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<0 NBS TECHNICAL NOTE 979

U.S. DEPARTMENT OF COMMERCE/ National Bureau of Standards

Lead Paint Abatement Costs:

Some Technical and

Theoretical Considerations

n^^ "^(Qj^ SHEir

»Wr 2 2 )9?9
NATIONAL BUREAU OF STANDARDS

The National Bureau of Standards' was established by an act of Congress March 3, 1901. The

Bureau's overall goal is to strengthen and advance the Nation's science and technology and

facilitate their effective application for public benefit. To this end, the Bureau conducts

research and provides: (1) a basis for the Nation's physical measurement system, (2) scientific and technological services for industry and government, (3) a technical basis for equity in trade, and (4) technical services to promote public safety. The Bureau's technical work is performed by the National Measurement Laboratory, the National Engineering Laboratory,

and the Institute for Computer Sciences and Technology.

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physical and chemical and materials measurement; coordinates the system with measurement

systems of other nations and furnishes essential services leading to accurate and uniform

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research and provides scientific and technical services to aid Federal Agencies in the selection, acquisition, application, and use of computer technology to improve effectiveness and

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The Institute consists of the following divisions:

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'Headquarters and Laboratories at Gaithersburg, Maryland, unless otherwise noted; mailing address Washington, D.C. 20234. -Some divisions within the center are located at Boulder, Colorado, 80303.

The National Bureau of Standards was reorganized, effective April 9, 1978.
Lead Paint Abatement Costs:

Some Technical and Theoretical Considerations

Robert E. Chapman

Joseph G. Kowalski

Building Economics and Regulatory

Technology Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D.C. 20234

Sponsored by the: Office of Policy Development and Research Department of Housing and Urban Development

Washington, D.C. 20410

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U.S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued February 1979

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

Stock No. 003-003-02022-2 Price $3.25

(Add 25 percent additional for other than U.S. mailing).
National Bureau of Standards Technical Note 979

Nat. Bur. Stand. (U.S.). Tech. Note 979, 153 pages (Feb. 1979) CODEN: NBTNAE

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1979

For sale by the Superintendent of Documents, U.S. Government Printing Office. Washington, D.C. 20402

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PREFACE

This research was conducted under the sponsorship of the Department

of Housing and Urban Development by the Applied Economics Program,

the Center for Building Technology, National Engineering Laboratory,

National Bureau of Standards, This report analyzes the technical and

theoretical considerations which went into collection and analysis of

the economics portion of Phase II of the Experimental Hazard Elimination Program (EHEP). This report develops a procedure for manually

calculating the costs of lead-based paint abatement.

Appreciation is extended to Dr, Harold E. Marshall, Applied Economics

Program, and Dr. John S. McConnaughey , Applied Economics Program, who

reviewed the economic aspects of this paper. Appreciation is also

extended to Mr, Harvey W, Berger, National Engineering Laboratory, who

provided useful suggestions for improving the treatment of certain

topics in this paper. Special appreciation is extended to Ms, Barbara

Cassard, formerly with the Applied Economics Program, and Ms, Klmberly

Hockenbery, Applied Economics Program, for their valuable assistance

in the data analysis phase of this research effort.

111
ABSTRACT

Public and private concern about the potential for lead poisoning in

children due to the ingestion of lead-based paint chips has resulted

in a Federally sponsored program to develop technologies by which the

lead-based paint hazard may be eliminated from the nation's housing.

The nature and design of the Experimental Hazard Elimination Program

(EHEP) is thought to be unique in that it permitted the costs of the

alternative lead paint abatement techniques to be rigorously analyzed.

The focus of this report is on the design, implementation and analysis

of EHEP and the cost information it produced. Statistical analyses

which permitted the development of econometric models capable of estimating abatement technique costs and expected contractor markup are described. Structural equations relating changes in the values of

certain key factors to variations in direct cost and contractor markup

are also presented. Guidelines, including a national deleading cost

estimate, are given so that these econometric models can be used by

municipal officials and building owners to estimate deleading costs

as well as provide input to policy evaluation and formulation.

Key words: Abatement; building economics; building materials; cost

estimation; econometric models; economic analysis; housing; lead-based

paint; lead poisoning.

IV
TABLE OF CONTENTS

Page

PREFACE iii

ABSTRACT iv

LIST OF FIGURES vili

LIST OF DIAGRAMS vlii

LIST OF TABLES ix

SI CONVERSION UNITS xli

1. INTRODUCTION 1

2, DESCRIPTION OF TECHNICAL APPENDICES 2

Appendix A: Details of the National Cost Estimates , , . , 4

A.l Lead-Based Paint Survey Data ..... 5

A. 2 Dwelling Unit Cost Computations 7

A. 3 Housing Census Data 18

A. 4 The National Cost Estimates by Region, Age, and

Type of Housing 19

A. 5 Conclusions 27

Appendix B: Description of Abatement Techniques 30

B.l Barrier Materials 30

B.1.1 Gypsum Wallboard 30

B.l. 2 Plywood Paneling 31

B.l. 3 Cementitious Coating 31

B.l. 4 Gypsum Veneer Plaster .• . 31

B.l. 5 Vinyl-Coated Fabric 31

B.l. 6 Cement-Coated Fiberglass 32

B.l. 7 Melamine Coated Hardboard 32

V
Page

B.1.8 Aggregate Filled Paint 32

B.1.9 Gypsum Plaster with Metal Lath 32

B.1.10 Gypsum Impregnated Jute Fabric ...... 33

B.1.11 Plywood on Floors 33

B.2 Paint Removal Methods 3A

B.2.1 Infra-Red Heating Device 34

B.2. 2 Solvent-Based Paint Remover ........ 34

B.2. 3 Electrical Heat Gun 35

B.2. 4 Hand Scraping 35

B.2. 5 Propane Torch 35

B.2. 6 Dip Tank Method 36

B.3 Replacement of Components 36

Appendix C: Theoretical Considerations Underlying

Phase II of EHEP 37

C.l Design Considerations 37

C.2 Statistical Techniques Utilized 42

C.2.1 Analysis of Variance 45

C.2, 2 Multiple Regression 48

C.3 Parameter Estimates of the EHEP Cost Models ... 52

C.S.I Key Factor Impacts on Direct Costs 53

C.3. 2 Key Factor Impacts on Contractor Markup . . 70

C.3,3 Methods for Calculating Confidence

Intervals About Predicted Costs 75

vi
Page

Appendix D: Discussion of EHEP Phase II Data Characteristics and Empirical Implications 80

D.l Techniques for Deleading Walls and Trim 80

D.1.1 Plywood Paneling 80

D.l. 2 Vinyl-Coated Fabric 80

D.l. 3 Cement-Coated Fiberglass 81

D.l. 4 Solvent-Based Paint Remover and Infra-red

Heating Device 81

D.l. 5 Finish Painting 82

D,2 Ranges Over Which the Cost Models are Valid ... 82

D.3 Treatment of Intra-Unit and Inter-Unit Economies

of Scale 84

D.4 Potential Savings Resulting From the Use of the

EHEP Phase II Cost Models 86

Appendix E: Methodology for Performing Hand Calculations , 88

E.l Description of Method 88

E.2 Hand Calculation Worksheets 96

Appendix F: Listing of Computer Program ... 124

REFERENCES 140

VI

1
LIST OF FIGURES

Page

A.l Cumulative Distributions (Pittsburgh and Washington)

for Interior Walls in Pre 1940 Units 8

A. 2 Cumulative Distributions (Pittsburgh and Washington)

for Interior Walls in 1940 - 1959 Units 9

A. 3 Cumulative Distributions (Pittsburgh and Washington)

for Interior Doors in Pre 1940 Units 10

A. 4 Cumulative Distributions (Pittsburgh and Washington)

for Interior Doors in 1940 - 1959 Units 11

A. 5 Regional Breakdown Used in National Cost Estimate ... 13

A, 6 Distribution of Total Deleading Cost Burden by Region. . 29

LIST OF DIAGRAMS

Page

C.l Random Assignment of Wall Abatement Techniques

in Occupied Units 40

C.2 Random Assignment of Trim Abatement Techniques in

Occupied Units 44

E.l How to Calculate Direct Costs for Wall Techniques ... 90

E.2 How to Calculate Direct Costs for Trim Techniques ... 92

E.3 How to Calculate Minimum Bid Price 95

viii
LIST OF TABLES

Table Page

A.l Distribution of Lead-Based Paint in Housing by Type

and Age 6

A, 2 Dwelling Unit Abatement Costs in 1976 Dollars Including

Markup for Pre 1940 Multifamily Dwelling Units (Walls

Only — 2.0 mg lead/cm^) 14

A, 3 Dwelling Unit Abatement Costs in 1976 Dollars Including

Markup for Pre 1940 Multifamily Dwelling Units (All

Trim — 2.0 mg lead/cm^) 15

A. 4 Dwelling Unit Abatement Costs in 1976 Dollars Including

Markup for Pre 1940 Single Family Dwelling Units (Walls

Only — 2.0 mg lead/cm^) 16

A. 5 Dwelling Unit Abatement Costs in 1976 Dollars Including

Markup for Pre 1940 Single Family Dwelling Units (All

Trim — 2.0 mg lead/cm^) 17

A. 6 Distribution of Dwelling Units by Age and Region Used

in Making National Cost Estimates 20

A. 7 Number of Dwelling, Units with a Lead Content on Walls

of 2.0 mg lead/cm or More 21

A. 8 Cost of Deleading Pre 1940 Single Family Dwelling Units . 22

A. 9 Expected Cost in 1976 Dollars of Deleading the Nation's

Housing by Region and Age/Type of Housing — 2.0 mg lead/

cm^ 23

A. 10 Expected Cost in 1976 Dollars of Deleading the Nation's

Housing by Region and Age/Type of Housing — 1.5 mg lead/

cra^ 24

A. 11 Estimated Costs of Administering a Nationwide Program

to Delead the Nation's Housing — 2.0 rag lead/cm .... 25

A. 12 Estimated Costs of Administering a Nationwide Program

to Delead the Nation's Housing — 1,5 mg lead/cm"^ .... 25

C.l Decision Model Symbol Identification 41

C.2 Occupied Unit Sound Wall Probability Function 43

IX
Table Page

C.3 The Key Factors Determining Direct Cost Per Square Foot

of Gypsum Wallboard 54

C.4 The Key Factors Determining Direct Cost Per Square Foot

of Plywood Paneling 55

C.5 The Key Factors Determining Material Cost Per Square

Foot of Plywood Paneling 56

C,6 The Key Factors Determining Direct Cost Per Square Foot

of Ceraentitious Coating and Veneer Plaster 57

0,7 The Key Factors Determining Material Cost Per Square

Foot of Cementitious Coating and Veneer Plaster 57

C,8 The Key Factors Determining the Productivity of Labor

in the Installation of Vinyl-Coated Fabric 58

C.9 The Key Factors Determining Material Cost Per Square

Foot of Vinyl-Coated Fabric 58

C.IO The Key Factors Determining the Productivity of Labor

in the Installation of Cement-Coated Fiberglass . . ^ . . 59

C.ll The Key Factors Determining Material Cost Per Square Foot

of Cement-Coated Fiberglass 60

C.12 The Key Factors Determining Labor Cost Per Linear Foot

Using the Infra-Red Heating Device 61

C.13 The Key Factors Determining Labor Cost Per Linear Foot

Using Solvent-Based Paint Remover 62

C.14 The Key Factors Determining Direct Cost Per Linear Foot

of Paint Removal Using the Electric Heat Gun 63

C.15 The Key Factors Determining Direct Cost Per Linear Foot

of Paint Removal Using the Hand Scraping Method 64

C.16 The Key Factors Determining Direct Cost Per Unit of

Component Replacement 65

C.17 The Key Factors Determining Direct Cost Per Square Foot

of Painting Walls and Ceilings 66

CIS The Key Factors Determining Direct Cost Per Unit of

Painting Trim 67
Table Page

C.19 Explanatory Power of EHEP Phase II Cost Models 68

C,20 Percentiles of the t Distribution for Selected

Degrees of Freedom 69

C.21 The Key Factors Determining Markup 70

C.22 Basic Data Matrix 71

C,23 Preliminary Contract Package Assignment 73

C,24 Variance-Covariance Matrix for Plywood Paneling

Cost Model 78

D.l Boundary Values of Key Factors Used to Compute Direct

Costs 83

E.l Index of the Worksheets Needed to Perform Hand

Calculations 91

E.2 Dwelling Unit Summary of Direct Costs 93

XI
SI CONVERSION UNITS

The conversion factors and units contained in this report are in accordance with the International System of Units (abbreviated SI for Systems

International d' Unites). The SI was defined and given official status

by the 11th General Conference on Weights and Measures which met in Paris,

France in October 1960. For assistance in converting U.S. customary units

to SI units, see ASTM E 380, ASTM Standard Metric Practice Guide, available

from the American Society for Testing and Materials, 1916 Race Street,

Philadelphia, PA. 19103. The conversion factors for the units found in

this Standard are as follows:

Length

1 in = 0.0254* meter

1 ft = 0.3048* meter

1 mil = 0.001* in

1 yd = 0.9144* meter

Area

1 in^ = 6.4516* x 10~^meter^

1 ft^ = 0.0929 meter^

1 yd^ = 0.836 meter^

Volume

1 in-^ = 1.639 X 10"^meter^

1 liter = 1.00* x 10~\eter^

1 gallon = 3.785 liters

Temperature

°C = 5/9 (Temperature °F -32)

* Exactly

xii
LEAD PAINT ABATEMENT COSTS: SOME TECHNICAL

AND THEORETICAL CONSIDERATIONS

1. INTRODUCTION

Through the "Lead-Based Poisoning Prevention Act" Congress has delegated to the Department of Housing and Urban Development (HUD) the

leadership role in developing the technical information which is

required to determine the abatement procedures which will: (1) effectively deal with the lead-based paint (LBP) poisoning problem, and

(2) promote economic efficiency in the allocation of resources to

eliminate high levels of LBP in housing.

The Experimental Hazard Elimination Program (EHEP) is intended to con- tribute to the accomplishment of these goals in three ways. First, it

permits the technical evaluation of experimental LBP abatement techniques in field deleading operations. Second, through the collection

of cost data, it provides a framework for estimating the costs of future

LBP hazard abatement operations. Third, it provides a data base against

which procedures for estimating abatement costs can be tested for accu- racy and ease of application.

EHEP consists of two phases. Phase I of EHEP involved deleading operations in 110 dwelling units. Thirty dwelling units were initially

deleaded in Washington, D.C.; the remaining 80 were deleaded in Atlanta,

Georgia, shortly thereafter. Phase II of EHEP involved deleading operations in 71 dwelling units in Boston, Massachusetts. Phase II differed

from Phase I in that an experimental design was rigorously defined and

controlled so that variations in abatement technique cost could be

analyzed with regard to such important factors as type and condition

of housing as well as prevailing supply and demand conditions for construction skills and materials. In addition, the cost information

collected during Phase I of EHEP was used as a data base against which

procedures for estimating abatement costs were validated.

The purpose of this report is to describe the technical and theoretical

considerations which went into the planning, implementation, and analysis of EHEP Phase II and the cost information it produced. The level

of detail and treatment of the technical and theoretical considerations

implicit in EHEP Phase II provide firm technical underpinnings for the

The "Lead-Based Paint Poisoning Prevention Act" (PL 93-695) was enacted by Congress on January 13, 1971 and amended (PL 93-151) on November 9, 1973. These acts provide for Federal participation,

including grants to local governments for detection, treatment, and

prevention of LBP poisoning.
research findings presented in Guidelines for Cost-Ef f ective Lead Paint

Abatement. Such an analysis is necessary because the evaluation of the

EHEP Phase II cost data is a significant aid in the identification of

those LBP abatement techniques which promote economic efficiency at the

dwelling unit level. Econometric models based on data from Phase II

of EHEP are developed that (1) identify and quantify those variables

which have the greatest impact on direct costs and contractor markup,

(2) show how the least-cost abatement technique may be identified,

(3) pro\'lde guidelines for estimating abatement costs at the dwelling

unit level or for a major program, and (4) provide baseline estimates

for the expected national and regional deleading costs,

2. DESCRIPTION OF TECHNICAL APPENDICES

The bulk of this report is composed of a set of technical appendices.

