Jones Lang Lasalle (JLL) Non-Current Debt (2009 - 2026)
Jones Lang Lasalle's Non-Current Debt was -$3.8 million in FY2015, compared with $18.73 million in FY2014.
Analysis
Jones Lang Lasalle (JLL) Non-Current Debt (2009 - 2026) Analysis & Trends
- Per Business Quant data, Non-Current Debt in earlier years was $18.73 million in FY2014 and $940,000 in FY2013.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Non-Current Debt (Qtr) |
|---|---|---|---|---|---|
| 1 | Cbre | 39.21 Bn | 32.52 Bn | 2.09 Bn | 5.73 Bn |
| 2 | KE Holdings | 20.01 Bn | -7.18 Bn | 1.03 Bn | - |
| 3 | Jones Lang Lasalle | 14.29 Bn | 12.37 Bn | - | 596.40 Mn |
| 4 | Compass | 7.20 Bn | 5.65 Bn | - | 3.14 Bn |
| 5 | Colliers International | 4.73 Bn | 3.80 Bn | 635.11 Mn | 2.49 Bn |
| 6 | Cushman & Wakefield | 2.70 Bn | 179.42 Mn | 512.00 Mn | 2.41 Bn |
| 7 | Newmark | 2.08 Bn | 1.15 Bn | - | 867.28 Mn |
| 8 | Marcus & Millichap | 1.06 Bn | 156.09 Mn | - | - |
| 9 | Agnt | 677.11 Mn | 206.79 Mn | 98.80 Mn | - |
| 10 | Rmr | 295.52 Mn | 45.60 Mn | - | 163.81 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 596.40 Mn |
| Mar 31, 2026 | 798.90 Mn |
| Dec 31, 2025 | 805.90 Mn |
| Sep 30, 2025 | 806.10 Mn |
| Jun 30, 2025 | 805.30 Mn |
| Mar 31, 2025 | 772.10 Mn |
| Dec 31, 2024 | 756.70 Mn |
| Sep 30, 2024 | 783.70 Mn |
| Jun 30, 2024 | 767.90 Mn |
| Mar 31, 2024 | 770.20 Mn |
| Dec 31, 2023 | 779.30 Mn |
| Sep 30, 2023 | 369.50 Mn |
| Jun 30, 2023 | 380.70 Mn |
| Mar 31, 2023 | 379.20 Mn |
| Dec 31, 2022 | 372.80 Mn |
| Sep 30, 2022 | 341.50 Mn |
| Jun 30, 2022 | 364.40 Mn |
| Mar 31, 2022 | 387.80 Mn |
| Dec 31, 2021 | 395.60 Mn |
| Sep 30, 2021 | 678.60 Mn |
API Access
Jones Lang Lasalle Non-Current Debt 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=non-current-debt&ticker=JLL&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "non-current-debt", "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=non-current-debt&ticker=JLL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();