Neighborhood Intelligence (NXH) Operating Expenses (2010 - 2026)
Neighborhood Intelligence's Operating Expenses was $139.56 million in Q2 2026, up 75.8% from $79.4 million a year earlier and up 80.3% from the prior quarter.
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Analysis
Neighborhood Intelligence (NXH) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Neighborhood Intelligence's Operating Expenses was $374.97 million through Jun 30, 2026, down 2.9% year-over-year; for FY2025, it was $318.74 million, down 32.8% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of -7.7% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $474.25 million in FY2024 (-7.0%), $510.01 million in FY2023 (+6.1%), $480.61 million in FY2022 (-6.3%) and $512.83 million in FY2021 (+8.0%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2023.
- Compared with a year earlier, Operating Expenses was higher in 1 of the last eight quarters, with an average decline of 13.2%.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2023 (growth of 78.2%); the worst was Q1 2025 (a decline of 38.3%).
- Per Business Quant data, NXH's Operating Expenses in the three quarters before Q2 2026 was $77.41 million (Q1 2026), $80.38 million (Q4 2025) and $77.62 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,686.58 Bn | 2,203.28 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 283.87 Bn | 277.11 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 145.82 Bn | 123.36 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 103.43 Bn | 96.39 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 74.61 Bn | 57.54 Bn | 2.12 Bn | 1.02 Bn |
| 6 | Target | 71.16 Bn | 65.74 Bn | 8.94 Bn | 5.73 Bn |
| 7 | O Reilly Automotive | 69.71 Bn | 68.80 Bn | 2.52 Bn | 1.53 Bn |
| 8 | Carvana | 68.78 Bn | 60.38 Bn | 1.38 Bn | 704.00 Mn |
| 9 | Autozone | 46.23 Bn | 45.13 Bn | 2.52 Bn | 1.60 Bn |
| 10 | Neighborhood Intelligence | 240.37 Mn | -337.28 Mn | 96.68 Mn | 139.56 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 139.56 Mn |
| Mar 31, 2026 | 77.41 Mn |
| Dec 31, 2025 | 80.38 Mn |
| Sep 30, 2025 | 77.62 Mn |
| Jun 30, 2025 | 79.40 Mn |
| Mar 31, 2025 | 81.34 Mn |
| Dec 31, 2024 | 114.30 Mn |
| Sep 30, 2024 | 111.18 Mn |
| Jun 30, 2024 | 127.17 Mn |
| Mar 31, 2024 | 131.88 Mn |
| Dec 31, 2023 | 164.31 Mn |
| Sep 30, 2023 | 123.83 Mn |
| Jun 30, 2023 | 111.82 Mn |
| Mar 31, 2023 | 110.05 Mn |
| Dec 31, 2022 | 92.20 Mn |
| Sep 30, 2022 | 101.81 Mn |
| Jun 30, 2022 | 109.56 Mn |
| Mar 31, 2022 | 112.76 Mn |
| Dec 31, 2021 | 119.72 Mn |
| Sep 30, 2021 | 127.86 Mn |
API Access
Neighborhood Intelligence 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=NXH&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "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=operating-expenses&ticker=NXH&period=max&api_key=YOUR_API_KEY");
const data = await res.json();