Spectrum Workshop - Issues and challenges in spectrum allocations and spectrum valuation

Spectrum workshop
Issues and challenges in spectrum
allocations and spectrum valuation
Stefan Zehle, CEO
Coleago Consulting
stefan.zehle@coleago.com
+44 7974 356 258

2
Introductions
Market, spectrum and technology overview
Principles of spectrum valuation
Spectrum valuation in practice
Spectrum renewal
Summary
Appendix 1 - Steps in Network Consultation
Appendix 2 - The Modelling Process
Agenda
© Copyright Coleago 2012Spectrum Workshop

Our skills and experience
Introductions
3© Copyright Coleago 2012Spectrum Workshop

Coleago offers advisory services focused on the TMT sector
4
Strategy &
Business
Planning
Marketing &
Customer
Management
Regulation &
Interconnect
Business
Transformation &
Cost Reduction
Due Diligence
Improving
Network
Performance
Spectrum and
Licences
Digital Content &
Media
Training

A refreshingly different approach to consulting
5
Junior Consultant
Senior Consultant
Manager
Senior
Managers
Partner
Analyst
Traditional
Consulting Firm
Model

Spectrum related work
6
Country Year
France 1994/5
Hong Kong 1995
Singapore 1995
Poland 1995
Austria 1995
Belgium 1995
Finland 1996
Denmark 1996
Italy 1996
Taiwan 1996
Brazil 1996/7
Mexico 1996/7
Argentina 1997
Country Year
Czech Republic 1999
Netherlands 2000
United Kingdom 2000
Belgium 2000
Egypt 2006
US (AWS) 2006
Canada (AWS) 2008
India (3G & BWA) 2009/10
UK (2.6GHz) 2009
Poland (2.6GHz) 2009
Ukraine (3G) 2010
Thailand (3G) 2010
Ireland (Renewal) 2010
Country Year
Canada 2001
Ireland 2002
Sudan 2003
Algeria 2004
Iran 2004
Maldives 2004
Oman 2004
Saudi Arabia 2004
USA 2006
Belgium 1997
Kuwait 1997
Ireland 1997
Norway 1998

2011 was an interesting year and 2012 will be similar!
7
Country Year
Australia renewal 2011/12
Bangladesh 3G 2011
New Zealand 2011
Switzerland 2011/12
United Kingdom 2011/12
Syria 2011
Belgium 2011
Pakistan 2012

Our spectrum expertise is much broader than simply spectrum
modelling and valuation
Spectrum
Consultations
Auction Strategy &
Auction Readiness
Administered
Incentive Pricing
Live Auction Support
© Copyright Coleago 2012Spectrum Workshop 8

The role of spectrum in determining
operator performance
Market development, spectrum
and technology overview

Significant traffic growth driven by mass market adoption of
smartphones and large-screen mobile broadband
10
Step Change in Mobile
Broadband
Data speeds capable of
delivering a good user
experience
Data optimised and
desirable devices for
consumer and business
applications
Flat fees; pricing
consistent with the
application
Compelling applications
and content
Time
Data Traffic
Minimum Acceptable User Experience
0
1
10
100 LTE
5
50
HSPD
A+
CSD
HSCS
D
GPRS
HSPD
A
Mbit/s
Time
EDGE
-
10
20
30
40
50
60
70
80
90
3 O2 Orange Vodafone T-Mobile
GB£/GByte
Jun-06 May-08 Mar-09

Mobile data traffic is not just about laptops and smartphones…
11
Cisco forecasts imply an average data usage CAGR of ~70% p.a.

Mobile data traffic is rapid and consumer create most of the traffic,
example Sweden
© Copyright Coleago 2012
0
50
100
150
200
250
300
2H 061H 072H 071H 082H 081H 092H09
Mbytes/Customer/Month
Mobile Data Usage in Sweden
Consumer Business Blended
0
5,000
10,000
15,000
20,000
25,000
30,000
1H 07 1H 08 1H 09
Terrabytes
AnnualMobile Data Traffic in Sweden
Private Business Total
12Spectrum Workshop

Singapore exhibits similar trends as see in Europe
131
244
550
802
1,007
1,615
2,197
2,626
0
500
1,000
1,500
2,000
2,500
3,000 1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
TbytesperQuarter
StarHub Singapore PacketData
Traffic
102
164
327
439
514
779
1,005
1,141
0
200
400
600
800
1,000
1,200
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
Mbytes/3GUser/Month
StarHub Singapore PacketData
Traffic per 3G Customer
© Copyright Coleago 2012 13Spectrum Workshop

Technology developments: LTE is now a commercial reality and
WiMAX is potentially being marginalised
14
FDD TDD
HSPA LTE WiMAX LTE
200+ operators
support 3.6Mbps+
circa 40 operators
support 14.4Mbps
The first commercial
LTE service launched
December 2009 in
Sweden and Norway
by TeliaSonera
Test networks
launched at 800MHz
and 2.6GHz in
Germany (Sep 2010)
LTE 1800 trials in
Finland and Australia
Russian telecoms
operator Yoto (2nd
largest worldwide
WiMax player)
intends to switch
from WiMax to LTE
for its future roll-out.
Clearwire, the largest
WiMax player, and
Sprint, have also
made statements
that neither would
rule out LTE
technology.
Nortel’s Chinese
R&D unit completed
a streaming video
session over LTE
TDD.
Likely that LTE rather
than WiMax will be
rolled out in India.
MWC Barcelona:
TDD LTE equipment
1-2 years from mass
production.

