000s [,], has seen significant adoption in countries such as England and Canada [-], although its uptake has been slower in low- and middle-income countries []. Online appointment booking, including online scheduling and access to test and laboratory results [], has been implemented in many countries, including England, Australia, and Canada [-]. eHealth commerce presents a promising opportunity to expand access to medications and has been implemented in many countries []. However, significant disparities exist in the formats and complexity of these eHealth services [,,], which may result in varying levels of awareness, want, and adoption among individuals across different eHealth service categories [-]. Unfortunately, there is a lack of studies that specifically examine the awareness of, want for, and adoption of eHealth services within these distinct categories.
Study Objectives
This study specifically focuses on hospitalized patients. Hospitalized patients often encounter challenges that exacerbate their exposure to a deeper digital divide and increase their demands for eHealth services [,]. These challenges include mobility impairments [], prolonged waiting times [], and additional barriers to follow-up care and chronic disease management after discharge [-]. The heightened demand for eHealth services among inpatients highlights their importance as a key target group for such interventions. Addressing the digital divide in eHealth services for this population is critical, as it may provide valuable insights for other countries in developing targeted strategies to promote eHealth adoption. Therefore, this study aimed to analyze the digital divide in awareness of, want for, and adoption of information-based, treatment intermediary, and treatment eHealth services using the awareness, want, and adoption (AWAG) segment matrix. In addition, it seeks to explore the underlying factors contributing to these disparities.
Methods
Clinical Context
In China, grade A tertiary hospitals (commonly referred to as Sanjia hospitals) represent the highest level of medical institutions, with responsibilities that encompass the provision of specialized health care, the advancement of medical education, and the conduct of advanced research []. Considering the differences in models of eHealth services, the hierarchy, and representativeness of patient sources, this study purposively selected 2 grade A tertiary hospitals and 1 tertiary hospital in Jinan, recognized for offering eHealth services, as the sampling sites. These 3 general hospitals represent distinct tiers of tertiary health care institutions, including national-level hospitals, provincial hospitals, and municipal hospitals. They serve diverse patient populations and reflect varying scales of hospital organization. Each facility is equipped with comprehensive inpatient departments and exhibits unique characteristics in the development of eHealth [-], as detailed in .
Table 1. Characteristics of 3 tertiary hospitals in Jinan for the eHealth survey from June to October 2023, including their eHealth service models and functional features.
Hospitals
Characteristic
A
Innovative “internet plus smart medical” model
One-stop-shop online follow-up service
Health science communication popularization matrix based on short video
B
Docking with Jinan “internet+medical health” convenience service platform
Digital inpatient ward system
The smart hospital built is in the forefront of Jinan municipal hospitals
C
The most functional internet hospital in the province
A full range of eHealth services
Study Design and Data Collection
In this study, the sample size calculation accounted for the issue of multiple comparisons. To address this, the significance level (α=.0167, 0.05/3) was adjusted using multiple Bonferroni corrections. The calculation was performed using the following standard formula, indicating that a minimum of 895 participants was needed. Considering an anticipated 30% nonresponse rate, the final required sample size was adjusted to 1279 participants.
where = 2.393, = 0.5, and = 0.04.
This study used a multistage stratified sampling approach to select participants from 3 participating hospitals. First, the sample size for each hospital was allocated proportionally based on the number of beds in each respective hospital. Subsequently, inpatients were randomly selected from all departments, excluding the emergency and obstetric departments. Specifically, in hospital A, 587 inpatients were selected from 17 departments. In hospital B, 207 inpatients were randomly chosen across 15 departments. In hospital C, 484 inpatients were selected from 15 departments. All wards within each department were included in the investigation, and a total of 305, 104, and 268 wards were included from hospitals A, B, and C, respectively. Two bed numbers were randomly selected using a random number method, and the patients occupying the corresponding beds in each ward were systematically surveyed. If the invited patient declined, the patient in the adjacent bed was approached as a replacement. As a result, a total of 1354 inpatients participated in this survey.
A face-to-face questionnaire survey was conducted across inpatient departments in these hospitals from June to October 2023. The investigators are interns majoring in preventive medicine, who have a relatively high level of medical literacy. To ensure the data quality, a comprehensive training program was implemented to clarify the questionnaire’s content and establish standardized criteria for questioning before conducting the survey. All respondents completed the questionnaire face-to-face with trained investigators, after providing their informed consent and signing the questionnaire.
