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Rural Broadband-Electric Co-Op 1

Bringing Broadband to a Rural Community and Incorporating it with an

Electric Cooperative

Group Members

Jeffery Willis

Akshay P Dhawale

Mohd Aamir BhatkarCarey Sonsino

Advisors

Dr. David Reed

Kevin Short

Kurt Shaubach

Dr. Scott Savage

TLEN 5710: Capstone

April 25, 2014




Abstract

This research project has attempted to answer the questions of whether the district of

Anza, CA could deploy a broadband service that residents could afford, and whether it would

make sense to incorporate this ISP into the local rural electric cooperative. In this effort, we

have developed a business model to integrate economic supply and demand data. This was

achieved through the acquisition of cost models for the opex, capex, analysis of demographics,

likely broadband penetration, likely transit estimates, and approximate household density. In

addition to this, several scenarios for providing broadband have been explored. This is essential

to determine a reasonable cost estimate for the provision of broadband to the citizens of this

region by assessing the lowest cost option. Furthermore, we have explored the strategic potential

for economies of scope by incorporating the ISP into the local electric cooperative to offer

models that yield a more economically feasible service. This research has been a case study, as

it specifically focuses on the Anza geographic area but the model developed could progressively

be applicable to other similar rural areas in need of broadband. In conclusion, this research effort

has been conducive to the universal service objectives of the National Broadband Plan as we

have supported our original hypothesis about affordability, and provided a business model to

promote the quicker adoption of broadband in similar rural areas throughout the US.




Table of Contents

I. Introduction .................................................................................................................. 4

A. Problem Statement .................................................................................................. 4

B.

Research Question ................................................................................................... 4

C. Terms and Acronyms .............................................................................................. 4

D. Assumptions ............................................................................................................ 5

II. Literature Review ........................................................................................................ 6

III. Research Methodology .............................................................................................. 6

A. Sub-Problem 1 - Evaluating Demand ..................................................................... 7

B. Sub-Problem 2 - Supply .......................................................................................... 8

C. Sub-Problem 3 - Economic Model ........................................................................ 13

IV. Research Results ...................................................................................................... 13

A.

Sub-Problem 1 - Demand ...................................................................................... 13

B. Sub-Problem 2 - Supply ........................................................................................ 16


V. Discussion of Results ................................................................................................ 21

A.

Sub-Problem 1 - Demand ...................................................................................... 21

B. Sub-Problem 2 - Supply ........................................................................................ 21


VI. Conclusions and Future Research ............................................................................ 22

VII. References .............................................................................................................. 23

VIII. Appendices ............................................................................................................ 25

Appendix A - Figures.....................................................Error! Bookmark not defined.




I. Introduction

A. Problem Statement

Despite efforts by the FCC, there are many rural areas in the USA where residents do not

currently have access to affordable broadband services [1] [2] [3]. Our hypothesis was that a

market existed for acceptance of broadband Internet in rural areas like Anza, CA, and this market

would best be served by the local electric cooperative.

B. Research Question

Would the residents of Anza, CA be able to afford and willing to adopt a DSL service

from an ISP managed by a CLEC, and if so, would it make sense to incorporate this CLEC with

the local rural electric cooperative?

Sub-Problem 1 - Demand. The first problem we evaluated was the economic1.

demand of broadband service in Anza area using statistical analysis of income levels and Census

Bureau data to evaluate service affordability and consumers’ willingness to adopt.

Sub-Problem 2 - Supply. From the supply side, we performed a cost analysis for2.

the capex and the opex of services to represent the cost to consumers. We analyzed policy and

business models to evaluate the viability of a standalone telecom ISP against the viability for

service integration with the local rural electric Co-Op.

Sub-Problem 3 - Economic Model. We created an economic model to integrate3.

supply and demand data to determine if an ISP was viable and whether it was advantageous for

this ISP to be integrated into the local electric cooperative.

