NBS

Publications

Reference

NAT'L INST. OF STAND & TECH

AlllOb MDTIMS

NBSIR 81-2402

Estimating Economic Impacts of

Building Codes

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Economics and Regulatory

Technology Division

Washington, DC 20234

October 1 981

100 U.S. DEPARTMENT OF COMMERCE

U56 NATIONAL BUREAU OF STANDARDS

31-2TO

1981

MATIONAL UVRRAU

09 STAMDABDa

UBBAXT

JAN 8 1982

NBSIR 81-2402 nOi: ClU ' flju^

OUoo

ESTIMATING ECONOMIC IMPACTS OF

BUILDING CODES

no.

1 ^%}

Carol Chapman Rawie

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Economics and Regulatory

Technology Division

Washington, DC 20234

October 1 981

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS. Ernest Ambler, Director
V

- - -'t

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. ,1AI
PRFFACE

The aim of this report is to help those involved in building regulation use economic analysis to determine the cost~eff cctiveness of code changes.

In considering a code change, it is sometimes tempting to focus on the physical

impacts — impacts on lives and injuries, the durability of a building component, Btu ' s of energy conserved, and so forth. However, knowledge of physical

effects alone may not be sufficient for making a decision; a common basis may

be needed for comparing these diverse impacts.

Economic analysis provides a common denominator — dollar value — which makes

it possible to consol’ date many of the Impacts of a code change into a single

figure. There are also other reasons to use. econom.ic analysis in making code

decisions

:

* It provides a framework which helps assure that all important effects

are systematically considered.

* It shows which information is essential to the code change decision,

sometimes pointing out glaring gaps in knowledge about code impacts.

This report is intended to assist government officials, architects, engineers,

builders, and others to: (1) conduct in-depth analyses of code change impacts;

and (2) further develop methods for doing such analyses. A simplified version

of this report is presented in another National Bureau of Standards publication,

Est imating Benefits and Costs of Building Regulations: A Step-by-St ep Guide

(ITbSIR 81-2223, June 1981). The simplified version would be useful to those

who wish to know the general method but are not concerned with the various

refinements to the m.ethod. It would also be useful as a summary of this report.

I wish to thank Htrold Marshall, Stephen Weber, James Pielert, Porter Driscoll,

Belinda Reeder, Pat DonVito and Wayne Stiefel of the National Bureau of Standards for their valuable comments on drafts of this report. Thanks are also

due to James Gross for his encouragement of this research. I am also indebted

to other NBS staff members and to members of the building community who provided useful insights during this research. Mike Usle deserves credit for his

assistance in making the calculations and preparing the lists of references.

Thanks are due also to Forrest Wilson for providing the illustrations.

This report drew on a number of building economics publications by economists

at the National Bureau of Standards. In addition, a report by John McConnaughey,

An Economic Analysis of Building Code Impacts: A Suggested Approach , published

by NBS in 1978, provided important stimulus to the research leading to this

report

.

iii
CONTENTS

Page

List of Tables vi

List of Figures and Examples vii

List of Worksheets viii

Executive Summary lx

1 . INTRODUCTION 1

1.1 Purpose and Background 1 1.2 Scope 3

2 . DEFINING THE PROBLEM „ . . .

,

4

2.1 Selecting Representative Cases 6

2.2 Code Analysis Period 11

3. DISCOUNTING 13

3.1 Discount Factors 13

3.2 Discount Rates 16

4. ESTIMATING IMPACTS ON BUILDING-RELATED COSTS 17

4.1 Construction Costs 17

4.2 Operation and Maintenance Costs 20

4.3 Government Costs 24

4.4 Effects on Particular Groups 26

5 . IMPACTS ON BUILDING SAFETY AND PERFORMANCE . 29

5.1 Building Safety 29

5.2 Simplified Approach 29

5.3 Detailed Approach 36

5.4 Building Performance 48

APPENDIX 52

6. ECONOMIC VALUE OF LIFE SAFETY 56

6.1 Controversies over Valuing Safety 56

6.2 Values Used Previously 57

6.3 Estimating Cost Components 58

6.4 Estimating Willingness to Pay 60

7. THE BENEFIT-COST RELATIONSHIP 63

7.1 Adjustments 63

7.2 Net Benefits 64

7.3 Benefit/Cost or Savings-to-Investment (SIR) Ratio 70

iv
Page

8. CALCULATING AGGREGATE IMPACTS 79

9. SENSITIVITY ANALYSIS AND PRESENTING RESULTS 85

9.1 Sensitivity Analysis 85

9.2 Presenting Results 92

10. CONCLUSIONS 95

10.1 Research Needs 95

APPENDIX A - TABLES OF DISCOUNT FACTORS A-1

APPENDIX B - GLOSSARY OF ECONOMIC TERMS B-1

APPENDIX C - THREE STUDIES OF SAFETY IMPACTS C-1

APPENDIX D - SOURCES OF DATA D-1

D.l Sources of Data on Characteristics and Growth of

the Building Stock 0-1

D.2 Sources of Construction Cost D?>ta 0-3

D.3 Sources of Data on Operation and Maintenance Costs 0-6

D. 4 Sources of Data on Building Hazards and Property

Losses 0-8

APPENDIX E - ADDITIONAL READING E-1

E. l Benefit-Cost Analysis 0-1

E.2 Building Economics in General E-2

E.3 Fire Safety 0-2

E.4 Energy 0-4

E.5 Miscellaneous 0-5

APPENDIX F - BLANK WORKSHEETS F-1

APPENDIX G - REFERENCES G-1

V
LIST OF TABLES

Page

Table 3.1 Summary of Discount Factors 15

Table 5.1 Types of Property Losses 35

Table 5.2 Dollar Losses Due to Accidents 43

Table 5.3 Change in Expected Life Safety and Property Dollar

Losses in a Single Year 45

Table 5.4 Change in Life Safety and Property Dollar Losses Over

Building Analysis Period 47

Table 5.5 Comparison of Simplified and Detailed Approaches for

Estimating Safety Impacts 52

Table 7.1 Hypothetical Impacts of Increased Attic Insulation 69

Table 9.1 Examples of Parameters that May Be Altered in a Sensitivity

Analysis 85

Table A.l Discounting Formulas A-2

Table A. 2 Single Present Worth Factors (SPW) A-3

Table A. 3 Uniform Present Worth Factors (UPW) A-4

Table A. 4 Modified Uniform Present Worth Factors for Discount Rate of

8% (UPW*) A-5

Table A. 5 Modified Uniform Present Worth Factors for Discount Rate of

10% (UPW*) A-6

Table A. 6 Modified Uniform Present Worth Factors for Discount Rate of

12% (UPW*) A7
LIST OF FIGURES AND EXAMPLES

Figures Page

Figure i Steps in the Economic Analysis xi

Figure 5.1 Events Leading to Fire 37

Figure 5.2 Probabilities of Fire Situations 39

Figure 5.3 Expected Losses Due to Accidents 41

Figure 5.4 Effects of Code Change on Consumer Surplus 55

Figure 7.1 Illustration of Potential Problems in Using B/C Ratio to

Rank Code Changes 75

Figure 7.2 Choosing Among Alternative Levels of Insulation; Method 1:

Greatest Net Benefits 76

Figure 7.3 Choosing Among Alternative Levels of Insulation; Method 2:

Marginal Benefits = Marginal Costs 77

Figure 7.4 Selecting the Best Package of Code Changes with "Budget"

Constraint 78

Examples

Example 2.1 Identifying What is Required by a Proposed Code Change 4

Example 2.2 Selecting Representative Cases for Analysis 7

Example 3.1 Finding the Differential Cost Increase 14

Example 6.1 Finding the Value of a Statistical Life 57

Example 7.1 Selecting the Best "Package" of Code Changes:

Net Benefits Approach 71

Example 7.2 Net Benefits vs. Benefit/Cost Ratio 72

Example 7.3 Selecting the Best Package of Code Changes:

Beneflt/Cost Ratio 73

Example 9.1 Breakeven Analysis 91

vii
LIST OF WORKSHEETS

1^ , f

Worksheets with Hypothetical Data Page

Worksheet 2.1 Proposed Code Change 5

Worksheet 2.2 Representative Cases 9

Worksheet 4.1 Impact on Unit Initial Costs 19

Worksheet 4.2 Impact on Unit Operation and Maintenance Costs 21

Worksheet 4.3 O&M Costs Rising at the Rate of Inflation 22

Worksheet 4.4 O&M Costs Rising at a Rate Different from Inflation .... 23

V/orksheet 4.5 Impacts on Government Costs per Unit 25

Worksheet 5.1 Impact on Expected Property Losses per Unit 31

Worksheet 5.2 Impact on Expected Life Safety per Unit 33

Worksheet 5.3 Impacts per Unit of a Change in Space 49

Worksheet 5.4 Impacts on Unit Residual Value 51

Worksheet 7.1 Adjusting O&M Costs for Differential Cost Changes 65

Worksheet 7.2 Present Value of Net Monetary Benefits per Unit 67

Worksheet 8.1a Aggregate Impacts (Net Monetary Benefits) 81

Worksheet 8.1b Aggregate Impacts (Lives Saved) 83

Worksheet 9.1 Values to Alter in a Sensitivity Analysis 87

Worksheet 9.2 Sensitivity Analysis 89

Worksheet 5.3 Impacts per Unit of a Change in Space

(Sensitivity Analysis) 90

VJorksheet 9.3 Results of the Benefit-Cost Analysis 93

Blank Worksheets

Appendix F F-1

viii
EXECUTIVE SUMMARY

This report describes a method for analyzing the cost effectiveness of changes in

building code requirements.^

The method can help in identifying the least costly way to protect against

building hazards and in deciding on the right level of protection to provide

through building codes. If adequate data concerning building costs and safety

are not available, the method can be used to help determine which data are most

needed to estimate costs and benefits of proposed code changes.

Throughout the report, sample worksheets have been completed for a hypothetical

problem in order to illustrate the method. Blank worksheets, tables of discount factors, and additional references are provided. The seven steps in the

analysis are described below and summarized in figure i on page xi.

During the past few years, there have been many claims that building code

requirements sometimes unnecessarily increase building costs. These claims

are very briefly summarized in chaptei 1 of this report.

Chapter 2 explains how to define the problem . This involves describing the

exact code changes to be analyzed and identifying several representative cases

for study. Estimating effects for representative cases shows the cost effectiveness of the proposed change under different circumstances and lays the

basis for estimating overall impacts in the code jurisdiction.

Chapters 3 and 4 explain discounting and how to estimate Impacts on buildingrelated costs . This includes effects on construction costs, operation and

maintenance costs over the building's life, and government costs for fire

protection, police protection, and code administration.

Chapters 5 and 6 are concerned with estimating Impacts on building safety and

performance . One approach is to estimate safety impacts in terms of the number

of fatalities or injuries and leave it up to those responsible for building

regulation to make the trade-off between dollars and lives. An alternative

approach is to estimate life safety impacts in dollar terms. Since there is no single generally accepted way to assign dollar values to life safety, chapter 6

discusses several methods of valuing life safety.

Chapter 7 discusses two methods of comparing benefits and costs . Of the

several measures which are commonly used to compare benefits and costs, the

one recommended here for most applications is the net benefits measure.

Chapter 8 shows how to estimate aggregate Impacts . This step involves

calculating the overall impact of a code change for all buildings in a specific

code jurisdiction. The recommended approach is to multiply the effects for

A simplified version of this method is described in Rawie, Carol Chapman,

Estimating Benefits and Costs of Building Regulations; A Step-by-Step Guide, NBSIR 81-2223 (Washington, D.C.: National Bureau of Standards, 1981).

IX
a particular type building by the number of buildings of that type expected

to be constructed in the jurisdiction and sum the products.

Chapter 9 explains how to perform a sensitivity analysis . Sensitivity analysis

is a technique for finding out how changes in data or assumptions affect final

results. It shows which information is most needed to improve the estimates

of benefits and costs and whether possible errors in data might drastically

alter the predicted impacts.

Chapter 9 also briefly explains how to write up the results of the benefit-cost

analysis in a way that highlights important effects of a code change. Four

kinds of information will be useful to decisionmakers: key assumptions,

quantitative impacts, qualitative impacts, and areas of uncertainty.

Chapter 10 describes the need for further information concerning the numbers of

building accidents, costs of building accidents, and what people are willing to

pay to reduce risk.

Blank worksheets and an extensive list of references are given in the Appendices

Decisions are being made daily in the building community which affect the costs

of buildings throughout the country. Yet often these decisions must be made

with only a general notion of the ultimate impacts of the code requirements on building safety and performance. It is hoped that this report will provide a means of obtaining and organizing information to help in making such decisions.

X
Figure i STEPS IN THE ECONOMIC ANALYSIS

xi
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1 . INTRODUCTION

1.1 PURPOSE AND BACKGROUND

The purpose of this report is to describe a method for estimating benefits and

costs of building code changes. The method can be used in deciding whether to

delete, add, or modify a code requirement. It can be used to compare alternative code changes to see which produces the greatest net benefits or has the

lowest costs. And it can be used to compare groups of code changes.^

In 1978, a builders association in Portland, Oregon, placed signs on a house

under construction pointing out ten code-required features v/hich increased

construction costs. This display symbolizes the concern of many builders,

building users, and government officials about the effects of building codes on building costs.^

In the past few years, there have been many claims that building codes add

unnecessarily to construction costs, but there have been few careful studies

which document the extent of the Impact. In 1979, the Federal Trade Commission

held hearings at which Arthur Young & Company summarized past studies of the

cost Impacts of building codes. The company concluded that, although most of

the studies had shortcomings, the consensus was that excessive building requirements imposed compliance costs ranging from one to five percent of home prices.^

^ In this report, "benefit-cost analysis" is a method of computing and

comparing benefits and costs of a code change. Some economists take a broader view and include in benefit-cost analysis the statement of the problem, the statement of goals, and the search for alternative solutions, as well as evaluation of the alternatives. (The author is indebted to Pat

Donvlto of the National Bureau of Standards for this observation.) This

report assumes that the problem, goals, criteria for approving a code change,

and alternative solutions have already been identified; the benefit-cost

analysis involves only evaluating these alternatives to see how well they

satisfy the criteria.

^ "Building Code Costs Demonstrated in Parade of Homes," Professional Builder , October 1978. See also Stanley Denn, "Builders Often Resist Tough Safety

Codes, Citing Added Expense," Wall Street Journal , January 27, 1981.

O Young, Arthur and Company, Testimony Before the Federal Trade Commission on

the Proposed Rule for Standards and Certification , May 1979. Studies reviewed

by Arthur Young included research by the National Commission on Urban Problems

(the Douglas Commission) which surveyed home manufacturers ( Building the American City , Washington, D.C., 1968); a study by Richard Muth who used regression analysis to relate home prices to code restrictions ("The Effects of Constraints on House Costs," Journal of Urban Economics , January 1976); a study

of Colorado code requirements which was sponsored by a builders association

(Lincoln, James R. , Coddington, Dean C. and Penberthy, John R. , An Analysis

of the Impact of State and Local Government Intervention on the Home Building

Process in Colorado 1970-1975 , Denver: Colorado Association for Housing and

Building, 1976); and a study by Stephen Seidel of code-related costs at a New

Jersey housing development ( Housing Costs and Government Regulation , New Brunswick, N.J.: Rutgers Center for Urban Policy Research, 1978).

1
In a 1978 study, a Department of Housing and Urban Development (HUD) task force

concluded that the problem of excessive building code requirements was most

likely to occur in jurisdictions which did not follow an up-to-date version of

a model code.^ This report did not include any data on code costs. A General

Accounting Office 1978 report concluded that building codes were generally not

a major factor contributing to rising housing prices, but that there might be some potential for reducing costs through code changes.^ A 1979 study by the

California Building Officials Organization dealt with the effects of codes on housing costs, among other topics.^

These studies have not really answered the question of which code requirements

unnecessarily add to building costs, nor have they adequately assessed the

extent of any adverse Impact. There are several reasons for this: (1) Some

studies apparently were based on opinions rather than on factual analysis; (2)

In several studies, a code requirement was labeled "excessive" if it deviated

from the model code; this criterion assumes that the model code provisions are neither too lenient nor too strict, which may not always be true; (3) Some of

the criticized code requirements called for features that builders would have

Installed anyway, even without a building code requirement; and (4) The studies

apparently did not subtract out whatever benefits would be realized from

"unnecessary" features before they estimated the excess costs.

Responding to this lack of data, HUD sponsored research in 1980 to determine

whether code requirements have significantly increased housing costs without

corresponding benefits.^

In summary, there is much opinion and scattered evidence that building codes

raise housing costs unnecessarily. But there is great uncertainty about the

extent of the effect and which code provisions are to blame, and there are very

few analyses to show the overall costs and benefits of the provisions most often

criticized

.

The controversy and concern about this 5.mportant question suggests that studies

such as the one HUD is sponsoring are very much needed. Future studies should

follow a rigorous methodology which adequately accounts for benefits as well as costs of code requirements and which carefully defines a baseline representing

what would have happened without the code requirement

.

^ U.S. Department of Housing and Urban Development, Final Report of the Task

Force on Housing Costs (Washington, D.C., 1978).

^ Comptroller General, Why Are New House Prices So High, How Are They

Influenced By Government Regulations, and Can Prices Be Reduced (Washington,

D.C.: General Accounting Office, 1978).

^ California Building Officials Organization, California’s Building Departments:

A Critical Evaluation and Commentary , July 1979.

^ Request for Proposals, Cost Impact of Duplicate Life and Safety Requirements

in Codes, H-5220 (Washington, D.C.: U.S. Department of Housing and Urban

Development, 1980).

2
This report describes a methodology that can be used to analyze changes in

individual building code requirements.

1 . 2 SCOPE

The scope of this report has been defined in the following ways: it is

concerned with building codes, not with zoni ig and environmental regulations,

nor with regulation of building components;^ it does not treat impacts on national employment or inflation;^ it emphasizes estimating the overall impacts

of a code change rather than estimating gains or losses to particular groups;^

and it is concerned with the process of estimating economic values of physical

effects. It does not provide a method for estimating the physical effects

themselves

Before beginning the analysis, there are three points the reader should keep in

mind concerning benefit-cost analysis and the use of this report.

First , a full-fledged benefit-cost analysis is most likely to be warranted if

the proposed code change is important (for example, if it affects a great number

of buildings); if the proposed change is controversial; if the magnitude of

economic effects is very uncertain without a formal analysis; and if data are available on the most important impacts of the code change.

Second , benefit-cost analysis is still a very imprecise tool, especially where

underlying data are lacking, as is often the case in the building area. Some

attempts at analyzing benefits and costs of coda changes may simply show important areas of ignorance. No benefit-cost analysis should be blindly accepted

by those making regulatory decisions.

Third , in using the method described in this report, it is only necessary to

complete the steps and fill out the worksheets that are appropriate for the

particular code change being studied. For example, the safety-related worksheets would probably not be needed for analyzing energy conservation codes.

New worksheets may be needed for situations not covered in this report.

^ However, the concepts presented here can be applied to other types of

regulation as well.

9 t# ^ These macroeconomic" impacts depend heavily on public policy and overall

economic conditions and, hence, are very difficult to predict.

^ The method can be adapted to calculate impacts on particular groups such as builders, homeowners, or construction materials suppliers. See section 4.4.

^ Sources of information which may be useful in estimating physical impacts are listed in appendix D, and several publications listed in appendix E, such as the report by S.G. Helzer e^ al . Decision Analysis of Strategies for Reducing

Upholstered Furniture Fire Losses , National Bureau of Standards Technical

Note 1101 (Washington, D.C.: U.S. Government Printing Office, 1979), demonstrate how physical impacts have been estimated by researchers.

3
2. DEFINING THE PROBLEM

To analyze the economic impacts of a proposed building code change, it is

necessary to determine the scope of the problem. This involves choosing

the perspective that will be used in the analysis and recording pertinent

information about the code change to be analyzed. The analyst must also

choose representative cases to be used in analyzing the impacts, the basic

unit of analysis, and the periods of time for which impacts will be analyzed.

Perspective

The first step is to decide what perspective to take, that is, will the

analysis deal with impacts on the local jurisdiction, or on a larger geographical area? Within that geographical area, will the code change be analyzed

from the viewpoint of a specific interest group — such as builders, materials

suppliers, homeowners, or union members -- or from the viewpoint of the community as a whole? For the most part, the discussion in this report assumes the

reader will analyze effects on everyone in the selected geographical area.

Describing the code change

The next step is to determine which parts of the code section will be altered

and record pertinent information for easy reference later on. Worksheet 2.1

is provided for this purpose. Its use is illustrated in example 2.1 which

involves a hypothetical code change for a fictitious jurisdiction called

Springfield County.

Example 2.1: IDENTIFYING WHAT IS REQUIRED BY A PROPOSED CODE CHANGE

Problem : A hypothetical proposed provision of the Springfield County building

code reads as follows: Except for sprinklered buildings, all new buildings

in residential use groups R-2 , R-3, and R-4 of two stories or more in height

must have at least one Fire Safety Feature of design A or B installed in the

kitchen -of each dwelling unit.

Use groups R-2, R-3, and R-4 are defined in the Springfield County code as including single-family houses and multifamily dwellings except hotels and

motels. The current code provision, which would be deleted if the revision

is approved, requires a Fire Safety Feature of design C.

What are the key changes required under the proposed provision?

Solution : Pertinent information that should be presented for this proposed

code change is shown in worksheet 2.1.

4
Worksheet 2.1 hypothetical example

PROPOSED CODE CHANGE

Title or number of code change Fire Safety Feature (FSF) Requirement

Units of analysis

The third step is to choose a unit of analysis, i.e., the unit for which impacts

will be calculated. For example, the effects of a plumbing code change might be

analyzed per bathroom. An attic insulation requirement might be analyzed per

square foot of attic floor space. A unit should be chosen such that Impacts are proportional to the number of units and, if possible, such that data are available on the number constructed. The impacts of the hypothetical Fire Safety

Feature (FSF) code change described above will be calculated per dwelling unit.

5
2.1 SELECTING REPRESENTATIVE CASES

Often, the effects of a code change depend on characteristics — such as system

type, building design, and location — that may vary from building to building.

As a practical matter, it is impossible to analyze the Impacts of a code change

for all types of buildings under all significant conditions. Therefore, to make

the analysis feasible, it is useful to choose a few representative cases for

study and use these case studies as a basis for calculating aggregate impacts.

Choosing a representative case involves selecting a reference building,

identifying construction practices with and without the code change, and selecting certain other characteristics v;hich will influence the impact of a code

change

.

Worksheet 2.2 is provided to record pertinent characteristics of representative

cases. Its use is illustrated in example 2.2, which is a continuation of exam- ple 2.1. Particular aspects of the representative case are discussed below.

The reference design

It is important for reference buildings to be representative of buildings that

will be constructed during the code analysis period and would be affected by a proposed code change. It may be convenient to select actual buildings as reference buildings. But if there are no actual buildings which are sufficiently representative, hypothetical designs can be used.

There are several ways to select reference designs. One approach would be to

estimate how many buildings of various types will be constructed in the code

jurisdiction during the code analysis period and select several typical designs.

Table D.l in appendix D shows some sources of data on the numbers of various

types of buildings. In some cases, reference designs might be selected partly

because information from engineering models or field data collection is available for these particular building types. Finally, reference designs described

in previous studies might be used..^

^ For example, reference designs are described in the following publications:

Hastings, Robert, Three Proposed Typical House Designs For Energy Conservation Research , NBSIR 77-1309 (Washington, D.C.: National Bureau of Standards,

1977); and Chapman, Robert E.; Chen, Phillip T.; and Hall, William G., Economic Aspects of Fire Safety in Health Care Facilities: Guidelines for CostEffective Retrofits, NBSIR 79-1902 (Washington, D.C.: National Bureau of

Standards, 1979).

6
Example 2.2: SELECTING REPRESENTATIVE CASES FOR ANALYSIS

m

Problem : The code change described in example 2.1, requiring a Fire Safety

Feature in residential buildings, is proposed for adoption in 1981. The code

jurisdiction is made up of two towns. Poker Flat and Springfield, for which the

following number of new dwelling units will be constructed between 1981 and

1990:

Springfield Poker Flat

Single-family houses 2000

Multifamily low-rise units 580

Residential high-rise units 400

500

20

0

Fifty percent of the single-family houses in Springfield are expected to be

one-story and 75 percent of the residential high-rises are expected to be

sprinklered. All other buildings will not have sprinkler systems and will be

at least two stories. With the code requirement, almost all single-family

houses are expected to use design A of the FSF, and all multifamily low-rise

and high-rise buildings are expected to use design B.

What representative cases should be selected as a basis for analyzing costs and

benefits of the code change?

Solution : Representative cases are selected as follows: Case I , a singlefamily house constructed in Springfield in 1985; builder installs a FSF/design

A in lieu of Installing design C; Case II , a single-family house constructed

in Poker Flat in 1985; builder Installs a FSF/design A in lieu of installing

design C; Case III , an apartment in a multifamily low-rise building constructed

in Springfield in 1985; builder installs FSF/design B in lieu of installing

design C; and Case IV , an apartment in a residential high-rise constructed in

Springfield in 1985; builder Installs FSF/design B in lieu of installing design

C.

These four cases are described in worksheet 2.2 on page 9.

7
There are several questions to keep in mind when specifying a reference design:

*^jyhleh building features will dLetermine the impact of the code change?

(For example, the type of heating system is relevant in analyzing

cost effectiveness of an energy conservation provision, but usually

not in analyzing a fire safety provision.)

* Which buildings will be affected by the code change after taking into

account exceptions and expected waivers?

* Are major rehabilitation and/or historic preservation projects common

in the jurisdiction; if so, how will new code requirements be applied

to them?

Location . The building location should usually be identified because it

affects wages and materials costs and, therefore, construction costs. It also

determines climate and other factors which may be relevant in analyzing the

impact of the code change.

Date constructed . Because costs change, the effect of a code requirement may

depend on when the building is to be constructed. For example, prices of

innovative fire safety devices may decline in real terms as wider use allows mass production. Escalating energy prices (prices rising faster than the rate

of inflation) mean that energy conservation requirements may have a greater

effect on buildings constructed in 1990 than in 1985.

Type of building . The type of building is important to specify. For instance,

for residential buildings, is it a single-family residence, a low-rise apartment building, or a high-rise? How many stories is it? How many square feet?

What is the construction type? It may also be useful to list the number, age,

and type of occupants and any other information about design and use that seems pertinent to the proposed code change.

Practice without the code change . This is the practice that the builder would

use in the absence of a code change when constructing the feature affected by

the code change. It is also called the "baseline practice" since it is^the

baseline for measuring code impacts. In situations where construction practices

are changing relatively rapidly, the baseline practice should reflect likely

trends

.

8
Worksheet 2.2 hypothetical example

REPRESENTATIVE CASES

Code analysis period 1981 - 1990 Unit of analysis IMelling unit

Characteristics

Representative Case

II III IV

Location Springfield Poker Flat Springfield Poker Flat

Date Constructed 1985 1985 1985 1985

Type of Building

Single-family

house

Singl e-family

house

Multi-family

low-rise

Residential

high-rise

Practice Without

the Code Change

Design C Design C Design C Design C

Practice With

the Code Change

Design A Design A Design B Design B

Building Analysis

Period (Years)

25 25 25 25

Other Factors

9
For example, suppose a code change would require a certain amount of insulation.

For a reference building constructed in 1980, the "baseline” against which

improvements should be measured is the Insulation used in current new construction. However, for a reference building constructed in 1990, the baseline

should be the greater amounts of insulation that would be used even without a code requirement. If today’s level of Insulation were used as a baseline in

analysd-ng -t^e i99G>bq;^^,ding, the analysis would incorrectly suggest that the

code" cdainge-was ¥e^^<iinfribie for improvements which would have taken place in

any case.

