Applications of Life-Cycle Cost Analysis to 

Homeland Security Issues in Constructed 

Facilities: A Case Study 

Robert E. Chapman 

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, MD 20899 

NISTIR 7025 

U.S. Department of Commerce 

Technology Administration

National Institute of Standards and Technology

 

NISTIR 7025

Office of Applied Economics

Building and Fire Research Laboratory 

Gaithersburg, MD 20899 

U.S. Department of Commerce 

Technology Administration

National Institute of Standards and Technology

Applications of Life-Cycle Cost Analysis to 

Homeland Security Issues in Constructed 

Facilities: A Case Study 

 Robert E. Chapman 

Sponsored by: 

National Institute of Standards and Technology

Building and Fire Research Laboratory 

October 2003 

U.S. DEPARTMENT OF COMMERCE 

Donald L. Evans, Secretary

TECHNOLOGY ADMINISTRATION 

Philip J. Bond, Under Secretary for Technology 

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Arden L. Bement, Jr., Director 
ii
Abstract 

The Office of Applied Economics, a unit of the Building and Fire Research Laboratory at 

the National Institute of Standards and Technology, is developing economic tools—

evaluation methods and software for implementing these methods—for evaluating the

management of terrorist risks. This report is one in a series focused on these economic 

tools. It illustrates how to apply a series of standardized methods to evaluate and 

compare the cost-effectiveness of security-related investments in constructed facilities. 

This report describes a renovation project for a prototypical data center. The renovation 

has been planned for some time to upgrade the data center’s HVAC, telecommunications 

and data processing systems and to address a number of generic security concerns. The

building owners employ two different renovation strategies. The first, referred to as the 

Base Case, employs upgrades which are consistent with pre-9/11 levels of security. The 

second, referred to as the Proposed Alternative, recognizes that in the post-9/11 

environment the data center faces heightened risks in two areas. These risks are 

associated with the vulnerability of information technology resources and the potential 

for damage to the facility and its contents from chemical, biological, radiological, and 

explosive (CBRE) hazards. Two scenarios—the potential for a cyber attack and the 

potential for a CBRE attack—are used to capture these risks. 

The results of this study demonstrate that the Proposed Alternative results in lower lifecycle costs and is hence the more cost-effective choice. Additional economic measures 

are reported that underscore the superior performance of the Proposed Alternative. 

Finally, this study demonstrates how a detailed cost-accounting framework promotes 

better decision making by identifying unambiguously who bears which costs, how costs 

are allocated among several widely-accepted budget categories, and how costs are 

allocated among key building components. 

Keywords

Building economics; commercial buildings; construction; economic analysis; hazard 

mitigation; homeland security; life-cycle costing

iii
iv
Preface 

This study was conducted by the Office of Applied Economics (OAE) in the Building 

and Fire Research Laboratory (BFRL) at the National Institute of Standards and 

Technology (NIST). The study is designed to illustrate how to use economic analysis to 

evaluate security-related investments in constructed facilities. The intended audience is 

the National Institute of Standards and Technology as well as other government and 

private sector organizations that are concerned with evaluating how to efficiently allocate 

scarce financial resources among security-related investment alternatives. 

v
vi
Acknowledgements 

The author wishes to thank all those who contributed so many excellent ideas and 

suggestions for this report. They include Dr. William Grosshandler of the Fire Research 

Division at NIST’s Building and Fire Research Laboratory (BFRL), manager of BFRL’s 

Research and Development for the Safety of Threatened Buildings Program, for his 

technical guidance, suggestions, and support. Special appreciation is extended to Dr. 

Harold E. Marshall, Dr. Sieglinde K. Fuller, Ms. Chi J. Leng, Ms. Amy S. Rushing, Ms. 

Christine A. Izzo, and Ms. Jennifer Helgeson of BFRL’s Office of Applied Economics 

for the thoroughness of their reviews and for their many insights and to Mrs. J’aime L. 

Maynard for her assistance in preparing the manuscript for review and publication. 

Special thanks are due to each of the Steering Committee members—Ms. Janet S. Baum, 

Health, Education + Research Associates, Inc.; Mr. Robert N. Harvey, Washington 

Group Infrastructure Corporation; Mr. David Henry, U.S. Department of Commerce; Mr. 

Muthiah Kasi, Alfred Benesch & Company; Mr. Douglas N. Mitten, Project Management

Services, Inc.; and Dr. Stephen R. Thomas, Construction Industry Institute—for their 

guidance throughout the development of our cost-effectiveness tool. The report has also 

benefited from the review and technical comments provided by Dr. Christoph Witzgall of

NIST’s Information Technology Laboratory. 

vii
viii
Table of Contents 

Abstract..............................................................................................................................iii

Preface................................................................................................................................. v

Acknowledgements........................................................................................................... vii

List of Tables..................................................................................................................... xi

List of Figures.................................................................................................................... xi

List of Exhibits.................................................................................................................. xii

List of Acronyms .............................................................................................................. xii

1 Introduction................................................................................................................. 1 

1.1 Background......................................................................................................... 1 

1.2 Purpose................................................................................................................ 2 

1.3 Scope and Approach ........................................................................................... 3

2 A Methodology for Measuring Economic Performance............................................. 5 

2.1 Types of Analysis ............................................................................................... 5 

2.1.1 Baseline Analysis........................................................................................ 5 

2.1.2 Sensitivity Analysis .................................................................................... 6 

2.2 Overview of Evaluation Methods....................................................................... 6 

2.2.1 Life-Cycle Cost Method ............................................................................. 9 

2.2.2 Present Value of Net Savings Method...................................................... 10 

2.2.3 Savings-to-Investment Ratio Method ....................................................... 10 

2.2.4 Adjusted Internal Rate of Return Method................................................. 11 

2.2.5 Summary of Methods................................................................................ 12 

2.3 The Need for a Detailed Cost-Accounting Framework .................................... 13 

2.4 Presentation and Analysis of the Results of an Economic Evaluation ............. 16 

2.4.1 Significance of the Project........................................................................ 18 

2.4.2 Analysis Strategy ...................................................................................... 18 

2.4.3 Calculation of Life-Cycle Costs and Additional Measures ...................... 19

3 Formulating the Economic Evaluation of the Data Center Renovation ................... 21 

3.1 Data Center Renovation: Overview of the Case Study..................................... 21 

3.2 Defining the Base Case and the Alternative ..................................................... 21 

3.3 Key Assumptions and Analysis Issues ............................................................. 22 

3.3.1 Key Assumptions...................................................................................... 22 

3.3.2 Analysis Issues.......................................................................................... 24

4 Baseline Analysis...................................................................................................... 25 

4.1 Cost Items Entering the Analysis...................................................................... 25 

ix
4.1.1 Input Costs................................................................................................ 25 

4.1.2 Event-Related Costs.................................................................................. 28 

4.2 How Cost Items are Modeled and Analyzed via the Cost-Accounting 

Framework........................................................................................................ 31 

4.3 Baseline Results................................................................................................ 37 

4.4 How the Baseline Analysis Links to the Software Product .............................. 39

5 Sensitivity Analysis .................................................................................................. 41 

5.1 Methodology..................................................................................................... 41 

5.2 Key Variables.................................................................................................... 42 

5.3 Sensitivity Results............................................................................................. 45 

5.3.1 Changing One Variable............................................................................. 45 

5.3.2 Changing All Variables in Combination................................................... 49 

5.4 How the Sensitivity Analysis Links to the Software Product........................... 67

6 Summary and Suggestions for Further Research...................................................... 69 

6.1 Summary........................................................................................................... 69 

6.2 Suggestions for Further Research ..................................................................... 72

References......................................................................................................................... 75 

Disclaimer: 

Certain trade names and company products are mentioned in the text in order to 

adequately specify the technical procedures and equipment used. In no case does such 

identification imply recommendation or endorsement by the National Institute of 

Standards and Technology, nor does it imply that the products are necessarily the best 

available for the purpose. 

x
List of Tables 

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation 

Method for Each Decision Type ......................................................................13 

Table 4-1. Summary of Input Costs for the Base Case.....................................................26 

Table 4-2. Summary of Input Costs for the Proposed Alternative ...................................27 

Table 4-3. Summary of Event-Related Information for the Base Case ............................29 

Table 4-4. Summary of Event-Related Information for the Proposed Alternative...........30 

Table 4-5. Base Case: Capital Investment Costs..............................................................32 

Table 4-6. Base Case: O&M Costs...................................................................................33 

Table 4-7. Base Case: Other Costs ...................................................................................33 

Table 4-8. Proposed Alternative: Capital Investment Costs.............................................35 

Table 4-9. Proposed Alternative: O&M Costs..................................................................36 

Table 4-10. Proposed Alternative: Other Costs................................................................37 

Table 4-11. Summary of Life-Cycle Costs.......................................................................38 

Table 5-1. Baseline and Extreme Values of the Twenty-one Variables Used in the 

Sensitivity Analysis .........................................................................................44 

Table 5-2. Results of Deterministic Sensitivity Analysis: Summary of Present 

Value Net Savings Calculations.......................................................................46 

Table 5-3. Summary Statistics Due to Changes in the Input Variable Discount Rate......48 

Table 5-4. Summary Statistics Due to Changes in the Input Variable Enhanced 

Renovation for the Proposed Alternative.........................................................48 

Table 5-5. Summary Statistics Due to Changes in the Input Variable Probability of

Major Damage for the Base Case ....................................................................49 

Table 5-6. Summary Statistics Due to Changes in the Input Variable Basic Renovation 

for the Base Case .............................................................................................49 

Table 5-7. Summary Statistics Due to Changes in All of the Variables...........................50 

Table 5-8. Percentiles for Statistical Measures Due to Changes in All of the Variables .51 

Table 5-9. Statistical Measures by Cost Classification and Cost Type Due to 

Changes in All of the Input Variables..............................................................61 

Table 5-10. Statistical Measures by Cost Category and Bearer for Event–Related Cost 

Items Due to Changes in All of the Input Variables........................................63 

List of Figures 

Figure 2-1. Overview of the Cost-Accounting Framework..............................................14 

Figure 5-1. Life-Cycle Costs for Each Alternative in Thousands of Dollars Due to 

Changes in All of the Variables .....................................................................56 

Figure 5-2. Present Value Net Savings in Thousands of Dollars Due to Changes in 

All of the Variables ........................................................................................57 

Figure 5-3. Savings-to-Investment Ratio Due to Changes in All of the Variables...........58 

Figure 5-4. Adjusted Internal Rate of Return Due to Changes in All of the Variables....59 

xi
List of Exhibits 

Exhibit 2-1. Format for Summarizing an Economic Evaluation ......................................17 

Exhibit 6-1. Summary of the Data Center Case Study .....................................................70 

List of Acronyms 

Acronym Definition

AHP Analytical Hierarchy Process

AIRR Adjusted Internal Rate of Return

ASTM American Society for Testing and Materials

BFRL Building and Fire Research Laboratory

CBRE Chemical, Biological, Radiological, and Explosive

CDF Cumulative Distribution Function

HVAC Heating, Ventilation, and Air-Conditioning

IAQ Indoor Air Quality

LCC Life-Cycle Cost

MARR Minimum Acceptable Rate of Return

NIST National Institute of Standards and Technology

OAE Office of Applied Economics

O&M Operations and Maintenance

OMB Office of Management and Budget

PVC Present Value of Non-Investment Costs

PVE(L) Present Value of Expected Losses

PVI Present Value of Investment Costs

PVII Present Value of Increased Investment Costs

PVNS Present Value of Net Savings

PVS Present Value of Savings

SIR Savings-to-Investment Ratio

TV Terminal Value

xii
1 Introduction 

1.1 Background 

The September 11th attacks on the World Trade Center buildings and the Pentagon 

galvanized the nation in strengthening its defenses against future terrorist attacks. Such 

attacks could result in physical damage to various constructed facilities, such as

buildings, industrial facilities, and infrastructure (e.g., bridges and dams). It is also likely 

that any attacks on constructed facilities would result in personal injury and financial 

losses to employees, tenants, occupants, and other facility stakeholders (e.g., owners and 

managers, investors, and third parties). 

Owners and managers of constructed facilities now face the challenge of responding in a 

financially responsible manner to the potential for future terrorist attacks. Three 

strategies for reducing exposure to terrorist-related losses are: (1) engineering 

alternatives; (2) management practices; and (3) financial mechanisms. Engineering 

alternatives include building designs and materials that can better withstand attack, and 

retrofits of existing structures to reduce the estimated loss of life and property from a

terrorist attack. Management practices include selecting safer building sites and the use 

of physical security personnel. Financial mechanisms are of two types: insurance and 

financial incentives. The owners and managers of constructed facilities may transfer the

risk of losses from terrorism by purchasing insurance for some types of losses. Financial 

incentives include tax write-offs, reduced insurance premiums, and government costsharing arrangements for investments that protect against terrorism. How investments in 

these “mitigation” strategies are integrated into a cohesive risk mitigation plan is a 

complex decision problem. 

Economic tools—evaluation methods and software for implementing these methods—are 

needed to direct limited resources to investments in mitigation strategies that will provide

the most cost-effective reduction in personal injuries, financial losses, and damages to 

constructed facilities. Such tools will enable key decision makers—the intended 

customers1

—to produce a risk mitigation plan that responds to the potential for future 

terrorist attacks in a financially responsible manner. By using economic tools to promote 

more informed decisions, both intended customers and other stakeholders will accrue 

significant benefits through reduced exposure to terrorist-related losses. 

The economic tool envisioned is a flexible decision methodology, embedded in userfriendly, decision-support software, that helps the owners and managers of buildings, 

industrial facilities, and infrastructure to maximize the likely reduction in terrorist-related 

losses while considering the tradeoffs among alternative levels of reliance on the three 

 

1

 Customers are the intended users of the economic tools; they are either directly or indirectly empowered

to decide which combination of mitigation strategies to employ. Stakeholders are organizations or 

individuals directly affected by mitigation activities or disaster-related losses. Therefore, customers are a 

subset of stakeholders. 

1
mitigation strategies. The economic tool will provide decision makers with the basis for

generating a risk mitigation plan. 

The Office of Applied Economics (OAE) in the Building and Fire Research Laboratory 

(BFRL) at the National Institute of Standards and Technology (NIST) is now developing 

such a tool. This report is the first in a series on how to apply economic tools to 

homeland security-related issues in constructed facilities. 

1.2 Purpose 

The report employs a case study approach to identify the most cost-effective levels of 

security-related investments in a typical constructed facility. This case study approach 

illustrates how to apply economic tools and interpret the results. It also provides the 

technical and theoretical foundations of these tools. A companion, follow-on document,2

also planned for publication in 2003, provides an in-depth discussion of those

foundations as well as additional data, material, and techniques needed to address a wide 

variety of homeland security-related issues in constructed facilities. 

This report has four main purposes. First, it demonstrates how to apply life-cycle cost 

analysis to a complex homeland security investment decision. Life-cycle cost analysis is 

a widely used evaluation method for conducting economic evaluations in constructed 

facilities; it is supported by an ASTM (American Society for Testing and Materials) 

International voluntary industry consensus standard.3

 Economic analysis over a project’s 

life cycle is the basis for formulating a number of other economic evaluation methods. 

Three of these methods—present value of net savings, savings-to-investment ratio, and 

adjusted internal rate of return—are also employed in this report and are supported by 

voluntary industry consensus standards.4

 These economic measures are all useful in 

evaluating whether or not to undertake a particular investment, since each measure 

provides a different perspective.5

Second, the case study demonstrates how a detailed cost-accounting framework promotes 

better decision making by identifying unambiguously who bears which costs, how costs 

are allocated among several widely-accepted budget categories, and how costs are 

allocated among key building components. A detailed cost-accounting framework is 

needed because costs affect stakeholders in different ways. Thus, knowing who bears 

 

2

 Chapman, Robert E., and Chi J. Leng. Expected publication date 2003. Cost-Effective Responses to 

Terrorist Risks. NISTIR (in preparation). Gaithersburg, MD: National Institute of Standards and 

Technology. 3

 ASTM International. 2002. “Standard Practice for Measuring Life-Cycle Costs of Buildings and 

Building Systems.” E 917. Annual Book of ASTM Standards: 2002. Vol. 04.11. West Conshohocken, 

PA: ASTM International. 4

 ASTM International. Fourth Edition, 1999. ASTM Standards on Building Economics. West 

Conshohocken, PA: ASTM International. 5

 The present value of net savings measures the overall magnitude of cost savings. The savings-toinvestment ratio measures the cost savings per unit of capital investment. The adjusted internal rate of

return measures the annual percentage yield from the capital investment. 

2
which costs leads to a better understanding of stakeholder perspectives and helps create 

mutually beneficial solutions. 

Third, the case study demonstrates how to combine information from two generic types 

of analysis—a baseline analysis and a sensitivity analysis—into a risk mitigation plan. 

The two generic types of analysis are designed to complement and reinforce each other. 

The baseline analysis provides a detailed snapshot of all costs associated with alternative

levels of investment in mitigation strategies. These costs are summarized via the costaccounting framework. The baseline analysis serves as the reference point for the 

sensitivity analysis. The baseline analysis also produces a preliminary ranking of the 

investment alternatives under consideration. The most cost-effective alternative 

identified in the baseline analysis is the starting point for creating a risk mitigation plan.

The sensitivity analysis measures the impact on project outcomes (e.g., life-cycle cost) of 

changing the values of one or more key input values about which there is uncertainty. 

The sensitivity analysis enables the decision maker to evaluate the conditions under

which other investment alternatives might result in a lower life-cycle cost. Coupling the 

two generic types of analysis with the cost-accounting framework promotes a better 

understanding of both expected costs and the variability of these costs across stakeholder 

groups. 

Fourth, this case study provides the software development process with the basis for 

specifying linkages among screens, checking computational algorithms, and assessing 

analysis capabilities for the prototype of OAE’s software product. 

1.3 Scope and Approach 

The “case study” approach employed here illustrates how to apply life-cycle cost analysis

to a complex homeland security investment decision—the process of creating a risk 

mitigation plan. A case study approach is useful because creating a risk mitigation plan

is complicated by the fact that investment costs often result in significant outlays, 

operations and maintenance costs are distributed over a period of many years, and costs 

affect stakeholders in different ways. The nature of these “cost considerations” 

introduces four complicating factors into the capital asset decision-making process. First, 

how do we identify which constructed facilities to protect and why? Second, which 

mitigation strategies do we employ and how will they operate, both singly and in 

combination?6

 Third, who bears which costs? Fourth, how do we produce a risk 

mitigation plan that demonstrates superior economic performance? Thus, a formal 

methodology is needed to insure that all relevant costs are captured and are analyzed via 

well-defined metrics to identify a risk mitigation plan that results in superior economic 

performance. Life-cycle cost analysis and the related economic methods covered in this 

report provide those metrics. 

This report has five chapters in addition to the Introduction. The methodology and the 

standardized methods employed in the case study are described in Chapter 2. 

 

6

 Responding to this complicating factor involves identifying whether there are any interdependencies that 

either positively or negatively affect the performance of any mitigation strategies. 

3
Standardized methods are used to define the key measures of economic performance. 

The cost-accounting framework recommended for use in homeland security economic

evaluations is then presented. A format for summarizing the results of an economic 

evaluation is also presented. This format is used to produce a two-page summary of the 

case study shown in Section 6.1. 

The body of the report, Chapters 3 through 5, is a case study of a data center renovation 

project being undertaken to remedy a number of generic security concerns. The approach 

is to present all security-related information in sufficient detail for the reader to 

understand the basis for the economic evaluation and make it possible to reproduce the 

results of the economic evaluation. 

Chapter 3 formulates the economic evaluation of the data center renovation. Following 

an overview of the case study, information on how the investment alternatives are 

specified is presented. Key assumptions and analysis issues are then presented. 

Chapter 4 presents the baseline analysis. The chapter begins with a description of the 

cost items entering into the analysis. Information is presented on renovation costs, 

service life estimates, and estimates of operations, maintenance, and repair expenditures. 

Attack scenarios are also presented. These scenarios include probabilities of each attack

outcome and its associated costs. Emphasis is then shifted to how the cost items are 

modeled and analyzed via the cost-accounting framework. Summary information is then 

presented to demonstrate how life-cycle cost analysis facilitates the identification of a 

cost-effective risk mitigation plan. Life-cycle cost information is supplemented with

additional economic measures to demonstrate new insights provided by these measures. 

The chapter concludes with a discussion of how the baseline analysis links to the

software product. 

Chapter 5 includes a sensitivity analysis to provide the reader with additional background 

and perspective on the data center case study. The purpose of the sensitivity analysis is 

to evaluate the impact of changing the values of a number of key variables whose values 

are uncertain. Monte Carlo techniques are employed to evaluate how changing the values 

of these key variables in combination affects the calculated values of the key economic 

measures. The chapter concludes with a discussion of how the sensitivity analysis links 

to the software product. Chapter 6 concludes the report with a summary and suggestions

for further research. 

