Jones Lang Lasalle (JLL) Operating Expenses (2009 - 2026)
Jones Lang Lasalle (JLL) recorded Operating Expenses of $6.64 billion in Q2 2026, up 9.7% from $6.05 billion a year earlier and up 7.4% from the prior quarter.
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Analysis
Jones Lang Lasalle (JLL) Operating Expenses (2009 - 2026) Analysis & Trends
On a TTM basis, Jones Lang Lasalle's Operating Expenses came in at $26.16 billion as of Jun 30, 2026, up 10.1% year-over-year; for FY2025, it came in at $25.02 billion, up 10.9% from FY2024.
- Annual Operating Expenses has increased for five straight years, with a five-year compound annual growth rate of 9.3% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $22.56 billion in FY2024 (+11.8%), $20.18 billion in FY2023 (+1.0%), $19.99 billion in FY2022 (+9.1%) and $18.32 billion in FY2021 (+14.3%).
- Quarterly Operating Expenses has ranged from $4.6 billion in Q3 2021 to $7.1 billion in Q4 2025 over the past five years.
- On a year-over-year basis, Operating Expenses has increased for 12 consecutive quarters, with growth averaging 11.4% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 21.8% in Q4 2021, against a decline of 2.8% in Q2 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $6.18 billion (Q1 2026), $7.1 billion (Q4 2025) and $6.24 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | KE Holdings | 38.10 Bn | 18.80 Bn | 1.03 Bn | -587.69 Mn |
| 2 | Cbre | 37.79 Bn | 31.56 Bn | 2.09 Bn | - |
| 3 | Jones Lang Lasalle | 14.45 Bn | 12.66 Bn | - | 6.64 Bn |
| 4 | Compass | 6.85 Bn | 5.32 Bn | - | 4.18 Bn |
| 5 | Colliers International | 4.59 Bn | 3.67 Bn | 635.11 Mn | 485.10 Mn |
| 6 | Cushman & Wakefield | 2.78 Bn | 264.20 Mn | 512.00 Mn | 2.63 Bn |
| 7 | Newmark | 1.99 Bn | 1.37 Bn | - | 848.02 Mn |
| 8 | Marcus & Millichap | 1.10 Bn | 194.81 Mn | - | 200.70 Mn |
| 9 | Rmr | 593.80 Mn | 518.26 Mn | - | 162.79 Mn |
| 10 | Agnt | 586.58 Mn | 116.27 Mn | 98.80 Mn | 97.16 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 6.64 Bn |
| Mar 31, 2026 | 6.18 Bn |
| Dec 31, 2025 | 7.10 Bn |
| Sep 30, 2025 | 6.24 Bn |
| Jun 30, 2025 | 6.05 Bn |
| Mar 31, 2025 | 5.63 Bn |
| Dec 31, 2024 | 6.44 Bn |
| Sep 30, 2024 | 5.64 Bn |
| Jun 30, 2024 | 5.48 Bn |
| Mar 31, 2024 | 5.01 Bn |
| Dec 31, 2023 | 5.59 Bn |
| Sep 30, 2023 | 4.99 Bn |
| Jun 30, 2023 | 4.90 Bn |
| Mar 31, 2023 | 4.70 Bn |
| Dec 31, 2022 | 5.35 Bn |
| Sep 30, 2022 | 4.97 Bn |
| Jun 30, 2022 | 5.04 Bn |
| Mar 31, 2022 | 4.63 Bn |
| Dec 31, 2021 | 5.50 Bn |
| Sep 30, 2021 | 4.60 Bn |
API Access
Jones Lang Lasalle 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=JLL&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "JLL", "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=JLL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();