Practice with the code change . This item describes how builders will respond

to the code change. The responses selected should reflect probable interpretations pf_ the , cede change. If the code allows some flexibility, alternative

ways of meeting requirements might be considered, perhaps including innovative

practices. (If it is not possible to define innovative practices well enough

to include them in a representative case, it may still be useful to list possible innovations in presenting the results of the analysis. This information

may help in choosing between more flexible and less flexible versions of a code

requirement .

)

Building analysis period . This period usually reflects the expected lifetime

of the building and so may differ for each reference building. If there is

great uncertainty about actual building lifetimes, it may be necessary to simply

assume a lifetime, such as 25 years. If good estimates concerning code change

impacts are available only for a shorter period, or if the code-mandated feature has a shorter lifetime, the building analysis period may be shorter than

the expected building life.

Other factors . Other factors may be important to specify. For example, project

size may be important. The scale of construction affects unit costs, and it

may also affect whether the builder will be a leader or a follower in adopting new designs. Builders may respond differently if they are constructing large

housing developments than if they are building houses one by one.

Another significant factor is whether new construction or modifications to

existing buildings are involved. In many jurisdictions, if the alterations on existing buildings’ represent more than 25 percent of the building’s value, then

changes may be required to comply with code requirements for new structures;

if alterations represent more than 50 percent of the building’s value, then

the entire building must meet code requirements for new structures.^ Where

o’wners rehabilitate low-priced, decaying buildings, the new work may easily

amount to more than 25 percent or even 50 percent of of the building’s value.

^ Impacts of building codes on rehabilitation projects are discussed in:

Gross, J.; Pielert, J.; and Cooke, P., Impact of Building Regulations on Rehabilitation—Status and Technical Needs , National Bureau of Standards

Technical Note 998 (Washington, D.C.: Government Printing Office, 1979),

and Berry, Sandra A., Proceedings of the National Conference on Regulatory

Aspects of Building Rehabilitation , NBS Special Publication 549 (Washington,

D.C.: National Bureau of Standards, 1979).

10
Thus, the economic effects of a code change may be quite different for rehabilitating or altering existing buildings than for new construction. For example,

it may be considerably more expensive to modify an old building to meet new code

requirements under the 50 percent rule than to meet the requirements during initial construction. If the code will apply in a jurisdiction with the "25-50

percent rule," if there is a great deal of rehabilitation going on, and if few

waivers are granted, then the analysis should be carried out to determine the

Impacts of a code change for a rehabilitation project as well as for new construction.

Considerations in selecting a representative case

The analyst should keep in mind four points in selecting representative

cases

.

(1) If the population of affected buildings is very diverse, using a large

number of reference buildings gives a better picture of the code's impacts

and a sounder basis for aggregating. Ideally one would do the analysis for

hundreds or thousands of different reference buildings. However, because of

the cost of the analysis, it is necessary to divide the affected building

population into groups of similar buildings and analyze impacts for a building

representing each group.

(2) Availability of data should be considered in specifying reference

buildings. For example, if climate is important, cities should be selected

for which there is sufficient climate data.

(3) Within each group, a representative building should be selected for

which impacts/unit are likely to be average for the group. This is not

always the most prevalent type of building. For example, if a group represented by a single building Includes many small buildings, a few mediumsized buildings, and many large buildings, a medium-sized building might

have closest to average impacts and be used as a representative building, even though the small and large buildings are more common.

(4) If the effects of various features are interdependent, a common combination of features should be selected for the representative case.

Because of these potential difficulties, much architectural and engineering

judgment is needed to select reference buildings.

2.2 CODE ANALYSIS PERIOD

Code changes do not last forever. Therefore, it is necessary to assume some code analysis period. Impacts will be estimated for all buildings constructed

during this period, which is the same for all representative cases. In the

example in worksheet 2.2, 1981 through 1990 was selected as the code analysis

period

.

11
The simplest approach is to select a single year (e.g., "1985") for the code

analysis period. This shows impacts over time for all buildings constructed in one year. However, for a more complete estimate of likely benefits and costs,

one would select the time period that the requirement is likely to be in effect

before it is carefully reviewed. Field and Rivkln,^ and Seidel^ provide data

on how often municipalities revise their codes. Another source of Information

is a California study which found that more than half of the building

departments surveyed reviewed their codes every three to four years.

Another approach is to select an analysis period for which reasonably good data

forecasts are available. This period depends on particular data needs. For

example, Marshall, Ruegg, and Wyly forecasted the impacts of reduced-size

venting for plumbing through 1985 since this was the period for which housing

start projections were available.^

If a code change is being phased in over a period of time, the code analysis

period should cover at least the time it takes to put the requirement into

force, since aggregate Impacts are likely to increase from year to year during

this time.

^ Field, Charles G. and Rivkin, Steven R., The Building Code Burden

(Lexington, MA: Lexington Books, 1975), p. 46.

^ Seidel, Housing Costs and Governmental Regulation , p. 83.

3 California Building Officials, A Critical Evaluation and Commentary , p. III-25

^ Marshall, Harold E.; Ruegg, Rosalie T.; and Wyly, Robert S. "Cost Savings

from Reduced Sized Venting," Plumbing Engineer , July-August 1977, pp. 35-42

and September-October 1977, pp. 45-46, 64-65.

12
3. DISCOUNTING

To compare effects that occur at different points in time, it is necessary to

put all costs on a time-equivalent basis through a process called discounting.^

This chapter describes how to allow for the timing of impacts through methods

of discounting and treating Inflation.

Future dollar effects can be discounted to their present value by means of a discount factor , a number (such as ".3855") which can be computed from a formula or looked up in a table. To determine the discount factor, first it is

necessary to select a discount rate , a percent (such as "10%") which reflects

the investment returns foregone by deferring income to the future, or the

returns gained by deferring costs. The discount rate used should reflect the

rates of return that could be earned by the people who will be affected by the

code change.^

3.1 DISCOUNT FACTORS

Three kinds of discount factors are described below and summarized in table 3.1.

Tables of discount factors are given in appendix A.

Single Present Worth factor (SPW)

A Single Present Worth factor is useful for discounting effects that occur only

once, such as a $100 cost incurred in ten years. The present value (P) of the

one-time future cost or benefit (F) is calculated from the formula, P = (F)(SPW).

The SPW can be selected from table A. 2 in appendix A for the assumed discount

rate and the year of the impact, or it can be calculated using the formula in

table A.l.

This factor is particularly useful for discounting non-recurring (or irregularly

recurring) repair and replacement costs.

Uniform Present Worth factor (UPW)

The Uniform Present Worth factor is used for costs and benefits that recur annually and are expected to be the same each year. For example, it might be

used to discount a cost equal to $100/year (in constant dollars) that will

^ Discounting is necessary because a one-dollar cost or benefit of a code change

is worth less if it occurs in the future than if it occurs in the present, even in the absence of inflation. This is because money received now can be

invested at a profit which is lost if the money is not received until later.

^ Various building studies have used real discount rates ranging from 1 percent

to 10 percent or higher. Ten percent is the rate specified by the Federal

Office of Management and Budget for evaluating most Federal investments in

Circular A-94, "Discount Rates to be Used in Evaluating Time Distributed Costs

and Benefits," March 27, 1972. Selection of the discount rate is discussed

further in section 3.2

13
occur throughout a 20-year building life. The present value (P) of the

annually-recurring cost or benefit (A) is determined by the formula P = (A)(UPW).

The UPW can be found in table A. 3 in appendix A for the assumed discount rate

and the period over which impacts occurs, or it can be calculated using the

formula in table A.l.

This factor is useful for analyzing uniformly recurring effects such as annual inspection costs and routine maintenance.

Modified Uniform Present Worth factor (UPW*)

The Modified Uniform Present Worth factor is used for discounting costs and

other impacts whose real value (value in constant dollars) rises at a constant

percentage rate. It is particularly useful when underlying prices rise faster

or slower than the general rate of Inflation. For example, this factor would

be useful for discounting energy-related operating costs in cases where there

is a continual increase in the price of energy relative to other prices.

Example 3.1 shows how to compute real (differential) rates of cost increase.

The formula in the example can also be used for computing differential cost

decreases

.

The present value (P) of future costs or benefits whose real value is "A” at

present prices, and is rising at a fixed percentage rate, can be calculated from

the formula P = (A) (UPW*). To determine this factor, you will need to estimate

the rate at which costs are Increasing over and above the rate of inflation.

The UPW* can be selected from tables A. 4 through A. 6 in appendix A for the

differential rate of increase in costs, the discount rate, and the period over which costs occur, or it can be calculated using the formula in table A.l

Example 3.1: FINDING THE DIFFERENTIAL COST INCREASE

Problem ; Find the differential ("real") rate of increase in a cost item if

the overall rate of inflation is 10 percent and the nominal (observed) rate

of cost increase is 15 percent.

Solution :

e = 1 - 1

1 + i

e = real rate of cost Increase for the item

e' = nominal rate of cost increase for the item

i = overall rate of Inflation

e = ^ + -15 _ 1 =4.5%

1 + ,10

14
Table 3.1 SUMMARY OF DISCOUNT FACTORS

TYPE

FACTOR

Single Present

Worth (SPW)

Uniform Present

Worth (UPW)

Modified Uniform

Present Worth (UPW*)

(table A. 2,

appendix A)

(table A. 3,

appendix A)

(tables A.4-A.6,

appendix A)

USE

P? 4- F

Find present value

of single future

amount

.

P? - A + A + . .

.

Find present value

of annually

recurring amount.

P? A(l+e) + A(l+e)2 + . .

.

Find present value

of annual amount

with differential

cost increase.

FORMULA P = (F)(SPW) P = (A) (UPW) P = (A) (UPW*)

SAMPLE

PROBLEM

Find present value

of cost equal to

$100 in constant

dollars, incurred

in 10th year, if

discount rate is

10%.

Find present value

of $100 cost (constant dollars)

incurred each year

for 20 years, with

10% discount rate.

Find present value of an annual cost recurring for 20 years,

which would be $100/

year at current prices

and is escalating at

5%/year, with a 10%

discount rate.

SOLUTION P = ($100)(.3855) P = ($100)(8.514) P = ($100)(12.7178)

= $38.55 = $851.40 = $1271.78

15
3.2 DISCOUNT RATES

^T^^sr;section ^jLsxiusses’ sever*! considerations in selecting the discount rate

^^ed WPcVTobsing ’discount factors from the tables in appendix A.

Discount rates reflect rates of return which could be earned by those affected

by the code change. Rates of return may be stated in terms of either market or real rates.

Market (nominal) rates are the actual rates observed in the market. They

include percentage points to compensate for inflation. If the code impacts are stated in current dollars, then the discount rate should be based on market

rates of return.

Some market rates that may be useful in determining discount rates are the

business prime rate of Interest, returns to business Investment, construction

loan rates, savings account rates of interest, mortgage and auto loan rates,

municipal and corporate bond rates, and treasury bill rates.

Real rates, on the other hand, cannot be directly observed and must be computed

(or assumed). In inflationary times, they are lower than market rates because

they show the real rate of return on an investment after subtracting out the

effects of inflation. If code change impacts are stated in constant dollars,

as they are in this report, the discount rate should be based on real rates of

return.

The following formula can be used to find a real rate of return corresponding

to a market rate;

r = ^ - 1, (3.1)

1 + i

where

:

r = the real rate of interest,

r' = the market rate of interest, and

*i = the rate of ’inflation.

Thus, with a 10 percent rate of inflation, an investment that returns 20 percent

nominally would return only [ (H-.2)/(l+. 1) ] - 1 = .091, or 9.1 percent, in real

terms

.

The example in this report uses a before-tax rate of return as the basis for

the discount rate. The before-tax rate of return reflects amounts that could

be earned on an investment from the national perspective (the taxes paid are

lost to the taxpayer, but they still represent gains to the economy as a whole).

However, in analyzing a code change strictly from the viewpoint of the local

community, you may* wish to use the rate of return after Federal and State taxes,

since tViis reflects the net gain to the local economy.

16

k
4. ESTIMATING IMPACTS ON BUILDING-RELATED COSTS

A code change may affect construction costs, energy costs, maintenance costs,

other costs of operating a building, and even costs of disposal at the end of

the building's life. It may also affect public costs related to buildings such

as costs of code administration and fire protection. This section explains how

to estimate impacts of a code change on building-related costs.

Impacts are measured by comparing costs without the code change to costs with

the code change.

The approach recommended in this report is to estimate costs before taking into

account any tax effects. For example, costs of adding insulation would be

estimated before considering possible savings due to tax credits. Treatment of

taxes was very briefly discussed in section 3.2.

The discussion in this chapter and the next assumes that the building in

question is constructed during the current year. Later, in chapter 7, the

report explains how to adjust the estimates to apply to buildings constructed

in future years.

4.1 CONSTRUCTION COSTS

Construction costs are discussed in this section and operating and maintenance

costs are discussed in the next section.

Worksheet 4.1 is provided to list effects on materials, labor, equipment,

overhead and profits, and other construction costs. The analyst should take

into account costs associated with plan-checking delays, construction modifications needed to obtain approval, certification requirements, and record-keeping

requirements. However, fees paid by builders to the code jurisdiction should

not be counted here since the code administration costs covered by the fees

will be counted in government costs, discussed in section 4.3. Section D.2 in

appendix D shows some sources of construction cost data.

Worksheet 4.1 summarizes the effects of code changes on construction costs for

the hypothetical case involving a Fire Safety Feature which was introduced

earlier. The data in this and other worksheets are for Representative Case I described earlier on worksheet 2.2. Because of limited space, computations

for other representative cases are not shown in this report. However, in an actual analysis, worksheets would be filled out for each representative case.

A blank worksheet is provided in appendix F to use in summarizing costs.

Qualitative considerations

Hard-to-quantlfy effects of a code change should also be presented in the

results of the benefit-cost analysis. Three hard-to-quantify factors which may

affect construction costs are described below.

The first factor is market uniformity.

17
Fragmentation of building markets because of codes that differ from jurisdiction

to jurisdiction has been criticized as one source of high building costs.

On the other hand, due to differences in local situations, code uniformity is

not always desirable. For example, code requirements appropriate in one type

of climate may be inappropriate in other climates. Jurisdictions with small

fire-fighting budgets may wish to have stringent fire safety requirements.

Citizens in one jurisdiction may prefer to spend more on life safety than

citizens in another jurisdiction. Finally, varying prices for labor and

building products may make code requirements which are reasonable in some jurisdictions excessively expensive in others.

The second factor affecting construction costs is the prescriptive versus the

performance nature of the requirements. Prescriptive code requirements have

been criticized for excluding cost-effective products or designs.^ They may

discourage manufacturers from developing new products. Performance standards,

on the other hand, are often said to be more conducive to innovation; but they

may also be more difficult for code officials to interpret and enforce. In

analyzing impacts of a code change, the analyst should take into account the

possible long-run indirect effects due to the "prescriptive" or "performance"

form of the provision.

The third factor affecting costs is construction delays. Adding a major code

requirement (or set of requirements) may increase the time it takes to get

plans and site work approved.^

^ McConnaughey , John, An Economic Analysis of Building Code Impacts; A

Suggested Approach , NBSIR 78-1528 (Washington, D.C.; National Bureau of

Standards, 1978), pp. 77-78; Field and Rlvkln, The Building Code Burden , pp. 27-30; Oster, Sharon and Quigley, John, "Regulatory Barriers to the

Diffusion of Innovation," in Cooke, Patrick, Ed., Research and Innovation in

the Building Regulatory Process; Proceedings of the First NBS/NCSBCS Joint

Conference , NBS Special Publication 473 (Washington, D.C.: National Bureau

of Standards, June 1977), p. 115; and Seidel, Housing Costs and Government

Regulation , pp. 85-86 and 92-93. However, a study by Arthur Young & Co.

asserts that there has been little reliable research on compliance costs

resulting from production inefficiency attributable to lack of code uniformity. See Young, Arthur and Company, Testimony Before the Federal Trade

Commission on the Proposed Rule for Standards and Certification , pp. 43-44.

2 Field and Rivkin, The Building Code Burden , pp. 27-30. A "prescriptive"

code provision is one which requires a specific method of construction or type of product, although it may allow use of "equivalent" products at the

discretion of the local building official. A performance standard states

performance criteria and methods of test or evaluation.

^ The times required for plan checking and site inspections are discussed in

California Building Officials Organization, A Critical Evaluation and Commentary (pp. 1-4-5) and Hendrickson, P.L. ^ al ^ An Analysis of the Impact of

State and Local Government Intervention on the Home Building Process in

Colorado 1970-1975, pp. 6-8.

18
The costs of any additional delays should be counted in analyzing code change

impacts. For example, there might be higher construction costs if personnel

and equipment are idled or inefficiently employed because of delays. Even if

it is not possible to estimate costs of the delays, the extent and nature of

added delays should still be described so that this effect can be considered

by those making code change decisions.

Worksheet 4.1 hypothetical example

IMPACT ON UNIT INITIAL COSTS^

Representative case j (from worksheet 2 •2)

Proposed Original

Type Cost Requirement - Requirement = Change

Materials and

Components $ 100 $ 50 =

$ 50

Wages and

Salaries $ 40 $ 20 =

$ 20

Construction

Equipment $ 0 $ 0 =

$ 0

Builder's overhead

, (general & admin.) $ 60 $ 30 =

$ 30

Other costs $ 0 $ 0 $ 0

TOTAL $ 200 $ 100 =

$ 100

^ Costs may be calculated per building. per dwelling unit

,

per square foot, or for some other basic unit of analysis.

19
4.2 OPERATION AND MAINTENANCE COSTS

Some code requirements are designed to decrease costs of operating and

maintaining buildings (O&M costs). Other code requirements may increase O&M

costs; for example, a requirement for a smoke control system might impose periodic testing costs. In some cases, the cumulative impact on O&M costs over the

years may be much greater than effects on initial construction costs.

O&M costs that might be affected by code changes include regularly recurring

energy costs and non-energy costs such as water use, security, cleaning, testing, inspection, and routine maintenance. (However, insurance costs should not

be included since this type of cost is covered in the section of this report

that deals with building safety and performance.) O&M costs also include

irregularly recurring costs such as costs associated with repair, replacement,

fire safety training, and planned alterations.

O&M costs include replacement of code-mandated components which wear out. In

estimating replacement costs it is necessary to determine both the expected life

of the component and whether the component will actually be replaced when it

wears out. For examj)le, how long will weatherstripping required under an energy

conservation code last? Will it be replaced when it wears out?

Three steps are needed in calculating the discounted value of the change in O&M

costs resulting from a code change.

First , enter the amount of the cost change and its timing on worksheet 4.2.

Sources of information for operation and maintenance costs are listed in section

D.3 in appendix D. In the example, repair costs for the FSF are assumed to be zero under the original code requirement and $25 after ten years for the

proposed requirement.

Second , Identify the appropriate factor for discounting each cost change. (See

chapter 3 on discounting.) For example, in part A of worksheet 4.2, the $25

one-time impact on repair costs will be discounted with a Single Present Worth

factor. The factor, .3855, was selected from table A. 2 in appendix A for a 10

percent discount rate and 10 years. A SPW factor was also selected for discounting replacement costs listed in part A for the original code requirement.

A Uniform Present Worth factor was selected for discounting routine maintenance

costs since these are equal annual costs. The factor, 9.077, was selected from

table A. 3 in appendix A for 10 percent and a period of 25 years (the building

analysis period).

In part B of the worksheet, energy prices were expected to rise more rapidly

than prices in general. Therefore, a Modified Uniform Present Worth factor,

9.8919, was selected from table A. 5 in appendix A for the differential price

rise of one percent and a period of 25 years.

20
Worksheet 4.2 hypothetical example

IMPACT ON UNIT OPERATION AND MAINTENANCE COSTS^

Representative case Discount rate 10 %

Building analysis period (from worksheet 2.2) 25 years

A. COSTS RISING AT THE RATE OF INFLATION

Proposed Requirement Original Requirement

Cost Type Amount^ Timing^ SPW^ UPW^ Amount^ Timing^ SPW^ UPW^

once at

Repair $25 10 yrs. .3855 $ 0

once at

Replacement $ $50 20 yrs. .1486

Routine

maintenance $J5 annual 9.077 $ 5 annual 9.077 \

B. COSTS RISING AT A RATE DIFFERENT FROM INFLATION

Cost Type

DifferentialPrice

Change

Proposed Requirement

Amount^ Timing^ SPW^^ UPW*^

Original Requirement

Amount^ Timing^ SPW^ UPW*^

Energy 1%

annually

for 25

$ 20 years 9 .8919

annuall y

for 25

$ 10 years 9 .8919

Replace

1 ment -5%

once at

$100 20 yrs. .1486 $

^ The unit of analysis should be the one listed on worksheet 2.2.

^ At present prices.

^ How often and when (years after construction year).

^ From discount factor tables A. 2 through A. 6 in appendix A for assumed discount

rate, timing of impact, and (for UPW*) the rate of differential price change.

21
In part B also, replacement costs under the proposed code requirement were expected to decline relative to prices in general at a differential rate of

five percent per year, due to a growing market and larger scale production of

the FSF/deslgn C. Because replacement is a one-time cost, it is treated differently from regularly recurring costs subject to differential price changes.

In particular, the five percent differential price decline will be taken into

account on worksheet 4.4 and so need not be considered in selecting the SPW.

The SPW discounting factor, .1486, was selected from table A. 2 in appendix A

for 20 years and 10 percent.

Third , after selecting discount factors, the data in worksheet 4.2 should be

transferred to either worksheet 4.3 (for costs rising at the same rate as inflation) or to worksheet 4.4 (for costs changing at a different rate than

inflation). By carrying out the calculations indicated in the worksheets, you can determine the impacts of the code change on operation and maintenance costs

over the building analysis period, discounted to the construction year.

22
Worksheet 4.4 hypothetical example

O&M COSTS RISING AT A RATE DIFFERENT FROM INFLATION

Representative case I

FUTURE ONE-TIME

COSTS^’^ Amount X U+e)t X SPW Discounted Value

Proposed

Requirement

$ 100 X ,3585 X ,1486 = $5.33

$ X X = $

Original - $ X X $

Requirement

- $ X X $

ElECURRING Amount at Present Prices

ANNUAL COSTS^

(Proposed - Original) X UPW* = Discounted Value

($ 20 - $ 10 ) X 9,8919 =

$ 98,92

($ - $ ) X = $

^ Amount, SPW, and UPW* are from worksheet 4.2, part B.

^ (1+e)*- = (1 H—.05)^^ = *3585 y where the rate of differential price rise ”e’ and year of impact ”t" are from worksheet 4.2, part B. This factor adjusts

for cost changes after the construction year.

23
4 . 3 GOVERNMENT COSTS

This section explains how to calculate the impacts of a code change on government costs for building code enforcement, fire protection, police protection, or other building-related services.

Code enforcement

To estimate the effects on code enforcement and other building department costs,

it is necessary to make an assumption about the effectiveness of enforcement

and the use of waivers. Then the budget needed to achieve the target compliance

rate and to handle the expected number of waivers is estimated.

To determine the effects of a code change on enforcement costs, one might ask

the following questions: Will a new concept or technology (such as solar energy

systems) require training or hiring of additional building officials? Is a new building aspect regulated that may require new enforcement procedures? Is a performance requirement Involved that will require extensive effort to determine compliance? Will periodic Inspection be required to assure continued

compliance?

Impacts on government costs should be discounted using one of the discount

factors described earlier. They should be calculated per building or other

unit of analysis so that they can be added to other impacts calculated on a similar basis.

Information on State and local costs related to adoption and enforcement of

building codes may be found in the testimony prepared for Federal Trade commission hearings by Arthur Young and Company. The data sources included the State

budget and State Controller reports.^ A survey of California building departments provides information on departmental budgets and fees.^

Worksheet 4.5 Illustrates how to estimate government costs for the hypothetical

FSF requirement in terms of costs per dwelling unit.

^ Arthur Young and Company, Testimony Before the Federal Trade Commission on the Proposed Rule for Standards and Certification , pp. 22-25.

^ California Building Officials, A Critical Evaluation and Commentary , July 1979.

24
Worksheet 4.5 hypothetical example

IMPACTS ON GOVERNMENT COSTS PER UNIT

Representative case J Discount rate 10 % Building analysis period 25 years

Assumptions J00% compliance; no waivers

^ From table A. 2 in appendix A for assumed discount rate and timing of cost.

The cost occurs only one time for a particular building although it is an ongoing cost for the building department.

® From table A. 3, appendix A, for assumed discount rate and building analysis

period

.

25
4.4 EFFECTS ON PARTICULAR GROUPS

This guide focuses primarily on the effect of a code change on the community as a whole. However, often it is helpful to know how a code change will affect a specific group such as building owners, tenants representing various Income

levels, designers, builders, materials and otljer product suppliers, taxpayers.

Insurance companies, or insurance customers. No worksheets are specifically

provided for these estimates, but some of the worksheets in this report may be

adapted for this purpose.

There are two questions to answer in estimating effects on particular groups:

First, who is most directly affected by the code change? For example,

construction cost changes directly affect the builder or building owner,

construction workers, and materials suppliers; operating and maintenance costs are paid by the building owner and/or user. These can be estimated from worksheets 4.1, 4.3 and 4.4, possibly with adjustments for tax effects.

It is possible to estimate effects on suppliers of various construction

products by breaking down construction costs into their components — e.g.,

estimating the change in the value of lumber purchased, the change in

electrician’s labor, etc.

For example, the economic analysis of the proposed Energy Performance Standards

for New Buildings^ estimated effects of proposed energy conservation standards on shipments of eight industries, including lumber. Insulation, and heating,

venting and air-conditioning equipment suppliers. The approach is to determine

what additional materials and equipment are needed to comply with a code change

and which products are displaced. Then, physical quantities and values can be

estimated per building and aggregated to find total effects on the industry.

Similarly, labor required or displaced as a result of compliance with a code

change can be estimated per building and in the aggregate, by type of labor.

Fire department and other government costs paid for out of government funds can

be estimated from worksheet 4.5.

Second, how are impacts on building costs shifted from those directly affected

to others? This information is necessary in order to determine accurately who

ultimately gains or loses from a code change.

Ideally, estimates of effects on particular groups should reflect the extent to

which builders pass on construction cost changes to building purchasers, landlords pass through energy costs to tenants, businesses pass through building

costs to their customers, hospitals raise prices, or the government alters fees

and taxes to reflect a change in enforcement or fire protection costs.

^ U.S. Department of Energy, Office of Conservation and Solar Energy and

Office of Buildings and Community Systems, Economic Analysis: Energy

Performance Standards for New Buildings, November 1979.

26
In practice, however, it may be difficult to trace this shifting of costs in

the market. If it cannot be estimated in dollar terms, any important shifting

of costs and benefits should be described in qualitative terms. This will

help decisionmakers understand who ultimately pays for, and gains from, code

changes

.

