Stewart Information Services (STC) Operating Expenses (2010 - 2026)
Stewart Information Services' Operating Expenses was $844.14 million in Q2 2026, up 25.0% from $675.41 million a year earlier and up 11.4% from the prior quarter.
Stewart Information Services (STC) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Stewart Information Services' Operating Expenses was $3.08 billion through Jun 30, 2026, up 21.3% year-over-year; for FY2025, it came in at $2.76 billion, up 16.0% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 5.9% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $2.38 billion in FY2024 (+8.2%), $2.2 billion in FY2023 (-22.6%), $2.84 billion in FY2022 (-1.2%) and $2.87 billion in FY2021 (+38.7%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2021.
- Compared with a year earlier, Operating Expenses has increased for ten straight quarters, with growth averaging 16.8% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q3 2021 (growth of 38.7%); the worst was Q1 2023 (a decline of 30.9%).
- Per Business Quant data, STC's Operating Expenses in the three quarters before Q2 2026 was $757.68 million (Q1 2026), $738.83 million (Q4 2025) and $735.74 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Berkshire Hathaway | 1,083.69 Bn | -333.79 Bn | 55.86 Bn | 86.07 Bn |
| 2 | Chubb | 127.81 Bn | 79.86 Bn | 9.13 Bn | 12.19 Bn |
| 3 | Progressive | 121.79 Bn | 97.63 Bn | 9.04 Bn | 19.40 Bn |
| 4 | Marsh & Mclennan Companies | 80.80 Bn | 72.54 Bn | - | 5.51 Bn |
| 5 | Travelers Companies | 75.76 Bn | 52.01 Bn | 6.23 Bn | 9.39 Bn |
| 6 | Manulife Financial | 74.06 Bn | 75.27 Bn | - | -902.21 Mn |
| 7 | Metlife | 61.12 Bn | -45.36 Bn | 7.82 Bn | 18.12 Bn |
| 8 | Arthur J. Gallagher | 58.82 Bn | 53.25 Bn | - | 3.59 Bn |
| 9 | Allstate | 57.81 Bn | 19.06 Bn | 8.73 Bn | 14.45 Bn |
| 10 | Stewart Information Services | 1.75 Bn | 533.81 Mn | 877.17 Mn | 844.14 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 844.14 Mn |
| Mar 31, 2026 | 757.68 Mn |
| Dec 31, 2025 | 738.83 Mn |
| Sep 30, 2025 | 735.74 Mn |
| Jun 30, 2025 | 675.41 Mn |
| Mar 31, 2025 | 606.08 Mn |
| Dec 31, 2024 | 630.56 Mn |
| Sep 30, 2024 | 625.15 Mn |
| Jun 30, 2024 | 573.23 Mn |
| Mar 31, 2024 | 547.17 Mn |
| Dec 31, 2023 | 563.39 Mn |
| Sep 30, 2023 | 574.65 Mn |
| Jun 30, 2023 | 523.98 Mn |
| Mar 31, 2023 | 534.46 Mn |
| Dec 31, 2022 | 635.16 Mn |
| Sep 30, 2022 | 670.89 Mn |
| Jun 30, 2022 | 757.29 Mn |
| Mar 31, 2022 | 773.31 Mn |
| Dec 31, 2021 | 847.60 Mn |
| Sep 30, 2021 | 720.28 Mn |
Stewart Information Services Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=STC&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "STC", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=STC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();