0%
5%
10%
15%
20%
25%
30%
-
10
20
30
40
50
60
4Q05
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
ARPU
tarHub Singapore - Voice & Data ARPU
Voice Data Data %
Decoupling of traffic and revenues: growth in data usage far outstrips
data ARPU growth
15
0%
5%
10%
15%
20%
25%
30%
0
5
10
15
20
25
30
35
40
1Q06
2Q06
3Q06
4Q06
1Q07
2Q07
3Q07
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
Data%ofARPU
MonthlyARPU€
Orange France Data Voice and Data ARPU
Voice Data Data ARPU %
0%
5%
10%
15%
20%
25%
30%
35%
0
10
20
30
40
50
60
Q107
Q207
Q307
Q407
Q108
Q208
Q308
Q408
Q109
Q209
Q309
Q409
Data%ofARPU
MonthlyARPUUS$
T&T USA - Voice and Data ARPU
Data Voice Data % of ARPU
MonthlyARPUS$
Orange France voice and data ARPU
0
10
20
30
40
50
60
Q107
Q207
Q307
Q407
Q108
Q208
Q308
Q408
Q109
Q209
Q309
Q409
MonthlyARPUUS$
AT&T USA - Voice and Data ARPU
Data Voice Data % of ARPU
 Data revenue growth (partly) offsets declining voice ARPU
 Relative to data traffic however, data revenue growth remains modest
 Significant investments relative to revenue
StarHub Singapore voice and data ARPU
MonthlyARPU€

More spectrum becoming available for mobile communications
16
Mobile
DigitalTV
Digital
Dividend
Mobile
GSM
3G
3G
3G
LTE
Mobile
Expansion
450MHz
470MHz
698MHz
806MHz
850MHz
900MHz
1800MHz
1900MHz
2100MHz
2300MHz
2500MHz
3400MHz
3600MHz
Better for
Coverage
Geographic
Coverage
Indoor
Coverage
Better for
Capacity

The cost of geographic coverage is lower with lower frequency bands
17
100% 126%
323%
455%
675%
1230%
0%
200%
400%
600%
800%
1000%
1200%
1400% 700MHz
850MHz
2100MHz
2500MHz
3500MHz
5800MHz
Relative Capex to Build Coverage
Coverage of rural areas
with digital dividend
700/800 MHz spectrum is
about 30% of the cost
compared with 2100 MHz
coverage.
Source: SFR, France

 The superior propagation characteristics of low band spectrum yield a higher
probability of (deep) in-building service at a given performance
 This may be translated in terms of a % of demand that is only addressable with low
band spectrum (or with potentially expensive in-building solutions)
Indoor propagation characteristics
18
Probability of
service
With spectrum
below 1GHz
With spectrum
above 1GHz
% DemandDeep indoor

The surge in data traffic results in a incremental capex and a squeeze
on margins
 The capex-to-sales ratio is increasing
again, reversing the declining trend
– Threat of negative returns on
incremental customers
 In response to high traffic volumes,
operators have started to abandon
unlimited data plans in 2010
 Increased prevalence of service
restrictions (e.g. P2P, continuous
video)
– However, future net neutrality
regulations could preclude this
19
Congestion squeezes margins
Time
Revenues per
incremental
customer decline
Marginal costs per customer
increase at capacity limits
Squeeze on margins
due to costs of
congestion-relief

Shifting emphasis: Is capacity to data what geographic coverage was
to Voice?
 As networks ‘fill up’:
– Capacity becomes a scarce resource
– Mobile communications shifts from a “buyer’s market” to a “seller’s market”
 Key premises:
– It may not be feasible to build oneself out of congestion
– Traffic will tend to migrate from more congested networks to less congested
networks
 If so, market shares will tend to converge towards shares of industry capacity
– Direct impact: market shares of data traffic and revenues
– Indirect impact: market shares of voice, through linked / correlated accounts
 Maintaining a fair share of industry capacity is of strategic importance

There may be no alternative to securing spectrum
 But securing fair-share of capacity is a strategic imperative for individual operators
– To ensure a level playing field and to protect (or grow) market share
 Once other means have been exhausted, spectrum acquisition may be the only
remaining option
21
Shares of Spectrum and capacity
Share of
Industry
Capacity
MNO average
MNO
Amount of
Spectrum
acquired by
an MNO
Minimum
required
for capacity
parity
Demand vs. Industry capacity
Time
Capacity
Market Congestion?

Spectrum is moving centre stage
 GSM Spectrum liberalisation / re-farming
– Introduction of technology neutrality, enabling introduction of HSPA and LTE in
former GSM bands
 New Mobile Spectrum releases
– 2.6GHz and the digital dividend spectrum in 700 / 800 MHz
 Licence Expiry
– In many countries the 900 and 1800 MHz GSM licences are near to expiry, and
some 2.1 GHz licence are only 4 years away from expiry
– Renewal may involve negotiations around Administered Incentive Pricing
New mobile spectrum releases and spectrum renewal negotiations invariably call
for a spectrum valuation exercise, in which all existing and expected future
spectrum needs to be considered jointly

The basic principles of valuing
spectrum
Spectrum valuation

Spectrum valuation is central to spectrum award and renewal
processes
Shareholder
Value
What and
how much
spectrum do
we want?
How do we obtain the
spectrum as cheaply as
possible?
What is the
value of the
spectrum?