To estimate accurately effects on particular groups, it may be necessary to

consider tax effects. Several past building economics studies are useful as examples of how to calculate effects on particular groups. For example, a report by Arthur D. Little, Inc., analyzes effects of energy conservation

requirements on suppliers of various building products and on builders, engineers, and code authorities.^ A Department of Energy report also estimates

effects of proposed energy conservation requirements in particular groups.^

Two NBS economists, Marshall and Ruegg, describe ways of dealing with tax

effects on building owners.^

^ Arthur D. Little, An Impact Assessment of ASHRAE Standard 90-75 .

2 U.S. Department of Energy, Economic Analysis: Energy Performance Standards

for New Buildings .

O Marshall, Harold E. and Ruegg, Rosalie T. Energy Conservation in Buildings;

An Economic Guidebook for Investment Decisions , National Bureau of Standards

Handbook 132 (Washington, D.C.: Government Printing Office, 1980), p. 38.

27
28
5. IMPACTS ON BUILDING SAFETY AND PERFORMANCE

This chapter explains how to estimate the effects of a code change on building

safety and performance.

Building codes can affect property damages, lives lost, and injuries due to

building accidents. A code requirement can also affect the performance or usefulness of a building. These and other effects of a proposed code change

should be measured against the existing code requirement as a baseline.

The term "building quality" is used here to refer to both the safety and

usefulness of a building. Some changes in building quality may be Impossible

to quantify in dollar terms. However, many other effects can be quantified, at

least approximately.

5.1 BUILDING SAFETY

One of the most difficult problems facing code officials is determining the

economic effects of a code change on safety. Frequently, the cause-and-ef f ect

relationship between a particular building feature and an accident is poorly

understood. If the physical impacts of a code change on safety are not known,

then it will not be possible to calculate accurate dollar values for safety

impacts. However, even when the data are poor, economic analysis can still be

helpful in identifying which information is most needed for the analysis. This

is accomplished by performing a sensitivity analysis (discussed in chapter 9).

Effects of the code revision on property losses should be estimated in dollars.

However, in determining life safety effects, there are two possible approaches.

One approach is to assess life safety effects in terms of the number of lives

saved or injuries avoided. This approach does not avoid the need to balance

dollars against life safety. Instead, it shifts this difficult and sometimes

controversial task to those who make code change decisions. The analyst can

assist decisionmakers by providing information on the number of lives saved and

injuries prevented, the timing of safety effects, and net monetary effects of

the code change. This approach is described in section 5.2.

Another approach is to estimate the dollar value of lives saved and injuries

prevented as a result of the code change. This approach has the advantage that

it measures all safety effects in units — dollars — which can be readily com- pared with other effects. However, following this approach requires assigning

a dollar value to lives saved and Injuries prevented. This raises a number of

practical, theoretical, and philosophical questions, some of which are discussed

in the next chapter. The approach itself is described in section 5.3.

5.2 SIMPLIFIED APPROACH

This section presents a simplified approach to estimating safety impacts. It

emphasizes presenting life-safety impacts in physical terms. The analysis

29
should be done for each representative case identified in worksheet 2.2.^ The

eight steps in the analysis, which use worksheets 5.1 and 5.2, are described

below.

1 . Determine what types of building accidents or resulting outcomes might be

affected by the code change . For example, the hypothetical fire safety code

change described earlier might affect the frequency of three types of fires:

(1) those which are confined to the source of the fire such as, for example, a stove; (2) room fires; and (3) building fires. A code change concerning wiring

might affect the frequency of ignitions. A code change related to stairs might

affect the frequency of falls leading to Injury. If possible, try to select

categories of events which are directly related to the code change and/or for

which frequency and loss data are available. Fill in this Information on worksheets 5.1 and 5.2.

2 . Estimate how the proposed code revision would change the probability of

each of these events occurring in the reference building in a single year . In

the example, the change is given per dwelling unit. Fill in the Information on worksheets 5.1 and 5.2.

In the example, the annual probability per dwelling unit of a room fire

occurring declines by .001 (.1%, or one chance in a thousand) as a result of

the code change. Only the change in probability is recorded on worksheet 5.1,

but you may wish to estimate the "before" and "after" probabilities in order

to determine the amount of change. For example, a change of .001 might represent a change from .006 to .005 (from 0.6% to 0.5%).

If there is no change in ignitions, a decrease in room and building fires means

that the number of fires confined to the source must increase, since fewer

small fires grow to be large fires. Therefore, worksheets 5.1 and 5.2 show an increase of .002 in confined fires.

Estimating effects on probabilities of accidents may be the most difficult task

in the entire analysis. One approach is to determine how often relevant types

of events are likely to occur without the code ch^ge and then to estimate the

percent of these that would be prevented by the code change. Later in this

chapter, several examples are given of how researchers have attempted to estimate the safety effects of code changes.

Some sources of accident and loss information are published data bases,

technical reports, insurance company figures, and the opinions of experts.

However, since information is often sparse about the safety effects of building

features, estimates may have to be based on "informed judgment" or on admittedly

arbitrary assumptions. Section D.4 in appendix D lists a few sources of

information about building accidents and losses.^

^ This approach is also described in a simplified version of this report:

Rawie , Estimating Benefits and Costs of Building Regulations.

2 Three reports briefly described in appendix C show how researchers estimated

losses related to fires, mobll home hazards, and electric shocks.

30
Worksheet 5.1 hypothetical example

IMPACT ON EXPECTED PROPERTY LOSSES PER UNIT

Representative case I Discount rate 10 % Building analysis period 25 yea rs

^ Probability of accident after code change minus probability of accident

before code change, based on available information and engineering judgment.

^ From table A. 3, appendix A, for assumed discount rate and building analysis

period or life of required feature.

31
Sometimes the direct impact of a code provision may be to increase safety,

but — paradoxically — because of market effects, there may be indirect negative impacts on safety. For example, if code requirements increase housing

costs significantly, some people may choose to stay in old housing to save money — possibly reducing safety for these people. This report concentrates

on direct effects of accidents; it is beyond the scope of this paper to

describe ways of rigorously estimating safety and other effects that occur through the market. However, information on code-caused increases in building

costs may help those involved in changing codes to consider possible indirect

effects on safety.

3 . Determine the dollar value of property losses from each type of accident . Property losses due to fire, earthquake, snow, winds, etc. include not only the

actual damage to the building and its contents, but also the less obvious costs

such as costs due to disruption of a company's business. Examples of these

costs are listed in table 5.1. Fill in the information on worksheet 5.1.

4. Multiply the average cost per accident by the change in the probability

that the particular accident will occur . Do this for each type of accident and

add these figures to get the total change in annual expected dollar loss. Fill

in this information on worksheet 5.1.

5 . Multiply the annual dollar loss by a Uniform Present Worth (UPW) discounting

factor. This will give the discounted expected value of property losses over

the building analysis period (or over the effective life of the required feature, if this is less than the building analysis period). ^ The UPW is found in

table A. 3 in appendix A. Fill in this information on worksheet 5.1.

* 6 . Estimate the number of fatalities and/or injuries resulting from each type

of accident or other event . Fill in this information on worksheet 5.2.

Sources of loss information are included in appendix D. A report by Helzer,

Buchbinder, and Offensend, described in appendix C, shows how one group of

researchers estimated the losses associated with various types of fires.

7. Multiply the value which was estimated in step 6 by the change in probability

estimated in step 2 that the particular type of accident will occur . Add these

products for fatalities and for each type of injury. This shows the change in

the expected annual number of fatalities and various types of injuries due to

the code change. Fill in this information on worksheet 5.2.

In some cases the nature of available hazards data will make it easier to

estimate directly the number of deaths and injuries associated with various

types of accidents, rather than first estimating the numbers of accidents. If

so, there may be no need to fill out the second and third column in worksheet

5.2; instead, the analyst could skip to the last three columns and directly

fill in the expected change in deaths and injuries.

^ The "expected value" is a way of accounting for uncertainty; it shows the

average impact per building. For example, if one building in a thousand

suffers a $50,000 fire loss, the expected value of fire losses for a single

building is $50.

32
Worksheet 5.2 hypothetical example

IMPACT ON EXPECTED LIFE SAFETY PER UNIT

Representative case I

Change in

Annual No of Deaths

Expected Change in;

Accident Probability and Injuries Major Minor

Type of Accident^ X per Accident Deaths Injuries Injuries

.005 (deaths) = + .00001

Confined

fire + .002 X .05 (major Inj.) 33 +.0001

.5 (minor inj .

)

= + .001

.02 (deaths) = -.00002

Room fire -.001 X .1 (major inj.) = -.0001

1.0 (minor inj.) — -.001

.1 (deaths) -.0001

Building

fire -.001 X .5 (major inj.) -.0005

2.0 (minor inj.) — -.002

1 TOTAL CHANGE IN ANNUAL EXPECTED LOSS -.00011 -.0005 -.002

1 Building analysis period or life X 25 25 25

1 of required feature

1 TOTAL CHANGE OVER TIME -.00275 -.0125 -.05

^ From worksheet 5.1.

33
8. Multiply the fatalities or Injuries prevented annually by the number

of years In the building analysis period or the life of the repaired feature,

whichever Is less. This will show the expected life safety impacts over time.

Fill in this information on worksheet 5.2.

Table 5.1 TYPES OF PROPERTY LOSSES

* Damage to the building, including demolition and cleanup.

* Damage to building contents.

* Loss of company profits and benefits to customers due to disruption of

business

.

* Costs of temporary shelter for residents or for commercial enterprises.

* Moving costs.

* Costs of insurance administration. Legal fees and administrative costs

should be counted, but not claims payments, which are already counted in

estimating damages.

* Miscellaneous costs such as costs of child care, lost wages for residents

as they deal with the aftermath of an accident, and extra meal costs for

displaced residents.

* Property losses to neighboring buildings.

* Losses which are difficult to quantify in dollars, such as loss of family

mementos, psychological effects of property damages, and loss of family

pets

.

* Any savings in operating expenses because a damaged building is not being

used should be deducted from costs of the accident.

34
35
5 . 3 DETAILED APPROACH

The simplified method described in section 5.2 may be the best approach if

the resources for doing the analysis are limited. However, it does not provide a rigorous method for estimating changes in the probability of an accident and it does not allow for impacts to vary over the building analysis

period. Therefore, this report also presents a detailed approach to estimating safety effects. (The two approaches are compared in the appendix to

this chapter.) The detailed approach has seven steps, described below:

1 . Determine what types of building accidents or resulting outcomes might

be affected by the code change . See the discussion of Step One in

section 5.2.

2 . Estimate how the code change would affect the probability that a specific

type of accident will occur . This can be done using the following

procedure

.

a

.

b.

Specify sets of events leading to accidents . To follow this detailed

approach, it is necessary to specify the conditions that lead to accidents.

These conditions can be set forth, as alternative chains-of-events . A

probability tree such as the in figure 5.1 for a hypothetical Fire

Safety Feature (FSF) is useful in depicting possible sets of events.

Estimate probabilities pf various: steps in the scenarios . Ultimately, the

analyst needs to know the probabllltJ.es that various chains of events

leading to accidents will occur in' the reference building in a given year,

and how these probabilities #re altered by a code change. This requires

estimating the probabilities of various conditions, or steps. This is

illustrated in figure 5.1. The' humhers on each segment are the probabilities that the event will occur in a single building (or other unit of

analysis such as dwelling unit or per square foot) within a year, providing

that the preceding events occurred.

1

For example, in figure 5.1 the heavy line corresponds to one chain of

events. In that chain of events, the probability of an Ignition is .0001.2

The probability that some one will be home if there is an ignition is .95.

The probability that there will be a FSF is .10. The probability that the

fire will be confined to its origin, if someone is home and there is a FSF, is .8.

Revised probabilities are needed to reflect a code change. In the example,

the probability of there being a FSF is set at the higher figure in parentheses, (.80), to reflect the code change requiring FSFs.

^ Sources of accident data are listed in appendix D. However, "expert

judgment" will also be needed to estimate these probabilities. For an example of how such probabilities have been estimated, see Helzer et al. Decision

Analysis of Strategies for Reducing Upholstered Furniture Fire Losses .

2 That is, an ignition will occur each year in one out of 10,000 dwelling units.

36
Figure 5.1 EVENTS LEADING TO FIBUS; (HYPOTHETICAL EXAMPLE)

Is there an ignition?

Is someone

home?

Is there®

a FSF?

Extent of

flame damage

confined

® Numbers in parentheses are for after the code change
c. Specify situations which include accident types and key conditions . In the

simplified approach, losses were assigned to each type of accident without

regard to other conditions such as whether someone is home. In reality,

losses depend not only on the accident type, but also on other factors.

Thus, the analyst should identify key elements that determine the amount of

loss. In the FSF example, two key elements determining the extent of loss are the type of fire (e.g., confined, room, or building) and whether someone is home. Thus, the six "modified" situations that will be used later

in the analysis are: 1) someone home/confined fire; 2) someone home/room

fire; 3) someone home/building fire; 4) nobody home/conf ined fire; 5) nobody

home/room fire; and 6) nobody home/building fire. These are shown in

separate columns in figure 5.2.

d . Estimate the change in probabilities of the situations listed in 2c due to

the code change . The next step is to compute the probabilities of each

situation occurring and to calculate how these probabilities change with a code change. A way to do this is explained below.

First, multiply the probabilities along a single path in the probability

tree to find the probability that the particular chain of events described

by the path will occur. For example, the probability that there will be an ignition, someone will be home, there will be a FSF, and a confined fire

will result, is .0001 x .95 x .10 x .8 = .0000076. "Path probabilities"

can be computed for each chain of events, as is illustrated in figure 5.2.

To find the probability that a particular situation described in 2c will occur in the reference building, add the "path probabilities" for all paths

leading to the situation in question. For example, the someone home/conf ined

fire situation encompasses two paths on the tree: one where someone is home,

there is a FSF, and a confined fire results; and one where someone is home,

.there is FSF, and a confined fire results. Adding the two path probabilities gives a probability of a someone home/conf ined fire of .0000503.

Probabilities should be calculated for each of the situations specified in

2c. They should be calculated twice, once for the original code provision,

and once for the revised code requirement, as illustrated in figure 5.2.

By subtracting probabilities computed with a code change from the

probabilities computed without a code change, the analyst can determine how

the code change alters the probabilities of various situations. For example, figure 5.2 illustrates that imposing a code requiring a FSF would

increase the probability of a confined fire occurring when someone is home

by +.00002 per building per year.

38
fign.

iOO^

Figure 5.2 PROBABILITIES OF FIRE SITUATIONS (HYPOTHETICAL EXAMPLE)^

.10 0.2

,.S0)\

0

homei

.95

MO \0.3

.80) B

I not

\homey

‘i05

.90 10.4

(. 20 ) B

Path

Probability

.0000076

(.0000608)

.0000019

(.0000152)

( 0 )

.0000427

(.0000095)

.0000342

(.0000076)

.0000085

(.0000019)

.0000003

(.0000024)

.00000015

(

.

0000012 )

.00000005

(.0000004)

.00000135

(.0000003)

.0000018

( .0000004)

.00000135

0.3 (.0000003)

jAccide.t Probabilities - Original

- Revised

Change

Someone Home

Confined Room Building

.0000076

(.0000608)

.0000019

(.0000152)

( 0 )

.0000427

(.0000095)

.0000342

(.0000076)

.0000085

(.0000019)

.0000503 .0000361 .0000085

.0000703 .0000228 .0000019

+.00002 -.0000133 -.0000066

Nobody Home

Confined Room Building

.0000003

(.0000024)

.00000015

(. 0000012 )

.00000005

(.0000004)

.00000135

(.0000003)

.0000018

(.0000004)

.00000135

(.0000003)

.00000165 .00000195 .00000140

.0000027 .0000016 .0000007

+.00000105 -.00000035 -.0000007

l> Figures in parentheses are for after the code change.

39
3 . Assign dollar values to life safety Impacts and determine dollar value of

property losses associated with each situation . The next step Is to determine what losses result from each situation described In 2c. The starting

point for determining these losses Is historical hazards data. However,

since losses may depend on factors not reflected In the available data,

the analyst may have to make loss estimates to account for various conditions surrounding the accident. For example, suppose the historical data

suggested that the probability of major Injury In a room fire Is 0.1.^

Since an Injury Is more likely to happen If someone Is home when the fire

starts, this figure might need to be adjusted up to 0.2 for fire with

someone home and down to .01 for fires with nobody home.

Losses assigned to various accident types for the hypothetical example are shown In figure 5.3. For example, the probability that there will be a death due to a confined fire when someone Is home Is estimated at .005 In

the hypothetical example;^ the property losses from such a fire are estimated at $2500. (These would be average figures. Some buildings would

have above-average losses and other would have below-average losses.)

In the simplified approach, deaths and Injuries were presented In physical

terms, without assigning a dollar value. However, In the detailed approach,

life safety Impacts will be converted to dollars. The dollar figure represents the amount society Is willing to pay (or require Individuals to pay)

to prevent a "statistical" death or Injury. Methods of arriving at these

dollar values are discussed In chapter 6.

For purposes of Illustration In the example, we have assumed the following

dollar values for preventing death, major Injury, and minor injury: death - $300,000; major injury - $50,000; and minor injury - $1,000. It should be

emphasized that these values were selected arbitrarily and are used for

Illustration only.

^ That is, a major injury would occur In one out of every ten room fires.

40
Figure 5,3 EXPECTED LOSSES DUE TO ACCIDENTS (HYPOTHETICAL EXAMPLE)

Ignition

Loss Type

Death

Major

Injury

Minor

Injury

Property

Loss

Confined ^

.005 .05 .5 $ 2,500

Someone Home / Room .02 .2 2 $10,000

\Building .2 .7 3 $50,000

Confined

/ 0 0 0 $ 2,500

^No one Home / Room .02 .05 1 $10,000

\Building .05 .3 1 $50,000
Once the deaths, injuries, and property losses associated with each

situation are known, and dollar values are assigned, it is possible to

calculate expected dollar losses for each situation. The average number of

deaths or injuries from an accident are multiplied by the appropriate value

and summed, together with property losses, to find the expected loss if an accident of that type occurs. For example, in the hypothetical case in

table 5.2, a confined fire that occurs when someone is home results in an average of .005 deaths. Multiplying this by the $300,000 assumed value per

deaths gives an expected loss of $1,500 due to fatalities if that particular type of fire occurs. Adding this to losses due to property damages and

injuries gives an expected cost of $7,000 if a confined fire occurs when

someone is home. (As noted before, this is an average loss. In some con- fined fires the losses would be greater, and in others they would be less.)

42

il
Table 5.2

DOLLAR LOSSES DUE TO ACCIDENTS (HYPOTHETICAL EXAMPLE)

Average $ Value of

No. of Deaths x Deaths or = Average Loss if Accident Occurs

1. Someone home/

confined fire

Death .005 $300,000 $ 1,500

Major Injury .05 50,000 2,500

Minor Injury .5 1,000 500

Property Loss 2,500 $ 7,000

2. Someone home/ room fire

Death .02 $300,000 $ 6,000

Major Injury .2 50,000 10,000

Minor Injury 2 1,000 2,000

Property Loss 10,000 $ 28,000

3. Someone home/

building fire

Death .2 $300,000 $60,000

Major injury .7 50,000 35,000

Minor injury 3 1,000 3,000

Property loss 50,000 $148,000

4. No one home/

confined fire

Death 0 $300,000 $ 0

Major Injury 0 50,000 0

Minor Injury 0 1,000 0

Property Loss 2,500 $ 2,500

5. No one home/ room fire

Death .02 $300,000 $ 6,000

Major Injury .05 50,000 2,500

Minor Injury 1 1,000 1,000

Property Loss 10,000 $ 19,500

6. No one home/

building fire

Death .05 $300,000 $15,000

Major Injury .3 50,000 15,000

Minor Injury 1 1,000 1,000

Property Loss 50,000 $ 81,000

^ From figure 5.3. ^ Assumed.

A3
4 . Calculate per-unit property and life safety impacts for each year In the

building analysis period . The next step Is to compute expected losses

by multiplying the loss associated with each situation by the change in

probability of that accident occurring, and summing. This gives a weighted

average which represents the change in loss that is statistically expected

for a single building (or other unit of analysis) in a given year. This

should be done for each year of the building analysis period, unless impacts are expected to be the same each year.

In the hypothetical example in table 5.3, the code change decreases the

probability of a room fire occurring when someone is home by .0000133. The

average loss if a fire occurs is $28,000. Multiplying the decreased probability by the loss if the fire occurs shows that the dollar losses statistically expected per building, in a given year, due to this particular

accident type, will decline by about 37(|:. Summing the changes shows that

the hypothetical code change would cause expected dollar life safety and

property losses per building in a given year to decline by $1.28. (With

this very slight impact on safety, the code change would only be costeffective if compliance and enforcement costs were also extremely low.)

44
Table 5.3

CHANGE IN EXPECTED LIFE SAFETY AND PROPERTY DOLLAR LOSSES IN

(HYPOTHETICAL EXAMPLE)

Accident Type

1. Someone home/

confined fire

2. Someone home/ room fire

3. Someone home/

building fire

4. No one home/

confined fire

5. No one home/ room fire

6. No one home/

building fire

Change in

Probability x

of Accident^

+ .00002

- .0000133

- .0000066

+ .00000105

- .00000035

- .0000007

Loss if

Accident Occurs^

$ 7,000

$ 28,000

$148,000

$ 2,500

$ 19,500

$ 81,000

Change in Expected Loss

^ From figure 5.2

A SINGLE YEAR

Change in

Expected

Loss

$0.14

$ .3724

$ .9768

$ .002625

$ .006825

$ .0567

$1.2701

45
5 . Calculate per-unit Impact over the building analysis period by summing

annual impacts . If the change in annual expected losses is likely to be

the same each year, then the change need only be calculated one time. The

present dollar value of expected life safety and property losses over time can be estimated using a Uniform Present Worth factor (UPW) , as was done in

Step 5 of the simplified approach.

But if the change in expected losses is likely to vary unevenly over the

life of the building, the calculations will have to be repeated for each

year of the building analysis period. This variation may occur, for example, if a code feature loses its effectiveness as the building ages. Or,

the variation may occur because of changes in the dollar losses connected

with a particular accident. For example, an increase in the cost of medical

care may increase the cost of an injury.

The example in table 5.4 Illustrates how to calculate the change in losses over time. First, each loss for a given year must be discounted to the

construction year by multiplying the loss by a Single Present Worth factor

selected for the appropriate year in the building analysis period. The

discounted values are then summed to find the change in the discounted

value losses due to the code change, -$6.82 in the example.^

^ It may also be useful to calculate the effect on the number of deaths and

injuries in physical terms. This can be done by multiplying the average

number of deaths (or injuries) of an accident by the changed probability of

that accident as a result of the code change. Then, life safety impacts over

time can be calculated by summing impacts in each year of the building analysis period. In presenting these results, you should make clear that life

safety effects were also Included in dollar losses and you should explain the

timing of life safety effects.

46
Table 5.4

CHANGE IN LIFE SAFETY AND PROPERTY DOLLAR LOSSES

OVER BUILDING ANALYSIS PERIOD

(HYPOTHETICAL EXAMPLE)

Year in Building

Analysis Period

SPW X

(10%)

Change

Losses^

in Expected = Discounted Value

of Change in

Expected Losses

1 .9091 X (- $1.27) — -$1.15

2 .8264 X (- 1.27) = - 1.05

3 .7513 X (- 1.27) = - .95

4 .6830 X (- 1.27) = - .87

5 .6209 X (- 1.27) = - .79

6 .5645 X (- 1) = - .56

7 .5132 X (- 1) = - .51

8 .4665 X (- 1) = - .47

9 .4241 X (- .50) = - .21

10 .3855 X (- .50) = - .19

11 .3505 X (- .10) = - .04

12 .3186 X (- .10) = - .03

13 .2897 X (- 0) - 0

14 .2633 X (- 0) = 0

15 .2394 X (- 0) = 0

16 .2176 X (- 0) = 0

17 .1978 X (- 0) = 0

18 .1799 X (- 0) = 0

19 .1635 X (- 0) = 0

20 .1486 X (- 0) = 0

TOTAL -$6.82

^ These hypothetical figures reflect declining effectiveness of a FSF over time.

The figure for year one is from table 5.3.

47
5.4 BUILDING PERFORMANCE

Code changes affect the performance or usefulness of a building in many ways.

A few of these impacts are discussed here and ways are presented to estimate

the costs resulting from such changes.

Usable space

Building code requirements may reduce the amount of building space available.

For example, fire safety requirements that apply only to buildings which exceed

certain floor area and height limits might lead to construction of a smaller

building in order to avoid those requirements, or a building may not be con- structed at all if code requirements make it excessively expensive. In such

cases, the owner sacrifices rentals or other benefits of the lost space but

also avoids some construction and operating costs.

Worksheet 5.3 Illustrates a method of calculating the costs of space foregone,

using rentals as a measure of the value of lost space. Effects are calculated

for the building analysis period and discounted to the construction year.

Valuing space . If the change in space is small relative to the size of the

market for that type of space, the change will not affect rental rates and current rentals can be used to value the space. However, if the change in space

is large relative to the size of the market, rentals may go up or down, and a figure lower than current rentals may be needed to value space. This is

explained further in the appendix to this chapter.

How space is used . Building codes may also affect how all or part of a building

is used. For example, a basement may not be finished for use as a "habitable

room" if doing so would trigger code requirements for minimum celling height.

The code could even affect the type of occupancy for an entire building; it

might Inhibit the use of buildings for occupancies with more stringent construction requirements. For example, fire safety requirements might inhibit use of

buildings as nursing homes. There would be a loss equal to the additional benefits of the foregone use as compared with the actual use. This effect may be

difficult to quantify; but if it cannot be quantified, it should at least be

described qualitatively so that decisionmakers can weigh it in considering code

revisions

.

Rehabilitation

If a rehabilitation project would be subject to new building requirements, a code change may forestall rehabilitation by making it economically impractical.

If this occurs, the net benefits of the foregone rehabilitation (increased value

of the space minus the rehabilitation costs) are lost and represent a cost of

the code change. Impacts of codes on rehabilitation are discussed in Berry^ and

Gross, Pielert, and Cooke.

^ Berry, Proceedings of the National Conference on Regulatory Aspects of

|| Building Rehabilitation .

^ Gross, Pielert, and Cooke, Impact of Building Regulations on Rehabilitation. '

48
Worksheet 5.3 hypothetical example

IMPACTS PER UNIT OF A CHANGE IN SPACE

Representative case I Discount rate 10 % Building analysis period 25 years

^ Rental excluding any owner-paid operating costs.

b From table A. 3, appendix A, for assumed discount rate and building analysis

period

.
Delays in occupancy

A code change may lead to delays in occupancy because of the time needed to

obtain approvals, resulting in a loss of rentals or other benefits of that

space during the delay.

Efficiency and amenities

Codes may also affect a building's durability, efficiency, comfort, convenience,

and attractiveness. For example, a code change permitting surface-mounted

flat conductor cable wiring makes it more convenient and efficient to change

wiring layouts.^ On the other hand, a requirement for a fire extinguisher

may interfere with the aesthetic quality of a historic building. Or mandatory

double-glazing might cause builders to use fewer windows — sacrificing the

psychological and emergency exit benefits of the omitted windows.

One measure of lost benefits may be the effect on the rent that can be charged

or the selling price of a building. Lost value may also be estimated in other

ways. For example, lost benefits might be measured by the resulting reduction

in a firm's profits or by how much a tenant or owner spends to compensate for an inconvenient or unappealing design.