These appendices are designed to be self-contained and hence may be

read independently. The major thrust of each appendix is to lay out

in detail the theoretical, technical, and empirical considerations upon

which the results of the EHEP Phase II cost analysis are based. In

that sense the technical appendices are supporting evidence for results

presented in the companion report Guidelines for Cost-Ef f ective Lead

Paint Abatement . The appendices go beyond the results of the companion

report, however. In particular, a methodology for hand calculations is

developed.

Details of the national cost estimates are presented in Appendix A,

These estimates should be useful for planning purposes in that they show

expected deleading costs by region, by age of housing, and by type of

housing (single family, multi-family). Housing characteristics based on research findings in Analysis of Housing Data Collected in a Lead-Based

Paint Survey in Pittsburgh, Pennsylvania Part II are also presented.

These housing characteristics in conjunction with cost information may

aid in policy planning and analysis in that they provide some insight

into the expected scale of deleading operations in a dwelling unit.

Estimates are presented for two levels of lead in paint. The first

Robert E. Chapman and Joseph G. Kowalski, Guidelines for CostEf f ective Lead Paint Abatement , National Bureau of Standards,

Technical Note 971 (In Press).

Direct costs are the costs to the contractor for performing the

deleading work. They include the costs of labor, material, and

special equipment required to perform the task. They do not include

any contractor markup for overhead and profit.

Douglas R. Shier and William G. Hall, Analysis of Housing Data

Collected in a Lead-Based Paint Survey in Pittsburgh, Pennsylvania

Part II , National Bureau of Standards, Interagency Report 77-1293,

June 1977.
estimate assumes that a minimum lead content of 2.0 mg/cm serves as abatement criteria. The second estimate assumes a minimum lead content

of l.*? mg/cm .

Appendix B is concerned with the engineering aspects of the alternative

LBP abatement techniques.

The theoretical concepts implicit in the EHEP Phase II experimental

design are presented in Appendix C. The structure of the EHEP Phase II

experimental design is laid out in detail. The emphasis, however, is on identifying the necessary experimental criteria and showing how an experimental design may be tailored to the dual objectives of satisfying

statistical requirements and recognizing budgetary constraints. An

"efficient" experimental design is important because it maximizes the

amount of information which can be collected and analyzed. Thus, these

(theoretical) guidelines may be useful to planners and policy analysts

concerned not only with collecting and analyzing LBP abatement costs

but also with other housing problem areas such as rehabilitation and

weather Ization.

In Appendix D a critique of the cost data collected during Phase II of

EHEP is presented. Special emphasis is given to those problems which

resulted in the deletion of a small number of observations from the

data base. Ranges, based on statistical tolerance intervals over which

the cost models are valid and a discussion of the treatment of scale

economies are also presented. Empirical tests which support the claim

that savings resulting from the use of the EHEP Phase II cost models

are approximately $100 per dwelling unit are presented in the final

section of Appendix D.

The methodology for using the EHEP Phase II cost models to make hand

calculations is described in Appendix E. This appendix includes worksheets and sample calculations which illustrate how the cost models

can be exercised. The major focus is on showing how the least-cost

combination of abatement techniques may be identified.

The report concludes with a listing of the computerized cost estimating

procedure. The computer program, written in the BASIC language, permits

the user to estimate LBP abatement costs by inputting Information on housing characteristics and wage and material prices via a time-sharing

terminal. The output shows the least-cost combination of abatement

techniques for each dwelling unit. An estimate of the contract bid

price is also provided.
APPENDIX A

DETAILS OF THE NATIONAL COST ESTIMATES

Combining the results of this report with those of two recent publications, ' has created an opportunity for estimating the national cost

of lead paint abatement. The two reports by Shier and Hall provide

information on the percentage of dwelling units (by age and building

type) which contain lead painted surfaces; they also provide information on the average number of surfaces per dwelling unit which contain

a given level of lead in paint. Such detailed information on the

distribution of lead in dwelling units was not available before the two

reports by Shier and Hall were published. It is important to recognize

that such data is necessary if a meaningful national cost estimate is

to be made. (A comparison of the results of the Pittsburgh survey to

other cities is also necessary if the housing data is to be projected

onto cities in other regions.) The results of the Pittsburgh survey

and the cost estimation procedures of this report, provide a means for

converting quantities of lead-based paint per dwelling unit into an

estimate of the deleading costs for that dwelling unit. This information may then be projected onto regional and national scales. To do

this, census data on the number of housing units by region, by age, and

by building type is used.

In Chapter 1 of the companion report Guidelines for Cost-Ef fective Lead

Paint Abatment , we asserted that it would cost between $28 and $35

billion to abate the lead-based paint hazard from the interiors of the

3 nation's housing stock. These figures are based on a set of assumptions detailing the amount of deleading work to take place and prevailing supply and demand conditions in the regional markets for labor and

materials.

Douglas Shier and William Hall, Analysis of Housing Data Collected in

Lead-Based Paint Survey in Pittsburgh, Pennsylvania Part 1 , National

Bureau of Standards, Interagency Report 77-1250, May 1977.

Douglas Shier and William Hall, Analysis of Housing Data Collected

in a Lead-Based Paint Survey in Pittsburgh, Pennsylvania Part II , National Bureau of Standards, Interagency Report 77-1293, June 1977.

No estimate was made on the cost of abating the lead-based paint

hazard from the exterior surfaces of a dwelling unit due to a general

lack of information on the appropriate techniques for abatement, the

cost of abatement, and the distribution of lead levels on exterior

surfaces. It is important to point out that these figures do not

include the costs of administering a nationwide lead paint abatement

program. It will be shown in Section A. 4 that including administration costs in the national estimate will increase the cost figures

cited above by approximately 30 percent.
The purpose of this appendix is to describe our method and assumptions

with respect to our manipulation of (1) the Pittsburgh lead-based paint

survey data, (2) the EHEP Phase II cost models as applied to the results

of (1), and (3) the Census of Housing data. This appendix will also

present a summary of the national cost estimate broken down by region

and type/age of housing.

A.l LEAD-BASED PAINT SURVEY DATA

The second Shier and Hall report is based on housing data collected in

a sample of approximately 3300 dwelling units during 1974 and 1975 in

Pittsburgh, Pennsylvania. The findings of this report are summarized in

three sets of tables. The first set of tables provides (for specified

lead levels) by age and building type (single family or multifamily)

data on the number and fraction of dwelling units, rooms, and surfaces

that equal or exceed the specified lead level. Table A.l was derived

from the Pittsburgh survey results. The values of the dwelling unit

specific characteristics associated with each age/type category may be

thought of as characterizing a "statistical" dwelling unit of that age/

type category. The percent at risk (dwelling units with lead levels

equal to or exceeding the specified lead level, 2.0 mg/cm or 1.5 mg/cm ) columns in Table A.l are calculated directly from the Pittsburgh survey

data. The number of square feet of wall area is based on the average

number of surfaces per dwelling unit at risk times an assumed value of

96 square feet per surface (12' by 8'). The number of linear feet of

doors needing deleading is based on the average number of door surfaces

needing deleading at the specified lead level times 8 linear feet (a

four foot abatement height was assumed). The number of linear feet of

windows reported in Table A.l is based on the average number of window

surfaces per dwelling unit at risk at the specified lead level times an assumed value of 7 linear feet per window.

Finally the number of linear feet of baseboard trim assumes that baseboards follow the lead distribution of windows and that the average

number of rooms with lead painted windows will also contain lead paint

on the baseboard trim. Forty-four linear feet of baseboard trim per room was assumed. This last mentioned procedure was necessary because

the tables presented in Shier-Hall report do not contain data on baseboards. (The 44 foot figure is based on empirical data established

during EHEP.) In using the data in Table A.l to compile average

deleading costs per dwelling unit, it was assumed that the smaller

sets at risk (in percentage terms) were subsets of the larger sets at

risk and that the largest percentage at risk for the three trim components held for all three of the trim components. Thus, for example,

at the 1.5 mg/cm'^ lead level, 78 percent of all dwelling units had

lead painted doors, windows and baseboards.

It is important to point out that only those surfaces which contain

lead paint will be treated. Consequently the costs of "cosmetic"

treatment for other surfaces is not included in the cost estimates.

Although some readers might object to this approach on aesthetic
TABLE A.l

DISTRIBUTION OF LEAD-BASED PAINT IN HOUSING BY TYPE AND AGE^^^

Linear

Dwelling % at Square % Linear % Linear % Feet

at Feet at Feet at Base-

.s Risk Doors Risk Windows Risk boards

Unit mg Lead Risk Feet

Type/ Age Per cm (b) Walls

Single 2.0 62 450 71 33 71 31 71 116

Pre40 1.5 72 469 78 34 77 33 77 122

Multi 2.0 61 454 68 32 68 26 68 102

Pre40 1.5 73 447 72 33 73 27 73 107

Single 2.0 37 359 27 21 27 24 27 93

40-59 1.5 50 353 45 24 34 24 34 93

Multi 2.0 21 384 29 14 29 15 29 73

40-59 1.5 29 381 46 15 28 15 28 76

Single 2.0 21 268 23 18 23 21 23 101

60-75 1.5 33 278 49 23 27 19 27 91

Multi 2.0 8 408 10 18 10 9 10 55

60-75 1.5 11 328 26 19 12 10 12 63

(a) 2 The upper set of numbers are associated with lead reading of 2.0 mg/cm

while the lower set are associated with lead reading of 1.5 mg/cm .

This column refers to the percent of all the building elements at risk

listed in the column on the right. Source in Douglas Shier and William

Hal 1 , Analysis of Housing Data Co llected in a Lead -Based Paint Survey

in Pittsburgh, Pennsylvania Part II, National Bureau of Standards Interagency Report 77-1293, June 1977, Appendix A,
grounds, the assumption that if only one surface in the room would

require treatment all surfaces in the room would require treatment

is contrary to the usual practice of HUD and local lead paint programs. Note that in cases where either gypsum wallboard or veneer plaster are used, the surface can be finished to the same color and

texture as in the rest of the room.

All dwelling units (72 percent of the total) with lead painted walls were a subset within the 78 percent at risk for trim. This procedure

maximizes the cost estimate per dwelling unit. A natural concern is

whether the Pittsburgh survey results generalize to different regions

in the country. While it would certainly be desirable to have survey

results as reliable as those from Pittsburgh for different regions,

there is some evidence that the Pittsburgh results will not prove to

be anomalous. Evidence of this is contained in the first Shier-Hall

report. A much smaller lead-based paint survey (100 dwelling units)

was undertaken in Washington, D.C. It was found that "in most

instances the distributions for Pittsburgh and Washington follow quite

-similar shapes. [This] suggests that there is very little difference

in the" wall lead levels for the two cities; the most pronounced difference occurs for doors in pre 1940 units." Figures A.l through A.

4

are taken from the Shier-Hall report. Because of the close correspondence between Pittsburgh and Washington case results, we believe that

assuming the Pittsburgh results projected onto other regions will not

produce distorted cost estimates. In each figure the cumulative distribution for painted surfaces is given. The cumulative distribution

is shown as a curve plotted against the lead level (in mg/cm ). Each

curve shows the percent of the total number of painted surfaces which

exceed a given lead level. For example, Figure A.l shows that approximately 10 percent of the interior walls in Pittsburgh exceed a lead

level of 3.0 mg/cm whereas approximately 13 percent of the interior

walls in Washington exceed a lead level of 3.0 mg/cm .

A. 2 DWELLING UNIT COST COMPUTATIONS

Table A.l of the previous section provided the basic information necessary to exercise the EHEP/Phase II cost models. Several supplementary

steps were required however, in order to fulfill the data requirements

of the EHEP models. In this section we shall describe these steps.

Since the national cost estimate is based on the aggregation of regional

estimates, variations between regions in wage rates and material prices

had to be introduced. Utilizing the cost indices found in Building r struction Cost Tata, 1977 for labor and materials, it x-^as pcssitle

1

2

Douglas Shier and William Hall, Analysis of Housing Data Collected

in a Lead Based Paint Survey in Pittsburgh, Pennsylvania Part I , pp. 61-62.

Robert S. Godfrey, ed. , Building Construction Cost Data, 1977 , Robert S. Means, Inc., Duxbury, Massachusetts, 1976.

7
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PITTSBURGH

.5 1.0 1.5 2.0 2.5 3.0 4.0 5.0 7.5 10.0

LEAD LEVEL (mg/cm2)

Figure A.

3

Cumulative Distributions (Pittsburgh and Washington) for Interior

Doors in Pre 1940 Units

10
100

90

80

70

-J;<o

o

60

50

6^ 40

30

20

10 -

PITTSBURGH

WASHINGTON

1 -[ T "sri

.5 1.0 1.52.02.53.0 4.0 5.0 7.5 10.0

LEAD LEVEL (mg/cm^)

Figure A.

4

Cumulative Distributions (Pittsburgh and Washington) for Interior Doors

in 1940-1959 Units

11
construct weighted average (weighted by the percent of the regions population found in a given state) regional cost indices. All weights were computed using Boston, Mass. as the base point. The indices were then

used to inflate or deflate the actual values of wages and material costs

found in Boston for each technique in order to arrive at regional estimates for wages and material costs.

Data on the "statistical" dwelling unit by age/type category (See

Table A.l) were then used in conjunction with regional estimates of

wage rates and material prices to generate an estimate for deleading

the "statistical" dwelling unit. All estimates were calculated using

the "policy analysis" routine of a computer time-sharing program based

on the EHEP/Phase II cost models. This routine calculates the direct

costs for each wall and trim technique based on the representative

("statistical") values of the key factors. These direct cost figures

are then averaged. A markup ratio based on the representative values

of the key factors is also calculated. (The markup ratio calculated

assumes that, on the average, three dwelling units will be in each

contract, ) The markup ratio is then used to calculate the total cost

(i.e. the sum of direct costs, overhead costs, and profit) of deleading

the "statistical" dwelling unit. Figure A. 5 shows the regional breakdowns used in making the national cost estimate. Tables A. 2 through A.

5

summarize the expected cost per dwelling unit for each region for

selected building types and ages. The figures presented in Tables A.

2

through A. 5 do not include an estimate of the costs per dwelling unit

associated with the administration of a lead-based paint program.

(Details of how these costs were calculated are given in Section A. 4.)

In the paragraph above it was stated that the direct costs of the six

wall and four trim techniques were averaged for each "statistical"

dwelling unit. This approach is equivalent to the assumption that the

wall and trim techniques are uniformly distributed. Since this assumption is of prime importance in the development of the national cost

estimates, we shall explore it in some detail.

The assumption of a uniform distribution of the abatement techniques

used in computing the national costs estimates, as well as baseline

program costs, can be supported in two ways. First, if an individual

chooses among n abatement techniques without knowing their expected

cost, each technique would be equally likely to be chosen. Hence,

under individual choice a uniform distribution is not unreasonable.

Second, suppose the techniques were assigned on the basis of engineering judgment or based on some other type of expert opinion. The abatement techniques used in Phase I of EHEP fit this scenario since they were assigned on the basis of engineering judgment. (Extensive laboratory tests were conducted prior to Phase I of EHEP to identify the

performance characteristics of each of the techniques to be used in

See Section D.3 for a discussion of the sizing of contract

packages.

12
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13
TABLE A.

