Realty Income (O) Accumulated Expenses (2010 - 2018)
Realty Income's (O) quarterly Accumulated Expenses came in at $21.6 million in Q1 2018, up 2.54% year-over-year from $21.1 million in Q1 2017, and down 24.76% quarter-over-quarter from $28.7 million in Q4 2017.
Realty Income (O) Accumulated Expenses (2010 - 2018) Analysis & Trends
Realty Income has disclosed Accumulated Expenses across 9 years of filings, most recently posting $21.6 million for Q1 2018.
- In Q1 2018, Accumulated Expenses rose 2.54% year-over-year to $21.6 million; the TTM figure through Mar 2018 stood at $21.6 million (up 2.54% YoY), while the FY2017 annual figure was $28.7 million, down 1.21% from the prior year.
- Accumulated Expenses came in at $21.6 million for Q1 2018 at Realty Income, down from $28.7 million in the prior quarter.
- In the past five years, Accumulated Expenses ranged from a high of $30.4 million in Q2 2016 to a low of $17.6 million in Q2 2017.
- Average Accumulated Expenses over 5 years is $23.7 million, with a median of $23.4 million recorded in 2014.
- Year-over-year, Accumulated Expenses gained 22.7% in 2016 and slumped 42.02% in 2017.
- Over 5 years, Accumulated Expenses stood at $25.2 million in 2014, then fell by 3.81% to $24.2 million in 2015, then rose by 20.09% to $29.1 million in 2016, then decreased by 1.21% to $28.7 million in 2017, then decreased by 24.76% to $21.6 million in 2018.
- Per Business Quant data, the three most recent Accumulated Expenses figures were $21.6 million in Q1 2018, $28.7 million in Q4 2017, and $20.7 million in Q3 2017.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Welltower | 165.94 Bn | 165.00 Bn | 1.39 Bn |
| 2 | Prologis | 126.84 Bn | 131.24 Bn | 2.41 Bn |
| 3 | Simon Property | 67.22 Bn | 68.48 Bn | 1.76 Bn |
| 4 | Realty Income | 53.47 Bn | 55.85 Bn | - |
| 5 | Public Storage | 50.86 Bn | 50.70 Bn | 1.20 Bn |
| 6 | Ventas | 44.55 Bn | 44.86 Bn | 1.73 Bn |
| 7 | Extra Space Storage | 28.52 Bn | 29.39 Bn | 642.43 Mn |
| 8 | Vici Properties | 26.41 Bn | 25.98 Bn | 1.05 Bn |
| 9 | Vivmark Residential | 23.37 Bn | 23.56 Bn | - |
| 10 | Invitation Homes | 16.02 Bn | 16.06 Bn | 705.34 Mn |
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2018 | 21.60 Mn |
| Dec 31, 2017 | 28.71 Mn |
| Sep 30, 2017 | 20.66 Mn |
| Jun 30, 2017 | 17.64 Mn |
| Mar 31, 2017 | 21.06 Mn |
| Dec 31, 2016 | 29.06 Mn |
| Sep 30, 2016 | 22.19 Mn |
| Jun 30, 2016 | 30.42 Mn |
| Mar 31, 2016 | 25.56 Mn |
| Dec 31, 2015 | 24.20 Mn |
| Sep 30, 2015 | 20.77 Mn |
| Jun 30, 2015 | 24.79 Mn |
| Mar 31, 2015 | 21.84 Mn |
| Dec 31, 2014 | 25.16 Mn |
| Sep 30, 2014 | 26.08 Mn |
| Jun 30, 2014 | 23.37 Mn |
| Mar 31, 2014 | 19.76 Mn |
| Dec 31, 2013 | 30.05 Mn |
| Sep 30, 2013 | 26.86 Mn |
| Jun 30, 2013 | 23.23 Mn |
Realty Income Accumulated 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=accumulated-expenses&ticker=O&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "O", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=O&period=max&api_key=YOUR_API_KEY");
const data = await res.json();