Mays J W (MAYS) Property Operating Expenses (2010 - 2026)
Mays J W's Property Operating Expenses was $2.54 million in fiscal Q4 2014 (quarter ended Jul 31, 2014), up 14.7% from $2.21 million a year earlier and up 4.3% from the prior quarter.
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Analysis
Mays J W (MAYS) Property Operating Expenses (2010 - 2026) Analysis & Trends
For FY2014 (ended Jul 31, 2014), Property Operating Expenses at Mays J W came in at $9.63 million, up 9.1% from FY2013.
- Property Operating Expenses has now increased for four consecutive fiscal years, with a four-year compound annual growth rate of 6.2% (FY2010 to FY2014).
- In earlier fiscal years, Property Operating Expenses was $8.82 million in FY2013 (+9.7%), $8.04 million in FY2012 (+2.6%), $7.84 million in FY2011 (+3.3%) and $7.58 million in FY2010.
- The fiscal Q4 2014 figure marks the highest quarterly Property Operating Expenses in data going back to fiscal Q1 2011.
- Compared with a year earlier, Property Operating Expenses has increased for seven straight quarters, with growth averaging 9.5% over the last eight quarters.
- The best year-over-year quarter for Property Operating Expenses over five years was fiscal Q2 2013 (growth of 16.2%); the worst was fiscal Q3 2012 (a decline of 6.2%).
- Per Business Quant data, MAYS's Property Operating Expenses in the three fiscal quarters before Q4 2014 was $2.44 million (Q3 2014), $2.5 million (Q2 2014) and $2.15 million (Q1 2014).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Property Expenses (Qtr) |
|---|---|---|---|---|---|
| 1 | Texas Pacific Land | 18.94 Bn | 17.93 Bn | 106.12 Mn | - |
| 2 | LandBridge | 5.09 Bn | 5.45 Bn | 69.80 Mn | - |
| 3 | Howard Hughes Holdings | 5.05 Bn | -3.72 Bn | 435.75 Mn | -14.51 Mn |
| 4 | St Joe | 4.12 Bn | 3.63 Bn | 80.65 Mn | - |
| 5 | Millrose Properties | 4.03 Bn | 3.85 Bn | - | - |
| 6 | Vesta Real Estate Corporation, S.A.B. de C.V | 3.35 Bn | 2.16 Bn | - | -8.50 Mn |
| 7 | EagleRock Land | 2.64 Bn | 4.02 Bn | - | - |
| 8 | Opendoor Technologies | 1.22 Bn | -2.55 Bn | 38.00 Mn | - |
| 9 | Forestar | 1.18 Bn | 1.18 Bn | - | - |
| 10 | Mays J W | 89.66 Mn | 78.71 Mn | - | 3.90 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 3.90 Mn |
| Apr 30, 2026 | 4.00 Mn |
| Jan 31, 2026 | 4.13 Mn |
| Oct 31, 2025 | 4.08 Mn |
| Jul 31, 2025 | 3.95 Mn |
| Apr 30, 2025 | 3.82 Mn |
| Jan 31, 2025 | 4.13 Mn |
| Oct 31, 2024 | 3.75 Mn |
| Jul 31, 2024 | 3.81 Mn |
| Apr 30, 2024 | 3.83 Mn |
| Jan 31, 2024 | 3.83 Mn |
| Oct 31, 2023 | 3.69 Mn |
| Jul 31, 2023 | 3.79 Mn |
| Apr 30, 2023 | 3.85 Mn |
| Jan 31, 2023 | 3.96 Mn |
| Oct 31, 2022 | 3.79 Mn |
| Jul 31, 2022 | 3.60 Mn |
| Apr 30, 2022 | 3.76 Mn |
| Jan 31, 2022 | 3.67 Mn |
| Oct 31, 2021 | 3.63 Mn |
API Access
Mays J W Property 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=property-operating-expenses&ticker=MAYS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "property-operating-expenses", "ticker": "MAYS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=property-operating-expenses&ticker=MAYS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();