If dollar impacts of a change in efficiency or amenities cannot be estimated,

these impacts can still be described qualitatively so that they will not be

overlooked completely in making code change decisions.

Residual value

The feature required by a code change may have some residual value after

the assumed building analysis period. For example, if the true lifetime of

a building is 40 years, a sprinkler system may still provide protection

beyond an assumed 25-year building analysis period. Also, after its useful

life, there may be a salvage value for that component.

Estimating the discounted residual value of the code-required feature may be

a difficult task. However, for a long building analysis period, an error in

estimating residual value may not be very Important because impacts that occur in the distant future are heavily discounted. For example with a 10

percent discount rate, a $1,000 Impact occurring after 25 years has a present

value of only $92. Thus, even a rough approximation of residual value may

be sufficient. In some cases residual value may be so small that it can be

neglected altogether. Worksheet 5.4 may be used to calculate the impacts on discounted residual value.

^ MacFadyen, David J. "A Case History of the Integrity of the National

Electrical Code," in Cooke, Patrick W., Ed., Proceedings of the Third

Annual NBS/NCSBCS Joint Conference , National Bureau of Standards Special

Publication 552 (Washington, D.C.: Government Printing Office, 1979),

pp. 58, 59.

50
Unintended effects

A code change may result in unintended impacts on building amenities and

usefulness. For example, consider the case of a code provision that is aimed

at protecting against fire deaths. That provision might also increase the dayto-day convenience of the building by providing more exits and/or it might

decrease building security.

Any significant unintended effects on building safety and performance should

also be estimated and presented separately for consideration by decisionmakers.

^ At end of building analysis period. The change will be positive for an Increase in the building's residual value.

^ From table A. 2, appendix A for assumed discount rate and building analysis

period

.

51
APPENDIX to Chapter 5

Table 5.5 COMPARISON OF SIMPLIFIED AND DETAILED APPROACHES FOR

ESTIMATING SAFETY IMPACTS

Simplified Approach Detailed Approach

1. Determine accident types or other

outcomes.

2. Estimate how the code change

would affect the probability

of the accident occurring.

3. Determine property losses

associated with each accident type.

4. Calculate annual per-unit property

Impact

.

5. Calculate per-unit Impact over the

building analysis period using UPW

factor

.

6. Determine fatalities and Injuries

from each type of accident.

7. Determine expected annual

fatalities and injuries by multiplying per-accident impacts by change

in probability of accident.

8. Calculate life safety impacts over

time by multiplying annual impacts by

number of years in building analysis

period

.

1. Determine accident types

or other outcomes.

2. Estimate how the code change

would affect the probability

that a specific type of accident

will occur, using the following

procedure

:

a. Specify events leading to

accidents.

b. Estimate probabilities of

various events.

c. Specify situations which include

accident types and key conditions.

d. Estimate change in probability

of the situations listed in 2c

due to code change.

3. Assign dollar values to life

safety impacts, and determine

dollar value of property and

life safety losses associated

with each accident type under

certain key conditions.

4. Calculate per-unit property and

life safety impacts for each year

in the building analysis period.

5. Calculate per-unit impact over

the building analysis period by

summing annual impacts. (Determination of fatalities and injuries

is included in step 3.)

6. Determine expected fatalities and

injuries in a given year by multiplying per-accident impacts by the

change in probability of accident.

7. Calculate life safety impacts over

time by summing impacts in each year

of the building analysis period.

52
APPENDIX to Chapter 5 (Continued)

CALCULATING THE VALUE OF LOST SPACE

This section describes a method for calculating costs of lost space if a code

change leads to reduced construction.

Usually a single code change has little effect on building space, so that the

loss of building space can be neglected in calculating costs of code changes.

However, if there is a large effect on building space, as in the present example, the loss of space may be a significant cost of the code change and should

be computed. If it is not possible to compute this cost quantitatively, it

should at least be described so that decisionmakers will consider it when they

approve code revisions.

Demand curve for building space

The demand curve (DD') for building space in figure 5.4 shows the amounts people

are willing to pay for varying amounts of space of a given quality and location

in a hypothetical jurisdiction. The height of the curve at a certain point on

the x-axls shows how much the "marginal" renter is willing to pay per square

foot.l

For example, if 9,000,000 square feet are available, the marginal renters are willing to pay up to $14 per square foot. If only 5,000 square feet are available, the marginal renters would be willing to pay up to $20 per square foot.

(Naturally, they will try to rent space for less, but will pay $20 if necessary.)

The rent they would be willing to pay is a measure of the value of the space to

them.

Before the code change

Suppose that before a code change, 10,000,000 square feet of space are available.

To rent all this space, landlords must charge no more than $10 per square foot;

otherwise some space will remain vacant. This means a renter who would be willing to pay as much as $14 per square foot need only pay $10. This renter gets a "consumer surplus" of $4 per square foot. Renters willing to pay other maximum

amounts get surpluses of different sizes.

The total consumer surplus is equal to the area of the triangle DCG under the

demand curve.

^ It is convenient to treat all space as rented, but the same principles apply

to purchased space.

53
After the code change

Now, suppose a code change reduces the amount of space available so that the

price goes up to $14 per square foot. Some renters—those willing to pay less

than $14 per square foot—are priced out of the market and there is a loss of

consumer surplus equal to the striped triangle, EFC.^ This is a net loss to

society.

In the example in figure 5.4, the demand curve is a straight line, so that the

lost benefits to the community are one-half of EF x FC, or 1/2 ($14/ft^ - $10/ft2) X (lOM ft^ - 9M ft2) = $2, 000, 000/year.

We did not provide a worksheet for making this type of calculation and, unlike

other effects discussed in this report, it is not calculated on a per unit

basis. It should be added to aggregate impacts with appropriate discounting.

(See chapter 8.)

^ There is also a loss of consumer surplus equal to the rectangle EFGH.

However, this share of the renters* loss is the building owners' gain, rather

then being a net loss to the community. It is true that some of this payment

may compensate for higher costs to the building owner due to the code change;

but these higher costs are included elsewhere in the analysis and need not

be counted here.

54
Figure 5.4 EFFECTS OF CODE CHANGE ON CONSUMER SURPLUS

Available Space

55
6. ECONOMIC VALUE OF LIFE SAFETY

This chapter concerns the dollar value of life safety. There is no generally

accepted approach for imputing a dollar value to death and injury. Therefore,

this chapter discusses factors to be considered, but it does not attempt to

provide a definitive way to value life safety impacts.^

Assigning a dollar value to the prevention of a death or injury raises

philosophical, theoretical, and practical problems. It may require an exercise of judgment which the analyst is not prepared to make. Even so,

it is still useful for the analyst to collect and present information about

the economic aspects of death and injury in order to aid decisionmakers in

imputing values to life safety. The discussion in this chapter should be

helpful whether the analyst assigns dollar values to life safety or leaves

this burden to those who make code decisions.

6.1 CONTROVERSIES OVER VALUING SAFETY

The notion of putting a dollar value on safety is controversial, especially

where lives are in question. At the heart of the controversy is the feeling

that it cheapens or dehumanizes life to put a dollar value on it. In examining this issue, however, it is important to remember that the dollar value

is not assigned as a measure of human worth; it is assigned in order to make

the unavoidable decision: How much should the community spend (or require

building owners and others to spend) to reduce building risks? Assigning a higher value for purposes of evaluating code change decisions does not neces- sarily make life more sacrosanct. Rather, it may mean that code changes

will be passed which provide greater protection against building hazards at

the expense of other aspects of safety or quality of life. For example,

assigning a high dollar value to life safety in making decisions concerning

hospital fire protection might mean that a large portion of health care spending would go into hospital construction to meet stringent code requirements and would not be available for other needs such as better nursing care.

^ One of the best works on this subject is a book by M. W. Jones-Lee, The

Value of Life: An Economic Analysis , (Chicago: University of Chicago Press,

1976). See also: Anderson, Lee G. and Settle, Russel F., Benefit-Cost Analsis: A Practical Guide (Lexington, MA. : Lexington Books, 1978); Walters,

Jeffrey, L. Economic Benefit-Cost and Risk Analysis of Results of Mobile

Home Safety Research: Wind Safety Analysis (Washington, D.C.: U.S.

Department of Housing and Urban Development, 1979); Cornell, M. et al. Survey

of Methods for Estimating the Cost of a Human Life (prepared for the U.S.

Coast Guard, 1976); Faigin, B. M. , 1975 Societal Costs of Motor Vehicle

Accidents (Washington, D.C.: U.S. Department of Transportation, 1976); and

Technology + Economics, Inc., The Consumer Product Safety Commission Injury

Cost Model: Complete Documentation, 1980.

56
Statistical life

Another objection to assigning a dollar value to life safety rests on the

idea that, to the person involved, life has value limited only by how much

that person could pay to preserve it. This objection can be met by introducing the concept of a "statistical life."

In considering a code change, the decisionmaker is not contemplating the

certainty of death to a few identifiable individuals, but rather a small

change in risk to a large number of people. The lives saved as a result of a code requirement are "statistical lives," since those who otherwise would

have been victims could not have been identified in advance. The distinction

between certain death versus a risk of death means that the relevant question

is not "How much should be spent to prevent certain death?" but rather, "How

much should be spent to reduce risk?" This concept is illustrated in example

6 . 1 .

Example 6.1: FINDING THE VALUE OF A STATISTICAL LIFE

Problem : In a community of 1,000, each individual is willing to pay up to

$400 to prevent a .002 risk of death due to fire. What is the value implicitly

put on a "statistical life" by this community, when contemplating a fire

protection code change which would reduce the probability of death by .002 for

each citizen?

Solution : If the risk of death is reduced by .002, this will save .002 x 1,000 = 2 statistical lives. The community is willing to pay $400 x 1,000 = $400,000 to save these two lives, or $200,000 per life. Thus $200,000 is

the value that the community implicitly puts on a statistical life.

People routinely make decisions concerning how much to spend to reduce

individual risk by a small amount. For example, homeowners must decide how

much to spend on smoke detectors. Cities and counties must determine how much

to spend on fire protection, knowing that failure to increase the fire protection budget increases risk slightly to many individuals and may result in addi

tional lives lost. Thus, the need to assign a value to saving a statistical

life is not an unusual one.

6.2 VALUES USED PREVIOUSLY

One method of determining what value to assign to a statistical life is to use values that have been assigned in previous analyses of government safety and

health regulation, or values which are implied in public policy decisions.

However, using such numbers may have serious drawbacks.

57
First , some past studies have estimated only monetary costs of accidents,

neglecting the intrinsic value of life and health. For example, a study for

the National Highway Traffic Safety Administration covers only the monetary

impacts of accidents.^ Second , even the monetary estimates may be based on a methodology which is unacceptable to those who make code change decisions. Third, even if the monetary values were properly computed, they may

have been estimated for populations and types of accidents significantly different from those affected by building codes. For example, a study on the

economic costs of accidents for the Coast Guard may be relevant only to the

boating population and boating accidents, but not to building accidents.

The values estimated in previous studies may have other problems as well,

such as inadequate data to support them. Moreover, the values implied in

public policy decisions may reflect tradition or political considerations

rather than the ideal balance between cost and safety. Thus, using the

values for life safety previously estimated for policy purposes or implied

in policy decisions may be politically convenient, but it may perpetuate use

of Inappropriate numbers. Agencies interviewed in one study suggested that

the specific number used to value life safety is not important so long as a consistent figure is used for various policy decisions. ^ This may be true

from a political point of view, but it does not necessarily promote

efficiency or fairness: it may be better to be occasionally right than

consistently wrong.

6.3 ESTIMATING COST COMPONENTS

There are three types of losses due to death or injury which should be

considered in assigning values to life safety: (1) direct monetary costs such

as medical bills; (2) Indirect monetary costs because the victim no longer

works; and (3) non-mohetary costs due to pain and suffering and loss of the

intrinsic value of life. These losses will be discussed in the following

sections. However, before beginning that discussion, there are several

points to keep in mind.

First, it may be feasible to calculate monetary costs of accidents, but

not nonmonetary costs such as pain and suffering. Even though the monetary

costs represent only a fraction of overall costs of death and injury, they

are still worth estimating because they set a lower boundary on what should

be spent to protect life safety.

Second , costs of death and injury accrue not only to the victim, but also to

the victim’s family and friends, insurance companies, government agencies,

employers, and others. Costs to all of these groups should be considered.

^ Faigin, 1975 Societal Costs of Motor Vehicle Accidents .

2 Cornell, Survey of Methods for Estimating the Cost of a Human Life .

^ Ibid , pp. 3-2, 3-3.

58
Direct Costs of Death and Injury

Direct costs of accidents which should be considered include medical expenses

such as hospital bills (including those paid by insurance companies); costs

of medical supplies such as drugs, prosthetics, and wheelchairs; costs of

home nursing care; and psychiatric counseling. The level of medical expenses

depends both on the number of days of care and the type of care needed. For

example, costs of treating burn victims are higher than costs of treating

many other kinds of injuries. Some data on medical costs of burns are given

in Stacey and Smith.

Accidental death and Injury also create a host of other direct costs, such

as legal costs, funeral costs, and costs of administering health-related

Insurance. (The insurance claims payments are counted under medical expenses.)

Indirect monetary costs

Most economists would agree that the value of a potential victim’s productive

labor should be considered in assigning values to life safety. However,

there is some disagreement as to how labor should be valued.

A death or Injury deprives society of the victim’s productive labor. It

may impose additional costs of retraining new employees. Even the portion

of earnings that was paid in taxes is a loss to society, since the government

foregoes tax revenues.

Foregone earnings are the before-tax amounts that would have been earned —

over the lifetime in case of death, or over the period of recovery from an

injury — discounted to their present value. A value is imputed to unpaid productive labor such as housework and volunteer work. It may also include Income

foregone by family members who have to stay home to care for a sick person.

If an accident results in injury , the gross amount of foregone earnings are

a good measure of indirect monetary costs. However, when an accident results

in a fatality , economists disagree on whether to measure indirect monetary

costs by; (1) gross value of earnings; (2) net earnings, after subtracting

out the value of consumption; or (3) some other measure related to earnings.

The approach taken may vary depending on who is to pay for a code change and

the level of risk Involved.

The value of life from society’s point of view is the value to the victim plus

the value to others. The author believes that where fatalities are involved,

earnings net of consumption are relevant when considering the value of a life to

those other than the potential victim, since earnings net of consumption deter-

^ Stacey, Gary S. and Smith, Kathy S., "Methodology for Estimating Costs of

Injuries and Property Losses," Fire Technology , August 1979, pp. 195-209.

2 Technology + Economics, Injury Cost Model.

59
mine the individual's net monetary contribution to others. From the victim's

point of view, foregone earnings are not a direct measure of monetary costs.

However, earnings are one of several factors affecting wealth and borrowing

ability which, in turn, affect willingness and ability to pay to preserve

life, discussed in the next section. Readers who wish to pursue this difficult

but intriguing subject should see the works cited in the footnote.

Pain and suffering, and the intrinsic value of life

The prevention of injury and death has an intrinsic value apart from the gain

of an Individual's productive labor or the savings in medical costs. Most

people would probably agree that pain and suffering and the loss of the intrinsic value of life should be counted in assigning values to life safety. However, because of the great difficulty in measuring these effects, they are sometimes omitted from estimates of the value of life safety.

It is difficult to set values on freedom from pain and suffering and

preservation of life for its own sake. These benefits do not have established

market values. One approach might be to value these aspects of life safety at

what a person is willing to pay to avoid risk over and above the purely monetary

costs of accidents. However, it is difficult to distinguish between what a person would pay to avoid non-monetary consequences and what he or she would

pay to avoid monetary consequences. Therefore, the willingness-to-pay approach

is best used to estimate overall costs to the victim rather than only

non-monetary costs.

Another approach which has been used to estimate costs of these intangibles is

jury awards for Injury and death in negligence cases. Data concerning jury

awards, together with a discussion of the drawbacks of this measure of the cost

of pain and suffering, are presented in the Consumer Product Safety Commission

Injury Cost Model.

6.4 ESTIMATING WILLINGNESS TO PAY

In the "willingness to pay" approach, the value of reducing risk is based on what people would be willing to pay to reduce risks. It may be an alternative

to the approach described earlier of simply adding up costs of accidents, or

it may be a tool for inferring values of cost items which are difficult to

quantify, such as pain and suffering.

1 If someone loses their life, the marginal utility of money becomes zero and

the added cost from loss of income is zero, i.e., you can't take it with you.

2 Jones-Lee, The Value of Life , p.46; Walters, Wind Safety Analysis , p. 48;

Dardis, Rachel and Thompson, Ruth, "Strategies for Reducing Residential,

Fire Losses," Journal of Consumer Product Flammability , June 1979, pp. 136-

151; Faigin, 1975 Societal Costs of Motor Vehicle Accidents , p. 3; and

Technology + Economics, Injury Cost Model , p. 3-55 through 3-86.

^ Technology + Economics, Injury Cost Model, pp. 3-39 through 3-51.

60
Policy-makers must decide whether to assign values to life safety which reflect

what people are willing to pay for safety, or whether to assign values which

reflect what people "ought to" pay in light of costs of accidents such as those

discussed above.

The willingness to pay approach, as described here, focuses on what people

would pay to avoid a small increase in risk, not what they would pay to avoid a certainty of death or injury. This is because code changes normally cause small

changes in risk to individuals, and the amount someone would spend on safety is

not necessarily proportional to the risk.

For example, if someone would pay $500,000 to prevent certain death, we might

expect them to be willing to pay 1/10,000 x $500,000 or $50 to avoid a one-in-aten-thousand risk of death. But people do not necessarily behave this way.

People who believe "it can’t happen to me" may be willing to pay less to avoid

an accident than the expected cost savings. People who believe "it very well

could happen to me" may be willing to pay more. Thus, a risk-taking person

might be willing to pay only $10 to avoid a one-in-ten-thousand risk of death, even if they would pay $500,000 to prevent certain death.

One method of determining what a person would spend to reduce risk is to look

at market behavior, e.g., the wage premium received by people in hazardous

occupations, how much more people pay for safer products, or how much they

pay for safety features.^ This approach is objective, but it has drawbacks:

(1) people may be ignorant of the true risks; (2) people who work in hazardous

jobs may put a relatively low value on safety compared to the average person;

those who pay extra for safety features may put a relatively high value on

safety; and (3) it may be difficult to determine to what extent a wage or price

difference is due to difference in safety as opposed to other factors.

Another approach is to survey people to find out what they would pay to avoid

risk. This method has the disadvantage that people may not know what they

would pay (or may not be willing to reveal it).

^ Melinek, Stanley J., "A Method of Evaluating Human Life for Economic

Purposes," Accident Analysis and Prevention , October 1974, pp. 103-114.

2 Thaler, R. and Rosen, S., "The Value of Saving a Life: Evidence from the

Labor Market," in Nestor E. Terleckyj, ed.. Household Production and

Consumption (New York: Columbia University Press, 1976), pp. 265-297.

^ Dardis, Rachel, "The Value of Life: New Evidence from the Market Place,"

American Economic Review, December 1980, pp. 1077-1082.

61
Whether to use consistent values for life

Some methods of valuing lives result in higher values for some groups of people

than for others. For example, approaches which emphasize lifetime earnings as a measure place a higher value on lives of males over females, of adults in their

working years over the elderly, and of the rich over the poor.^ Using such

figures in making building code decisions can create a bias toward providing

greater protection to some groups, such as adult males.

If this bias is considered undesirable, the differences may be reduced by

altering the estimating approach or by assigning a value to life safety that

represents an average of various populations, regardless of sex, age, or wealth.

In some cases, however, using different values for life safety may be desirable.

Where occupants of a building bear the costs of providing greater protection,

using the same dollar value regardless of who is affected may result in code

requirements that are excessively high for lower-income people and insufficient

for upper-income people. For example, elderly people living on fixed incomes

might be forced to spend high amounts for fire protection in situations where

they would be better off spending more on other items.

Some people may feel that deaths and Injuries due to hazards that the building

occupant is not likely to perceive, or which affect children or people who

cannot easily avoid exposure to the risk, are not the same as those affecting

adults who knowingly and voluntarily take risks. The reasoning may be that more should be spent to protect life safety in some situations than in others.

This point can be accommodated by assigning a higher value to life safety in

certain situations, e.g., if the hazard in question is not voluntarily and

knowingly risked by responsible adults.

^ See, for example, Walters, Wind Safety Analysis, p. 51.

62
7. THE BENEFIT-COST RELATIONSHIP

This chapter discusses ways of comparing benefits and costs and shows how to

calculate Net Monetary Benefits for the hypothetical Fire Safety Feature.

Two methods for comparing benefits and costs are: (1) net benefits (benefits

minus costs), and (2) the benefit/cost or savings-to-investment ratio (SIR).

There are also variations of these methods which can be used when some of the

code change impacts are not quantified in economic terms.

This report emphasizes using the net benefits measure because it is the most

useful in comparing alternative versions of a code requirement. Other methods

will also be discussed here because some of these may be useful under certain

c ircumst ances .

Before describing how to compute these measures, two adjustments are needed. These are explained in the next section.

Net monetary benefits are the portion of net benefits which can be quantified

in monetary terms. A method for calculating net monetary benefits is

explained below and illustrated for the hypothetical Fire Safety Feature

introduced earlier

.

7.1 ADJUSTMENTS

In previous worksheets we calculated code change impacts based on the

assumption that the reference buildings were constructed in the current year.

To analyze a building constructed in future years, two further adjustments

are needed to find the accurate present value of code change impacts.

Adjust for differential price changes

Chapter 3 described how to discount when the cost of an item is rising at a rate different from overall inflation, using a UPW* factor. However, that

The various methods are discussed in Grant, Eugene L. and Ireson, W. Grant,

Principles of Engineering Economy , 5th edition (New York: Ronald Press,

1970); Smith, Gerald W. , Engineering Economy: Analysis of Capital Expenditures , 2nd edition (Ames, Iowa: The Iowa State University Press, 1973);

and Marshall, Harold E. and Ruegg, Rosalie T. , Efficient Allocation of

Research Funds: Economic Evaluation Methods with Case Studies in Building

Technology , National Bureau of Standards Special Publication 558 (Washington, D.C.: Government Printing Office, 1979). Additional methods of

comparing benefits and costs, such as the discounted payback and Internal

rate of return methods, are discussed in these works.

63
Another calculation is needed to account for real cost changes that occur between the present year and the construction year. In the hypothetical example, the present year is taken to be 1980. Thus, for a building constructed

in 1985, it is necessary to account for the fact that energy prices will rise

relative to other prices between 1980 and 1985 as well as after 1985.

To account for real cost changes before the construction year, transfer data

from worksheets 4.2, 4.3, and 4.4 to worksheet 7.1. Then, multiply the change

in O&M cost subject to differential cost changes by a "cost change factor" of

(1 + e)t, computed using the annual rate of differential cost change "e" and

years before construction "t." Next, sum the various O&M costs to find total

O&M costs adjusted for differential cost change. The result is a value which

fully reflects the change in relative prices. This is illustrated in worksheet

7.1 for the hypothetical code change. Adjusted O&M costs are $201.07.

Discount to present value

At this point, all monetary impacts have been discounted to the future

construction year. For example, in the illustration, impacts occurring

after 1985 were discounted to 1985. Now it is necessary to further discount

to find the present values.

To do this, first , enter the values of changes in building performance on worksheet 7.2 (see page 67). Where building performance improves, use a positive number. Otherwise, use a negative number. These values are taken from

worksheets 5.3, 5.4, and other worksheets you may have devised to estimate

changes in performance.

Second , enter the changes in building costs and property losses on worksheet

7.2. Use positive numbers for cost or loss increases and negative numbers for

cost or loss reductions. The cost changes are taken from worksheets 4.1, 4.5,

and 7.1. Property loss changes are from worksheet 5.1.

Third , select a Single Present Worth factor from table A. 2 in appendix A for

the assumed discount rate and the number of years before the building is con- structed, and enter it on worksheet 7.2.

Fourth , multiply the SPW factor by the values discounted to the construction

year to find the present value of each Impact.

7.2 NET BENEFITS

The net benefits method involves summing the benefits and costs of a proposed

code change, where Impacts are measured relative to effects of the current

provision and discounted to the present value. ^ An expression for net benefits

(benefits minus costs) is:

NB = AQ - AC - AG, (7.1)

1 Net benefits and the other measures described in this chapter can be calculated

per unit (e.g., per building) or on an aggregate basis. Aggregating is also

discussed in chapter 8.

64
Worksheet 7.1 hypothetical example

ADJUSTING O&M COSTS FOR DIFFERENTIAL COST CHANGES

Representative case Years before building constructed 5 years^

^Based on construction date in worksheet 2.2.

^The cost change factor, (1 + e)^, adjusts for the rate of differential

cost change, e, listed above and in worksheet 4.2; "t" is the number of

years before the building is constructed.

Calculations for first line in the table: (1 + e)^ = (1 + .01

Calculations for second line in the table: (1 + e)^ = (1 “ .05

1.051 .

,774 .

65
where

:

NB = net benefits of the code change,

AQ = the dollar value of the change in building quality (safety and

performance) due to the code change,

AC = the change in building costs due to the code change, and

AG = the change in government costs due to the code change.

All amounts are discounted to present value and may be either per unit or aggregate, depending on whether net benefits were calculated per unit or in the

aggregate.

Worksheet 7.2 illustrates a method of comparing benefits and costs. After

carrying out the calculations described in section 7.1, sum the first group of

values in the worksheet to find the total effect on building performance, and sum the second group to find the total effect on building cost plus property

loss

.

Next, subtract the change in cost-plus-loss from the change in building

performance. This shows Net Monetary Benefits discounted to present value. In

the hypothetical^, example. Net Monetary Benefits are negative, -$209. At first

it might seem that the code change is not cost effective for this particular

case. However, worksheet 7.2 shows only monetary effects; there are also life

safety impacts not shown in worksheet 7.2. Therefore, we cannot determine just

by looking at worksheet 7.2 whether the code change would be cost effective for

Representative Case I.

The Net Monetary Benefits on worksheet 7.2 are given only for a single unit,

such as a dwelling unit or building, and they refer only to one representative

case. Worksheet 7.2 should be calculated for each representative case. Also,

since the example analyzes the code change from the perspective of the community

as a whole, the Net Monetary Benefits figure includes all monetary effects,

without distinguishing whether they accrue to builders, building owners, tenants,

taxpayers, or other groups in the economy.

How to use the net benefits measure

Net benefits is a very useful measure of the impact of alternative versions of a code requirement.^ If there are two competing proposed code changes, and if

both proposals have benefits which exceed their costs, the best code provision

would be the one with the greatest net benefits. (This assumes that there is no ceiling on any cost element, such as construction costs.)

^ Net benefits may be calculated on a per unit basis, as in the example here.

Alternatively, they may be calculated on an aggregate basis, either by summing

individual net benefits (as is shown in chapter 8) or by summing individual

costs and benefits by type from worksheet 7.2, and then computing aggregate

net benefits.