4
2 A Methodology for Measuring Economic Performance 

This chapter focuses on laying out a methodology for measuring and summarizing the 

economic performance of alternative levels of investments in mitigation strategies. The 

economic evaluation methodology that emerges is both comprehensive and consistent. 

The methodology is based on two types of analysis, four measures of economic 

performance, a cost-accounting framework, and a format for summarizing the results of 

an economic evaluation. The two types of analysis are baseline analysis and sensitivity 

analysis. They are described in Section 2.1. The four measures of economic

performance are life-cycle cost, present value of net savings, savings-to-investment ratio, 

and adjusted internal rate of return. They are described in Section 2.2. The costaccounting framework is described in Section 2.3. The format for summarizing the 

results of the economic evaluation is described in Section 2.4. 

An economic evaluation may be divided into four stages: (1) identification; (2) 

classification; (3) quantification; and (4) presentation. The identification stage involves 

identifying and listing all of the “effects” of the project/program being analyzed. In 

principle, this set of effects produces a checklist of all items that should be taken into 

consideration. The second stage entails classifying these effects into cost categories. The 

third stage produces year-by-year estimates of the values of each of the cost categories.

The final stage is the presentation and analysis of the relevant information in a 

straightforward manner (i.e., in a form that clearly spells out the important assumptions 

underlying the economic evaluation and the implications of these assumptions for the 

study’s conclusions). 

2.1 Types of Analysis 

2.1.1 Baseline Analysis 

The starting point for conducting an economic evaluation is to do a baseline analysis. In 

the baseline analysis, all data (i.e., all input variables and any functional relationships 

among these variables) entering into the calculations are set at their likely values. For 

selected types of data, the input values are fixed (e.g., a physical constant or a value that 

is mandated by legislation). The input values associated with these data types are

considered to be known with certainty. For other types of data, the likely values7

 reflect 

the fact that some information associated with these data is uncertain. Consequently, the

values of any data subject to uncertainty are set based on some measure of central 

tendency.8

 Baseline data represent a fixed state of analysis based on likely values. For 

 

7

 Throughout this report, the terms likely value and baseline value are used interchangeably. 8

 Two common measures of central tendency are the arithmetic mean (e.g., the sum of the individual values 

of the items divided by the number of items in the sample) and the median (e.g., the middle value in a rank

ordering of the individual values of the items in the sample). In most cases in this report, the mean is used

as the measure of central tendency. Any case where the median is used as the measure of central tendency

is clearly indicated in the text. Consequently, if no explicit reference is made to the measure of central 

tendency, the measure used is the mean. 

5
this reason, the results and the analysis of these results are referred to as the baseline 

analysis. Throughout this report, the term baseline analysis is used to denote a complete 

analysis in all respects but one; it does not address the effects of uncertainty. 

2.1.2 Sensitivity Analysis 

Sensitivity analysis measures the impact on project outcomes of changing the values of

one or more key input variables about which there is uncertainty. Sensitivity analysis can 

be performed for any measure of economic performance (e.g., life-cycle cost, present 

value of net savings, savings-to-investment ratio, adjusted internal rate of return). Since 

sensitivity analysis is easy to use and understand, it is widely used in the economic 

evaluation of government and private-sector applications. Office of Management and 

Budget Circular A-94 recommends sensitivity analysis to federal agencies as one 

technique for treating uncertainty in input variables.9

 Therefore, a sensitivity analysis

complements the baseline analysis by evaluating the changes in output measures when 

selected key sets of data vary about their baseline values. Readers interested in a

comprehensive survey on methods for dealing with uncertainty for use in government and 

private-sector applications are referred to the study by Marshall10 and the subsequent 

video11 and workbook.12

2.2 Overview of Evaluation Methods 

Several methods of economic evaluation are available to measure the economic

performance of a new technology, a building, a building system, or like investment, over 

a specified time period. These methods include, but are not limited to, life-cycle cost, 

present value of net savings, savings-to-investment ratio, and adjusted internal rate of 

return. These methods differ in their mathematical formulation and, to some extent, in 

their applicability to particular types of investment decisions. 

To ensure consistency in computation, application, and interpretation, the four methods 

described in this section are based on ASTM International standard practices.13 The four 

“standardized” evaluation methods used in this report are generic. Readers interested in 

an in-depth survey covering these as well as other methods are referred to Ruegg and 

Marshall.14

 

9

 Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office of Management

and Budget. 10 Marshall, Harold E. 1988. Techniques for Treating Uncertainty and Risk in the Economic Evaluation of 

Building Investments. NIST Special Publication 757. Gaithersburg, MD: National Institute of Standards 

and Technology. 11 Marshall, Harold E. 1992. Uncertainty and Risk—Part II in the Audiovisual Series on Least-Cost

Energy Decisions for Buildings. Gaithersburg, MD: National Institute of Standards and Technology. 12 Marshall, Harold E. 1993. Least-Cost Energy Decisions for Buildings—Part II: Uncertainty and Risk 

Video Training Workbook. NISTIR 5178. Gaithersburg, MD: National Institute of Standards and

Technology. 13 ASTM International. Fourth Edition, 1999. ASTM Standards on Building Economics. West 

Conshohocken, PA: ASTM International. 14 Ruegg, Rosalie T. and Harold E. Marshall. 1990. Building Economics: Theory and Practice. New

York: Chapman and Hall. 

6
Once all costs have been identified and classified, it becomes necessary to develop yearby-year estimates for each of the cost categories for each alternative under analysis. We 

denote the alternatives as Aj (where the index for j ranges from 0,..., N, for a total of N+1 

alternatives). Associated with each alternative are investment cost categories k (where 

the index k ranges from 1,..., Kj) and non-investment cost categories m (where the index 

m ranges from 1,..., Mj). The potential for future terrorist attacks—as well as other 

natural and man-made hazards—are measured by the expected value of annual losses. 

Associated with each alternative are expected loss categories p (where the index p ranges 

from 1,..., Pj). Some of the expected loss categories accrue to investment costs and some 

accrue to non-investment costs. Expected losses are modeled separately from investment 

costs and non-investment costs to better characterize the nature of low-probability highconsequence events. It is important to note that some costs entering the analysis may be 

negative. For example, the sale of equipment and components at the end of the study 

period results in a salvage value whose present value is subtracted from investment costs. 

Similarly, better indoor air quality may result in productivity improvements which 

favorably impact occupants; these “savings” are subtracted from non-investment costs. 

Any pure benefits which result (e.g., increased rental income due to improvements) are 

subtracted from project costs (i.e., benefits are treated as negative costs). 

At the heart of the economic evaluation methodology is an economic concept referred to 

as the time value of money. This concept relates to the changing purchasing power of 

money as a result of inflation or deflation, along with consideration of the real earning 

potential of alternative investments over time.15 The discount rate reflects the decision 

maker’s time value of money. The discount rate is used to convert, via a process known 

as discounting, costs which occur at different times to a base time. Throughout this 

report, the term “present value” will be used to denote the value of a cost found by 

discounting cash flows (present and future) to the base time. The base time is the date 

(base year) to which costs are converted to time equivalent values. 

In order to describe each of the four standardized methods, it is necessary to first 

introduce and define a series of terms. These terms are used to define each of the 

standardized methods. Throughout this section, the following terms are used as the basis 

for defining the standardized methods: 

t = a unit of time;16

 

15 Inflation reduces the purchasing power of the dollar over time; deflation increases it. When amounts are 

stated in actual prices as of the year in which they occur, they are said to be in current dollars. Current 

dollars are dollars of any one year’s purchasing power, inclusive of inflation/deflation. That is, they reflect 

changes in purchasing power of the dollar from year to year. In contrast, constant dollars are dollars of 

uniform purchasing power, exclusive of inflation/deflation. Constant dollars indicate what the same good

or service would cost at different times if there were no change in the general price level to change the 

purchasing power of the dollar. For additional information on conducting economic analyses using either 

constant dollars or current dollars, see Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle 

Costing Manual for the Federal Energy Management Program. NIST Handbook 135. Gaithersburg, MD: 

National Institute of Standards and Technology. 16 Denote the beginning of the study period as the base year (i.e., t=0) and end of the study period as T. 

Thus, the length of the study period in years is T.

7
T = the length of the study period in years; 

d = the discount rate expressed as a decimal.17

Throughout this section the prefix, PV, is used to designate dollar denominated quantities 

in present value terms. The present value is derived by discounting (i.e., using the 

discount rate) all costs—present and future—to the base year (i.e., t=0). The present 

value terms are: the present value of investment costs (PVI), the present value of noninvestment costs (PVC), and the present value of expected losses (PVE(L)). 

The cost terms that make up the four mathematical formulations are given in equations 

(2.1) through (2.6). While there may be many different ways of classifying costs (i.e., 

classification schemes), their explicit treatment in both the mathematical formulation and 

the standardized methods ensures that a comprehensive and consistent coupling results 

between the mathematical formulation and each standardized method. 

The investment costs for alternative Aj in year t may now be expressed as: 

1

K j

jt kjt

k

I I

=

= ∑ (2.1) 

The non-investment costs for alternative Aj in year t may now be expressed as: 

1

M j

jt mjt

m

C C

=

= ∑ (2.2) 

The expected losses for alternative Aj in year t may now be expressed as: 

( ) (

1

Pj

jt pjt

p

E L E L

=

= ∑ )

+

+

 

 (2.3) 

The present value of investment costs for alternative Aj may now be expressed as: 

0 1

/(1 )

T K j

t

j kjt

t k

PVI I d

= =

⎛ ⎞ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠

∑ ∑ (2.4) 

The present value of non-investment costs for alternative Aj may now be expressed as: 

0 1

/(1 )

T M j

t

j mjt

t m

PVC C d

= =

⎛ ⎞ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ∑ ∑ (2.5) 

17 The discount rate used with constant-dollar amounts is different from the discount rate used with currentdollar amounts. A real discount rate (net of general inflation) is used with constant-dollar amounts. A 

market or nominal discount rate (inclusive of general inflation) is used with current-dollar amounts. 

8
The present value of expected losses for alternative Aj may now be expressed as: 

( ) ( )

0 1

/(1 )

T Pj

t

j pjt

t p

PVE L E L d

= =

⎛ ⎞ = ⎜ ⎟ ⎜ ⎟ ⎝ ⎠

∑ ∑ +

⎟ + ⎟

 (2.6) 

2.2.1 Life-Cycle Cost Method18

The life-cycle cost (LCC) method measures, in present-value or annual-value terms, the 

sum of all relevant costs associated with owning and operating a constructed facility over 

a specified period of time. The basic premise of the LCC method is that to an investor or 

decision maker all costs arising from that investment decision are potentially important to

that decision, including future as well as present costs. Applied to constructed facilities,

the LCC method encompasses all relevant costs over a designated study period, including 

the costs of designing, purchasing/leasing, constructing/installing, operating, maintaining, 

repairing, replacing, and disposing of a particular design or system. 

The LCC method is particularly suitable for determining whether the higher initial cost of 

a constructed facility or system specification is economically justified by lower future 

costs (e.g., losses due to natural or manmade hazards) when compared to an alternative 

with a lower initial cost but higher future costs. If a design or system specification has 

both a lower initial cost and lower future costs relative to an alternative, an LCC analysis 

is not needed to show that the former is the economically preferable choice. 

The LCC for alternative Aj may now be expressed as: 

( )

0 1 1 1

/(1 )

T K M j j Pj

t

j kjt mjt pjt

t k m p

LCC I C E L d

= = = =

⎛ ⎞

= + ⎜ + ⎜

⎝ ⎠

∑ ∑ ∑ ∑ (2.7) 

Denote the alternative with the lowest initial investment cost (i.e., first cost) as A0; it is 

referred to as the base case. Then: 

00 j0 I < I for j = 1,..., N (2.8) 

The LCC method compares alternative (mutually exclusive) designs or system

specifications that satisfy a given functional requirement on the basis of their life-cycle

costs to determine which is the least-cost means (i.e., minimizes life-cycle cost) of

satisfying that requirement over a specified study period. Should any pure benefits result 

from the project (e.g., increased rental income due to improvements); include them in the 

calculation of LCC. 

 

18 ASTM International. 2002. “Standard Practice for Measuring Life-Cycle Costs of Buildings and

Building Systems.” E 917. Annual Book of ASTM Standards: 2002. Vol. 04.11. West Conshohocken, 

PA: ASTM International. 

9
2.2.2 Present Value of Net Savings Method19

The present value of net savings (PVNS) method is reliable, straightforward, and widely 

applicable for finding the economically efficient choice among investment alternatives. 

It measures the net savings from investing in a given alternative instead of investing in 

the foregone opportunity (e.g., some other alternative or the base case). Any pure 

benefits that result from the project (e.g., increased rental income due to improvements) 

are included in the calculation of PVNS, since they are included in the LCC calculation. 

The PVNS for a given alternative, Aj, vis-à-vis the base case, A0, may be expressed as: 

:0 0 - PVN j S = LCC LCCj

j

 

 (2.9) 

If PVNS j:0 is positive, alternative Aj is economic; if it is zero, the investment is as good 

as the base case; if it is negative, the investment is uneconomical. 

2.2.3 Savings-to-Investment Ratio Method20

The savings-to-investment ratio (SIR) is a numerical ratio whose size indicates the 

economic performance of a given alternative instead of investing in the foregone 

opportunity. The SIR is savings divided by investment costs. The LCC method provides 

all of the necessary information to calculate the SIR. The SIR for a given alternative, Aj, 

is calculated vis-à-vis the base case. The numerator and denominator of the SIR are 

derived through reference to equations (2.4) and (2.5). 

The numerator equals the difference in the present value of non-investment costs between 

the base case and the given alternative, Aj. The resultant expression, denoted as present 

value of savings, is given by: 

PVS j:0 = − PVC0 PVC (2.10) 

The denominator equals the difference in the present values of investment costs for the

given alternative, Aj, and the base case. The resultant expression, denoted as present 

value of increased investment costs, is given by: 

PVIIj j :0 = PVI − PVI0 (2.11) 

The SIR for a given alternative, Aj, vis-à-vis the base case may be expressed as: 

19 ASTM International. 2002. “Standard Practice for Measuring Net Benefits for Investments in Buildings 

and Building Systems.” E 1074. Annual Book of ASTM Standards: 2002. Vol. 04.11. West

Conshohocken, PA: ASTM International. 20 ASTM International. 2002. “Standard Practice for Measuring Benefit-to-Cost and Savings-toInvestment Ratios for Investments in Buildings and Building Systems.” E 964. Annual Book of ASTM 

Standards: 2002. Vol. 04.11. West Conshohocken, PA: ASTM International. 

10
:0

:0

:0

j

j

j

PVS

SIR

PVII = (2.12) 

A ratio less than 1.0 indicates that Aj an uneconomic investment relative to the base case; 

a ratio of 1.0 indicates an investment whose benefits or savings just equal its costs; and a 

ratio greater than 1.0 indicates an economic project. A ratio of, say, 2.75 means that the 

investor (e.g., the general public for a public-sector project/program) can expect to 

receive $2.75 in cost savings for every $1.00 invested (e.g., public funds expended), over 

and above the required rate of return imposed by the discount rate. 

2.2.4 Adjusted Internal Rate of Return Method21

The adjusted internal rate of return (AIRR) is the average annual yield from a project 

over the study period, taking into account reinvestment of interim receipts. Because the 

AIRR calculation explicitly includes the reinvestment of all net cash flows, it is 

instructive to introduce a new term, terminal value (TV). The terminal value of an 

investment, Aj, is the future value (i.e., the value at the end of the study period) of 

reinvested net cash flows excluding all investment costs. The terminal value for an 

investment, Aj, is denoted as TVj. 

The reinvestment rate in the AIRR calculation is equal to the minimum acceptable rate of 

return (MARR), which is assumed to equal the discount rate, d, a constant. When the 

reinvestment rate is made explicit, all investment costs are easily expressible as a time

equivalent initial outlay (i.e., a value at the beginning of the study period) and all noninvestment cash flows as a time equivalent terminal amount. This allows a 

straightforward comparison of the amount of money that comes out of the investment 

(i.e., the terminal value) with the amount of money put into the investment (i.e., the time

equivalent initial outlay). 

The AIRR is defined as the interest rate, rj, applied to the terminal value, TVj, which 

equates (i.e., discounts) it to the time equivalent value of the initial outlay of investment 

costs. It is important to note that all investment costs are discounted to a time equivalent 

initial outlay (i.e., to the beginning of the study period) using the discount rate, d. 

Several procedures exist for calculating the AIRR. These procedures are derived and 

described in detail in the report by Chapman and Fuller.22 The most convenient 

procedure for calculating the AIRR is based on its relationship to the SIR. This 

procedure results in a closed-form solution for rj:0. The AIRR for a given alternative, Aj, 

vis-à-vis the base case—expressed as a decimal—is that value of rj:0 for which: 

 

21 ASTM International. 2002. “Standard Practice for Measuring Internal Rate of Return and Adjusted 

Internal Rate of Return for Investments in Buildings and Building Systems.” E 1057. Annual Book of

ASTM Standards: 2002. Vol. 04.11. West Conshohocken, PA: ASTM International. 22 Chapman, Robert E. and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case Studies 

in Building Technology. NISTIR 5840. Gaithersburg, MD: National Institute of Standards and 

Technology. 

11
1

:0 :0 (1 )( ) 1 Tj j r d = + SIR − (2.13)

If rj:0 is greater than the discount rate (also referred to as the hurdle rate), alternative Aj is

economic; if rj:0 equals the discount rate, the investment is as good as the base case; if rj:0

is less than the discount rate, the investment is uneconomical. 

2.2.5 Summary of Methods23

The methods presented in the previous sections provide the basis for evaluating the 

economic performance of homeland security-related investments in constructed facilities. 

The equations underlying the methods presented earlier are all based on ASTM standard 

practices. All of the methods are appropriate for evaluating accept or reject type 

decisions. But among the methods are several distinctions that relate to the type of 

investment decision that the decision maker is facing. 

There are four basic types of investment decisions for which an economic analysis is 

appropriate: 

(1) whether to accept or reject a given project; 

(2) the most efficient project size/level, system, or design; 

(3) the optimal combination of interdependent projects (i.e., the right mix of sizes/levels,

systems, and designs for a group of interdependent projects); and 

(4) how to prioritize or rank independent projects when the allowable budget can not 

fund them all. 

Each type of investment decision is important. First and foremost, decision makers need 

to know whether or not a particular project or program should be undertaken in the first 

place. Second, how should a particular project/program be configured? The third type of

decision builds on the second and introduces an important concept, interdependence. 

Consequently, for a given set of candidate projects and implied interdependencies, the 

problem becomes how to choose that combination of projects that minimizes LCC (or 

equivalently maximizes PVNS). The fourth type of decision introduces a budget 

constraint. The key here is how to get the most impact for the given budget amount. 

Table 2-1 provides a summary of when it is appropriate to use each of the evaluation

methods described earlier. Note that the LCC and PVNS methods are appropriate in 

 23 For a comprehensive treatment of how to choose among economic evaluation methods, see the 

NIST/BFRL video (Marshall, Harold E. 1995. Choosing Economic Evaluation Methods—Part III in the 

Audiovisual Series on Least-Cost Energy Decisions for Buildings. Gaithersburg, MD: National Institute of 

Standards and Technology) and workbook (Marshall, Harold E. 1995. Least-Cost Energy Decisions for 

Buildings—Part III: Choosing Economic Evaluation Methods Video Training Workbook. NISTIR 5604. 

Gaithersburg, MD: National Institute of Standards and Technology). 

12
three of the four cases. Only in the presence of a budget constraint is the use of either 

LCC or PVNS inappropriate and even in that case it plays an important role in computing

the aggregate measure of performance. 

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation Method 

for Each Decision Type24

 Decision Type LCC PVNS SIR AIRR 

Accept/Reject Yes Yes Yes Yes

Design/Size Yes Yes No No

Combination 

(Interdependent) 

Yes Yes No No

Priority/Ranking 

(Independent) 

No No Yes Yes

In summary, no single evaluation method works for every decision type. First and 

foremost, managers want to know if a particular project is economic. Reference to Table 

2-1 shows that all of the evaluation methods address this type of decision. Second, as 

issues of design, sizing, and packaging combinations of projects become the focus of 

attention—as often occurs in conjunction with budget reviews—the LCC and PVNS 

methods emerge as the principle means for evaluating a project’s or program’s merits.25 

Finally, the tightening budget picture involves setting priorities. Consequently, decision 

makers need both measures of magnitude, provided by LCC and PVNS, and of return, 

provided by either the SIR or the AIRR, to assess economic performance. Multiple 

measures, when used appropriately, ensure consistency in both setting priorities and 

selecting projects for funding. 

