AMRC vs BWMN

Ameresco, Inc. vs Bowman Consulting Group Ltd. — Valuation Comparison 2026

AMRC

Engineering & Construction
Ameresco, Inc.
Quality
7.0
out of 10
Value Trap
30
LOW
Price
$36.56
Last close
Models
10/13
Active
VS

BWMN

Engineering & Construction
Bowman Consulting Group Ltd.
Quality
8.5
out of 10
Value Trap
35
LOW
Price
$32.79
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType AMRC Fair ValueAMRC Upside BWMN Fair ValueBWMN Upside
Bayesian DCF Intrinsic $8.43 -74.3%
Earnings Power Value Intrinsic $3.23 -90.1%
EROIC Spread Intrinsic $2.42 -93.4% $6.94 -78.8%
First Chicago Scenario $59.86 +63.7% $43.98 +34.1%
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 AMRC vs BWMN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AMRC vs BWMN — Which Stock Is More Undervalued?

BWMN scores higher with a 8.5/10 quality rating vs AMRC's 7.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ameresco, Inc. (AMRC) and Bowman Consulting Group Ltd. (BWMN) 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.

AMRC currently trades at $36.56 with a QOC of 7.0/10, while BWMN trades at $32.79 with a QOC of 8.5/10.

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).