Recent auction outcomes show big variations in prices
25
2.6GHz prices paid

Very large variations in prices paid are due to different auction
dynamics and levels of competition
26
Country Year €/MHz/pop Commentary
Hong Kong 2009 0.252 5 bidders for 3 blocks
Sweden 2008 0.130 5 bidders for 4 blocks; one new entrant
Denmark 2010 0.164 4 bidders; 2nd price auction, low spectrum
caps
Norway 2007 0.036 2 operators plus Craig Wireless
Germany 2010 0.028 4 operators and 140MHz; substitute spectrum
available
Finland 2009 0.004 3 operators and 140MHz; single TDD block
sold for 50% higher price than FDD
Netherlands 2010 0.001 3 operators and two cablecos; low spectrum
caps, and 2nd price rule
2.6GHz FDD prices paid in recent auctions:

Digital dividend spectrum is more expensive: Demand and supply is
the key driver
27
0.73
0.03
0.11
0.02 0.02
-
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
800 MHz
Paired
1.8 GHz
Paired
2.0 GHz
Paired
2.6 GHz
Paired
2.6 GHz
Unpaired
€/Mhs/PopsGerman Spectrum Auction Prices €/MHz/Pop

Valuing spectrum is conceptually straightforward
28
Net Present Value with
NPV without
Valuation
Business value with
extra spectrum
Business value
without extra
spectrum
Maximum value of
extra spectrum
The valuation needs to take mitigating strategies into account: We
compare optimal ‘acquire’ with optimal ‘non-acquire’ cases (i.e.
assume optimal strategy is pursued in all cases)

Valuations are typically based on a 10 year forecast and a terminal
value using Discounted Cash Flow Analysis
29
Free Cash Flow
Forecast
NPV of 10
Year Forecast
Terminal
Value
EV
Valuation
Aligned with management forecasts
in the early years
Valuations are then subject to
benchmarking using company
comparables such as EV / EBITDA

How value is created from spectrum is not always fully appreciated
 There are two options to end an auction:
 Option A, you could obtain 20 MHz
– You value 20 MHz at € 500 million
– You can obtain it for € 480 million
 Option B, you could obtain 10 MHz
30
Which would you
choose?

Value creation arises where spectrum valuation and the price paid in
the award process meet
 There are two options to end an auction:
 Option A, you could obtain 20 MHz
 Option B, you could obtain 10 MHz
31
Value created:
€ 20 million
Value created:
€ 60 million

It’s about increasing shareholder value, not about acquiring the
largest or most valuable spectrum blocks
32
Value placed on a block
Price paid
Value created
 Oddly, the point about value creation is often the most difficult to
communicate to senior management.
€ / $

33
Increasingly there are multiple-bands to be valued
Business value with
spectrum
Business value
without spectrum
Maximum values of
spectrum

Synergies between and within blocks requires a holistic approach
34
800MHz
900MHz
1800MHz
2100MHz
2600MHz

The degree of complexity and the number of valuations can be
difficult to comprehend
Spectrum valuation is increasingly
complex
 Large number of feasible spectrum
combinations all require values
 Edge-block interference means the
position of spectrum blocks is important
 Key uncertainties around demand and
technology require a scenario-driven
approach
35
Band Mode Available MHz
800MHz
FDD
TDD
2 x 30
900MHz FDD 2 x 35
1800MHz FDD 2 x 75
2.1GHz
FDD
TDD
2 x 60
1 x 20
1 x 15
2.6GHz
FDD
TDD
2 x 70
1 x 45
Total 620MHz
Example: Swiss Spectrum Auction

Operational sources of spectrum value
Blocking value
Capacity competition
Quality & coverage competition
Access speed claim competition
Site-build capex avoidance
Technology migration benefits
Any assumptions that have a market
share impact will lead to high
spectrum valuations
Technically related sources usually
generate lower spectrum valuations

Non operational sources of spectrum value
Tax shield
Terminal value assumptions

A good general philosophy
38
“It’s better to be roughly right than precisely wrong”
John Maynard Keynes

Issues in spectrum valuation

40
Spectrum valuation must take all potential resources into account,
in different parts of the network
Deep indoor
Deep indoor
RuralUrban
Feasible coverage with
low band spectrum only
Addressable with
all bands
Macro network
Macro densification
layer
Capacity solutions
layer
Lower scope for cell-
splits in (dense) urban
given high site density
Higher scope for cell-
splits in rural given
low site density
Outdoor: small cells
Indoor: Femto cells and
WiFi
Limited scope for small
cells and WiFi offload

Identifying industry bottlenecks is paramount
Technology roadmap and device penetration
 Low (initial) penetration of LTE-enabled devices limit the utilisable capacity in LTE
bands such as 800MHz and 2.6GHz
– 3G capacity likely to remain the bottleneck in the near/medium term
 The site upgrade path may have a significant impact on the valuation
Regulatory network deployment constraints
 Severe emission limits may prevent operators from deploying additional mobile
channels on individual sites (e.g. Switzerland)
 Planning constraints may limit the scope for site build, and constrain site densification
(e.g. no permission to build on residential property in Ukraine)
The problem may be capacity, in-building quality, geographic coverage, or all three
 Securing the right mix of spectrum may be as important as obtaining the right quantity