2

DWELLING UNIT ABATEMENT COSTS IN 1976 DOLLARS INCLUDING

MARKUP FOR PRE 1940 MULTIFAMILY DWELLING UNITS

(WALLS ONLY — 2.0 mg lead/cm^)

Cemen- Cement

Gypsum Plywood titious Veneer Fiber- Vinyl

Region Wallboard Paneling Coating Plaster glass Fabric Average^

New England 676 658 513 556 813 834 675

Mid Atlantic 701 679 591 638 873 834 719

East North

Central 695 675 567 614 855 836 707

West North

Central 657 645 475 515 783 830 651

South

Atlantic 566 594 425 453 744 791 596

East South

Central 576 598 425 453 744 795 599

West South

Central 513 567 430 450 747 764 579

Mountain 618 626 479 514 786 810 638

Pacific 649 656 613 655 890 805 711

The simple average of the six barrier techniques

14
TABLE A.

3

DWELLING UNIT ABATEMENT COSTS IN 1976 DOLLARS INCLUDING

MARKUP FOR PRE 1940 MULTIFAMILY DWELLING UNITS

(ALL TRIM ~ 2.0 mg lead/cm^)

Region

Hand Solvent

Heat Gun Scraping Strip Infra-Red Average*

New England 363 466 500 396 431

Mid Atlantic 413 516 573 451 488

East North

Central 398 502 551 435 471

West North

Central 338 442 464 369 403

South Atlantic 299 402 411 326 359

East South

Central 300 403 411 327 360

West South

Central 297 400 410 324 358

Mountain 337 440 464 368 402

Pacific 421 526 590 462 500

The simple average of the four paint removal techniques

15
TABLE A.

DWELLING UNIT ABATEMENT COSTS IN 1976 DOLLARS INCLUDING

MARKUP FOR PRE 1940 SINGLE FAMILY DWELLING UNITS

(WALLS ONLY — 2.0 mg lead/cm^)

Cemen- Cement

Gypsum Plywood titious Veneer Fiber- Vinyl

Region Wallboard Paneling Coating Plaster glass Fabric Average^

New England 691 665 512 557 824 847 683

Mid Atlantic 716 686 591 641 885 847 728

East North

Central 710 682 567 616 867 849 715

West North

Central 672 651 473 516 794 843 658

South

Atlantic 579 600 423 452 755 804 602

East South

Central 589 604 422 453 755 808 605

West South

Central 525 572 427 450 757 776 585

Mountain 633 632 477 515 797 823 646

Pacific 663 663 614 658 902 818 720

The simple average of the six barrier techniques

16
TABLE A.

5

DWELLING UNIT ABATEMENT COSTS IN 1976 DOLLARS INCLUDING

MARKUP FOR PRE 1940 SINGLE FAMILY DWELLING UNITS

(ALL TRIM — 2.0 mg lead/cm^)

Regi on Hand Solvent

Heat Gun Scraping Strip Infra-Red Average'

New England 387 517 561 441 477

Mid Atlantic 444 576 645 505 542

East North

Central 427 559 620 486 401

West North

Central 358 489 519 409 444

South Atlantic 312 443 457 360 393

East South

Central 313 444 458 360 394

West South

Central 310 440 457 357 391

Mountain 356 487 519 408 443

Pacific 455 586 664 517 555

The simple average of the four paint removal techniques

17
the field.) Table 4.1 of the report, Economic Analysis of Experimental

Lead Paint Abatement Methods: Phase I , shows the number of dwelling

units which were assigned to each abatement technique. The information

in Table 4.1 permits the hypothesis that the choice of any wall technique was equally likely (i.e., a uniform distribution) to be tested.

The results of the chi-squared goodness of fit test performed to test

this hypothesis indicated that a uniform distribution was satisfactory.

(The test statistic was 3.283 where X

05 6 ~ 12.592.) The following

assumptions were made in performing this £est: 1) cementitious coating

and filled paint were combined; 2) plywood paneling was substituted for

melamine paneling; and 3) the gypsum jute observations were divided

equally among vinyl-coated fabric and cement-coated fiberglass. In

each case the techniques which were grouped or substituted were almost

identical (see Appendix B). In the case of trim techniques, engineering judgment tended to favor the more expensive techniques. Thus the

assumption of a uniform distribution results in a lower rather than a higher estimate of expeced costs.

A. 3 HOUSING CENSUS DATA

The 1970 Census of Housing contains data on occupied dwelling units

which cross tabulate for each tenure category the number of dwelling

units by age and region. However, since the tenure data does not

correspond to the single f amily/multif amily classification used in the

Pittsburgh survey, it was necessary to convert the tenure data by relying on data found in the census volume on Metropolitian Housing Characteristics. Table B-6 of that report contains for each of the four

1

3

4

Robert E. Chapman, Economic Analysis of Experimental Lead Paint

Abatement Methods: Phase I , National Bureau of Standards, Technical Note 922, September 1976.

An alternative assumption would be to assume that the contractor

performing the work could choose the least costly technique.

This would imply that an averaging of abatement technique costs

would overstate the baseline program costs. Unfortunately, such

an approach is inconsistent with existing HUD policy which requires

specifications to be written prior to invitation to bid. Another

problem with this approach is that the planning and scheduling of

program resources would be unnecessarily complicated since the

preparation of budgetary estimates and specifications would have

to wait until after the abatement contracts had already been

awarded,

U.S. Bureau of the Census, Census of Housing: 1970 Detailed

Housing Characteristics Final Report HC(1)-B1, United States

Summary , Table 22.

U.S. Bureau of the Census, Census of Housing: 1970 Metropolitan

Housing Characteristics Final Report HC, United States Summary , Table B-6.

18
major regions cross tabulated data which report for each occupancy

category the number of single family units and multifamily units by age

of dwelling unit. This data was used to establish percentage distributions of single and multifamily units for each occupancy category by

age which was then used to convert the subregional age/tenure data into

age/building type data. Tables No. 1261 and 1262 of the Statistical

Abstracts were utilized in a similar manner to derive estimates by

region of the number of single and multifamily units constructed between

1970 and 1975, These data were added to the 1960-1970 data that were available in the 1970 Census of Housing . This permitted the most recent

age category corresponding to the Pittsburgh survey data results to be

included. Table A. 6 resxilted from these procedures. It was used to provide the basis of the estimates of the number of dwelling units at risk

by region and age for each of the specified lead levels. Table A.

7

presents the number of dwelling units at risk for walls with lead

readings equal to or greater than 2,0 mg/cm .

The last step was to multiply the number of units at risk by the cost

(including markup) of deleading the "statistical" dwelling unit. In

order to arrive at a single cost figure per unit, the results from

tables like Tables A. 2 and A. 4 were used to compute the weighted

average cost per unit based on all of the six wall techniques and four

of the trim techniques (component replacement was not used in these

calculations because of its extreme cost). Thus the product of the

weighted average cost per dwelling unit for walls and trim (at a specified lead level) times the number of dwelling units at risk enabled

us to arrive at regional estimates of the total cost of deleading walls

and trim. Table A, 8 is representative of these calculations. For both

the 1.5 and 2,0 mg/cm lead levels, there are six of these tables, one

for each age/building type category.

A. 4 THE NATIONAL COST ESTIMATES BY REGION, AGE, AND TYPE OF HOUSING

The tables presented in this section represent the end product of

the previous three sections. They summarize, for the 1.5 mg/cm

(Table A. 10) and 2,0 mg/cm (Table A, 9) levels, the national cost by

region, age, and building type of deleading housing which contains

lead-based paint.

In addition to the costs of physically installing the lead-based paint

abatement techniques, (Tables A. 9 and A. 10) there will be costs associated with administering a lead-based paint program (Tables A. 11 and

A. 12). In principle the costs of administering such a program must

also be included in any assessment of the cost to the nation of a program or plan to eliminate the lead-based paint hazard from housing.

U.S, Bureau of the Census, Statistical Abstracts of the United

States: 1976 , U.S. Department of Commerce, 97th ed., July 1976.

19
TABLE A.

6

DISTRIBUTION OF DWELLING UNITS^ BY AGE AND REGION

USED IN MAKING NATIONAL COST ESTIMATES

Pre '40 '40-'59 '60-'75 Pre '40 '40-'59 '60-'75 Regional

Region Single Single Single Multi Multi Multi Total

NE 975.5 684.0 680.1 1083,4 231.5 453.0 4107.5

MA 3007.0 2245.3 1693.6 3457.8 1088.1 1322.8 12814.6

ENC 3517.7 3115.1 2567.2 2334.3 412.1 1482.0 13428.4

WNC 1674.4 1031.1 1072.8 981.5 367.0 594.0 5720.8

SA 2146.8 2901.5 3675.0 527.2 791.7 1646.0 11688.2

ESC 1061.0 1150.8 1164.8 241.0 303.4 421.1 4342.1

WSC 1319.6 2045.0 1864.7 299.4 550.8 781.6 6861.1

MT 518.2 890.0 921.7 205.7 199.8 612.3 3347.7

PAC 1507.1 2905.9 2385.8 735.9 870.9 1865.3 10270.9

NATIONAL TOTAL

15727.3 16968.7 16025.7 9866.2 4815.3 9178.1 72581.3

In Thousands

20
TABLE A.

7

NUMBER OF DWELLING UNITS^ WITH A LEAD CONTENT

ON WALLS OF 2.0 mg lead/cm^ OR MORE

Pre '40 Pre '40 '40-'59 •40''59 '60-'75 '60-'75 Regional

Region Single Multi Single Multi Single Multi Total

NE 604.8 660.9 253.1 48.6 142.8 36.2 1746.4

MA 1864.3 2109.3 830.8 228.5 355.7 105.8 5494.4

ENC 2181.0 1423.9 1152.6 86.5 539.1 118.6 5501.7

WNC 1038.1 598.7 381.5 77.1 225.3 47.5 2368.2

SA 1331.0 321.6 1073.6 166.3 771.8 131.7 3796.0

ESC 657.8 147.0 425.8 63.7 244.6 33.7 1572.6

WSC 818.1 182.6 756.6 115.7 391.6 62.5 2327.6

MT 321.2 125.5 329.3 42.0 193.6 49.0 1060.6

PAG 934.4 448.9 1075.2 182.9 501.0 149.2 3291.6

NATIONAL TOTAL

9750.7 6018.4 6278.5 1011.3 3365.5 734.2 27158.6

In Thousands

21
TABLE A.

8

COST OF DELEADING PRE 1940 SINGLE FAMILY DWELLING UNITS^'^

Region Wall Cost Trim Cost Total Cost Present

National Cost

NE 412.9 329.9 742.8 6.2

MA 1356.6 1158.3 2514.9 21.1

ENC 1559.8 1306.2 2866.0 24.1

WNC 683.4 527.3 1210.7 10.2

Wall Cost Trim Cost Total Cost

412.9 329.9 742.8

1356.6 1158.3 2514.9

1559.8 1306.2 2866.0

683.4 527.3 1210.7

801.3 599.0 1400.3

398.1 296.6 694.7

478.2 366.5 844.7

207.5 162.9 370.4

672.5 594.6 1267.1

SA 801.3 599.0 1400.3 11.8

ESC 398.1 296.6 694.7 5.8

WSC 478.2 366.5 844.7 7.0

MT 207.5 . 162.9 370.4 3.1

PAC 672.5 594.6 1267.1 10.6

^ In millions of 1976 dollars.

Administrative costs are not included.

22
TABLE A.

9

EXPECTED COST IN 1976 DOLLARS^»^ OF DELEADING THE NATION'S HOUSING

BY REGION AND AGE/TYPE OF HOUSING — 2.0 mg lead/cm^

Region

New

England

Mid

Atlantic

East North

Central

West North

Central

East South

Central

West South

Central

Pre '40 Pre '40 '40-'59 '40-'59 'eO-'yS '60-'75

Single Multi Single Multi Single Multi

South

Atlantic 1400.3 320.4 768.7 131.1 566.3

Percent

Regional National

Total Cost

742.8 763.8 207.7 44.0 120.1 29.6 1908.0 6.7

2514.9 2665.0 739.3 224.6 326.0 93.5 6563.3 23.1

2866.0 1754.9 1002.8 83.1 482.3 102.5 6291.6 22.1

1210.7 658.7 299.5 66.6 180.8 37.1 2453.4 8.6

94.0 3280.8 11.5

694.7 147.0 306.0 50.4 180.1 24.1 1402.3 4.9

844.7 178.5 531.2 89.5 283.3 43.7 1970.9 6.9

Mountain 370.4 136.4 255.4 35.9 154.0 37.8 989.9 3.5

Pacific 1267.1 569.3 958.1 180.3 462.5 131.9 3569.2 12.7

NATIONAL TOTAL

11,911.6

(Percent) (41.9)

7194.0 5068.7 905.5 2755.4 594,2 28,429.4 100

(25.3) (17.8) (3.2) (9.7) (2.1)

In millions.

Administrative costs are not included.

23
TABLE A. 10

EXPECTED COST IN 1976 DOLLARS^'^ OF DELEADING THE NATION'S HOUSING

BY REGIONS AND AGE/TYPE OF HOUSING ~ 1.5 mg lead/cm^

Percent

Region Pre '40 Pre '40 '40-'59 '40-'59 '60-'75 '60-'75 Regional National

Single Multi Single Multi Single Multi Total Cost

New

England 865.8 i3.5 305.3 55.9 178.4 40.1 2329.0 6.6

Mid

Atlantic 2932.7 3080.1 922.8 284.6 482.4 126.9 7829.5 22.3

East North

Central 3341.8 2028.9 1357.2 105.4 714.5 138.9 7686.7 21.9

West North

Central 1410.7 762.3 405.3 84.7 269.1 50.3 2982.4 8.5

South

Atlantic 1629.1 370.9 1040.5 166.8 844.6 127.5 4179.4 11.9

East South

Central 808.4 170.2 414.3 64.2 268.6 32.7 1758.4 5.0

West South

Central 981.9 206.6 719.2 113.9 421.

i

59.5 2502.9 7.1

Mountain 431.4 157.9 345.6 45.6 229.0 51.3 1260.8 3.6

Pacific 1476.7 657.8 1297.0 228.2 682.9 179.5 4522.1 12.9

NATIONAL TOTAL

13,878.5 8318.2 6807.2 1149.3 4091.3

(Percent) (39.6) (23.7) (19.4) (3.3) (11.7)

806.7 35,051.2 100

(2.3)

In millions.

Administrative costs are not included.

24
TABLE A. 11

ESTIMATED COSTS OF ADMINISTERING A NATIONWIDE

PROGRAM TO DELEAD THE NATION'S HOUSING —

2.0 mg lead/cm

Admi nistrative Nationa 1 Costs^ By Age/Type of Hous Lng

Costs per

Dwelling Pre '40 Pre '40 '40-'59 '40-' 59 '60-'75 •60-' 75 Total

Unit Single Multi Single Multi Single Multi

$250 2,440 1,500 1,570 250 840 180 6 ,780

$350 3,410 2,110 2,200 350 1,180 260 9 ,510

$450 4,390 2,710 2,830 460 1,510 330 12 ,230

In millions.

TABLE A. 12

ESTIMATED COSTS OF ADMINISTERING A NATIONWIDE PROGRAM

TO DELEAD THE NATION'S HOUSING ~

1 .5 mg lead/cm

Administrative Nationa 1 Costs^ By Age /Type of Housirig

Cost per

Dwelling Pre '40 Pre '40 '40-'59 '40-'59 '60-'75 '60-' 75 Total

Unit Single Multi Single Multi Single Multi

$250 2,830 1,800 2,120 350 1,320 250 8 670

$350 3,960 2,520 2,970 490 1,850 350 12 ,140

$450

i 5,100 3,240 3,820 630 2,380 450 15 ,620

In millions.

25
Administrative costs per dwelling unit are dependent upon the nature of

a lead-paint abatement program. A program whose purpose is primarily

aimed at identifying dwelling units with lead-based paint hazards and

then attempting to achieve voluntary compliance to local health requirements will be quite different, in terms of administrative costs, from a program which pursues a more active policy of undertaking the actual

installation of the abatement technique when voluntary compliance cannot be achieved. In the latter case administrative costs will also

include the costs associated with administering the abatement contracts

involving the installation of specific abatement techniques.

A large number of administrative scenarios are possible. Each scenario

will have associated with it different levels of administrative costs

per dwelling unit.

In order to achieve some order of magnitude estimate of administration

costs we shall make the following assumptions. First, we shall not

attempt to estimate the costs associated with any litigation that may

be required to enforce compliance with a local housing code which

specifies some hazardous level of lead-based paint in dwelling units.

The liability problem has posed some difficult legal questions regarding the responsibilities of building owners and occupants and consequently will not be dealt with in this report. The reader interested

in the legal implications of lead paint abatement policies is referred

to the articles by Tepper and Sarb . Secondly, we shall assume that

the primary administration costs are those associated with detecting

lead-painted units, determining what corrective actions are called for,

finding and selecting contractors, getting bids, and administering the

actual contract.

Since little reliable data is available on the costs of administering

what amounts to small rehab jobs in residential dwelling units, we have

relied on the experience of the Boeing team which administered the

Experimental Hazard Elimination Program and on conversations with contracting officials from local housing authorities in the Washington, D.C. area who have responsibility for administering rehabilitation contracts

in residential units.

The per dwelling unit administrative cost estimates of installing leadbased paint abatement techniques ranges from $250-$450 per dwelling

unit. This range estimate includes the following administrative tasks:

surveying dwelling units for the presence of lead-based paint, preparing

1

2

R. Bruce Tepper, Jr. , "Lead Paint Poisoning: The Response in Litigation," St. Louis University Law Journal , Vol. 19, Winter 1974,

Thomas B. Sarb, "Lead Paint Poisoning: Remedies for the HUD Low-Income

Homeowner When Neglect is No Longer Benign," University of Michigan

Journal of Law Reform , Vol. 8, Spring 1975. i.