2.3 The Need for a Detailed Cost-Accounting Framework 

The cost categories represented in equations (2.1) through (2.6) provide the basis for

calculating life-cycle costs. The flexibility of the life-cycle cost method, however,

enables us to go beyond the generic cost categories represented in equations (2.1) through 

(2.6). The result is a more focused representation of costs; it is referred to as the detailed

cost-accounting framework. The objective of producing this framework is to promote 

better decision making by identifying unambiguously who bears which costs, how costs 

are allocated among several widely-accepted budget categories, and how costs are 

allocated among key building components. A detailed cost-accounting framework is 

needed because costs affect stakeholders in different ways. Thus, knowing who bears 

 24 ASTM International. 2002. “Standard Guide for Selecting Economic Methods for Evaluating

Investments in Buildings and Building Systems.” E 1185. Annual Book of ASTM Standards: 2002. Vol.

04.11. West Conshohocken, PA: ASTM International. 25 If incremental values of the SIR or AIRR are computed, they can be used to make design/size and

packaging decisions. See Ruegg and Marshall, Building Economics, pp. 54-58 and 85-87. 

13
which costs leads to a better understanding of stakeholder perspectives and helps create 

mutually beneficial solutions. Finally, the cost-accounting framework promotes a 

detailed, consistent breakdown of life-cycle costs so that a clear picture emerges of the

cost differences between competing alternatives. 

Costs are classified along three dimensions within the detailed cost-accounting

framework. To differentiate these costs from the generic cost categories, they are 

referred to as cost types and cost items. Each dimension contains a collection of cost

types. The cost types are used as placeholders for summarizing and reporting aggregated 

cost information. Each cost type is a collection of cost items. Each cost item has a 

unique set of identifiers that places it within the cost-accounting framework. The three 

dimensions employed in the cost-accounting framework are: (1) bearer of costs; (2) 

budget category; and (3) building/facility component. Each dimension captures the full 

spectrum of costs (i.e., all costs summed across each dimension add up to the same total).

A schematic representation of the cost-accounting framework is given in Figure 2-1. 

Figure 2-1. Overview of the Cost-Accounting Framework 

Bearer Budget 

Category

Component 

Owner/Manager 

Occupant/User 

Third Party

Capital 

Investment

O&M 

Other 

Building/Facility

Elements

Building/Facility Site 

Work

Non-Elemental 

The description of the cost-accounting framework given here employs a project-oriented 

approach. Such an approach is instructive since most construction activity is summarized

on a project basis. This approach also helps to link the methodology to the software

product. A project could be the construction of a building, industrial facility, or 

infrastructure. A project could also be the renovation of an existing constructed facility. 

The first of the three dimensions covers who bears which costs. This dimension covers 

all stakeholder groups.26 A stakeholder group is defined as any collection of 

 26 The companion document outlines a strategy for identifying, collecting, and measuring benefits and 

costs. See Chapman and Leng, Cost-Effective Responses to Terrorist Risks, pp. 15-28. The strategy 

includes a hierarchy of individual stakeholders and stakeholder groups and a hierarchy of benefits and

costs. Although the benefits and costs appearing in their respective hierarchies are generic, the hierarchies 

are considered to be exhaustive. Two crosswalks are provided to demonstrate how benefits and costs

accrue to stakeholder groups. 

14
organizations or individuals directly affected by the project (e.g., by construction or risk 

mitigation activities or by disaster-related losses). The first dimension, Bearer, has three

cost types based on who bears the costs. The three cost types are: (1) Owner/Manager; 

(2) Occupant/User; and (3) Third Party. Owner/Manager costs are all costs incurred by 

the project’s owner or agent. These costs include but are not limited to design costs, 

capital investment costs, and selected types of repairs to the constructed facility. 

Occupant/User costs accrue to the direct users of the project. Occupant/User costs 

frequently include operations and maintenance costs and selected types of repairs not 

covered by the project’s owner or agent. Occupant/User costs can also include delay 

costs and business interruption costs due to temporary closures for repair and

reconstruction activities. Third-Party costs are all costs incurred by entities who are 

neither the project’s owner or agent nor direct users of the project. One example of a 

Third-Party cost is the lost sales for a business establishment whose customer access has

been impeded (e.g., due to a road closure during construction/reconstruction). Another 

example is damage to the environment from a construction process that pollutes the 

water, land, or atmosphere. 

The second dimension, Budget Category, has three cost types based on which category of 

the budget the funds come from. These cost types are: (1) Capital Investment; (2) O&M 

(Operations and Maintenance); and (3) Other. These cost types correspond to widely 

used budget categories for private and public sector cost accounting. In the context of the 

previous section, Capital Investment costs accrue to the investment cost category (see 

equations 2.1 and 2.4) and O&M and Other costs accrue to the non-investment cost 

category (see equations 2.2 and 2.5). NIST Handbook 135 is especially helpful in 

determining how to allocate cost items among Capital Investment and O&M.27 All

acquisition costs, including costs related to planning, design, purchase, and construction, 

are investment-related costs and fall under the Capital Investment cost type. Residual

values (resale, salvage, or disposal costs) and capital replacement costs are also 

investment-related costs. Capital replacement costs are usually incurred when replacing 

major systems or components and are paid from capital funds. Cost items falling under 

the O&M cost type include energy and water costs, maintenance and repair costs, minor

replacements related to maintenance and repair activities, and insurance premiums paid

by owners and/or occupants to reduce their risk exposure. O&M costs are usually paid 

from an annual operating budget, not from capital funds. Other costs are non-capital 

costs that can not be attributed to the O&M cost type. An example of an Other/ThirdParty cost is damage to the environment stemming from the project. 

The third dimension, Component, has three cost types. These cost types are: (1) 

Building/Facility Elements; (2) Building/Facility Site Work; and (3) Non-Elemental. The

first two cost types are associated with the elemental classification UNIFORMAT II.28 

 27 Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle Costing Manual for the Federal Energy

Management Program. NIST Handbook 135. Gaithersburg, MD: National Institute of Standards and 

Technology. 28 ASTM International. 2002. “Standard Classification for Building Elements and Related Site Work—

UNIFORMAT II.” E 1557. Annual Book of ASTM Standards: 2002. Vol. 04.11. West Conshohocken,

PA: ASTM International. 

15
Elements are an integral part of any construction project; they are often referred to as 

component systems or assemblies. Each element performs a given function regardless of 

the materials used, design specified, or method of construction employed. NonElemental costs are all costs that can not be attributed to specific functional elements of 

the project. An example of a Non-Elemental/Capital/Owner cost is the purchase of a 

right-of-way or an easement. An example of a Non-Elemental/Other/Third-Party cost is 

damage to the environment stemming from the project. 

The previous discussion serves to highlight some of the differences in perspective 

between public sector and private sector decision makers. A private sector decision 

maker may not be concerned with costs that are external to their firm. This perspective

has a significant impact on what is included and what is excluded in the Third Party and 

Other cost types. Generally, this is in contrast to the public sector decision maker who 

must assess all costs to whomsoever they accrue. In the case of homeland security 

activities, however, the private sector perspective is often more in line with the public 

sector’s perspective. Natural hazards, industrial accidents, and terrorist acts that occur 

infrequently, but whose consequences are devastating, highlight the importance of 

including Third Party and Other cost types in the private sector’s life-cycle cost calculus. 

Including these costs also helps to identify areas for public policy analysis (e.g., the role

of economic incentives), bringing private sector and public sector perspectives into closer

alignment. 

2.4 Presentation and Analysis of the Results of an Economic Evaluation 

The presentation and analysis of the results of an economic evaluation are central to

understanding and accepting its findings. If the presentation is clear and concise, and if

the analysis strategy is logical, complete, and carefully spelled out, then the results will 

stand up under close scrutiny. This section first outlines a generic format for economic

evaluations that meets the two previously cited conditions. The generic format is 

patterned after an ASTM Standard Guide for summarizing economic impacts.29 The 

generic format is built upon the following three factors: (1) why the project is important; 

(2) how the analysis strategy is employed; and (3) how the key cost measures are 

calculated, summarized, and traced to relevant standards, codes, and regulations. These 

factors, taken together, constitute a three-step procedure for summarizing the results of an 

economic evaluation. 

A template for a specific format, tailored to homeland security applications, is given in 

Exhibit 2-1. The specific format is focused at the project or program level. The specific 

format is designed to provide a concise summary of key project- or program-related 

results within the organization. Documenting results for homeland security applications 

at the project or program level provides a means for “rolling up” results to senior

management while supporting performance improvement activities for the organization’s 

project managers. For example, the specific format is well suited to summarize lessons

 29 ASTM International. 2002. “Standard Guide for Summarizing the Economic Impacts of BuildingRelated Projects.” E 2204. Annual Book of ASTM Standards: 2002. Vol. 04.12. West Conshohocken, 

PA: ASTM International. 

16
learned from using a particular technology or practice on a project or group of projects 

and to highlight why efforts should be made to incorporate these lessons learned into ongoing or planned projects. Thus, the specific format provides a vehicle for information

sharing across the organization’s major units/divisions and geographic regions. 

The discussion that follows relates the three factors for the generic format referenced 

above to the specific format given in Exhibit 2-1. Exposition of the generic format serves 

two purposes. First, it provides a means for organizing the way to present material 

associated with an economic evaluation. Second, it provides a vehicle for clearly and 

concisely presenting the salient results of the analysis. Such a short summary is 

appropriate for use by senior managers as the basis for statements on the benefits of the 

project or program to the public, employees, or other stakeholders. A two-page summary 

of the results of the baseline analysis for the data center case study is provided at the 

beginning of Chapter 6 (see Exhibit 6-1). 

Exhibit 2-1. Format for Summarizing an Economic Evaluation 

1.a Significance of the Project: 

Describe why the project or program is important.

Describe the changes brought about by the effort. 

1.b Key Points: 

Highlight two or three key points which 

convey why this project or program is

important. 

2. Analysis Strategy: 

Describe how the life-cycle cost of each alternative was calculated. 

Describe how the present value of net savings of the preferred alternative was determined. 

Describe how any additional measures were calculated. 

Summarize key data and assumptions: (a) Base year; (b) Length of study period; (c) Discount rate or 

minimum acceptable rate of return; (d) Data; and (e) Any special homeland security considerations. 

3.b Key Measures: 

Report the calculated value of the Present 

Value of Net Savings (PVNS) and at least 

one of the following: 

 Savings-to-Investment Ratio (SIR)

 Adjusted Internal Rate of Return

(AIRR)

3.a Calculation of Life-Cycle Costs and Additional

Measures: 

Life-Cycle Costs: 

Report the life-cycle costs of each alternative. 

Net Savings: 

Report the present value of net savings of the preferred 

alternative. 

Additional Measures: 

Report the values of any additional measures calculated. 

3.c Traceability

Cite references to specific ASTM

standard practices, ASTM adjuncts, or 

any other standards, codes, or regulations

used. 

17
2.4.1 Significance of the Project 

This section of the summary format sets the stage for the results that follow. It consists

of two subsections, designated as 1.a and 1.b. 

Subsection 1.a calls for a concise summary of the project. The summary is sufficiently

detailed to enable senior management and non-technical readers to understand the 

significance of the project. The goal at this point is to clearly describe: 

(1) why the project is important; and 

(2) why some or all of the changes brought about are noteworthy. 

The objective of subsection 1.b is to highlight two or three points which convey why this

project is important. These points are intended for use as talking points by senior 

management when they make presentations to non-technical audiences or for use in press

releases. 

2.4.2 Analysis Strategy 

This section of the summary format focuses on documenting the steps taken to ensure 

that the analysis strategy is logical and complete. Particular emphasis is placed on 

summarizing the key assumptions, including any constraints that limited the scope of the

study. Responses are provided for key assumptions concerning: (a) the base year for the

study; (b) the length of the study period; (c) the discount rate or minimum acceptable rate 

of return used; and (d) any special homeland security considerations. 

Special emphasis is placed on documenting the sources and validity of any data used to 

make estimates or projections of key cost measures. This section establishes an audit 

trail from the raw data, through data manipulations (e.g., represented by equations and 

formulae), to the results which describe how to determine: 

(1) the life-cycle cost of each alternative evaluated;30

(2) the present value of net savings of the preferred alternative;31 and 

(3) the way in which any additional measures were calculated.

 30 As part of the discussion of life-cycle costs, include information on how life-cycle costs are distributed

across the key stakeholder groups. 31 If the preferred alternative is not the minimum life-cycle cost alternative, provide a rationale on why it is 

preferable to the minimum life-cycle cost alternative. For example, a rationale for not preferring the

minimum life-cycle cost alternative could be due to its adverse impact (i.e., cost burden) on one of more of

the key stakeholder groups. 

18
2.4.3 Calculation of Life-Cycle Costs and Additional Measures 

This section of the summary format focuses on reporting the calculated values of the key 

cost measures, as well as any additional measures that are deemed appropriate, and 

establishing traceabilty to standardized practices or, where appropriate, to statutory 

documents or procedures. It consists of three subsections, designated as 3.a, 3.b, and 3.c. 

Subsection 3.a reports using text, mathematical expressions, tables, graphs, and 

comparative statistics the following information: 

(1) the life-cycle cost of each alternative; 

(2) the present value of net savings of the preferred alternative; and 

(3) the values of any additional measures calculated.

Subsection 3.b reports the calculated value of the life-cycle cost for each alternative, the 

present value of net savings of the preferred alternative and at least one of the following: 

(a) the savings-to-investment ratio, or 

(b) the adjusted internal rate of return. 

Subsection 3.c cites references to specific ASTM standard practices, ASTM adjuncts, or 

any other standards, codes, or regulations used. 

19
20
3 Formulating the Economic Evaluation of the Data Center

Renovation 

This chapter formulates the economic evaluation of the data center renovation. 

Following an overview of the case study, information on how the investment alternatives 

are specified is presented. The chapter concludes with a discussion of key assumptions 

and analysis issues. 

3.1 Data Center Renovation: Overview of the Case Study 

This case study describes a renovation project for a prototypical data center for a 

financial institution. It is based on an actual building renovation project. However, for

purposes of confidentiality, a number of key building features have been changed. Thus, 

the cost estimates are for purposes of illustration—actual renovations of different 

building types will face different costs and different risk profiles. 

The case study, as in the actual building renovation project, focuses exclusively on two of 

the three mitigation strategies—engineering alternatives and management practices. The 

renovation has been planned for some time to upgrade the data center’s HVAC,

telecommunications and data processing systems and to address a number of generic 

security concerns. 

The data center undergoing renovation is a single-story structure located in a suburban 

community. The floor area of the data center is 40,000 ft2

 (3,716 m2). The replacement

value of the data center is $20 million for the structure plus its contents. The data center 

corresponds to the type of structure that would be used by a major bank, credit card 

company, or insurance company as its primary data repository. It contains financial 

records that are in constant use by the firm and its customers. Thus, any interruption of 

service will result in both lost revenues to the firm and potential financial hardship for the

firm’s customers. The occupants of the data center are part of the same parent company, 

but not part of the same corporate division responsible for facilities construction and 

renovation. Thus, costs associated with the “facility owners” are recorded separately 

from those associated with the data center’s occupants. 

The site upon which the data center is located is traversed by a thoroughfare that has been 

used by local residents since the data center was constructed. Alternative routes are

available and convenient to local residents, subject to a short detour. Plans have been 

made by the community to put in a new street which better links the affected 

neighborhoods and does not traverse the data center’s site. The new street will be 

available for use within two years of the renovation. 

3.2 Defining the Base Case and the Alternative 

The building owners employ one of two different renovation strategies. The first, 

referred to as the Base Case, employs upgrades which are consistent with pre-9/11 levels 

21
of security. Thus, the Base Case represents maintenance of the status quo. The second, 

referred to as the Proposed Alternative, recognizes that in the post-9/11 environment the 

data center faces heightened risks in two areas. These risks are associated with the 

vulnerability of information technology resources and the potential for damage to the 

facility and its contents from chemical, biological, radiological, and explosive (CBRE) 

hazards. Two scenarios—the potential for a cyber attack and the potential for a CBRE 

attack—are used to capture these risks. The Proposed Alternative augments the Base 

Case by strengthening portions of the exterior envelope, limiting vehicle access to the 

data center site, significantly improving the building’s HVAC, telecommunications and

data processing systems, and providing better linkage of security personnel to the 

telecommunications network.32 

3.3 Key Assumptions and Analysis Issues 

A clear statement of the assumed values of key sets of parameters underlying the analysis 

is vital to understanding how the analysis was conducted. Documenting the assumptions 

and the rationale behind the setting of the assumed values of these key sets of parameters 

is necessary to ensure that: (1) all costs are discounted to an equivalent time basis for

purpose of comparison; and (2) readers can follow the flow of the analysis, gain insights 

useful for their own applications, and reproduce our results. 

Finally, there is the “analysis issue” concerned with the treatment of uncertainty. This

analysis issue provides the necessary “direct” linkage between the baseline analysis and 

the sensitivity analysis. It is crucial in measuring how variations about the baseline input 

values affect the economic outcome measures. Dealing with uncertainty is the core

concept in structuring the sensitivity analysis. 

3.3.1 Key Assumptions 

The assumptions covered in this section focus on the setting of the assumed values of the 

following key sets of parameters: (1) the base year; (2) the starting and ending points in 

the study period; and (3) the discount rate. The base year establishes the anchor point for

all cost calculations. The starting and ending points in the study period define both the 

scope of the study period—those years over which costs are tabulated—and the length of

the study period—a key parameter in the AIRR calculation. Because cash flows are 

distributed throughout the study period, the choice of the discount rate is of central 

importance to the analysis. 

 32 A risk mitigation plan is usually composed of a combination of mitigation strategies. A well-chosen

combination of mitigation strategies enables them to complement and reinforce each other, producing a 

more robust risk mitigation plan. The use of both engineering alternatives (e.g., strengthening portions of 

the exterior envelope) and management practices (e.g., limiting vehicle access to the site) in the data center 

renovation is an example of such a combination. Engineering alternatives and management practices differ

in two key ways. First, engineering alternatives, once put in place, can be expected to remain in place for 

an extended period of time. Engineering alternatives are thus long-term investments. Second, management 

practices can be varied over the project life cycle. Management practices are thus capable of responding to

changing conditions, giving them more of a short-term investment perspective. 

22
The base year for computing life-cycle costs is 2003. There are two reasons why 2003 

was selected as the base year. 

(1) 2003 reflects heightened awareness to security-related problems. Since the 

September 11th attacks, a number of efforts have been launched to strengthen the 

nation’s defenses against future terrorist attacks.33

(2) 2003 is a year for which construction industry cost data are available. The use of 

historic/industry cost data is desirable because this study employs constant dollar 

estimates for all costs. 

The study period begins in 2003 and ends in 2027. Thus, the length of the study period is 

25 years. A study period of 25 years is often used in both private sector and public sector 

life-cycle cost analyses. Any costs that occur after 2027 are not included. Two factors 

were instrumental in determining the beginning and end of the study period. 

(1) The study period begins in 2003, since it reflects both heightened awareness to 

security-related issues and is grounded in the present. 

(2) The end of the study period is 2027. By 2027, we can expect significant and 

fundamental changes in our nation’s response to threats from terrorism. 

The baseline analysis uses a real discount rate of 4 % to convert dollar amounts to present 

values. A 4 % real discount rate is used because it is appropriate for both private sector 

and public sector security-related investments. 

The discount rate also figures prominently in the sensitivity analysis. Generally, both 

private sector and public sector investment decisions benefit from a critical analysis of 

the impact of changing the discount rate on project outcomes. For public sector projects, 

OMB recommends that separate analyses be used to evaluate the sensitivity of key 

economic measures to variations in the discount rate.34 The sensitivity analysis presented

in Chapter 5 evaluates the implications of raising the discount rate to 8 % or lowering the

discount rate to 0 %. The 0 % to 8 % range of values for the real discount rate was 

chosen to bracket the historical values of real Treasury interest rates. These rates are 

periodically updated by OMB and published in Appendix C of OMB Circular A-94; they 

apply to government lease-purchase and cost-effectiveness analyses. All values of the

discount rate used in this report are real rates, since constant dollar estimates of costs are 

used. 

 33 Interested readers are referred to the report on critical infrastructures (Executive Office of the President. 

2003. The National Strategy for the Physical Protection of Critical Infrastructures and Key Assets. 

Washington, DC: The White House.) and the Committee on Science and Technology for Countering

Terrorism report (National Research Council. 2002. Making the Nation Safer: The Role of Science and

Technology in Countering Terrorism. Washington, DC: The National Academies Press.) for 

recommendations on how to strengthen the nation’s defenses against future terrorist attacks. 34 Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office of Management

and Budget. 

23
3.3.2 Analysis Issues 

Two types of analyses are employed to evaluate the merits of the Proposed Alternative 

vis-à-vis the Base Case. First, a baseline analysis is performed in which all values are 

fixed. Second, a sensitivity analysis is performed in which a number of variables are 

allowed to vary in combination according to an experimental design. 

The two generic types of analysis are designed to complement and reinforce each other.

The baseline analysis serves as a reference point for the sensitivity analysis. The 

sensitivity analysis uses the same data and assumptions as the baseline analysis for its

starting point. 

