Automatic Data Processing (ADP) Operating Leases (2019 - 2026)
Automatic Data Processing (ADP) posted Operating Leases of $306.7 million for fiscal Q4 2026 (quarter ended Jun 30, 2026), down 4.5% from $321.2 million a year earlier and down 3.6% from the prior quarter.
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Analysis
Automatic Data Processing (ADP) Operating Leases (2019 - 2026) Analysis & Trends
Since fiscal Q1 2020, Automatic Data Processing has reported Operating Leases for 28 quarters.
- Annual Operating Leases has declined for four consecutive fiscal years, with a five-year compound annual growth rate of -2.2% (FY2021 to FY2026).
- In prior fiscal years, Automatic Data Processing's Operating Leases was $321.2 million in FY2025 (-2.3%), $328.6 million in FY2024 (-6.1%), $349.9 million in FY2023 (-5.7%) and $370.9 million in FY2022 (+8.1%).
- The fiscal Q4 2026 figure stands as the lowest quarterly Operating Leases since fiscal Q3 2025.
- On a year-over-year basis, Operating Leases increased in three of the last eight quarters, with growth averaging 0.9%.
- The strongest year-over-year quarter for Operating Leases in the past five years was fiscal Q2 2026, with growth of 15.8%; the weakest was fiscal Q3 2025, with a decline of 8.4%.
- According to Business Quant data, Operating Leases for the three prior fiscal quarters was $318.2 million (Q3 2026), $347.6 million (Q2 2026) and $341.7 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palantir Technologies | 480.73 Bn | 449.68 Bn | 1.64 Bn |
| 2 | Oracle | 399.91 Bn | 272.04 Bn | - |
| 3 | Sap Se | 262.30 Bn | 190.08 Bn | 8.40 Bn |
| 4 | Salesforce | 202.26 Bn | 158.13 Bn | 8.70 Bn |
| 5 | ServiceNow | 153.84 Bn | 132.30 Bn | 2.82 Bn |
| 6 | Automatic Data Processing | 114.84 Bn | 96.97 Bn | 2.51 Bn |
| 7 | Intuit | 91.04 Bn | 70.38 Bn | 3.44 Bn |
| 8 | Relx | 70.51 Bn | 69.60 Bn | - |
| 9 | Strategy | 53.24 Bn | 46.23 Bn | 81.55 Mn |
| 10 | Thomson Reuters | 48.40 Bn | 45.18 Bn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 306.70 Mn |
| Mar 31, 2026 | 318.20 Mn |
| Dec 31, 2025 | 347.60 Mn |
| Sep 30, 2025 | 341.70 Mn |
| Jun 30, 2025 | 321.20 Mn |
| Mar 31, 2025 | 283.20 Mn |
| Dec 31, 2024 | 300.30 Mn |
| Sep 30, 2024 | 316.10 Mn |
| Jun 30, 2024 | 328.60 Mn |
| Mar 31, 2024 | 309.10 Mn |
| Dec 31, 2023 | 326.80 Mn |
| Sep 30, 2023 | 334.30 Mn |
| Jun 30, 2023 | 349.90 Mn |
| Mar 31, 2023 | 332.10 Mn |
| Dec 31, 2022 | 334.10 Mn |
| Sep 30, 2022 | 348.50 Mn |
| Jun 30, 2022 | 370.90 Mn |
| Mar 31, 2022 | 344.90 Mn |
| Dec 31, 2021 | 363.50 Mn |
| Sep 30, 2021 | 345.70 Mn |
API Access
Automatic Data Processing Operating Leases 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-leases&ticker=ADP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-leases", "ticker": "ADP", "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-leases&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();