26

(.:
specifications, making preliminary cost estimates, contacting contractors and advertising for bids, conducting prebid conferences,

reviewing bids, inspecting work in progress, administering progress payments, writing reports, and other miscellaneous administrative tasks.

Table A. 11 presents estimates of the national administrative costs

associated with deleading dwelling units with a lead content on walls of

2,0 mg lead/cm or more. The estimates are broken down by the age of

the dwelling unit and are estimated for the end points of the range and

for the mid-point. As seen in Table A.ll, administrative costs would

run from 6,8 billion to $12.25 billion. Another way of putting it is

that when administrative costs are included, the cost of deleading the

nation's housing stock (2,0 mg lead/cm ) would range between 35 and 41

billion dollars.

Estimates of the national administrative costs associated with the 1,5

mg/cm abatement criterion are presented in Table A, 12, Administrative

costs range from $8,7 billion to $15.6 billion. Total abatement costs are thus expected to range between $44 billion and $51 billion,

A, 5 CONCLUSIONS

The installation of barrier materials or the removal of paint from trim

surfaces are tasks which light rehabilitation or remodeling contractors

have the capability of doing. Installing a barrier material is fundamentally a remodeling task, e,g,, installing gypsum wallboard or covering a wall with a vinyl fabric material. Paint removal from trim surfaces is

also a task which remodeling contractors are called on to do on occasion.

Because of the great differences in housing characteristics throughout

the nation, and because the amount of deleading required varies between

units, the cost of deleading a dwelling unit will vary widely from

region to region and among techniques. In order for a national cost

estimate to be reliable, it must be based on a method of estimation

which is sensitive to the diversity of conditions discussed above,

A credible estimate of the cost of deleading the nation's housing stock

can be made if we know how much lead paint exists and how much it will

cost to delead specific dwelling units. Evidence on the quantity of

lead paint in housing is contained in the Pittsburgh survey while information on the cost of deleading is contained in this report.

Our results from the national cost estimates reported in this appendix

are as follows:

1. The "incremental cost" of meeting a 1.5 mg/cm abatement criterion

versus a 2.0 mg/cm abatement criterion is approximately $9.25 billion.

This figure includes an administrative cost of $350 per dwelling

unit for both the 1,5 mg lead/era and the 2,0 mg lead/cm abatement

criteria.

27
In relative terms this represents an increase in national cost of almost

25 percent.

2. Almost half the nation's deleading cost burden is concentrated in

eight states. Furthermore, this relationship holds both for the 1.5

mg lead/cm and 2.0 mg lead/cm abatement criteria. The eight states are located in the Middle Atlantic and East North Central regions.

(See Figure A. 6 for a detailed breakdox^n of the regional deleading cost

burdens .

)

3. The amount of deleading work required in any given dwelling unit

remains fairly constant when the minimum abatement criteria is reduced

from 2.0 mg/cm to 1.5 mg/cm . The substantial increase in regional

and national deleading costs experienced in going from the 2.0 mg/cm

to the 1,5 mg/cm abatement criterion is therefore due almost entirely

to additions to the stock of dwelling units "at risk."

4. The proportions of the nation's deleading cost burden associated with

the pre 1940 dwelling units declines only slightly (from 65 percent to

61 percent) when the minimum abatement criterion is reduced from 2.0

mg/cm to 1.5 mg/cm , This is true because the number of pre 1940 dwelling units "at risk" and the amount of deleading work they require exceed

that of all post 1940 dwelling units.

5. The order of magnitude of deleading costs ($30-$50 billion) is

roughly equivalent to the 1976 level of output of the remodeling and

alteration construction sector.

28
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29
APPENDIX B

DESCRIPTION OF ABATEMENT TECHNIQUES

This appendix is intended to supply background information on the three

basic methods of lead-based paint (LBP) abatement. The three abatement

methods are subdivided into individual abatement techniques. The abatement techniques are then described individually in some detail with

regard to their composition, method of application, and/or any special

skill or material /equipment requirements.

Since the body of this appendix is formed of excerpts from publications

in which these abatement techniques are described in greater detail,

the interested reader is referred to the two reports by Boone et al

.

B.l BARRIER MATERIALS

The complete stripping of LBP from surfaces as well as the removal and

replacement of building components which are coated with LBP are defined

here as LBP elimination methods. The effectiveness of such methods is

essentially total. However, the application of these methods to large

wall surfaces appears to be both impractical and prohibitively expensive.

The application of materials, which are intended to act as barriers, is

frequently the most reasonable approach to the abatement of LBP on planar surfaces. The effectiveness of barrier materials is therefore

a function of the degree to which they prevent access to the existing

LBP which they cover. The material's strength, durability and permeance

are the primary characteristics that establish its effectiveness in LBP

abatement.

B.1.1 GYPSU^l WALLBOARD

Gypsum wallboard is a product which is composed of a thick layer of

gypsum to which paper or other materials are bonded to provide a finished or finishable surface. It is intended for use on walls, ceilings,

or partitions, and can be applied directly to existing surfaces or to

wood or metal furring strips.

It can be applied directly to existing walls with adhesives and nails.

The installed wallboard is prepared for painting by finishing the joints

between the sheets vjith joint tape and compound. Nail heads are then

covered with compound and both areas are sanded to a smooth finish.

1 Thomas H. Boone, Harvey W. Berger, A. Philip Cramp, Herbert A. Jackson,

The Demonstration of Experimental Lead Paint Hazard Abatement Methods

in Washington, D.C. , National Bureau of Standards, Interagency Report

75-761, June 1975, and TTiomas H. Boone, Harvey W. Berger, A. Philip

Cramp, Herbert A. Jackson, The Demonstration of Experimental Lead Paint

Haz a rd Abatement Method s in Atlanta, Georgia, National Bureau of Standards, Interagency Report 75-974, December 1975.

30
B.1.2 PLYWOOD PANELING

This product is a 5/32 inch (4 mm) thick lauan mahogany plywood panel

which has been treated with a multiple coat catalyzed varnish finish.

The material is supplied in 4 x 8 feet panels (1,2 x 2.4 m) and is

applied to existing walls with either fasteners or an adhesive. Preformed strips are used to cover vertical butted seams and as a molding

at the ceiling line. Wood baseboards are applied over the paneling

along the floor line.

B.1.3 CEMENTITIOUS COATING

The cementitious coating consists of portland cement, sand, and acrylic

resin to which water is added to make a slurry. It can be tinted to

desired colors by the addition of pigments recommended by the manufacturer.

The cementitious coating is viscous and has a rough texture. It can

be applied by brush or roller or any other equipment designed to apply

viscous filled coatings.

The surface must be clean and free from loose particles. Glossy surfaces must be roughened with an abrasive, and chaulky surfaces sealed

with a sealer to ensure good adhesion. The applied coating has a final

thickness of about 1/16-inch (about 1.6 mm).

B.1.4 GYPSUM VENEER PLASTER

This product is a component pre-packaged material which becomes plastic

when mixed with water so that it can be trowel applied to form a highly

polished finish or can be worked to achieve a textured finish. It may

be applied to solid plaster walls which have first been treated with

a vinyl polymer bonding compound to improve adhesion to the existing

painted surfaces. The finished thickness of the plaster is approximately

1/8-inch (3.2 mm) thick. Skilled workmen are required for the satisfactory application of this product.

B.1.5 VINYL-COATED FABRIC

Vinyl-coated fabric is basically similar to wallpaper or vinyl-clad

paper. Its strength and durability exceed that of the other two materials because of its combination of vinyl surface and textile fabric

backing.

Vinyl-coated fabric may be installed on any wall surface that is free

of dirt, grease, oil and moisture. The method used for hanging vinylcoated fabric is the same as used for hanging wallpaper. The adhesive

used depends upon the weight of the vinyl-coated fabric. These fabrics

normally resist soiling and staining even by objects such as crayon,

lipstick, or ink, A mild detergent and a sponge usually will remove most soils and stains. They conform to the bends and contours of most

31
surfaces and are installed readily by workmen who are familiar with

wallpaper installation using ordinary hand tools and equipment.

B.1.6 CEMENT-COATED FIBERGLASS

This product consists of glass fabric impregnated with portland cement.

It is applied to walls in the same manner as wallpaper, with a waterbase

adhesive. As the portland cement absorbs moisture from the environment

it hardens and becomes a fairly rigid and penetration-resistant material.

A protective coating should be applied if the material is to be used in

wet areas.

A clean substrate in good repair is necessary for adequate adhesion.

The covering will, however, bridge minor voids and mask minor subsrate

imperfections

.

The following barrier materials were used in Phase I of EHEP but were dropped from consideration during the Phase II deleading operations

because of excess cost, logistical problems or similarities with other

techniques that were used in Phase I.

B.1.7 MELAMII^-COATED HARDBOARD

This product is a 1/4-inch (6.4 mm) thick tempered hardboard to which

a 1-1/2 mil (.038 mm) thick melamine film has been laminated to provide

a serviceable, decorative finish. The material is supplied in 4 x 8

feet panels (1.2 x 2.4 m) and is applied to existing walls with fasteners or an adhesive. Pre-formed strips are used to cover vertical

butted seams and as a molding at the ceiling line. Baseboards are applied over the paneling at the floor.

B.1.8 AGGREGATE FILLED PAINT

This coating is a latex based product which contains sand or other

mineral aggregate which provides a thick finish comparable in texture

to sand finished plaster.

Filled paint is viscous. The product used was formulated for application by airless spray equip.nent, but it and most other formulations can

be applied using a brush or roller. It requires no mixing or thinning

and it can be applied successfully to any surface which is free of dirt,

oil or grease. Primers are not required for use on previously painted

surfaces and thicknesses as high as 20 mils (0.02 inch or 0.05 cm) can

be obtained with one coat. The finish texture, ranging from smooth

to coarse, depends upon the aggregate quantity and particle size.

B.1.9 GYPSU?! PLASTER WITH METAL LATH

Gypsum plaster is applied to a metal lath which serves as a support

for the plaster and a means of adhesion to the surface being treated.

32
The lath is a netting of 20 gauge galvanized steel which is fastened

to a surface with nails, staples or screws.

The plaster is usually applied in two stages resulting in a final

thickness of 1/2-inch (1,3 cm). In the first application, wood fiber

is added to the gypsum plaster to improve its working characteristics

by making it easier to apply and to increase its adhesion to the metal

lath. This first coat is applied by trowel to a uniform thickness of

about 3/8-inch (1 cm). The plaster sets up (hardens) rapidly and must

be applied quickly. Highly calcined gypsum plaster is used for the

second or top-coat. This plaster contains a retarder which delays

hardening so that the desired finish (smooth or textured) can be

achieved. The final coat is about 1/8-inch (0.3 cm) thick. The finished dried surface is usually painted.

The application of plaster to lath should be done by skilled workmen.

The temperature of the work area and surface should be above 50° F and adequate ventilation (for proper drying of the plaster) should be

provided.

B.1.10 GYPSUM IMPREGNATED JUTE FABRIC

This wall covering product consists of jute fabric which is impregnated

with uncrystallized gypsum (hydrated calcium sulfate). It is applied

to walls in the same manner as wallpaper, with a water base adhesive

which is recommended by the manufacturer of the fabric. As the gypsum

absorbs moisture from the air, it hydrates, hardens and becomes a fairly

rigid and penetration-resistant material. A protective coating can be

applied if the material is to be used in wet areas.

A clean substrate in good repair is necessary for adequate adhesion.

The covering will, however, bridge minor voids and mask minor substrate

imperfections.

B.1.11 PLYWOOD ON FLOORS

Plywood may be used to cover-up lead painted exterior wood decks on porches. Warped and rotted boards are replaced with new or sound ones and a 15 lb. (6.8 kg) roofing felt is put down as an underlayment before

installing the plywood.

Four by 8 feet (1.2 x 2.4 m) sheets of 3/8-inch (9.5 mm) A-B grade,

exterior or marine plywood are used. All edges are treated with wood

preservative and the sheets are nailed or screwed to the existing deck

with their A grade surface up. All exposed edges are covered with wood

trim and the new surface is painted with one coat of primer and two

coats of exterior paint.

Skilled workmen are required to install plywood on existing porch decks

with satisfactory workmanship. Although the job can be done with hand

33
tools, it can be accomplished much more easily with ordinary power tools

such as electric drills and saws.

B.2 PAINT REMOVAL METHODS

The safety of workmen and dwelling occupants is a primary consideration

in the implementation of paint removal methods. Procedures should be

taken to avoid the inhalation or absorption of lead fumes or dust. In

addition, attention should be given to the potential fire hazard associated with the use of open flame paint removal techniques.

B.2.1 INFRA-RED HEATING DEVICE

The infra-red heating device used in Phase II of EHEP consists of a propane burner whose flame is recessed in a metal reflector covered with a metal grid. When the flame is lit, the grid is heated. The device is

held near the surface that is to be stripped. The heat radiating from

the grid causes the paint to soften, swell and/or blister so that it can

be removed easily from its substrate with metal scrapers.

This device is somewhat less hazardous than a propane torch because the

open flame is recessed. Completely portable, it uses one pound disposable propane cylinders or a 25 pound refillable propane tank. The

process of heating and scraping can be carried out by a single workman.

B.2. 2 SOLVENT-BASED PAINT REMOVER

A number of "industrial grade" paint removers may be used to remove LBP

from windows, doors and other wood trim components. These viscous

liquids are applied to both horizontal and vertical surfaces by brush,

allowed to react with the lead paint coatings and then removed along

with the softened paint using various metal scrapers.

Paint removers are extremely variable in their ability to penetrate and

react with multiple layers of dried paint. The effectiveness of the remover depends not only on its own composition but also on the nature

of the multi-layered coating to which it is applied. Most removers will

react with only 2 to 4 layers of paint at a time. In such cases where

more than that number of layers of paint is present, the remover-paint

agglomeration must be removed and fresh remover applied as often as necessary to achieve complete removal.

Generally, the reaction time for paint removers is about one-half hour.

The remover-paint waste, which is scraped from the treated surfaces,

is collected on polyethylene drop cloths and discarded. Prior to light

sanding and repainting, the surface is wiped with a water-saturated rag

in order to remove any residue of remover-paint mixture.

Proper precautions regarding open flames, adequate ventilation and

avoidance of contact with skin is necessary to assure the safe use of

this class of products.

34
B . 2 . 3 ELECTR ICAL HEAT GUN

It was seen earlier that when paint is heated sufficiently, it softens,

swells and usually blisters so that it can be removed easily from its

substrate with metal scrapers. One technique available for removing

paint from doors, windows and other wood surfaces, by means of heat

generation is a portable flameless electric hot air blowing device.

It is quite effective in removing paint from wood substrates as demonstrated in laboratory tests; it also presents an apparent low risk of

fire and injury to operators or bystanders.

This commerically manufactured device consists essentially of an air

blower attached to a reinforced flexible plastic hose, with a cylindrical

electric heater at the outlet. The heater is heavily insulated, so that

it can be hand held while in operation. This apparatus is mounted on a stand which makes it quite mobile. It is capable of producing a strong

stream of hot air at temperatures of between 350° and 1000°F (175° and

540°C). The working end of the device is very light in weight and a single workman can perform the continuous operations of heating the paint

to its softening point and scraping it off with metal tools. Electrical

circuits or portable generators supplying 20 ampere currents at 110-120

volts are required for operation of the hot air blower.

B . 2 . 4 HAND SCRAPING

Simple hand tools such as paint scrapers and putty knives may be used

to remove loose and flaking paint. Electrical sanders may then be

used to achieve the complete removal of paint which adhered too tightly

to those surfaces for hand scraping.

The following paint removal methods were used in Phase I of EHEP but were dropped from consideration during the Phase II deleading operations

because of excess cost, potential fire hazards, logistical problems, or similarities with other techniques that were used in PViase I.

B.2.5 PROPANE TORCH

The open flame produced by a propane torch can be used to heat paint

directly to the softening point so that it can be scraped off with

metal tools. The process is easily carried out by a single workman

using a one pound propane bottle.

Until recently, the use of propane torches had been quite widespread

for this purpose. They are now falling into disfavor, however, due

to the extreme fire hazard of an open flame (especially in occupied

and furnished dwellings) and the danger of over-heating paints to the

point of vaporization of the lead pigments.