C. Terms and Acronyms

BAM Broadband Assessment Model (by the FCC)

CDP Census Designated Place

CLEC Competitive Local Exchange Carrier




DSL Digital Subscriber Line

ESA Economics and Statistics Administration

ESRI Environmental Systems Research Institute

FCC Federal Communications Commission

FiOS Fiber Optic Service (Verizon)ILEC Incumbent Local Exchange Carrier

ITU International Telecommunication Union

ISP Internet Service Provider

NRTC National Rural Telecommunications Cooperative

NTIA National Telecommunications and InformationAdministration

PON Passive Optical Network

POP Point of Presence

QoS Quality of ServiceROI Return on Investment

USF Universal Service Funds

D. Assumptions

• The customer demand would follow the adoption rates presented in the 2013 NTIA/ESA

report.

• Subsidies from the Universal Service Fund would not be available (this was decided

because of a judicious prescience of auction outcomes, the regulatory environment for USF, the

presence of an ILEC [Verizon], and the unlikely hood of receiving aid).

• The information obtained on the ESRI website is current and up-to-date.

• Each of the existing microwave transmitting towers utilized is accessible/leasable.

• Verizon would permit a lease of their copper (or a policy change may force them to).

•

Costs used for capex and opex are fair market estimates obtained by NRTC experts.




II. Literature Review

We began our literature review by first considering all the parameters of an affordability

model. Before constructing ours from scratch, several cost models were sought through Internet

searches, deliberate requests from faculty and professional advisers in the industry. Of the items

reviewed, the most meaningful was a power point provided by the NRTC [4], the BAM (by the

FCC) [5], a “Rural Broadband Model” for the Yurok Tribe [6], and an expert training document

provided by the ITU in telecom cost modeling [7]. These documents were useful because of the

dynamic variety of cost modeling approaches contained within and instrumental to our decision

to use a bottom up, cost-plus model.

For the demand side of our cost model, we studied Census Bureau information on

income, demographics and statistics [11] as well as data obtained from the ESRI on population

and housing density [10]. On the supply side, we retrieved a fair estimate of capex and opex [12]

to determine if it is affordable when compared with competing technologies, in a community

where Verizon has abandoned a plan for providing cable or FiOS to the residents [8], [9].

This data will prove valuable and contribute to the body of knowledge in the pursuit of

meeting the universal service objectives of the National Broadband Plan; first as a case study for

this region for evaluating economic feasibility, but also by providing a method to apply to similar

rural areas that have cooperatives present but telecoms absent

III. Research Methodology

In our policy research with the NRTC, we learned that the Telecommunications act of

1996 does not require any ILEC to unbundle their network elements and lease them for the

purpose of broadband; only for a landline (aka normal voice telephone services). This is found

to be most discouraging; as such an effort is to meet universal service objectives of the National




Broadband Plan. Ironically, the Anza Co-Op is required by law to lease the use of their poles to

the incumbent, Verizon, for attaching communication lines to help meet universal service

objectives. This defeats any possibility of the Co-Op using the lease rate of poles as leverage to

incent Verizon to allow the use of their communication lines.

Because there is no policy that requires Verizon to unbundle its network elements and

lease their copper communication lines to an ISP for the purpose of broadband, determining a

lease cost is impossible. Such a pursuit is ambiguous. Our research effort has then shifted its

methodology from one of a realistic deployment of DSL on Verizon’s copper, to calculating the

opportunity cost of this policy. In addition, we have explored the cost of an ISP owned PON

overbuild on existing telephone poles.

A. Sub-Problem 1 - Evaluating Demand

For the demand side of the economic model, we estimated the adoption rate of the

households in the Anza area and the amount of bandwidth that it would take to service those

customers.

Adoption Rate. We determined the customer demand for broadband in the area1.

covered by Anza Co-Op by estimating the number of households that would likely subscribe to

the service. By using statistics for households rather than individuals, we estimated the number

of DSL subscribers. The referenced report by the NTIA & ESA [13] provided estimates for rural

broadband adoption rates based on the metrics of race, income, and education. We applied these

estimated adoption rates to our statistical data in order to estimate the actual number of

subscribers that an ISP would gain. This was done for the zip codes: Anza, Aguanga, and

Mountain Center instead of the city or CDP with these same names, specifically because the zip




code geographic areas more closely resemble the Anza Electric Cooperative service footprint

than the city or CDP geographic areas.

