Network-1 Technologies, Inc (NTIP) Fair Value & Analysis
Industrials · US · Market cap $33.6M
Fair value as of: Jun 25, 2026
Analysis
Network-1 Technologies, Inc (NTIP) currently trades at $1.47, while our model-based Fair Value estimate is $2.19 — implying the stock looks roughly 49.0% undervalued today. We read business quality at 95/100 (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: low) — always confirm before acting.
About the company
Network-1 Technologies, Inc. engages in the development, licensing, and protection of intellectual property assets in Connecticut. The company owns 119 patents, including the Cox patent portfolio related to enabling technology for identifying media content on the Internet; M2M/IoT patent portfolio related to enabling technology for authenticating, provisioning, and using embedded subscriber identification module technology in IoT, machine-to-machine, and other mobile devices, such as smartphones, tablets, and computers, as well as automobiles; and HFT patent portfolio covering technologies related to high frequency trading that addresses technological problems associated with speed and latency, and provide critical latency gains in trading systems. Its patents also comprise the Mirror Worlds patent portfolio related to foundational technologies that enable unified search and indexing, displaying, and archiving of documents in a computer system; and the remote power patent covering t…
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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.