Instituto de Diagnóstico S.A (INDISA) Fair Value & Analysis
Healthcare · CL · Market cap 412B CLP
Fair value as of: Jun 24, 2026
Analysis
Instituto de Diagnóstico S.A (INDISA) currently trades at 2,679 CLP, while our model-based Fair Value estimate is 1,735 CLP — implying the stock looks roughly 35.2% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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
Instituto de Diagnóstico S.A. provides health services in Chile. It offers patient information and exams instruction, and online services; specialties services; and outpatient services including urgency, laboratory, imaging, vaccination, blood bank, outpatient procedures, and reproductive medicine unit. The company also provides hospitalization services, such as maternity, adult and pediatric hospitalization, adult critical patient unit, pavilion and surgical procedures, neonatology, and robotic surgery; and telemedicine services comprising online consultation and rehabilitation. In addition, it operates health centers, which includes metabolic surgery and nutrition, thyroid and parathyroid, physical medicine and rehabilitation, endometriosis, hemodynamics service, and dialysis centers, as well as provide insurance products. Further, the company offers research and training services, as well as newborn, school, and accident agreements. Instituto de Diagnóstico S.A. was founded in 19…
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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.