66
Worksheet 7.2 hypothetical example

PRESENT VALUE OF NET MONETARY BENEFITS PER UNIT

Representative case I Discount rate 10 %

Years before building constructed 5 years Unit of analysis Dwelling unit

Worksheet Change in Building Present

Number Type Impact Performance^ X SPW^ = Value

5.3 Building Space $ -127.08 X .6209 $ -78.90

5.4 Residual Value $ 1.85 X .6209 $ 1.15

(Other Impact) $ X $

TOTAL EFFECT ON PERFORMANCE $ -125.23 X .6209 —

$ -77.75

Worksheet Change in Present

Number Type Impact Building Cost^ X SPW^ = Value

4.1 Initial Costs $ 100.00 X .6209 = $ 62.09

1 4.5 Government Costs $ 10.00 X .6209 =

$ 6.21

7.1 O&M Costs $ 201.07 X .6209 = $ 124.84

5.1 Property Loss $ - 99.85 X .6209 —

$ -62.00

(Other cost or loss)^$ X

—

$

TOTAL EFFECT ON COST-PLUS-LOSS $ 211.22 X .6209 $ 131.14

Total Effect

on Performance

$ -77.75

Total Effect on

- Cost-Plus-Loss

- $ 131.14

= Net Monetary Benefits

= $ -208.89

^ Value discounted to construction year. Use a positive nvimber for Increases in

value, cost, or loss. Use a negative number for decreases.

^ From table A. 2, appendix A, for assumed discount rate and number of years

before building is constructed.

^ If life safety impacts are quantified in dollars, they should be included here.

67
For example, the net benefits approach could be used to determine what R-value

should be required for insulation. Two methods for doing this are explained

below for a code requirement affecting insulation. The methods are illustrated

in figures 7.2 and 7.3 in the appendix to this chapter.

Method 1 ; One method is to calculate the net energy savings and net costs

(compared with the current code requirement) of a number of alternative levels

of insulation in order. to find the insulation level with the greatest net

benefits. In the example in table 7.1, the increase to R-30 insulation

provides the greatest net benefits.

Method 2 ; A second method is to calculate only the added benefits and the

added costs of each increase in insulation. For example, starting with R-0,

calculate the marginal benefits of having one more increment of insulation,

calculate the marginal cost of this increase, and compare the two. In the

example in table 7.1, moving from R-11 to R-19 increases the cost by $80.

The added savings, $184, exceeds added costs by a considerable amount.

Moving to R-30 increases the cost by an additional $110, which is only

slightly less than the added savings of $119.

If the marginal benefit is greater than the marginal cost, the added insulation

is cost-effective. Repeating these calculations for further Increases in insulation, one can find the point at which the marginal benefits of another increment of insulation no longer exceed the marginal cost. This is the ideal level

for the code requirement.^ Beyond this point, added costs are greater than

added benefits. In the example, this point occurs at close to R-30 insulation.

Possible disadvantages of the net benefits approach

The main disadvantages of using the net benefits approach exclusively are that

it requires effects to be quantifiable in dollar terms and a simple net benefit figure may not provide decisionmakers with enough information. Some effects,

such as those related to life safety, are very difficult to quantify in dollar

terms, and, in any case, people making code changes are likely to want more

information about impacts than is given by a simple dollar figure. Therefore,

it is often useful to make up a table which gives both the net benefits figure

and Individual benefits and costs.

In addition, the net benefits approach may not always be the simplest way to

find the most cost-effective code change. This is true when people involved

in the code change process have in mind a "regulatory budget" — the maximum

^ While this statement is true for insulation, in other cases there may be some exceptions to this general statement. One difficulty might be that the indicated "optimal" level is better than any other levels close to the apparent

optimum, but that there is some other level which is very different from, and

better than, the apparent optimum. In other words, the marginal analysis

might select a "local" optimum and overlook the "global" optimum. Also, if

the marginal benefits curve crosses the marginal cost curve from below, the

marginal benefits will equal marginal costs at the least optimal level.

68
Table 7.1

HYPOTHETICAL IMPACTS OF INCREASED ATTIC INSULATION^

Method 1; Greatest Net Benefits

Incremental Net Savings

Increase Cost Savings (Savings - Cost)

From R-0 to R-11 $180 $1,051 $871

From R-11 to R-19 260 1,235 975

From R-19 to R-30 370 1,354 984 (greatest net benefits)

From R-30 to R-38 450 1,401 951

Method 2: Marginal Benefit = Marginal Cost

Change in Change in

Cost Savings

From R-O to R-11 $180 $1,051

From R-11 to R-19 80 184

From R-19 to R-30 110 119 (marginal benefit

closest to marginal cost)

From R-30 to R-38 80 47

^ Numbers are based on a hypothetical example used in Department of Energy/

National Bureau of Standards Federal LCC Workshop for Energy Conservation

and Solar Energy in Federal Facilities , 1979, p. 95. (workshop notebook,

unpublished)

.

69
added construction cost they are willing to impose through code changes made

in a particular year. For example, the aim might be to keep construction

costs from rising more than a certain amount because of energy conservation

cost changes, regardless of the eventual benefits of the code change. This

requires a way to choose the most beneficial combination of code changes

which still stays within the "budget ." It may take a good deal of calculation

to solve this problem through the net benefits approach, as is illustrated in

example 7.1. Often, it is simpler to solve the problem by calculating benefit/

cost ratios, described in the next section.

7.3 BENEFIT/COST OR SAVINGS-TQ-INVESTMENT (SIR) RATIO

Another way of relating benefits and costs is the benefit/cost or savlngs-toinvestment (SIR) ratio (benefits divided by costs). This ratio is useful if one cost item is subject to a constraint. For example, it is useful if there

is a ceiling on construction costs. Here we assume that there is a constraint

on construction costs, I. Therefore the benefit/cost ratio used here has only

construction costs in the denominator; all other effects are in the numerator,

i . e . : ^

B/C = AQ - AO&M - AG

^ (7.2)

AI

where:

B/C = Benefit/cost ratio,

AQ = Change in the dollar value of building quality due to the code

change

,

AO&M = Change in operation and maintenance costs due to the code change,

AG = Change in government costs due to the code change, and

AI = Increase in initial costs due to the code change.

All amounts are discounted to present value.

How to use the benefit/cost ratio

The benefit/cost ratio shows whether benefits exceed costs. This ratio may

be useful in ranking code changes in order to select the set of code changes

which will provide the greatest protection for a given increase in a constrained

item.

^ In general, the cost item(s) subject to a constraint goes into the denominator

other costs and benefits go into the numerator.

^ The benefit/cost ratio can be computed per unit or on an aggregate basis. To

compute the aggregate ratio, first calculate the aggregate amount of the

constrained cost item (from worksheet 7.2); this is the "C” of the "B/C" ratio

Then calculate the aggregate amounts of all other impacts; these are the "B"

of the ratio.

70
Example 7.1; SELECTING THE BEST "PACKAGE" OF CODE CHANGES: NET BENEFITS

APPROACH (HYPOTHETICAL EXAMPLE)

Problem ; Five code changes have been proposed. Each of the five is costeffective. However, collectively they would raise construction costs by

$320 per house. The present values of their costs, gross benefits, and net

benefits are shown in the table below. Those making the code change decision

do not want construction costs to increase by more than $100. Which combination of code changes would give the greatest net benefits while not exceeding

the $100 ceiling on construction costs increases?

Code

Change

Gross

Benefits

Added Construction

Cost Net Benefits

A $180 $ 30 $150

B 100 20 80

C 200 50 150

D 60 20 40

E 400 200 200

Solution ; Code change "E" is immediately eliminated from consideration because

its construction cost increase of $200 exceeds the $100 ceiling. (This is

true even though "E" would have the greatest net benefits of any of the proposals.) Various combinations of the remaining proposals. A, B, C, and D, are evaluated for their impact on present value construction costs and net

benefits, as shown below.

Alternative

Packages of

Code Changes Construction Cost Net Ben

A $ 30 $150

A+B 50 230

A+B+C 100 380

A+B+C+D 120 420

B $ 20 80

B+C 70 230

B+C+D 90 270

C 50 150

C+D 70 190

D 20 40

The table shows that the combination with the greatest net benefits is A + B + C + D. However, this combination would raise construction costs by $120,

which exceeds the $100 ceiling. Therefore, this is rejected in favor of the

next most beneficial combination, A + B + C, which only raises construction

costs by $100.

71
As noted earlier in this chapter, those involved in the decisionmaking process

might sometimes decide to set a ceiling on how much construction costs might be

increased as a result of a set of code changes. In these situations, the selfimposed "budget" keeps them from making all the code changes which might have

net benefits. Instead, they must select the package of code changes that

delivers the greatest net benefits for a specified maximum increase in construction costs. (This is analogous to the situation of an investor who has a limited budget to invest to make the greatest profits.)

The method of selecting this package is illustrated in example 7.3 on the facing

page. It is the same problem as example 7.1 and results in the same solution,

but it involves less computation.

The package is selected by first calculating benefit/cost ratios for the

various possible code changes. To maximize the net benefits subject to a constraint on construction costs, the change in construction costs (the cost

which is limited by the budget constraint) is included in the denominator and

all the other costs and benefits are included in the numerator.

Next, code changes are selected in order of their benefit/cost ratios. The

code change with the highest ratio would be selected first, then the change

with the next highest ratio, and so on, until the "budget" is used up. This

gives the package of code changes which provides the greatest net benefits

within the "budget" restriction.

A modified approach may be needed if approval of one code provision affects the

benefits and costs of other proposals.^

Example 7.2: NET BENEFITS VS. BENEFIT/COST RATIO

Problem : There is no "ceiling" on allowable construction costs, and two

mutually exclusive alternative code changes have the following costs and

benefits. Which code change is more desirable?

Code Change A Code Change B

Benefits $1,000

Costs $ 200

Benefit/cost ratios 5:1

$10

$ 1 10:1

Solution : The benefit/cost ratios are 5:1 for code change A and 10:1 for

code change B= However, the net benefits for code change A are $800 compared

with only $9 for code change B. Therefore, code change A yields the greatest

net gain to society. While both are cost effective, if only one code change can be selected, code change A is more desirable.

^ McConnaughey , An Economic Analysis of Building Code Impacts , p. 16,

describes a method of calculating the benefit/cost ratio in such cases.

72
Disadvantages of the benefit/cost approach

The benefit/cost ratio can be misleading. If only one code requirement can be

approved from among several alternatives, it is tempting to think that the code

requirement with the highest benefit/cost ratio is the best choice. This is not

necessarily the case. A minor change might have smaller net benefits but a higher benefit/cost ratio than a more major code change. Generally, the best

code provision is the one with the highest net benefits, regardless of whether

it has a higher or lower benefit/cost ratio than some other version. Example

7.2 on the facing page illustrates this point.

Example 7.3: SELECTING THE BEST PACKAGE OF CODE CHANGES: BENEFIT/ COST RATIO

(HYPOTHETICAL EXAMPLE)

Problem: Each of five proposed code changes is cost-effective, but collectively

they raise construction costs by $320 per house. Their impacts are shown below.

Those making the code change decision do not want construction costs to increase

by more than $100. Which combination of code changes would give the greatest

net benefits while not exceeding the $100 ceiling on construction costs

increases?

Code Gross Added Construction

Change Benefits Cost Net Benefits

A $180 $ 30 $150

B 100 20 80

C 200 50 150

D 60 20 40

E 400 200 200

Solution : As is shown in the table below, the benefit/cost ratios are computed

for each proposed code change and the proposals are ranked according to their

B/ C ratio. Then, the cumulative increase in construction costs is calculated

as each proposal is added to the "package," starting with the highest ranked

change, "A". After "C" is selected, the cumulative increase in construction

costs would just equal the $100 ceiling. Thus, ”D" and "E" are not included

in the package. This is depicted graphically in Figure 7.4 in the appendix to

this chapter. (Note that this gives exactly the same solution as computing

net benefits, but requires less calculation.)

Code Gross Increase in Cumulative Increase

Change Benefits Construction Costs in Constr. Costs B/

C

A $180 $ 30 $ 30 6 1 B 100 20 50 5 1 C 200 50 100 4 1 D 60 20 120 3 E 400 200 320 2

73
In addition, there is one situation in which the benefit/cost method may not

work for choosing the best package of code changes within a limited "budget."

This occurs when the "last" code change selected does not leave enough "budget"

to include the code change with the next highest benefit/cost ratio. In such

cases, a code change with a lower benefit/cost ratio might have to be added to

the package instead of one or more of the code changes with higher benefit/cost

ratios, so as to use up the remaining budget in a way that maximizes net benefits. Figure 7.1 illustrates this situation, which forces the analyst to use

the net benefits approach to find the best package. In the example, the "budget" is assumed to be $70, so that code change "C" cannot be selected, since

it exceeds the budget. Code change "D" must be selected Instead.

Cost/Effectiveness Ratio

It may be useful to find the ratio of the dollar cost for a non-monetary benefit

such as lives saved. Decisionmakers can use this ratio to decide whether it is

worth spending the indicated amount to save a life, prevent an injury, or obtain some other non-monetary benefit.

However, this measure should be used with caution. If several mutually

exclusive proposals are all cost-effective, this ratio would not necessarily

Indicate which proposal is best. For example, a code provision costing $100

for each life saved is not necessarily more desirable than an alternative

requirement which costs $1,000 for each life saved. If the $1, 000/life alternative saves 10 lives (at a total cost of $10,000) and the $100/life alternative saves only one life, the higher priced alternative would be the more desirable code change, unless a budget constraint prevents the $1000/life

alternative from being chosen. (We assume that lives are valued at more than

$1000 each.)

74
Figure 7.1 ILLUSTRATION OF POTENTIAL PROBLEMS IN USING

BENEFIT/COST RATIO TO RANK CODE CHANGES

Benefit/Cost

Ratio

Increase in Construction Costs

75
APPENDIX to Chapter 7

Figure 7.2 CHOOSING AMONG ALTERNATE LEVELS OF INSULATION

METHOD 1: GREATEST NET BENEFITS^

Total Cost

$1500

Gross

Savings

$1000

R-0 R-11 R-19 R-30 R-38

Insulation R-Value

^ See footnote "a" to table 7.1.

76
Figure 7.3 CHOOSING AMONG ALTERNATIVE LEVELS OF INSULATION

METHOD 2: MARGINAL BENEFITS = MARGINAL COSTS^

Marginal Cost

1

^ See footnote "a" to table 7.1.

t 77
Figure 7.4 SELECTING THE BEST PACKAGE OF CODE CHANGES

WITH "BUDGET" CONSTRAINT

Benefit/ Cost

Ratio

6

5

Code

Change

A

Construction Cost Ceiling = $100

4

Code

Change

B

Code

3 _ -( Change

C Code

Chng

D

2 _

Code Change E

1 $30 $20 $50 $20 $100

$50 $100

Cumulative Increase in Construction Costs
8. CALCULATING AGGREGATE IMPACTS

So far, this report has focused on calculating the effects of a code change for

individual buildings. But these calculations do not show the overall impact of

a code change. To find the overall Impact, it is necessary to aggregate the

effects on many buildings into one number.

Aggregating may be necessary in situations where the code change has differing

effects on different buildings, and where it is not feasible to apply the

requirement only to buildings where it is cost effective. In such cases, it is

necessary to aggregate to find out whether the code change is cost effective

overall.

For example, suppose a proposed fire safety requirement would result in expected

net benefits of $200 for each house occupied by someone who smokes, but would

have an expected net cost of $100 for all other houses. The code change would

only be cost effective in communities with a large number of smokers. In such

cases, aggregating would be helpful to show whether the code change is cost

effective for the community as a whole.

Aggregating may not be essential if a code change is cost effective for all

buildings, or if the code change is not cost effective for any type of building.

In such cases, it may be possible to accept or reject on the basis of effects

on individual buildings.

Also, if a code change has net benefits for some occupancies or construction

types and net costs for others, it may be possible to modify the proposed code

change so that it applies only to types of buildings for which it is cost

effective. In such cases, aggregating might not be essential to make the code

change decision.

Even in such cases, however, aggregating may be useful when presenting life

safety effects. By aggregating, effects can be presented in terms of the

number of lives statistically expected to be saved for the entire jurisdiction.

This figure, rounded to a whole number such as "six,” may be more meaningful to

those using the analysis than the probability of saving a life in one building

or dwelling unit, which is likely to be a small fraction such as ".00275."

Steps in the calculations

Worksheets 8.1a and 8.1b illustrate the procedure for calculating aggregate

impacts of a code change for the hypothetical FSF example used earlier in this

report. The figures on the number of affected dwelling units are taken from

example 2.2 in chapter 2. The example and discussion assume that the analyst

is following the net benefits approach described earlier. There are two steps.

First , estimate how many buildings or other units will be affected by the code

change during the code analysis period, for each representative case. Affected

buildings which were not included in the original representative cases may be

assigned to a representative case for the purpose of aggregating. For example.

79
duplexes might be grouped with single-family houses, or buildings constructed

in 1983 might be grouped with buildings constructed in 1985.^

For a model code or voluntary state code, you will need to determine which

jurisdictions follow the code. It may be necessary to reduce the estimate of

the number of affected buildings to allow for delays in adopting the model or state code.

2

Buildings should not be counted if they will not be affected by the code change

because of exceptions, waivers, non-compliance, or because the builder would

have adopted the code-mandated design in any case. An alternative is to

assume that there is 100 percent use of the code-mandated feature in the jurisdictions adopting the code change. However, this assumption might introduce

some error, since it overstates the impacts of the code change and does not

allow for the softening effects of waivers, which may be granted if the code

change is not cost effective and if safety and health can be protected through

other measures.

For a model code change, the analyst will also need to determine which

jurisdictions follow the code. This may involve reducing the estimate of the

number of affected buildings to allow for lags in adopting the model or state

code . 3

Also, some jurisdictions may have adopted the requirement even without a model

code change; if the aim is to determine impacts of changing the model code,

buildings in these jurisdictions should not be counted in aggregating. Thus,

the analyst should estimate how many jurisdictions will adopt the requirement

as a result of the model code change. One approach is to use a table such as the following to describe the adoption of a code change over time:

^ A simplified approach to estimating numbers of buildings is to assume that

there is no long-term growth in construction over the code analysis period.

For example, McConnaughy^ assumed that annual new construction would equal

the average of new construction over the past five years. This approach

does not account for either long-term growth in the building stock or changes

in the composition of new construction. However, it allows the analyst to use data from past years rather than having to seek out, or make, forecasts.)

See McConnaughey , An Economic Analysis of Building Code Impacts , p. 36.

2 Some information on delays is given in Field and Rlvkin, the Building Code

Burden .

^ Cooke, Patrick W. and Elsenhard, Robert M. , A Preliminary Examination of

Building Regulations Adopted by the States and Major Cities , NBSIR 77-1390

(Washington, D.C.; National Bureau of Standards, 1977).

80
Worksheet 8.1a hypothetical example

AGGREGATE IMPACTS

Type impact(s) Net Monetary Benefits* Unit of analysis Dwelling unit

Representative Case

Number of

Affected Units^ X

1 ,000

II SFH/Poker Flat/1985 500

III Low-rise/Spring! ./1 985 600

IV High-rise/Spring! ./1 985 100

TOTAL FOR ALL UNITS

Impact per

Unite

$ -208.89

- 91.50

- 95,72

-203 .61

Impacts for

= All Units

= $ -208,890

= - 45,750

= - 57 ,432

- 20,361

$ -332,433

^This worksheet should be filled out separately for each impact of interest.

^May include buildings similar to buildings specified in representative cases.

eprom worksheets 5.2 (life safety) or 7.2 (monetary impacts).

* Underlying calculations for Case I are shown in worksheets 4.1 through 7.2

in this guide. The underlying calculations for Cases II, III, and IV,

using similar worksheets, are not shown due to limited space.

81
Year

Jurisdictions Adopting

Model Code Change

(% of buildings)

1980 (year of code change) 0

1981 5%

1982 10%

1983 40%

1984 60%

1985 70%

In the example on worksheet 8.1a and 8.1b, the number of dwelling units for

each case was determined from the information in example 2.2 in chapter 2 and

entered onto the worksheets. For example, 50 percent of the 2000 single-family

houses in Springfield are one story. Since the code change does not apply to

one-story buildings, the number of buildings in Case I is the remaining 1000.

Second , multiply the number of buildings (or other units) by the per-unit

impacts calculated for that representative case. Do this for each representative case, as is illustrated on worksheet 8.1a and 8.1b, and sum the products.^

You will need separate worksheets for aggregating monetary effects, lives,

major Injuries, and minor injuries. You may also calculate aggregates for con- struction cost changes, energy cost changes, and any other effect of interest

to decisionmakers. This guide shows only calculations for Net Monetary

Benefits and lives saved.

1 If there are some buildings which do not seem to fit in any category

represented by a reference buiding, then the impacts will have to be

separately estimated for these buildings. One approach would be to assume

that impacts for the "other" category are a certain percent of impacts for a category containing a reference building. See for example Arthur D. Little,

Inc., Energy Conservation in New Building Design: An Impact Assessment of

ASHRAE Standard 90-75 (Washington, D.C.; Federal Energy Administration,

1976), p. 97. Such estimates are rough, since they are made without a benefit-cost analysis, but presumably they are better than completely Ignoring

impacts on "other" buildings.

82
Worksheet 8.1 b hypothetical example

AGGREGATE IMPACTS^

Type impact (s) Lives Saved Unit of analysis Dwelling unit

Number of

Representative Case

I SFH/Spring!ield/1 985 1,000 X

II SFH/Poker Flat/1 98

5

500 X

III Low-rise/Spring! ./1985 - 600

IV High-rise/Spring! ./1985 100 X

TOTAL FOR ALL UNITS

Impact per

Affected Units^ X Unit^

.00275

.00275

.003

.0001

.;r

Impacts for

All Units

2.750

1 .375

1 .800

.01

5.935

^This worksheet should be filled out separately for each impact of interest.

^May include buildings similar to buildings specified in representative cases.

^From worksheets 5.2 (life safety) or 7.2 (monetary impacts).

83
84
9. SENSITIVITY ANALYSIS AND PRESENTING RESULTS

The final steps in a benefit-cost analysis are to conduct a sensitivity

analysis and to present the results in a format that will be useful to those

who must make code change decisions.

9.1 SENSITIVITY ANALYSIS

Sensitivity analysis is a way of finding out how changes in data or assumptions

will affect the final results. For example, how would doubling the assumed

code analysis period affect the calculated net benefits? Table 9.1 gives exam- ples of parameters that may be altered as part of a sensitivity analysis.^

Table 9.1 EXAMPLES OF PARAMETERS THAT MAY BE ALTERED

IN A SENSITIVITY ANALYSIS

Current levels of accidents

Effect of code change on accidents

Cost per accident

Discount rate

Building analysis period

Code analysis period

Method and cost of compliance by builders

Use of waivers and extent of non-compliance

Durability of code-mandated feature

Owner/tenant behavior with respect to maintenance and replacement

Rate of energy price escalation

Any important and controversial assumption

Any Important piece of data about which there is substantial

uncertainty

^ Another method for dealing with uncertainty is probability analysis . This

involves estimating the probability that an event will occur (e.g., that a smoke detector will fail) and multiplying that probability by the impact if

the event occurs. This gives an "expected value". However, estimating the

probabilities is itself a difficult and uncertain task. See Ruegg, Rosalie

T . , Solar Heating and Cooling in Buildings: Methods of Economic Evaluation , NBSIR 75-712 (Washington, D.C.: National Bureau of Standards, 1975), p. 33.

I
One reason for performing a sensitivity analysis is to decide which estimates

should be further refined. If the code change decision would be affected

by a relatively small error in estimating a factor, then it may be desirable

to spend more effort improving the estimate.

Another reason for doing a sensitivity analysis is to aid decisionmakers who

will use the results of the benefit-cost analysis. It shows whether disagreeing with assumptions or underlying data is sufficient reason to reject the

results of an analysis. For example, suppose the basic analysis uses a discount rate of 10 percent, but a code official believes that the rate should be

8 percent and a builder believes it should be 15 percent. A sensitivity analysis would show whether changing the rate from 10 percent to 8 percent or to 15

percent would significantly alter the results of the analysis. If it does not,

then both parties might still accept the general conclusions.

How to do a sensitivity analysis

This section describes how to do sensitivity analysis. The method is

illustrated on worksheet 5.3 redone for the sensitivity analysis, and on worksheets 9.1 and 9.2, for the hypothetical Fire Safety Feature requirement.

These worksheets are for Representative Case I. A sensitivity analysis is

needed for each representative case in order to determine the sensitivity of

the aggregate figures.

There are seven steps in the sensitivity analysis:

1. Identify "sensitive” variables . Start by listing the important variables on worksheet 9.1, along with the value used in the original analysis. If a potential change in a variable might change the results of the analysis enough

to affect the code change decision, this variable is a good candidate for the

sensitivity analysis.

In the hypothetical example, the size of the safety impact is critical to the

code change decision. There is much uncertainty about the effect of the FSF on the number of fires and, therefore, on safety. Thus, the effect of the

code change on various types of fires is varied in the sensitivity analysis.

2. Determine your approach . One method of testing for sensitivity is to change

values one at a time. For example, one might recompute Net Benefits, changing

only the discount rate, and then recompute Net Benefits changing only the code

analysis period. A second approach is to change a number of variables at once. For example, one may recompute Net Benefits for an "optimistic case" and "pessimistic case," varying a number of factors each time. In the example, several

factors are varied at once.

86
Worksheet 9.1 hypothetical example

VALUES TO ALTER IN A SENSITIVITY ANALYSIS^

Representative case

Variable Value in Base Case^

Include in Sensitivity Analysis?^

FSF/design A price $100 no

FSF/design A life 10 years no

Replacement patterns

Replaced

after 20 years yes

Electricity cost

$10 and $20/year

in base year prices no

Residual value $20 no

Discount rate 10% yes

Number of affected bldgs 1,000 no 1 Change in probabilities

:

1 of confined fire + ,002 yes

H of room fire -,001 yes

1 of building fire -,001 yes

1

1

1

^This should be filled out for each representative case in worksheet 2.2.

Walues in the basic analysis are from worksheets 4.1 through 5.4, and 8.1

^The factor should be included in the sensitivity analysis if its value is

very uncertain and if a change may affect the code decision.

87
3. Determine values to use In the sensitivity analysis . Select values which

might occur under some plausible set of circumstances. Record these values on the top half of worksheet 9.2.

4. Recompute worksheets 4.1 through 7.2 . It is necessary to repeat the

calculations on most worksheets using the altered values. For the hypothetical

example, it was necessary to recalculate most worksheets, but only the

recalculation of worksheet 5.3 is shown in this guide.

5. Record the results on worksheet 9.2 . This should show the results of a sensitivity analysis for one representative case, before aggregating. It shows

which variables were altered, how they were altered, and results using the

original and altered values.

In the example, worksheet 9.2 was calculated for Case I for the original set

of values and for a set of altered values. Using a lower discount rate, a reduced safety impact, and the assumption that the FSF is never replaced. Net

Monetary Benefits fell (i.e., net monetary costs increased) from -$209 to

-$291. Life safety benefits also declined.

6. Repeat steps one through five for each representative case . Assumptions and

data may be varied differently for each representative case.

7. Compute new aggregate values . Worksheet 8.1 should be recomputed using the

new values.

The analyst should present results of the sensitivity analysis with other

findings so that decisionmakers can determine whether a change in underlying

variables might affect the code change decision.

In some cases, changes in underlying variables may not significantly affect the

results of the economic analysis. If so, decisionmakers could base their decision about a code change on the analysis, even if they disagree somewhat with

underlying data or assumptions. In other cases, however, the uncertainty about

underlying data may be so great that the economic analysis is not a reliable

guide to making decisions. If so, decisionmakers should be cautious in using

the results; more research may be needed to refine estimates of certain factors

before making a code change.

