Geo (GEO) Operating Expenses (2009 - 2026)
Geo's Operating Expenses was $530.7 million in Q2 2026, up 11.7% from $475.22 million a year earlier and up 1.8% from the prior quarter.
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Analysis
Geo (GEO) Operating Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Geo's Operating Expenses was $2.09 billion through Jun 30, 2026, up 15.0% year-over-year; for FY2025, it came in at $1.97 billion, up 10.9% from FY2024.
- Operating Expenses has now increased for four consecutive years, with a five-year compound annual growth rate of 2.1% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $1.77 billion in FY2024 (+1.7%), $1.74 billion in FY2023 (+4.9%), $1.66 billion in FY2022 (+2.1%) and $1.63 billion in FY2021 (-8.0%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses in data going back to Q4 2011.
- Compared with a year earlier, Operating Expenses has increased for 17 straight quarters, with growth averaging 9.0% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2025 (growth of 18.5%); the worst was Q1 2022 (a decline of 10.0%).
- Per Business Quant data, GEO's Operating Expenses in the three quarters before Q2 2026 was $521.51 million (Q1 2026), $529.91 million (Q4 2025) and $508.88 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Accenture | 116.45 Bn | 76.86 Bn | 6.13 Bn | 15.54 Bn |
| 2 | Cintas | 80.26 Bn | 79.45 Bn | 1.48 Bn | 792.12 Mn |
| 3 | Iron Mountain | 33.05 Bn | 32.57 Bn | 1.07 Bn | 1.66 Bn |
| 4 | APi | 16.57 Bn | 13.61 Bn | 703.00 Mn | 528.00 Mn |
| 5 | Rollins | 14.72 Bn | 14.26 Bn | 569.95 Mn | 877.22 Mn |
| 6 | Aramark | 14.30 Bn | 12.32 Bn | 430.34 Mn | 4.84 Bn |
| 7 | UL Solutions | 13.09 Bn | 11.87 Bn | 417.00 Mn | 267.00 Mn |
| 8 | Gartner | 11.73 Bn | 5.42 Bn | 1.19 Bn | 1.30 Bn |
| 9 | Rentokil Initial | 10.36 Bn | 3.71 Bn | - | - |
| 10 | Geo | 4.00 Bn | 3.61 Bn | - | 530.70 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 530.70 Mn |
| Mar 31, 2026 | 521.51 Mn |
| Dec 31, 2025 | 529.91 Mn |
| Sep 30, 2025 | 508.88 Mn |
| Jun 30, 2025 | 475.22 Mn |
| Mar 31, 2025 | 453.78 Mn |
| Dec 31, 2024 | 447.36 Mn |
| Sep 30, 2024 | 441.92 Mn |
| Jun 30, 2024 | 443.53 Mn |
| Mar 31, 2024 | 441.68 Mn |
| Dec 31, 2023 | 441.94 Mn |
| Sep 30, 2023 | 440.67 Mn |
| Jun 30, 2023 | 428.13 Mn |
| Mar 31, 2023 | 433.49 Mn |
| Dec 31, 2022 | 429.72 Mn |
| Sep 30, 2022 | 436.21 Mn |
| Jun 30, 2022 | 411.79 Mn |
| Mar 31, 2022 | 385.16 Mn |
| Dec 31, 2021 | 395.99 Mn |
| Sep 30, 2021 | 399.90 Mn |
API Access
Geo 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=GEO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "GEO", "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=GEO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();