Spectrum valuation must take account of mitigating strategies
 Capacity solutions
– Indoor: Femto cells, Private WiFi
– Outdoor: small cells
– Traffic offloading on public WiFi
 Yield management / repositioning the offer
– Focus on existing and new voice customers (converged offerings)?
– Prioritize access to network capacity to high-value customers?
 Infrastructure-sharing
– Potential source of capacity and coverage synergies as well as cost rationalisation
 Future spectrum releases?
 Buy capacity from a competitor?
 Mitigate limited geographic footprint through national roaming?
A valuation must reflect all that is relevant to the asset being valued

Challenging strategic questions need to be addressed
Should operators deploy HSPA in 900MHz or go straight to LTE?
 900MHz enabled HSPA devices widely available today
 Potential first mover advantage
 LTE offers higher performance
 Migrating from to LTE from HSPA in the future introduces extra costs
Should operators pursue a strategic or tactical LTE deployment?
 Strategic: urban or national LTE footprint?
– What is the business case?
 Tactical: only deploy LTE as a capacity resource in busiest parts of the network?
How much GSM spectrum can or should one re-farm for 3-4G?
 When can GSM be switched off?

Generic methodological principles

Key sources of spectrum value
45
Greater capacity
(all bands)
Fewer sites
for given coverage
and capacity
(all bands)
More traffic
Greater coverage
footprint
(low band)
Extra Spectrum
Higher costs of
sale & overheads
In-building quality
(low band)
Greater
performance
(2x20MHz LTE)
More customers
Higher network
costs
Lower network
costs
Spectrum roll-out
costs
More revenues

Valuing capacity: Network congestion puts customers and
revenues at risk
46
% of cells
overloaded
Mbps per
Cell
Network Cells
(ranked by peak demand)
Maximum Throughput per Cell
(including densification uplift and
other capacity solutions)
Need to take an industry-wide view: consider congestion levels versus any spare
capacity across all operators
Unserved demand
leads to loss of
customers and
revenues if there’s
a better alternative
elsewhere
Unconstrained Peak
Demand per Cell
Constrained Peak
Traffic per Cell

Illustration
 Operator A has more spectrum, leading to higher cell throughput
 Operator A is positioned as a quality network, and prices its services at a premium
 Operator B is a low cost operator; as a result, customers are more willing to accept a
lower quality of service during peak hours
 Operator B has a lower cell throughput, but higher overall congestion tolerance
Valuing capacity: Customer migration between networks will
depend on relative congestion-tolerance levels
47
Cell
Throughput
(Mbps)
Congestion-
Tolerance
Threshold
Cell
Throughput
(Mbps)
Congestion-
Tolerance
Threshold
Operator A Operator B

Valuing capacity: Which customers have highest propensity to
churn?
The relative impact of congestion across key segments is likely to reflect
 Degree of customer exposure to the problem (hence sensitivity to congestion)
 Yield management strategies pursued by operators
– De-prioritising least desirable customers (lowest % margin contributors)
48
Device type Segment Description
Average %
margin
Sensitivity to
congestion
Small
screen
(“HS”)
“Standalone HS Low”
Low data usage; not
sensitive to congestion
High (low
data)
Negligible
“Standalone HS High”
High small-screen data
usage
High (limited
data)
Moderate
“Linked HS-PC”
Correlated small & large-
screen; incl. tethering
Medium
(heavy data)
High
Large-
screen
(“PC”) “Standalone PC”
Independent of small
screen voice and data
Low (heavy
data no voice)
High

49
Valuing in-building quality: Benefit of Low Band spectrum
Probability of
service
With spectrum
below 1GHz
With spectrum
above 1GHz
Deep indoor
RuralUrban
Deep indoor
 In-building quality benefits of LB
spectrum can be expressed in terms
of addressable demand
– Without low band spectrum, none
of this ‘deep indoor’ demand can be
served
– Unless potentially costly indoor
solutions are deployed
– Such solutions can only solve part
of the problem
 Cannot be mitigated using yield
management strategies
 Hong Kong: 850MHz worth 5x more
per MHz than 2.6GHz spectrum!
 Value of additional blocks of low band
spectrum can be quantified using the
‘capacity-value’ approach
% Demand

Valuing deep indoor capacity: Additional Low Band spectrum
increases the % demand that can be served
50
% of cells
overloaded with
1 block
Deep
indoor
Mbps per
Cell
Network Cells
(ranked by peak demand)
Max Indoor Throughput per Cell
with 2 LB Blocks
The first block of low band spectrum provides the largest increase in addressable
indoor demand; subsequent blocks generate decreasing marginal returns
Constrained Deep
Indoor Traffic per Cell
Max with
1 LB Block
Extra demand
addressable with
additional LB
block
Unconstrained Deep
Indoor Demand per Cell

 Low Band spectrum may enable an operator to extend the geographic footprint
 Potential impact on addressable market, hence market share (if significant rural
demand exists)
 Impact of wider footprint on marketing productivity
– Share of gross ads, retention rate (reduced churn)
– Scope for price premium (ARPU impact)
51
Valuing geographic coverage: Low Band impact on footprint
Deep indoor
Deep indoor
RuralUrban

