LPRO vs NNI

Open Lending Corporation vs Nelnet, Inc. — Valuation Comparison 2026

LPRO

Personal Credit Institutions
Open Lending Corporation
Quality
7.2
out of 10
Value Trap
32
LOW
Price
$2.28
Last close
Models
13/13
Active
VS

NNI

Personal Credit Institutions
Nelnet, Inc.
Quality
8.4
out of 10
Value Trap
12
SAFE
Price
$130.57
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType LPRO Fair ValueLPRO Upside NNI Fair ValueNNI Upside
Bayesian DCF Intrinsic $5.54 +142.9% $164.35 +25.9%
Earnings Power Value Intrinsic $7.75 +337.9%
EROIC Spread Intrinsic $0.88 -61.3% $56.13 -57.0%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for LPRO vs NNI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LPRO vs NNI — Which Stock Is More Undervalued?

NNI scores higher with a 8.4/10 quality rating vs LPRO's 7.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Open Lending Corporation (LPRO) and Nelnet, Inc. (NNI) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

LPRO currently trades at $2.28 with a QOC of 7.2/10, while NNI trades at $130.57 with a QOC of 8.4/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).