This survey recruited inpatients based on the following inclusion criteria: (1) aged ≥15 years; (2) able to communicate effectively and complete questionnaires independently or with assistance; (3) not hospitalized due to childbirth or accidental injuries; (4) no history of major mental illness, language impairment, or cognitive impairment; and (5) provided informed consent. After excluding samples with missing key information or those that did not meet the inclusion criteria, a total of 1322 inpatients from 3 hospitals were included as survey participants.
Ethical Considerations
The study was approved by the Ethics Committee of the School of Public Health, Shandong University, P.R. China (LL20230602). Participants who provided written informed consent were included in the study. Data were collected anonymously by the research team and stored securely in locked files. Participation was entirely voluntary, and no compensation was offered for participation.
Measures
Measures of Dependent Variables
This study examined 12 eHealth services based on the established definition and scope of eHealth []. Existing literature indicates that eHealth can produce 3 primary effects.
The signaling effect: The internet enables patients to access and evaluate information about health care services, providers, and their quality at a reduced cost []. This helps mitigate information asymmetry in health care, which, in turn, enhances individuals’ health literacy, improves patient-provider matching efficiency, and elevates the overall quality of care [-].
The intermediary effect: eHealth facilitates the use of various nondiagnostic and nontreatment resources, such as online triage and appointment scheduling for examinations and surgeries. These services enhance both the accessibility and equity of high-quality medical resources by leveraging eHealth’s intermediary role [-].
The substitution effect: a range of medical services, including online consultations, telemedicine, and follow-up care, expands the allocation of health care resources through the application of information technology. By substituting traditional in-person services, eHealth improves the fairness and accessibility of high-quality medical care [,].
On the basis of these 3 effects and the stages of the patient journey [], these 12 services were categorized into 3 groups: information-based services, treatment intermediary services, and treatment services, as detailed in . Information-based services provide health and medical information aimed at enhancing health literacy while reducing costs [,]. Treatment intermediary services support the preadmission process by leveraging nonmedical resources, thereby improving accessibility and the efficiency of health care delivery [,]. Treatment services, on the other hand, use information technology to optimize the allocation of medical resources, complement traditional health care practices, and promote equitable access to high-quality medical services [-].
Table 2. Classification of 12 eHealth services by functional role as information-based, treatment intermediary, and treatment services.
Items
eHealth services
Information-based services
Seek disease or health information online
Seek doctor or hospital information online
Seek medical review information online (patients’ evaluation of doctors)
Give or receive peer-to-peer feedback about health status in online communities or chat platforms
Treatment intermediary services
Outpatient appointment booking online
Pay medical bills online
Access electronic medical records and medicinal examination reports online
Appoint a medical examination or surgery online
Online hospitalization appointment
Online drugstore purchases or online pharmacy, excluding dietary supplements
Treatment services
Electronic consulting, including email, chat, or video health care consultations
Chronic disease monitoring and management, online telemonitoring, or remote monitoring to manage chronic diseases, such as social networking or eHealth communities, telehealth, and mobile health, including wearable devices or apps
In this study, the awareness, want for, and adoption of eHealth services were treated as dependent variables. Inpatients were asked whether they had heard of any eHealth services (1=no, 2=yes). The awareness of eHealth services was categorized as 1 if the inpatient had heard of them and 0 otherwise. For patients who had not heard of eHealth services, the investigator provided a brief explanation of their purpose and functionality. Following this explanation, patients were asked about their intention to use eHealth services (1=no, 2=yes). Want for eHealth was then categorized as 1 if the patient expressed a willingness to use them and 0 otherwise. In addition, inpatients were asked about their experience with using eHealth services (1=no, 2=used with the help of family members, 3=used independently). Adoption of eHealth services was categorized as 1 if the inpatient reported using them independently or with the help of family members and 0 otherwise.
Measures of Independent Variables
On the basis of Wilson’s model of information behavior [], this study divided the factors influencing the use of eHealth services into digital technology factors, health status, and general demographic characteristics.
Digital Technology Factors
eHealth literacy [], perceived usefulness, and perceived ease of use [] are categorized as digital technology factors.
The eHealth literacy scale (eHEALS), an 8-item instrument, was used to measure eHealth literacy []. Responses were collected using a 5-point Likert scale, ranging from very inconsistent to very consistent. The total score, calculated as the sum of scores across all 8 items, ranged from 8 to 40, with higher scores indicating greater eHealth literacy. eHEALS has demonstrated strong validity and reliability in China []. In this study, the Cronbach α for eHEALS was .976, indicating excellent internal consistency.
