Mays J W (MAYS) Operating Expenses (2010 - 2026)
Mays J W's Operating Expenses came in at $5.64 million for fiscal Q3 2026 (quarter ended Apr 30, 2026), up 2.1% from $5.52 million a year earlier but down 4.9% from the prior quarter.
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Analysis
Mays J W (MAYS) Operating Expenses (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Apr 30, 2026, Mays J W reported Operating Expenses of $23.09 million, up 3.1% year-over-year; for FY2025 (ended Jul 31, 2025), it came in at $22.62 million, up 1.8% from FY2024.
- Operating Expenses carries a four-year compound annual growth rate of 1.8% (FY2021 to FY2025).
- Going back by fiscal year, Operating Expenses was $22.21 million in FY2024 (-0.6%), $22.35 million in FY2023 (+1.4%), $22.05 million in FY2022 (+4.8%) and $21.05 million in FY2021.
- The five-year range for quarterly Operating Expenses is $5.08 million (fiscal Q4 2021) to $5.92 million (fiscal Q2 2026).
- Year-over-year, Operating Expenses has increased for four consecutive quarters, with growth averaging 2.1% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in fiscal Q4 2022 (growth of 9.6%), and the weakest in fiscal Q1 2024 (a decline of 1.4%).
- Business Quant data shows MAYS's Operating Expenses at $5.92 million (Q2 2026), $5.74 million (Q1 2026) and $5.79 million (Q4 2025) in the three fiscal quarters before Q3 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Texas Pacific Land | 23.31 Bn | 22.14 Bn | - | 54.24 Mn |
| 2 | Eason Technology | 6.61 Bn | 6.61 Bn | - | - |
| 3 | LandBridge | 6.20 Bn | 6.54 Bn | 65.71 Mn | 15.90 Mn |
| 4 | Millrose Properties | 4.06 Bn | 4.06 Bn | - | 29.22 Mn |
| 5 | St Joe | 3.76 Bn | 3.26 Bn | 73.42 Mn | 104.06 Mn |
| 6 | Vesta Real Estate Corporation, S.A.B. de C.V | 2.89 Bn | 2.89 Bn | - | -8.70 Mn |
| 7 | Opendoor Technologies | 2.35 Bn | -1.47 Bn | 86.00 Mn | 230.00 Mn |
| 8 | Forestar | 1.34 Bn | -3.23 Mn | 84.10 Mn | 38.30 Mn |
| 9 | Five Point Holdings | 705.60 Mn | 734.37 Mn | -23.72 Mn | 21.49 Mn |
| 10 | Mays J W | 80.63 Mn | 80.63 Mn | - | 5.64 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Apr 30, 2026 | 5.64 Mn |
| Jan 31, 2026 | 5.92 Mn |
| Oct 31, 2025 | 5.74 Mn |
| Jul 31, 2025 | 5.79 Mn |
| Apr 30, 2025 | 5.52 Mn |
| Jan 31, 2025 | 5.83 Mn |
| Oct 31, 2024 | 5.49 Mn |
| Jul 31, 2024 | 5.57 Mn |
| Apr 30, 2024 | 5.52 Mn |
| Jan 31, 2024 | 5.74 Mn |
| Oct 31, 2023 | 5.38 Mn |
| Jul 31, 2023 | 5.52 Mn |
| Apr 30, 2023 | 5.59 Mn |
| Jan 31, 2023 | 5.79 Mn |
| Oct 31, 2022 | 5.45 Mn |
| Jul 31, 2022 | 5.56 Mn |
| Apr 30, 2022 | 5.47 Mn |
| Jan 31, 2022 | 5.54 Mn |
| Oct 31, 2021 | 5.49 Mn |
| Jul 31, 2021 | 5.08 Mn |
API Access
Mays J W 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=MAYS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "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=operating-expenses&ticker=MAYS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();