O Reilly Automotive (ORLY) Operating Expenses (2009 - 2026)
O Reilly Automotive (ORLY) recorded Operating Expenses of $1.53 billion in Q2 2026, up 8.4% from $1.41 billion a year earlier and up 1.7% from the prior quarter.
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Analysis
O Reilly Automotive (ORLY) Operating Expenses (2009 - 2026) Analysis & Trends
On a TTM basis, O Reilly Automotive's Operating Expenses came in at $5.96 billion as of Jun 30, 2026, up 8.2% year-over-year; for FY2025, it was $5.71 billion, up 7.7% from FY2024.
- Annual Operating Expenses has increased for 16 straight years, with a five-year compound annual growth rate of 9.3% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $5.3 billion in FY2024 (+7.8%), $4.92 billion in FY2023 (+11.1%), $4.43 billion in FY2022 (+7.9%) and $4.1 billion in FY2021 (+11.9%).
- The Q2 2026 figure is the highest quarterly Operating Expenses in data going back to Q1 2009.
- On a year-over-year basis, Operating Expenses has increased for 66 consecutive quarters, with growth averaging 8.1% over the last eight quarters.
- Across the past five years, year-over-year growth in Operating Expenses ran from 5.4% in Q2 2022 to 13.5% in Q2 2023.
- Per Business Quant, the preceding three quarters came in at $1.51 billion (Q1 2026), $1.46 billion (Q4 2025) and $1.46 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,654.24 Bn | 2,170.93 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 289.19 Bn | 282.43 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 143.39 Bn | 120.94 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 105.29 Bn | 98.25 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 75.79 Bn | 58.72 Bn | 2.12 Bn | 1.02 Bn |
| 6 | Target | 72.00 Bn | 66.59 Bn | 8.94 Bn | 5.73 Bn |
| 7 | O Reilly Automotive | 70.61 Bn | 69.69 Bn | 2.52 Bn | 1.53 Bn |
| 8 | Carvana | 66.52 Bn | 58.12 Bn | 1.38 Bn | 704.00 Mn |
| 9 | Autozone | 47.57 Bn | 46.47 Bn | 2.52 Bn | 1.60 Bn |
| 10 | JD.com | 32.28 Bn | -77.90 Bn | 8.71 Bn | -1.53 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.53 Bn |
| Mar 31, 2026 | 1.51 Bn |
| Dec 31, 2025 | 1.46 Bn |
| Sep 30, 2025 | 1.46 Bn |
| Jun 30, 2025 | 1.41 Bn |
| Mar 31, 2025 | 1.38 Bn |
| Dec 31, 2024 | 1.36 Bn |
| Sep 30, 2024 | 1.35 Bn |
| Jun 30, 2024 | 1.30 Bn |
| Mar 31, 2024 | 1.28 Bn |
| Dec 31, 2023 | 1.25 Bn |
| Sep 30, 2023 | 1.26 Bn |
| Jun 30, 2023 | 1.23 Bn |
| Mar 31, 2023 | 1.17 Bn |
| Dec 31, 2022 | 1.17 Bn |
| Sep 30, 2022 | 1.13 Bn |
| Jun 30, 2022 | 1.09 Bn |
| Mar 31, 2022 | 1.04 Bn |
| Dec 31, 2021 | 1.06 Bn |
| Sep 30, 2021 | 1.06 Bn |
API Access
O Reilly Automotive 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=ORLY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ORLY", "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=ORLY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();