Transit Estimate. One important factor in estimating the opex of the broadband2.

provider is the amount of bandwidth needed to feed the DSL plant. The appropriate amount of

bandwidth to accommodate all of the potential subscribers must be evaluated and brought to the

POP. ISP’s typically offer several different tiers of service speeds and subscribers would receive

different amounts of bandwidth for various reasons. Some users will only need the minimum

speed for applications (such as email and web browsing) and will subscribe to the lowest tier of

service while others will want to use applications that require higher speeds (such as video

streaming or large file transfers) and will subscribe to the higher service tiers. Some customers

may desire higher speeds but will be unable to afford or unwilling to pay the incrementally

higher rate. They also may be unable to receive higher speeds due to distance limitations of DSL

technology.

B. Sub-Problem 2 - Supply

When considering the costs of capital investment, several models were explored as noted

in the preceding literature review section. After much research, we decided on our own cost

model as a hybrid between several sources in addition to training in cost modeling provided by

CU Boulder’s Engineering Management Program to our team members. We concluded that a

cost-plus, bottom up model using housing density and broadband penetration parameters would

be appropriate to estimate supply and to meet an estimated demand. While much effort needed

to be placed on researching the opex and capex elements to meet the demand, it was first

essential to determine our needed lengths of transmission to ensure coverage capability.




Determining length of required for transmission distances to customers. 1.

Since this research effort includes exploring the potential for economies of scope with the local

cooperative, it made sense to use the pre-existing telephone poles (which we learned from Kevin

Short were owned by the cooperative and leased to the ILEC, Verizon) for electrical distribution

as a basis for potential broadband provision. This is because Verizon’s communication lines

share the same path as the electrical lines. In order to do this, we were able to acquire a map of

Anza Electric Co-Op’s pre-existing transmission and distribution layout. With much effort, and

the useful aid of Google Earth, we were able to estimate the actual area determined feasible

potential customers for DSL and a PON. Three scenarios were evaluated utilizing three

techniques: 1) The “Complete Transmission Line” approach, 2) The “Partial Transmission Line”

for considering only current landline customers as potential DSL customers, and 3) The “Cherry

Picking” approach.

a. Complete transmission line approach. The first technique considered all 737

miles of pre-existing energized electrical lines as a potential for a network overbuild. There are

490 miles of overhead distribution, 220 miles of underground distribution, and 25 miles of

overhead transmission. The “complete” approach simply eliminates the 25 miles of

transmission, leaving 712 miles of energized lines; our middle mile connection will not take the

same path as the corresponding 25 miles Anza’s electric transmission lines. This model was

suspected to be unfeasible from the outset of this technique due to obvious highest capital costs.

b. Partial transmission line approach. The second technique is to roughly estimate

how many of the Co-Op owned poles currently have Verizon’s communication lines on them.

Of the 10,000 poles, only 3700 of them have Verizon’s lines attached to the poles. This equates




to 37% of the total 712 miles, or a ball-park figure of 264 miles of Verizon’s copper attached to

Anza Co-Op’s poles.

c. Cherry picking approach. The third technique is far more conservative and

precise in the selection of the zones with the highest population density throughout the entirety

of the Co-Op’s distribution region with pre-existing poles. There were six zones selected in this

effort: Central Anza (the CDP zone), Aguanga (Northern), Lake Riverside, Terwilliger, Garner

Valley, and Sugarloaf. These six zones still left much unanswered about exactly how much

distribution lines resided within the boundaries, however, a common proxy is to use the

perimeter of a zone to estimate the length needed for its communication lines. This technique

was advised by Dr. Scott Savage, and applied to each of the six zones.

Table 1 - Transmission Distances for Zones

Once these lengths have been determined, it is now possible to estimate the total

transmission line costs for our ISP from knowing our cost of installation per mile.