A goal of this case study is to illustrate how to combine the results of the two generic

types of analysis to provide decision makers with the basis for generating a risk 

mitigation plan that responds to the potential for future cyber and CBRE attacks in a 

financially responsible manner. Creating a risk mitigation plan involves choices among 

investment alternatives. An investment alternative is comprised of a combination of any

number of engineering alternatives, management practices, and financial mechanisms.

Each investment alternative has a well-defined set of costs associated with it which must 

be estimated, discounted to a present value, and evaluated. The baseline analysis 

establishes the frame of reference for the economic evaluation by rank ordering the 

investment alternatives from lowest life-cycle cost to highest life-cycle cost. The 

investment alternative with the lowest life-cycle cost is the most cost-effective 

alternative. The sensitivity analysis provides the means for evaluating financial risks 

associated with a wide variety of project-related costs. The sensitivity analysis enables

the decision maker to evaluate the conditions under which other investment alternatives 

might result in lower life-cycle costs vis-à-vis the “most cost-effective alternative” 

identified in the baseline analysis. Together the baseline analysis and the sensitivity

analysis provide the necessary insights to produce the risk mitigation plan. 

24
4 Baseline Analysis 

The baseline analysis presented in this chapter is the reference point from which the Base 

Case is compared with the Proposed Alternative. The chapter begins with a description 

of the cost items entering into the analysis. Information is presented on renovation costs, 

service life estimates, and estimates of operations, maintenance, and repair expenditures.

Attack scenarios are also presented. These scenarios include probabilities of each attack

outcome and its associated costs. Emphasis is then shifted to how the cost items are 

modeled and analyzed via the cost-accounting framework. Summary information is then 

presented to demonstrate how life-cycle cost analysis facilitates the identification of a 

cost-effective risk mitigation plan. Life-cycle cost information is supplemented with

additional economic measures to demonstrate new insights provided by these measures. 

The chapter concludes with a discussion of how the baseline analysis links to the

software product. 

4.1 Cost Items Entering the Analysis 

Cost items are classified under two broad headings: (1) input costs and (2) event-related 

costs. Input costs represent all costs tied to the building or facility under analysis that are 

not associated with an event. Input costs include the initial capital investment outlays for 

facilities and site work, future costs for electricity for lighting and space heating and 

cooling, future renovations, and any salvage value for plant and equipment remaining at 

the end of the study period. Event-related costs are based on annual outcomes, each of 

which has a specified probability of occurrence. Each outcome has a non-negative 

number of cost items associated with it (i.e., an outcome may have no cost items

associated with it if it results in zero costs). Event modeling is used to evaluate natural 

and man-made risks. In this case study, we model the risks associated with cyber attacks 

and CBRE attacks exclusively. The event modeling methodology, however, can also be

used to model multiple hazards, such as those associated with earthquakes, high winds, or 

an accident resulting in widespread damage due to fire or chemical spills.

4.1.1 Input Costs 

Tables 4-1 and 4-2 summarize input cost information on the Base Case and the Proposed 

Alternative, respectively. The information presented in Tables 4-1 and 4-2 specifies the 

baseline values of input costs for the Base Case and the Proposed Alternative. Each table 

includes the individual cost items, its cost category, its occurrence rate, its escalation rate,

and its estimated cost in constant 2003 dollars. Information on the cost category is 

included to help link the entries in Tables 4-1 and 4-2 to the entries in Tables 4-5 through 

4-7 for the Base Case and Tables 4-8 through 4-10 for the Proposed Alternative. This is 

the first step in linking individual cost items into the cost-accounting framework. Note 

that several cost entries are negative (e.g., salvage value). Negative entries reflect either

revenues received or costs avoided. The dollar entries in Tables 4-5 through 4-11 include 

these input costs, expressed in present value terms. 

25
Table 4-1. Summary of Input Costs for the Base Case 

Cost Ite

m Cost Category Occurrence Escalation 

Basic Renovation Capital Investment Initial 0.00% Site Protection Capital Investment Initial 0.00% HVAC Upgrade Capital Investment Future (

year 17) 0.00% 

Salvage Capital Investment Future (

year 25) 0.00% 

Site Security O&M Annuall

y Recurring 0.50% 

Site Lightin

g O&M Annuall

y Recurring -0.10% 

Electricity

O

&

M Annuall

y

Recurrin

g -0.10% 

Teleco

m Services O&M Annuall

y Recurring 0.00% 

HVAC Repairs O&M Periodic (years 1 through 24 in intervals of 4) 0.00% Duct Cleaning O&M Future (

year 17) 0.00% 

26
Table 4-2. Summary of Input Costs for the Proposed Alternative 

Cost Ite

m Cost Category Occurrence Escalation 

Enhanced Renovation Capital Investment Initial 0.00% Site Protection Capital Investment Initial 0.00% Special Security Features Capital Investment Initial 0.00% HVAC Upgrade Capital Investment Future (

year 17) 0.00% 

Salvage Capital Investment Future (

year 25) 0.00% 

Site Security O&M Annuall

y Recurring 0.50% 

Site Lightin

g O&M Annuall

y Recurring -0.10% 

Electricity

O

&

M Annuall

y

Recurrin

g -0.10% 

Teleco

m Services O&M Annuall

y Recurring 0.00% 

HVAC Repairs O&M Periodic (years 1 through 24 in intervals of 6) 0.00% Duct Cleaning O&M Future (

year 17) 0.00% 

Improved Productivit

y (IAQ) O&M Annuall

y Recurring 0.00% 

Change in Traffic Pattern Other Costs Annuall

y Recurring (

years 1 and 2) 0.00% 

27
The timing of costs is an important factor in any life-cycle cost analysis. The entries

under the Occurrence column heading in Tables 4-1 and 4-2 provide information on the 

timing of cash flows. Four occurrence types are used to specify when costs are incurred: 

(1) initial; (2) future (one time); (3) annually recurring (multiple years); and (4) periodic 

(every n years). Qualifications on timing are recorded in the tables. For example, the 

HVAC upgrade for the Base Case occurs once, in year 17, and the change in the traffic

pattern associated with the Proposed Alternative is an annually recurring cost, incurred 

only in years 1 and 2. 

Tables 4-1 and 4-2 also contain information on differential escalation rates. Differential 

escalation rates are needed because not all cost items change at the same rate, even when 

expressed in real terms. For example, the real cost of electricity is expected to decline 

slightly over the next 25 years. This trend is recorded as a value of –0.10 % per year for

both the Base Case and the Proposed Alternative. Conversely, the real costs of site 

security are expected to increase modestly over the next 25 years. This trend is recorded 

as a value of 0.50 % per year for both the Base Case and the Proposed Alternative. 

4.1.2 Event-Related Costs 

Annual probabilities for the outcomes associated with each attack scenario are postulated

along with associated outcome costs. The annual probabilities and outcome costs differ 

by renovation strategy. However, both the Base Case and the Proposed Alternative have

similar types of outcome costs. Should a cyber attack occur, it results in damage to 

financial records and identity theft for a small set of corporate customers. Should a 

CBRE attack occur, it results in several non-fatal injuries, physical damage to the data 

center, interruption of business services at the data center, and denial of service to 

corporate customers during recovery. 

Tables 4-3 and 4-4 summarize event-related information on the Base Case and the 

Proposed Alternative, respectively. The information presented in Tables 4-3 and 4-4 

specifies the baseline values of event-related costs for the Base Case and the Proposed

Alternative. Each table includes the scenario—cyber attack or CBRE attack—the years 

over which the scenario is run, the scenario-specific outcomes, their annual probabilities, 

their outcome-associated cost items, their cost categories, and their estimated costs in

constant 2003 dollar amounts. For example, the cyber attack scenario covers two time

periods—years 1 through 10 and years 11 through 25. Two time periods are used 

because cyber crime is on the rise and although new countermeasures are being produced

regularly, hackers are becoming more adept at finding and exploiting weaknesses in 

countermeasures software. Note that a given outcome may have multiple cost items. For 

example, if the outcome in the cyber attack scenario is “record theft,” then outcomeassociated cost items for “record reconstruction” and “identity theft” both occur. For

each outcome-associated cost item, the annual outcome probability is multiplied by the 

dollar amount in the last column to produce an expected dollar value of event-related 

costs. The resultant “stream” of expected dollar values is discounted to a present value. 

The dollar entries in Tables 4-5 through 4-11 include these event-related costs, expressed 

in present value terms. 

28
Table 4-3. Summary of Event-Related Information for the Base Case 

Scenario Years Outcome Probability

Cost Item Cost Categor

Cyber Attack 1 Through 1

0 No Bre

aches 0.6

None Non

e

Record Theft 0.4

R

ecord Reconstruction O&M 

Identity Theft Other 

11 Through 2

5 No Bre

aches 0.5

None Non

e

Record Theft 0.5

R

ecord Reconstruction O&M 

Identity Theft Other 

CBRE Attack 1 Through 2

5 No Bre

aches 0.994 Non

e Non

e

Minor Dama

g

e 0.005 Damage to Data Center Capital Investm

Business Interruption O&M One Non-Fat

al Injury Other 

Denial of Service Other 

Major Dama

g

e 0.001 Damage to Data Center Capital Investm

Business Interruption O&M 

20 Non-Fatal Injuries Other 

Denial of Service Other 

29
Table 4-4. Summary of Event-Related Information for the Proposed Alternative 

Scenario Years Outcome Probability Cost Item Cost Category 

Cyber Attack 1 Through 10 No Breaches 0.75 None None 

Record Theft 0.25 Record Reconstruction O&M 

Identity Theft Other 

11 Through 25 No Breaches 0.65 None None 

Record Theft 0.35 Record Reconstruction O&M 

Identity Theft Other 

CBRE Attack 1 Through 25 No Breaches 0.996 None None 

Minor Damage 0.0035 Damage to Data Center Capital Investmen

Business Interruption O&M One Non-Fatal Injury Other Denial of Service Other 

Major Damage 0.0005 Damage to Data Center Capital Investmen

Business Interruption O&M 8 Non-Fatal Injuries Other Denial of Service Other 

30
4.2 How Cost Items are Modeled and Analyzed via the Cost-Accounting 

Framework 

Tables 4-5 through 4-10 summarize input and event-related costs for the key budget 

categories for the Base Case and the Proposed Alternative. In order to differentiate those 

costs which are input costs from those which are event-related in Tables 4-5 through 4-

10, all event-related costs are expressed as expected values; they are shown in a bolditalics font face. 

Table 4-5 records the capital investment costs for the Base Case. All costs recorded in 

Table 4-5 are borne by the Owner/Manager (i.e., the division responsible for construction 

and renovation). Four input cost items are identified in Table 4-5. All but one of the four 

input cost items are listed under the Component column heading of Building/Facility 

Elements. The exception is the $100K for site protection; it appears under the 

Building/Facility Site Work column heading. Since the costs of site protection occur at 

the beginning of the 25-year study period, they are already expressed in present value 

terms. Referring to the column heading Building/Facility Elements, we see that “basic”

renovation costs are $1,000K. Since these costs occur at the start of the 25-year study 

period, they are already in present value terms. The next two cost items represent cash

flows which occur in the future. The HVAC upgrade occurs in year 17; the cost of the 

HVAC upgrade is $25K. The present discounted value of the HVAC upgrade cost is 

$12.8K. Since the HVAC system is expected to have several remaining years of service 

life, it is appropriate to record a salvage value at the end of the 25-year study period. 

Since the salvage value produces a net cash inflow, it appears as a negative cost (i.e., its 

value at the end of year 25 is -$10K). The present discounted value of the salvage value

is -$3.7K. Table 4-5 also includes event-related cost information. All event-related costs

which appear in Table 4-5 stem from the CBRE attack scenario. One event-related cost 

item is recorded in Table 4-5, damage to the data center. Damage to the data center is 

expected to amount to $59.4K over the 25-year study period; it results from two 

outcomes—a CBRE attack resulting in major damage and a CBRE attack resulting in

minor damage. Table 4-5 also records life-cycle costs across all bearers and all 

components. This total is $1,168K; it is recorded under the column heading Total. 

Table 4-6 records the operations and maintenance (O&M) costs for the Base Case. Note 

that costs are borne by both the Owner/Manager and the Occupant/User, both of whom 

are part of the same parent company. Turning first to the Owner/Manager input cost 

items, we see that the bulk of these costs are associated with site security. Site security

costs occur on an annual basis. Site security costs of $125K are assumed to escalate at 

0.5 % per year. The present value of site security costs is therefore $2,064K. Site 

lighting costs are also borne by the Owner/Manager. Site lighting costs occur on an 

annual basis. Site lighting costs of $3.6K are assumed to escalate at –0.1 % per year. 

The present value of site lighting costs is $55.6K. The Occupant/User input cost items

include electricity and telecommunication services, both of which occur on an annual 

basis. Electricity costs of $72K escalate at –0.1 % per year. Telecommunication services 

costs are $40K each year. These two cost items, expressed in present value terms, are 

31
$1,113K and $625K, respectively. HVAC repairs in the amount of $5K occur every four 

years and a major duct cleaning procedure in the amount of $5K is done when the HVAC 

upgrade occurs in year 17. The present values of these cost items are $18.0K and $2.6K,

respectively. Table 4-6 also records event-related cost information from both the CBRE 

and cyber attack scenarios. The costs of business interruption are expected to amount to

$97.6K over the 25-year study period. The costs of record reconstruction due to record 

theft associated with a cyber attack are expected to amount to $107K over the 25-year 

study period. 

Note that O&M costs over the 25-year study period exceed capital investment costs. 

Compare the entry in the Total column heading for all bearers in Table 4-6 to the 

corresponding entry in Table 4-5. Such differences point out opportunities for cost 

savings, such as might result from additional capital investments in energy conservation

measures or improved telecommunication services. 

Table 4-5. Base Case: Capital Investment Costs 

Elements Site Work

All Bearers 1,068,484 100,000 0 1,168,484

Owner/Manager 1,068,484 100,000 0 1,168,484

Basic Renovation 1,000,000 0 0

Site Protection 0 100,000 0

HVAC Upgrade 12,834 0 0

Salvage -3,751 0 0

Damage to Data Center 59,401 0 0

Occupant/User 0000

Third Party 0000

NonElemental

Cost Item

Component

Building/Facility Total

Table 4-7 records the Other costs for the Base Case; it includes only event-related cost 

information from the CBRE and cyber attack scenarios. Event-related cost items

associated with the CBRE attack scenario are non-fatal injuries and denial of service to 

corporate customers during the recovery period. Note that the costs of non-fatal injuries

are apportioned equally to each bearer (i.e., $9.8K). The costs resulting from denial of 

service are expected to amount to $39K. The cyber attack scenario results in a single 

event-related cost item, identity theft. The costs to corporate customers due to identity

theft are expected to amount to $619K. 

32
Table 4-6. Base Case: O&M Costs 

Elements Site Work

All Bearers 1,757,917 55,626 2,268,349 4,081,892

Owner/Manager 0 55,626 2,064,088 2,119,714

Site Security 0 0 2,064,088

Site Lighting 0 55,626 0

Occupant/User 1,757,917 0 204,260 1,962,178

Electricity 1,112,515 0 0

Telecom Services 624,883 0 0

HVAC Repairs 17,953 0 0

Duct Cleaning 2,567 0 0

Business Interruption 0 0 97,638

Record Reconstruction 0 0 106,622

Third Party 0000

NonElemental

Cost Item

Component

Building/Facility Total

Table 4-7. Base Case: Other Costs 

Elements Site Work

All Bearers 0 0 687,233 687,233

Owner/Manager 0 0 9,764 9,764

Non-fatal Injuries 0 0 9,764

Occupant/User 0 0 9,764 9,764

Non-fatal Injuries 0 0 9,764

Third Party 0 0 667,705 667,705

Non-fatal Injuries 0 0 9,764

Denial of Service 0 0 39,055

Identity Theft 0 0 618,886

NonElemental

Cost Item

Component

Building/Facility Total

33
Table 4-8 records the capital investment costs for the Proposed Alternative. All costs 

recorded in Table 4-8 are borne by the Owner/Manager (i.e., the division responsible for

construction and renovation). Five input cost items are identified in Table 4-8. Three of 

the five input cost items are listed under the Component column heading of 

Building/Facility Elements. The first of the two exceptions is the $200K for site 

protection; it appears under the Building/Facility Site Work column heading. The second 

is the $50K for special security features; it appears under Non-Elemental column 

heading. Since the costs of site protection and special security features occur at the 

beginning of the 25-year study period, they are already expressed in present value terms.

Referring to the column heading Building/Facility Elements, we see that “enhanced” 

renovation costs are $1,500K. Since these costs occur at the start of the 25-year study 

period, they are already in present value terms. The costs of the “enhanced” renovation

and associated site work are significantly higher than the Base Case. For example, the 

improved HVAC system includes sensors and filters to address certain chemical and 

biological hazards. The higher initial costs of the “enhanced” renovation, however, result 

in reductions in several key operations and maintenance costs. In addition, the improved 

HVAC system results in improved indoor air quality which is expected to have a

favorable impact on occupant productivity. The next two input cost items in Table 4-8 

represent cash flows which occur in the future. The HVAC upgrade occurs in year 17;

the cost of the HVAC upgrade is $30K. The present discounted value of the HVAC 

upgrade cost is $15.4K. Since the HVAC system is expected to have several remaining 

years of service life, it is appropriate to record a salvage value at the end of the 25-year 

study period. Since the salvage value produces a net cash inflow, it appears as a negative 

cost—its value at the end of year 25 is -$12.5K. The present discounted value of the 

salvage value is -$4.7K. Table 4-8 also records event-related cost information. All 

event-related costs which appear in Table 4-8 stem from the CBRE attack scenario. One 

event-related cost item is recorded in Table 4-8, damage to the data center. Damage to 

the data center is expected to amount to $11.1K over the 25-year study period; it results 

from two outcomes—a CBRE attack resulting in major damage and a CBRE attack 

resulting in minor damage. Table 4-8 also records total life-cycle costs across all bearers 

and all components. This total is $1,772K; it is recorded under the column heading Total. 

Table 4-9 records the O&M costs for the Proposed Alternative. Note that costs are borne 

by both the Owner/Manager and the Occupant/User, both of whom are part of the same

parent company. Turning first to the Owner/Manager input cost items, we see that the 

bulk of these costs are associated with site security. Site security costs occur on an 

annual basis. Site security costs of $100K are assumed to escalate at 0.5 % per year. The

present value of site security costs is therefore $1,651K. Site security costs are lower for 

the Proposed Alternative than for the Base Case due to additional investments in security 

technologies which reduce overtime demands for physical security staff. Site lighting 

costs are also borne by the Owner/Manager. Site lighting costs occur on an annual basis. 

Site lighting costs of $3.0K are assumed to escalate at –0.1 % per year. The present value 

of site lighting costs is $46.4K. The Occupant/User input cost items include electricity 

and telecommunication services, both of which occur on an annual basis. Annual 

electricity costs are $60K; they escalate at –0.1 % per year. Annual telecommunication 

services costs are $36K each year. These two cost items, expressed in present value 

34
terms, are $927K and $562K, respectively. The improved HVAC and 

telecommunications systems result in cost savings for space heating and cooling and 

telecom services for the Proposed Alternative. Specifically, annual electricity costs are 

reduced from $72K in the Base Case to $60K for the Proposed Alternative. Annual 

telecom services costs are reduced from $40K to $36K. HVAC repairs in the amount of 

$6K occur every six years and a major duct cleaning procedure in the amount of $7.5K is 

done when the HVAC upgrade occurs in year 17. The present values of these cost items

are $13.8K and $3.9K, respectively. The improved HVAC system produces a spillover 

benefit in the form of improved indoor air quality. Better indoor air quality results in

improved productivity for data center tenants. Previous studies have cited productivity 

improvements ranging from 3 % to 15 %.35, 36 The data center case study uses a very

conservative estimate of 0.5 %. The resultant annual value of improved productivity is 

$4.0K. Since improved productivity is a net benefit to the Occupant/User, it is subtracted 

from total life-cycle costs. The present value of improved productivity is estimated to be 

-$62.5K. Table 4-9 also records event-related cost information from both the CBRE and

cyber attack scenarios. The costs of business interruption are expected to amount to 

$29.3K over the 25-year study period. The costs of record reconstruction due to record 

theft associated with a cyber attack are expected to amount to $29.1K over the 25-year 

study period. 

Table 4-8. Proposed Alternative: Capital Investment Costs 

Elements Site Work

All Bearers 1,521,858 200,000 50,000 1,771,858

Owner/Manager 1,521,858 200,000 50,000 1,771,858

Enhanced Renovation 1,500,000 0 0

Site Protection 0 200,000 0

Special Security Features 0 0 50,000

HVAC Upgrade 15,401 0 0

Salvage -4,689 0 0

Damage to Data Center 11,146 0 0

Occupant/User 0000

Third Party 0000

NonElemental

Cost Item

Component

Building/Facility Total

 35 Fisk, William J., and Arthur H. Rosenfeld. 1997. “Estimates of Improved Productivity and Health from

Better Indoor Environments,” Indoor Air (Vol. 7): pp. 158-172. 36 Lomonaco, Carol, and Dennis Miller. 1997. “Comfort and Control in the Workplace,” ASHRAE Journal

(September): pp. 50-56. 

