Dixie (DXYN) Operating Expenses (2010 - 2026)
Dixie (DXYN) recorded Operating Expenses of $17.23 million in Q2 2026, up 2.0% from $16.9 million a year earlier and up 7.0% from the prior quarter.
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Analysis
Dixie (DXYN) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Dixie's Operating Expenses came in at $67.68 million as of Jun 27, 2026, down 4.4% year-over-year; for FY2025, it came in at $68.22 million, down 4.2% from FY2024.
- Annual Operating Expenses has declined for three straight years, though with a five-year compound annual growth rate of 2.0% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $71.18 million in FY2024 (-8.8%), $78 million in FY2023 (-4.3%), $81.54 million in FY2022 (+19.6%) and $68.18 million in FY2021 (+10.1%).
- Quarterly Operating Expenses has ranged from $16.11 million in Q1 2026 to $25.7 million in Q4 2022 over the past five years.
- On a year-over-year basis, Operating Expenses rose in two of the last eight quarters, with an average decline of 4.7%.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 49.7% in Q4 2022, against a decline of 16.4% in Q4 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $16.11 million (Q1 2026), $17.86 million (Q4 2025) and $16.48 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | SharkNinja | 28.10 Bn | 25.76 Bn | 860.34 Mn | 680.96 Mn |
| 2 | Somnigroup International | 13.42 Bn | 12.96 Bn | 817.20 Mn | - |
| 3 | Hni | 3.31 Bn | 2.88 Bn | 647.20 Mn | 550.90 Mn |
| 4 | Newell Brands | 2.42 Bn | 1.58 Bn | 812.00 Mn | 529.00 Mn |
| 5 | Sonos | 2.06 Bn | 962.88 Mn | 189.31 Mn | 157.78 Mn |
| 6 | Whirlpool | 1.82 Bn | -1.65 Bn | 442.00 Mn | 412.00 Mn |
| 7 | Arhaus | 1.52 Bn | 596.72 Mn | 172.07 Mn | 117.81 Mn |
| 8 | Millerknoll | 1.47 Bn | 771.78 Mn | 385.30 Mn | 333.50 Mn |
| 9 | Corsair Gaming | 1.41 Bn | 938.61 Mn | 104.29 Mn | 96.68 Mn |
| 10 | Dixie | 5.36 Mn | 3.26 Mn | 20.23 Mn | 17.23 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 17.23 Mn |
| Mar 28, 2026 | 16.11 Mn |
| Dec 27, 2025 | 17.86 Mn |
| Sep 27, 2025 | 16.48 Mn |
| Jun 28, 2025 | 16.90 Mn |
| Mar 29, 2025 | 16.99 Mn |
| Dec 28, 2024 | 19.10 Mn |
| Sep 28, 2024 | 17.84 Mn |
| Jun 29, 2024 | 17.62 Mn |
| Mar 30, 2024 | 16.61 Mn |
| Dec 30, 2023 | 21.49 Mn |
| Sep 30, 2023 | 19.30 Mn |
| Jul 1, 2023 | 19.76 Mn |
| Apr 1, 2023 | 17.46 Mn |
| Dec 31, 2022 | 25.70 Mn |
| Sep 24, 2022 | 19.57 Mn |
| Jun 25, 2022 | 18.86 Mn |
| Mar 26, 2022 | 17.41 Mn |
| Dec 25, 2021 | 17.17 Mn |
| Sep 25, 2021 | 18.22 Mn |
API Access
Dixie 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=DXYN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DXYN", "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=DXYN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();