Innoviz Technologies Ltd (INVZ) Fair Value & Analysis
Consumer Cyclical · US · Market cap $129M
Fair value as of: Jun 25, 2026
Analysis
Innoviz Technologies Ltd (INVZ) currently trades at $0.5701, while our model-based Fair Value estimate is $0.1300 — implying the stock looks roughly 77.2% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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: low).
About the company
Innoviz Technologies Ltd. manufactures and sells automotive grade LiDAR sensors and perception software to enable safe autonomous driving at a mass scale. The company offers InnovizOne, a solid-state LiDAR sensor designed for automakers and robotaxis, shuttles, trucks, and delivery companies requiring an automotive-grade and mass-producible solution to achieve autonomy. It also provides InnovizTwo Long-Range, a automotive-grade LiDAR sensor solution for various levels of autonomous driving; InnovizTwo Short- to Mid-Range, a automotive-grade LiDAR sensor designed to cover the short- and medium-range vehicles; InnovizThree, third-generation LiDAR platform, designed to deliver range detection with cost efficiency and installation flexibility for behind-the-windshield, rooftop or front grille integration; and perception application, a software application that turns raw point cloud data into perception ready outputs designed to serve as functionally safe software into a vehicle's drivin…
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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.