35
B.2.6 DIP TANK METHOD

Commercial furniture stripping plants have facilities for removing

old paint and varnish by totally immersing the object of interest in

large tanks containing heated alkaline solvents in an aqueous medium.

Doors may be stripped by this process, requiring two to three hours

of treatment during which time they are alternately soaked and scrubbed

with heavy bristle brushes. The total stripping time is dependent on the type of paint, the number of paint layers and the temperature and

strength of the solvent bath. Doors can be stripped with hinges and

other hardward still attached. After the paint is completely removed

the doors are washed with water, allowed to air dry, planed, and then

returned to the dwelling unit from which they were taken where they

are reinstalled and repainted.

B.3 REPLACEMENT OF COMPONENTS

Removal of doors, door frames, windows and frames, and other trim com- ponents and replacement with new materials is an accepted technique of

LBP abatement. Replacement is used primarily when building components

have deteriorated or are damaged beyond the point of serviceability.

This method, in addition to restoring the building component to serviceability, also provides an opportunity to upgrade the quality of the

dwelling unit significantly.

Carpentry skills are required for rehanging, fitting and remounting

hardware. Custom carpentry may be required for replacing wooden sash

windows and frames in some dwelling units.

36
APPENDIX C

THEORETICAL CONSIDERATIONS UNDERLYING PHASE II OF EHEP

The building component cost data collected in Phase II of EHEP were

analyzed by using a series of statistical tests. Statistical analyses

were needed because the data collected from Phase II deleading operations have some degree of uncertainty associated with them. This uncertainty is due to the fact that the data are based on only a small sample

of units, not enough to capture all of the potential sources of variation

due to the effects of changes in wage rate, the efficiency of labor, or the size of the abatement contract.

A statistical approach to the data analysis provides a methodology which

identifies and quantifies differences which may exist in costs at the

dwelling unit level. In addition, the use of statistical procedures

permits probabilistic levels of significance to be attached to any con- clusion regarding differences in direct costs or the expected bid price.

Finally, a statistical approach permits the development and straightforward application of a model capable of predicting abatement technique

costs.

The level of confidence (i.e., the probabilistic level of significance)

which can be attached to any conclusion is dependent upon several factors.

First, the underlying assumptions for any statistical procedure must be

known and must be relevant to the data under consideration. Second, the

relative importance of rejecting a hypothesis when it is true or accepting it when it is false must be assessed. Finally, due to budgetary

constraints on the amount of deleading work which could take place in

EHEP, there was the trade-off between the size of the sample and the

power of the test.

C.l DESIGN CONSIDERATIONS

A detailed experimental design was required to adequately measure variations in abatement technique cost and at the same time recognize the

constraints under which EHEP had to operate.

The probability of rejecting the formulated (null) hypothesis when it

is true is denoted a type I error; the probability of accepting the

alternative hypothesis when it is false is denoted a type II error.

The probability of accepting the alternative hypothesis when it is

true is denoted the power of the test. Other things being equal,

the larger the sample, the greater is the power of the test.

37
The considerations which went into the planning of the Phase II EHEP

experimental design relied heavily on the concepts presented in

Natrella's text Experimental Statistics .

Five requisites, presented in Natrella, determined the approach taken

in the design portion of EHEP Phase II. They are:

(1) there should be a clearly defined objective;

(2) the effects of the factors should not be obscured by other

variables

(3) the results should not be influenced by conscious or unconscious bias in the experiment;

(4) the experiment should provide a measure of precision; and

(5) the precision should be sufficient to meet objectives.

The first requisite, really an overall goal of EHEP Phase II, was to

identify the candidate abatement techniques, their methods of application, and appropriate criteria which had to be met for deleading

operations to take place in order to adequately identify and measure

the factors which cause technique costs to vary.

The second requisite involved the establishment of an experimental

pattern which identified the "theoretical" key factors affecting technique costs (i.e., the technique, the occupancy status, the substrate

condition, the contract package size, and the ownership category).

The third requisite involved the development of a decision model capable

of assigning the individual abatement techniques to the stock of dwelling

units in a random fashion. This greatly reduced the risk of systematic

biases entering the experiment.

The fourth and fifth requisites involved replication and grouping of

observations. The experimental design presented in Chapter 4 of the

companion report. Guidelines for Cost Effective Lead Paint Abatement , showed that each of the factors thought to affect costs had several

observations. In most cases this level of replication permitted the

objective of the experiment to be met. However, in some instances it was necessary to group observations. The concepts underlying this

"grouping" methodology will be explained in part C.2 of this appendix.

Mary Natrella, Experimental Statistics , National Bureau of Standards,

Handbook 91, August 1963.

2 Mary Natrella, Experimental Statistics , p. 11-2.

38
We shall now turn to the decision model which permitted the abatement

techniques to be randomly assigned to the stock, of dwelling units.

The decision model used in this portion of EHEP is similar to an "urn

problem" found in most texts on probability theory. The implementation of the model made use of a flowchart to provide a structure for

exercising the theoretical concepts. The flowchart shown in Diagram C.l

makes use of both decision blocks and process blocks.

In the Phase II EHEP experiment the term cell refers to an attribute

within the framework of the model, for example in the wall experiment

there is a cell with the following attribute: occupied - unsound walls - abatement technique A. Observations on the other hand are dwelling unit

specific characteristics, for example an occupied dwelling unit having

an unsound - dry wall surface. Also there may be more than one observation per dwelling unit, since any one dwelling may have both sound and

unsound wall surfaces. The number of observations is initially set

equal to 5.

Before turning to the flowchart and examining how the decision model

operates, it is first necessary to review the symbols used and the

implied functions they perform. The symbols are defined in Table C.l.

An illustration of how the model assigns the abatement techniques to

the various dwelling units is now in order. The flowchart for the wall

experiment, Diagram C.l, provides a convenient starting place. To start

the process, a dwelling unit is chosen from the pool of dwelling units

which met the criteria for deleading operations. At decision block 1 the occupied units are separated from the unoccupied units.

For brevity we shall trace through the flow chart for occupied units

since the two branches of Diagram C.l are identical. Next, dwelling

units which contain more than one wall observation have their observations numbered consecutively from 1 to N. The first observation then

proceeds to the next block, and continues through the model until it comes to a flag (block 7) which allows the second observation to move. This continues until all observations for the dwelling unit have been

assigned. Returning to decision block 2, the observation moves to the

next block where a random number is assigned. The value of the random

number is then associated with a probability function to insure random

assignment.

It is important to note that for occupied (or unoccupied) dwelling units

abatement techniques A and B are assigned on the basis of whether the

wall surface is wet or dry rather than sound or unsound since relevant

costs for these techniques are independent of substrate condition.

See, for example, the text by Marek Fisz, Probability Theory and Mathematical Statistics , Third Edition, John Wiley and Sons, Inc., New York,

1963.

39
Pool of

Ehsjelling Un i t s

Draw a Random -^

No

Let 1 OBS move

Yes

No

Yes

Assign to Wet/Drv Cell

Assign to Sound /Unsound

Cell

Yes

Number each

OBS from 1 to

N

KEY:

OBS = Observation

DU = Dvyelling Unit

Yes Assign

Technique

Assign

Technique

Assign to Cell

Recompute

Probabilities

Let Next

OBS Move

Mo

No

re ^v^

1 OBS \Yes

Ass emb:

all OBS

ethis

s DU?/

^

DU

->f^

Any

Assignment

^This DU:

No

Return

to pool

of DU's

Yes

(Join Pools "\

for Trim y

DIAGRAM C.l RANDOM ASSIGNMENT OF WALL ABATEMENT TECHNIQUES IN OCCUPIED UNITS

40
TABLE C.l

DECISION MODEL SYMBOL IDENTIFICATION

SYMBOL OPERATION

Pool of dwelling units available for wall

surface abatement

Either yes or no decision

Processing of an observation

O Q Connections from one part of the chart to

another

(__) Pool of dwelling units available for door,

window or trim abatement

Done

41
When the observation reaches decision block 3, those assigned abatement

technique A or B are sent to decision block 5 where wet wall surfaces

are separated from dry wall surfaces. Should the wall observation have

wet wall surfaces, decision block 6A deterraj.nes whether or not the cell

is already filled^ In the event that the cell is filled, the observation is rerouted via connection A» Those observations not encountering

their appropriate cell filled are assigned to that cell. Observations

are then assigned to the appropriate cell in the sound/unsound wall

experiments Observations are then rerouted via connection B.

Returning to decision block 3, if neither technique A nor B was indicated, the wall condition is determined and one of the abatement techniques Cj D, E or F is assigned to the dwelling unit.

This is accomplished by matching the random number of this observation

against the occupied unit sound wall probability function given in

Table C.2, Probability functions for other wall characteristics and

other experiments are defined similarly.

For instance, if the random number falls between (R "^ Rk + Rp "** ^d "*" ^e^^*^

and 1, technique F is selected, A similar argument holds for the assign- ^ ment of the other abatement techniques.

The observation is then assigned to the appropriate cell and the probability function is recomputed. The next observation is then permitted

to move. Dwelling units identified in decision block 2 as having more

than one observation are separated in decision block 7 and are detained

until all observations are assembled. Any dwelling unit which did not

have at least one of its wall observations assigned to a cell is returned

to the pool of dwelling units. Those dwelling units where a wall observation was placed in a cell are now candidates for door, window and trim

abatement.

The selection of abatement techniques for doors, windows, and baseboard

trim surfaces is identical. The trim abatement for occupied units is

given schematically in Diagram C,2,

Those units which are occupied are separated from those which are unoccupied at decision block 1, Decision block 2 determines if enough trim

is available for abatement; if there is, a random number is assigned

to the dwelling unit. The random number is then matched against the

occupied unit trim probability function and assigned an abatement technique. The observation is assigned to the appropriate cell and the

probability function is recomputed. When this task is completed the

next dwelling unit is selected from the pool. The one just assigned

joins the done pool,

C.2 STATISTICAL TECHNIQUES UTILIZED

To facilitate the analysis of the Phase II EHEP cost data, the statistical procedures of one-way analysis of variance and multiple regression

42
TABLE C.2

OCCUPIED UNIT SOUND WALL PROBABILITY FUNCTION

Technique

Selected

Greater than or equal to Less than

A

B

C

D

E

F

R^/T

R^/T

Ra + Rb)/T

(^a + ^b + Rc)/T

(R3 + Rb + Re + Rd>/T

(Ra + Rb + Re -^ Rd + Re)/T

(R3 + \)/T*

(Ra + Rb + Rc)/T

(R + Rk + R. + R,,)/T

(R. + Ri, + R^ + Rj + R^)/T

a b c d e

where

Ri

T =

T =

the number of observations required to fill the cell occupied - sound - technique i; i = a, b, c, d, e, f

the total number of observations remaining required to fill all

the occupied cells, i.e.,

R^ + R, + R^ + R , + R + R.

a b c d e f

*Observations with a random number less than (R + R, )/T are assigned

to either technique A or B and sent from decision block 3 to decision

block 5.

43
f Trim Pool \. No

Yes

Yes

Draw a Random Number

Assign

Technique

Assign to Appropriate

Cell

Recompute

Probabil ity

Function

Let Next

Unit Move

DONE

No \Done

DIAGRAM C.2

RANDOM ASSIGNMENT OF TRIM ABATEMENT TECHNIQUES IN OCCUPIED UNITS

44
were used. Both were selected because the type and nature of the data

collected indicated that rather sophisticated methods were needed.

Furthermore, they permit the data to be reduced to a form from which

a prediction mechanism can be constructed. The application of these

statistical procedures to the cost data was accomplished through the use of the NBS computer package OMNITAB II."*- OMNITAB II was selected

because of its availability and its highly reliable statistical programs.

Also, its numerical and graphical output provide an effective way of

testing if any of the underlying assumptions have been violated,

C.2.1 ANALYSIS OF VARIANCE

The initial testing of the Phase II EHEP cost data involved the analysis

of variance for a one-way classification, » As a statistical procedure,

the one-way analysis of variance permits the testing of the null hypothesis, that the observed differences among two or more sample means can

be attributed to chance, versus the alternative hypothesis, that the

observed differences are indicative of actual differences among the

means of the corresponding populations. That is, it provides a way

to test the hypothesis that, for a particular building component, the

observed differences in direct costs for each abatement technique are attributable to chance versus the hypothesis that they indicate that

real differences in direct costs do exist between the different abatement techniques. On the surface this may seem a rather simplistic

statement since the chance of having two or more abatement techniques

1

2

3

5

David Hogben, Sally T, Peavy , Ruth N, Varner, OMNITAB II User's Reference Manual , National Bureau of Standards, Technical Note 552,

October 1971.

If the power of a test is relatively insensitive to the relaxation of one or more of its underlying assumptions, it is refered to as a robust test. In the absence of robustness, some means of testing for

for violation of the underlying assumptions is required.

The analysis of variance used here is referred to as one-way since

the data being analyzed are subject to only one dimension of classification, the abatement technique.

Due to the nature of the experimental design discussed in section C.l,

less emphasis was placed on the use of the analysis of variance in

Phase II of EHEP than in Phase I, The OMNITAB II analysis of variance

package does however have several features which make a discussion of

the technique and some of its implications quite useful.

An introduction to the analysis of variance is given in K, A. Brownlee,

Statistical Theory and Methodology in Science and Engineering , John

Wiley and Sons, Inc., 1960. For a more mathematical treatment see

H, Scheffe, The Analysis of Variance , John Wiley and Sons, Inc., 1959.

45
with identical direct costs is very unlikely. Because of uncertainty,

however, finding a difference does not necessarily indicate that one

is significantly more or less expensive than the other. What is of

central importance here is that an analysis of variance approach permits

the statement to be made with a given level of confidence that there

does or does not exist, for a given building component, a significant

difference in direct costs for different abatement techniques. The

acceptance or rejection of the null hypothesis was based on whether

or not the resulting F ratio was greater than the theoretical F ratio

for the predetermined confidence level. For the purposes of EHEP, the

set of abatement techniques may be considered to be fixed. This has

a consequence that the type of analysis of variance model under consideration is a fixed effects model.

The data base was prepared for analysis by first separating it into its

respective building components. The data were then subdivided within

each building component into abatement techniques. To provide an adequate point of reference from which abatement techniques could be compared,

they were tabulated with respect to their dwelling units. Once the data

base was arranged in this way, it was possible to read the relevant information directly from the Dwelling Unit Cost Data Form. However, before

1

4

A confidence level of 95 percent does not mean that the null hypothesis is 95 percent true; it is either true or false. It implies that

if the null hypothesis were true and the experiment was repeated over and over under identical circumstances 95 out of every 100 times the

null hypothesis would be accepted.

The acceptance region for the null hypothesis is given by the 95 percent

k

where point of the F distribution with k-1 and Z n.-k degrees of freedom,

i = l

where k equals the number of abatement techniques and n- equals the

number of experimental observations for the ith technique. The F ratio

resulting from the observed data is computed by dividing the amongtechniques mean square by the within-techniques mean square.

This assumption has some interesting theoretical implications in that

it permits, generally speaking, more definitive statements to be made

with the same set of data than other analy sis-of-variance models.

Future studies may indicate that additional abatement techniques are

feasible in which case the assumption of a fixed effects model would

be a bit stronger than required. In the absence of any such indication, however, it appears that the assumption of a fixed effects model

is justified.

It should be recalled that building components denote walls, doors and

frames, windows and frames, and other miscellaneous trim.

46
the actual analysis could begin, it was necessary to reduce all cost data

to a per unit basis. To accomplish this, the figures for labor costs,

material/equipment costs, the quantity of surface deleaded, and the total

person-hours expended were calculated. At this time several other variables which were involved in the analysis were computed. These were the

average wage rate and the productivity of labor. The average wage rate

reflects the average wage which would be paid per person-hour for a given

abatement technique excluding the costs of finish painting „ It may be

expressed mathematically as

n

x\WR = Z P.W. , 1 1'

i=l

where AWR = average wage rate,

p. = the proportion of the total person-hours expended by the ith

labor type,

Wj^ = the wage of the ith labor type, and

n

I Pi = 1-

i=l

The average productivity of labor is the number of units which can be

processed in one person-hour, that is the total number of units processed divided by the total person-hours expended.

The analysis of variance model was applied to data for each building

component on per unit direct cost, per unit labor cost, per unit

material /equipment cost, and the productivity of labor,

2 To insure that the underlying assumptions were not violated, two backup

tests were used. Both tests were provided in the OMNITAB II output package. First, the Cochran C test was used to test the validity of the

assumption that the variance of the abatement techniques was the same.