35
Note that O&M costs over the 25-year study period exceed capital investment costs. 

Compare the entry in the Total column heading for all bearers in Table 4-9 to the 

corresponding entry in Table 4-8. However, these differences are less than in the Base 

Case and O&M costs for the Proposed Alternative are less than the O&M costs of the 

Base Case, which serves to highlight that some cost savings result from the additional 

capital investments associated with the Proposed Alternative. 

Table 4-9. Proposed Alternative: O&M Costs 

Elements Site Work

All Bearers 1,507,133 46,355 1,647,198 3,200,685

Owner/Manager 0 46,355 1,651,271 1,697,625

Site Security 0 0 1,651,271

Site Lighting 0 46,355 0

Occupant/User 1,507,133 0 -4,073 1,503,060

Electricity 927,096 0 0

Telecom Services 562,395 0 0

HVAC Repairs 13,792 0 0

Duct Cleaning 3,850 0 0

Improved Productivity (IAQ) 0 0 -62,488

Business Interruption 0 0 29,291

Record Reconstruction 0 0 29,124

Third Party 0000

NonElemental

Cost Item

Component

Building/Facility Total

Table 4-10 records the Other cost entries for the Proposed Alternative. Table 4-10 

includes a single input cost entry. This entry, change in traffic pattern, corresponds to 

$94.3K in “driver delay” costs incurred by local residents who no longer can traverse the 

data center site and hence must take a detour around the data center site. These nonelemental input costs are borne by third parties (i.e., local residents). When the new 

street is opened two years after the renovation, a more direct route is created and these 

“driver delay” costs disappear. Table 4-10 also records event-related cost information

from both the CBRE and cyber attack scenarios. Event-related cost items associated with 

the CBRE attack scenario are non-fatal injuries and denial of service to corporate 

customers during the recovery period. The costs of non-fatal injuries are apportioned 

equally to each bearer (i.e., $2.9K). Costs resulting from denial of service are expected 

to amount to $13.3K. The cyber attack scenario results in a single event-related cost 

item, identity theft. The costs to corporate customers due to identity theft are expected to 

amount to $166K. 

36
Table 4-10. Proposed Alternative: Other Costs 

Elements Site Work

All Bearers 0 0 282,359 282,359

Owner/Manager 0 0 2,929 2,929

Non-fatal Injuries 0 0 2,929

Occupant/User 0 0 2,929 2,929

Non-fatal Injuries 0 0 2,929

Third Party 0 0 276,501 276,501

Change in Traffic Pattern 0 0 94,305

Non-fatal Injuries 0 0 2,929

Denial of Service 0 0 13,279

Identity Theft 0 0 165,988

NonElemental

Cost Item

Component

Building/Facility Total

Because both the Base Case and the Proposed Alternative have similar sets of cost items, 

it is useful to compare some of the event-related costs. Consider the cyber attack 

scenarios for the Base Case (Table 4-3) and the Proposed Alternative (Table 4-4). 

Comparing the outcome probabilities and associated costs (see Tables 4-3 and 4-4), we 

see that the additional capital investments in telecommunications and data processing 

services for the Proposed Alternative, result in significantly lower expected costs both to 

the corporation (i.e., Occupant/User) and their corporate customers (i.e., Third Party). 

For example, compare the estimated values for the cost items record reconstruction and 

identity theft in Tables 4-6 and 4-7 for the Base Case to the corresponding entries in 

Tables 4-9 and 4-10 for the Proposed Alternative. 

4.3 Baseline Results 

Table 4-11 summarizes the results of the baseline analysis for the Base Case and the 

Proposed Alternative. All costs reported in Table 4-11 are life-cycle costs. Since Table 

4-11 includes all input and event-related costs, it represents a complete picture of the 

baseline analysis. The entries in the Base Case column of Table 4-11 come from Tables 

4-5, 4-6, and 4-7. The entries in the Proposed Alternative column of Table 4-11 come

from Tables 4-8, 4-9, and 4-10. 

Turning first to the Base Case column heading and the Budget Category classification, 

we see that the entries correspond to the All Bearers/Total cell in Table 4-5 for Capital 

Investment, in Table 4-6 for O&M, and in Table 4-7 for Other. Bearer costs for the 

Owner/Manager equal the sum of the Owner/Manager/Total cells in Tables 4-5, 4-6, and 

4-7 (i.e., 3,297,962 = 1,168,484 + 2,119,714 + 9,764). Bearer costs for the 

37
Occupant/User equal the sum of the Occupant/User/Total cells in Tables 4-6 and 4-7,

since the corresponding entry in Table 4-5 is zero. Component costs for each component 

equal the sum of the respective Bearer/Component cells in Tables 4-5, 4-6, and 4-7. For 

example, the life-cycle costs of Building/Facility Elements equals the sum of the

Bearer/Elements cells in Tables 4-5 and 4-6 (i.e., 2,826,402 = 1,068,484 + 1,757,917). 

Note that the Life-Cycle Cost for the Base Case of $5,937,608 equals the sum of the cost 

items listed under each Cost Classification37 (i.e., 5,937,608 = 3,297,962 + 1,971,941 + 

667,705 = 1,168,484 + 4,081,892 + 687,233 = 2,826,402 + 155,626 + 2,955,581). Thus, 

whether we look at costs from the Bearer perspective, from the Budget Category 

perspective, or by Component, all costs are included and classified accordingly.

Table 4-11. Summary of Life-Cycle Costs 

Decision Criterion/ 

Cost Type

Base Case Proposed 

Alternative

Life-Cycle Cost 5,937,608 5,254,903

Bearer: Owner/Manager 3,297,962 3,472,413

Occupant/User 1,971,941 1,505,989

Third Party 667,705 276,501

Category: Capital Investment 1,168,484 1,771,858

O&M 4,081,892 3,200,685

Other 687,233 282,359

Component: Building/Facility Elements 2,826,402 3,028,991

Building/Facility Site Work 155,626 246,355

Non-Elemental 2,955,581 1,979,557

Cost 

Classification

Life-cycle costs for the Proposed Alternative are calculated in exactly the same manner 

as for the Base Case, but using Tables 4-8, 4-9, and 4-10. The Life-Cycle Cost for the 

Proposed Alternative is $5,254,903. Note that this cost is less than the Life-Cycle Cost of 

the Base Case. This is because the Proposed Alternative includes a number of features 

that produce future cost savings. These cost savings partially offset the increased Capital 

Investment costs for the Proposed Alternative. As a general rule, whenever the potential 

for a spillover benefit exists (e.g., improved indoor air quality), consider incorporating it 

into the risk mitigation plan and evaluating its impact on life-cycle cost. 

 37 Due to rounding, some Cost Classification totals may differ slightly from the Life-Cycle Cost total. 

38
Reference to Table 4-11 demonstrates that the Proposed Alternative is the most costeffective choice, since it results in the lowest life-cycle cost (i.e., $5,254,903 versus 

$5,937,608). 

The life-cycle cost figures presented in Table 4-11 enable us to calculate several 

additional economic measures that taken together provide useful information to decision 

makers. First, the difference between the life-cycle cost of the Base Case and the 

Proposed Alternative equals the present value of net savings (PVNS) resulting from 

choosing the Proposed Alternative. For the baseline analysis, the PVNS of the Proposed 

Alternative amounts to $682,706. Second, the way in which the Budget Category cost 

items are defined enables us to calculate both the savings-to-investment ratio (SIR) and

the adjusted internal rate of return (AIRR). The SIR equals the difference in noninvestment costs—the savings stemming from the use of the Proposed Alternative rather 

than the Base Case—divided by the increased capital investment cost for the Proposed 

Alternative. Reference to Table 4-11 shows that the increased capital cost of the 

Proposed Alternative of $603,374 results in savings of $1,286,080. These figures 

translate into an SIR of 2.13 (i.e., every dollar invested in the Proposed Alternative is 

expected to generate $2.13 in cost savings). Using the computed value of the SIR, we 

can calculate the AIRR. In this case, the AIRR over the 25-year study period is 7.2 %, 

which exceeds the hurdle rate of 4 %. Finally, the use of multiple economic measures 

provides alternative views of the same decision process. Specifically, PVNS provides a 

measure of magnitude, whereas the SIR is a multiplier, and the AIRR is an annual rate of 

return. 

4.4 How the Baseline Analysis Links to the Software Product 

The software product has two analysis options: (1) baseline analysis and (2) sensitivity 

analysis. The baseline analysis, as presented in Sections 4.1 through 4.3, links directly to 

four key features of the software product. These features are illustrated in Tables 4-1

through 4-11; they are concerned with: (1) the cost-accounting framework; (2) selected

data inputs; (3) selected output reports; and (4) the “Cost Summary” screen of the 

software product. 

The software product employs the same cost-accounting framework as described in 

Section 2.3 and illustrated in Sections 4.1 through 4.3. As the user of the software 

product inputs data on each cost item, each cost item is classified according to its Bearer, 

Budget Category, and Building Component. The input screens, Cost Summary screen, 

and output reports for the baseline analysis option of the software product are all identical 

in their use of the cost-accounting framework to the information recorded in Tables 4-1

through 4-11. 

Tables 4-1 through 4-4 record the types of cost-related information requested of the 

software product’s user in the input screens. Cost-related input screens for the software 

product are of two basis types: (1) input costs and (2) event-related costs. The user

accesses these screens by selecting the Costs or Events options on the Main Menu, which

appears as a side bar on the Cost Summary screen. The input screens presented to the 

39
user ask for the same type of information for input costs as recorded in Tables 4-1 and 4-

2 and for event-related costs as recorded in Tables 4-3 and 4-4. The software product 

includes additional input screens designed to collect information on the project under

analysis and its associated investment alternatives (e.g., the Base Case and the Proposed 

Alternative). This more basic information is covered in Chapter 3 (e.g., description of the

data center, specification of the Base Case and the Proposed Alternative, the base year, 

the years over which costs are analyzed, and the discount rate). 

Tables 4-5 through 4-10 are indicative of selected sets of output reports from the software

product. The software product is designed to help the user “drill down” on how 

individual cost items are distributed across Bearer, Budget Category, and Building 

Component. The software product drills down according to the Budget Category, just as 

is done in Tables 4-5 through 4-10. This approach gives users a snapshot of all of the 

costs entering the analysis, expressed in present value terms, which “roll up” into the lifecycle costs recorded in the Cost Summary screen. 

With the exception of the Main Menu options, editing/screening options for the 

alternatives under analysis, and some descriptive information, Table 4-11 is identical to 

the Cost Summary screen. Both Table 4-11 and the Cost Summary screen of the software 

product employ the cost-accounting framework, express costs in present value terms, and 

support the calculation of additional economic measures. 

It is also worth noting that the information presented in Tables 4-1 through 4-11 may be 

used as a test problem for prospective users of the software product. Thus, the users of 

the software product have a convenient frame of reference through which they can gain 

familiarity with the software product. 

40
5 Sensitivity Analysis 

This chapter includes a sensitivity analysis to provide the reader with additional 

background and perspective on the data center case study. The purpose of the sensitivity 

analysis is to evaluate the impact of changing the values of a number of key variables 

whose values are uncertain on the calculated values of the key economic measures (e.g.,

PVNS). The results of the sensitivity analysis are summarized in a series of tables and 

figures. The chapter concludes with a discussion of how the sensitivity analysis links to 

the software product. 

5.1 Methodology 

Sensitivity analysis may be divided into two polar cases: (1) deterministic; and (2)

probabilistic. Deterministic sensitivity analyses are the most straightforward. Their 

advantage is that they are easy to apply and the results are easy to explain and 

understand. Their disadvantage is that they do not produce results that can be tied to 

probabilistic levels of significance (i.e., the probability that PVNS is greater than zero). 

For example, a deterministic sensitivity analysis might use as inputs a pessimistic value, a 

value based on a measure of central tendency (e.g., mean or median), and an optimistic 

value for the variable of interest. Then an analysis could be performed to see how each

outcome (e.g., PVNS) changes as each of the three chosen values for the selected input is 

considered in turn, while all other input variables are maintained at their baseline values. 

A deterministic sensitivity analysis can also be performed on different combinations of 

input variables. That is, several variables are altered at once and then an outcome

measure is computed. 

In a probabilistic sensitivity analysis, a small set of key input variables is varied either

singly or in combination according to an experimental design. In most cases, 

probabilistic sensitivity analyses are based on Monte Carlo techniques, or some other

form of simulation. The major advantage of probabilistic sensitivity analysis is that it 

permits the effects of uncertainty to be rigorously analyzed. For example, not only the 

expected value of each economic measure can be computed but also the variability of that 

value. In addition, probabilistic levels of significance can be attached to the computed 

values of each economic measure. The disadvantage of a probabilistic sensitivity 

analysis is that it requires many calculations carried out according to an experimental 

design, and is therefore practical only when used with a computer. 

Both deterministic and probabilistic sensitivity analyses are employed in the data center 

case study. A deterministic sensitivity analysis is first performed to identify the input 

variables with the greatest impact on PVNS. PVNS is used as the ranking criterion 

because it measures the difference between the life-cycle costs of the Base Case and the 

Proposed Alternative. Probabilistic sensitivity analyses, based on Monte Carlo 

simulation, are then performed on selected variables singly (i.e., those with the greatest

impact on PVNS) and then for a selected set of variables in combination.

41
5.2 Key Variables 

Information on the 21 variables that are the focus of the sensitivity analysis is presented

in this section. The sensitivity analysis uses the same data and assumptions as the

baseline analysis for its starting point. The 21 variables are listed in Table 5-1. Three of 

the 21 variables apply to both the Base Case and the Proposed Alternative. Eight of the 

variables apply to the Base Case. The 10 remaining variables apply to the Proposed 

Alternative.

The objective of the sensitivity analysis is to evaluate how uncertainty in the values of the

21 variables translates into changes in each of five key economic measures. The five 

economic measures evaluated in the sensitivity analysis are: (1) the life-cycle costs of the 

Base Case (LCCBC); (2) the life-cycle costs of the Proposed Alternative (LCCAlt); (3) the

present value of net savings (PVNS) resulting from the Proposed Alternative; (4) the

savings-to-investment ratio (SIR) produced by the additional capital investment in the 

Proposed Alternative; and (5) the adjusted internal rate of return (AIRR) on the additional 

capital investments associated with the Proposed Alternative. Variations in the values of 

the 21 variables translate into the value of each outcome (e.g., the SIR) in such a manner 

that the impacts of uncertainty can be measured quantitatively. 

The approach selected for this study makes use of both deterministic sensitivity analyses

and probabilistic sensitivity analyses. The probabilistic sensitivity analyses presented in 

this report are based on the method of model sampling. Model sampling is a procedure 

for sampling from a stochastic process to determine, through multiple trials, the 

characteristics of a probability distribution. 

Any software product that has the capability to randomly sample from the known or 

hypothesized parent probability distribution for each variable of interest may be used to 

implement the method of model sampling. For the data center case study, the method of 

model sampling was implemented through application of the @RISK software product.38 

This software product is a risk analysis tool for spreadsheets. For the case at hand,

selected columns of the spreadsheet were associated with the 21 variables. The @RISK

software product allows the user to specify a unique probability distribution for each 

variable. Specification of the experimental design involves defining which variables are 

to be simulated and the number of simulations. Throughout this sensitivity analysis,

1,000 simulations were run for the combination of the 21 variables under analysis. The

number of simulations was chosen to ensure that values in the tails of the distribution for

each variable would be selected for inclusion in the analysis.

In reality, the exact nature of the parent probability distribution for each variable is

unknown. Estimates of the parameters (e.g., mean and variance) of the parent probability

distribution can be made and uncertainty can be reduced by investigation and research.

However, uncertainty can never be eliminated completely. Therefore, in order to

implement the procedure without undue attention to the characterization of the parent 

 38 Palisade Corporation. 1997. Guide to Using @RISK: Risk Analysis and Simulation Add-In for Microsoft 

Excel or Lotus 1-2-3. Newfield, NY: Palisade Corporation.

42
probability distribution, it was decided to focus on only two probability distributions: (1)

the triangular and (2) the uniform. One reason for using these probability distributions is 

that they are both defined over a finite interval. They are also used frequently in cost-risk

analyses.39 Furthermore, the specification of each probability distribution is

accomplished with as few as two data points. The triangular distribution is widely used 

in simulation modeling; its specification requires three data points, the minimum value, 

the most likely value, and the maximum value. The triangular distribution is used 

whenever the range of input values is continuous and a clustering about some central 

value is expected. The uniform distribution is also widely used in simulation modeling;

its specification requires only two data points, the minimum value and the maximum 

value. In addition, all values between the minimum and maximum are equally likely. 

The uniform distribution is used whenever the range of input values is continuous but no 

a priori reason can be given for expecting clustering about some central value. 

Table 5-1 summarizes information on each of the 21 variables. The table includes

information on the applicability of the variable of interest (i.e., whether it applies to the 

Base Case, the Proposed Alternative, or both), information on the type of probability 

distribution used to model variations about the baseline value for each variable, the 

baseline value for each variable, and the minimum and maximum values for each

variable. References to the entries under the heading Probability Distribution shows that

seven of the 21 variables use the uniform distribution and 14 use the triangular 

distribution to model variations about the baseline value for that variable. The next three

headings record, for each variable, its setting (i.e., baseline, minimum, and maximum)

and value. For each variable, the baseline value is recorded first. For example, the 

baseline value for the discount rate is 4 % (real); it is recorded in decimal form as 0.04.

Two other values for the discount rate, 0 % and 8 %, are selected to bracket the baseline 

value. These values are recorded in decimal form as 0 and 0.08, respectively. 

The seven variables that use the uniform distribution are all associated with either 

percentage rates or probabilities. The first three variables—the discount rate, escalation 

rate for site security, and escalation rate for electricity—are percentage rates; they apply

to both the Base Case and the Proposed Alternative. The two sets of probabilities (i.e., 

major damage and minor damage) apply to the Base Case and the Proposed Alternative,

respectively. The baseline value for the probability of major damage is drawn directly 

from Table 4-3 for the Base Case and from Table 4-4 for the Proposed Alternative. In the 

sensitivity analysis, the probabilities for minor damage is assumed to be functionally 

related to the probability of major damage. For example, in the Base Case, the 

probability of major damage is 0.001 and the probability of minor damage is 0.005. 

Thus, the probability of minor damage differs from the probability of major damage by a 

factor of five (i.e., it is multiplied by 5.0). Because the probabilities of all outcomes 

associated with an attack scenario must add up to 1.0, applying a factor to the probability 

of major damage establishes a relationship that forms the basis for a constraint. 

 39 ASTM International. 2002. “Standard Practice for Measuring Cost Risk of Buildings and Building 

Systems.” E 1946. Annual Book of ASTM Standards: 2002. Vol. 04.12. West Conshohocken, PA: ASTM

International. 

43
Table 5-1. Baseline and Extreme Values of the Twenty-one Variables Used in the 

Sensitivity Analysis 

Baseline Minimum Maximum

 (1) Discount Rate Both Uniform 0.04 0 0.08

 (2) Escalation Rate 

 for Site Security Both Uniform 0.005 0.003 0.007

 (3) Escalation Rate 

 for Electricity

Both Uniform -0.001 -0.0015 -0.0005

 (4) Probability of

 Major Damage

Base Case Uniform 0.001 0.0005 0.0015

 (5) Probability of

 Minor Damage 

 Factor

Base Case Uniform 5 3 7

 (6) Electricity Base Case Triangular 1.80 1.675 1.925

 (7) Basic Renovation Base Case Triangular 1,000,000 900,000 1,100,000

 (8) Damage to Data

 Center

Base Case Triangular 3,000,000 2,500,000 3,500,000

 (9) Business 

 Interruption Base Case Triangular 5,000,000 4,000,000 6,000,000

(10) Non-fatal Injuries

 for Major Attack Base Case Triangular 20 15 25

(11) Denial of Service Base Case Triangular 2,000,000 1,500,000 2,500,000

(12) Probability of

 Major Damage

Alternative Uniform 0.0005 0.0003 0.0007

(13) Probability of

 Minor Damage

 Factor

Alternative Uniform 7 5 9

(14) Electricity Alternative Triangular 1.50 1.375 1.625

(15) Enhanced 

 Renovation

Alternative Triangular 1,500,000 1,300,000 1,700,000

(16) Improved 

 Productivity (IAQ) Alternative Triangular -4,000 -5,500 -2,500

(17) Change in Traffic

 Pattern 

Alternative Triangular 50,000 40,000 60,000

(18) Damage to Data

 Center

Alternative Triangular 1,000,000 750,000 1,250,000

(19) Business 

 Interruption Alternative Triangular 2,000,000 1,500,000 2,500,000

(20) Non-fatal Injuries

 for Major Attack Alternative Triangular 8 6 10

(21) Denial of Service Alternative Triangular 1,000,000 750,000 1,250,000

Variable Name

Probability

Distribution

Setting and Value

Applies to

44
All of the variables that use the triangular distribution vary about a central value. For the 

Base Case, four of the six variables that use the triangular distribution are associated with

the costs stemming from the CBRE attack scenario. The two exceptions are electricity

and the costs of the basic renovation. For the Proposed Alternative, four of the eight 

variables that use the triangular distribution are associated with the costs stemming from 

the CBRE attack scenario. Two of the four remaining are similar to the Base Case; they

are electricity costs and the costs of the enhanced renovation. The two remaining

variables are associated with increases in productivity due to improved indoor air quality,

which appear as a negative cost, and the added costs to local residents due to the change 

in traffic patterns during the first two years. 

