Dine Brands Global (DIN) EBITDA (2010 - 2026)
Dine Brands Global's EBITDA was $23.6 million in Q2 2026, down 19.5% from $29.3 million a year earlier and down 4.5% from the prior quarter.
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Analysis
Dine Brands Global (DIN) EBITDA (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Dine Brands Global's EBITDA was $70.62 million through Jun 28, 2026, down 29.8% year-over-year; for FY2025, it was $74.2 million, down 38.9% from FY2024.
- EBITDA shows a five-year compound annual growth rate of 5.3% (FY2020 to FY2025).
- In earlier years, EBITDA was $121.4 million in FY2024 (-15.0%), $142.8 million in FY2023 (+7.0%), $133.51 million in FY2022 (-11.0%) and $150.04 million in FY2021 (+162.1%).
- Quarterly EBITDA has moved between -$915,000 (Q4 2025) and $40.02 million (Q2 2024) over five years.
- Compared with a year earlier, EBITDA was higher in two of the last seven quarters, with an average decline of 22.4%.
- The best year-over-year quarter for EBITDA over five years was Q4 2021 (growth of 156.3%); the worst was Q4 2024 (a decline of 61.2%).
- Per Business Quant data, DIN's EBITDA in the three quarters before Q2 2026 was $24.7 million (Q1 2026), -$915,000 (Q4 2025) and $23.24 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | EBITDA (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 163.51 Bn | 158.34 Bn | 6.42 Bn | 3.90 Bn |
| 2 | Starbucks | 107.11 Bn | 94.75 Bn | - | 1.37 Bn |
| 3 | Chipotle Mexican Grill | 40.42 Bn | 36.40 Bn | - | 623.92 Mn |
| 4 | Yum Brands | 37.23 Bn | 34.12 Bn | 1.47 Bn | 715.00 Mn |
| 5 | Restaurant Brands International | 24.82 Bn | 21.92 Bn | 1.38 Bn | 793.00 Mn |
| 6 | Darden Restaurants | 22.06 Bn | 21.17 Bn | -113.70 Mn | 663.10 Mn |
| 7 | Restaurant Brands International Limited Partnership | 17.01 Bn | 12.57 Bn | 1.38 Bn | 793.00 Mn |
| 8 | Yum China Holdings | 14.18 Bn | 8.54 Bn | 537.00 Mn | 468.00 Mn |
| 9 | Texas Roadhouse | 10.25 Bn | 9.62 Bn | - | 201.13 Mn |
| 10 | Dine Brands Global | 352.51 Mn | -145.39 Mn | 91.20 Mn | 23.60 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 23.60 Mn |
| Mar 29, 2026 | 24.70 Mn |
| Dec 28, 2025 | -915,000.00 |
| Sep 30, 2025 | 23.24 Mn |
| Jun 29, 2025 | 29.30 Mn |
| Mar 30, 2025 | 22.30 Mn |
| Dec 29, 2024 | 13.43 Mn |
| Sep 30, 2024 | 35.52 Mn |
| Jun 30, 2024 | 40.02 Mn |
| Mar 31, 2024 | 32.39 Mn |
| Dec 31, 2023 | 34.58 Mn |
| Sep 30, 2023 | 33.75 Mn |
| Jun 30, 2023 | 34.79 Mn |
| Mar 31, 2023 | 39.64 Mn |
| Dec 31, 2022 | 22.85 Mn |
| Sep 30, 2022 | 33.25 Mn |
| Jun 30, 2022 | 38.48 Mn |
| Mar 31, 2022 | 39.02 Mn |
| Dec 31, 2021 | 35.22 Mn |
| Sep 30, 2021 | 35.99 Mn |
API Access
Dine Brands Global EBITDA 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=ebitda&ticker=DIN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ebitda", "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=ebitda&ticker=DIN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();