Ltc Properties (LTC) Operating Expenses (2010 - 2026)
Ltc Properties' Operating Expenses came in at $75.54 million for Q2 2026, up 69.6% from $44.54 million a year earlier and up 7.0% from the prior quarter.
Ltc Properties (LTC) Operating Expenses (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Ltc Properties reported Operating Expenses of $294.36 million, up 111.4% year-over-year; for FY2025, it was $223.37 million, up 78.1% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 15.7% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $125.39 million in FY2024 (-13.3%), $144.58 million in FY2023 (+26.9%), $113.9 million in FY2022 (+5.5%) and $107.98 million in FY2021 (+0.1%).
- The five-year range for quarterly Operating Expenses is $26.76 million (Q1 2022) to $87.49 million (Q3 2025).
- Year-over-year, Operating Expenses has increased for six consecutive quarters, with growth averaging 62.5% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q3 2025 (growth of 198.8%), and the weakest in Q2 2024 (a decline of 26.7%).
- Business Quant data shows LTC's Operating Expenses at $70.63 million (Q1 2026), $60.7 million (Q4 2025) and $87.49 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Welltower | 166.01 Bn | 148.52 Bn | 1.39 Bn | 3.22 Bn |
| 2 | Prologis | 121.16 Bn | 125.55 Bn | - | 1.47 Bn |
| 3 | Simon Property | 65.49 Bn | 66.75 Bn | - | 900.79 Mn |
| 4 | Realty Income | 51.38 Bn | 53.76 Bn | - | 1.19 Bn |
| 5 | Public Storage | 49.67 Bn | 48.75 Bn | - | 766.20 Mn |
| 6 | Ventas | 43.61 Bn | 42.81 Bn | - | 1.68 Bn |
| 7 | Extra Space Storage | 27.79 Bn | 27.79 Bn | 642.43 Mn | 481.97 Mn |
| 8 | Vici Properties | 25.29 Bn | 23.41 Bn | 1.05 Bn | 315.61 Mn |
| 9 | Vivmark Residential | 22.78 Bn | 22.97 Bn | - | 566.18 Mn |
| 10 | Ltc Properties | 2.31 Bn | 2.38 Bn | - | 75.54 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 75.54 Mn |
| Mar 31, 2026 | 70.63 Mn |
| Dec 31, 2025 | 60.70 Mn |
| Sep 30, 2025 | 87.49 Mn |
| Jun 30, 2025 | 44.54 Mn |
| Mar 31, 2025 | 30.65 Mn |
| Dec 31, 2024 | 34.79 Mn |
| Sep 30, 2024 | 29.28 Mn |
| Jun 30, 2024 | 31.02 Mn |
| Mar 31, 2024 | 30.30 Mn |
| Dec 31, 2023 | 38.65 Mn |
| Sep 30, 2023 | 31.92 Mn |
| Jun 30, 2023 | 42.32 Mn |
| Mar 31, 2023 | 31.69 Mn |
| Dec 31, 2022 | 30.04 Mn |
| Sep 30, 2022 | 30.10 Mn |
| Jun 30, 2022 | 27.00 Mn |
| Mar 31, 2022 | 26.76 Mn |
| Dec 31, 2021 | 26.96 Mn |
| Sep 30, 2021 | 29.44 Mn |
Ltc Properties 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=LTC&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "LTC", "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=LTC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();