Second, should the assumption of an underlying normal distribution be

Units denote square feet, linear feet, the number of doors and frames,

or windows and frames.

2 The sample observations were assumed to be independent normally distributed random variables \7ith equal variance.

3

W. J, Dixon and F. J. Massey, Introduction to Statistical Analysis , McGraw-Hill Book Company, 1957, p. 180o

47
unjustified, the Kruskal-Wallis H test was used. The Kruskal-Wallis H

test was selected because it is a non-parametric test which uses the

ranks of the experimental observations and avoids any assumption about

the underlying distribution.

If the one-way analysis of variance results indicated that the difference between abatement techniques for a particular building component

was not attributable to chance, a pair wise multiple comparison of means was performed. The purpose of this test was to group the abatement techniques into categories such that the means of the abatement

techniques within a category were not significantly different at the

95 percent confidence level, but that the means in different categories were significantly different at this level. The grouping into categories was accomplished through the application of the Scheffe method. This

method was selected since it did not require an equal number of observations for each abatement technique. This is the approach which was used in Phase I of EHEP so that sample size could be increased by grouping observations. Generally speaking, during Phase II of EHEP there

were enough observations on each abatement technique that grouping into

cost categories was not necessary.

C.2.2 MULTIPLE REGRESSION

The design of the Phase II EHEP experiment permitted an in-depth analysis of the structure and determinants of direct cost and bid price

through the use of multiple regression.

In the course of the analysis of the Phase II cost data, two measures

of per unit direct cost were used as response variables: (1) the cost

per square foot, and (2) the cost per linear foot. Explanatory variables (those which determine per unit direct cost) included the average

K. A. Brownlee, Statistical Theory and Methodology in Science and

Engineering , pp. 194-196.

K. A. Brownlee, Statistical Theory and Methodology in Science and

Engineering , pp. 252-254.

In the event that an equal number of observations for each technique

does occur, the Newman-Keuls-Hartley method may be used. Details of

this method are given in G, W. Snedecor, Statistical Methods , Fifth

Edition, Iowa State University Press, 1956.

The term structure is used to denote the basic relationship between

the variables and how they affect direct cost; determinants refer

to specific variables which affect direct costs.

48
wage rate and the quantity of surface deleaded. Throughout the analysis,

the structure of the model was assumed to be of the linear form

m

PDC =60+2 B-jX. + e, j=l

where PDC = per unit direct cost, the response variable,

Bq = the intercept term,

J = the coefficients of the explanatory variables.

X. = the explanatory variables, and

e = the error term.

The methodology through which the underlying structure and determinants

of per unit direct cost were analyzed involved a four stage iterative

process of (1) selecting a model, (2) estimating the coefficients, 8j,

(3) testing the validity of the underlying assumptions, and (4) testing

the adequacy of the model. Once estimated, the finalized model could

be used to predict the direct costs of the alternative abatement techniques at the dwelling unit level. A similar approach was used in the

the estimation of the markup ratio.

To begin the four stage iterative process for the regression model

development, three variables were selected for initial testing: (1) the

quantity of surface deleaded, (2) the average wage rate, and (3) a measure of the material price. The response variable for this portion of

the model development phase was per unit direct cost. Attempts were

also made for each building component and abatement technique to develop

separate regression models for per unit labor costs, per unit material/

equipment costs, and the productivity of labor.

The construction of the regression model was facilitated by the use of

two statistical procedures, the sequential F test and the partial F test,

These tests were used to assess the relative importance of variables

added to the structure of the model. The sequential F test is used to

test whether the model is made significantly better by the addition

of a particular variable, whereas the partial F test is used to test

The intercept term is included since the assumption that the response

is zero when all explanatory variables are zero is a rather strong and

usually unjustified assumption.

49
whether the whole model is significantly better than a reduced model

which includes this particular variable.

Taken in conjunction, they permit the deletion of variables which have

negligible impact on direct costs and provide a well defined stopping

procedure to insure that the model does not become unwieldly.

New variables continue to be added to the model until the value of the

partial and sequential F tests are no longer significant. The coefficients of the explanatory variables (6^) are estimated through the use

of the method of least squares. The method of least squares was used

When the form of the model is given by

m

Y = 6q + I Q.X. + e,

the sequential F test will tell if

t Y = Bq + Z 6.x. + e,

is significantly better than

t-1

Y = Bq + I BjXj + e, j=l

whereas the partial F test will tell if

m

Y = Bq + Z Q.X. + e,

j = l

is significantly better than

t-1

Y = Bq + 2 B .X. + e,

j = l

where m _> t > 1.

2 The method of least squares provides a methodology whereby the sum of the squared difference of the observed and predicted values

of the n experimental observations

(cont. on next page)

50
because the estimates it provides possess several highly desirable

statistical attributes. The finalized model is assessed for adequacy

through the use of a graphical technique which plots the standardized

residuals under the assumption that they are independent normally

distributed random variables. This test for adequacy is of great importance since, if the standardized residuals are not independent normally

distributed random variables, it is not possible to perform either the

sequential or partial F test as an aid in determining when to stop adding

variables.

A second test used involved correlation techniques. These techniques

describe the linear statistical relationship between two normally distributed random variables. As such they provide a tool for identifying

potential sources of multicollinearity , a situation that arises when one

or more of the explanatory variables provide essentially the same information. Multicollinearity is undesirable since it reduces the precision

of the estimates of the B-. The correlation coefficient takes on values

between +1, Values close to +1 indicate a high degree of positive or

1

2

3

4

5

(Continued from previous page.)

"" ^ 2

Z (Y- - Y .

)

is minimized,

i=l

where Y. = the experimental value of the observations, and

Y. = the predicted value of the observation.

Least squares estimates are both linear combinations of the experimental observations, Y . , and minimum variance unbiased estimators

of the 8^. Furthermore, they exhibit normality whenever the Y. are normally distributed.

This graphical technique is sometimes referred to as a probability

plot.

The standardized residuals are equal to the residuals (the difference

between the observed and predicted values) divided by the standard

deviation of the predicted values (a measure of dispersion between

the observed and predicted values).

This as well as other consequences are outlined in N. R. Draper and

H. Smith, Applied Regression Analysis , John Wiley and Sons, Inc.,

1966, p. 59.

David Hogben, Sally T. Peavy, and Ruth N. Varner, OMNITAB II User's

Reference Manual , pp. 155-162.

51
negative correlation respectively. A value of indicates that the two

variables are uncorrelated. When explanatory variables were found to be

highly correlated at the 95 percent confidence level, they were either

transformed or deleted from the analysis to prevent the problems for

estimation caused by multicollinearity. Also provided in the standard

OMNITAB II "CORRELATION" command output is the significance level of a quadratic fit over a linear fit. For example, it would be useful to

know where the per unit direct costs for each abatement technique

"bottom out." One factor which is likely to be quadratic is job size.

Denoting job size as Q and assuming a quadratic model, we would then

expect the sign of Q to be negative and the sign of Q to be positive.

Under the assumption of a quadratic model, the value of Q which minimizes direct cost could thus be solved through differentiation. Unfortunately, the output from the OMNITAB II "CORRELATION" command did not

support the hypothesis that a quadratic model is more appropriate than

a linear model. This implies that costs rise or fall at a constant rate

as the job size changes.

C.3 PARAMETER ESTIMATES OF THE EHEP COST MODELS^

In Phase II of EHEP, data was collected on six techniques for covering

the LBP on walls. Four methods of paint removal on doors and frames,

windows and frames, and baseboards were also tested. In this section

we shall present the results of our cost analysis for these techniques.

The results of our cost analysis for finish painting, component replacement, and contractor markup are also presented. The section concludes

with a discussion of how dwelling units may be grouped into contract

packages to minimize the expected bid price.

For each technique a table is presented which tabulates the weighting

factors (measured effects) associated with the key factors. Combining

the values of the weighting factors with those of the key factors

permits abatement technique cost to be predicted. The row entries in

Positive correlation means that the values of the two variables move

in the same direction; negative correlation means that they move in

opposite directions.

Because of this linear relationship it was necessary to specify boundary values for each key factor. Note that the use of boundary values

is not necessarily a weakness of the Phase II EHEP cost models.

Regardless of the form of the model, its use should be restricted to

those regions over which the assumptions upon which it is based are valid. In view of the fact that the cost models discussed in the next

section were calibrated at the level of the individual dwelling unit,

the boundary values presented in Section D.2 seem quite reasonable.

For an excellent discussion of the general topic of cost functions see Gerald L. Musgrave and Robert H. Rasche, "Estimation of Cost Functions,

The Engineering Economist , Volume 22, Number 3, Spring 1977.

52
Tables C.3 through C,18 present our numerical estimates of the amount

by which per unit direct costs will change given a unit change in the

key factor. If the change in the key factor is more or less than a unit change, the quantitative impact on per unit direct cost will be

the product of the row entry in the table (the weighting factor) times

the amount of the change in the key factor. For example, one key factor,

price per sheet, is measured in dollars. The weighting factor associated

with this key factor may be 0.5, This would mean that a one dollar

increase in the sheet price would result in a $0,50 increase in unit per

direct cost, A $.60 decrease in the sheet price would result in a $0,30

decrease (= 0,50 x 0,60) in direct cost per unit. The format of the

table for computing contractor markup (Table C,19) is identical to that

of the other tables with the exception that the weighting factors show

the quantitative impact on percent markup rather than per unit direct

costs,

C.3,1 KEY FACTOR IMPACTS ON DIRECT COSTS

In this subsection we shall first tabulate the results of the analysis

of the direct cost data for the six techniques for abating the LBP hazard on walls. Next we shall tabulate the results of our analysis of

the direct costs of paint removal for each of the four techniques tested

in Phase II of EHEP, The data analysis on component replacement collected in Phase I of EHEP will then be summarized. The analyses of

finish painting costs for walls, ceilings and trim will then be presented.

In each table which follows, the numerical row entries are statistical

measurements of the impact on direct cost, material cost, or output per

hour of a unit change in a key factor. Multiplying the weighting factor

times the value of the key factor and summing yields the direct cost,

material cost, or output per hour. Summary statistics which show the

explanatory power of each model are presented in Table C,19,

In addition to the weighting factors, the t statistic associated with

each parameter estimate (weighting factor) is included in each of the

cost models presented in Tables C,3 through C,18, The value of the t statistic is shown within parentheses to distinguish it from the

weighting factor. For those tables where more than one model is presented (Table C.12 for example), the number of degrees of freedom is

shown immediately below the name of the cost model. For example, in

Table C,12 v = 4 indicates that the t statistics for the infra-red

heating device/door and window model have four degrees of freedom.

Percentiles of the t distribution for selected degrees of freedom are

given in Table C,20 so that the level of significance associated with

each parameter estimate presented in Tables C,3 through C,18 can

easily be determined. For example, comparing the t statistics which

appear in Table 0,3 to the Tq g^^ value with eight degrees of freedom

53
in Table C.20 reveals that all parameter estimates are significant at

the 5 percent level. Note that the tQ nyc column is used to test significance at the 5 percent level. Similarly the tQ gc column is used

to test significance at the 10 percent level, and ttie tQ qq column is

used to test significance at the 20 percent level. In each case, to

test significance at the e percent level the percentile of the t distribution examined is equal to 1 - e/2. This is due to the fact that

we are performing a two-tailed test.

TABLE C.3

THE KEY FACTORS DETERMINING DIRECT COST

PER SQUARE FOOT OF GYPSUM WALLBOARD

Key Factors

Price Per

4' X 8' Dummy

Reciprocal Average Sheet of Variable

of Net Hourly Gypsum For PanIntercept Square Feet Wage Rate Wallboard try Work^

Weighting

Factors^ -1.7700 306.5874 0.0620 0.5009 0.2740

tg (-3.32) (2.62) (3.23) (4.02) (3.07)

a The numerical row entries are statistical measurements of the impact

on direct cost per square foot (in dollars) of a unit change in a key

factor. Multiplying the weighting factor times the value of the key

factor and summing yields the direct cost per square foot.

Net square feet is the area measurement of the amount of gross wall

area minus the areas not covered by gypsum wallboard, e.g., door

openings, window openings, and wall areas covered by cabinets.

This variable takes on a value of 1 if pantry repair work was involved,

otherwise. This condition, although specific to the dwelling units

in Boston, must be accounted for in order to prevent biases from entering into the other measured effects.

54
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55
TABLE C.5

THE KEY FACTORS DETERMINING MATERIAL COST

PER SQUARE FOOT OF PLYWOOD PANELING

Key Factors

Average Hourly Poor Substrate

Price per Sheet Wage Rate Condition

Weighting

Factors^ 0.07995 -0.0231 0.0829

t;Li (7.08) (-2.50) (2.67)

The numerical row entries are statistical measurements of the impact on material cost per square foot (in dollars) of a unit change in a key factor. Multiplying the weighting factor times the value of the

key factor and summing yields the material cost per square foot.

The dummy variables take on a value of 1 if the statement is true and

if the statement is false.

56
TABLE C.6

THE KEY FACTORS DETERMINING DIRECT COST PER SQUARE

FOOT OF CEMENTITIOUS COATING AND VENEER PLASTER

Key Factors

Intercept

Gross

Square

Feet

Average Material Wallpaper Veneer

Hourly Cost per Stripping Plaster

Wage Rate Square Foot Required Used'

Weighting

Factor^

14

-0.8817 -0.0004 0.1154 3.8636 0.1416 0.2066

(-2.156) (-1.41) (5.02) (10.92) (2.52) (3.99)

TABLE C.7

THE KEY FACTORS DETERMINING MATERIAL COST PER SQUARE

FOOT OF CEMENTITIOUS COATING AND VENEER PLASTER

Key Factors

Intercept

Ratio of

Net-toGross

Square

Feet

(Doors & Windows)

Divided by Gross

Square Feet

Veneer

Plaster

Used^

Poor

Substrate

Condition

Weighting

Factor^ -0.5002 0.6872

15 (-2.26) (2.72)

1.3296

(2.31)

-0.0847 0.1741

(-2.54) (4.97)

The numerical row entries are statistical measurements of the impact on direct .cost (material cost) per square foot (in dollars) of a unit change

in a key factor. Multiplying the weighting factor times the value of the

key factor and summing yields the direct cost (material cost) per square

foot.

The dummy variables take on a value of 1 if the statement is true and

if the statement is false,

57
TABLE C.8

THE KEY FACTORS DETERMINING THE PRODUCTIVITY OF LABOR

IN THE INSTALLATION OF VINYL-COATED FABRIC

Key Factors

Intercept

Net

Square

Feet

Ratio of

Net -toGross

Square Feet

Average

Hour ly

Wage

Rate

Percent of

Wall Area

Walnscotted

Weighting

Factor^

t3

-25,2456

(-1.21)

0.0276

(2.32)

22.0745

(1.31)

1.2933

(2.07)

-24.8364 j

(-3.78)

^ The numerical row estimates are statistical measurements of the impact

on productivity of a unit change in a key factor. Multiplying the

weighting factor times the value of the key factor and summing yields

a productivity estimate for a particular dwelling unit.

TABLE C.9

THE KEY FACTORS DETERMINING MATERIAL COST

PER SQUARE FOOT OF VINYL-COATED FABRIC

Key Factors

Price per Square

Yard of VinylCoated Fabric

Percent of

Wall Area

Walnscotted

Weighting

Factor^ 0.0958

(9.32)

0.5447

(2.05)

^ The numerical row estimates are statistical measurements of the impact

on material cost per square foot (in dollars) of a unit change in a key factor. Multiplying the weighting factor times the value of the

key factor and summing yields the material cost per square foot,

58
TABLE C.IO

THE KEY FACTORS DETERMINING THE PRODUCTIVITY OF LABOR

IN THE INSTALLATION OF CEMENT-COATED FIBERGLASS

Key Factors

Ratio of

Net-to- Percent of Poor

Gross Wall Area Substrate

Intercept Square Feet Wainscotted Condition Occupied

Weighting

Factor^ 43.5809 -33.6107 -45.5105 -3.4202 9.0642

t,. (4.37) (-2.59) (-3.10) (-1.22) (2.11)

^ The numerical row entries are statistical measurements of the impact on productivity of a unit change in a key factor. Multiplying the weighting

factor times the value of the key factor and summing yields a productivity

estimate for a particular dwelling unit.

The dummy variables take on a value of 1 if the statement is true and

if the statement is false.

