$41.730
+0.021 (+0.05%)Al cierre
Fuentes de ingresos de PCH
Potlatchdeltic Corp (PCH) generates its revenue through a diversified portfolio of business segments. Currently, the largest contributor to its top-line growth is Lumber, accounting for 37.7% of total sales, equivalent to $120.89M. Other significant revenue streams include Rural real estate and Northern region-Sawlogs. Understanding this composition is critical for investors evaluating how PCH navigates market cycles within the Specialized REITs industry.
Rentabilidad y márgenes de PCH
Evaluating the bottom line, Potlatchdeltic Corp maintains a gross margin of 18.16%. This metric reflects the company's pricing power and manufacturing efficiency. Further down the income statement, the operating margin stands at 11.76%, while the net margin is 8.24%. These profitability ratios, combined with a Return on Equity (ROE) of 3.25%, provide a clear picture of how effectively PCH converts its operational activities into shareholder value.
Comparativa sectorial de PCH
In the context of the broader market, PCH competes directly with industry leaders such as SLG and BNL. With a market capitalization of N/A, it holds a significant position in the sector. When comparing efficiency, PCH's gross margin of 18.16% stands against SLG's 22.88% and BNL's 87.91%. Such benchmarking helps identify whether Potlatchdeltic Corp is trading at a premium or discount relative to its financial performance.
Desempeño financiero de Potlatchdeltic Corp
Latest quarter: 2025/Q3.
- Revenue: $314.18M, +23.14% YoY.
- Net income: $25.89M, +682.50% YoY.
- EPS: $0.33, +725.00% YoY.
- Gross margin: 18.16%, +67.99% YoY.
Takeaway: The last reported quarter shows strong YoY acceleration and margin expansion, but near-term trading may be dominated by merger mechanics and timber/pulpwood outlook rather than standalone quarterly momentum.
Upcoming: QDEC 2025 earnings on 2026-02-09 (after hours), EPS est. -0.12.
Financials
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.