White Mountains Insurance (WTM) Operating Expenses (2009 - 2026)
White Mountains Insurance (WTM) reported Operating Expenses of $588.6 million for Q2 2026, up 14.8% from $512.7 million a year earlier and up 8.3% from the prior quarter.
White Mountains Insurance (WTM) Operating Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, White Mountains Insurance's Operating Expenses came in at $2.52 billion, up 20.2% year-over-year; for FY2025, it came in at $2.41 billion, up 25.1% from FY2024.
- Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 59.2% (FY2020 to FY2025).
- By year, Operating Expenses came in at $1.92 billion in FY2024 (+20.1%), $1.6 billion in FY2023 (+22.5%), $1.31 billion in FY2022 (+47.2%) and $888 million in FY2021 (+277.6%).
- Five-year quarterly Operating Expenses spans a low of $230 million in Q4 2021 and a high of $702.8 million in Q4 2025.
- Year over year, Operating Expenses has now increased in each of the last 17 quarters, with growth averaging 20.2% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q3 2021 (growth of 93.9%); the low point was Q1 2022 (a decline of 13.2%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $543.3 million (Q1 2026), $702.8 million (Q4 2025) and $685.4 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 | White Mountains Insurance | 4.81 Bn | -5.87 Bn | 764.50 Mn | 588.60 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 588.60 Mn |
| Mar 31, 2026 | 543.30 Mn |
| Dec 31, 2025 | 702.80 Mn |
| Sep 30, 2025 | 685.40 Mn |
| Jun 30, 2025 | 512.70 Mn |
| Mar 31, 2025 | 505.40 Mn |
| Dec 31, 2024 | 478.70 Mn |
| Sep 30, 2024 | 599.60 Mn |
| Jun 30, 2024 | 430.10 Mn |
| Mar 31, 2024 | 414.70 Mn |
| Dec 31, 2023 | 409.00 Mn |
| Sep 30, 2023 | 498.50 Mn |
| Jun 30, 2023 | 361.00 Mn |
| Mar 31, 2023 | 332.80 Mn |
| Dec 31, 2022 | 308.60 Mn |
| Sep 30, 2022 | 414.20 Mn |
| Jun 30, 2022 | 309.30 Mn |
| Mar 31, 2022 | 275.20 Mn |
| Dec 31, 2021 | 230.00 Mn |
| Sep 30, 2021 | 268.20 Mn |
White Mountains Insurance 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=WTM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WTM", "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=WTM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();