Dine Brands Global (DIN) Change in Accured Expenses (2010 - 2026)
Dine Brands Global's Change in Accured Expenses was $5.7 million in Q2 2026, down 68.2% from $17.9 million a year earlier.
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Analysis
Dine Brands Global (DIN) Change in Accured Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Dine Brands Global's Change in Accured Expenses was -$7.1 million through Jun 28, 2026; for FY2025, it was $14 million.
- Change in Accured Expenses shows a five-year compound annual growth rate of 2.7% (FY2020 to FY2025).
- In earlier years, Change in Accured Expenses was -$7.1 million in FY2024, -$28.9 million in FY2023, -$16.26 million in FY2022 and $19.71 million in FY2021 (+61.2%).
- Quarterly Change in Accured Expenses has moved between -$26.65 million (Q1 2022) and $17.9 million (Q2 2025) over five years.
- The best year-over-year quarter for Change in Accured Expenses over five years was Q2 2025 (growth of 615.7%); the worst was Q3 2022 (a decline of 98.8%).
- Per Business Quant data, DIN's Change in Accured Expenses in the three quarters before Q2 2026 was -$17.9 million (Q1 2026), $3.32 million (Q4 2025) and $1.78 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 164.13 Bn | 158.95 Bn | 6.42 Bn | - |
| 2 | Starbucks | 107.97 Bn | 95.60 Bn | - | - |
| 3 | Chipotle Mexican Grill | 41.04 Bn | 37.02 Bn | - | 100.69 Mn |
| 4 | Yum Brands | 37.16 Bn | 34.04 Bn | 1.47 Bn | 4.00 Mn |
| 5 | Restaurant Brands International | 24.42 Bn | 21.52 Bn | 1.38 Bn | -8.00 Mn |
| 6 | Darden Restaurants | 22.67 Bn | 21.77 Bn | 1.68 Bn | -51.80 Mn |
| 7 | Restaurant Brands International Limited Partnership | 17.01 Bn | 12.57 Bn | 1.38 Bn | -8.00 Mn |
| 8 | Yum China Holdings | 13.79 Bn | 8.16 Bn | 537.00 Mn | 107.00 Mn |
| 9 | Texas Roadhouse | 10.24 Bn | 9.60 Bn | - | 15.18 Mn |
| 10 | Dine Brands Global | 379.50 Mn | -118.40 Mn | 91.20 Mn | 5.70 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 5.70 Mn |
| Mar 29, 2026 | -17.90 Mn |
| Dec 28, 2025 | 3.32 Mn |
| Sep 30, 2025 | 1.78 Mn |
| Jun 29, 2025 | 17.90 Mn |
| Mar 30, 2025 | -9.00 Mn |
| Dec 29, 2024 | 3.93 Mn |
| Sep 30, 2024 | -2.09 Mn |
| Jun 30, 2024 | 2.50 Mn |
| Mar 31, 2024 | -11.45 Mn |
| Dec 31, 2023 | -15.31 Mn |
| Sep 30, 2023 | -3.42 Mn |
| Jun 30, 2023 | 1.63 Mn |
| Mar 31, 2023 | -11.80 Mn |
| Dec 31, 2022 | 2.47 Mn |
| Sep 30, 2022 | 156,000.00 |
| Jun 30, 2022 | 7.75 Mn |
| Mar 31, 2022 | -26.65 Mn |
| Dec 31, 2021 | 10.15 Mn |
| Sep 30, 2021 | 13.41 Mn |
API Access
Dine Brands Global Change in Accured 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=change-in-accured-expenses&ticker=DIN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-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=change-in-accured-expenses&ticker=DIN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();