Mota-Engil, SGPS, S.A (EGL) Fair Value & Analysis
Industrials · PT · Market cap €1.4B
Analysis
Mota-Engil, SGPS, S.A (EGL) currently trades at €4.71, while our model-based Fair Value estimate is €9.11 — implying the stock looks roughly 93.4% undervalued today. We read business quality at 83/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Mota-Engil, SGPS, S.A. provides public and private construction works and related services in Europe, Africa, and Latin America. The company constructs various infrastructures, such as roads, motorways, airports, ports, dams, railways, and residential and commercial buildings, as well as construction of industry, hospitals, and in specialised areas, such as oil and gas, aggregates, precast, electromechanics, foundations and geotechnics. It also collects, treats, recovers, and disposal urban solid, hazardous, and non-hazardous waste; exploits water markets; mineral prospection and exploration; agroforestry activities; maintains streets; set-up of a coliving space and provides ancillary or related services; real estate development; and manages financial holdings. In addition, the company provides food and beverages, terminals exploration, inspection, commercial, shipping, sea transport, earthmoving, mobility, logistics, civil engineering and architecture, road signs, landscape gardeni…
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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.