Ensuring coverage capabilities. The transmit distance limitations of DSL’s2.

current technology on copper was a big consideration when estimating whether the subscribers

are within acceptable QoS range. For the sake of time, we have done a rough estimate, instead

of a meticulous and precise measurement along each road. From our research, the DSL protocol

most appropriate with primary residential subscribers is asynchronous DSL [16], which has a

distance of 18,000 ft, or 3.4 miles [1]. With the addition of DSL accelerators, a distance of

27,000 ft, or 5.11 miles could be achieved. Theoretically, these could be used at the necessary

Cherry Picking Zone Needed Transmiss ion Distance (perimeter in miles)

Anza 24.9

Aguanga 16.1

Lake Riverside 10.4

Terwilliger 26.8

Garner Valley 33.7

Sugarloaf 24.4

Total: 136.3




distances for the subscribers on the outskirts of the DSLAM’s 18,000 ft transmission radius.

Although a radius is not a realistic measure of transmission distance along square corners, and

city blocks, it is approximate enough to be acceptable, as it will estimate who will or will not be

covered. Each DSLAM transmission radius is then placed adjacent to its corresponding tower to

form a network of multiple towers as a “microwave ring.” This is mapped out with enhanced

icons and a legend in the Appendix, Figure A.1. Complete coverage would require the addition

of two new towers annotated in RED on the map and would most certainly have to be factored

into the capital costs of broadband provision.

3. Middle mile architecture. Middle mile connections and their associated costs

were of primary concern when estimating transmission length because of the remote rural setting

and likely long distance to the nearest POP for connection to the backbone of the Internet. Three

options were considered: An optical fiber (run from the nearest Level 3 Network connection

along the 79 or 74 highways), importing broadband access through satellite communication as a

POP, or a series of microwave-hop transmission towers.

Of the three options, the most feasible and was the middle microwave hop because of its

lowest overall cost displayed in the Appendix, Figure A.2. The wireless microwave method of

importing broadband through the towers is preferred to satellite because of the propagation delay

associated with satellite communications (not to mention high capital for satellite transmission

equipment never pursued), and preferred to fiber because of the high capital costs of deploying a

middle mile fiber transmission line.

4. Integrating PON Research Efforts. It was strongly encouraged by Kurt

Shaubach to pursue the deployment of a PON overbuild primarily because of policy, but also

because a fiber overbuild would be cheaper than using copper lines with a far superior QoS.




With a PON, transmit distance is not as limited as DSL and frees up many more possible

locations for points of synergy. Any of the three substations are primary locations for a point of

synergy as well as three different possible locations for a central office/data center.

Determining Housing Density Characteristics. The process of determining3.

housing density required that we derive the figure from other known parameters: the population

density, and the average members per household. These parameters can be obtained from the

ESRI [10]. Because different households vary in the number of members, an approximation of

the household density was calculated based on the percentage of 2, or 3+ member households.

Table 2 - Household Density Results Per Zip Code

C. Sub-Problem 3 - Economic Model

The economic model was created with Microsoft Excel to integrate all of the parameters

mentioned in our research methodology so far. It spanned several spreadsheet pages and

employed more advanced financial functions using the capex and opex for long term feasibility

projections. Most importantly, it computed the time required to achieve a ROI; our primary

factor on which we based affordability conclusions.

IV. Research Results

A. Sub-Problem 1 - Demand

1. Adoption Rate. Table 3 shows the estimated number of households in

Anza/Aguanga that would likely subscribe to broadband service when using the race and

ethnicity adoption estimates. For each race category, we multiplied the NTIA/ESA adoption rate

County Zip Total Pop Total H.H. Pop. Density % of 2/HH % of >2/HH Estimated H.H. Density

Aguanga 92536 3,810 1,591 34 people/mi2 86% 14% 16/mi

2

Anza 92539 4,734 2,114 35 people/mi2

63% 37% 15/mi2

Mountain Center 92561 1,661 1,118 11 people/mi2

100% 0% 6/mi2

From American Fact Finder Census 2008-2012 5 year

estimateFrom ESRI to date




by the number of households in the Anza Co-Ops service area, resulting in the estimated number

of subscribers for that demographic. We summed the estimated subscriber count for each race to

yield the total number of estimated subscribers for this area. This calculation ultimately resulted

in an estimated 2080 subscribing households out of a possible 3506, or approximately 59.3%.