Performance (widest LTE channel)
 One 2x10MHz LTE channel provides greater performance for a given level of traffic
than two 2x5MHz channels,
 One 2x20MHz LTE channel provides greater performance for a given level of traffic
than two 2x10MHz channels, but only marginally
 Potential impact on marketing productivity of “speed claim” / experienced performance
Network cost avoidance
 To achieve a given level of capacity: deploying spectrum on existing sites is typically
less expensive than splitting cells and deploying micro solutions (indoor: Femto cells;
outdoor: small cells)
 To achieve a given geographic footprint: rolling out a network with lower bands is less
expensive (and yields better quality of coverage) than doing so with higher bands
Positioning of spectrum blocks can also impact on value
 Edge block interference (e.g. 2.6GHz FDD versus TDD)
 Interference with digital terrestrial TV (800MHz)
52
Other spectrum value components

Model structure overview
53
Customers,
ARPU/AUPU,
Revenues
Network
Dimensions
Network
Costs
Scenario Manager, User Interface
Traffic
Segmented
Analysis
Total
Market
Demand
Capacity /
Congestion
Analysis
Consolidation Model Network ModelMarket Model
Market
Scenarios
Technical
Scenarios
Regulatory
Scenarios
Competition
Scenarios
Coverage
Analysis
Spectrum valuation: cash flows ‘With’
minus ‘Without’ extra spectrum
Cash flows, Financial Statements
Device
Diffusion
Traffic
Distribution
Profile
All MNO’s
(Industry-
wide
view)

Spectrum renewal costs also bear on the
terminal value of new spectrum acquired
Spectrum Renewal

Uncertainties around the conditions, process and costs of spectrum
renewal pose further challenges for operators
Typical approaches to spectrum renewal
 Setting of renewal fees using Administered Incentive Pricing (AIP)
 Re-auctioning existing spectrum holdings upon licence expiry (e.g. Singapore
2008 for 900MHz renewal, proposed in Switzerland and the Netherlands
 Private, bi-lateral negotiations between regulator and individual operators, with
threat of auction if no agreement is reached (e.g. Australia)
Typical regulatory objectives
 Secure an adequate return to society and incentivise efficient use of spectrum
 Maintain competition
 By setting prices that will appear:
– Comparatively cheap for those who make best economic use of it and
comparatively expensive for less efficient users of the spectrum, thus
‘incentivise’ more efficient use of spectrum resources

Operators who face licence expiry are usually better off with a
negotiated settlement rather than a re-auction
 Operators faced with the expiry in their licence and refarming usually go
through a negotiation process
 Case study: Australian 800MHz, 1.8GHz, 1.9GHz TDD and 2.1GHz renewal
– Separate negotiations with each operator on renewal fees
– Threat of auctioning the spectrum to be renewed if individual operators fail to
reach negotiated agreement
 Developing a compelling negotiation case for favourable spectrum renewal
terms is essential
– Covering both renewal fees as well as conditions (e.g. technology neutrality)
– Ensuring competitors may no less per MHz, and ideally more!
 “Independent” reports commissioned by operators that support their case may
help to sway the argument in their favour

Administered Incentive Pricing (AIP)
 Most regulators are obliged to agree an allocation methodology for renewal
that:
– yields a return for society and ensures that economic benefit is generated
– ensures that the mobile market is sufficiently competitive
 Recognising that a simple re-auction may not be appropriate, many regulators
have opted for negotiated renewal prices using different methods, such as:
– Spectrum fee benchmarking
– Optimum Deprival Value (ODV)
– Best Alternative Use (BAU)

Benchmarking
 Benchmarking is the process of setting prices against a proxy taken from
another, comparable market.
 Benchmarks do not reflect valuations but prices paid.
 International benchmarks suffer problems of translation to the local context.
– Spectrum demand and supply are very different is each country
– Markets vary in terms of population, population density, geography, income
levels / willingness to pay and input cost levels (e.g. staff costs).
– Technically, benchmarking is further affected by such factors as choice of
exchange rate, the comparator set and so on.
– Timing also matters, since perceptions of the prospective value of mobile
spectrum has varied significantly over time.
 Benchmarks are not considered as a suitable method in setting spectrum prices,
but cash strapped governments have used them to justify high reserve prices.
– A situation best avoided, but depends on regulatory policy

Optimal Deprival Value (ODV) – Academic description
Deprival value is a cost-based valuation approach that answers the question:
 “What is the least cost system or bundle of assets needed to provide
customers with the existing level and quality of services, should a certain
existing asset be removed?”
This approach can be applied to valuing spectrum rights by addressing the
following:
 “If an operator was deprived of incremental spectrum rights, what incremental
costs would be incurred to replicate the existing level and quantity of services
using the remaining spectrum rights?”
 These costs are “avoided” by owning the incremental spectrum rights and so
represent the value of those rights. The rights holder should be prepared to
pay up to the value of the incremental costs to avoid being deprived of its
spectrum rights, so long as the incremental costs are less than the present
value of the free cash flows generated from the spectrum services.

