Asbury Automotive (ABG) Operating Expenses (2010 - 2026)
Asbury Automotive's Operating Expenses was $506.4 million in Q2 2026, up 6.5% from $475.5 million a year earlier but down 0.8% from the prior quarter.
Asbury Automotive (ABG) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Asbury Automotive's Operating Expenses was $2.07 billion through Jun 30, 2026, up 10.5% year-over-year; for FY2025, it came in at $1.99 billion, up 5.2% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 20.5% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $1.89 billion in FY2024 (+16.8%), $1.62 billion in FY2023 (-8.3%), $1.76 billion in FY2022 (+64.2%) and $1.07 billion in FY2021 (+37.3%).
- Quarterly Operating Expenses has moved between $268.7 million (Q3 2021) and $528.7 million (Q4 2025) over five years.
- Compared with a year earlier, Operating Expenses has increased for four straight quarters, with growth averaging 9.2% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q1 2022 (growth of 89.9%); the worst was Q1 2023 (a decline of 11.5%).
- Per Business Quant data, ABG's Operating Expenses in the three quarters before Q2 2026 was $510.4 million (Q1 2026), $528.7 million (Q4 2025) and $527.1 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,826.65 Bn | 2,343.34 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 307.75 Bn | 300.99 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 162.60 Bn | 140.14 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 109.37 Bn | 102.33 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 75.83 Bn | 58.75 Bn | 2.12 Bn | 1.02 Bn |
| 6 | O Reilly Automotive | 73.47 Bn | 72.55 Bn | 2.52 Bn | 1.53 Bn |
| 7 | Target | 69.62 Bn | 64.21 Bn | 8.94 Bn | 5.73 Bn |
| 8 | Autozone | 51.01 Bn | 49.91 Bn | 2.52 Bn | 1.60 Bn |
| 9 | Carvana | 42.20 Bn | 32.69 Bn | 1.38 Bn | 704.00 Mn |
| 10 | Asbury Automotive | 3.11 Bn | 2.97 Bn | 753.10 Mn | 506.40 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 506.40 Mn |
| Mar 31, 2026 | 510.40 Mn |
| Dec 31, 2025 | 528.70 Mn |
| Sep 30, 2025 | 527.10 Mn |
| Jun 30, 2025 | 475.50 Mn |
| Mar 31, 2025 | 456.40 Mn |
| Dec 31, 2024 | 476.90 Mn |
| Sep 30, 2024 | 466.50 Mn |
| Jun 30, 2024 | 476.50 Mn |
| Mar 31, 2024 | 468.60 Mn |
| Dec 31, 2023 | 414.10 Mn |
| Sep 30, 2023 | 391.70 Mn |
| Jun 30, 2023 | 408.60 Mn |
| Mar 31, 2023 | 403.00 Mn |
| Dec 31, 2022 | 421.50 Mn |
| Sep 30, 2022 | 438.20 Mn |
| Jun 30, 2022 | 448.20 Mn |
| Mar 31, 2022 | 455.50 Mn |
| Dec 31, 2021 | 295.70 Mn |
| Sep 30, 2021 | 268.70 Mn |
Asbury 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=ABG&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ABG", "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=ABG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();