To measure inpatients’ acceptance of eHealth services, the constructs of perceived usefulness and perceived ease of use, derived from the Technology Acceptance Model proposed by Davis, were used []. Each construct was measured using 4 items, scored on a 5-point Likert scale, ranging from very inconsistent to very consistent. Total scores ranged from 4 to 20, with higher scores reflecting a more favorable evaluation of eHealth services. In this study, Cronbach α was .944 for perceived usefulness and 0.969 for perceived ease of use, indicating high reliability.
Health Status
Health status included self-rated health (SRH) and chronic disease (no=1, yes=2). SRH was initially assessed using 5 categories: very poor, poor, fair, good, and very good. For the purposes of this study, SRH was reclassified into 3 categories: negative (very poor or poor), fair, and positive (good or very good).
General Demographic Characteristics
General demographic characteristics also were selected in this study, such as sex (male=1, female=2), age (in years), marital status (married=1, unmarried=2), educational attainment (in years), and place of residence (urban=1, rural=2). Income, categorized into 3 groups based on per capita disposable household income and the average per capita urban and rural household income, was also considered []: lowest (below 40% of the per capita disposable household income of Jinan), middle (between 40% and 100% of the average per capita disposable income of Jinan), and highest (above average per capita disposable income of Jinan).
AWAG Matrix Analysis
This study used a matrix analysis based on the 3-stage consumer purchase decision model, which includes cognition, interest, and final decision []. As illustrated in , individuals initially form an understanding or awareness of innovative things under the influence of both external factors and personal characteristics. On the basis of this awareness and their own needs, they develop a willingness to engage with the service. Ultimately, driven by their personal circumstances and external triggers, this willingness is transformed into actual adoption behavior [,].
Figure 1. Schematic diagram of the association between awareness, want, and adoption.
To assess the digital divide in eHealth services, the study measured the percentage of participants who were aware of, wanted, and adopted eHealth services. In addition, the adoption gap rate of eHealth services was calculated to quantify the disparities in eHealth service adoption across the population [], using the following formula:
where represents the awareness rate for eHealth service x;
represents the want rate for eHealth service x; and
represents the adoption rate for eHealth service x.
Adoption gap rates range from 0% to 100%. Among those who were already aware of or wanted eHealth services, the adoption gap rate represents the percentage of individuals who have never used eHealth services. When the adoption rate is equal to the minimum value of the awareness rate and the want rate, the adoption gap rate is 0, indicating that all the people who have awareness or want for eHealth services have used them. Conversely, if no one has used eHealth services, the adoption gap rate is 100%.
On the basis of technology adoption lifecycle (bell curve) and the AWAG matrix method by Liang [], this study grouped the innovators and the early majority stage and subdivided the 4 types of adopters into 3 adjusted adoption lifecycle accumulation rates of 15%, 50%, and 85%, including innovators or early adopters, early majority, late majority, and laggards []. The middle point of 50% divides both the awareness rates and the want rates into 2 levels. The AWAG matrix is thus divided into 4 primary categories: opened group, perception deficiency group, desire deficiency group, and closed group. According to Liang’s studies [], the opened group includes individuals who are receptive to innovation, demonstrating a strong interest in seeking new information and exploring innovative ideas. Conversely, individuals classified as the closed group show little interest in innovation and are resistant to adopting new ideas. The perception deficiency group includes individuals who lack a strong awareness of innovation. While they remain open to new information and are willing to explore innovative things, they tend to lag behind in receiving new information. The desire deficiency group, on the other hand, consists of individuals who, despite being early recipients of new information, are not interested in innovation and even show resistance to trying something new. Using the cumulative rate of 15% or 85%, each category was further divided into 4 subcategories, that is, strong (S), generic (G), want-bias (Wb), and awareness-bias (Ab). The strong subgroup represents the most open, closed, perception deficient, or desire deficient group, whereas the generic subgroup represents the least in each respective category. The position of each circle on the matrix reflects the awareness and want rates for eHealth services, whereas the size of the circle indicates the adoption gap rate. Larger circles correspond to greater usage gaps for a specific eHealth service, implying lower overall utilization. For instance, an eHealth service in the Wb opened group has high awareness but lagging desire, suggesting a need for strategies that build trust and perceived usefulness rather than mere information campaigns. Conversely, an eHealth service in the Ab opened group has strong desire but low awareness, indicating that marketing and education efforts should be prioritized.