Optimal Deprival Value (ODV)
 ODV calculates this price on the basis of the ‘deprival value’ of a marginal block
of spectrum (typically 5MHz), for an ‘average reference operator’
– Deprival value: the network costs that would need to be incurred to offer the
same quality of mobile service, if the marginal spectrum block were lost
– The only source of value is technical, hence ODV produces a low value
 Since this is calculated for an ‘average operator’ with ‘average spectrum
holdings’, the ODV price per MHz should appear:
– Less expensive for operators with less spectrum, who would value it more
highly
– More expensive for those with surplus spectrum, who should value the
marginal block less highly
– In this sense, ODV would reflect an ‘optimal price’ in terms of incentives, by
encouraging ‘spectrum hoarders’ to give up marginal blocks to more efficient
users
 ODV generates a uniform price per MHz in any band, for all operators

Best Alternative Use (BAU)
 In contrast to ODV, BAU considers the value placed on an incremental block of
spectrum by alternative users or uses, such as:
– An alternative technological use
– A new mobile entrant
– Competitors
 The case for new mobile market entry is typically weak, and the alternative
technological uses of mobile spectrum are invariably less valuable than mobile
 Hence BAU is normally driven by the value placed by existing competitors on a
marginal block of spectrum
– Unlike ODV, BAU may generate different prices per MHz for each operator
 If all other things are equal, BAU should generate a higher price per MHz for
operators with spectrum surpluses, and a lower price per MHz for those with
smaller holdings

In some markets, regulators propose to align renewal fees of expiring
licences with auction outcomes for new spectrum
 Current consultation in the UK: base 900MHz and 1800MHz renewal fees on
forthcoming 800MHz and 2.6GHz auction
– 900MHz renewal based on 800MHz proceeds
– 1800MHz renewal based on linear average between 800MHz and 2.6GHz
proceeds (!)
 India: proposal to base 1800MHz renewal on proceeds of 2.1GHz auction
 No longer an auction for new spectrum, but a simultaneous auction and renewal
fee negotiation!

How to obtain the spectrum as
cheaply as possible
Auction strategy

No auction design is perfect
Design Principle SMRA SMRA-AS CCA
Supports simultaneous award of spectrum
in multi-bands
✔✔✔ ✔✔✔ ✔✔✔
Reduces exposure or aggregation risk ✘ ✔ ✔✔✔
Flexibility over the use of specific or
generic lots
✔✔ ✔✔ ✔
Transparency of bidders and bids ✔✔✔ ✔✔✔ ✔✔
Certainty over prices paid ✔✔✔ ✔✔✔ ✘
Certainty over lots awarded ✔✔✔ ✔✔✔ ✘
Certainty over total expenditure ✔✔✔ ✔✔ ✘
Simplicity and ease of presentation and
transparency of results
✔✔✔ ✔✔✔ ✘

CCA second price auctions
Key characteristics
 Winners do not pay the price they bid but a function of the next best allocation of
resources (if the winning bidder were absent)
– Very complex combinatorial algorithms
– Predicting prices paid is challenging for bidders
 The price paid by any bidder is determined by marginal valuations expressed by
competing bidders
 Prices paid per lot can differ significantly between bidders
– It is possible for a bidder to pay more for less spectrum than a competitor!

Real Example: impact of second price rule in the Denmark CCA
based 2.6GHz auction in 2010
 Hutchison paid €0.9 million for 2x10 MHz
of FDD plus 25 MHz of TDD
 The other bidders who acquired 2x20
MHz FDD paid ~20x more per MHz
 This dramatic outcome was a product of a
second price combinatorial auction with
tight spectrum caps:
– TDC, Telenor and Telia’s prices
reflected Hutchison’s bid value for an
additional lot of 2x10MHz FDD
– Hutchison’s 2x10MHz FDD could not
have been sold to anyone else, hence
the 2nd price was the reserve price
66
*Prices paid per MHz
H3G TDC Telia Telenor

Conclusions
67
Spectrum auctions are increasingly complex
 These are multi-dimensional problems
 Complex game-theoretical aspects
 A thorough understanding of the auction rules and the risks and opportunities is
critical
Valuations for each feasible package is required
 Not just own valuations, but also the (likely) valuations of other bidders
 Extensive competitor analysis and intelligence gathering is required (cash
position, strategic objectives, public announcements, past auction behaviour,…)
Implementation work-streams need to start well in advance
 Influence auction rules at consultation stage
 Influence government spectrum policies

Overview of auction strategy development and auction readiness
1
Auction objectives,
valuations, bid limits
Understand the work of the valuation team. To agree auction
objectives. Set bid limits and explain rationale.
2
Develop competitor
intelligence
Identify competing bidders and ascertain their auction objectives,
auction strategies and bid limits relative to own valuation.
3 Develop bid strategy
Generate actionable insight into the auction format and rules, and into
the underlying opportunities, risks, and mitigating strategies.
4
Develop bid
optimisation &
support tools
Create the tools that allow Coleago to validate bid strategy and
support the implementation of the bid strategy in a live auction
environment.
5
Bid strategy testing
and refinement
Validate strategic hypotheses through auction simulations and
quantify risks and opportunities
6
Mock auctions &
auction readiness
Test the bidding strategy and to train the auction team and to test
auction support tools, bid room protocols, procedures and disaster
recovery as well as providing insight to senior management .
7 Game plan
To be able to execute the bid strategy in accordance with robust
corporate governance.