For example, in the hypothetical example used in this guide, changing to more pessimistic assumptions reduced the projected aggregate number of lives saved

from six to three. Some decisionmakers seeing this result might conclude that

more research is needed before going ahead with the code change. Further

sensitivity analysis could help pinpoint needs for more research.

88
Worksheet 9.2 hypothetical example

SENSITIVITY ANALYSIS^

Representative case

Variable Values in

Basic Analysis

Values in

Sensitivity

Analysis

Replacement patterns 20 years never

Discount rate 10% 8%

Change in probability of: |

confined fire + .002 + .001

room fire -.001 -.0005

building fire -.001 -.0005

Impact

Calculated Using

Original Values

Calculated Using

Altered Values

Met Monetary Benefits^

$ -209 $ -291

Fatalities Prevented^

.00275 .0011

Major Injuries Prevented^

.0125 .005

Minor Injuries Prevented^

.05 .02

1

^This and previous worksheets should be recalculated for each representative case listed in worksheet 2.2.

^From worksheet 7.2 calculated using original and altered values.

^From worksheet 5.2 calculated using original and altered values.

89
Worksheet 5.3

Sensitivity Analysis

hypothetical example

IMPACTS PER UNIT OF A CHANGE IN SPACE

Representative case I Discount rate 8 % Building analysis period 25 years

^Rental excluding any owner-paid operating costs.

^From table A. 3, appendix A, for assumed discount rate and building analysis period.

90
Breakeven Analysis

Breakeven analysis is a type of sensitivity analysis which can be used if all

impacts are given in monetary terms. It does not require determining a possible

range of values for key parameters. The breakeven value is the value a parameter

must take for benefits to just equal costs (i.e., for net benefits to be zero).

This type of analysis is illustrated in example 9.1

Example 9.1; BREAKEVEN ANALYSIS

Problem ; An energy conservation code change would require double glazing on windows. Aggregate net benefits of the code change are related to the price

of heating oil as follows;

Oil Price ($/gallon) Net Benefits of Code Change

$.50 - $ 200,000

.75 - 100,000

1.00 0

1.25 + 50,000

1.50 + 75,000

What is the breakeven price of heating oil?

Solution ; The breakeven price for heating oil is $1. 00/gallon. At this

price, the costs of double glazing are just offset by benefits from energy

conservation. At any price greater than $1. 00/gallon the code change would

be cost-effective.
9.2 PRESENTING RESULTS

A single number representing net benefits usually would not give decisionmakers

enough information about the economic impacts of a proposed code change. It

may not be possible to represent the effects of a code change in a single

number and some effects may not be quantifiable at all. Therefore, it is

useful to present the results of the analysis in a table of benefits and

costs which includes both numbers and verbal descriptions of code impacts.^

Such a table summarizes a variety of information in a simple format. It

should highlight the most important effects of the code change — even if

these effects are not quantified . This will assure that important qualitative

effects are not overlooked by those making code change decisions.

Worksheet 9.3 is provided to help organize the results of the analysis. Its use is illustrated for the hypothetical Fire Safety Feature code change that

has been used as an example throughout this report.

A summary table should include the following Information:

Assumptions . State the key assumptions used in the analysis. For example,

what was the unit of analysis? What discount rate was used? What was the

building analysis period? The code analysis period? What other possible

important or controversial assumptions were made?

Quantitative impacts . List quantitative Information which may be useful,

such as overall Net Monetary Benefits, initial construction cost impacts,

and the number of fatalities and injuries prevented.

Qualitative impacts . Describe the Impacts which can not be quantified.

For example. Impacts on innovation, historic preservation, or national

security might be difficult to quantify, but could be Important in a particular situation. The shifting of code impacts from one group to another

might be described.

Uncertainty . State the areas of uncertainty in the results. To summarize

these, present results of the sensitivity analysis and/or verbally qualify

the results. For example, the table might state: "Safety effects are highly

uncertain due to lack of good fire incidence data.”

Concisely presented results of an economic analysis will assist decisionmakers

in identifying the building code changes which will more efficiently achieve

building safety, energy conservation, and other goals.

^ See for example, Hendrickson, Paul L.; McDonald, Craig L.; and Schilling,

A. Henry, Review of Decision Methodologies for Evaluating Regulatory Actions

Affecting Public Health and Safety (prepared by Battelle Pacific Northwest

Laboratories, Richland, Washington, for the U.S. Nuclear Regulatory

Commission, Washington, D.C., 1976), p. 53

92
Worksheet 9.3 hypothetical example

RESULTS OF THE BENEFIT-COST ANALYSIS

Code change Fire Safety Feature requirement

SELECTED ASSUMPTIONS

Value Used in Values Used in ' Subject Basic Analysis Sensitivity Analysis

Unit of analysis Dwelling unit Dwelling unit

Discount rate 10% 8%

\ Building analysis period 25 years 25 years

1 Code analysis period 10 years 10 years

\ Replacement patterns After effective life Never

COSTS AND BENEFITS

Type Impact Base Case^ Sensitivity Analysis^ Comment

Net Monetary

Benefits

$ -332,000 $ -500,000

Fatalities

Prevented

6 3 Safety effects

occur over a

Major Injuries

Prevented

35 10

2 5 -year period

and are very

uncertain.

Minor Injuries

Prevented

100 40

^From worksheet 8.1. ^From worksheet 8.1 recomputed for sensitivity analysis

93
94
10. CONCLUSIONS

Today, every community faces a dilemma: How can It obtain adequate building

protection for Its businesses and residents without an excessive rise In

construction costs?

One solution to this problem Is to approve code revisions which will afford

the greatest protection for the construction dollar. The purpose of this

report Is to describe a method of using benefit-cost analysis to Identify such

code revisions. If the needed data are available, frequently economic analysis

can help Identify less costly ways to achieve the desired level of building

safety. It can also help find the right balance between dollars spent on building safety and dollars spent on other needs. This Is because money spent on building safety competes with other types of spending.

For example, a school district with a limited budget may have to choose between

Improving buildings or Increasing teachers’ salaries; a hospital may have to

choose between Increased fire safety or better nursing care, and so on. Should

quallty-of-llfe goals be achieved through better buildings or through other

ways of spending the money? Economic analysis can be very helpful In striking

this balance.

A formal benefit-cost analysis Is not feasible for many code changes, and It

Is not justified for minor, noncontroverslal changes. This report describes

how to conduct an economic analysis of those building code changes for which

economic analysis Is appropriate. This method Involves defining representative

cases to analyze; estimating the discounted Impacts on building cost, safety,

and performance, and on government costs; calculating net benefits by subtracting costs from benefits; aggregating over the various representative cases to

find overall Impacts In a code jurisdiction; conducting a sensitivity analysis;

and writing up the results. Including both monetary and nonmonetary effects.

To Illustrate the method, sample worksheets have been completed for a hypothetical problem. However, no one method and no one set of worksheets

would be suitable for the many types of code changes that come before standards

committees, model code organizations, and government bodies each year. Thus,

to conduct a benefit-cost analysis. It will probably be necessary for the Individual analyst to devise his or her own set of worksheets, using the blank

worksheets found In appendix F as a starting point.

10.1 RESEARCH NEEDS

More data

Decisions are being made which affect costs of buildings throughout the country.

Yet, often they must be made with only a general notion of the ultimate Impacts

of the code requirements on building safety and/or performance. The lack of

data concerning engineering and safety effects of building features makes It

Impossible In many cases to carry out a useful benefit-cost analysis.

95
The many gaps in data concerning impacts of building features have been pointed

out a number of times. For example, a 1980 article in Fire Journal assessed

the quantity of research concerning automatic sprinkler systems as "little" and

the quality as only "fair," and suggested that further research to determine

costs and effectiveness should have very high priority. Research on smoke

detectors was also considered insufficient.^ A 1978 U.S. Fire Administration

study of fire detectors encountered serious problems with the quality of fire

incidence reports. ^ There are similar data problems in other areas.

One reason for these gaps in data is that information is expensive to collect

and organize and costly engineering studies may be needed to provide it. But

this expense must be balanced against the cost of Inappropriate building

requirements due to inadequate information. Even a modest effect on the cost

of millions of buildings adds up to a staggering national total. As a preliminary step, areas should be identified where the lack of good information

concerning the impacts of building features is imposing the greatest costs.

Value of life safety

Numbers have been developed to reflect the "value of life" or "value of injury"

for use in public policy decisions. However, these are usually tailored to

problems other than building code decisions, and a value developed for one purpose is not likely to be appropriate for other kinds of decisions.

An ambitious but useful research project would be to develop information tViat

would help decisionmakers determine how much money should be spent to reduce

accident risks. It is probably more efficient to develop such information in

a research project which concentrates on this question than for analysts to

tackle this difficult issue as part of each individual analysis of code change

impacts. 3 A study tailored to building problems would provide information on various costs of building-related accidents and what Individuals or society

might be willing to spend (or require to be spent) to prevent them, including

information on:

* Factors which may warrant spending more, or less, on building

protection — e.g., whether a risk is hidden or obvious, the age group

protected, and whether the community or the occupants ultimately pay

for the protection.

^ Swersey, Arthur J., and Ignall, Edward, "What Does Fire Research Have to

Do with Fire Protection," Fire Journal , January 1980, pp. 63-74.

^ Waterman, T. E., Mniszewskl, K. R. , and Spadoni, D. J., Cost/Benefit of Fire

Detectors , (prepared by the IIT Research Institute, Chicago, for the

National Fire Data Center, U.S. Fire Administration, Washington, D.C., 1978).

^ Such a study has been done for product safety. See Technology + Economics,

Injury Cost Model.

96
* What building occupants are willing to pay to reduce their

personal risks, for various types of risk and occupants.

* What communities are willing to pay to preserve life safety through

fire department or police protection.

* What health care facilities normally spend to reduce patient risks.

* Medical costs resulting from fatalities and injuries related to

fire, tornadoes, earthquakes, and other building-related hazards.

* Other monetary costs of life safety hazards in buildings.

* Characteristics of the populations which are at risk for various

types of building hazards.

Case studies

There is a need for economic analysis of proposed or existing code requirements

which impose significant costs that may not be justified by the level of benefits. Case studies would contribute directly to improving building code decisions and would help in developing methods of analysis that could be used in

later studies.

It would be useful to further test and refine the worksheets in this report

and to develop new worksheets which are specific to particular types of code

change decisions.

An important question is how the methodology described in this report can be

made simpler and yet still be useful. For example, which factors are likely

to be relatively unimportant in most analyses so that they do not need precise

estimates, and which factors are likely to be crucial and need careful estimates? Such information would assist analysts to know in advance where to

direct their greatest efforts in estimating code change impacts.

97

Appendix A: TABLES OF DISCOUNT FACTORS^

A.l Discounting Formulas

A. 2 Single Present Worth Factors (SPW)

A. 3 Uniform Present Worth Factors (UPW)

A. 4 Modified Uniform Present Worth Factors

for Discount Rate of 8% (UPW*)

A. 5 Modified Uniform Present Worth Factors

for Discount Rate of 10% (UPW*)

A. 6 Modified Uniform Present Worth Factors

for Discount Rate of 12% (UPW*)

^ Other tables of discount factors can be found in many building economics

texts, such as Marshall, H. E., and Ruegg, R. T., Energy Conservation in

Buildings, National Bureau of Standards Handbook 132 (Washington, D.C.:

Government Printing Office, 1980); or in Investment analysis texts.

A-1
Table A.l Discounting Formulas

Nomenclature Abbreviation Use When Algebraic Form

Single Present Value Formula SPW Given F; to find P = F1

(i+ip

Uniform Present Value Formula UPW Given A; to find P

P

N

Modified Uniform Present Value

Formula

UPW* Given A; escalating

at rate e; to find P

Where

P = a present sum of money

F = a future sum of money

i = an interest rate

N = number of interest periods

A = an end-of-period payment (or receipt) in a uniform series of pa5nnents (or

receipts), usually annually

e = annual rate of increase of annual payment (or receipt)

Source: Gerald W. Smith, Engineering Economy: Principles of Capital Expenditures , 2nd Ed. (Ames, Iowa: The Iowa State University Press, 1973), p. 47.

A2
Table A.

2

SINGLE PRESENT WORTH FACTORS^

(SPW)

P F

Present Value Future Amount

P = (SPW) X (F)

II Real Discount Rate

Year 6% 8% 10% 12% 15% Year 6% 8% 10% 12% 15%

1 .9434 .9259 .9091 .8929 .8696 26 .2198 .1352 .0839 .0525 .0264

2 .8900 .8573 .8264 .7972 .7561 27 .2074 .1252 .0763 .0469 .0230

3 .8396 .7938 .7513 .7118 .6575 28 .1956 .1159 .0693 .0419 .0200

4 .7921 .7350 .6830 .6355 .5718 29 .1846 .1073 .0630 .0374 .0174

5 .7473 .6806 .6209 .5674 .4972 30 .1741 .0994 .0573 .0334 .0151

6 .7050 .6302 .5645 .5066 .4323 31 .1643 .0920 .0521 .0298 .0131

7 .6641 .5835 .5132 .4523 .3759 32 .1550 .0852 .0474 .0266 .0114

8 .6274 .5403 .4665 .4039 .3269 33 .1462 .0789 .0431 .0238 .0099

9 .5919 .5002 .4241 .3606 .2843 34 .1379 .0730 .0391 .0212 .0086

10 .5584 .4632 .3855 .3220 .2472 35 .1301 .0676 .0356 .0189 .0075

11 .5268 .4289 .3505 .2875 .2149 40 .0972 .0460 .0221 .0107 .0037

12 .4970 .3971 .3186 .2567 .1869 45 .0727 .0313 .0137 .0061 .0019

13 .4688 .3677 .2897 .2292 .1625 50 .0543 .0213 .0085 .0035 .0009

14 .4423 .3405 .2633 .2046 .1413

15 .4173 .3152 .2394 .1827 .1229

16 .3936 .2919 .2176 .1631 .1069

17 .3714 .2703 .1978 .1456 .0929

18 .3503 .2502 .1799 .1300 .0808

19 .3305 .2317 .1635 .1161 .0703

20 .3118 .2145 .1486 .1037 .0611

21 .2942 .1987 .1351 .0926 .0531

22 .2775 .1839 .1228 .0826 .0462

23 .2618 .1703 .1117 .0738 .0402

24 .2470 .1577 .1015 .0659 .0349

25 .2330 .1460 .0923 .0588 .0304

^ Formula for P is given in table A.l.

A-3
Table A.

3

UNIFORM PRESENT WORTH FACTORS^

(UPW)

p? «-

Present Value

A + A + A + ....

Annually Recurring

Future Amounts

P = (UPW) X (A)

Real Discount Rate

Year 6% 8% 10% 12% 15% Year 6% 8% 10% 12% 15%

1 0.943 0.926 0.909 0.893 0.870 26 13.003 10.810 9.161 7.896 6.491

2 1.833 1.783 1.736 1.690 1.626 27 13.211 10.935 9.237 7.943 6.514

3 2.673 2.577 2.487 2.402 2.283 28 13.406 11.051 9.307 7.984 6.534

4 3.465 3.312 3.170 3.037 2.855 29 13.591 11.158 9.370 8.022 6.551

5 4.212 3.993 3.791 3.605 3.352 30 13.765 11.258 9.427 8.055 6.566

6 4.917 4.623 4.355 4.111 3.784 31 13.929 11.350 9.479 8.085 6.579

7 5.582 5.206 4.868 4.564 4.160 32 14.084 11.435 9.526 8.112 6.591

8 6.210 5.747 5.335 4.968 4.487 33 14.230 11.514 9.569 8.135 6.600

9 6.802 6.247 5.759 5.328 4.772 34 14.368 11.587 9.609 8.157 6.609

10 7.360 6.710 6.144 5.650 5.019 35 14.498 11.655 9.644 8.176 6.617

11 7.887 7.139 6.495 5.938 5.234 40 15.046 11.925 9.779 8.244 6.642

12 8.384 7.536 6.814 6.194 5.421 45 15.456 12.108 9.863 8.283 6.654

13 8.853 7.904 7.103 6.424 5.583 50 15.762 12.233 9.915 8.305 6.661

14 9.295 8.244 7.367 6.628 5.724

15 9.712 8.559 7.606 6.811 5.847

16 10.106 8.851 7.824 6.974 5.954

17 10.477 9.122 8.022 7.120 6.047

18 10.828 9.372 8.201 7.250 6.128

19 11.158 9.604 8.365 7.366 6.198

20 11.470 9.818 8.514 7.469 6.259

21 11.764 10.017 8.649 7.562 6.312

22 12.042 10.201 8.772 7.645 6.359

23 12.303 10.371 8.883 7.718 6.399

24 12.550 10.529 8.985 7.784 6.434

25 12.783 10.675 9.077 7.843 6.464

^ Formula for P is given in table A.l.

A-4
Table A.

4

ii-'TH MODIFIED UNIFORM PRESENT WORTH FACTORS

FOR DISCOUNT RATE OF 8%^

(UPW*)

P? ^ A + A + A+ ...

Present Annually Recurring

Value Future Amounts Valued

at Present Prices

P = (UPW*) X (A)

Rate of Differential Cost Change

Year 1% 2% 3% 4% 5%

1 0.9352 0.9444 0.9537 0.9630 0.9722

2 1.8093 1.8364 1.8633 1.8903 1.9174

3 2.6276 2.6788 2.7307 2.7832 2.8364

4 3.3925 3.4745 3.5580 3.6431 3.7298

5 4.1078 4.2259 4.3470 4.4711 4.5985

6 4.7768 4.9356 5.0994 5.2685 5.4429

7 5.4023 5.6058 5.8170 6.0363 6.2640

8 5.9874 6.2388 6.5014 6.7757 7.0622

9 6.5345 6.8367 7.1541 7.4877 7.8382

10 7.0461 7.4013 7.7766 8.1734 8.5923

11 7.5246 7.9346 8.3703 8.8336 9.3263

12 7.9726 8.4382 8.9365 9.4694 10.0394

13 8.3906 8.9138 9.4765 10.0817 10.7328

14 8.7819 9.3631 9.9915 10.6712 11.4069

15 9.1479 9.7873 10.4826 11.2390 12.0622

16 9.4902 10.1880 10.9510 11.7857 12.6994

17 9.8103 10.5665 11.3977 12.3121 13.3189

18 10.1096 10.9239 11.8237 12.8191 13.9211

19 10.3895 11.2615 12.2300 13.3073 14.5066

20 10.6513 11.5803 12.6175 13.7774 15.0759

21 10.8961 11.8814 12.9871 14.2301 15.6293

22 11.1251 12.1657 13.3395 14.6660 16.1674

23 11.3992 12.4343 13.6757 15.0858 16.6905

24 11.5894 12.6880 13.9963 15.4900 17.1991

25 11.7287 12.9275 14.3020 15.8792 17.6936

ESKZUI

A-5

^ See table A.l for formula for P
Table A.

5

MODIFIED UNIFORM PRESENT

FOR DISCOUNT RATE

WORTH FACTORS

OF 10%a

(UPW*)

p? <- A + A + A+ ... P = (UPW*) X (A)

Present

Value

Annually Recurring

Future Amounts Valued

at Present Prices

Rate of Differential Cost Change

Year 1% 2% 3% 4% 5%

1 0.9182 0.9273 0.9364 0.9455 0.9546

2 1.7612 1.7871 1.8131 1.8393 1.9657

3 2.5353 2.5844 2.6340 2.6844 2.7354

4 3.2460 3.4027 3.4027 3.4834 3.5656

5 3.8986 4.0092 4.1225 4.2388 4.3581

6 4.4978 4.6449 4.7966 4.9531 5.1146

7 5.0480 5.2344 5.4278 5.6284 5.8367

8 5.5521 5.7810 6.0188 6.2669 6.5260

9 6.0159 6.2878 6.5722 6.8705 7.1839

10 6.4417 6.7577 7.0903 7.4411 7.8118

11 6.8328 7.1935 7.5755 7.9807 8.4113

12 7.1919 7.5977 8.0299 8.4909 8.9837

13 7.5216 7.9725 8.4553 8.9733 9.5300

14 7.8243 8.3199 8.8536 9.4293 10.0513

15 8.1022 8.6421 9.2266 9.8604 10,5490

16 8.3574 8.9408 9.5758 10.2680 11.0240

17 8.5918 9.2179 9.9029 10.6535 11.4776

18 8.8069 9.4747 10.2090 11.0177 11.9103

19 9.0044 9.7129 10.4957 11.3622 12.3235

20 9.1857 9.9337 10.7641 11.6878 12.7178

21 9.3512 10.1385 11.0154 11.9957 13.0942

22 9.5042 10.3285 11.2509 12.2870 13.4537

23 9.6446 10.5046 11.4714 12.5623 13.7968

24 9.7735 10.6679 11.6777 12.8225 14.1241

25 9.8919 10.8193 11.8710 13.0686 14.4367

^ See table A.l for formula for P
Table A.

6

MODIFIED UNIFORM PRESENT

FOR DISCOUNT RATE

WORTH FACTORS

OF 12%a

(UPW*)

p? A (1+e) + (1+e)^ + . . . P = (UPW*) X (A)

Present

Value

Annually Recurring

Future Amounts Valued

at Present Prices

Rate of Differential Cost Change

Year 1% 2% 3% 4% 5%

1 0.9018 0.9107 0.9196 0.9286 0.9375

2 1.7150 1.7401 1.7654 1.7908 1.8164

3 2.4484 2.4955 2.5432 2.5915 2.6404

4 3.1097 3.1834 3.2585 3.3349 3.4129

5 3.7061 3.8099 3.9163 4.0253 4.1371

6 4.2438 4.3804 4.5212 4.6664 4.8160

7 4.7288 4.9000 5.0775 5.2616 5.4525

8 5.1662 5.3732 5.8292 5.8144 6.0492

9 5.5606 5.8042 6.0597 6.3276 6.6086

10 5.9162 6.1967 6.4924 6.8042 7.1331

11 6.2370 6.5541 6.8903 7.2468 7.6248

12 6.5262 6.8796 7.2563 7.6577 8.0857

13 6.7870 7.1761 7.5928 8.0393 8.5179

14 7.0222 7.4461 7.9023 8.3936 8.9230

15 7.2343 7.6920 8.1870 8.7227 9.3028

16 7.4256 7.9159 8.4487 9.0282 9.6589

17 7.5981 8.1198 8.6895 9.3119 9.9927

18 7.7536 8.3056 8.9108 9.5753 10.3057

19 7.8939 8.4747 9.1144 9.8200 10.5991

20 8.0204 8.6288 9.3017 10.0471 10.8741

21 8.1345 8.7691 9.4739 10.2580 11.1320

22 8.2373 8.8968 9.6322 10.4539 11.3737

23 8.3301 9.0132 9.7778 10.6357 11.6004

24 8.4137 9.1191 9.9118 10.8046 11.8129

25 8.4892 9.2156 10.0349 10.9614 12.0121

^ See table A.l for formula for P.

A-7
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, \ ^ - Appendix B: GLOSSARY OF ECONOMIC TERMS^

AGGREGATE IMPACTS - Impacts on all affected buildings within a single code

jurisdiction or other geographical area* n

ANNUALLY RECURRING COSTS - Cost incurred each year in an equal amount or in an _ amount ^that is increasing at a constant rate throughout the study period.

BASELINE PRACTICE - The construction practice that would be used in the

absence of a code change.

BENEFIT-COST ANALYSIS - A method of evaluating an alternative by comparing

the discounted value of expected benefits with the discounted value of

expected costs. ~ , --

BENEFIT-COST RATIO - Discounted benefits divided by discounted costs; the

ratio must be .greater than one for a code change to represent an economic

Improvement, .

BREAKEVEN ANALYSIS - A method of finding that value of a variable for which

discounted costs and benefits of a code change are equal.

BUILDING ANALYSIS PERIOD - The period (often building life) for which effects

are analyzed for a particular building.

CODE ANALYSIS PERIOD - The period for which effects are analyzed for a particular code change.

CONSTANT DOLLARS - Values expressed in terms of- the general purchasing power

of the dollar in a base year. Constant dollars do not reflect price inflation.

DIFFERENTIAL PRICE ESCALATION RATE -? The difference between a general rate of

inflation and the rate of increase assumed for a given cost item, such as energy.

DISCOUNT FACTOR - A multiplicative number used to convert costs and benefits

occurring at different times to a common basis.

^ Definitions are based on Ruegg, R. T.; Petersen, S. R. , and Marshall, H. E.,

Recommended Practice for Measuring Life-Cycle Costs of Buildings and Building

Systems , NBSIR 80-2040 (Washington, D.C.: National Bureau of Standards,

1980); Marshall, H. E. and Ruegg, R. T., Simplified Energy Design Economics , National Bureau of Standards Special Publication 544 (Washington, D.C.:

U.S. Government Printing Office, 1980); and comments by various reviewers.

For further definitions of economic terms, see the Recommended Practice

cited above.

B-1
DISCOUNT RATE - The rate of interest reflecting the time value of money that

is used to convert benefits and costs occurring at different times to

equivalent values at a common time.

DISCOUNTING - A technique for converting cash flows that occur over time to

equivalent amounts at a common point in time.

MODIFIED UNIFORM PRESENT WORTH FACTOR - A factor used to convert a series of

annually recurring costs which are escalating at a constant rate to their

present value.

NET BENEFITS - The difference between discounted benefits and discounted

costs

.

NONMONETARY IMPACTS - Impacts, such as changes in life safety, for which it

is not feasible to assign dollar values.

PRESENT VALUE - The value of a benefit or cost at the present time (i.e., as of the base period), found by discounting future cash flows to the present.

REAL DISCOUNT RATE - The rate of interest reflecting the real earning power

of money over time. This is the discount rate to use in discount formulas or

to select discount factors when future benefits and costs are expressed in

constant dollars.

REAL PRICE RISE - The rise in a price in constant dollars, i.e., the price

rise after allowing for the overall level of inflation.

REAL RATE OF INTEREST - A rate of interest calculated based on constant

dollars. (The real rate of interest shows the true return on an investment

after allowing for effects of inflation.)

RESIDUAL VALUE - The net value of an asset at the end of its economic life,

at the end of the building analysis period, or when it is no longer to be

used.

SENSITIVITY ANALYSIS - Testing the outcome of an evaluation by altering the

value of one or more parameters from the initially assumed values(s).

SINGLE PRESENT WORTH FACTOR (SPW) - A discount factor used to convert a future sum to its present value.

UNIFORM PRESENT WORTH FACTOR (UPW) - A discount factor used to convert a series of recurring sums to their present value.

B-2
Appendix C: THREE STUDIES OF SAFETY IMPACT

This appendix gives examples, drawn from three studies, of how researchers

have estimated safety effects of code changes. The descriptions below are not

intended to be comprehensive summaries. Rather, they are intended to give the

reader a feel for how data can be obtained and used in estimating safety impacts.

Ground Fault Circuit Interrupters . In a 1978 report, economist

John McConnaughey assessed effects of a code requirement for Ground Fault Circuit Interrupters (GFCI's), which protect against electric shocks.^ McConnaughey

calculated impacts for the housing stock as a whole. However, to help the

reader relate McConnaughey ' s approach to the method in this guide, we divide

by the number of buildings to find the per-building impact.