5.3 Sensitivity Results 

The results of the sensitivity analysis are summarized in a series of tables and figures.

Two sets of results are presented. The first set covers the case where each of the 21 input 

variables is varied singly. The first set of results is designed to show the effect of each 

input on PVNS. This is done by varying each input variable singly while holding all 

other input variables at their baseline values. The results of this deterministic sensitivity 

analysis are summarized in Table 5-2. Tables 5-3 through 5-6 summarize the results of a

probabilistic sensitivity analysis for the four input variables having the greatest impact on 

PVNS. The second set covers the case where all 21 input variables are varied in 

combination. The second set of results is designed to produce a data set that facilitates an 

in-depth analysis of the results, and promotes an understanding of what these results 

mean. These results are summarized in Tables 5-7 through 5-10 and in Figures 5-1 

through 5-4. To facilitate comparisons among each of the Monte Carlo simulations,

Tables 5-3 through 5-7 use the same presentation format. Table 5-8 summarizes in 

tabular form the results plotted in Figures 5-1 through 5-4. Tables 5-9 and 5-10 use the 

same presentation format, which is a slight variation in the presentation format used in 

Tables 5-3 through 5-7, due to its focus on life-cycle cost comparisons. 

5.3.1 Changing One Variable 

Table 5-2 summarizes the results of a deterministic sensitivity analysis in which each of

the 21 variables are varied singly while holding all other input variables at their baseline 

values. The table records the variable’s name, its applicability (i.e., whether it applies to

the Base Case, the Proposed Alternative, or Both), its values of PVNS resulting from its 

minimum and maximum settings, its delta PVNS, and its rank. Note that minimum and 

maximum refer to the value of the input variable (see Table 5-1 for these settings) and not 

the value of PVNS. For example, the minimum setting of the discount rate (i.e., 0 %)

results in a higher value of PVNS (i.e., $1,533K) than the maximum setting (i.e., 8 % and 

$246K). Each variable is assigned a rank based on the value of delta PVNS. The value 

of delta PVNS equals the absolute value of the difference between the values under the

Minimum and Maximum column headings. Variables are ranked from most important 

(i.e., a rank of 1) to least important (i.e., a rank of 20). The least important variable is

assigned a rank of 20 instead of 21 because two variables produced the same value of 

delta PVNS; based on their computed value of delta PVNS, they are assigned a rank of 5. 

45
Table 5-2. Results of Deterministic Sensitivity Analysis: Summary of Present Value 

Net Savings Calculations 

Minimum Maximum

 (1) Discount Rate Both 1,533.143 246.479 1,286.664 1

 (2) Escalation Rate 

 for Site Security Both 673.604 692.077 18.473 11

 (3) Escalation Rate 

 for Electricity Both 681.643 683.776 2.133 20

 (4) Probability of

 Major Damage

Base Case 570.013 795.398 225.385 3

 (5) Probability of

 Minor Damage

 Factor

Base Case 666.631 698.780 32.149 9

 (6) Electricity Base Case 601.585 763.826 162.241 5

 (7) Basic Renovation Base Case 582.706 782.706 200.000 4

 (8) Damage to Data

 Center Base Case 673.970 691.441 17.471 12

 (9) Business 

 Interruption Base Case 667.084 698.328 31.244 10

(10) Non-fatal Injuries

 for Major Attack Base Case 676.847 688.564 11.717 15

(11) Denial of Service Base Case 674.895 690.517 15.622 13

(12) Probability of

 Major Damage

Alternative 707.707 657.704 50.003 6

(13) Probability of

 Minor Damage

 Factor

Alternative 690.171 675.241 14.930 14

(14) Electricity Alternative 763.826 601.585 162.241 5

(15) Enhanced 

 Renovation Alternative 882.706 482.706 400.000 2

(16) Improved 

 Productivity (IAQ) Alternative 706.139 659.272 46.867 7

(17) Change in Traffic

 Pattern 

Alternative 701.567 663.845 37.722 8

(18) Damage to Data

 Center

Alternative 684.770 680.641 4.129 17

(19) Business 

 Interruption Alternative 686.611 678.800 7.811 16

(20) Non-fatal Injuries

 for Major Attack Alternative 683.877 681.534 2.343 19

(21) Denial of Service Alternative 684.658 680.753 3.905 18

Variable Name

Delta

PVNS Rank

Setting and PVNS

Applies to

46
Reference to the table reveals that the discount rate has the highest impact on PVNS,

resulting in a delta PVNS of $1,287K. Note that the discount rate affects both the Base

Case and the Proposed Alternative. The next most important variable is the cost of the 

enhanced renovation; it affects only the costs of the Proposed Alternative. The third and 

fourth most important variables affect only the costs of the Base Case; they are the 

probability of major damage and the costs of the basic renovation. 

Tables 5-3 through 5-6 report a series of statistical measures for the four variables having 

the greatest impact on PVNS. To facilitate comparisons among the economic measures, 

a shorthand notation for each is used. The five economic measures reported in the tables 

are: (1) the life-cycle costs of the Base Case (LCCBC); (2) the life-cycle costs of the 

Proposed Alternative (LCCAlt); (3) the present value of net savings (PVNS) resulting 

from the Proposed Alternative; (4) the savings-to-investment ratio (SIR) produced by the 

additional capital investment in the Proposed Alternative; and (5) the adjusted internal 

rate of return (AIRR) on the additional capital investments associated with the Proposed 

Alternative. The statistical measure and its corresponding value are recorded under the 

heading Statistical Measure. Seven statistical measures are reported to characterize the 

results of each Monte Carlo simulation. The calculation of these statistical measures is 

based on a “sample of 1,000 observations” produced by each Monte Carlo simulation.

These statistical measures are: (1) the minimum; (2) the 25th percentile, denoted by 25%; 

(3) the 50th percentile (i.e., the median), denoted by 50%; (4) the 75th percentile, denoted 

by 75%; (5) the maximum; (6) the mean; and (7) the standard deviation. The minimum 

and the maximum define the range of values for the results from each of the Monte Carlo

simulations. The 50th percentile and the mean are measures of central tendency. The 25th

and 75th percentiles define the interquartile range, a range that includes the middle 50

percent of the observations. The interquartile range is also a crude measure of central 

tendency. The standard deviation measures the variability of the results from each of the 

Monte Carlo simulations. The values reported for LCCBC, LCCAlt, and PVNS are all in

thousands of 2003 dollars. 

Table 5-3 shows how variations about the baseline value of the discount rate (4 % (real))

affect each economic measure. The discount rate affects calculations in a number of

ways. For example, LCCBC, LCCAlt, and PVNS are all affected by the discount rate. 

Reference to Table 5-3 reveals that LCCBC is more sensitive to changes in the discount 

rate than is LCCAlt. This is due to the fact that while the Base Case has lower first costs

than the Proposed Alternative, it has higher future costs. Thus, costs occurring in the out

years are penalized by a lower discount rate and benefit from a higher discount rate. 

Note that the range of values for LCCBC (i.e., maximum minus minimum) is about 25 % 

greater than the range of values for LCCAlt (i.e., $4.4 million versus $3.2 million). The 

value of PVNS also varies considerably. However, in no instance does PVNS turn

negative. Thus, for all “observations” associated with this Monte Carlo simulation, the

Proposed Alternative is the most cost-effective investment choice. The computed values 

of the SIR and the AIRR provide additional support for the superior performance of the 

Proposed Alternative vis-à-vis the Base Case when the discount rate is allowed to vary 

about its baseline value. 

47
Table 5-3. Summary Statistics Due to Changes in the Input Variable Discount Rate 

Statistical Measure 

Economic

Measure Minimum 25% 50% 75% Maximum Mean

Standard 

Deviation 

LCCBC 4,408.711 5,059.926 5,894.800 7,128.006 8,865.249 6,144.757 1,247.033

LCCAlt 4,162.097 4,628.684 5,224.410 6,101.412 7,333.412 5,400.784 887.470

PVNS 246.613 431.242 670.390 1,026.594 1,531.837 743.973 359.567

SIR 1.399 1.704 2.110 2.736 3.671 2.251 0.631

AIRR 0.054 0.062 0.072 0.083 0.096 0.073 0.012

Table 5-4 summarizes the results of the Monte Carlo simulation of variations about the 

baseline value for the cost of the enhanced renovation. Because the costs of the enhanced 

renovation only apply to the Proposed Alternative, LCCBC does not change from its 

baseline value. The range of values for LCCAlt is nearly $400K. The range of values for

PVNS is also nearly $400K, since it is the difference between LCCBC and LCCAlt and 

LCCBC is a constant. This close coupling is seen through reference to the value of the 

standard deviation for LCCAlt and PVNS, which are equal. Note that PVNS is always 

positive, indicating that the Proposed Alternative is the most cost-effective investment

choice. The computed values for the SIR and the AIRR serve to reinforce the previous 

statement. 

Table 5-4. Summary Statistics Due to Changes in the Input Variable Enhanced 

Renovation for the Proposed Alternative 

Statistical Measure 

Economic

Measure Minimum 25% 50% 75% Maximum Mean

Standard 

Deviation 

LCCBC 5,937.609 5,937.609 5,937.609 5,937.609 5,937.609 5,937.609 0.0

LCCAlt 5,062.580 5,192.650 5,251.010 5,307.479 5,443.325 5,250.707 81.751

PVNS 494.284 630.130 686.599 744.959 875.029 686.902 81.751

SIR 1.624 1.961 2.145 2.377 3.129 2.188 0.309

AIRR 0.060 0.068 0.072 0.077 0.089 0.073 0.006

Table 5-5 shows how variations about the baseline value of the probability of major 

damage for the Base Case affect each economic measure. Because this probability only 

applies to the Base Case, LCCAlt does not change from its baseline value. The range of 

values for LCCBC is approximately $225K. The range of values for PVNS is also 

approximately $225K. Because PVNS equals the difference between LCCBC and LCCAlt, 

which is a constant, the standard deviations of the two economic measures—LCCBC and

PVNS—are equal in value. Note that PVNS is always positive, indicating that the

Proposed Alternative is the most cost-effective investment choice. The computed values 

for the SIR and the AIRR serve to reinforce the previous statement. 

48
Table 5-5. Summary Statistics Due to Changes in the Input Variable Probability of 

Major Damage for the Base Case 

Statistical Measure 

Economic

Measure Minimum 25% 50% 75% Maximum Mean

Standard 

Deviation 

LCCBC 5,824.959 5,879.687 5,938.149 5,990.735 6,049.548 5,937.191 64.131

LCCAlt 5,254.903 5,254.903 5,254.903 5,254.903 5,254.903 5,254.903 0.0

PVNS 570.056 624.784 683.246 735.832 794.645 682.288 64.131

SIR 1.900 2.010 2.133 2.249 2.385 2.134 0.138

AIRR 0.067 0.069 0.072 0.074 0.077 0.072 0.003

Table 5-6 shows how variations about the baseline value of the cost of the basic 

renovation affect each economic measure. Because the cost of the basic renovation only 

applies to the Base Case, LCCAlt does not change from its baseline value. The range of 

values for LCCBC is approximately $200K. The range of values for PVNS is also 

approximately $200K. Because PVNS equals the difference between LCCBC and LCCAlt, 

which is a constant, the standard deviations of the two economic measures—LCCBC and

PVNS—are equal in value. Note that PVNS is always positive, indicating that the

Proposed Alternative is the most cost-effective investment choice. The computed values 

for the SIR and the AIRR serve to reinforce the previous statement. 

Table 5-6. Summary Statistics Due to Changes in the Input Variable Basic 

Renovation for the Base Case 

Statistical Measure 

Economic

Measure Minimum 25% 50% 75% Maximum Mean

Standard 

Deviation 

LCCBC 5,838.695 5,908.166 5,938.153 5,967.072 6,035.233 5,937.996 41.652

LCCAlt 5,254.903 5,254.903 5,254.903 5,254.903 5,254.903 5,254.903 0.0

PVNS 583.792 653.263 683.249 712.169 780.330 683.093 41.652

SIR 1.831 2.032 2.133 2.241 2.543 2.143 0.150

AIRR 0.065 0.070 0.072 0.074 0.080 0.072 0.003

5.3.2 Changing All Variables in Combination 

The results of Monte Carlo simulations may be presented in a number of ways. In this

section, they are presented in both tabular and graphical formats. The tabular formats 

record information on each of the five economic measures. The two tables, Table 5-7 

and Table 5-8, report a variety of computed statistics for each economic measure. The 

four figures record the distribution of the observed values for the Base Case and the 

Proposed Alternative either side-by-side (Figure 5-1) or as an indication of the degree to 

which the Proposed Alternative is preferred to the Base Case (Figures 5-2 through 5-4). 

49
Table 5-7 summarizes the results of the Monte Carlo simulation in which all 21 of the 

variables were varied in combination. A close examination of Table 5-7 reveals several 

interesting outcomes. First, the range of values—the difference between the minimum 

and maximum—is very wide. For example, the minimum value of life-cycle costs for the 

Base Case (LCCBC) is approximately $4.3 million, whereas the maximum is 

approximately $9.0 million. Life-cycle costs for the Proposed Alternative (LCCAlt) range 

from slightly more than $4.0 million to almost $7.5 million. Second, the computed value 

of the mean equals or exceeds the computed value of the median for each of the 

economic measures. This is because a small number of very large observations are 

pulling up the computed value of the mean. Finally, the computed values of the mean of 

each of the five economic measures are higher than the corresponding baseline value.

This is due to a small number of very large observations. 

Table 5-7. Summary Statistics Due to Changes in All of the Variables 

Statistical Measure 

Economic

Measure Minimum 25% 50% 75% Maximum Mean

Standard 

Deviation 

LCCBC 4,344.264 5,090.509 6,007.620 7,196.295 9,022.518 6,216.082 1,300.610

LCCAlt 4,012.033 4,648.762 5,319.521 6,157.292 7,428.776 5,450.631 925.923

PVNS 45.546 438.144 707.783 1,049.742 1,884.364 765.451 396.182

SIR 1.055 1.718 2.196 2.864 6.144 2.357 0.827

AIRR 0.042 0.063 0.073 0.085 0.118 0.074 0.014

The fact that the range of outcomes is so wide suggests that an in-depth examination of 

the results of this Monte Carlo simulation is warranted. We now turn to this in-depth

examination. 

Additional tabular results of the sensitivity analysis are recorded in Table 5-8. The table 

lists each of the calculated percentiles from the distribution of observed values. The 

range of percentiles included in the table goes from the 1st to the 99th. For purposes of 

this analysis, the 0th percentile is set equal to the minimum value, and the 100th percentile 

is set equal to the maximum value, both of which are recorded in Table 5-7. This enables 

a close coupling of the values recorded in Table 5-7 and the values used to plot each 

figure. Table 5-8 includes for each percentile the computed value for LCCBC, LCCAlt, 

PVNS, SIR, and AIRR. The percentiles are computed based on all 1,000 data points (i.e.,

observations) for each economic measure. The percentiles are estimated by first ordering 

each economic measure and then applying a statistical procedure. 

50
Table 5-8. Percentiles for Statistical Measures Due to Changes in All of the 

Variables 

LCC 

Base Case

LCC 

Proposed 

Alternative

PVNS SIR AIRR

1ST 4,408.939 4,131.020 113.571 1.170 0.047

2ND 4,447.807 4,166.421 165.689 1.239 0.049

3RD 4,471.871 4,192.933 196.449 1.275 0.050

4TH 4,499.119 4,215.310 212.869 1.307 0.051

5TH 4,514.646 4,232.637 231.645 1.332 0.052

6TH 4,556.499 4,251.961 240.219 1.358 0.053

7TH 4,590.336 4,278.672 258.973 1.377 0.053

8TH 4,616.113 4,300.847 272.651 1.403 0.054

9TH 4,635.000 4,325.696 284.012 1.416 0.055

10TH 4,654.661 4,343.490 295.988 1.436 0.055

11TH 4,681.120 4,364.229 302.565 1.459 0.056

12TH 4,721.964 4,383.464 310.806 1.479 0.056

13TH 4,758.266 4,403.611 314.475 1.496 0.057

14TH 4,770.549 4,421.375 323.981 1.518 0.058

15TH 4,791.359 4,439.894 333.398 1.531 0.058

16TH 4,811.403 4,458.578 348.714 1.546 0.058

17TH 4,825.715 4,487.720 358.007 1.556 0.059

18TH 4,847.179 4,505.870 373.479 1.573 0.059

19TH 4,864.959 4,530.502 382.957 1.601 0.060

20TH 4,893.068 4,549.341 392.742 1.629 0.060

21ST 4,927.469 4,563.671 404.246 1.647 0.061

22ND 4,961.376 4,585.233 412.618 1.665 0.061

23RD 4,994.424 4,605.595 418.045 1.677 0.062

24TH 5,045.221 4,629.449 428.964 1.689 0.062

25TH 5,090.509 4,648.762 438.144 1.718 0.063

26TH 5,139.193 4,667.035 446.522 1.738 0.063

27TH 5,165.694 4,687.329 454.925 1.753 0.064

28TH 5,183.902 4,703.428 465.935 1.775 0.064

29TH 5,200.027 4,715.920 473.315 1.784 0.064

30TH 5,229.603 4,743.970 487.034 1.806 0.065

31ST 5,260.299 4,774.719 496.304 1.824 0.065

32ND 5,292.044 4,794.741 509.277 1.845 0.066

33RD 5,316.582 4,806.027 518.374 1.859 0.066

Percentile

Economic Measure

51
Table 5-8. Percentiles for Statistical Measures Due to Changes in All of the 

Variables (continued) 

LCC 

Base Case

LCC 

Proposed 

Alternative

PVNS SIR AIRR

34TH 5,355.592 4,837.197 531.068 1.879 0.067

35TH 5,386.135 4,856.793 540.145 1.901 0.067

36TH 5,423.031 4,892.186 547.338 1.918 0.067

37TH 5,474.066 4,910.952 559.021 1.942 0.068

38TH 5,512.934 4,935.236 569.980 1.955 0.068

39TH 5,555.517 4,959.474 583.781 1.969 0.069

40TH 5,595.710 4,981.664 592.758 1.978 0.069

41ST 5,635.408 5,028.569 601.838 1.994 0.069

42ND 5,686.331 5,065.051 615.758 2.011 0.069

43RD 5,721.935 5,099.141 625.336 2.044 0.070

44TH 5,772.212 5,123.306 638.736 2.070 0.071

45TH 5,791.412 5,152.538 651.871 2.079 0.071

46TH 5,832.964 5,182.370 658.751 2.104 0.071

47TH 5,883.409 5,217.566 667.990 2.130 0.072

48TH 5,934.622 5,255.633 678.921 2.151 0.072

49TH 5,968.606 5,297.741 694.183 2.172 0.073

50TH 6,007.620 5,319.521 707.782 2.196 0.073

51ST 6,063.680 5,332.664 716.300 2.229 0.074

52ND 6,106.014 5,359.889 728.052 2.260 0.074

53RD 6,148.621 5,381.234 747.341 2.269 0.075

54TH 6,207.593 5,419.186 755.026 2.299 0.075

55TH 6,225.512 5,462.661 768.750 2.319 0.076

56TH 6,264.406 5,500.216 780.426 2.338 0.076

57TH 6,298.690 5,527.885 788.764 2.352 0.076

58TH 6,326.879 5,563.722 799.785 2.378 0.077

59TH 6,377.325 5,589.093 812.249 2.397 0.077

60TH 6,426.842 5,627.975 822.709 2.410 0.077

61ST 6,460.302 5,643.796 826.608 2.442 0.078

62ND 6,519.807 5,671.304 844.042 2.466 0.078

63RD 6,558.826 5,691.569 866.645 2.495 0.079

64TH 6,618.551 5,729.749 879.277 2.517 0.079

65TH 6,651.548 5,758.683 892.120 2.544 0.080

66TH 6,703.771 5,793.341 911.049 2.574 0.080

Percentile

Economic Measure

52
Table 5-8. Percentiles for Statistical Measures Due to Changes in All of the 

Variables (continued) 

LCC 

Base Case

LCC 

Proposed 

Alternative

PVNS SIR AIRR

67TH 6,770.153 5,834.585 925.657 2.605 0.081

68TH 6,827.283 5,869.818 937.659 2.626 0.081

69TH 6,870.662 5,900.864 954.495 2.657 0.081

70TH 6,913.685 5,948.606 976.590 2.684 0.082

71ST 6,952.237 5,989.425 989.883 2.728 0.083

72ND 6,990.303 6,015.056 1,000.831 2.777 0.083

73RD 7,068.004 6,057.059 1,020.912 2.823 0.084

74TH 7,138.904 6,111.740 1,034.297 2.841 0.084

75TH 7,196.295 6,157.292 1,049.742 2.864 0.085

76TH 7,263.727 6,194.589 1,077.578 2.893 0.085

77TH 7,309.931 6,249.632 1,097.065 2.935 0.086

78TH 7,356.209 6,285.977 1,113.470 2.959 0.086

79TH 7,455.522 6,321.390 1,137.340 2.993 0.087

80TH 7,547.515 6,372.358 1,157.499 3.020 0.087

81ST 7,617.376 6,424.954 1,175.125 3.055 0.088

82ND 7,683.960 6,495.491 1,189.019 3.106 0.088

83RD 7,771.339 6,601.461 1,208.726 3.140 0.089

84TH 7,843.874 6,651.722 1,229.132 3.179 0.089

85TH 7,909.649 6,686.210 1,251.006 3.228 0.090

86TH 7,997.986 6,722.381 1,273.997 3.276 0.091

87TH 8,042.787 6,773.367 1,292.204 3.313 0.091

88TH 8,124.799 6,806.513 1,310.231 3.374 0.092

89TH 8,197.230 6,859.746 1,339.281 3.417 0.092

90TH 8,260.406 6,890.234 1,361.483 3.469 0.093

91ST 8,316.132 6,915.155 1,383.361 3.551 0.094

92ND 8,391.509 6,987.816 1,404.434 3.635 0.095

93RD 8,425.666 7,026.362 1,422.436 3.703 0.096

94TH 8,500.261 7,058.668 1,442.569 3.793 0.097

95TH 8,541.554 7,103.044 1,471.245 3.934 0.099

96TH 8,625.981 7,136.917 1,500.697 4.015 0.099

97TH 8,725.047 7,203.183 1,530.541 4.190 0.101

98TH 8,801.704 7,266.987 1,585.247 4.400 0.103

99TH 8,856.323 7,341.796 1,665.578 4.884 0.108

Percentile

Economic Measure

53
The graphical results of the sensitivity analysis where all 21 variables were varied in 

combination are shown in Figures 5-1 through 5-4. The figures were constructed by first 

sorting the values of each economic measure from smallest to largest. The resultant 

cumulative distribution function (CDF) was then plotted. In each figure, the vertical axis 

records the probability that the economic measure (e.g., PVNS) is less than or equal to a

specified value. The values recorded on the horizontal axis cover the range of values 

encountered during this Monte Carlo simulation. 

