ENPLUS Co (074610) Fair Value & Analysis
Industrials · KR · Market cap 4.5B KRW
Fair value as of: Jun 24, 2026
Analysis
ENPLUS Co (074610) currently trades at 5,110 KRW, while our model-based Fair Value estimate is 1,345 KRW — implying the stock looks roughly 73.7% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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: medium).
About the company
ENPLUS Co., Ltd. manufactures and sells fire trucks in South Korea. The company provides fire pump cars, fire chemical vehicles, fire water tank cars, rescue trucks, supporting fuel vehicles, elevated ladder trucks, articulated ladder trucks, unmanned destruction waterproof vehicles, explosion chemical cars, multi-purpose small wreckers, versatile compact CAFS, fire bikes, ambulances, experience cars, protest trucks, convoys, haru top cars, and aur cushion, as well as chemical, biological, and radiological analysis vehicles. It offers arm roll trucks, compression cars, jingoe dumps, vacuum suction cars, sewer dredge, street sweeper, oil tank lorry, aircraft tankers, self-loaders, and sprinkler truck and water trucks. In addition, it offers EV ton cargo and built-in-vehicles; and smart air purification vehicles for air pollutions, landfills, fire scenes, and construction sites. The company was formerly known as NANOMEDICS Co., Ltd. and changed its name to ENPLUS Co., Ltd. in April 20…
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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.