Statistical Analysis
In the descriptive analysis, the mean and SD were used to summarize continuous variables with a normal distribution, whereas median and interquartile range (IQR) were adopted to describe those with a nonnormal distribution, including age, educational attainment, eHealth literacy, perceived usefulness, and perceived ease of use. For categorical variables, such as sex, marital status, place of residence, economic status, SRH, and chronic disease status, frequency and percentage were calculated to describe their distributions. Sample description and univariate analysis results are presented in Tables S1, S2, and S3 in the .
To identify significant factors influencing the awareness of, want for, and adoption of eHealth services, binary logistic regression analysis was conducted, adjusting for potential confounding factors. The results were reported as odds ratios with corresponding 95% CIs, and statistical significance was set at a P value <.05 (2 sided). To account for potential clustering effects within hospitals, cluster-robust SEs were used in the logistic regression analysis. All statistical analyses were performed using Stata 17.0 (StataCorp LLC, USA), and the AWAG matrix figure was generated using MATLAB R2019b (The MathWorks, Inc, USA).
Results
Descriptive Statistics
Nearly half of the participants were male (611/1322, 46.2%). The median age was 53 years (IQR 40‐60). Of 1322 participants, 174 (13.2%) were unmarried. Rural residents accounted for 21.6% (285/1322), whereas the majority resided in urban areas (1037/1322, 78.4%). The median years of educational attainment was 9 (IQR 6‐13). The largest income group was those with the lowest income, representing 52.8% (698/1322) of the sample.
SRH showed a balanced distribution between negative (326/1322, 24.7%) and positive (388/1322, 29.3%) assessments, with the largest proportion reporting fair SRH (608/1322, 46.0%). Most inpatients had chronic diseases, accounting for 60.67% (802/1322) of the sample. Among the 3 digital technology factors, a median eHealth literacy score of 26 (IQR 16‐32) was reported, whereas the median scores of perceived usefulness and perceived ease of use were 16 (IQR 13‐17) and 14 (IQR 9‐16), respectively. The detailed information regarding inpatients is displayed in Table S1, S2, and S3 in .
AWAG Matrix Analysis Results
presents the awareness, want, and adoption rates for eHealth services, including information-based, treatment intermediary, and treatment eHealth services. Overall, the awareness, want, and adoption rates for eHealth services were relatively high (all exceeding 50%), 1204 of 1322 inpatients (91.1%) had awareness of eHealth services, 88.4% (1169/1322) of them had a want for eHealth services, and 847 of 1322 inpatients (64.1%) adopted 1 or more of these services. The adoption gap ratio of eHealth services was 27.6%, categorizing it within the strong opened group. Among the 3 types of eHealth services, treatment intermediary eHealth services demonstrated the highest awareness (1182/1322, 89.4%), want (1142/1322, 86.4%), and adoption (753/1322, 57.0%) rates, with an adoption gap ratio of 34.1%. Conversely, treatment eHealth services showed the lowest rates, with awareness at 74.6% (986/1322), want at 69.9% (924/1322), and adoption at 23.5% (310/1322). The adoption gap ratio for treatment eHealth services was the highest, at 66.5%. As shown in , the adoption gap ratio for treatment eHealth services (66.5%) was the highest and fell into the generic opened group. In contrast, information-based eHealth services had the lowest adoption gap ratio (32.1%), placing it within the Wb opened group.
Table 3. Awareness, want, and adoption gap of information-based, treatment intermediary, and treatment eHealth services.
Variables
Awareness, n (%)
Want, n (%)
Adoption, n (%)
Adoption gap ratio (%)
Region in awareness-want segment matrix
eHealth services
1204 (91.1)
1169 (88.4)
847 (64.1)
27.6
O _ S
Information-based eHealth services
1128 (85.3)
1037 (78.4)
704 (53.3)
32.1
O _ Wb
Treatment intermediary eHealth services
1182 (89.4)
1142 (86.4)
753 (57.0)
34.1
O _ S
Treatment eHealth services
986 (74.6)
924 (69.9)
310 (23.1)
66.5
O _ G
aGroups are opened (O), desire deficiency (D), perception deficiency (P), and closed (C); regions are strong (S), generic (G), awareness-bias (Ab), and want-bias (Wb).