Understand valuations, bid limits and auction
objectives as well as highlighting auction risks
An initial workshop with senior management is often useful to
highlight the key issues that will arise in the auction
A typical workshop agenda is as follows
 Overview of the spectrum on offer and any caps
 High level overview of the auction process
 Explanation of key auction issues and risks that must be addressed
 Discussion and agreement of primary and secondary auction objectives
 Explanation of the role of bid limits and agreement on the nature of any
limits to be imposed on the bid team
 Discussion surrounding management’s attitude towards risk taking in the
auction
Spectrum Workshop © Copyright Coleago 2012 69

Competitor intelligence
Competitor intelligence is
critical in this auction
 The valuation workstream also
provides valuation matrices for
all other bidders
 We will need to make an
assessment of other bidder’s
likely bid limits
 We analyse other bidders
relative to your operation and
adjust their bid limits accordingly
 The exercise is highly subjective
so having a framework is useful
Bidder Limit Analysis
Bidder
1
Bidder
2
Bidder
3
Bidder
4
Financial
Strength
1 1 3 3
Strategic
Imperative
2 3 2 2
Previous
Bidding
History
3 2 1 3
Public
Announce-
ments
1 3 3 2
Capacity
Constraints
2 3 1 1

Develop bidding strategy
In the many auctions there is no “dominant strategy”
 The academics will analyse the auction rules in detail in conjunction with
– Your auction objectives, valuations and budget limits
– Competitor intelligence, valuations and bid limits
 The analysis will allow them to develop a range of potential auction
strategies
 We will then “War Game” the potential strategies to
– Test how robust each strategy is to different competitor strategies
– Identify the actions of other bidders that would cause a change in
strategy
– Develop an initial “Auction Game Plan”
 We would present the initial “Game Plan” at a bid strategy workshop

Develop the auction simulation tool

Identify and develop appropriate live auction support
tools
With potentially only ten minutes per round many aspects of bid
execution will need to be automated
There are a range of auction support tools that may be appropriate
 Auction bid tracker
– Tracks historic bids (audit and governance)
– Predicts when bid limits may be breached for both you operation and
other bidders based on bid increments and bid levels
– Analyses dynamic lot valuations and prices and provides data on
absolute and relative value creation
 Bid optimisation tool
– Utilises the results from previous auctions and updates spectrum
values for current and future auctions and generates automated
recommendations for bids

An example of a bid optimiser from the Canadian AWS
spectrum auction

Bid strategy testing and refinement
 Extensive auction simulations will be used to test the impact of
deviations from the ‘dominant bid strategy’ under numerous competitive
scenarios, covering:
– Alternative competitor valuation and cash-constraint assumptions
– Alternative ‘price discovery’/ bid inflation assumptions
– Individual and collective deviations from ‘sincere bidding’ strategies
including strategic demand reduction and ‘eligibility parking’
 Key outputs
– Comprehensive testing of alternative bid strategies under a broad
range of assumptions to validate approach
– Identification and quantification of risks and opportunities
– Empirical validation of supplementary-bid optimisation and price
prediction capabilities (subject to auction format)

Mock auctions – bid team training
Mock auctions are often more about training the bid team than
developing bid strategy
We recommend running a minimum of two mock auctions
 Plan the mock auction process and schedule of auctions
 Identify and invite the right participants
 Prepare instructions and bidder briefs, including bid limits for each mock
auction for all participants
 Explain the auction design, key issues and mock auction process
 Run the mock auctions, providing support and training as required
The results and conclusions will be presented at a final auction bid
strategy workshop where the strategy will be refined if appropriate

Mock auctions – testing protocols and procedures
The Belgian auction will require extremely robust auction room
protocols and procedures
The mock auctions provide an opportunity to test all aspects of bid strategy
execution
 The effectiveness of the live auction support tools
 Bidding protocols
– Deciding the bid strategy, preparing the next bid, entering the bid,
checking the bid, submitting the bid, updating the auction activity
journal
 Escalation procedures in the event of anticipated bid limit breaches
 Disaster recovery

Auction Game Plan Document
 Pre-auction activity – e.g. aggressive statements prior to the auction
 Statement of hierarchy of primary and secondary auction objectives
 Your valuation and bid limit matrices
 Competitor bid limits and triggers for re-assessment of competitors
 Opening auction bid strategy
 Description of bidding patterns which might indicate strategic behaviour
 Description of trigger points for switching auction objectives / strategy
 Alternative bidding strategies in response to strategic behaviour
 Triggers for bid limit escalation procedures
 Bid team roles and responsibilities
 Bidding protocols and procedures
 Disaster recovery
 Post auction press releases prepared for win / lose outcomes

Summary

Summary
 Operators typically under estimate the complexities and challenges
associated with spectrum valuation
– Often regarding auction bidding strategy as the more challenging
 Early engagement with regulators is essential to ensure that any
spectrum award or renewal process is favourable to you
 Coleago can support you across a broad range of issues
– Regulatory strategy and lobbying
– Support during formal spectrum consultation
– AIP lobbying and modelling
– And of course, spectrum valuation, auction strategy and readiness
and live auction support