The first step was to determine the number of electric shock deaths without the

code change. For 1963-1974, there was an average of 290 deaths per year due to

electric shock in the home. Data for this estimate came from Department of

Health and Human Services’ National Center for Health Statistics, including its

publication. Vital Statistics of the United States .

Since the code change affected only new homes, McConnaughey was concerned with

estimating the number of electric shock deaths in the 2.4 percent of the housing stock that is new homes. If hazards were similar in old and new homes,

this number would be 290 x 2.4 percent. However, new residences are likely to

have fewer electric shock deaths because receptacles are grounded. McConnaughey

estimated that grounding would prevent 50 percent of the potential deaths in new homes, based on a newspaper clipping study and an NBS study of GFCI usage.

Thus, the number of electric shock deaths occurring in new buildings annually

would be 290 x 2.4 percent x 50 percent = 3.48. Dividing by the number of new buildings (1,736 million) gives an annual probability of .000002 that there

will be an electric shock death in a single building.

To determine the percent of accidents prevented by the code change, McConnaughey

estimated the percent of fatalities occurring outdoors or in bathrooms — the areas potentially protected by GFCI’s. This number, 45.5 percent, was also

based on the newspaper clipping study. Next, he estimated the effectiveness

of the GFCI in preventing these deaths, taking into account homeowner practices

and causes of deaths, to be 77 percent.

Multiplying these figures together (.000002 x .455 x .77) shows the impact of

the code change on the annually expected number of deaths per building to be

.0000007. This is the figure that would be entered on worksheet 5.2 under the

"Total Change in Annual Expected Loss."

^ McConnaughey, An Economic Analysis of Building Code Impacts.

C-1
Mobile home wind safety . In a study for the Department of Housing and Urban

Development, Jeffrey Walters analyzed effects of potential regulations on mobile home wind safety.^

The first step was to find existing frequency and cost of wind damages to

mobile homes. Using insurance company data, Walters estimated the average

frequency without the potential regulations. However, since the primary

concern was structural failure, the frequency figures were adjusted downward

to reflect only accidents Involving structural failure. These adjustments

were based on the expert opinions of leading mobile home insurers. Walters

estimated fatalities and Injuries based on mortality statistics from the

National Weather Service's publication. Storm Data . These data were adjusted

to allow for underreporting. The change in frequency of accidents due to the

regulation was estimated based on the definitions of wind zones and on NBS

technical research. An insurance company also provided data on property

losses due to wind damage to mobile homes. These data were adjusted, based

on engineering judgment, to find costs due to structural failure only.

The report concluded that the potential code revision would be cost effective

in 70 m.p.h. wind zones. In 105 m.p.h. wind zones, it would not be cost

effective, and in 90 m.p.h. wind zones, it may be cost effective depending on

the type of mobile home and the weight given to intangible benefits.

Fire safety . In a third study, Helzer and Buchbinder of the NBS Center for

Fire Research and Offsensend of SRI Inc. estimated the effect of potential

standards on the likelihood of upholstered furniture fires. 2 They identified

12 situations defined by three factors; whether someone is home, when the

fire is discovered, and the extent of flame damage.

In estimating probabilities of various types of fire, the researchers drew on

U.S. Fire Administration fire incidents data. In estimating loss of life and

property for the 12 situations, they drew on U.S. Fire Administration statistics which related losses to the extent of flame damage. However, because of

the lack of detailed data, they used expert judgment to estimate fire probabilities and how certain factors — such as whether someone was home — would

affect the losses.

The changes in likelihood of various situations were estimated based on engineering judgment and assumptions concerning the extent of compliance.

^ Walters, Jeffrey L., Economic Benefit-Cost and Risk Analysis of Results of

Mobile Home Safety Research; Wind Safety Analysis (prepared for U.S.

Department of Housing and Urban Development, March 1979).

Helzer, Susan Godby; Offensend, Fred L.; and Buchbinder, Benjamin, Decision

Analysis of Strategies for Reducing Upholstered Furniture Fire Losses , NBS

Technical Note 1101 (Washington, D.C.; National Bureau of Standards, 1979).

C-2
The report concluded that, under certain assumptions, the cost-plus-loss for

the nation of a "no action" policy would be $6.33 billion. For a smoke

detector requirement, the cost-plus-loss would be $5.95 billion, and for an upholstered furniture flammability standard, it would be $5.96 billion.

Varying the underlying assumptions in a sensitivity analysis affected the

cost-plus-loss figures, sometimes significantly.

C-3

Appendix D: SOURCES OF DATA

D . 1 SOURCES OF : DATA'-QN CHARACTERISTICS AND GROWTH OF THE BUILDING STOCK^

Housing dat4 (general)

Construction authorized and put

in place ^

’

Housing forecasts '

Housing characteristics 'and

defects in housing

Housing characteristics, e.g., type

structure; location; number of bathrooms and bedrooms; external wall

material; type foundation; heating

fuel; parking facilities; number of

stories; floor space

U.S. Department of Housing and

Urban Development, Statistical

Yearbook , U.S. Government Printing

Office, Washington, D.C.

Construction Review , U.S. Department

of Commerce, Bureau of Domestic

Business Development, Industry and

Trade Administration, Washington,

D.C.

Marcin, Thomas. The Effects of

Declining Population Growth on the

Demand for Housing , U.S. Department

of Agriculture, General Technical

Report, NC-11, 1974.

U.S. Bureau of the Census, Annual

Housing Survey; 1977, United States

and Regions , Series H-150-77, U.S.

Government Printing Office,

Washington, D.C., 1979. Part A:

General Housing Characteristics, and

Part B; Indicators of Housing and

Neighborhood Quality.

New One-Family Houses Sold and for

Sale , Series C25, U.S. Bureau of

the Census, Washington, D.C.

(monthly publication)

.

Housing characteristics Characteristics of New and Existing

Single-Family Detached and MultiFamily Low-Rise Dwelling Units by

Region , NAHB Research Foundaion,

Rockville, Md., 1976.

^ For more detail concerning data in Census publications, see U.S. Bureau of

Census, Catalog , U.S. Government Printing Office, Washington, D.C.; or Construction Statistics Data Finder , U.S. Bureau of the Census, Washington, D.C.

D-1
D.l SOURCES OF DATA ON CHARACTERISTICS AND GROWTH OF THE BUILDING STOCK - (Continued)

Characteristics of apartments completed

Number of low-income and elderly

housing units, by state

Family characteristics

Building features; common materials

or designs

Climate data

U.S. Bureau of the Census,

Characteristics of Apartments

Completed; 1978 , Series H-131A,

U.S. Government Printing Office,

Washington, D.C.

U.S. Department of Housing and

Urban Development, 1977 Statistical Yearbook , U.S. Government

Printing Office, Washington, D.C.

Social and Economic Characteristics

of the Metropolitan and Nonmetropolitan Population , U.S. Department

of Commerce, Bureau of the Census,

Washington, D.C., 1974 and 1970.

Trade journals, interviews with

contractors or architects in HUD

regional offices; or collect through

original building survey.

Environmental Design Service, Climatic Atlas of the United States , U.S. Department of Commerce,

Washington, D.C. 1968, 1974.

D-2
D.2 SOURCES OF CONSTRUCTION COST DATA

Construction costs

Construction costs

Construction costs

Overall unit costs of building components

Construction costs; includes remodeling/

renovation costs

Construction costs

Construction costs

Construction costs

Construction costs

Costs of selected building components:

foundations and floors; exterior wall

framing and sheathing; windows and doors;

furrings; insulation; heating and cooling

systems

Building Construction Cost Data , Robert Snow Means Company, Inc.,

Duxbury, Mass, (published annually).

Mechanical and Electrical Cost Data , Robert Snow Means Company, Inc.,

Duxbury, Mass., 1979.

Building Cost File , Van Nostrand

Relnhold Co., New York (published

annually)

.

Design Cost File , Van Nostrand

Reinhold Co., New York, 1979.

Dodge Manual for Building Construction

Pricing and Scheduling , McGraw-Hill

Information Systems Company, New York

(published annually).

Dodge Building Cost and Specification

Digest , McGraw-Hill, New York

(subscription service - updated semiannually) .

Engelsman, Coert, Engelsman*s General

Construction Cost Guide , Van Nostrand

Reinhold Co., New York, 1979.

National Construction Estimator , Craftsman Book Co., Solana Beach,

California, 1980.

General Construction Estimating

Standards , Volume I (sltework,

piling and concrete); Volume II

(masonry, metals, doors, finishes,

windows, specialities); and Volume

III (mechanical and electrical

systems) Richardson Engineering

Services, Inc., Solana Beach,

California (annual publication)

.

Selected Cost Data on Residential

Construction (prepared for the

National Bureau of Standards), NAHB

Research Foundation, Inc., Rockville,

Md., December 1977.

D-3
D.2 SOURCES OF CONSTRUCTION COST DATA - (Continued)

Wage rates for construction trades by

geographical area; building materials

prices; cost Indices (rate of Inflation

for construction costs)

Union hourly wages in building trades

Construction cost Indices

Construction cost indices

Insulation costs

Housing weatherization costs

Lead paint abatement costs

Selling expenses, taxes, profits

Fire protection systems

Engineering News Record , McGraw-Hill,

New York (various issues).

Department of Labor; Bureau of Labor

Statistics

.

Construction Review , U.S. Department

of Commerce, Bureau of Domestic Busi- ness Development, Industry and Trade

Administration, Washington, D.C.

(monthly publication)

.

Value of New Construction Put in Place , Series C30, U.S. Bureau of the Census,

Washington, D.C. (monthly publication).

Masonry Advisory Council, ”1978 Masonry Cost Guide,” Park Ridge, 111.,

1978.

Weber, S. F.; Boehm, M. J.; and

Lipplatt, B. C., Weatherization

Investment Costs for Low-Income Housing , National Bureau of Standards

NBSIR 80-2167, U.S. Government

Printing Office, Washington, D.C.,

1980.

Chapman, Robert and Kowalski, Joseph.

Guidelines for Cost-Effective Lead

Paint Abatement , Technical Note

971, National Bureau of Standards,

U.S. Government Printing Office,

Washington, D.C., 1979.

Mobile Home Manufacturers' Cost and

Profile Survey , published by MobileModular Housing Dealer Magazine , Chicago, 111., 1977.

Various issues of the Fire Journal , published monthly by National Fire

Protection Association, Boston.

D-4
D.2 SOURCES OF CONSTRUCTION COST DATA - (Continued)

Construction costs; materials,

component, and system list prices

Individual builders; state and local

home builders associations; trade

associations; building materials

and equipment suppliers.

Wholesale prices of construction materials U.S. Department of Labor, Bureau of

Labor Statistics, Producer Prices

and Price Indexes, U.S. Government

Printing Office, Washington, D.C.

(monthly publication)

.

D-5
D.3 SOURCES OF DATA ON OPERATION AND MAINTENANCE COSTS

Expenditures for residential maintenance

and alterations

Apartment building operation and

maintenance (O&M) data: by building

location, age, and type of ownership

Shopping center O&M data; e.g. maintenance, utilities, by size of shopping

center

Office building O&M data: data by age,

height, size and location

Hospital operations data

Income and costs for suburban office

buildings

Obsolescence (product life) information

Price indices to be used to convert

current dollars to constant dollars

Residential Alterations and Repairs , Series C50, U.S. Bureau of the

Census, Washington, D.C. (quarterly

publication)

.

Income/Expense Analysis: Apartments,

Condominiums and Cooperatives , Institute of Real Estate Management,

Chicago, 111. (annual publication).

Dollars and Cents of Shopping Centers , Urban Land Institute, Washington, D.C.,

1978.

Office Building Experience Exchange

Report , Building Owners and Managers

Association International, Washington,

D.C.

American Hospital Association,

Hospital Administrative Services

Division. Chicago, 111., (published

twice a year).

Institute of Real Estate Management,

Chicago, 111.

Manufacturer, industry associationsMonthly Labor Review , U.S. Department of Labor, Bureau of Labor

Statistics, Washington, D.C.

Energy Use

Energy price forecasts Department of Energy, Energy Information Administration (See 45

Federal Register 5620, January 23,

1980).

Energy use End Use Energy Consumption Data

Base: Series 1 Tables, 1974 , U.S. Department of Energy, Energy

Information Administration, Washington, D.C., June 1978.

D-6
D.3 SOURCES OF DATA ON OPERATION AND MAINTENANCE COSTS - (Continued)

Energy use

Energy consumption annually for 1960

through 1978, by state, fuel, type, and

major end use sector

Conservation activities performed by

households, January 1977 to December

1978; status of households with respect

to insulation, storm windows, etc.

Tabulations of fuel oil used by singlefamily households

Residential and Commercial Energy

Use Patterns, 1970-1990, Report

to the Council on Environmental

Quality and the Federal Energy

Administration , Arthur D. Little,

Inc., Cambridge, Mass., November

1974.

State Energy Data Report, Statistical

Tables and Technical Documentation,

1960 through 1978 , Department of

Energy, Energy Information Administration, Washington, D.C., April 1980.

Residential Energy Consumption Survey:

Conservation , Department of Energy,

Energy Information Administration,

Washington, D.C., February 1980.

Single-Family Households; Fuel Oil

Inventories and Expenditures , Department of Energy, Energy Information

Administration, Washington, D.C.,

December 1979.

D-7
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY LOSSES

Data from the National Electronic

Injury Surveillance System on injuries related to consumer products Including appliances, building parts such as glazing and

stairs, home furnishings and fixtures such as bathtubs and carpets

NEISS Data Highlights, U.S. Consumer

Product Safety Commission, Washington,

D.C. (published quarterly).

Causes of deaths in fires Vital Statistics of the United States:

Mortality, Volume II, Parts A and B,

U.S. Department of Health, Education

and Welfare, National Center for

Health Statistics, Washington, D.C.

Test reports and supporting statements submitted with proposed standards and changes in model codes

concerning hazards and effects of

code changes in reducing hazards

Model building code organizations;

standards, committees, and testing

laboratories

.

Loss data, indirect costs of accidents Insurance companies.

Includes abstracts of publications

containing data on building accidents

National Technical Information Service,

Building Industry Technology (abstracts

of reports and other publications),

NTIS, Springfield, VA 22161 (weekly

publication)

.

Fire Hazards

Summaries of data on fire deaths from Fristrom, Geraldine, Fire Deaths in

various sources, descriptions of sources the United States; Review of Data

of data on fire incidents, fire deaths,

and property losses

Sources and Range of Estimates,

National Fire Data Center, U.S.

Fire Administration, Washington

D.C., 1977.

Fire incident reports (computerized

system)

National Fire Incident Reporting

System, U.S. Fire Administration,

Washington, D.C.

Fire deaths, incidents, dollar losses;

by cause, occupancy

"Fire and Fire Losses Classified,"

Fire Journal, National Fire Protection Association, Boston, Mass,

(published annually).

D-8
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY LOSSES - (Continued)

Fire Hazards - (Continued)

Cumulative fire experience: fatalities,

age, time of day, other factors

Fire Injury costs

Major fire Incident reports

(computerized system)

Local fire problems

Fire frequency (Including unreported

fires)

Fire loss data

Data on building fires, costs of

accidents

Data on deaths. Injuries, dollar

losses and Incidents

Fire Injury costs

Indirect costs of residential

fires

Fire Protection Handbook , National

Fire Protection Association,

Boston Mass., (published every six

or seven years).

Stacey, Gary S. and Smith, Kathy S.,

’’Methodology for Estimating Costs of

Injuries and Property Losses," Fire

Technology , August 1979, pp. 195-209.

Fire Incident Data Organization

(FIDO), National Fire Protection

Association, Boston, Mass.

State Fire Marshall reports (e.g.,

Michigan and California)

.

Highlights of the National Household Survey , U.S. Fire Administration, Washington, D.C., 1976.

1977 Fire Losses In Ontario , Province of Ontario, Office of the

Fire Marshall.

Accident Facts , National Safety

Council, Chicago, 111. (annual

publication)

.

Fire In the United States: Deaths , Injuries, Dollar Loss, and Incidents

at the National, State and Local

Levels , U.S. Fire Administration,

Washington, D.C., Dec. 1978.

Baranclk, J. I. and Shapiro, M. A.,

Pittsburgh Burn Study , Graduate

School of Public Health, University

of Pittsburgh, May 1972.

Munson, M.J. and Ohls, J.C.,

"Indirect Costs of Residential

Fires," Fire Journal , January 1979,

pp. 42-48.

D-9
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY LOSSES - (Continued)

Fire Hazards - (Continued)

Social and economic effects of

residential fires

Assessments of smoke detectors, remote

alarm system, automatic suppression

system

Safety impacts of retrofit features for

health care facilities (as rated by a panel of experts)

Smoke detector effectiveness

Smoke detector effectiveness

Smoke detector cost effectiveness

Smoke detector sales and installations

Performance of fire protection and

control equipment

Rapkin, Chester, ed.. The Social and

Economic Consequences of Residential

Fires , (forthcoming; editor is at

Princeton University).

Assessment of the Potential Impact

of Fire Protection Systems on Actual

Fire Incidents , Applied Physics Lab,

Johns Hopkins University, Laurel,

Md., October 1978.

Nelson, H. E. and Shibe, A. J.,

A System for Fire Safety Evaluation

of Health Care Facilities , NBSIR

78-1555, National Bureau of Standards,

Washington, D.C., November, 1978.

Available through NTIS.

Gratz, D. V. and Hawkins, R. E,

Evaluation of Smoke Detectors in Homes , International Association of Fire Chiefs,

Foundation, Washington, D.C., 1980;

available through NTIS.

Smoke Detectors in Ontario Housing

Corporation Dwellings , Ministry of

Housing, 4th floor. Queen's Park

Toronto, M7A2K5, Canada, 1978.

Potter, J. J.; Smith, M. L.; and

Panwalke, S. S., Cost Effectiveness

of Residential Fire Detection Systems , Texas Tech University, Lubbock, 1976.

Young, J. K. and Feignenbaum, E. L.,

"Survey and Analysis: OccupantInstallable Smoke Detectors," Aerospace Report ATR-77(2819)-2 , September

1977.

Independent testing laboratories

(e.g.. Factory Mutual); product manufacturers; trade associations (e.g..

Fire Equipment Manufacturers

Association)

.

D-10
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY LOSSES - (Continued)

Fire Hazards - (Continued)

Flammability of building materials

NBS fire research

Natural Disasters

Statistics on deaths and property losses

from hurricanes, floods, earthquakes and

tornadoes

Hurricane, flood and earthquake damages

and death

Hurricane, flood, earthquake and tornado

damages and deaths

Hurricane, flood, earthquake and tornado

damages and deaths

Hurricane, flood, earthquake and tornado

damages and deaths

Hurricanes, tornados, and floods

Hurricane and flood damages and deaths

Trade associations (e.g.. Society of

the Plastics Industry).

Jason, Nora H. , Fire Research

Publications, 1980 , NBSIR 81-2272,

National Bureau of Standards,

Washington, D.C., April 1981.

Sav, G. Thomas, Natural Disasters:

Some Empirical and Economic Considerations , NBSIR 74-473, National Bureau

of Standards, Washington, D.C.,

February 1974.

Dacy, Douglas C. and Kunreuther,

Howard, The Economics of Natural

Disasters , The Free Press, New

York, 1969.

Disaster Preparedness , Vol . 3

,

Executive Office of the President,

Office of Emergency Preparedness,

Washington, D.C., U.S. Government

Printing Office, January 1972.

Annual Summary of Disaster Services

Activities , American Red Cross,

Washington, D.C.

Highlights of Disaster Relief Services , American Red Cross, Washington, D.C. (annual publication).

Sorkin, Alan L., Economics of

Natural Disasters (forthcoming;

author is at the Johns Hopkins University and the University of Maryland,

Baltimore County).

Climatological Data, National

Summary , U.S. Department of Commerce,

National Oceanic and Atmospheric

Administration, Environmental

Data Service, Ashville, N.C.

D-11
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY IDSSES - (Continued)

Natural Disasters - (Continued)

Earthquake damages

Earthquake property losses

Earthquake damages

Tornado incidents and deaths

Tornado data

Economic, safety, and other Impacts of

natural disasters and unusual climatic

conditions

McClure, F. E., "Studies in Gathering

Earthquake Damage Statistics," Coast

and Geodetic Survey, U.S. Department

of Commerce Washington, D.C., 1967.

A Study of Earthquake Losses in

the San Francisco Bay Area , U.S.

Department of Commerce, National

Oceanic and Atmospheric Administration, Environmental Research

Laboratories; report for the

Office of Emergency Preparedness,

Boulder, Colo.

Studies in Seismicity and Earthquake Damage Statistics, 1969 , National Ocean Survey, U.S.

Department of Commerce; prepared

for Department of Housing and

Urban Development, 1969, Rockville,

Md.

Tornado Preparedness Planning , U.S. Department of Commerce,

National Oceanic and Atmospheric

Administration, National Weather

Service, U.S. Government Printing

Office, Washington, D.C., 1970.

General Summary of Tornadoes , U.S. Department of Commerce,

National Oceanic and Atmospheric

Administration, Environmental

Data Service, U.S. Government

Printing Office, Washington, D.C.

Impact Assessment of Major Climatic

and Other Natural Events , U.S.

Department of Commerce, National

Oceanic and Atmospheric Administration, Environmental Data and Information Service, Washington, D.C.

(published monthly).

(i D-12
D.4 SOURCES OF DATA ON BUILDING HAZARDS AND PROPERTY LOSSES - (Continued)

Natural Disasters - (Continued)

Damage due to wind, tornadoes, hurricane

etc., by state

Miscellaneous Hazards

Indoor air pollution

Electric shock fatalities

Lead paint poisoning

Housing needs of the handicapped;

survey presented in support of

proposed model code change

Storm Data , National Weather Service, National Oceanic and Atmospheric Administration, National

Climatic Center, Ashville, N.C.

(monthly publication)

.

Benson, F. B., ^ • > Indoor - Outdoor Air Pollution Relationships; A Literature Survey , Publication AP-112, Environmental

Protection Agency, Washington,

D.C., August 1972.

Newspaper clippings summarized for

the National Electric Code Panel,

Preprint of the Proposed Amendments

to the 1971 NEC , National Fire

Protection Association, Boston,

Mass

.

Gllsinn, Judith F., Estimates of

the Nature and Extent of Lead

Paint Poisoning in the United

States , NBS Technical Note 746,

National Bureau of Standards,

Washington, D.C., December 1972.

1980 Proposed Changes to the BOCA

Basic Codes , Vol. II, Building

Officials and Code Administration

International, Homewood, 111.,

p. 55.

Effects of housing project design on crime Newmann, Oscar, Defensible Space:

Crime Prevention through Urban

Design , Macmillan, New York, 1972.

Burglary losses Crime in the United States;

Unified Crime Reports , Federal

Bureau of Investigation, U.S.

Department of Justice, Washington,

D.C. (annual publication).

D-13
D,4 SOURCES OF DATA ON BUIIDING HAZARDS AND PROPERTY LOSSES - (Continued)

Miscellaneous Hazards - (Continued)

Costs of death and Injury

Costs of death and injury

Insurance claims records

containing information on value

of hospital and professional

medical services, by age, sex,

and type injury for 1976 through

1978.

Accident costs

Annual per capita earnings by age,

sex, race, education, etc.

Faigin, B. M. , 1975 Societal Costs

of Motor Vehicle Accidents, U.S.

National Highway Traffic Safety

Administration, Washington, D.C.,

1976.

Cornell, M. , A Survey of

Methods for Estimating the Cost

Value of a Human Life , U.S. Coast

Guard, Washington, D.C., 1976.

Civilian Health and Medical

Program for the Uniformed Services

(CHAMPUS), Department of Defense.

Kurtz, Robert D. and Stone,

Robert F., "A Model of Injury

Costs," Consumer Product Safety

Commission and Technology + Economics, Inc. (paper presented

at the Atlantic Economic Conference, October 10, 1980).

Statistical Abstract of the United

States , U.S. Department of Commerce,

Bureau of the Census, U.S. Government Printing Office, Washington,

D.C.

D-14
Appendix E: ADDITIONAL READING

E.l BENEFIT-COST ANALYSIS

American Telephone and Telegraph Company,

Construction Plans Department. Engineering

Economy; A Manager's Guide to Economic

Decisionmaking, 3d edition. New York; McGrawHill, 1977.

Anderson, Lee G. and Settle, Russell F. Benefit-Cost

Analysis; A Practical Guide . Lexington, Mass.;

Lexington Books, 1978.

Arrow, Kenneth. "The Rate of Discount for Long-Term Public

Investment," in Energy and the Environment; A Risk-Benefit

Approach , edited by H. Ashley, R. L. Rudman and C. Whipple.

New York; Pergammon Press, 1976, pp. 113-140.

Ashford, N. A. and Hill, C. T., et al. The Benefits of

Environmental, Health, and Safety Regulation , prepared for

the U.S. Senate Committee on Governmental Affairs.

Washington, D.C.; U.S. Government Printing Office, 1980.

Brown, David B. "Cost/Benefit of Safety Investments Using

Fault Tree Analysis," in Journal of Safety Research , Vol. 5.,

No. 2 (June 1973), pp. 73-81.

Department of the Navy, Naval Facilities Engineering

Command. Economic Analysis Handbook , NAVFAC P-442, July 1980.

(Available from Naval Publications and Form Center, 5801

Tabor Ave., Philadelphia 19120, Stock Control No. 0525-LP0377152.)

Grant, Eugene L. and Ireson, W. Grant. Principles of

Engineering Economy, 5th Edition. New York; Ronald Press,

1970.

Mishan, E. J. Cost-Benefit Analysis; New and Expanded

Edition . New York: Praeger Publishers, Inc., 1976.

Ruegg, Rosalie T.; Petersen, Stephen R.; and Marshall,

Harold E. Recommended Practice for Measuring Life-Cycle

Costs of Buildings and Building Systems , NBSIR 80-2040

(Washington, D.C.: National Bureau of Standards, 1980).

Smith, Gerald W. Engineering Economy: Analysis of Capital

Expenditures , 2nd edition. Ames, Iowa; The Iowa State

University Press, 1973.

Engineering

economics

Benefit-cost

analysis

Discounting

Federal benefitcost studies

Risk analysis

Benefit-cost

analysis for

facilities

planning

Investment

analysis

Benefit-cost

analysis

Methods of

estimating costs

of constructing

and operating

buildings

Investment

analysis

E-1
E.l BENEFIT-COST ANALYSIS - (Continued)

U.S. House of Representatives, Subcommittee on Oversight and

Investigations. Cost-Benefit Analysis: Wonder Tool or Mirage ? 96th Congress, 2nd Session, Washington, D.C.,

December 1980.

E.2 BUILDING ECONOMICS IN GENERAL

The American Institute of Architects. Life-Cycle Cost

Analysis; A Guide for Architects . Washington, D.C., 1977.

Arthur Young and Co. A Comparative Economic Impact Analysis

of the Present and Proposed State Building Standards Process . Prepared for the State of California, State and Consumer

Services Agency, October 15, 1978.

Banks, W. B. and Kroll, M. E. A Method for Predicting

Changes in Building Costs (Princes Risborough, England:

Princes Risborough Lab., cl980, available through NTIS).

Haviland, David S. Life-Cycle Cost Analysis 2; Using It in

Practice . Washington, D.C.: The American Institute of

Architects, 1978.