Table 3 - Anza/Aguanga/Mountain Center Broadband Demand and Adoption Estimates by

Race

Race

Occupied Housing Units by Zip Code, 2008-2012 AmericanCommunity Survey 5-Year Estimates [11] Rural Broadband

Adoption Percentage by

Race, 2011 [13]

Estimated Numberof Subscribing

Households

92536

(Aguanga)

92539

(Anza)

92561 (Mountain

Center)

Total (All 3 Zip

Codes)

White 1228 1455 616 3299 0.61 2012

African American 17 0 3 20 0.35 7

American Indian andAlaska Native 11 34 38 83 0.33 27

Asian American 0 0 0 0 0.81 0Other 43 59 2 104 0.33* 34

Total 1299 1548 659 3506 2080

*No data available for other races; conservatively using the lowest available group adoption rate

Table 4 applies the household income metric to estimate the number of broadband

subscribers. We used the method of multiplying the typical adoption rate for each income

bracket by the number of households in each bracket to yield a total of 2293 estimated

subscribers out of a possible 3506 (approximately 65.4%), higher broadband adoption numbers

than when using the race metric.


Income

Family Income



Income, 2011 [13]


Households

92536

(Aguanga)

92539

(Anza)

92561 (Mountain

Center)

Total (All 3

Zip Codes)

Less than $25,000 145 388 133 666 0.36 240

$25,000-$49,999 466 598 122 1186 0.59 700

$50,000-$74,999 193 359 29 581 0.77 447$75,000-$99,999 270 134 147 551 0.83 457

$100,000 or more 225 69 228 522 0.86 449

Total 1299 1548 659 3506 2293

Table 5 shows the broadband adoption numbers when we used the census data for

education. The calculation for the educational-based metric resulted in an adoption estimate of




2072 households out of 3506, or approximately 59.1%, slightly lower than the race-based

estimate.


Education

Education



Education, 2011 [13]


Households

92536

(Aguanga)

92539

(Anza)

92561 (Mountain

Center)

Total (All 3

Zip Codes)

No high school diploma 167 299 0 466 0.27 126

High school graduate(including equivalency) 392 416 230 1038 0.51 529

Some college or associate's

degree 599 690 252 1541 0.68 1048

College degree or more 141 143 177 461 0.8 369

Total 1299 1548 659 3506 2072

Transit Estimate. Given the complexities involved in estimating the amount of4.

bandwidth that each subscriber will ultimately receive, we simplified the calculation of the total

system bandwidth by using the National Broadband Plan goals of 4 Mbps (megabits per second)

downstream and 1 Mbps upstream as the average speed for each customer [14]. Multiplying the

lowest estimated number of subscribing households in the Anza region (2,072) by these average

speeds yielded a total estimated required bandwidth of 8,288 Mbps (8.288 Gbps, gigabits per

seconds) downstream and 2,072 Mbps (2.072 Gbps) upstream. The same calculation for the

highest estimate of subscribing households of 2,293 resulted in 9,172 Mbps (9.172 Gbps)

downstream and 2,293 Mbps (2.293 Gbps) upstream. One additional consideration is that under

typical circumstances, at any given time not all subscribers will be fully utilizing the bandwidth

in their subscription plan. It is common practice for broadband service providers to

oversubscribe the amount of bandwidth, but the exact ratio of oversubscription is not public

information and presumably varies among providers. For enterprise campus networks, Cisco

recommends an oversubscription ratio of 4:1 for the distribution-to-core links [15]. We applied

this ratio to the bandwidth calculations to our research effort, which resulted in 2.072 to 2.293




Gbps downstream and 518 to 573.25 Mbps upstream. If bandwidth oversubscription is used, it is

important to monitor the system congestion during peak utilization times and adjust the

oversubscription ratio if necessary in order to maintain a reasonable level of service. While these

numbers gave us a reasonable ballpark estimate, it should be noted that they do not account for

any business-class broadband services, growth in the population, housing market, or general

increase in demand for bandwidth.


