A GU I D E T O V A LU I NG T E CH S T A R T -‐ U P S
VALUATION OF EARLY STAGE VENTURES
AVI FOGEL Principal and Owner Majorlee Investments Board of Directors, New York Angels
Ø Avi is a 4 Kme start-‐up CEO, with companies in LAN switching, residenKal gateways, intrusion prevenKon soOware and enterprise process analysis and monitoring soOware. He has actually seen 6 exits in these 4 startups. Board member, advisor, angel investor and member of NY Angels and Launchpad Venture Group
Ø Avi has invested in 13 angel investments todate and has seen exits (of varying success) in 5 of them.
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AGENDA Ø The end game – liquidity percentages Ø Factors impacKng the relaKve shares
Ø ValuaKon methods and examples Ø General seed/startup valuaKon data Ø The Dave Berkus Model
Ø The VC Method Ø The Scorecard (Bill Payne) Method
Ø The Risk Factor SummaKon Model
Ø DCF (Discounted Cash Flow) – not really being used
3
THE END GAME – LIQUIDITY PERCENTAGES
4
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HP to Acquire Le
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PALO ALTO, Calif
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England,
Aug. 18, 2011
FACTORS IMPACTING SHARE IN OUTCOME
Ø MulKple factors impact the relaKve outcomes
Ø ValuaKon history, impacKng cap table, is just one of them
Ø Other factors are: Preference level, parKcipaKon, cap/no cap, dividends
Ø ValuaKon / cap table math is quite simple: Ø POM = PMV + Inv Ø POMx%=PMVx%*(PMV/POM) (where x= C(ommon); (Pref)A; (Pref)B; etc. – previous
round stakeholders)
Ø Example – Trifecta Inc. Ø Early exit, via sale to WebCo at $28M Ø Pref A -‐ $2M, parKcipaKng preferred X1, no cap, at a PMV of $3M Ø Pref B -‐ $4M, preferred with cumulaKve 8% dividend (over 2 years) at a PMV of $8M
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$10.4
$8.9
$8.7 Common
Pref A
Pref B
TRIFECTA – LIQUIDITY ALLOCATIONS
Pref B • Preference + Dividends or • 33.3% of balance, whichever is higher
Pref A • 1 x preference amount + • 40% of 66% of balance
Common • 60% of 66% of balance
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INSIGHT Ø While PMV negoKaKons are very important – since they determine cap table percentages at liquidity, pay just as much amenKon to other factors, which could override valuaKon in their liquidity impact
Ø ParKcipaKng or non-‐parKcipaKng preferred
Ø Level of parKcipaKon (typically X1 to X3)
Ø Dividends (cumulaKve or not)
Ø Overall cap or no cap
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SEED / STARTUP PHASE VALUATION DATA
8
General ValuaBon
Data
ENTREPRENEUR VALUATION EXPECTATIONS SURVEY
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69%
22%
9%
Too High
Appropriate
Too Low
General ValuaBon
Data
Source: Angel Capital associa3on, Fall 2009 Survey
BILL PAYNE 2011 SURVEY General ValuaBon
Data
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Ø Conducted in summer 2011
Ø 35 angel groups in 20 states and 2 provinces provided data
Ø Refers to pre-‐revenue companies
Ø Range from $800K in avg pmv (Boise, ID & Fargo, ND) to $3.4M for Band of Angels in Silicon Valley
Ø Average valuaKon across all groups is $2.1M, up from $1.7M in 2010
Ø Local differences are substanKal due to availability of capital and alternate sources and a buzz /hype factor in some of the locaKons (Silicon Valley, NYC, Boston)
BILL PAYNE 2011 SURVEY General ValuaBon
Data
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Group LocaBon Avg Pre-‐Rev PMV Maple Leaf Angels Toronto, ON $1.0M – flat
Tech Coast Angels San Diego $1.50M-‐flat to down
Vancouver Angels Vancouver, BC $1.50M decreasing
Ohio Tech Angels Columbus, OH $1.80M, flat
Mid-‐AtlanKc Angels Philadelphia $2.0M, flat
DC Dinner Clubs DC $2.0M, up slightly
St. Louis Arch Angels St. Louis $2.0M, flat
Launchpad Ventures Boston $2.1M, flat
Hub Angels Boston $2.5M, up pressure
Golden Seeds NYC $2.9M, down 10%
NY Angels NYC $3.0M, rising
Band of Angels Silicon Valley $3.4M, up
Source: Bill Payne & Associates
PWC MONEYTREE Q3/2011 General ValuaBon
Data
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Ø Average amount going into seed/startup phase investments in NYC area has declined to $1M
Ø Since investors like to see 30%-‐40% of the business in return, this suggests a pre-‐revenue PMV for VC investments of $1.5M -‐ $2.0M in NYC.