Figure 2. Awareness, want, and adoption gap matrix for information-based, treatment intermediary, and treatment eHealth services.
Logistic Analysis of Awareness, Want, and Adoption on 3 eHealth Services
presented logistic analysis results of 3 different eHealth services. Regarding general demographic characteristics, older patients were less likely to have awareness of, want for, and adoption of all 3 eHealth services (P<.001), except for want for and adoption of treatment eHealth services (P>.05). Inpatients living in rural areas were less likely to have a want for all 3 eHealth services (P<.05). Educational attainment displayed a significant positive association with awareness and adoption of these 3 services (P<.01). Compared to inpatients with the lowest income, inpatients with middle income were more likely to have awareness of information-based and treatment intermediary eHealth services, whereas those with the highest were more likely to have a want for treatment intermediary and treatment eHealth services (P<.05).
Table 4. Logistic regression results for awareness of, want for, and adoption of information-based, treatment intermediary, and treatment eHealth services.
Variables
Information-based eHealth service, OR (95% CI)
Treatment intermediary eHealth services, OR (95% CI)
Treatment eHealth services, OR (95% CI)
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Awareness
Want
Adoption
Awareness
Want
Adoption
Awareness
Want
Adoption
Gender
Female (ref: male)
1.392 (0.720-2.689)
1.140 (0.857- 1.517)
0.895 (0.677- 1.183)
1.142 (0.631-2.068)
0.896 (0.537- 1.495)
1.003 (0.755- 1.331)
1.310 (1.097- 1.564)
0.863 (0.651- 1.142)
1.084 (0.937- 1.253)
Age
0.927 (0.904-0.951)
0.956 (0.947- 0.965)
0.956 (0.934- 0.978)
0.940 (0.911-0.970)
0.940 (0.926- 0.954)
0.950 (0.932- 0.969)
0.943 (0.922- 0.964)
0.989 (0.961- 1.018)
0.994 (0.987- 1.001)
Marital status
Single (ref: married)
0.598 (0.317-1.129)
0.896 (0.588- 1.367)
0.675 (0.544- 0.838)
0.573 (0.267-1.228)
0.784 (0.548- 1.120)
1.078 (0.739- 1.573)
0.671 (0.603- 0.747)
1.101 (0.785- 1.544)
1.502 (0.525- 4.297)
Place of residence
Rural (ref: urban)
0.447 (0.299-0.669)
0.524 (0.363- 0.756)
0.559 (0.308- 1.013)
0.604 (0.312-1.171)
0.350 (0.155- 0.793)
0.428 (0.237- 0.772)
0.605 (0.227- 1.613)
0.583 (0.554- 0.613)
0.797 (0.440- 1.444)
Educational attainment
1.104 (1.066-1.142)
1.022 (0.969- 1.077)
1.034 (1.018- 1.050)
1.118 (1.105-1.131)
1.068 (0.992- 1.149)
1.106 (1.073- 1.140)
1.089 (1.028- 1.153)
0.993 (0.979- 1.007)
1.031 (1.001- 1.063)
Income
Middle (ref: lowest)
1.424 (1.277-1.588)
1.599 (0.568- 4.507)
1.304 (0.679- 2.504)
1.205 (1.029-1.412)
0.901 (0.514- 1.578)
1.057 (0.810- 1.380)
0.726 (0.296- 1.785)
1.670 (0.884- 3.153)
0.919 (0.592- 1.427)
Highest (ref: lowest)
1.426 (0.805-2.527)
2.237 (1.758- 2.847)
1.231 (0.841- 1.800)
2.309 (0.938-5.683)
2.206 (0.998- 4.877)
1.471 (0.919- 2.355)
0.880 (0.266- 2.912)
1.399 (1.185- 1.651)
0.954 (0.733- 1.243)
SRH
Fair (ref: negative)
0.595 (0.475-0.746)
0.355 (0.189- 0.667)
0.597 (0.373- 0.953)
0.480 (0.417-0.552)
0.471 (0.386- 0.576)
0.581 (0.418- 0.806)
0.796 (0.543- 1.167)
0.630 (0.502- 0.791)
0.689 (0.594- 0.799)
Positive (ref: negative)
0.360 (0.328-0.396)
0.287 (0.248- 0.332)
0.319 (0.152- 0.671)
0.450 (0.177-1.143)
0.491 (0.318- 0.757)
0.485 (0.421- 0.558)
0.640 (0.343- 1.195)
0.523 (0.273- 1.001)
0.510 (0.430- 0.604)
Chronic disease
Yes (ref：no)
0.724 (0.427-1.228)
1.265 (0.529- 3.024)
1.275 (0.600- 2.712)
0.693 (0.509-0.944)
1.003 (0.488- 2.061)
0.789 (0.473- 1.315)
0.550 (0.328- 0.921)