McConnaughey , John S. An Economic Analysis of Building Code

Impacts: A Suggested Approach , NBSIR 78-1528. Washington,

D.C.; National Bureau of Standards, 1978. Available through

NTIS.

Rawie, Carol Chapman. Estimating Benefits and Costs of

Building Regulations: A Step-by-Step Guide , Washington, D.C.,

National Bureau of Standards, June 1981.

Stone, P. A. Building Economy . New York; Pergammon Press,

1976 (second ed.)

Thompson, B. E. and Chapman, R.E. Productivity in

Residential Construction; An Annotated Bibliography , NBSIR 80-2150. Washington, D.C.: National Bureau of

Standards, February 1981.

E.3 FIRE SAFETY

Chapman, Robert E. "Cost-Effective Methods for Achieving

Compliance to Fire Safety Codes," Fire Journal , Vol. 73,

No. 5, September 1979, pp. 30-39, 123.

Cost-benefit

analysis in

regulatory

decisionmaking

Life-cycle costs

Building codes

Construction costs

Life-cycle costs

Building codes,

including ground

fault circuit

interrupter case study

Building codes

Building economics

Contains sections

on construction

costs

Fire safety in

hospitals

E-2
E.3 FIRE SAFETY - (Continued)

Chapman, Robert E.; Chen, Phillip T.; and Hall, William G.

Economic Aspects of Fire Safety in Health Care Facilities;

Guidelines for Cost-Effective Retrofits . NBSIR 79-1902.

Washington, D.C.: National Bureau of Standards, 1979.

Available through NTIS.

Comptroller General of the United States. Federal Fire

Safety Requirements Do Not Insure Life Safety in Nursing Home

Fires; Report to Congress , B-164031(3), Washington, D.C.;

General Accounting Office, June 1978.

Dardis, Rachel and Thompson, Ruth. "Analyzing the Effectiveness of Alternative Fire Protection Strategies," in

Fire Journal , September 1979, pp. 27-30.

Dardis, Rachel and Thompson, Ruth. "Strategies for Reducing

Residential Fire Losses," in Journal of Consumer Product

Flammability , Vol. 6, No. 2, June 1979, pp. 136-151.

Helzer, Susan Godby; Offensend, Fred L. ; and Buchblnder,

Benjamin. Decision Analysis of Strategies for Reducing

Upholstered Furniture Fire Losses , National Bureau of

Standards Technical Note 1101, Washington, D.C.: U.S.

Government Printing Office, 1979.

Munson, Michael J. and Ohls, James C. "Indirect Costs of

Residential Fires," in Fire Journal , January 1979, pp. 42-48.

Stacey, G. S. and Smith, K. S. "Methodology for Estimating in

Costs of Burn Injuries and Property Losses," in Fire Technology,

Vol. 15, No. 3, August 1979, pp. 195-209.

Waterman, T. E.; Mniszewski, K. R.; and Spadoni, D. J.

Cost/Benefit Analysis of Fire Detectors . IIT Research

Institute, Chicago, 111., Sept. 1978. (Report prepared for the

U.S. Fire Administration, Washington, D.C.)

E.4 ENERGY

Arthur D. Little, Inc. An Energy and Economic Impact Assessment of hud’s Minimum Property Standards , FEA/D-76/495.

Washington, D.C.: U.S. Government Printing Office, 1976.

Arthur D. Little, Inc. Energy Conservation in New Building

Design; An Impact Assessment of ASHRAE Standard, 90-75 . Washington, D.C.; U.S. Department of Energy, December 1975.

E-3

Fire safety in

hospitals

Sprinklers in

nursing homes

Upholstered

furniture standard;

smoke detectors

Upholstered

furniture standard;

smoke detectors

Upholstered

furniture standard;

smoke detectors

Fire costs

Fire losses

Fire detectors

HUD Minimum

Property

Standards

ASHRAE 90-75
E.4 ENERGY - (Continued)

Chapman, Robert E.; Crenshaw, Richard W.; Barnes, Kimberly A.;

and Chen, Phillip T. Optimizing Weatherization Investments in

Low- Income Housing: Economic Guidelines and Forecasts , NBSIR

79-1948. Washington, D.C.: National Bureau of Standards,

February 1980. Available through NTIS.

Lentz, Craig, "ASHRAE Standard 90-75 — Economic Impact on Selected Industries and the Design Profession," in ASHRAE

Journal , Vol. 18, No. 6, June 1976, pp. 33-38.

Lentz, Craig, "ASHRAE Standard 90-75 — Impact on Building

Energy Usage and Economics," ASHRAE Journal , Vol. 18, No. 4,

April 1976, pp. 23-28.

Marshall, Harold E. and Ruegg, Rosalie T. Energy Conservation in Buildings; An Economics Guidebook for Investment

Decisions . National Bureau of Standards Handbook 132.

Washington, D.C.: U.S. Government Printing Office, May 1980.

Marshall, Harold E. and Ruegg, Rosalie T. "Life-Cycle Costing

Guide for Energy Conservation in Buildings," in Energy Conservation through Building Design , McGraw-Hill Architectural

Record Books, 1979.

Marshall, Harold E.; Ruegg, Rosalie T.; and Wilson, Forrest.

Simplified Energy Design Economics; Principles of Economics

Applied to Energy Conservation Investments in Buildings . National Bureau of Standards Special Publication 544.

Washington, D.C.: U.S. Government Printing Office, 1980.

Petersen, Stephen R. and Kelly, George E. An Economic Analysis

of Efficiency Improvements to Residential Gas- and Oil-Fired

Central Heating Equipment . NBSIR 80-2079, Washington, D.C.:

National Bureau of Standards, July 1980. Available from NTIS.

Petersen, Stephen R. Economic Analysis of Insulation in

Selected Masonry and Wood-Frame Walls . NBSIR 79-1789.

Washington, D.C.: National Bureau of Standards, 1979.

Available through NTIS.

Petersen, Stephen R. Retrofitting Existing Housing for

Energy Conservation; An Economic Analysis . National Bureau

of Standards Building Science Series 64, Washington, D.C.:

U.S. Government Printing Office, December 1974.

Petersen, Stephen R. The Role of Economic Analysis in the

Development of Energy Standards for New Buildings . NBSIR

78-1471, Washington, D.C.: National Bureau of Standards,

July 1978. Available through NTIS.

E-4

Weatherization

ASHRAE 90-75

ASHRAE 90-75

Solar energy;

windows

Energy conservation

Energy conservation

Energy conservation

Insulation

Energy conservation

retrofits

Energy conservation
E.4 ENERGY - (Continued)

Ruegg, Rosalie T. and Chapman, Robert E. Economic Evaluation

of Windows In Buildings; Methodology . National Bureau of

Standards Building Science Series 119, Washington, D.C.:

U.S. Government Printing Office, April 1979.

Ruegg, R. T.; McConnaughey , J. S.; Sav, G. T., and

Hockenbery, K. A. Life-Cycle Costing; A Guide for Selecting

Energy Conservation Projects for Public Buildings . National

Bureau of Standards, Building Science Series 115, Washington,

D. C.; U.S. Government Printing Office, September 1978.

E. 5 MISCELLANEOUS

Chapman, Robert E. Cost Estimation and Cost Variability

in Residential Rehabilitation , National Bureau of Standards

Building Science Series 129. Washington, D.C.; U.S.

Government Printing Office, 1981.

Chapman, Robert E. and Colwell, Peter F. Economics of Protection Against Progressive Collapse . NBSIR 74-542,

Washington, D.C.: National Bureau of Standards, September 1974.

Chapman, Robert E. and Kowalski, Joseph G. Guidelines

for Cost-Effective Lead Paint Abatement , National Bureau of

Standards Technical Note 971. Washington, D.C.:

U.S. Government Printing Office, 1979.

Kunreuther, Howard. "Values and Costs", Building Practices

for Disaster Mitigation (R. Wright, S. Kramer and C. Culver,

ed.). National Bureau of Standards Building Science Series

46. Washington, D.C., U.S. Government Printing Office,

1973, pp. 41-62.

Marshall, Harold E. and Ruegg, Rosalie T. Efficient

Allocation of Research Funds; Economic Evaluation Methods

with Case Studies in Building Technology , National Bureau

of Standards Special Publication 558. Washington, D.C.;

U.S. Government Printing Office, 1979.

Marshall, Harold E.; Ruegg, Rosalie T.; and Wyly, Robert S.

"Cost Savings from Reduced Sized Venting," in Plumbing Engineer , July/August 1977, pp. 35-42 and September/October 1977,

pp. 45-46 and 64-65.

E-5

Windows

Energy conservation

Methods of

estimating costs

of residential

rehabilitation

Progressive collapse

Lead paint abatement

Earthquakes

Reduced-sized

venting for plumbing;

roofing shingles.

Reduced sized

venting for

plumbing
E.5 MISCELLANEOUS - (Continued)

Schroeder, Steven and Stelnfeld, Edward. Estimated Cost

of Accessible Buildings , prepared for Department of Housing

and Urban Development, April 1979. Available through NTIS.

Stiefel, S. Wayne. Procedural Options for Modification of

Architectural Glass in Residences to Improve Occupant Safety , NBSIR 75-791. Washington, D.C.: National Bureau of Standards,

1975.

Walters, J. L. ; Tucker, B. ; Shepard, T. ; and Weidner, S.

Economic Benefit-Cost and Risk Analysis of the Results of

Mobile Home Research, Transportation Safety and Durability

Analysis , PB81-107518. Washington, D.C.: Department of

Housing and Urban Development, 1980.

Walters, Jeffrey L. Economic Benefit-Cost and Risk Analysis

of Results of Mobile Home Safety Research; Wind Safety

Analysis . Washington, D.C.: U.S. Department of Housing and

Urban Development, 1979.

Accessibility

costs

Architectural

glass safety

Mobile home

standards

Mobile home

wind standards

E-6
Appendix F

BLANK WORKSHEETS
Worksheet 2. 1

PROPOSED CODE CHANGE

Title or number of code change

Occupancy or Use

Group Affected

Construction

Type Affected

Building Part

or System

Affected

1 Conditions or B Exceptions

fl Original

B Requirement

fl Proposed

fl Changes

F-2
Worksheet 2.2

REPRESENTATIVE CASES

Code analysis period Unit of analysis

1 Representative Case 1

I Characteristics I II III IV 1

Location

Date Constructed

Type of Building

Practice Without

the Code Change

Practice With

the Code Change

Building Analysis

Period (Years)

Other Factors

F-3
Worksheet 4,1

IMPACT ON UNIT INITIAL COSTS^

Representative case (from worksheet 2.2)

1 Type Cost

Proposed

Requirement

Original

- Requirement = Change

Materials and

Components $ - $ $

Wages and

Salaries $ - $ $

Construction

Equipment $ - $ $

Builder's overhead

(general & admin.) $ - $ $

Other costs $ - $ $

TOTAL $ - $ $

1

^ Costs may be calculated per building, per dwelling unit, per square foot, or for some other basic unit of analysis.

F-4
Worksheet 4.2

IMPACT ON UNIT OPERATION AND MAINTENANCE COSTS^

Representative case Discount rate %

Building analysis period (from worksheet 2.2) years

A. COSTS RISING AT THE RATE OF INFLATION

Proposed Requirement Original Requirement

Cost Type Amount^ Timing^ SPW^ UPW^i Amount^ Timing^ SPW*^ UPW*^

$ $

$ $

$ $

B. COSTS RISING AT A RATE DIFFERENT FROM INFLATION

Cost Type

Differ

entlal- Proposed Requirement

Price

Change Amount^ Timing^ SPW*^ UPW*^

Original Requirement

Amount^ Timing^ SPW^ UPW*d

$ $

$

^The unit of analysis should be the one listed on worksheet 2.2.

^At present prices.

^How often and when (years after construction year).

^^From discount factor tables A. 2 through A. 6 in appendix A for assumed discount

rate, timing of impact, and (for UPW*) the rate of differential price change.

F-5
Worksheet 4.3

O&M COSTS RISING AT THE RATE OF INFLATION

Representative case

FUTURE ONE-TIME

COSTS Amount^ X SPW^ = Discounted Value Total

Proposed $ X $ \

Requirement

$ X $ 1

Original - $ X

II 1 <r>

</>

Requirement

- $ X = - $ I

EQUAL ANNUAL Amount^

COSTS

(Proposed - Original) X UPW^ = Discounted Value Total

^From worksheet 4.2, part A.

F-6
Worksheet 4.4

O&M COSTS RISING AT A RATE DIFFERENT FROM INFLATION

Representative case

^Amount, SPW, and UPW* are from worksheet 4.2, part B.

^(I + e)^ = ( + ) = , where the rate of differential price

rise ”e" and year of Impact ”t" are from worksheet 4.2, part B. This factor

adjusts for cost changes after the construction year.

F-7
Worksheet 4.5

IMPACTS ON GOVERNMENT COSTS PER UNIT

Representative case Discount rate % Building analysis period years

Assumptions

^From table A. 2 In appendix A for assumed discount rate and timing of cost.

^The cost occurs only one time for a particular building although It Is an ongoing

cost for the building department.

®From table A. 3, appendix A, for assumed discount rate and building analysis period.

F-8
Worksheet 5.1

IMPACT ON EXPECTED PROPERTY LOSSES PER UNIT

Representative case Discount rate % Building analysis period years

Accident

Type

Change in Annual

Probability

of Accident^ X

Average Cost

per Accident

(Constant $) =

Change in Annual

Expected

Property Loss

X $ = $

X $ $

X $ —

$

TOTAL CHANGE IN ANNUAL EXPECTED LOSS $

UPW^ X

TOTAL CHANGE IN DISCOUNTED EXPECTED LOSS OVER TIME $

^ Probability of accident after code change minus probability of accident

before code change, based on available information and engineering judgment.

^ From table A. 3, appendix A, for assumed discount rate and building analysis

period or life of required feature.

F-9
Worksheet 5.2

IMPACT ON EXPECTED LIFE SAFETY PER UNIT

Representative case

F-10
Worksheet 5.3

IMPACTS PER UNIT OF A CHANGE IN SPACE

Representative case Discount rate % Building analysis period years

Change in Annual Rent

Usable Space X per Sq. Ft.^ =

Change in

Annual

Revenues

= $

X UPW^

X

Changes in

Discounted

Revenues (A)

$

Change in Construction

Built Space X Cost per Sq. Ft.

Change in

Construction

Costs (B)

Net Discounted

Value (A - B)

^ Rental excluding any owner-paid operating costs.

^ From table A. 3, appendix A, for assumed discount rate and building analysis period.

F-11
Worksheet 5.4

IMPACTS ON UNIT RESIDUAL VALUE

Representative case Discount rate %

Building analysis period years

^ At end of building analysis period. The change will be positive for an Increase In the building's residual value.

^ From table A. 2, appendix A for assumed discount rate and building analysis

period

.

F-12
Worksheet 7.1

ADJUSTING O&M COSTS FOR DIFFERENTIAL COST CHANGES

Representative case Years before building constructed years'

^ Based on construction date in worksheet 2.2.

^ The cost change factor, (1 = e)^, adjusts fo:

cost change, e, listed above and in workshee

years before the building is constructed.

Calculations for first line in table: (1 + e)

Calculations for second line in table: (1 + e)^ = (1

the rate of differential

4.2; t is the number of

II + )_ =

= (1 + )_ =

F-13
Worksheet 7.2

PRESENT VALUE OF NET MONETARY BENEFITS PER UNIT

Representative case Discount rate %

Years before building constructed years Unit of analysis

^ Value discounted to construction year. Use a positive number for increases

in value, cost, or loss. Use a negative number for decreases.

^ From table A. 2, appendix A, for assumed discount rate and number of years

before building is constructed.

^ If life safety Impacts are quantified in dollars, they should be included here.

F-14
Worksheet 8.1

AGGREGATE IMPACTS^

^ This worksheet should be filled out separately for each impact of interest.

^ May include buildings similar to buildings specified in representative cases.

^ From worksheets 5.2 (life safety) or 7.2 (monetary impacts).

F-15
Worksheet 9.1

VALUES TO ALTER IN A SENSITIVITY ANALYSIS^

Representative case

^ This should be filled out for each representative case in worksheet 2.2.

^ Values in the basic analysis are from worksheets 4.1 through 5.4, and 8.1.

^ The factor should be included in the sensitivity analysis if its value is

very uncertain and if a change may affect the code decision.

F-16
Worksheet 9.2

SENSITIVITY ANALYSIS^

Representative case

^ From worksheet 7.2 calculated using original and altered values.

^ From worksheet 5.2 calculated using original and altered values.

F-17
Worksheet 9.3

RESULTS OF THE BENEFIT-COST ANALYSIS

Code change

SELECTED ASSUMPTIONS

H Value Used in Values Used in

H Subject Basic Analysis Sensitivity Analysis

COSTS AND BENEFITS

Type Impact Base Case^ Sensitivity Analysis^ Comment

Net Monetary

Benefits $ $

Fatalities

Prevented

H Major Injuries

B Prevented

n Minor Injuries

B Prevented

r

^ From worksheet 8.1. ^ From worksheet 8.1 recomputed for sensitivity analysis

F-18
APPENDIX G; REFERENCES

Anderson, L.G. and Settle, R.F. Benefit-Cost Analysis; A Practical

Guide . Lexington, Mass.; Lexington Books, 1978.

Berry, Sandra A., ed. Proceedings of the National Conference on Regulatory

Aspects of Building Rehabilitation , NBS Special Publication 549.

Washington, D.C.: National Bureau of Standards, 1979.

California Building Officials Organization. California’s Building

Departments; A Critical Evaluation and Commentary . July 1979.

Chapman, R.E.; Chen, P.T.; and Hall, W.G. Economic Aspects of Fire Safety

In Health Care Facilities; Guidelines for Cost-Effective Retrofits , NBSIR 79-1902. Washington, D.C.; National Bureau of Standards, 1979.

Comptroller General of the United States. Why are New House Prices so High, How are they Influenced by Government Regulations, and Can Prices

be Reduced? Washington, D.C.; General Accounting Office, 1978.

Cooke, P.W. and Elsenhard, R.M. A P re liminary Examination of Building

Regulations Adopted by the States and Major Cities , NBSIR 77-1390.

Washington, D.C.; National Bureau of Standards, 1977.

Cornell, M. et al. A Survey of Methods for Estimating the Cost of a Human

Life , prepared for the U.S. Coast Guard, 1976.

Dardls, Rachel. "The Value of Life; New Evidence from the Marketplace,"

American Economic Review , December 1980, pp. 1077-1082.

Dardls, R. and Thompson, R. "Strategies for Reducing Residential Fire

Losses," Journal of Consumer Product Flammability , June 1979, pp. 136-151.

Falgln, B.M. 1975 Societal Costs of Motor Vehicle Accidents. Washington,

D.C.; U.S. Department of Transportation, 1976.

Field, C.G.; and Rlvkln, S.R. The Building Code Burden . Lexington,

Mass.; Lexington Books, 1975.

Grant, E.L. and Ireson, W.G. Principles of Engineering Economy , 5th

edition. New York; Ronald Press, 1970.

Gross, J.; Plelert, J.; and Cooke, P. Impact of Building Regulations on Rehabilitation—Status and Technical Needs , National Bureau of Standards

Technical Note 998. Washington, D.C.; Government Printing Office, 1979.

Hastings, Robert. Three Proposed Typical House Designs for Energy

Conservation Research , NBSIR 77-1309. Washington, D.C.; National Bureau

of Standards, 1977.

G-1
Helzer, S.G.; Offensend, F.L. ; and Buchblnder, B.. Decision Analysis of

Strategies for Reducing Upholstered Furniture Fire Losses , National

Bureau of Standards Technical Note 1101. Washington, D.C.: Government

Printing Office, 1979.

Hendrickson, P.L. ; McDonald, C.L. ; and Schilling, A.H. Review of Decision

Methodologies for Evaluating Regulatory Actions Affecting Public Health

and Safety (prepared by Battelle Pacific Northwest Laboratories,

Richland, Washington, for the U.S. Nuclear Regulatory Commission,

Washington, D.C., 1976).

Jones-Lee, M.W. The Value of Life; An Economic Analysis . Chicago:

University of Chicago Press, 1976.

Lincoln, J.R.; Coddington, D.C.; and Penberthy, J.R. An Analysis of the

Impact of State and Local Government Intervention on the Home Building

Process in Colorado 1970-1975 . Denver; Colorado Association for

Housing and Building, 1976.

Little, Arthur D., Inc. Energy Conservation in New Building Design; An Impact

Assessment of ASHRAE Standard 90-75 . Washington, D.C.: Federal Energy

Administration, 1976.

Marshall, H.E. and Ruegg, R.T. Efficient Allocation of Research Funds;

Economic Evaluation Methods with Case Studies in Building Technology , National Bureau of Standards Special Publication 558. Washington, D.C.;

Government Printing Office, 1979.

Marshall, H.E. and Ruegg, R.T. Energy Conservation in Buildings; An

Economic Guidebook for Investment Decisions , National Bureau of Standards

Handbook 132. Washington, D.C.; Government Printing Office, 1980.

Marshall, H.E. and Ruegg, R.T. Simplified Energy Design Economics . National

Bureau of Standards Special Publication 544. Washington, D.C.: Government

Printing Office, 1980.

Marshall, H.E.; Ruegg, R.T.; and Wyly, R.S. "Cost Savings from Reduced

Sized Venting," Plumbing Engineer , July-August 1977, pp. 35-42 and

September-October 1977, pp. 45-46, 64-65.

McConnaughey , John. An Economic Analysis of Building Code Impacts; A

Suggested Approach , NBSIR 78-1528. Washington, D.C.; National Bureau of

Standards, 1978.

McFadyen, David J. "A Case History of the Integrity of the National

Electrical Code," in Cooke, Patrick W., ed.. Research and Innovation in

the Building Regulatory Process; Proceedings of the Third Annual NBS/NCCSBCS

Joint Conference , National Bureau of Standards Special Publication 552.

Washington, D.C.; Government Printing Office, 1979, pp. 57-78.

G-2
REFERENCES (Continued)

Melinek, Stanley J. "A Method of Evaluating Human Life for Economic

Purposes," Accident Analysis and Prevention , October 1974, pp. 103-114.

Muth, Richard. "The Effects of Constraints on House Costs," Journal of

Urban Economics , January 1976.

National Bureau of Standards. Department of Energy/National Bureau of

Standards Federal LCC Workshop for Energy Conservation and Solar Energy

in Federal Facilities , 1979. (Unpublished workshop notebook.)

National Commission on Urban Problems. Building the American City . Washington, D.C., 1968.

Office of Management and Budget. Circular A-94, "Discount Rates to be

Used in Evaluating Time Distributed Costs and Benefits," March 27, 1972.

Oster, S. and Quigley, J. "Regulatory Barriers to the Diffusion of Innovation," in Cooke, Patrick, Ed., Research and Innovation in the Building

Regulatory Process; Proceedings of the First NBS/NCSBCS Joint Conference , NBS Special Publication 473. Washington, D.C.: National Bureau of

Standards, June 1977.

Rawle, Carol Chapman. Estimating Benefits and Costs of Building Regulations;

a Step-by-Step Guide , NBSIR 81-2223. Washington, D.C.: National Bureau of

Standards, 1981.

Ruegg, Rosalie T. Solar Heating and Cooling in Buildings; Methods of

Economic Evalaution , NBSIR 75-712. Washington, D.C.; National Bureau of

Standards, 1975.

Ruegg, R.T.; Petersen, S.R.; and Marshall, H.E. Recommended Practice for

Measuring Life-Cycle Costs of Buildings and Building Systems , NBSIR 80-

2040. Washington, D.C.: National Bureau of Standards, 1980.

Seidel, Stephen. Housing Costs and Government Regulation . New Brunswick,

N.J.: Rutgers Center for Urban Policy Research, 1978.

Smith, Gerald W. Engineering Economy; Analysis of Capital Expenditures , 2nd edition. Ames, Iowa: The Iowa State University Press, 1973.

Stacey, G.S. and Smith, K.S. "Methodology for Estimating Costs of Burn

Injuries and Property Losses," Fire Technology , August 1979, pp. 195-209.

Swersey, A.J. and Ignall, E. "What does Fire Research Have to Do with

Fire Protection," Fire Journal, January 1980, pp. 63-74.

G-3
REFERENCES (Continued)

Technology + Economics, Inc. The Consumer Product Safety Commission

Injury Cost Model; Complete Documentation , 1980 . (Prepared under subcontract to Battelle Columbus Laboratories for the Consumer Product

Safety Commission.)

Thaler, R. and Rosen, S. "The Value of Saving a Life: Evidence from the

Labor Market," in Nestor E. Terleckyj, ed.. Household Production and

Consumption (New York: Columbia University Press, 1975), pp. 265-297.

U.S. Department of Energy. Economic Analysis: Energy Performance

Standards for New Buildings , November 1979.

U.S. Department of Housing and Urban Development. Final Report of the

Task Force on Housing Costs . Washington, D.C.: U.S. Department of

Housing and Urban Development, 1978.

U.S. Department of Housing and Urban Development. Request for Proposals,

Cost Impact of Duplicate Life and Safety Requirements in Codes, H-5220 . Washington, D.C.: U.S. Department of Housing and Urban Development, 1980.

Walters, Jeffrey L. Economic Benefit-Cost and Risk Analysis of Results

of Mobile Home Safety Research: Wind Safety Analysis . Washington, D.C.:

U.S. Department of Housing and Urban Development, 1979.

Waterman, T.E.; Mniszewski, K.R.; and Spadoni, D.J. Cost/Benefit of Fire

Detectors (prepared by the IIT Research Institute, Chicago, for the

National Fire Data Center, U.S. Fire Administration, Washington, D.C., 1978).

Young, Arthur and Company. Testimony before the Federal Trade Commission

on the Proposed Rule for Standards and Certification , Sacramento: Arthur

Young and Company, 1979.

G-4
_NBS>114A (REV. 2«80 ) U.s. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBSIR 81-2402

2. Performing Organ. Report No, 3. Publication Date

November 1981

4. TITLE AND SUBTITLE

Estimating Economic Impacts of Building Codes

5. AUTHOR(S)

Carol Chapman Rawie

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions) 7. Contract/Grant No.

national bureau of STANDARDS

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

WASHINGTON, D.C. 20234 Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, City, State. ZIP)

National Bureau of Standards

Department of Commerce

Washington, D.C. 20234

10. SUPPLEMENTARY NOTES

I I Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here)

This report describes a method for estimating the benefits and costs of proposed

changes in building codes. A companion report by the author, Estimating Benefits and

Costs of Building Regulations; A Step-by-Step Guide , published by the National Bureau

of Standards as NBSIR 81-2223, provides a simplified description of the same basic

method.

This report shows the reader how to set up the problem, discount impacts to their

present value, estimate code impacts on building costs, estimate effects on building

safety, compute aggregate impacts, and conduct a sensitivity analysis. One chapter

discusses the problem of assigning a dollar value to life safety. Worksheets and an extensive list of references, including sources of data on building costs and hazards.

are included.

12. KEY WORDS (S/x to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

Benefit-cost analysis; building economics; building regulation; codes; construction

economics; construction regulation; economics; fire safety codes; regulation.

13. AVAILABILITY 14. NO. OF

PRINTED PAGES

[25*-*nl imited

1 1 For Official Distribution. Do Not Release to NTIS 162

r 1 Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

15. Price 20402.

[IQCOrder From National Technical Information Service (NTIS), Springfield, VA. 22161 $14.00
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