Douglas Elliman (DOUG) Operating Expenses (2021 - 2026)
Douglas Elliman (DOUG) reported Operating Expenses of $17.94 million for Q2 2026, down 32.2% from $26.48 million a year earlier and down 22.8% from the prior quarter.
Douglas Elliman (DOUG) Operating Expenses (2021 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Douglas Elliman's Operating Expenses came in at $99.97 million, down 16.8% year-over-year; for FY2025, it came in at $112.59 million, down 5.2% from FY2024.
- Operating Expenses has declined for three consecutive years, though with a five-year compound annual growth rate of 7.2% (FY2020 to FY2025).
- By year, Operating Expenses came in at $118.81 million in FY2024 (-7.0%), $127.82 million in FY2023 (-2.7%), $131.42 million in FY2022 (+41.6%) and $92.8 million in FY2021 (+16.7%).
- The Q2 2026 figure ranks as the lowest quarterly Operating Expenses in data going back to Q1 2021.
- Year over year, Operating Expenses has now declined in each of the last three quarters, with an average decline of 5.9% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q1 2022 (growth of 70.0%); the low point was Q2 2026 (a decline of 32.2%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $23.24 million (Q1 2026), $25.77 million (Q4 2025) and $33.01 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | KE Holdings | 37.89 Bn | 18.60 Bn | 1.03 Bn | -587.69 Mn |
| 2 | Cbre | 37.24 Bn | 31.01 Bn | 2.09 Bn | - |
| 3 | Jones Lang Lasalle | 14.06 Bn | 12.27 Bn | - | 6.64 Bn |
| 4 | Compass | 6.70 Bn | 5.16 Bn | - | 4.18 Bn |
| 5 | Colliers International | 4.59 Bn | 3.66 Bn | 635.11 Mn | 485.10 Mn |
| 6 | Cushman & Wakefield | 2.77 Bn | 247.78 Mn | 512.00 Mn | 2.63 Bn |
| 7 | Newmark | 1.95 Bn | 1.33 Bn | - | 848.02 Mn |
| 8 | Marcus & Millichap | 1.08 Bn | 173.61 Mn | - | 200.70 Mn |
| 9 | Agnt | 613.32 Mn | 143.01 Mn | 98.80 Mn | 97.16 Mn |
| 10 | Douglas Elliman | 137.24 Mn | -322.46 Mn | 41.42 Mn | 17.94 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 17.94 Mn |
| Mar 31, 2026 | 23.24 Mn |
| Dec 31, 2025 | 25.77 Mn |
| Sep 30, 2025 | 33.01 Mn |
| Jun 30, 2025 | 26.48 Mn |
| Mar 31, 2025 | 27.33 Mn |
| Dec 31, 2024 | 37.67 Mn |
| Sep 30, 2024 | 28.68 Mn |
| Jun 30, 2024 | 25.45 Mn |
| Mar 31, 2024 | 27.02 Mn |
| Dec 31, 2023 | 33.52 Mn |
| Sep 30, 2023 | 29.03 Mn |
| Jun 30, 2023 | 31.77 Mn |
| Mar 31, 2023 | 33.51 Mn |
| Dec 31, 2022 | 32.19 Mn |
| Sep 30, 2022 | 33.52 Mn |
| Jun 30, 2022 | 32.88 Mn |
| Mar 31, 2022 | 32.83 Mn |
| Dec 31, 2021 | 28.32 Mn |
| Sep 30, 2021 | 22.29 Mn |
Douglas Elliman 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=DOUG&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DOUG", "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=DOUG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();