PWC MONEYTREE Q3/2011 NYC AVG $$/DEAL-‐ SEED &
STARTUP
General ValuaBon
Data
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$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
$7.00
$8.00
Q1 2008
Q2 2008
Q3 2008
Q4 2008
Q1 2009
Q2 2009
Q3 2009
Q4 2009
Q1 2010
Q2 2010
Q3 2010
Q4 2010
Q1 2011
Q2 2011
Q3 2011
PWC MONEYTREE Q3/2011 General ValuaBon
Data
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Seed/startup phase
Amount % of Total Deals $$/deal
SouthWest $7M 3.92% 1 $7.00
Colorado $8M 4.26% 2 $4.00
Silicon Valley $99M 55.28% 31 $3.19
New England $30M 16.78% 13 $2.31
Southeast $8M 4.43% 4 $2.00
North Central $2M 0.84% 1 $2.00
Midwest $11M 6.09% 9 $1.22
NY Metro $10M 5.39% 10 $1.00
LA/Orange County $1M 0.82% 3 $0.33
San Diego $1M 0.50% 3 $0.33
Northwest $1M 0.43% 3 $0.33
Philadelphia Metro $1M 0.78% 4 $0.25
South Central $1M 0.46% 4 $0.25
Texas $0M 0.00% 1 $0.00
The iniKal stage. The company has a concept or product under development,
but is probably not fully operaKonal. Usually in existence less than 18 months.
PWC MONEYTREE Q3/2011 General ValuaBon
Data
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Early stage phase
Amount % of Total Deals $$/deal
SouthWest $41M 2.09% 4 $10.25
San Diego $71M 3.61% 7 $10.14
LA/Orange County $205M 10.50% 22 $9.32
Silicon Valley $774M 39.53% 107 $7.23
North Central $32M 1.62% 5 $6.40
Colorado $73M 3.72% 13 $5.62
Texas $70M 3.57% 13 $5.38
New England $256M 13.09% 48 $5.33
NY Metro $182M 9.32% 38 $4.79
Midwest $110M 5.63% 26 $4.23
Southeast $38M 1.92% 12 $3.17
Northwest $55M 2.79% 18 $3.06
Philadelphia Metro $35M 1.77% 15 $2.33
DC/Metroplex $11M 0.54% 7 $1.57
South Central $5M 0.24% 4 $1.25
Upstate NY $1M 0.06% 1 $1.00
AK/HI/PR $0M 0.00% 1 $0.00
The company has a product or service in tesKng or pilot producKon.
In some cases, the product may be commercially available. May or may not
be generaKng revenues. Usually in business less than three years.
VALUATION (PMV) MODELS General ValuaBon
Data
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Ø The Dave Berkus Method
Ø The VC Method
Ø The Scorecard (Bill Payne) Method
Ø The Risk Factor SummaKon Model
Ø DCF (Discounted Cash Flow) – not really used
POST COMPUTATION General ValuaBon
Data
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Ø ValuaKon negoKaKons between the parKes usually include all factors, including control and board posiKons, all intended to make investors feel that the potenKal reward matches the risk (i.e. – stronger board posiKon reduces risk, hence OK to slightly increase PMV, etc)
Ø The actual methods used may be a combinaKon of several – with a mean / median of the several ones being used as the most common pracKce
THE DAVE BERKUS METHOD
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The Dave Berkus Model
DAVE BERKUS METHOD The Dave Berkus Model
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Ø Based on the following assumpKons: Ø only 1 in 1,000 start-‐ups, meet or exceed their projecKons
Ø The startup can achieve a minimum of $20M in its year 5
Ø Pre-‐revenue or at rollout/early revenue Ø First published widely in “Winning Angels” by Harvard’s Amis & Stevenson
5 MODEL FACTORS The Dave Berkus Model
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If this exists Add to company value, up to: Sound idea (basic value) $0.5M Prototype (reducing technology risk)
$0.5M
Quality management team (reducing execuKon risk)
$0.5M
Strategic relaKonships (reducing market risk)
$0.5M
Product at rollout or early revenue (reducing producKon / delivery risks)
$0.5M
EXERCISE The Dave Berkus Model
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Ø GreatCo has the following characterisKcs: Ø Web product unifies all your contact lists from all apps
Ø Product beta is scheduled to start in July, limited demo available