0.829 (0.472- 1.457)
0.832 (0.742- 0.934)
eHealth literacy
1.133 (1.097-1.171)
1.050 (1.041- 1.058)
1.048 (1.016- 1.082)
1.082 (1.059-1.106)
0.963 (0.916- 1.013)
0.992 (0.960- 1.025)
1.114 (1.091- 1.138)
1.018 (0.979- 1.060)
1.062 (1.006- 1.120)
Perceived usefulness
—
1.365 (1.220- 1.528)
1.202 (1.180- 1.225)
—
1.379 (1.287- 1.479)
1.154 (1.109- 1.202)
—
1.335 (1.258- 1.417)
1.107 (1.088- 1.126)
Perceived ease of use
—
1.023 (0.854- 1.226)
1.046 (0.966- 1.132)
—
1.108 (0.938- 1.310)
1.110 (1.021- 1.207)
—
1.069 (0.929- 1.231)
1.095 (1.005- 1.193)
aOR: odds ratio.
bModel 1 (awareness), Model 4 (awareness), Model 7 (awareness): logistic regression model adjusted for sex, age, marital status, place of residence, educational attainment, income, SRH, chronic disease, and eHealth literacy.
cModel 2 (want) adds perceived usefulness and ease of use to the variables in model 1; model 5 (want) adds perceived usefulness and ease of use to the variables in model 2; model 8 (want) adds perceived usefulness and ease of use to the variables in model 7.
dModel 3 (adoption) includes all variables from model 2; model 6 (adoption) includes all variables from model 5; model 9 (adoption) includes all variables from model 8.
eP<.01.
fP<.001.
gP<.05
hSRH: self-rated health.
iNot applicable.
Health status was an important factor that influenced awareness, want, and adoption in eHealth services among inpatients. Inpatients with more positive SRH were less likely to have awareness of, want for, and adoption of 3 services (P<.05). Having chronic diseases was only significantly negative with awareness of treatment intermediary and treatment eHealth services and adoption of treatment eHealth services (P<.01).
Among digital technology factors, eHealth literacy demonstrated a positive correlation with awareness of all 3 services (P<.001), and it had a favorable influence on want for information-based eHealth services and adoption of information-based and treatment eHealth services (P<.05). Perceived usefulness exerted a positive effect on both want for and adoption of 3 services (P<.001). Finally, perceived ease of use had a positive influence on the adoption of treatment intermediary and treatment eHealth services (P<.05).
Among inpatients, age, living in rural areas, and better SRH negatively influenced awareness, want, and adoption in eHealth services, but educational attainment, eHealth literacy, perceived usefulness, and perceived ease of use were positively associated with these outcomes. In addition, the influence of these factors differed depending on the specific type of eHealth service. The logistic analysis results for awareness of, want for, and adoption of eHealth services were presented in Table S4 in .
Discussion
Principal Findings
The findings of this study confirmed the existence of a digital divide in eHealth services among information-based, treatment intermediary, and treatment eHealth services. In addition, the study further demonstrated a reciprocal relationship between the want and awareness rate, suggesting that a higher awareness rate may stimulate greater want rate for eHealth services, and consequently, adoption rate of eHealth services also tends to increase with elevated awareness and want rates. These observations align with the findings of Te-Hsin Liang’s research on 2 types of eHealth services in Taiwan []. However, it is important to note that these findings are based on a sample from 3 hospitals in a single city in China, which may limit the generalizability of the results to other regions or populations.
