Latham (SWIM) Operating Expenses (2020 - 2026)
Latham (SWIM) reported Operating Expenses of $37.62 million for Q2 2026, up 0.9% from $37.27 million a year earlier and up 2.8% from the prior quarter.
Latham (SWIM) Operating Expenses (2020 - 2026) Analysis & Trends
Over the twelve months ended Jun 27, 2026, Latham's Operating Expenses came in at $139.67 million, up 0.2% year-over-year; for FY2025, it came in at $122.57 million, down 3.6% from FY2024.
- Operating Expenses has declined for four consecutive years, though with a five-year compound annual growth rate of 7.5% (FY2020 to FY2025).
- By year, Operating Expenses came in at $127.21 million in FY2024 (-0.5%), $127.84 million in FY2023 (-19.3%), $158.44 million in FY2022 (-27.2%) and $217.78 million in FY2021 (+154.6%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses since Q2 2022.
- Year over year, Operating Expenses gained in six of the last eight quarters, with growth averaging 15.2%.
- The high point for year-over-year Operating Expenses in five years was Q3 2021 (growth of 139.2%); the low point was Q2 2022 (a decline of 56.1%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $36.59 million (Q1 2026), $31.42 million (Q4 2025) and $34.05 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Trane Technologies | 101.79 Bn | 96.51 Bn | 2.26 Bn | 1.04 Bn |
| 2 | Johnson Controls International | 89.46 Bn | 87.19 Bn | 2.47 Bn | 1.49 Bn |
| 3 | Comfort Systems Usa | 49.47 Bn | 44.73 Bn | 844.23 Mn | 287.05 Mn |
| 4 | Carrier Global | 45.96 Bn | 40.27 Bn | 1.94 Bn | 5.58 Bn |
| 5 | Otis Worldwide | 27.46 Bn | 23.87 Bn | 1.14 Bn | 3.28 Bn |
| 6 | James Hardie Industries | 15.27 Bn | 14.34 Bn | 548.70 Mn | 327.40 Mn |
| 7 | Masco | 14.85 Bn | 12.70 Bn | 868.00 Mn | 397.00 Mn |
| 8 | Carlisle Companies | 13.52 Bn | 9.86 Bn | 568.40 Mn | 210.70 Mn |
| 9 | Allegion | 13.34 Bn | 12.06 Bn | 517.50 Mn | 262.80 Mn |
| 10 | Latham | 741.28 Mn | 741.28 Mn | 70.08 Mn | 37.62 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 37.62 Mn |
| Mar 28, 2026 | 36.59 Mn |
| Dec 31, 2025 | 31.42 Mn |
| Sep 27, 2025 | 34.05 Mn |
| Jun 28, 2025 | 37.27 Mn |
| Mar 29, 2025 | 35.89 Mn |
| Dec 31, 2024 | 31.89 Mn |
| Sep 28, 2024 | 34.40 Mn |
| Jun 29, 2024 | 30.92 Mn |
| Mar 30, 2024 | 30.32 Mn |
| Dec 31, 2023 | 23.60 Mn |
| Sep 30, 2023 | 23.43 Mn |
| Jul 1, 2023 | 30.21 Mn |
| Apr 1, 2023 | 33.06 Mn |
| Dec 31, 2022 | 33.06 Mn |
| Oct 1, 2022 | 26.75 Mn |
| Jul 2, 2022 | 41.80 Mn |
| Apr 2, 2022 | 45.23 Mn |
| Dec 31, 2021 | 47.24 Mn |
| Oct 2, 2021 | 48.07 Mn |
Latham 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=SWIM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SWIM", "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=SWIM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();