Vivmark Residential (VMRK) Return on Sales [ROS] (2009 - 2020)
Vivmark Residential (VMRK) posted Return on Sales [ROS] of 61.86% for Q1 2020, up 30.17 percentage points from 31.69% a year earlier and up 2.07 percentage points from the prior quarter.
Vivmark Residential (VMRK) Return on Sales [ROS] (2009 - 2020) Analysis & Trends
For the trailing twelve months through Mar 31, 2020, Return on Sales [ROS] at Vivmark Residential was 57.64%, up 19.86 percentage points year-over-year; for FY2019, it came in at 50.21%, up 6.98 percentage points from FY2018.
- Annual Return on Sales [ROS] has increased for three consecutive years, with a five-year change of +14.96 percentage points (FY2014 to FY2019).
- In prior years, Vivmark Residential's Return on Sales [ROS] was 43.23% in FY2018 (+2.58 pp), 40.65% in FY2017 (+5.36 pp), 35.29% in FY2016 (-1.48 pp) and 36.77% in FY2015 (+1.52 pp).
- The Q1 2020 figure stands as the highest quarterly Return on Sales [ROS] in data going back to Q1 2009.
- On a year-over-year basis, Return on Sales [ROS] has increased in each of the last four quarters, with an average year-over-year change of +6.09 percentage points over the last eight quarters.
- The strongest year-over-year quarter for Return on Sales [ROS] in the past five years was Q1 2020, with a gain of 30.17 percentage points; the weakest was Q4 2018, with a drop of 25.47 percentage points.
- According to Business Quant data, Return on Sales [ROS] for the three prior quarters was 59.79% (Q4 2019), 53.76% (Q3 2019) and 55.13% (Q2 2019).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | Welltower | 177.74 Bn | 163.76 Bn | 1.47 Bn | 9.10% |
| 2 | Prologis | 120.05 Bn | 114.00 Bn | - | 43.76% |
| 3 | Simon Property | 74.18 Bn | 70.24 Bn | - | 46.02% |
| 4 | Public Storage | 62.09 Bn | 60.82 Bn | - | 23.20% |
| 5 | Realty Income | 55.76 Bn | 54.03 Bn | - | 48.67% |
| 6 | Ventas | 45.60 Bn | 44.27 Bn | - | 11.68% |
| 7 | Extra Space Storage | 33.58 Bn | 32.49 Bn | 650.96 Mn | 44.04% |
| 8 | Vici Properties | 27.29 Bn | 25.64 Bn | 1.06 Bn | 52.20% |
| 9 | Vivmark Residential | 24.52 Bn | 24.30 Bn | - | - |
| 10 | Essex Property Trust | 17.52 Bn | 17.27 Bn | - | 31.82% |
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2020 | 61.86% |
| Dec 31, 2019 | 59.79% |
| Sep 30, 2019 | 53.76% |
| Jun 30, 2019 | 55.13% |
| Mar 31, 2019 | 31.69% |
| Dec 31, 2018 | 33.48% |
| Sep 30, 2018 | 51.99% |
| Jun 30, 2018 | 34.01% |
| Mar 31, 2018 | 53.57% |
| Dec 31, 2017 | 58.95% |
| Sep 30, 2017 | 34.89% |
| Jun 30, 2017 | 34.38% |
| Mar 31, 2017 | 33.83% |
| Dec 31, 2016 | 37.01% |
| Sep 30, 2016 | 34.55% |
| Jun 30, 2016 | 34.62% |
| Mar 31, 2016 | 34.99% |
| Dec 31, 2015 | 40.82% |
| Sep 30, 2015 | 37.00% |
| Jun 30, 2015 | 36.26% |
Vivmark Residential Return on Sales [ROS] 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=return-on-sales-%5Bros%5D&ticker=VMRK&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "ticker": "VMRK", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=return-on-sales-%5Bros%5D&ticker=VMRK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();