In order to determine the cost to supply broadband service, we had to estimate the amount

of equipment needed for our capital expenses and the cost of operations and maintenance.

Determining the number of DSLAMS and accelerators needed. Based on the1.

transit estimate calculations and DSLAM specifications, we estimated that we would need

approximately 75 24-port DSLAMs to support the population of the Anza area. One DSLAM

can theoretically support up to 200 customers at lower speeds, however for higher ADSL/VDSL

speeds, approximately 20 customers per port was a more practical estimate. We also needed to

account for additional DSLAMs for cascading equipment in a copper architecture, and

aggregated uplink ports to connect to the aggregator and/or Internet. The philosophy behind

selecting a 24-port DSLAM lies in the lower household density, which means that higher port

density equipment may go underutilized. One higher capacity 24-port DSLAM was still required

as an aggregator for the Internet Gateway switch, which took into consideration an access-to-

distribution oversubscription ratio of 20:1 as recommended by Cisco [15].

Determining the cost of building two additional towers to ensure coverage. 2.

We determined that a fair market cost estimate to build a bare tower (without transmission

equipment) for our purposes was approximately $50,000, and $100,000 for a tower with installed




microwave equipment. There was no lease charge in this estimate because the cooperative

would own the facilities.

Estimating total number of towers needed and their lease rates for a3.

microwave ring. Based on the ADSL theoretical transmit distance coverage map, we

determined that a total of 6 existing towers could be leased and 2 new towers to be built in

strategic locations would achieve a more comprehensive footprint. The 2 additional towers

would be required for long-haul microwave links to the desired ISP backbone. We found the

lease rates for these towers to be relatively inexpensive at $1,500 per tower per year.

Integrating middle mile architecture costs. The middle mile architecture took4.

into account the total number of DSLAMs required, the cost of fiber deployment, and the cost of

the fiber itself. We also included additional installation charges as sunk costs in the capital

expenditure model for the middle mile. Fiber deployment would be carried out as micro

trenching at a cost of around $22/foot trenched. The fiber is assumed to be single-mode multi-

stranded fiber, which costs around $3/foot of fiber.

Table 6 - Middle Mile Costs, a snapshot

The middle mile architecture also consisted of operational expenditure (opex) for renting

pole attachments to overhead copper/fiber. There were 10,000 poles in our targeted area owned

and managed by the Anza Electric Co-Op, on which Verizon requires 3,700 pole attachments; a

similarly, a good approximation for building our PON. Verizon recently paid $15 per pole per

year to Anza Electric Co-Op for those contacts, so with that rate as an estimate, the total cost of

all the towers was around $56K per year.

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Integrating xDSL transmission line costs with three methods. Below is a5.

financial snapshot summary of the three approaches that we considered while deploying copper

in the target area.

Table 7 - Financial Snapshot of Three Approaches

a. Complete transmission line cost . The complete transmission line approach was

perhaps the most expensive approach for providing broadband service. This accounted for

approximately 712 miles of copper and labor costs, and totaled to nearly $62M in capital sunk

costs to provide service for the entire geographic region. This model was deemed likely to be

unfeasible from the outset.

b. Partial transmission line cost. Partial transmission line can be seen as a mid-way

approach from the cost perspective. This approach would serve only the areas with pre-existing

communication lines available. New copper would be laid along the same footprint as the

existing lines, which was effectively 264 miles, or only 37% of the total possible miles of copper

(712 miles). The cost of laying the new copper over the 264 miles was estimated to be around

$22M.

c. Cherry picking transmission line cost. The cherry picking approach was the most

cost effective approach based on the copper architecture. This served only selected zones with

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Reference source not found.. Only 136 miles of transmission lines were required for this

approach, and the total capital expenditure for this equaled $11M.