59
TABLE C.ll

THE KEY FACTORS DETERMINING MATERIAL COST PER

SQUARE FOOT OF CEMENT-COATED FIBERGLASS

Key Factors

Ratio of Net-to-Gross

Square Feet Average Labor Cost

Weighting

Factor^ 0.5600 1.0900

ty (2.90) (3.99)

^ The numerical row entries are statistical measurements of the impact

on material cost per square foot (in dollars) of a unit change in

a key factor. Multiplying the weighting factor times the value of

the key factor and summing yields the material cost per square foot.

The dummy variables take on a value of 1 if the statement is true and

if the statement is false.

60
TABLE C.12

THE KEY FACTORS DETERMINING LABOR COST PER LINEAR

FOOT USING THE INFRA-RED HEATING DEVICE

-

Weighting

Factor^

t

Key Factors

Windows

V = or Doors

= 4

Baseboard

V = Trim

7

Average

Hourly

Wage

Rate

XRF Time

Linear Feet

Average

Hourly

Wage

Rate

Dummy for

Infra-Red^

0.2903

(14.06)

-0.0007

(-2.46)

0.1354

(4.91)

-0.4913

(-1.16)

The numerical row entries are statistical measurements of the impact

on labor cost per linear foot (in dollars) of a unit change in a key

factor. Multiplying the weighting factor times the value of the key

factor and summing yields a cost estimate of the labor cost per

linear foot.

This key factor takes on a value of 1 if the labor costs of the infrared heating device are being analyzed and takes on a value of if

solvent strip labor costs are being analyzed. See Table C.13,

61
TABLE C.13

THE KEY FACTORS DETERMINING LABOR COST PER LINEAR

FOOT USING SOLVENT-BASED PAINT REMOVER

Key Factors

Windows or Doors Baseboard Trim

V = 13 V = 7

Average Average

Hourly Hourly

Wage XRF Time Wage Dummy for

Intercept Rate Linear Feet Rate Infra-Red

Weighting

Factor^ -1.3499 0.5727 -0.0013 0.1354 -0.4913

t (-1.54) (6.53) (-2.63) (4.91) (-1.16)

^ The numerical row entries are statistical measurements of the impact on labor cost per linear foot (in dollars) of a unit change in a key factor.

Multiplying the weighting factor times the value of the key factor and

summing yields a cost estimate of the labor cost per linear foot.

b This key factor takes on a value of 1 if the labor costs of the infra-red

heating device are being analyzed and takes on a value of if solvent

strip labor costs are being analyzed. See Table C.12

62
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63
TABLE C.15

THE KEY FACTORS DETERMINING DIRECT COST PER LINEAR FOOT

OF PAINT REMOVAL USING THE HAND SCRAPING METHOD

Key Factors

Windows or Doors

V = 9 Ba seboard Trim

V = 4

Intercept

Average

Hourly

Linear Wage

Feet Rate

Average

Hourly

Wage

Rate

Weighting

Factor^ 0.9468

(1.14)

-0.0213 0.3262

(-1.38) (5.29)

0.1146

(5.55)

The numerical row entries are statistical measurements of the impact

on direct cost per linear foot (in dollars) of a unit change in a key factor. Multiplying the weighting factor times the value of the

key factor and summing yields an estimate of direct cost per linear

foot.

64
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Vj 60

OJ

u c 5-1

C

O -H OJ o 3 O 4-1 &, C XI 4= CO 60 4-1 OJ

0) ^1 -H CD ^ X QJ QJ O CO H & S CJ H

65
TABLE C.17

THE KEY FACTORS DETERMINING THE DIRECT COST PER

SQUARE FOOT OF PAINTING^ WALLS AND CEILINGS

Key Factors

Gross Average Ratio of NetSquare Feet Hourly Paint Price to-Gross

Painted Wage Rate Per Gallon Square Feet

Weighting

Factor

63

^ -0.000296 0.0106 0.0257 0.1736

(-6.73) (2.13) (2.62) (1.78)

A primer coat and finish coat.

The numerical row entries are statistical measurements of the impact

on direct cost per square foot (in dollars) of a unit change in a key factor. The sum of the products of the row entries times the

observed values of the key factors yields an estimate of the direct

cost per square foot.

66
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67
TABLE C.19

EXPLANATORY POWER OF EHEP PHASE II COST MODELS

Model R' Table

Gypsum Wallboard

Plywood Paneling

Direct Cost

Material Cost

0.88

0.91

0.96

C.3

C.4

C.5

Cement itious Coating

and Veneer Plaster

Direct Cost

Material Cost

Vinyl-Coated Fabric

Productivity

Material Cost

Cement-Coated Fiberglass

Productivity

Material Cost

Infra-Red Heating Device

Windows and Doors

Baseboards

Solvent-Based Paint Remover

Windows and Doors

Baseboards

Heat Gun

Windows and Doors

Baseboards

0.91

0.66

0.88

0.96

0.80

0.86

0.95

0.79

0.85

0.79

0.92

0.97

c .6 c .7

c .8 c .9

c .10

c .11

c .12

c .12

c .13

c .13

c .14

c .14

Hand Scraping

Component Replacement

Door

Frames

Windows and Frames

0.88

0.80

0.96

0.93

C.15

C.16

C.16

C.16

Painting

Walls

Trim

Doors and Frames

Windows and Frames

0.89

0.90

0.91

C.17

C.18

C.18

68
TABLE C.20

PERCENTILES OF THE t DISTRIBUTION FOR SELECTED DEGREES OF FREEDOM^

Percentiles

Degrees of

Freedom ^0.70 ^0.80 ^0.90 ^0.95 ^0.975

3 0.584 0.978 1.638 2.353 3.182

4 0.569 0.941 1.533 2.132 2.776

5 0.559 0.920 1.476 2.015 2.571

6 0.553 0.906 1.440 1.943 2.447

7 0.5439 0.896 1.415 1.895 2.365

8 0.546 0.889 1.397 1.860 2.306

9 0.543 0.883 1.383 1.833 2.262

11 0.540 0.876 1.363 1.796 2.201

13 0.538 0.870 1.350 1.771 2.160

14 0.537 0.868 1.345 1.761 2.145

15 0.536 0.866 1.341 1.753 2.131

22 0.532 0.858 1.321 1.717 2.074

30 0.530 0.854 1.310 1.697 2.042

60 0.527 0.848 1.296 1.671 2.000

^ J. Johnston, Econometric Methods , Second Edition, McGraw--Hill

Book Company, New York, 1972, p. 426,

69
C.3.2 KEY FACTOR IMPACTS ON CONTRACTOR MARKUP

In this subsection we shall first discuss the analysis of the data on contractor markup. From Table C.21 it can be seen that the expected

contract markup is affected by the number of net square feet in the

contract and the number of linear feet of trim requiring paint removal.

Further examination of the table indicates that contractors tend to give

markup reductions on those products with which they are most familiar

(e.g., gypsum wallboard and plywood paneling). In our treatment of

direct costs, our emphasis was on being able to identify that technique

V7hich minimized direct costs for a given dwelling unit. We saw that

changing the values of the key factors could result in a different technique being chosen as the least costly. (Information on exactly how

this is done is given in Appendix E.) This indicates that some consideration should be given with regard to the way in which dwelling units

are grouped into contract packages. Basic guidelines are given in

Appendix E, but for those decision makers faced with deleading a large

number of dwelling units additional information is called for. Although

the method discussed in the following paragraphs could be carried out

by hand, it is most easily accomplished by using the time-sharing program presented in Appendix F,

TABLE C.21

KEY FACTORS DETERMINING MARKUP^

Key Factors

Hundreds

of Net Hundreds Fraction in

Square of Linear Gypsum Board

Intercept Feet Feet or Plywood^

Weighting

Factor^ 1.2972 -0.0079 0.0520 -0.2144

til (18.55) (-2.01) (3.45) (-2.82)

^ The R^ for this model is 0.77.

b

b

The numerical row entries are statistical measurements of the impact

on the markup ratio of a unit change in each of the key factors.

Multiplying the weighting factor times the value of a key factor in

a particular contract and summing these values yields an estimate of

the markup ratio for that contract.

Net wall area done using gypsum wallboard or plywood paneling divided

by total net wall area,

70
The first step in grouping the dwelling units into contract packages is

to establish a basic data matrix. This matrix identifies the dwelling

unit being deleaded (the row of the matrix corresponds to the dwelling

unit number), the total direct cost of deleading that unit, the nvimber

of net square feet of wall area, the number of linear feet of trim

requiring paint removal, and the net square feet of wall area over which gypsum wallboard or plywood paneling will be applied in the dwelling unit. Table C.22 shows the format for the basic data matrix.

TABLE C.22

BASIC DATA MATRIX

Dwelling Direct Net Square Linear

Unit // Cost Feet Feet

1 ^1 Nl H

2 D2 N2 H

Net Square Feet in

Gypsum Wallboard or Plywood Paneling

GPi

GP2

N Djj N^ Ln GPn

Using the data in Table C.22, it then becomes possible to study the differential markups. That is, how much does the markup ratio change

(either rise or fall) by grouping two arbitrary dwelling units i and j

together. The basic question here is can bid price be lowered by grov'^^-

ing dwelling units together? Suppose dwelling unit i has a direct co

of D. and a markup ratio of M. and dwelling unit j has a direct zost

D. and a markup ratio of M-. Now suppose dwelling unit i were to be

grouped with dwelling unit j, would the bid price then be lower? To see

if this criterion is met, we shall examine the following relation:

D-L (b^ + M|) + D2 (b^ + Mp > (D-L + D2) (b^ + M{ + Mp

where b = the intercept term in Table C.19; and

M/^ = the sum of the products of the weighting factors

the value of the key factos.

Upon cancellation we get

D-|^M^ + D2MI _<

71
The above relationship is particularly useful. The left hand side of

the first inequality is the total cost of letting two single dwelling

unit contracts whereas the right hand side is the total cost if the two

units are done as one contract. The second inequality tells us under

what condition the cost of grouping the two dwelling units together

will be lower than letting two single unit contracts.

If we think, of the M' as markup differentials, we may use them to

determine the impact that grouping dwelling unit j with dwelling unit i

will have on i's markup ratio. Let us refer to this impact as M. ., and

define M. ^ as: m A = biN.- + boL-i + b-j —

for i ?^ j

= 9 for i = j

(Setting M. . = 9 is a precautionary measure to avoid pairing a dwelling

unit with itself.)

Clearly the M .

• form a matrix of markup differentials which we shall

denote as M, where

M = (M^.) 1 £ i, j < N

We may now construct a matrix, D, with the direct cost per dwelling

unit along the diagonal and zeros elsewhere. Mathematically

D = (D^j) 1 < ,i,j < N

where D. . = D. if i=i ij 1

-J =0 if ±^2

Postmultiplying M by D yields the desired test matrix, MD, where

MD = [(MD)] = Y)^^ M.^ 1 £ i, j £ N.

We now wish to see the impact that grouping an arbitrary dwelling unit,

k, with another dwelling unit, i, will have on the contract's expected

bid price. This impact may be expressed mathematically as:

\i = ^k^k + ^i^iWe then wish to see which dwelling unit, when paired with k, will have

the most favorable impact on the expected bid price of the contract.

Denote this dwelling unit as Vj* = min (Vi .).

i

72
Once all N dwelling units have been examined, we will have a set of most

favorable impacts:

V* = (Vf, V^, . . ., V*).

The Vj^ are then ranked from smallest to largest. We denote this set as

V* = (V(J), Vif2), . . .. V*(j,)),

and yfi.) is the k order statistic of the set V*.

Clearly,

ni) 1 ^2) i -inN)

Now by starting with V*q\, we can be certain that the dwelling units

are paired to insure that the dwelling unit which has the most favorable

impact on overall bid price will be selected first. Let the first two

dwelling units chosen be denoted as J-|^ and J2. Through this process we may continue to group the dwelling units together. The end result will

be the preliminary contract package assignment. Table C.23 is an example

of what such a grouping would look like.

TABLE C.23

PRELIMINARY CONTRACT PACKAGE ASSIGNMENT

Dwelling Contract

Contract Units in Direct

Package # Contract Cost

Contract Net Square

Contract Contract Feet in Gypsum WallNet Square Linear board and Plywood

Feet Feet Paneling

J-L J2

J3 J4

Dt +Dt Nt +Nt Lt +Lt Jl J2 Ji J2 Jl J2

Oj3+Dj^ Nj^+Nj^ Lj3+Lj^

Notice that Table C.23 is similar to Table C.22. (Table C.22, in

addition to providing input data, serves as a map between the contract

number and the unit number.)

We now wish to see if grouping any two of the two dwelling unit contracts

together into a single four dwelling unit contract will reduce the overall

73
contract cost. (Note if an odd number of dwelling units were analyzed

in the previous step, there will be one single unit contract left over. Therefore the possible contract package sizes are two, three, four or five dwelling units.) To see if contract costs reductions result, we once more examine the markup differential now defined as

(GC).

(MC)ij = bi (NC)i -f b2(LC)i + b3

^^^^^^\^^^^ i ^ J

= 9 i = j

where (WC) . = the net square feet in "preliminary" contract i;

(LC). = the linear feet in "preliminary" contract i; and

(GPC). = the net square feet of gypsum wallboard and plywood paneling in "preliminary" contract i.

Once again the (MC). • form a matrix of markup differentials which we shall denote as MC, where

MC = ((MC)ij) 1 <i,^ < I

Similarly we may construct the matrix DC with the direct cost per "preliminary" contract along the diagonal and zeros elsewhere. Postmultiplying MC by DC yields the desired test matrix. We then calculate the

the contract cost differential, (VC)- ., defined as

(VC)^j = (MC)jLj (DC)j + (MC)j^ (DC)^.

We now wish to see which "preliminary" contract, when paired with i,

will have the most favorable impact on the total contract cost. We

denote this contract as (VC*). where

(VC*)^ = min (VC)^j,

J

We then test to see if (VC*). is less than zero. If not, then we will

not group contract i with any other contract since the overall cost

has not been reduced. All those (VC*). which were found to be less

than zero are then ranked from smallest to largest. Starting with the

smallest (VC*)., (VC*)/--i\, we then form a new and final contract package.

This process is continued until all contracts have been examined. Suppose the first two contracts chosen were "preliminary" contracts 1 and 3.

The final contract would then include all dwelling units in the two

"preliminary" contracts. From Table C.23 we would then identify the

dwelling unit numbers. The time-sharing program would then refer to

Table C.22 to determine all the dwelling unit specific data required

to make the final cost calculations.

74
The last set of calculations is to determine if contract costs can be

reduced by substituting gypsum wallboard or plywood paneling in those

dwelling units in which another barrier material was found to minimize

direct costs. Recall that gypsum wallboard and plywood paneling, due

to their familiarity, are subject to markup reductions.

As a first step, we identify the least costly of the two products

(gypsum wallboard and plywood paneling). We then subtract from it the

direct cost of the barrier material which was found to minimize direct

costs in the dwelling unit under study. We shall denote this cost difference as AD. Next we recompute the markup ratio under the assumption

that gypsum wallboard or plywood paneling will be installed in the

dwelling unit under study. Denote this new markup ratio as M-. . We may now calculate the change in the markup ratio (for the entire contract)

due to the discount for gypsum wallboard or plywood paneling. If we denote M as the original markup ratio, then AM is equal to M minus M-, . If we denote the direct cost of the contract as D, then the switching

criteria is given by the following inequality.

AD • M-|^ + AM • D _< 0.

If the above inequality is not satisfied then no reduction in overall

contract cost can be achieved by switching to gypsum wallboard or plywood paneling. The above test is performed for each dwelling unit.

After completion of this test no further reduction in contract package

costs can be achieved. Thus the packages assembled in the previous

step should reflect both the least costly combination of dwelling units

and of abatement techniques.

C.3.3 METHODS FOR CALCULATING CONFIDENCE INTERVALS ABOUT

PREDICTED COSTS

Each of the cost models presented in Sections C.3.1 and C.3.2 combine

one or more explanatory variables in order to estimate a response

variable (direct cost, material cost, output per hour, or markup).

Although these models are best linear unbiased estimators of the

desired response variable, they represent point estimates rather than

interval estimates. Thus there may be situations in whch other abatement techniques may have costs very near to the least costly technique.

This would imply that the least costly abatement technique can not be

unambiguously identified. In Section D.4 empirical evidence will be

presented to show that, on the average, there will be a wide range of

costs even if the three least costly techniques for deleading walls

and two least costly techniques for deleading trim can be identified.

