NGL Energy Partners LP (NGL) Fair Value & Analysis
Energy · US · Market cap $2.0B
Analysis
NGL Energy Partners LP (NGL) currently trades at $16.46, while our model-based Fair Value estimate is $3.97 — implying the stock looks roughly 75.9% overvalued today. We read business quality at 96/100 (high quality), in the Energy sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).
About the company
NGL Energy Partners LP engages in the transportation, storage, blending, and marketing of crude oil, natural gas liquids, refined products/renewables, and water solutions in the United States. It operates through three segments: Water Solutions, Crude Oil Logistics, and Liquids Logistics. The Water Solutions segment transports, treats, recycles, and disposes produced and flowback water generated from crude oil and natural gas production; aggregates and sells recovered crude oil; disposes solids, such as tank bottoms, drilling fluid, and muds, as well as performs truck and frac tank washouts; and sells produced water for reuse and recycle, and brackish non-potable water. The Crude Oil Logistics segment purchases crude oil from producers and marketers and transports it to refineries for resale at pipeline injection stations, storage terminals, barge loading facilities, rail facilities, refineries, and other trade hubs; and provides storage, terminaling, and transportation services thr…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.