Steps in Network Consultation
APPENDIX 1
81
Appendix

Typical steps in a public spectrum consultation process
Stage Activity Operator’s Actions
Policy
Formulation
The Government formulates policy for the allocation of spectrum.
Government departments or ministries involved may include
Telecoms, Development, Finance, Competition Commission, etc
Lobby politicians
PR campaign to influence the debate
Notice of
Consultation
At this stage the process moves to the telecoms regulatory authority
who issues a notice of consultation. This includes a description of the
consultation process and timetable. There may also be a workshop to
obtain inputs from stakeholders in an informal manner.
Lobby politicians
Prepare workshop materials
Meetings with regulatory agency
1st Round
Consultation
The regulator issues a formal consultation document which contains a
list of decisions that need to be made in relation to the spectrum
allocation, a discussion and analysis of the issues, perhaps a
preliminary conclusion (“we are minded to....”), and an invitation to
respond to questions relating to each decision that needs to made.
Analyse consultation document
Commission independent reports that support own view point
Prepare & submit response to consultation
Cross Submission
The regulator would normally publish the submissions received from
different stakeholders and invite cross submissions. This gives
stakeholders the opportunity to comment on points made by other
stakeholders.
Analyse other submissions quantitatively (how many agree
with x) and qualitatively
Prepare and file cross submission document
2nd Round
Consultation
The regulator issues a second consultation document which draws on
the submission received and presents its conclusion and a proposal
ahead of the final determination. If an auction is proposed this would
include a description of the auction format and rules.
Analyse consultation document
Submit response to consultation
Final
Determination
The regulator publishes the final determination, including the process,
detailed procedures and rules. The regulator answers questions for
clarification of the process, procedures and rules.
Analyse determination document
Questions for clarification
Explore legal recourse if necessary

Approach to modelling
APPENDIX 1
83
Appendix

Approach to Network Modelling
Overall approach:
Options:
 Calculate traffic offloaded onto public WiFi and/or small sites, subject to separate
offload limits in different parts of the network; model using abstract cost per Mbps
 Treat densification as a capacity resource relative to existing sites, subject to separate
densification limits in different parts of the network
84
Compare traffic in
each 1% of sites
(all operators) Adjust traffic on
basis of relative
congestion
(all operators)
Calc. resources
required to meet
adjusted traffic
(single operator)Compare max
capacity in each
1% of sites
(all operators)
Calculate capex
and opex
(units x unit costs)
- Spectrum
- Technologies
- Densification, etc.
- RAN
- Backhaul
- Core
LTE roll-out
(single operator)
Appendix

Model structure overview
85
Customers,
ARPU/AUPU,
Revenues
Network
Dimensions
Network
Costs
Scenario Manager, User Interface
Traffic
Segmented
Analysis
Total
Market
Demand
Capacity /
Congestion
Analysis
Consolidation Model Network ModelMarket Model
Market
Scenarios
Technical
Scenarios
Regulatory
Scenarios
Competition
Scenarios
Coverage
Analysis
Spectrum valuation: cash flows ‘With’
minus ‘Without’ extra spectrum
Cash flows, Financial Statements
Device
Diffusion
Traffic
Distribution
Profile
All MNO’s
(Industry-
wide
view)
Appendix

Impact of spectrum scenario on Reference Demand
86
Customers
ARPU and
AUPU
Reference
Demand
Revenues
and Traffic
Customers
ARPU and
AUPU
Pre-Congestion
Demand
Revenues
and Traffic
Customers
ARPU and
AUPU
Congestion-
Adjusted
Demand
Revenues
and Traffic
Adjustments based
on all commercial
value drivers except
capacity:
– Relative footprint
– Quality
– Performance
Impact on:
– Share of gross ads
– Churn
– ARPU
Adjustments based
on relative
congestion:
– Equalisation of
congestion levels
– Subject to relative
congestion
tolerance limits
– Traffic and subs
migrate from more
to less congested
networks
Impact on:
– Share of gross ads
– Churn
Traffic
Distribution
Profile
Capacity /
Congestion
Analysis
Net Traffic
Migration
Within Consolidation Model
Within Technical Model
Appendix

Valuing capacity: Combined congestion-impact biases
87
“Standalone HS High”
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Relativetoaverage
% of base
Relative AUPU versus ARPU
ARPU AUPU
“Linked HS-PC”
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Relativetoaverage
% of base
ARPU AUPU
“Standalone PC”
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Relativetoaverage
% of base
ARPU AUPU
Key
segments:
Bias within each
segment:
Bias across
segments:
Highest
(e.g. 60%)
Next highest
(e.g. 25%)
Lowest
(e.g. 15%)
Net impact of
congestion:
High impact
on traffic
relative to
revenues
Moderate
impact on
traffic relative
to revenues
Low impact
on traffic
relative to
revenues
Appendix

Modelling workflow (overview)
88
Reference
Demand
(all operators)
Within Consolidation Model
Pre-Congestion-
Demand
(all operators)
Spectrum
impact excl.
capacity
Congestion
Impact
Congestion-
Adjusted Demand
(all operators)
Impact on %
congestion
tolerance
(all operators)
Peak traffic
across network
(all operators)
Congestion
analysis
(all operators)
Within Network Model
Net traffic
migration
(all operators)
Adjusted traffic
(all operators)
Capacity upgrade
requirements
(selected operator)
P&L and Cash
Flows
(selected operator)
Network
dimensions
(selected operator)
Unit costs
Capex & Network
Opex
(selected operator)
Link budgets,
3G+ throughputs
Coverage
analysis
(all operators)
Max effective
capacity per cell
(all operators)
Costs of Sale
& Overheads
(selected operator)
Appendix

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