Is FTHM or LRHC more undervalued in 2026?

CirclFi compares Fathom Holdings Inc. (FTHM) and La Rosa Holdings Corp. (LRHC) using 13 institutional-grade valuation models. FTHM has a Quality of Company score of 5.7/10 while LRHC scores 5.1/10.

Which is a better buy — FTHM or LRHC?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do FTHM and LRHC valuations compare?

FTHM at $0.57 and LRHC at $1.31 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

FTHM vs LRHC Valuation Comparison 2026 — Intrinsic Value Analysis

FTHM

Real Estate Agents & Managers (For Others)

Fathom Holdings Inc.

Quality

5.7

out of 10

Value Trap

LOW

Price

$0.57

Last close

Models

4/13

Active

LRHC

Real Estate Agents & Managers (For Others)

La Rosa Holdings Corp.

Quality

5.1

out of 10

Value Trap

LOW

Price

$1.31

Last close

Models

3/13

Active

Model-by-Model Comparison

Model	Type	FTHM Fair Value	FTHM Upside	LRHC Fair Value	LRHC Upside
Bayesian DCF	Intrinsic	$0.21	-63.4%	$1.99	-8.7%
EROIC Spread	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
ML-RIV	Intrinsic	$0.54	-5.3%	$0.09	-95.0%
Dynamic NAV	Asset-Based	$0.14	-75.6%	—	—
PWERM	Option-Based	$•••.••	••.•%	$•••.••	••.•%

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FTHM vs LRHC — Which Stock Is More Undervalued?

FTHM scores higher with a 5.7/10 quality rating vs LRHC's 5.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fathom Holdings Inc. (FTHM) and La Rosa Holdings Corp. (LRHC) 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.

FTHM currently trades at $0.57 with a QOC of 5.7/10, while LRHC trades at $1.31 with a QOC of 5.1/10.

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).