Figure 5-1 shows how the life-cycle costs of the Base Case compare to those of the 

Proposed Alternative when all 21 variables are varied in combination. In analyzing 

Figure 5-1, it is useful to keep in mind that the values of LCCBC and LCCAlt from the 

baseline analysis were $5,937K and $5,255K, respectively. Comparisons between Figure 

5-1 and Table 5-8 are also helpful in interpreting the results of the Monte Carlo 

simulation. First, notice that the life-cycle cost trace of the Proposed Alternative in 

Figure 1 always remains to the left of the life-cycle cost trace of the Base Case. Thus, for

any given probability (e.g., 0.40, as measured by the 40th percentile recorded in Table 5-

8), the life-cycle cost of the Proposed Alternative ($4,982K) is less than the life-cycle 

cost of the Base Case ($5,596K). Similarly, for any given life-cycle cost (e.g., $5,000K),

the probability of being less than or equal to that cost is higher for the Proposed 

Alternative (0.40) than for the Base Case (0.23). Second, the horizontal distance between 

the Proposed Alternative and the Base Case gets larger as the cumulative probability

moves from 0.00 to 1.00. This translates into a wider range of life-cycle costs for the 

Base Case (i.e., maximum minus minimum); it is reflected in the higher standard

deviation for the Base Case recorded in the last column of Table 5-7. Figure 5-1 clearly 

demonstrates that the Proposed Alternative is the most cost-effective renovation strategy. 

However, it is instructive to examine how the use of other economic measures sheds light 

on other aspects of its cost-effectiveness. 

Figure 5-2 shows how present value net savings due to the use of the Proposed 

Alternative varies when all 21 variables are varied in combination. In analyzing Figure 

5-2, it is useful to keep in mind that the value of PVNS resulting from the baseline

analysis was $682K. As was seen in Table 5-7, the median value of the 1,000 

observations of $708K was $25K more than the value of PVNS calculated in the baseline 

analysis. The mean value of $765K exceeds the baseline value by $80K. It is important 

to note that the values of PVNS shown in Figure 5-2 and recorded in Tables 5-7 and 5-8 

are based on each of the 1,000 outcomes of the Monte Carlo simulation. Thus, for each

simulation s, where 1 ≤ s ≤ 1,000, values of LCCBC(s) and LCCAlt(s) are calculated.

These individual calculations are based on random draws for the 21 variables in Table 5-

1. Their difference equals PVNS(s). Since the values of LCCBC and LCCAlt are rankordered in both Figure 5-1 and Table 5-8, their differences approximate but do not equal 

the computed values for PVNS shown in Figure 5-2 or recorded in Table 5-8. Referring 

to Table 5-1, we see that only 3 of the 21 variables are common to both the Base Case 

and the Proposed Alternative. Both the Base Case and the Proposed Alternative have 

similar sets of variables analyzed in the Monte Carlo simulation (e.g., the probability of 

major damage), but these variables are treated independently in the Monte Carlo 

simulation. The Proposed Alternative also has two variables—improved productivity and 

54
the change in traffic patterns—that do not occur in the Base Case (see entries 16 and 17 

in Table 5-1). Thus, care is needed when comparing the economic measures calculated

via the Monte Carlo simulation. Turning now to Figure 5-2, we see that a value of PVNS 

equal to $400K occurs at 0.20 on the cumulative distribution function (CDF). Stated 

another way, there is a probability of 0.80 that PVNS will exceed $400K. Reference to

Figure 5-2 and Table 5-8 reveals that for almost 30 % of the observations resulting from 

the Monte Carlo simulation PVNS exceeds $1,000K. 

Figure 5-3 shows how the savings-to-investment ratio, SIR, on the Proposed 

Alternative’s investments varies when all 21 variables are varied in combination. In

analyzing Figure 5-3, it is useful to keep in mind that the value of the SIR resulting from

the baseline analysis was 2.13. As was seen in Table 5-7, the median value of the SIR for

the 1,000 observations was 2.196 and the mean value was 2.357. The difference between 

the mean value of the SIR in the sensitivity analysis and the baseline value can be 

explained through reference to Figure 5-3 and Table 5-8. Reference to Figure 5-3 

demonstrates that it trails off very slowly as the cumulative probability approaches 1.0. 

This means that the CDF for the SIR is highly, positively skewed (examine the upper tail 

of the CDF trace). Thus, a few very high values are pulling up the mean. To gain a more 

complete picture, refer both to Figure 5-3 and the entries under the SIR column heading 

of Table 5-8. First, note that the lower limit shown on Figure 5-3 extends almost to 1.0. 

Second, the trace of the SIR first increases rapidly until about the 10th percentile. The 

trace then increases at a fairly linear rate until about the 50th percentile, after which it 

increases at a slower (i.e., decreasing) rate. Third, the values for the 90th and 99th

percentile are 3.469 and 4.884, respectively. Finally, the maximum value for the SIR is

6.144 (see Table 5-7). Thus, nearly 25 % of the total range of the SIR values is due to 

less than 1 % of the observations. 

The presence of extreme values poses a number of challenges to the decision-making 

process. However, the results of the Monte Carlo simulation produce a wealth of 

information that may be employed by the decision maker. In this case, the extreme

values serve to reinforce the preferred outcome. Since this is not always the case, it is

useful to explore how some of the additional information from the Monte Carlo 

simulation can be brought into play. Both the mean and median are measures of central 

tendency for the CDF. In this case, the computed SIR values for both the mean and the 

median indicate that the Proposed Alternative is the most cost-effective renovation 

strategy. A generic measure of central tendency is the inter-quartile range. The interquartile range contains the middle 50 % of the observations. For the SIR values resulting 

from the Monte Carlo simulation, the inter-quartile range goes from 1.718 (i.e., the 25th

percentile) to 2.864 (i.e., the 75th percentile). These values imply that every dollar 

invested in the Proposed Alternative will generate between $1.72 and $2.86 in savings 

(expressed in present value terms). Thus, the bulk of the observations are located in a 

range that indicates strong preference for the Proposed Alternative. Another outcome

measure of use to decision makers, is the probability that the SIR exceeds 1.0. In this 

case, the probability is 1.0. 

55
Figure 5-1. Life-Cycle Costs for Each Alternative in Thousands of Dollars Due to Changes in All of the Va

0.000.100.200.300.400.500.600.700.800.901.00

3,900 4,400 4,900 5,400 5,900 6,400 6,900 7,400 7,900 8,400 

Life-Cycle Costs in Thous ands of Dollars

Cumulative Probability

A lt

Ba

56
Figure 5-2. Present Value Net Savings in Thousands of Dollars Due to Changes in All of the Variables 

0.000.100.200.300.400.500.600.700.800.901.00

0 200 400

600 800 1,000 1,200 1,400 1,600 

P

r

es e

nt V

alu

e N

et S

a

vin

gs in T

h

o

us a

n

ds o

f D

olla

rs

Cumulative Probability

57
Figure 5-3. Savings-to-Investment Ratio Due to Changes in All of the Variables 

0.000.100.200.300.400.500.600.700.800.901.00

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 

S avings-to-Investment Ratio

Cumulative Probability

58
Figure 5-4. Adjusted Internal Rate of Return Due to Changes in All of the Variables 

0.000.100.200.300.400.500.600.700.800.901.00

0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 

Adjusted Internal Rate of Return

Cumulative Probability

59
Figure 5-4 shows how the adjusted internal rate of return, AIRR, on the investments 

associated with the Proposed Alternative varies when all 21 variables are varied in 

combination. In analyzing Figure 5-4, it is useful to keep in mind that the value of AIRR 

resulting from the baseline analysis was 0.072. As was seen in Table 11, both the mean 

(0.074) and median (0.073) values of the 1,000 observations were nearly equal. Due to 

the way in which the AIRR is calculated, Figure 5-4 exhibits a pattern different from

those seen in the other figures.40 The CDF for the AIRR has both a fairly long lower tail 

as well as a long upper tail and appears to be nearly symmetrical. This helps to explain 

why the mean and median values in the sensitivity analysis are close to the value

computed in the baseline analysis. 

If the AIRR exceeds the discount rate, then the Proposed Alternative is a more costeffective renovation strategy than the Base Case. In this case, the discount rate is defined

as the minimum acceptable rate of return for an investment to be economically

acceptable; it is also equal to the hurdle rate of return. Since the discount rate is 4 % and 

the minimum observed AIRR value from the Monte Carlo simulation is 4.2 %, all 

computed values of the AIRR exceed that hurdle rate. Thus, the Proposed Alternative is 

the most cost-effective investment choice. 

The types of cost information presented in the baseline analysis and the sensitivity 

analysis have differed thus far. This stems from the fact that the focus of the baseline 

analysis was on a detailed representation of the cost data based on the cost-accounting 

framework. Up to this point, the focus of the sensitivity analysis was to rigorously 

evaluate the effects of uncertainty on the economic measures. Thus, the results of the 

sensitivity analysis, as presented in Tables 5-3 through 5-8 and Figures 5-1 through 5-4, 

treated the cost data at an aggregated level. However, the results of the Monte Carlo

simulation enable more detailed analyses to be conducted. For example, a table in the 

same format as Table 5-7 could be prepared which builds on the cost classification 

presented in Table 4-11. Such a table would be useful in analyzing how different 

stakeholder groups were affected by a particular investment strategy or how costs vary 

across budget categories. 

Tables 5-9 and 5-10 provide such a snapshot. Specifically, they summarize life-cycle 

cost information from the Monte Carlo simulations using the same format as Table 4-11.

Both tables are divided into two parts. Part A reports key statistical measures for the 

Base Case. Part B reports key statistical measures for the Proposed Alternative. The two 

tables focus on different “data slices,” however, in order to illustrate how the sensitivity

analysis complements the baseline analysis. Table 5-9 covers the same material that was

reported in Table 4-11. Table 5-10 provides additional detail on all event-related costs.

 40 Although the values for AIRR are a monotonic transformation of the values for SIR, the shapes of the 

two CDFs are quite dissimilar. This is because the AIRR is functionally related to (SIR)

1/25. This

relationship is highly non-linear, explaining why the two CDF traces are so dissimilar. 

60
Table 5-9. Statistical Measures by Cost Classification and Cost Type Due to Changes in All of the Input V

Part A. Base Case 

Statistical Measures for the Base Case Cost

Classification Cost Type Minimum 25% 50% 75% Maximum 

Bearer: Owner/Manager 2,538.714 2,909.572 3,339.187 3,880.696 4,797.152 

Occupant/User 1,336.802 1,648.684 1,992.238 2,451.522 3,175.978 

Third Party 422.708 535.835 681.143 868.386 1,157.819 

Category: Capital Investment 1,042.323 1,136.640 1,170.961 1,207.663 1,343.717 

O&M 2,783.707 3,371.207 4,152.394 5,117.372 6,588.994 

Other 430.409 550.434 701.302 892.625 1,204.773 

Component: Building/Facility Elements 2,176.572 2,503.133 2,852.649 3,293.802 4,007.353 

Building/Facility Site Work 136.109 145.872 156.115 169.980 192.043 

Non-Elemental 1,972.161 2,435.590 3,009.532 3,730.476 4,877.460 

61
Table 5-9. Statistical Measures by Cost Classification and Cost Type Due to Changes in All of the Input V

Part B. Proposed Alternative 

Statistical Measures for the Alternative Cost

Classification Cost Type Minimum 25% 50% 75% Maximum 

Bearer: Owner/Manager 2,734.617 3,165.627 3,489.845 3,921.559 4,669.915 

Occupant/User 995.680 1,243.041 1,532.129 1,891.290 2,436.117 

Third Party 189.734 237.344 281.978 333.799 426.369 

Category: Capital Investment 1,567.308 1,716.373 1,771.406 1,828.779 1,959.304 

O&M 2,132.068 2,630.246 3,253.768 4,011.710 5,216.230 

Other 192.555 241.878 287.215 341.011 440.176 

Component: Building/Facility Elements 2,375.873 2,764.691 3,055.764 3,436.134 4,048.389 

Building/Facility Site Work 229.685 238.056 247.254 258.308 276.205 

Non-Elemental 1,355.974 1,645.834 2,012.984 2,465.556 3,170.004 

62
Table 5-10. Statistical Measures by Cost Category and Bearer for Event–Related Cost Items Due to C

Input Variables 

Part A. Base Case 

Statistical Measures for the Base Case

Cost

Category Bearer Event-Related Cost Item Minimum 25% 50% 75% Maximum 

CapitalInvestment

Owner/ Manager 

Damage to Data Center 18.714 43.212 59.640 80.047 139.537 

O&M Occupant/ User Business Interruption 32.092 71.689 97.920 128.450 229.019 

Record Reconstruction 104.961 105.231 106.529 108.988 112.525 

Other Owner/ Manager Non-fatal Injuries 3.061 7.101 9.711 13.038 24.149 

Occupant/ User 

Non-fatal Injuries 3.061 7.101 9.711 13.038 24.149 

Third Party 

Non-fatal Injuries 3.061 7.101 9.711 13.038 24.149 Denial ofService 13.465 28.038 39.554 52.709 97.370 Identity Theft 399.563 494.094 626.434 805.262 1,049.672 

63
Table 5-10. Statistical Measures by Cost Category and Bearer for Event–Related Cost Items Due to C

Input Variables (continued) 

Part B. Proposed Alternative 

Statistical Measures for the Alternative 

Cost

Category Bearer Event-Related Cost Item Minimum 25% 50% 75% Maximum 

CapitalInvestment

Owner/ Manager 

Damage to Data Center 4.597 8.371 11.043 14.284 25.780 

O&M Occupant/ User Business Interruption 11.329 22.456 29.182 37.571 71.250 

Record Reconstruction 28.500 28.653 29.094 29.853 30.903 

Other Owner/ Manager Non-fatal Injuries 1.065 2.253 2.916 3.782 6.903 

Occupant/ User 

Non-fatal Injuries 1.065 2.253 2.916 3.782 6.903 

Third Party 

Non-fatal Injuries 1.065 2.253 2.916 3.782 6.903 Denial ofService 5.321 10.011 13.066 17.116 33.411 Identity Theft 105.838 131.710 168.065 217.354 284.909 

64
Table 5-9 employs the cost-accounting framework to summarize life-cycle costs by cost 

classification and by cost type. The three cost classifications are concerned with the 

bearer, the budget category, and the building component. Associated with each cost 

classification are its cost types. The cost types specify who bears which costs, how costs 

are allocated among three widely accepted budget categories, and how costs are allocated 

among key building components. Each individual cost item has associated with it a cost 

type for each cost classification. Thus, the combined costs across a given cost 

classification (e.g., bearer) equal the life-cycle cost of the investment alternative under

analysis. 

Each statistical measure reported in Table 5-9 (e.g., 25th percentile), when aggregated

across a cost classification (e.g., bearer), approximates but does not necessarily equal the 

corresponding statistical measure for the life-cycle costs of the Base Case, LCCBC, and 

the Proposed Alternative, LCCAlt, recorded in Table 5-7. This is because Table 5-9 

disaggregates the life-cycle cost total into its constituent parts (e.g., capital investment 

costs, O&M costs, and other costs) and calculates statistical measures for each constituent 

part. This approach provides an in-depth snapshot of how the cost of each constituent

part varies. Table 5-9 uses the same statistical measures as Table 5-7. 

Part A of Table 5-9 summarizes life-cycle cost information on the Base Case. Reference 

to the table reveals that the value of each statistical measure—expressed in thousands of 

2003 dollars—varies considerably across cost types. The mean exceeds the median

across all cost types, reinforcing the effects of high values on the mean as noted in the 

higher-level cost summaries presented in Tables 5-7 and 5-8 and in Figure 5-1. The 

median value tends to exceed slightly the baseline value for the corresponding cost type.

For example, the median value for capital investment is $1,171K, whereas the baseline 

value is $1,168K (see Table 4-11). The last column of the table records the standard 

deviation. The cost type with the highest standard deviation is O&M costs, followed by 

non-elemental costs. The cost types with the lowest standard deviations are capital

investment costs and building/facility site work. Because the differences in the

calculated value of the standard deviation are so large across cost types, it is instructive to

“drill down” on potential sources of that variability. This approach is taken for eventrelated cost items in Table 5-10. 

Part B of Table 5-9 summarizes life-cycle cost information on the Proposed Alternative. 

Reference to the table reveals that the value of each statistical measure—expressed in

thousands of 2003 dollars—varies considerably across cost types. The mean exceeds the 

median across all cost types, reinforcing the effects of high values on the mean as noted 

in the higher-level cost summaries presented in Tables 5-7 and 5-8 and in Figure 5-1. 

The median value tends to exceed slightly the baseline value for the corresponding cost 

type. For example, the median value for O&M costs is $3,254K, whereas the baseline

value is $3,201K (see Table 4-11). The last column of the table records the standard 

deviation. The cost type with the highest standard deviation is O&M costs, followed by 

non-elemental costs. The cost types with the lowest standard deviations are other costs, 

third-party costs, and building/facility site work. Note that with one important exception, 

the standard deviation for each cost type recorded for the Proposed Alternative is less 

65
than the corresponding standard deviation for the Base Case. The one exception is 

concerned with capital investment costs. As noted earlier, because the differences in the 

calculated value of the standard deviation are so large across cost types, it is instructive to

“drill down” on potential sources of that variability. This approach is taken for eventrelated cost items in Table 5-10. 

Table 5-10 builds on the material presented in Table 5-9. Table 5-10 records

distributional information on all event-related cost items. The table records the cost 

category and the bearer in the first two columns. Each event-related cost item is recorded

in the third column. 

Part A of Table 5-10 covers the Base Case; it builds on the material presented in Part A

of Table 5-9. Referring to Table 5-10, we see that the mean in every case exceeds the 

median—just as was observed in Table 5-9. The median value for each event-related 

cost item is fairly close to the corresponding baseline value (see event-related entries in 

Tables 4-5, 4-6, and 4-7). The last column of the table records the standard deviation.

With the exception of the identity theft cost item, the standard deviations are much 

smaller than the values recorded in Table 5-9. Comparisons between the entries in Part A 

of Tables 5-9 and 5-10 reveal that the large variations in O&M costs and non-elemental 

costs are not linked to event-related costs items.

