Neighborhood Intelligence (NXH) Total Liabilities (2010 - 2026)
Neighborhood Intelligence's Total Liabilities was $419.06 million in Q2 2026, up 84.7% from $226.87 million a year earlier and up 107.5% from the prior quarter.
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Analysis
Neighborhood Intelligence (NXH) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025, Total Liabilities at Neighborhood Intelligence came in at $207.53 million, down 13.2% from FY2024.
- Total Liabilities shows a five-year compound annual growth rate of -12.0% (FY2020 to FY2025).
- In earlier years, Total Liabilities was $239.22 million in FY2024 (-13.5%), $276.69 million in FY2023 (+18.9%), $232.72 million in FY2022 (-27.6%) and $321.58 million in FY2021 (-18.4%).
- The Q2 2026 figure marks the highest quarterly Total Liabilities since Q2 2021.
- Compared with a year earlier, Total Liabilities was higher in 1 of the last eight quarters, with an average year-over-year change of 0.0%.
- The best year-over-year quarter for Total Liabilities over five years was Q2 2026 (growth of 84.7%); the worst was Q2 2022 (a decline of 29.9%).
- Per Business Quant data, NXH's Total Liabilities in the three quarters before Q2 2026 was $202 million (Q1 2026), $207.53 million (Q4 2025) and $211.44 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,659.84 Bn | 2,176.54 Bn | 104.83 Bn | 544.07 Bn |
| 2 | Home Depot | 287.59 Bn | 280.83 Bn | 16.12 Bn | 92.77 Bn |
| 3 | Tjx Companies | 147.38 Bn | 124.93 Bn | 5.07 Bn | 26.46 Bn |
| 4 | Lowes Companies | 105.03 Bn | 97.99 Bn | 8.58 Bn | 63.32 Bn |
| 5 | Ross Stores | 74.93 Bn | 57.86 Bn | 2.12 Bn | 9.24 Bn |
| 6 | Target | 71.05 Bn | 65.64 Bn | 8.94 Bn | 43.39 Bn |
| 7 | O Reilly Automotive | 70.25 Bn | 69.33 Bn | 2.52 Bn | 19.22 Bn |
| 8 | Carvana | 69.96 Bn | 61.56 Bn | 1.38 Bn | 9.41 Bn |
| 9 | Autozone | 46.92 Bn | 45.82 Bn | 2.52 Bn | 23.70 Bn |
| 10 | Neighborhood Intelligence | 244.46 Mn | -333.19 Mn | 96.68 Mn | 419.06 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 419.06 Mn |
| Mar 31, 2026 | 202.00 Mn |
| Dec 31, 2025 | 207.53 Mn |
| Sep 30, 2025 | 211.44 Mn |
| Jun 30, 2025 | 226.87 Mn |
| Mar 31, 2025 | 220.36 Mn |
| Dec 31, 2024 | 239.22 Mn |
| Sep 30, 2024 | 232.15 Mn |
| Jun 30, 2024 | 246.43 Mn |
| Mar 31, 2024 | 289.51 Mn |
| Dec 31, 2023 | 276.69 Mn |
| Sep 30, 2023 | 254.60 Mn |
| Jun 30, 2023 | 237.39 Mn |
| Mar 31, 2023 | 246.88 Mn |
| Dec 31, 2022 | 232.72 Mn |
| Sep 30, 2022 | 275.59 Mn |
| Jun 30, 2022 | 298.24 Mn |
| Mar 31, 2022 | 331.84 Mn |
| Dec 31, 2021 | 321.58 Mn |
| Sep 30, 2021 | 355.80 Mn |
API Access
Neighborhood Intelligence 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=NXH&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "NXH", "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=NXH&period=max&api_key=YOUR_API_KEY");
const data = await res.json();