Re (RMAX) Operating Expenses (2012 - 2026)
Re (RMAX) posted Operating Expenses of $66.99 million for Q2 2026, up 14.1% from $58.71 million a year earlier but down 14.2% from the prior quarter.
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Analysis
Re (RMAX) Operating Expenses (2012 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Re was $261.8 million, up 1.0% year-over-year; for FY2025, it was $244.56 million, down 8.6% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of 1.5% (FY2020 to FY2025).
- In prior years, Re's Operating Expenses was $267.5 million in FY2024 (-20.5%), $336.31 million in FY2023 (+6.7%), $315.17 million in FY2022 (-7.2%) and $339.63 million in FY2021 (+49.3%).
- Quarterly Operating Expenses has run from a low of $54.93 million in Q3 2025 to a high of $128.57 million in Q3 2021 over five years.
- On a year-over-year basis, Operating Expenses increased in two of the last eight quarters, with an average decline of 8.3%.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q3 2021, with growth of 113.8%; the weakest was Q3 2024, with a decline of 38.1%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $78.05 million (Q1 2026), $61.82 million (Q4 2025) and $54.93 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Cbre | 37.92 Bn | 31.69 Bn | 2.09 Bn | - |
| 2 | KE Holdings | 37.35 Bn | 18.06 Bn | 1.03 Bn | -587.69 Mn |
| 3 | Jones Lang Lasalle | 14.31 Bn | 12.53 Bn | - | 6.64 Bn |
| 4 | Compass | 6.79 Bn | 5.25 Bn | - | 4.18 Bn |
| 5 | Colliers International | 4.60 Bn | 3.67 Bn | 635.11 Mn | 485.10 Mn |
| 6 | Cushman & Wakefield | 2.75 Bn | 233.71 Mn | 512.00 Mn | 2.63 Bn |
| 7 | Newmark | 1.99 Bn | 1.36 Bn | - | 848.02 Mn |
| 8 | Marcus & Millichap | 1.10 Bn | 195.57 Mn | - | 200.70 Mn |
| 9 | Agnt | 593.27 Mn | 122.95 Mn | 98.80 Mn | 97.16 Mn |
| 10 | Re | 263.49 Mn | -673.06 Mn | - | 66.99 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 66.99 Mn |
| Mar 31, 2026 | 78.05 Mn |
| Dec 31, 2025 | 61.82 Mn |
| Sep 30, 2025 | 54.93 Mn |
| Jun 30, 2025 | 58.71 Mn |
| Mar 31, 2025 | 69.10 Mn |
| Dec 31, 2024 | 68.20 Mn |
| Sep 30, 2024 | 63.27 Mn |
| Jun 30, 2024 | 62.28 Mn |
| Mar 31, 2024 | 73.76 Mn |
| Dec 31, 2023 | 86.30 Mn |
| Sep 30, 2023 | 102.22 Mn |
| Jun 30, 2023 | 69.30 Mn |
| Mar 31, 2023 | 78.49 Mn |
| Dec 31, 2022 | 72.80 Mn |
| Sep 30, 2022 | 83.71 Mn |
| Jun 30, 2022 | 75.26 Mn |
| Mar 31, 2022 | 83.40 Mn |
| Dec 31, 2021 | 78.73 Mn |
| Sep 30, 2021 | 128.57 Mn |
API Access
Re 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=RMAX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "RMAX", "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=RMAX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();