In the AWAG matrix, information-based, treatment intermediary, and treatment eHealth services were all categorized within the opened group. Compared to other studies, inpatients in this research exhibited relatively high levels of awareness and want for these services. The internet, recognized as a rapidly expanding platform for health information dissemination [,], has emerged as a pivotal platform for accessing comprehensive health-related data, effectively catering to diverse health care stakeholders []. Inpatients, specifically, rely heavily on the internet to remain informed about their health status and to educate themselves on disease treatments []. While treatment intermediary eHealth services were positioned within the strong subgroup, information-based eHealth services were categorized in the Wb subgroup. Previous research highlights an increasing reliance on internet searches when addressing health concerns []. However, challenges persist regarding the quality of online health information []. Overuse or inappropriate use of information-based eHealth services can lead to exaggerated or misinterpreted adverse symptoms, potentially heightening health anxiety or fear [,]. Governmental support and hospital-led initiatives to raise awareness and promote the use of these services are instrumental in their effectiveness [,]. The adoption gap between treatment intermediary and information-based eHealth services was comparable, indicating similar potential for increased utilization of treatment intermediary eHealth services. This highlights the importance of developing a robust maintenance strategy. Several pivotal factors contributing to the digital divide in the adoption of such services have been identified through research, including inadequate staffing levels in hospitals, outdated medical technology, system implementation challenges, and the overarching health care environment []. However, thanks to policy support and advancements in medical technology [], the adoption gap rate for eHealth services in this study is notably lower compared to that reported in comparable research endeavors [].
Furthermore, treatment eHealth services fell within the generic subgroup characterized by relatively lower rates of awareness and want compared to the other 2 service categories. The late introduction of treatment eHealth services has led to limited awareness among inpatients []. Some studies have also found that this lack of awareness is further exacerbated by the higher eHealth literacy requirements for using treatment eHealth services [], which contributes to lower want rates []. Similar findings were observed in this study, showing that eHealth literacy positively influenced awareness of treatment eHealth services, while the want for these services remained relatively low. Notably, the adoption gap rate for treatment eHealth services was 66.5%, suggesting ample prospects for augmenting their adoption. Addressing barriers such as inadequate awareness and low eHealth literacy could play a pivotal role in narrowing this gap and enhancing the utilization of treatment eHealth services.
Factors influencing all 3 eHealth services consistently indicated that younger participants were more likely to be aware of and want eHealth services, whereas older participants demonstrated a greater propensity to adopt them. The decline in physical function commonly observed in older patients presents challenges in using electronic devices [], thereby perpetuating the digital divide. Furthermore, apart from the lack of access to digital devices [], the process of learning to use the internet may evoke feelings of anxiety or embarrassment among older adults []. Cao et al [] demonstrated that weekly online and offline knowledge and psychological interventions significantly improved the knowledge, willingness, confidence, and usage of internet medical services among older patients with chronic diseases in China. This suggests that the digital divide in eHealth services, driven by nonmaterial barriers among the older, can be mitigated through targeted knowledge training and mindset improvement, particularly in middle- and high-income countries [].
Rural participants were less likely to be aware of, want, and adopt eHealth services, consistent with previous findings and further substantiating the digital divide [,]. Likewise, negative SRH has a negative effect on eHealth services’ awareness, want, and adoption. This finding aligns with Andersen and Newman’s individual determinants of disease levels [], which suggest that it is the direct cause of want for and adoption of eHealth services among inpatients in this study. A study on telemedicine in the United States also found that residing in rural areas and access to broadband had a greater impact on the use of telemedicine than other socioeconomic factors, which highlights the importance of understanding not only broadband access but also the broader relationship between the rural environment and telemedicine use [].
The impact of income and educational attainment on eHealth services varied notably depending on the specific type of service. Compared to treatment intermediary eHealth services [,], information-based eHealth services typically have lower costs and are easier to use []. Similarly, this study also identified a significant positive effect of educational attainment on awareness and adoption of all 3 types of eHealth services. The research by Limbu and Huhmann [] and Reinecke et al [] further support these findings, highlighting that both income and education serve as important enablers for access to more expensive or complex eHealth services, regardless of whether in high-income or middle- and low-income countries, in which reforms are expected to address the digital divide. However, inpatients with poor SRH and chronic diseases were likely to want or adopt all 3 eHealth services, especially treatment services. Some studies have indicated that individuals with poor health conditions or chronic diseases exhibit a heightened demand for eHealth services that can address health issues and provide monitoring, potentially overcoming barriers to usage [,].
Moreover, higher eHealth literacy implies a stronger ability to acquire information and benefit from eHealth services []. Beyond its positive influence on awareness, eHealth literacy was also significantly associated with the want for information-based eHealth services and the adoption of treatment eHealth services. Specifically, younger patients with higher eHealth literacy scores demonstrated a greater likelihood of desiring and adopting these services, aligning with the findings of other studies conducted globally [-].