Ø Prominent CEO with engineering background and very good CTO and 2 lead developers
Ø Company has had a dialog with senior execuKve at HP who seemed quite interested in hearing more
EXERCISE – FACTORS? The Dave Berkus Model
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If this exists Add to company value, up to:
Value (0% -‐ 100%)
Totals
Sound idea $0.5M Prototype $0.5M Quality management
$0.5M
Strategic relaKonships
$0.5M
Product rollout early revenue
$0.5M
EXERCISE – POSSIBLE RATINGS The Dave Berkus Model
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If this exists Add to company value, up to:
Value (0% -‐ 100%)
Totals
Sound idea $0.5M 100% $0.5M Prototype $0.5M 80% $0.4M Quality management
$0.5M 60% $0.3M
Strategic relaKonships
$0.5M 0% $0.0M
Product rollout early revenue
$0.5M 0% $0.0M
THE VENTURE CAPITAL METHOD
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The VC Method
VALUATION FORMULA The VC Method
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Terminal Value Year n (at exit) -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ MulKple Year n (cash-‐on-‐cash)
POM Value (post investment)
=
COMPUTING TERMINAL VALUE
The VC Method
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Ø Based on entrepreneur mulK-‐year financial plan, as handicapped by investors Ø Used frequently -‐ P/E raKo in terminal year Ø MulKple on business metric (seats for SaaS, uniques, etc)
Ø MulKple of revenues in terminal year
Ø Accuracy of +/-‐ 20%-‐30% is fine
TERMINAL VALUE BASED ON P/E RATIO
The VC Method
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Ø Start with handicapped year n revenue projecKon (ex: $23M)
Ø MulKply by net profit margin (earnings as % of rev) (ex: 15%, $23Mx15%=$3.45M)
Ø MulKply by comparable / industry group P/E (ex: 12)
Ø To get terminal value ($3.45M x 12= $41.4M)
COMMON CASH-‐ON-‐CASH (COC) MULTIPLES
The VC Method
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Phase IRR MulBple
Seed 80%+ X19 (5 yr)
Early Stage 50%+ X5 (4 yr)
RevRamp 35%-‐45% X3 (3 yr)
Late Stage 18%-‐30% X2 (3 yr)
FROM VC FINANCING, PROF BEN SOPANZETTI
EXERCISE The VC Method
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Variable Value
Year 5 revenues $32M
Year 5 NPM (%) 16%
Industry P/E (adjusted) 14
Terminal Value ?????
Desired IRR, and Kme span 80%+, 5 years
CoC MulKple ?????
Post Money ValuaKon ????????
Money to go in $1.27M
Pre-‐money value ????????
AllocaKon (C/P) post money ????????
EXERCISE -‐ SOLUTION The VC Method
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Variable Value
Year 5 revenues $32M
Year 5 NPM (%) 16%
Industry P/E (adjusted) 14
Terminal Value $71.7M
Desired IRR, and Kme span 80%+, 5 years
CoC MulKple X 19
Post Money ValuaKon =$71.7M/19= $3.77M
Money to go in $1.27M
Pre-‐money value $2.50M
AllocaKon (C/P) post money 66%/34%
THE SCORECARD METHOD
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The Scorecard Method
VALUATION STEPS
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Ø Start with a median value for pre-‐rev companies in region
Ø Determine valuaKon factors and weights
Ø Determine performance level for each factor
Ø Calculate the weighted total for factors Ø MulKply median value by weighted total
The Scorecard Method
ESTABLISHING MEDIAN VALUE
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Ø UKlize sources such as ACA, NVCA, MoneyTree and local investor groups
Ø Follow local and regional trends – VC’s trending lower, angels trending higher, top 3 market “bubbly”
Ø For the NYC area, based on VC / angel data we will assume: valuaKon ranges of $1.5M-‐$2.5M with a $2.0M median
The Scorecard Method
ESTABLISHING FACTORS & WEIGHTS
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The Scorecard Method
