Fairvalue-Calculator Fairvalue-Calculator
EN DE

Laureate Education, Inc (LAUR) Fair Value & Analysis

Consumer Defensive · US · Market cap $4.8B

Price$36.58
Fair Value$41.45
Upside+13.3%
Quality95/100
Evidence: High Range $28.26 – $53.06

Analysis

Laureate Education, Inc (LAUR) currently trades at $36.58, while our model-based Fair Value estimate is $41.45 — implying the stock looks roughly 13.3% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Defensive 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: high) — always confirm before acting.

About the company

Laureate Education, Inc., together with its subsidiaries, offers higher education programs and services to students through a portfolio of higher education institutions in Mexico, Peru, and the United States. The company offers a range of undergraduate, graduate, and specialized degree programs in the areas of medicine and health sciences, engineering and information technology, and business and management through campus-based, online, and hybrid programs. It also operates universities and technical-vocational institutions, as well as provides high school education services. The company was formerly known as Sylvan Learning Systems, Inc. and changed its name to Laureate Education, Inc. in May 2004. Laureate Education, Inc. was founded in 1989 and is headquartered in Miami, Florida.

Open the full interactive analysis →

Similar stocks

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.