Perceived usefulness has a significant positive impact on eHealth services’ demand and use, consistent with previous studies [,]. However, in this study, perceived ease of use only has a positive impact on the adoption of eHealth services. This result aligns with prior research, suggesting that perceived ease of use indirectly influences the intention to use eHealth services through perceived usefulness, rather than exerting a direct effect []. In addition, a survey by Wu et al [] on inpatient participation in telemedicine in Toronto reveals that perceived usefulness, along with prior positive experiences, was a key factor driving participants’ willingness to engage in various telemedicine services. These results further underscore the importance of enhancing patients’ experiences and perceptions, as doing so can foster greater use of eHealth and help address the digital divide.
This study holds significant implications for eHealth policies and practices. The AWAG matrix analysis shows a digital divide in eHealth exists among inpatients in Jinan, considering various adoption stages and service types. Future digital health initiatives should avoid adopting a one-size-fits-all strategy and, instead, aim to achieve digital health equity through tailored services that account for varying user characteristics and needs. Information-based services were characterized by want bias. To enhance the credibility of information, it is recommended to introduce doctor certification and user evaluation systems, whereas targeted promotional efforts should be directed toward rural and low-education groups, using community health lectures, bulletin boards, and broadcasts in primary health care institutions. These low-cost measures can be followed by low- and middle-income countries. For treatment intermediary services with higher awareness, want, and adoption, it is essential to optimize operational processes, improve system stability, and enhance response speed to elevate the user experience. This can be achieved by simplifying appointment scheduling, payment procedures, and query results. However, the awareness, want, and adoption rate of medical treatment remain relatively low. It is crucial for the government and hospitals to collaborate to promote policy support. Therefore, collaboration between the government and health care institutions is critical to foster policy support. For example, governments should include eHealth or telemedicine services within the scope of medical insurance reimbursement and encourage both doctors and patients to use online follow-up services.
Limitations
This study acknowledges several limitations. First, it uses a cross-sectional survey design, which inherently restricts the ability to analyze influence pathways and establish causality among the relevant variables. Future studies adopting a longitudinal design would be more appropriate to address this limitation. Second, the sample population consists of inpatients from 3 hospitals in Jinan, Shandong Province, who inherently exhibit a certain level of medical service demand. Consequently, caution should be exercised when generalizing the findings to other populations. Finally, as a retrospective study, it is susceptible to recall bias, which could impact the accuracy of the data and results.
Conclusions
This study delves into inpatients’ awareness of, want for, and adoption of eHealth services by using a matrix analysis conducted among inpatients at 3 hospitals in Jinan, China. Information-based eHealth services, categorized within the opened Wb group in the AWAG matrix, revealed a significant digital divide in their usage. To address this, targeted strategies should focus on enhancing privacy protection and improving the perceived ease of use of these services, which could help sustain and gradually increase demand. Treatment intermediary eHealth services, classified within the opened strong group, also demonstrated a substantial digital divide in adoption, necessitating further attention. Finally, treatment eHealth services, positioned in the opened generic group, continue to face significant adoption challenges, with both awareness and want requiring improvement. To bridge these gaps, initiatives such as frequent public awareness campaigns and improving response efficiency need to be implemented to enhance awareness and foster sustained demand. The findings also underscore the diverse health care needs of individuals, shaped by factors such as educational attainment and place of residence. These differences necessitate comprehensive strategies, particularly in addressing the challenges older adults face in navigating internet technologies.
This study was supported by the Humanities and Social Science Foundation of the Ministry of Education of China (grant 21YJC630060), the National Natural Science Foundation of China (grant 72274108), and the Natural Science Foundation of Shandong Province (grant ZR2022MG003). The funders had no involvement in the study design, data collection, analysis, interpretation, or the writing of the manuscript. The authors would like to thank the Shandong University School of Public Health and all participants for making this study possible.
Data will be made available on request.
WS and DD contributed to data curation, formal analysis, interpreted the data, and wrote the original draft of the manuscript. SD and ZY contributed to reviewing and editing the manuscript. JL acquired funding, administered the project, provided supervision, interpreted the data, and contributed to reviewing and editing the manuscript. All authors reviewed and approved the final paper before submission.
None declared.
Edited by Alicia Stone, Amaryllis Mavragani; submitted 07.Feb.2025; peer-reviewed by Sonia Butler, Xueting Ding; final revised version received 04.Oct.2025; accepted 06.Oct.2025; published 30.Oct.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