Factor WeighBng Strength of entrepreneur/team 30% Opportunity size 25% Product & technology 15% CompeKKve environment 10% MarkeKng/sales/partnerships 10% Need for addiKonal investment 5% Other factors (great early feedback) 5%
ESTABLISHING FACTOR PERFORMANCE -‐ EXAMPLE
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Ø CoolCo Inc has the following charcterisKcs: Ø A strong team (125% of norm) Ø Average technology (100% of norm) Ø Large market opportunity (150% of norm) Ø Single angel round needed (100% of norm) Ø CompeKKon is stronger (75% of norm) Ø More work needed on sales/partners (80%) Ø Excellent iniKal customer feedback (100%-‐other)
The Scorecard Method
COOLCO VALUATION EXAMPLE
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The Scorecard Method
Factor WeighBng Factor Value MulBply team 30% 125% 0.3*0.125=0.375
Opportunity 25% 150% 0.375
Technology 15% 100% 0.150
CompeKKon 10% 75% 0.075
Sales/Partners 10% 80% 0.080
More investments 5% 100% 0.050
Others 5% 100% 0.050
Grand Total 1.125
COOLCO VALUATION
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ValuaKon = median value x resulKng factor mulKplier
= $2.0M x 1.125 = $2.25M
The Scorecard Method
THE RISK FACTOR SUMMATION METHOD
38
The Risk Factor
SummaBon Method
VALUATION STEPS
39
Ø Start with a median value for pre-‐rev companies in region
Ø Determine risk factors
Ø Assign posiKve/negaKve value to each factor Ø Sum up the result for all risk factors = TotRisk
Ø PMV = median +TotRisk
The Risk Factor
SummaBon Method
ESTABLISHING MEDIAN VALUE
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Ø UKlize sources such as ACA, NVCA, MoneyTree and local investor groups
Ø Follow local and regional trends – VC’s trending lower, angels trending higher, top 3 market “bubbly”
Ø For the NYC area, based on VC / angel data we will assume: valuaKon ranges of $1.5M-‐$2.5M with a $2.0M median
The Risk Factor
SummaBon Method
DETERMINING RISK FACTORS
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Ø Management
Ø Stage of business Ø LegislaKon/poliKcal Ø Manufacturing
Ø Sales / MarkeKng
Ø Funding / capital
The Risk Factor
SummaBon Method
Ø CompeKKon
Ø Technology Ø LiKgaKon Ø InternaKonal Ø ReputaKon Ø LucraKve exit
ASSIGNING VALUES TO RISK FACTORS
42
Ø Each factor can receive a max (posiKve) value of +2 and a min (negaKve) value of -‐2
Ø Sum up the total of all the values=ValueTot
Ø MulKply ValueTot by $250,000 to get TotRisk
Ø PMV = Median value + TotRisk
The Risk Factor
SummaBon Method
EXAMPLE
43
The Risk Factor
SummaBon Method
Risk factor Value
Management +1 Stage -‐1 LegistlaKon/PoliKcal 0 Manufacturing 0 Sales/MarkeKng -‐1 Funding / Capital +2
Risk factor Value
CompeKKon +1 Technology +1 LiKgaKon 0 InternaKonal 0 ReputaKon 0 Exit +1
ValueTot = +4; TotRisk = 4 x $250,000=$1M
PMV = $2.0M + $1.0M = $3.0M
THE DCF METHOD
44
The DCF Method
WHAT IT IS AND WHY IT DOES NOT FIT
45
The DCF Method
Ø DCF uKlizes cash flow projecKons for the business in future years, discounKng them to the present as Net Present Value – the higher the risk, the higher the discount rate
Ø Usually 5 years forward are projected individually and years 6 and on are captured via a terminal value of cash flows
Ø While sKll very difficult to project even for mature companies, DCF is not appropriate for early stage firms with extreme lack of predictability of cash flows
RESOURCES Ø Angel Capital AssociaKon -‐ hmp://www.angelcapitalassociaKon.org/
Ø Angel Resource InsKtute -‐ hmp://www.angelresourceinsKtute.org/
Ø Rob Witbank Research on Angel InvesKng -‐ hmp://bit.ly/xt9sX1
Ø Bill Payne Angel ValuaKon Analysis -‐ hmp://bit.ly/w3vQ91
Ø Dave Berkus Method -‐ hmp://berkonomics.com/?p=131
Ø Winning Angels -‐ hmp://amzn.to/zA3VSO
Ø Common IRR’s and their mulKples -‐ hmp://bit.ly/xyK7SP
Ø The VC Method -‐ hmp://bit.ly/z73l1s
Ø The Scorecard (Bill Payne) Method -‐ hmp://bit.ly/yCO2Vj
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