Considering the cost of a PON instead.6.

a. Complete transmission line cost. The cost of fiber was around 620% more than

that of copper. A PON overbuild would entail nearly half a billion dollars in investments for

complete transmission cost.

b. Partial transmission line cost. As mentioned above, the cost of fiber was 620%

more than that of copper. Keeping all other parameters nearly the same, the cost of a partial

PON overbuild would take a total of $11.7M in capital investments. We concluded that this

PON model was unfeasible considering the take rates and total households in the targeted area.

c. Cherry picking transmission line cost. The cherry picking approach is the only

comparable model to a copper architecture. This would require an investment of around $11M

to install fiber as a PON overbuild (nearly the same to that of the copper architecture). We did

not pursue this model since it would entail additional equipment costs to handle a complete fiber

model. Additionally, this business case would not be profitable considering the sunk costs, and

would render a ROI of nearly 10 years on capital expenditure alone.

Table 8 – GPON Cost Considerations

7. The lowest cost combination of network architecture for acceptable QoS. The

ROI calculations are done as a key indicator of the economic feasibility of any combination of

the models. We decided to consider simple revenue based, not net-income based ROI as a fair

generalization to analyze different business models. The calculation of ROI does not include

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Sales and General Admin (SG&A) expenses, income tax, or amortization/depreciation. The

revenue is calculated by considering monthly broadband subscription charges fixed at $50/month

and steadily increasing adoption rate over time. The total investment can be calculated as the

sum of the middle mile architecture and fiber or copper cherry picking transmission line

approach, with respective ROI. In the case of microwave backhaul, investment cost is calculated

as the minimum cost for the middle mile architecture and microwave architecture based

equipment cost, tower construction, and licensing costs amongst others. Both fiber and copper

yield similar results for the cherry picking approach, with fiber marginally better than copper.

In the case of microwave, due to the nature of the business model involving considerable

leasing costs, we also included monthly operational expenditures in terms of rentals, lease fees,

and other maintenance. The maintenance cost is considered as an industry average of around 3-

5% annually [17]. Microwave architecture, even post opex inclusion, yields much better results

as compared to any wireline architecture. The majority of the better ROI can be attributed to the

minimal-to-no trenching and media buildout, which are usually highly capital intensive.


The population and dynamics of the Anza area yields the same investment for copper or

fiber to provide broadband service to majority of locations, however, this is a huge capital cost

and poses as a hindrance to broadband investment in such rural areas. On the other hand, if

CLECs or new businesses were able to lease the copper lines from incumbents, this would

drastically reduce the capital investment and such projects would likely appear as a standalone

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investable opportunity (even with absence of USF funding). Though the operational expense

would increase, annualized opex would still be a fraction of the capital costs required otherwise.

V. Discussion of Results

A. Sub-Problem 1 - Demand

The three metrics of race, income, and education produced slightly different broadband

adoption estimates when we applied the census data for the Anza area, and we used these

estimates as the basis for a reasonable range of broadband adoption rates. Assuming that the

broadband adoption rates for these specific racial, financial, and educational groups remain

relatively stable, and assuming that the demographic makeup of this area does not change

significantly, we expect a broadband adoption rate between 59% and 65%.


There can be various technologies that can provide Broadband access to any given

market. We looked at three ways of delivering broadband to Anza, over copper, over fiber, and

over microwave links. We also studied in detail the different wireline architectural approaches,

and we saw the cherry picking approach to be the most cost effective method of delivering

broadband to majority of densely populated regions. For our network architecture, we concluded

that microwave ring and longhaul was the most cost efficient way delivering broadband over

leased and/or built towers.


Based on a generalized income statement with achievable revenue, average workforce

size, and considering minimal other general expenses, the projected income statement reveals

that the EBITDA is the highest in year - 6 at 55% of revenue. The major reason for this slow

turnover is the economics of a rural geography and population. Lower computer literacy rate




hinders adoption of broadband services, and lower population blocks efficiencies associated with

network effects. As a counter example, if the number of households were to be increased only

by 30% leaving the adoption rate unchanged, we would achieve EBITDA at nearly 55% of

revenue from the first year itself.

