Village Super Market (VLGEA) Total Liabilities (2011 - 2026)
Village Super Market's Total Liabilities came in at $493.54 million for fiscal Q3 2026 (quarter ended Apr 25, 2026), down 4.3% from $515.89 million a year earlier and down 3.3% from the prior quarter.
Village Super Market (VLGEA) Total Liabilities (2011 - 2026) Analysis & Trends
At the end of FY2025 (ended Jul 26, 2025), Village Super Market's Total Liabilities was $511.75 million, down 4.2% from FY2024.
- Total Liabilities carries a five-year compound annual growth rate of -2.6% (FY2020 to FY2025).
- Going back by fiscal year, Total Liabilities was $534.11 million in FY2024 (-4.2%), $557.54 million in FY2023 (+0.9%), $552.34 million in FY2022 (+0.9%) and $547.53 million in FY2021 (-6.1%).
- The fiscal Q3 2026 figure represents the lowest quarterly Total Liabilities since fiscal Q3 2020.
- Year-over-year, Total Liabilities has declined for 11 consecutive quarters, with an average decline of 4.0% over the last eight quarters.
- The fastest year-over-year change in Total Liabilities over five years came in fiscal Q1 2023 (growth of 5.0%), and the weakest in fiscal Q4 2021 (a decline of 6.1%).
- Business Quant data shows VLGEA's Total Liabilities at $510.58 million (Q2 2026), $503.19 million (Q1 2026) and $511.75 million (Q4 2025) in the three fiscal quarters before Q3 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Walmart | 847.32 Bn | 810.03 Bn | 49.13 Bn | 189.41 Bn |
| 2 | Costco Wholesale | 410.07 Bn | 339.37 Bn | 9.01 Bn | 52.92 Bn |
| 3 | Sysco | 37.60 Bn | 31.85 Bn | 4.13 Bn | 25.73 Bn |
| 4 | Kroger | 35.90 Bn | 22.05 Bn | 7.86 Bn | 43.65 Bn |
| 5 | Dollar General | 27.06 Bn | 21.73 Bn | 3.68 Bn | 22.88 Bn |
| 6 | Caseys General Stores | 22.17 Bn | 20.17 Bn | 1.24 Bn | 5.03 Bn |
| 7 | Dollar Tree | 21.26 Bn | 17.89 Bn | 2.10 Bn | 10.60 Bn |
| 8 | US Foods Holding | 20.27 Bn | 20.07 Bn | 1.92 Bn | 10.06 Bn |
| 9 | Tractor Supply | 16.44 Bn | 15.61 Bn | 1.68 Bn | 9.53 Bn |
| 10 | Village Super Market | 657.83 Mn | 140.66 Mn | - | 493.54 Mn |
Historic Data
| Date | Value |
|---|---|
| Apr 25, 2026 | 493.54 Mn |
| Jan 24, 2026 | 510.58 Mn |
| Oct 25, 2025 | 503.19 Mn |
| Jul 26, 2025 | 511.75 Mn |
| Apr 26, 2025 | 515.89 Mn |
| Jan 25, 2025 | 530.15 Mn |
| Oct 26, 2024 | 532.88 Mn |
| Jul 27, 2024 | 534.11 Mn |
| Apr 27, 2024 | 534.00 Mn |
| Jan 27, 2024 | 541.28 Mn |
| Oct 28, 2023 | 557.48 Mn |
| Jul 29, 2023 | 557.54 Mn |
| Apr 29, 2023 | 549.01 Mn |
| Jan 28, 2023 | 557.02 Mn |
| Oct 29, 2022 | 568.15 Mn |
| Jul 30, 2022 | 552.34 Mn |
| Apr 30, 2022 | 552.20 Mn |
| Jan 29, 2022 | 542.05 Mn |
| Oct 30, 2021 | 541.06 Mn |
| Jul 31, 2021 | 547.53 Mn |
Village Super Market Total Liabilities 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=total-liabilities&ticker=VLGEA&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "VLGEA", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=VLGEA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();