Dine Brands Global (DIN) Operating Expenses (2010 - 2026)
Dine Brands Global's Operating Expenses was $79.5 million in Q2 2026, up 8.5% from $73.3 million a year earlier and up 6.7% from the prior quarter.
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Analysis
Dine Brands Global (DIN) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Dine Brands Global's Operating Expenses was $330.54 million through Jun 28, 2026, up 9.8% year-over-year; for FY2025, it came in at $328 million, up 11.9% from FY2024.
- Operating Expenses has now increased for five consecutive years, with a five-year compound annual growth rate of 7.0% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $293.1 million in FY2024 (+1.2%), $289.5 million in FY2023 (+2.7%), $281.8 million in FY2022 (+6.3%) and $265.09 million in FY2021 (+13.1%).
- Quarterly Operating Expenses has moved between $63.67 million (Q1 2022) and $104.65 million (Q4 2025) over five years.
- Compared with a year earlier, Operating Expenses was higher in six of the last eight quarters, with growth averaging 6.8%.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2025 (growth of 27.6%); the worst was Q4 2023 (a decline of 9.3%).
- Per Business Quant data, DIN's Operating Expenses in the three quarters before Q2 2026 was $74.5 million (Q1 2026), $104.65 million (Q4 2025) and $71.89 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 163.51 Bn | 158.34 Bn | 6.42 Bn | 3.76 Bn |
| 2 | Starbucks | 107.11 Bn | 94.75 Bn | - | 8.42 Bn |
| 3 | Chipotle Mexican Grill | 40.42 Bn | 36.40 Bn | - | 2.82 Bn |
| 4 | Yum Brands | 37.23 Bn | 34.12 Bn | 1.47 Bn | 1.51 Bn |
| 5 | Restaurant Brands International | 24.82 Bn | 21.92 Bn | 1.38 Bn | 1.80 Bn |
| 6 | Darden Restaurants | 22.06 Bn | 21.17 Bn | -113.70 Mn | 3.20 Bn |
| 7 | Restaurant Brands International Limited Partnership | 17.01 Bn | 12.57 Bn | 1.38 Bn | 1.80 Bn |
| 8 | Yum China Holdings | 14.18 Bn | 8.54 Bn | 537.00 Mn | 2.79 Bn |
| 9 | Texas Roadhouse | 10.25 Bn | 9.62 Bn | - | 1.54 Bn |
| 10 | Dine Brands Global | 352.51 Mn | -145.39 Mn | 91.20 Mn | 79.50 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 79.50 Mn |
| Mar 29, 2026 | 74.50 Mn |
| Dec 28, 2025 | 104.65 Mn |
| Sep 30, 2025 | 71.89 Mn |
| Jun 29, 2025 | 73.30 Mn |
| Mar 30, 2025 | 78.40 Mn |
| Dec 29, 2024 | 82.01 Mn |
| Sep 30, 2024 | 67.40 Mn |
| Jun 30, 2024 | 68.95 Mn |
| Mar 31, 2024 | 74.78 Mn |
| Dec 31, 2023 | 73.25 Mn |
| Sep 30, 2023 | 72.15 Mn |
| Jun 30, 2023 | 70.96 Mn |
| Mar 31, 2023 | 73.20 Mn |
| Dec 31, 2022 | 80.78 Mn |
| Sep 30, 2022 | 69.98 Mn |
| Jun 30, 2022 | 67.22 Mn |
| Mar 31, 2022 | 63.67 Mn |
| Dec 31, 2021 | 71.14 Mn |
| Sep 30, 2021 | 68.61 Mn |
API Access
Dine Brands Global 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=DIN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DIN", "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=DIN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();