Integrating broadband with an electric cooperative would reduce our capital investments

considerably allowing the business model to be profitable sooner. This factor majorly drives the

broadband investments in such rural areas. Furthermore, other challenges like building up the

long-term computer literacy rate, operating with multiple organizations with different business

drivers, and other broadband policy regulations make such areas unprofitable ventures.

VI. Conclusions and Future Research

The numbers speak for themselves, as the integration of an ISP with the electric

cooperative significantly reduces cost of broadband services, making the service affordable to

the residents of the Anza area. Our data supports our hypothesis that such an ISP deployment

can be made affordable if policy permitted the lease of Verizon’s communication lines, and

made even more affordable if integrated with the cooperative. The opportunity cost of policy not

requiring Verizon to unbundle its network elements for the purpose of broadband is substantial

and has been accordingly demonstrated through this research effort.

In the future, expansion of DSLAMs to accommodate more customers, trenching fiber for

middle mile architecture, and finally deploying a GPON for the Anza area remain possibilities,

as is the case with many of the USA’s unserved rural areas where cooperatives are present, but

lack broadband services. Additionally, we hope our model serves as an example to encourage a

change of policy in the near future to promote a quicker adoption of broadband.




VII.References

[1] V. Glass and S. K. Stefanova. "An empirical study of broadband diffusion in rural

America," J. Regulatory Econ., vol. 38, pp. 70-85, Aug. 2010.

[2] H. Kuttner. "Broadband for Rural America: Economic Impacts and Economic

Opportunities," presented at the Economic Summit on the Future of Rural

Telecommunications, Washington, DC, Oct. 15, 2012.

[3] M. J. Copps. "Bringing Broadband to Rural America: Report on a Rural Broadband

Strategy," FCC, Washington, DC, May 22, 2009.

[4]

Shaubach, K. NRTC Cost Model Doc, “Schaubach – CRN Panel (FINAL)” sent via e-mail.

[5] Federal Communications Commission and CostQuest Associates. "Broadband Assessment

Model," FCC, Washington, DC, Mar. 2010.

[6] Yurok Tribe Information Services Department. "A Rural Broadband Model," Klamath, CA,

2011.

[7] Holmes, J. ITU “Expert Level Training on Telecom Network Cost Modeling for the

Pacific.” Oct. 2012.

[8]

B. Arnason. (2012, Apr. 19). Verizon Confirms its Plans to Abandon DSL [Online].

Available: http://www.telecompetitor.com/verizon-confirms-its-plans-to-abandon-dsl/

[9] T. Cheredar. (2011, Dec. 9). Lame: Verizon is abandoning its FiOS TV & internet service

to pursue wireless partnerships [Online]. Available:

http://venturebeat.com/2011/12/09/verizon-stops-fios-build-out/

[10] Esri. (n.d.). Esri ZIP Lookup | Demographics and Lifestyle by ZIP Code [Online].

Available: http://www.esri.com/data/esri_data/ziptapestry




[11] United States Census Bureau. (n.d.). American FactFinder [Online]. Available:

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml

[12] Naveh, T. “Mobile Backhaul: Fiber vs. Microwave”. Paramus, NJ. Oct. 2009. [Online]

Available:

http://www.ceragon.com/images/Reasource_Center/White_Papers/Mobile_Backhaul_Fiber

_Microwave-White_Paper.pdf

[13] National Telecommunications and Information Administration and Economics and

Statistics Administration. "Exploring the Digital Nation: America's Emerging Online

Experience," U.S. Dept. of Commerce, Washington, DC, Jun. 2013.

[14] Federal Communications Commission. "Connecting America: The National Broadband

Plan," FCC, Washington, DC, n.d.

[15] Cisco Systems, Inc. (2008, May 21). Campus Network for High Availability Design Guide

[Online]. Available:

http://www.cisco.com/c/en/us/td/docs/solutions/Enterprise/Campus/HA_campus_DG/haca

mpusdg.html

[16] Broadband.gov [Online]. Available: http://www.broadband.gov/broadband_types.html

[17] R. Lev and V. Viniak. "Managing Maintenance and Support Costs," Accenture, 2012.

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