41 On the other hand, almost all of the 

variation in other costs and third-party costs are due to the identity theft cost item. 

Part B of Table 5-10 covers the Proposed Alternative; it builds on the material presented 

in Part B of Table 5-9. Referring to Table 5-10, we see that the mean in every case 

exceeds the median—just as was observed in Table 5-9. The median value for each

event-related cost item is fairly close to the corresponding baseline value (see eventrelated entries in Tables 4-8, 4-9, and 4-10). The last column of the table records the 

standard deviation. The standard deviations are much smaller than the values recorded in 

Table 5-9. Comparisons between the entries in Part B of Tables 5-9 and 5-10 reveal that 

the large variations in O&M costs and non-elemental costs are not linked to event-related 

costs items.

42 Note that the relatively high value of the standard deviation of capital 

investment costs is due to variations in the costs of the enhanced renovation (see Table 5-

443) rather than the event-related cost item damage to the data center. Comparisons 

between Part A and Part B of Table 5-10, reveal that the deviations of event-related cost 

items for the Proposed Alternative are much smaller than the corresponding values for 

the Base Case. This helps to reinforce the choice of the Proposed Alternative as the most

cost-effective risk mitigation strategy. 

 41 Due to the impact of variations in the discount rate, the input cost site security was the principal

component of the high value of the standard deviation for O&M costs, non-elemental costs, and 

owner/manager costs for the Base Case. 42 As was the case for the Base Case, due to the impact of variations in the discount rate, the input cost site

security was the principal component of the high value of the standard deviation for O&M costs, nonelemental costs, and owner/manager costs for the Proposed Alternative. 43 Note that the calculated value for the standard deviation of LCCAlt for the enhanced renovation recorded

in Table 5-4 is due to changes in the value of that variable alone, rather than for all variables 

simultaneously. 

66
5.4 How the Sensitivity Analysis Links to the Software Product 

The sensitivity analysis links directly to four key features of the software product. These 

features are illustrated in Tables 5-1 through 5-10 and in Figures 5-1 through 5-4; they 

are concerned with: (1) the cost-accounting framework; (2) selected data inputs for both 

deterministic and probabilistic sensitivity analyses; (3) selected output reports; and (4) 

the “drill down” on the “Cost Summary” screen of the software product.

The software product employs the same cost-accounting framework as described in 

Section 2.3. This promotes a close coupling between the sensitivity analysis and the 

baseline analysis. The “roll ups” to the cost types and cost classifications for individual 

cost items are identical to those used in the baseline analysis. Thus, any changes in lifecycle cost are traceable to variations in input variables about their baseline values. Such 

an approach promotes in-depth analyses via the “drill down” feature described at the end 

of this section. 

Table 5-1 records the types of cost-related information requested of the software 

product’s user for both deterministic and probabilistic sensitivity analyses. For 

deterministic sensitivity analyses, Table 5-1 records the minimum and maximum setting 

for each variable of interest. For probabilistic sensitivity analyses, Table 5-1 records both 

the minimum and maximum setting along with the appropriate probability distribution to

be used in any Monte Carlo simulations. The input screens for both types of sensitivity 

analysis are designed to collect information on both the variables of interest and the type

of analysis (e.g., single-variable deterministic). 

Tables 5-2 through 5-8 and Figures 5-1 through 5-4 are indicative of selected sets of 

output reports from the software product. Table 5-2 is indicative of the Most Significant 

Factors tab in the software product. This feature identifies those factors which have the 

greatest impact on life-cycle cost. The output from the software product will differ 

slightly from the material contained in Table 5-2, because Table 5-2 is constructed via a 

two-stage process that is based on net savings rather than a pure measure of life-cycle 

costs. Figure 5-1 is a standard output from the software product whenever a Monte Carlo 

simulation is performed. The user will also have the option to obtain additional graphical 

reports for economic measures other than life-cycle cost (e.g., Figures 5-2 through 5-4). 

Tabular summaries for Monte Carlo simulations, either singly (see Tables 5-3 through 5-

6) or in combination (see Table 5-7) will also be available to the user of the software 

product. 

The software product is designed to help the user “drill down” on how individual cost 

items are distributed across Bearer, Budget Category, and Building Component. The 

software product drills down according to a probabilistic version of the Cost Summary 

screen (see Table 4-11 and its supporting tables). This feature is illustrated via a twostage analysis. The first stage drills down from life-cycle cost (see Table 5-7) to the 

individual cost categories and cost types; it is illustrated in Table 5-9. The second stage 

drills down to the individual cost items to determine how they contribute to variations in 

life-cycle cost; it is illustrated in Table 5-10. This approach gives users a snapshot of all 

67
of the costs entering the analysis, expressed in present value terms, which “roll up” into 

the life-cycle costs recorded in the Cost Summary screen. 

It is also worth noting that the information presented in Tables 5-1 through 5-10 and in 

Figures 5-1 through 5-4 may be used as a test problem for prospective users of the 

software product. Prospective users of the software product should first attempt to 

reproduce the computed values for PVNS recorded in Table 5-2. These values are 

deterministic and will highlight any specification errors or deviations from the minimum 

and maximum values recorded in Table 5-1. Next, prospective users should perform a

Monte Carlo simulation using the values and the distributions recorded in Table 5-1. 

Although the computed values for the statistical measures (e.g., 25th percentile) will differ 

from one Monte Carlo simulation to another, differences between median values should 

be fairly small. Thus, the users of the software product have a convenient frame of 

reference through which they can gain familiarity with the sensitivity analysis features of

the software product. 

68
6 Summary and Suggestions for Further Research 

6.1 Summary 

This report illustrates how to apply the life-cycle cost method to a prototypical data 

center renovation project. The building owners are evaluating two renovation strategies: 

the Base Case, which employs pre-9/11 levels of security; and the Proposed Alternative,

which recognizes the increased potential for a cyber attack and a CBRE attack. Two

basic types of analysis are presented: a baseline analysis and a sensitivity analysis 

employing Monte Carlo simulation. The two types of analysis complement and reinforce 

each other. A detailed cost accounting framework is used to classify costs by bearer,

budget category, and building component for the two renovation strategies. 

The material presented in the baseline analysis (see Chapter 4) demonstrates how the cost

accounting framework promotes better-informed decision making by clearly identifying 

the most cost-effective renovation strategy. The cost data presented in Tables 4-5 

through 4-11 represent a simple but concise statement of who bears which costs, how 

much they bear, and how these costs are distributed. 

The cost information presented as part of the baseline analysis demonstrated how data

used to calculate life-cycle costs could also be used to compute three additional 

measures: (1) the present value of net savings (PVNS); (2) the savings-to-investment

ratio (SIR); and (3) the adjusted internal rate of return (AIRR). These economic 

measures are all useful in evaluating whether or not to undertake a particular investment, 

since each measure provides a different perspective. The PVNS measures the overall 

magnitude of cost savings. The SIR measures the cost savings per unit of capital 

investment. The AIRR is the annual percentage yield from the capital investment over 

the study period. Exhibit 6-1 summarizes the baseline analysis. It provides a brief 

description of each renovation strategy and covers the background, approach, and results 

of the economic evaluation. Exhibit 6-1 utilizes the summary format introduced in

Chapter 2 (see Exhibit 2-1); it provides a concise statement of why the Proposed 

Alternative is the “preferred” choice with a PVNS of $682K.

In the sensitivity analysis (see Chapter 5), each economic measure is first rigorously 

analyzed. Charts and tables are produced to demonstrate how changing assumptions, 

input data, and the two sets of attack scenarios affect each economic measure. The 

results of the sensitivity analysis reinforced how to choose the most cost-effective risk 

mitigation plan. The second part of the sensitivity analysis established a direct linkage to

several of the more detailed tables presented in the baseline analysis. Specifically, Tables

5-9 and 5-10 were used to “drill down” on the individual cost categories, cost types, and 

event-related cost items. This approach enabled the sources of variability to be identified

and quantified, thus producing a risk mitigation plan that identifies the most costeffective investment alternative as well as incorporates information on specific eventrelated risks.

69
Exhibit 6-1. Summary of the Data Center Case Study 

1.a Significance of the Project:

The data center undergoing renovation is a single-story

structure located in a suburban community. The floor area of 

the data center is 40,000 ft2

 (3,716 m2). The replacement value 

of the data center is $20 million for the structure plus its 

contents. The data center contains financial records that are in 

constant use by the firm and its customers. Thus, any 

interruption of service will result in both lost revenues to the 

firm and potential financial hardship for the firm’s customers. 

The occupants of the data center are part of the same parent

company, but not part of the same corporate division

responsible for facilities construction and renovation.

The building owners employ two different renovation 

strategies. The first, referred to as the Base Case, employs 

upgrades which are consistent with pre-9/11 levels of security. 

Thus, the Base Case represents maintenance of the status quo. 

The second, referred to as the Proposed Alternative, 

recognizes that in the post-9/11 environment the data center 

faces heightened risks in two areas. These risks are associated 

with the vulnerability of information technology resources and 

the potential for damage to the facility and its contents from

chemical, biological, radiological, and explosive (CBRE) 

hazards. Two scenarios—the potential for a cyber attack and 

the potential for a CBRE attack—are used to capture these 

risks. 

1.b Key Points:

1. The objective of the renovation project 

is to provide cost-effective operations

and security protection for the data

center. 

2. The renovation has been planned for

some time to upgrade the data center’s 

HVAC, telecommunications and data

processing systems and to address a

number of generic security concerns. 

3. Two upgrade alternatives are 

proposed:

 - Base Case (Basic Renovation) and 

 - Proposed Alternative (Enhanced 

Renovation), which augments the 

Base Case by strengthening

portions of the exterior envelope, 

limiting vehicle access to the data 

center site, significantly improving

the building’s HVAC, data 

processing and telecommunications 

systems, and providing better 

linkage of security personnel to the

telecommunications network.

2. Analysis Strategy: How Key Measures are Estimated 

The following economic measures are calculated as present-value (PV) amounts:

(1) Life-Cycle Costs (LCC) for the Base Case (Basic Renovation) and for the Proposed Alternative 

(Enhanced Renovation), including all costs of acquiring and operating the data center over the length of 

the study period. The selection criterion is lowest LCC. 

(2) Present Value Net Savings (PVNS) that will result from selecting the lowest-LCC alternative. 

PVNS > 0 indicates an economically worthwhile project. 

Additional measures: 

(1) Savings-to-Investment Ratio (SIR), the ratio of savings from the lowest-LCC to the extra investment

required to implement it. A ratio of SIR >1 indicates an economically worthwhile project.

(2) Adjusted Internal Rate of Return (AIRR), the annual return on investment over the study period. An

AIRR > discount or hurdle rate indicates an economically worthwhile project.

Data and Assumptions: 

- The Base Date is 2003. 

- The alternative with the lower first cost (Basic Renovation) is designated the Base Case. 

- The study period is 25 years and ends in 2027. 

- The discount or hurdle rate is 4.0 % real. 

- Annual probabilities for the outcomes for each attack scenario are given along with outcome costs. 

- Annual probabilities and outcome costs differ by renovation strategy. 

- However, both the Base Case and the Proposed Alternative have similar types of outcome costs. 

70
Exhibit 6-1. Summary of the Data Center Case Study (continued) 

3.b Key Results: 

 LCC 

Base Case $5,937K

 Proposed Alt. $5,255K 

 PVNS from Alt. $682K 

 SIR 2.13 

 AIRR 7.2 %

3.a Calculation of Savings, Costs, and Additional Measures 

Savings and Costs in Thousands of Dollars ($K) 

PV of Investment Costs Base Case Proposed Alt. 

 Capital Investment $1,168K $1,772K 

PV of Increased Investment Costs for Proposed Alt. $604K

PV of Non-Investment Costs Base Case Proposed Alt.

 O&M Costs 4,082K 3,201K 

 Other Costs 687K 282K

 $4,769K $3,483K 

 PV of Non-Investment Savings for Proposed Alt. $1,286K 

LCC Base Case Proposed Alt.

 PV of Investment Costs 1,168K 1,772K 

 PV of Non-Investment Costs 4,769K 3,483K

 $5,937K $5,255K 

PVNS from Proposed Alternative $682K 

Savings-to-Investment Ratio (SIR) 

 PV of Non-Investment Savings $1,286K

 Divided by PV of Incr. Investment 604K 

 SIR = 2.13 

Adjusted Internal Rate of Return (AIRR)

 (1+0.04) 2.131/25 – 1 = 0.072

 AIRR = 7.2 % 

which exceeds the hurdle rate of 4.0 % 

3.c Traceability: 

Life-cycle costs and supplementary 

measures were calculated according 

to ASTM standards E 917, E 964, 

E 1057, and E 1074. 

71
The data center case study highlights the merits of the life-cycle cost method and why we

have chosen this method as the core component of our decision methodology. The case 

study also provides a snapshot of the types of data inputs and cost summaries that our 

software product will produce, and serves as a valuable reference point for checking the 

computational algorithms in the software product. 

6.2 Suggestions for Further Research

The background work for this report uncovered additional areas of research that might be 

of value to government agencies and private-sector organizations concerned with 

homeland security-related issues. These areas of research are concerned with: (1) the 

specification of mitigation strategies with particular emphasis on spillover effects; (2) the 

construction of scenarios for modeling sequential investment decisions; and (3) 

evaluations based on multiattribute decision analysis. 

The data center case study illustrated an example of a beneficial spillover. In this case, 

the improved indoor air quality and reduced energy consumption associated with the 

Proposed Alternative’s HVAC upgrade resulted in a significant reduction in life-cycle 

cost. In comparing investment alternatives, it is important to recognize that spillovers—

either positive or negative—can tip the balance from one alternative to another. 

Consequently, every effort should be made to identify spillovers and quantify their 

impacts. This is especially true for cases where a particular mitigation strategy may 

address multiple hazards. 

Many investment decisions are sequential in nature. The data center case study included 

sequential elements related to capital replacements. However, additional research on 

scenario construction is needed to better capture the sequential nature of decision making 

in a life-cycle cost context. Because the sequence in which investment decisions are 

made impacts not only capital costs but O&M and other costs as well, research on 

scenario construction would help the users of the software product to identify those

investment sequences which have the most favorable impact on life-cycle cost. 

Many investment alternatives differ in characteristics that decision makers consider 

important but that are not readily expressed in monetary terms. Because the standardized 

evaluation methods employed in this report consider only monetary benefits and 

monetary costs associated with alternative investment choices, their application does not 

reflect the importance of these non-financial characteristics to the decision maker. When 

non-financial characteristics are important, decision makers need a method that accounts 

for these characteristics (also called attributes) when choosing among alternative 

investments. A class of methods that can accommodate non-monetary benefits and costs

is multiattribute decision analysis.44 

 44For more information on multiattribute decision analysis, see Norris, Gregory A., and Harold E. Marshall.

1995. Multiattribute Decision Analysis Method for Evaluating Buildings and Building Systems. NISTIR

5663. Gaithersburg, MD: National Institute of Standards and Technology. 

72
The analytical hierarchy process (AHP) is one of a set of multiattribute decision analysis

methods that considers non-financial characteristics in addition to common economic

evaluation measures when evaluating project alternatives. The AHP has several 

important strengths: (1) it is well-known and well-reviewed in the literature; (2) it 

includes an efficient attribute weighting process; (3) it incorporates hierarchical 

descriptions of attributes; (4) its use is facilitated by available software; and (5) it has 

been accepted by ASTM as a standard practice for investments related to buildings and 

building systems.45 

The AHP and its associated software represent a powerful and versatile management tool. 

How to apply this management tool most productively to homeland security-related 

issues suggests additional research on how decision makers view non-financial outcomes 

associated with low-probability, high-consequence events. 

 45 ASTM International. 2002. “Standard Practice for Applying Analytical Hierarchy Process (AHP) to

Multiattribute Decision Analysis of Investments Related to Buildings and Building Systems.” E 1765.

Annual Book of ASTM Standards: 2002. Vol. 04.12. West Conshohocken, PA: ASTM International. 

73
74
References 

ASTM International. 2002. “Standard Classification for Building Elements and Related 

Site Work—UNIFORMAT II.” E 1557. Annual Book of ASTM Standards: 2002. Vol. 

04.11. West Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Guide for Selecting Economic Methods for

Evaluating Investments in Buildings and Building Systems.” E 1185. Annual Book of 

ASTM Standards: 2002. Vol. 04.11. West Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Guide for Summarizing the Economic Impacts of 

Building-Related Projects.” E 2204. Annual Book of ASTM Standards: 2002. Vol. 

04.12. West Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Practice for Applying Analytical Hierarchy 

Process (AHP) to Multiattribute Decision Analysis of Investments Related to Buildings 

and Building Systems.” E 1765. Annual Book of ASTM Standards: 2002. Vol. 04.12. 

West Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Practice for Measuring Benefit-to-Cost and 

Savings-to-Investment Ratios for Investments in Buildings and Building Systems.” E

964. Annual Book of ASTM Standards: 2002. Vol. 04.11. West Conshohocken, PA: 

ASTM International. 

ASTM International. 2002. “Standard Practice for Measuring Cost Risk of Buildings and 

Building Systems.” E 1946. Annual Book of ASTM Standards: 2002. Vol. 04.12. West 

Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Practice for Measuring Internal Rate of Return 

and Adjusted Internal Rate of Return for Investments in Buildings and Building 

Systems.” E 1057. Annual Book of ASTM Standards: 2002. Vol. 04.11. West 

Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Practice for Measuring Life-Cycle Costs of 

Buildings and Building Systems.” E 917. Annual Book of ASTM Standards: 2002. Vol. 

04.11. West Conshohocken, PA: ASTM International. 

ASTM International. 2002. “Standard Practice for Measuring Net Benefits for

Investments in Buildings and Building Systems.” E 1074. Annual Book of ASTM

Standards: 2002. Vol. 04.11. West Conshohocken, PA: ASTM International. 

ASTM International. Fourth Edition, 1999. ASTM Standards on Building Economics. 

West Conshohocken, PA: ASTM International. 

75
Chapman, Robert E., and Chi J. Leng. Expected publication date 2003. Cost-Effective 

Responses to Terrorist Risks. NISTIR (in preparation). Gaithersburg, MD: National 

Institute of Standards and Technology. 

Chapman, Robert E. and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two 

Case Studies in Building Technology. NISTIR 5840. Gaithersburg, MD: National

Institute of Standards and Technology. 

Executive Office of the President. 2003. The National Strategy for the Physical 

Protection of Critical Infrastructures and Key Assets. Washington, DC: The White 

House. 

Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office

of Management and Budget. 

Fisk, William J., and Arthur H. Rosenfeld. 1997. “Estimates of Improved Productivity 

and Health from Better Indoor Environments,” Indoor Air (Vol. 7): pp. 158-172. 

Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle Costing Manual for the 

Federal Energy Management Program. NIST Handbook 135. Gaithersburg, MD: 

National Institute of Standards and Technology.

Lomonaco, Carol, and Dennis Miller. 1997. “Comfort and Control in the Workplace,” 

ASHRAE Journal (September): pp. 50-56. 

Marshall, Harold E. 1995. Choosing Economic Evaluation Methods—Part III in the 

Audiovisual Series on Least-Cost Energy Decisions for Buildings. Gaithersburg, MD: 

National Institute of Standards and Technology.

Marshall, Harold E. 1995. Least-Cost Energy Decisions for Buildings—Part III: 

Choosing Economic Evaluation Methods Video Training Workbook. NISTIR 5604. 

Gaithersburg, MD: National Institute of Standards and Technology. 

Marshall, Harold E. 1993. Least-Cost Energy Decisions for Buildings—Part II: 

Uncertainty and Risk Video Training Workbook. NISTIR 5178. Gaithersburg, MD: 

National Institute of Standards and Technology.

Marshall, Harold E. 1992. Uncertainty and Risk—Part II in the Audiovisual Series on 

Least-Cost Energy Decisions for Buildings. Gaithersburg, MD: National Institute of 

Standards and Technology. 

Marshall, Harold E. 1988. Techniques for Treating Uncertainty and Risk in the

Economic Evaluation of Building Investments. NIST Special Publication 757.

Gaithersburg, MD: National Institute of Standards and Technology. 

76
National Research Council. 2002. Making the Nation Safer: The Role of Science and 

Technology in Countering Terrorism. Washington, DC: The National Academies Press. 

Norris, Gregory A., and Harold E. Marshall. 1995. Multiattribute Decision Analysis

Method for Evaluating Buildings and Building Systems. NISTIR 5663. Gaithersburg, 

MD: National Institute of Standards and Technology. 

Palisade Corporation. 1997. Guide to Using @RISK: Risk Analysis and Simulation AddIn for Microsoft Excel or Lotus 1-2-3. Newfield, NY: Palisade Corporation. 

Ruegg, Rosalie T. and Harold E. Marshall. 1990. Building Economics: Theory and 

Practice. New York: Chapman and Hall. 

77