Hence it appears that even though the cost models presented in

Sections C.3.1 and C.3.2 produce only point estimates, there should be

enough difference in cost between techniques to assume that tVie least

costly technique can be unambiguously identified.

Even though in most cases there will be a wide enough variation in

costs between techniques to identify one as least costly, there may be

75
instances where confidence intervals about each estimated response are desired. The purpose of this section is to illustrate how this may be

accomplished. First, we must define precisely what we mean by a con- fidence interval. The 100(1 - e) percent confidence interval associated

with a particular cost model is defined as

C'0 + t /o ^/c'Cx'x)"^ e/2 C

where

^ = the vector of estimated coefficients (weighting factors);

C = the vector of explanatory variables (key factors);

-6/c'(X'X) C = an estimator of the standard deviation of the

predicted value, C ^; and

t /2 = the 1 - e/2 value of the t statistic with n - k

degrees of freedom.

The output of each cost model presented in Sections C,3,l and C,3,2

is the point estimate C'B, Suppose we denote the cost model associated with the least cost point estimate as

^(1) ^(1)

and the cost model associated with the second least cost point estimate

as

^(2) ^(2)

2 Then the hypothesis which we wish to test is

"o ' ^(1) ^(1) ^ ^(2) ^(2)'

the alternative being

^A = ^(1) ^(1) "^ ^(2) ^(2)-

The number of degrees of freedom is based on the information used

in estimating the cost model, in particular, n is the number of

observations and k is the number of explanatory variables,

2 Since the purpose of this section is to illustrate how to fit a confidence interval about the predicted value, no discussion of how

the null and alternative hypotheses stated above would be tested

will be given. For those readers interested in performing tests

of statistical hypotheses, the text by K. A, Brownlee, Statistical

Theory and Methodology in Science and Engineering , is recommended.

76
Notice that C/-i\ need not equal C/on since different cost models use different explanatory variables (key factors). From the definition of

the 100 (1 - e) percent confidence interval given above it is apparent

that the variance-covariance matrix associated with each cost model,

2 ~1

^ (X'X) , is needed in order to calculate the desired confidence

interval. Fortunately, the variance-covariance matrix associated with

each cost model is provided as part of the standard OMNITAB "FIT"

output.

Let us now examine how a 90 percent confidence interval may be fitted

around the direct cost per square foot estimate for plywood paneling.

The variance-covariance matrix for the plywood paneling cost model is

given in Table C,24. Note that each of the entries are given in a scientific notation format. For example, 1.70 -01 in scientific notation is merely 0,170, similarly -6,30 -03 is merely -0.00630. Thus

the use of scientific notation permits the variance-covariance matrix

to be presented in a more simple and compact manner.

Suppose we now wish to fit a confidence interval about the estimate

resulting from deleading 537 net square feet of wall area in a dwelling unit. Prior information on wage rates and prices for a four by

eight foot sheet of plywood paneling indicate that the average wage

rate is $10.09 per hour and the cost per square foot of plywood

,14.40, paneling is $0,45, ( ), The transpose of the vector of explanatory

variables (key factors), C', is thus

C = (1,537,10.09, 0.45, 0,0).

Similarly, the transpose of the vector of estimated coefficients

(weighting factors), §, is

t' = (-0.4081, 0.00054, 0.0748, L.2602, 0.3579, 0.13)

The predicted value, C'B, which results is

C'^ = (-0.4081)(l)+(537)(0.00054)+(10.09)(0.0748)+(0. 45) (1.2602)

+ +0

Simplyfying we have

C'^ = 1.20.

That is the direct cost of installing plywood paneling is $1.20

per square foot in this case.

The values of the weighting factors are taken from Table C.4.

77
w

pa<:H

QOHouo

^:MQO

>-'

D-i C

x:

I—

HU<

)—<;>ouI wM

Pi>

CO •H

t-l

•r-l e>aD

>-i CD

<U

O

4J

U

d CO

OJ

>-l CO3 4-1 cr q; en a;aou

0)

S-i o

(U CO o

00

OSI

CMO

IT) o

<3-

enooI oI o

CV] OOO

ino

CO o

c»

LO ro o1 O1

r--. r-i O O • •

Cs!1 1 OI 00

ino

ino

in

CM

CO <r

m o1 o1 o1 o <r in

CNl tH CNJ • • • v£3 1 ^

CNl CM <r o1 O1 o1 in CM •<r 00 O iH • • • CN rH 1

00o

00

in

00

CMo

in00

COo

COoI

CMoI 00

ino1

ino1

ino1

CM

O

1—1

—

<r CO rH Csl CO o1 o1 O1 O1 o1 o1 o <f o <r in 00

r^ tH

n r^ r-^ in

tH • I—

1 1 1 CMCo 1

4-1

4-J 0) (U •H •H

D.

W

w tH CD (-1 0) 3 CO CO •H CO

CJ crU Pi •H 4-1 > •r-l

i-l en QJ V-i cn

U i-H 01 (U Oi (U O 0) •H

4-1 4-1 fe OO 4-) CJ

a e c 0) CO CO 3 CO M Z :s s en tt,

78
We now wish to compute the width of the confidence interval, w, where

Obtaining the appropriate value for the t distribution with eight degrees

of freedom from Table C.20 reduces the above statement to

w = 1.86 /c'42(x'X) -1

C

Performing the indicated matrix multiplication, C' (X'X) C, and taking

the square root of the resulting scalar yields

« a2/' v» v^~l / C'4^(X'X) -"C = 0.06

Thus the width of the confidence interval is 11 cents (w - (0,06) (1 .86) )

,

The 90 percent confidence interval, Iq qq, about the predicted value for

plywood paneling in this case is thus

^0.90 " (1-09, 1.31) or

Iq gQ = 1.20 + 0.11 = C'^ + w.

The 90 percent confidence interval, or anyother percent confidence

interval, associated with any of the other cost models can be calculated

in a similar manner. Note however that new confidence interval must be

calculated each time the values of the key factor are changed. In addition, some care must be exercised in determining the number of degrees

of freedom for the t distribution and the appropriate percentile column.

Due to the number of cost models and the size and complexity of the variance-convariance matrices associated with the cost models, variancecovariance matrices for each cost model will not be included in this

report.

For those readers who may wish to calculate confidence intervals,

copies of the variance-covariance matrices for any cost models

presented in this report are available upon written request.

79
APPENDIX D

DISCUSSION OF EHEP PHASE II DATA CHARACTERISTICS

AND EMPIRICAL IMPLICATIONS

Section D.l of this appendix discusses any special data or estimation

problems that arose in the statistical analysis of the parameters of

the cost models. In Section D.2 the values of the lower and/or upper

bounds of the key factors will be detailed in two tables, one for wall

techniques and one for trim techniques. If a key factor takes on values

less than or greater than a boundary value (depending on the nature of

the boundary) the boundary value should be substituted for that value

of the key factor. Such occasions should rarely arise. But to avoid

producing estimates of costs which are outside of the reliable range

of the EHEP Phase II models, it is necessary to make such substitutions.

These boundary values have been incorporated into the BASIC Program so that such substitutions are made automatically by the program.

Section D.3 addresses the question of whether or not scale economies

due to increasing the job size and/or the contract package size exist.

Section D.4 presents empirical evidence which supports the assertion

that significant dollar savings can be achieved through the use of the

EHEP Phase II cost models.

D.l TECHNIQUES FOR DELEADING WALLS AND TRIM

D.1.1 PLWOOD PANELING

In four of the 14 dwelling units in which plywood paneling was installed,

relatively high costs per square foot were observed. It was also noted

that two firms were each responsible for two of these dwelling units.

One firm was primarily a painting contractor. The other firm was observed by the Boeing field staff to exercise almost no supervision

over that contract. Thus two dummy variables are included in the plywood

model as key factors. The first key factor took on the value of for

all dwelling units where the plywood installation was done by the firms

that exercised normal supervision. For the firm with inadequate supervision, the key factor took on a value of 1.

As can be seen in Table C.4 inadequate supervision raised that contractor's direct cost per square foot by $0.36. A similar procedure was followed for the painting contractor. That firm's unfamiliarity with

the techniques of installation of plywood paneling raised direct costs

per square foot by $0.13. Although these key factors may be unique to

the Boston firms, they do illustrate the importance of adequate management and practical experience.

D.l. 2 VINYL-COATED FABRIC

Although this product was installed in 11 dwelling units in Phase II of

EHEP, only eight of the dwelling units provided statistically reliable

observations. In the three dwelling units that were rejected, the

80
installation of vinyl-coated fabric was done by contractors whose cost

reports were considered very unreliable. In two of these cases the

contractor used a "profit sharing" arrangement with his workmen. This

arrangement precluded the payment of wages to those workmen. Their

recorded figures for hours worked are not considered reliable. In the

last case the contractor who installed the vinyl-coated fabric was a non-profit institution whose primary goal was training unskilled youth.

Their productivity figures are also extremely biased.

D,l,3 CEMENT-COATED FIBERGLASS

In Boston nine of the 13 dwelling units treated with this barrier material provided usable cost measurements. As was the case with vinylcoated fabric, the non-profit contractor, and the contractor who had

profit sharing arrangements with his workmen, provided additional but

unusable observations.

The ratio of net-to-gross square feet is a key factor which impacts on

the productivity of labor in installing cement-coated fiberglass. This

key factor was also important in the installation of vinyl-coated fabric.

However, this key factor has exactly the opposite quantitative impact on productivity for cement-coated fiberglass than it did in the case of

vinyl-coated fabric. For cement-coated fiberglass fewer openings (windows, doors or cupboards) in walls of a given gross square footage have

the effect of lowering productivity. The opposite was true for vinylcoated fabric. Since the installation procedures for these two barrier

materials are similar, no ready explanation for this difference comes

to mind. The statistical measurements of the differential effects of

this key factor are reliable. One speculation for this result is that

these results may be reflecting something relating to the ease of the

handling of these two materials. The Boeing representatives who handled

EHEP contract administration and monitoring made the following comments

about cement-coated fiberglass:

Cement-coated fiberglass had imperfections at the edges

which resulted in a loss of up to six inches of material

on a side causing a need to re-order. Handling and folding during preparation for installation produced flaking

of the cement from the fiberglass weave causing noticeable flaws which could not be corrected,

D,l,4 SOLVENT-BASED PAINT REMOVER AND INFRA-RED HEATING DEVICE

Since the infra-red heating device was used to remove paint from baseboards in only three dwelling units and the solvent-based paint remover

Boeing Aerospace Company, Experimental Hazard Elimination Program

Dwelling Unit Report; Phase II , Part II B General Data, for Department of Housing and Urban Development, December, 1976, unpublished,

p,15,

81
was used on baseboards in only six dwelling units, the nine observations

were combined into one data set. This combined data set was used to

produce parameter estimates for paint removal from baseboards which

apply to both the solvent-based paint remover and the infra-red heating

device. These results are reported twice, both in Tables C.12 and C.13

under the heading "Baseboard Trim," A model was estimated which included

a dummy for infra-red but it was not found to be statistically significant.

D.1.5 FINISH PAINTING

The costs of painting walls and ceilings demonstrated a very wide variation in the Boston Phase of EHEP. Four key factors however were able to

explain 89 percent of the total variation of the costs of painting walls

and ceilings. Increasing the gross square footage to be painted by 100

square feet will cause direct cost per square foot to fall by 3 cents.

A dollar an hour increase in the average wage rate raises per square

foot costs by 1 cent per square foot while a dollar per gallon increase

in the price of paint increases the cost per square foot by 2.6 cents.

Lastly an increase in the ratio of net square feet to gross square feet

of 0.1 will increase painting cost per square foot by 1.7 cents.

D.2 RANGES OVER WHICH THE COST MODELS ARE VALID

In order to minimize the possibility of the cost equations producing

spurious estimates of direct costs, it is necessary to place restrictions upon the values that are assigned to the key factors. Since a cost estimating equation will usually contain more than one key factor,

it is not possible to solve directly for the minimal or maximal values

that the key factors can assume. The possibility of negative forecasts

occuring is more likely when the values of the key factors differ substantially from the values taken by the key factors in the Boston data

set. Thus we have calculated for each key factor which must be entered

into the computer program or in the hand calculations a tolerance

interval. A tolerance interval (based upon the Boston data sets) tells

you with some known probability that a given proportion of the population will lie within a given interval. Thus by calculating a tolerance

interval for each of the key factors and specifying that the key factor

may not assume a value greater than or less than the end points of the

tolerance interval we defend against the possibility of extreme values

(extreme relative to the Boston experience) producing spurious cost

estimates. The tolerance intervals used to establish the boundary

values in Table D.l are based on a 90 percent probability that 90 percent of the population of a key factor will lie within the calculated

interval

Spurious values of the dependent variable are possible because of the

linear form of the estimated equations.

82
TABLE D.l

BOUNDARY VALUES OF KEY FACTORS USED TO COMPUTE DIRECT COSTS

Technique/

Key Factor

Value of

Lower Bound

Value of

Upper Bound

Gypsum Wallboard

Wage Rate

Sheet Price

$4.88

$1.83

N.A.*

N.A.

Plywood Paneling

Wage Rate

Gross Square

Sheet Price

Material Cost

Foot

Feet

Per Sq uare

$5.97

282

$6.07

$0.23

N.A.

930

N.A.

N.A.

Cementious Coating and

Plaster

Wage Rate

Gross Square Feet

Veneer

$6.51

406

N.A. . 770

Cement-Coated Fiberglass

Percent Wall Area Wainscotted 0% 40%

Vinyl-Coated Fabric

Wage Rate $4.11 N.A.

Gross Square Feet N.A. 916

Price Per Square Yard $4.46 $5.06

Percent Wall Area Wainscotted 0% 40%

Heat Gun

Wage Rate $3.68

Hand Scraping

Wage Rate $3.54

Infra -Red

Wage Rate $3.61

XRF Times Linear Feet of

Windows or Doo]rs N.A.

Solvent Strip

Wage Rate $3.82

XRF Times Linear Feet of

Windows or Doors N.A.

N.A.

N.A.

N.A.

1142

N.A.

1008

*Not applicable

83
D.3 TREATMENT OF INTRA-UNIT AND INTER-UNIT ECONOMIES OF SCALE

The presence or absence of scale economies is a question of fundamental

importance. If scale economies exist, can be identified, and can be

taken advantage of, the total costs of lead paint abatement will be

reduced. Two potential types of scale economies exist. They are:

(1) intra-unit (increasing the number of square feet, linear feet or components in a given dwelling unit) scale economies, and (2) interunit (increasing the number of dwelling units in a contract) scale

economies. By reference to Section C.3.2 it can be seen that fairly

strong intra-unit scale economies exist. In particular, in nine out

of the ten abatement techniques analyzed in Phase II of EHEP it was found that increasing job size (the number of square feet, linear

feet, or components in a given dwelling unit) caused the per unit

direct cost to fall. The question of whether or not inter-unit scale

economies exist however, must be approached in a different manner.

To answer this question reference will be made to the first phase of

EHEP.

In Phase I of EHEP the size of abatement contracts ranged from 1 to

30 dwelling units. With such a wide variation in contract package

size it is possible to thoroughly test the impact that increasing the

contract package size has on direct costs. The information required

to perform this test is presented in Economic Analysis of Experimental

Lead Paint Abatement Methods: Phase 1 .^ In this report regression

equations for direct cost were estimated which included the two types

of scale economies.

A close examination of the results of the Phase I cost data analysis,

reveals that the coefficient of contract package size is positive and

1

3

Robert E. Chapman, Economic Analysis of Experimental Lead Paint

Abatement Methods: Phase I , National Bureau of Standards,

Technical Note 922, September 1976.

It is important to point out that preliminary results from Phase I affected the experimental design of Phase II of EHEP. The formulation of the experimental design was also affected by preliminary

results from the Pittsburgh survey. (Although the results of this

survey had not been published at the time, information on the

approximate number of lead painted wall and trim surfaces were available.) Since the number of dwelling units included in Phase II was limited to around 80 by budgetary constraints, prior information

from Phase I and the Pittsburgh survey was of vital importance.

See Tables 4.6, 4.9, and 4.12 on pages 54, 60, and 65 in Robert E.

Chapman, Economic Analysis of Experimental Lead Paint Abatement

Methods: Phase I.

84
significant for trim techniques (paint removal methods). On the other

hand in five out of the eight wall techniques (barrier materials) the

coefficient of contract package size is negative. The presence or absence of scale economies thus depends on the relative strengths of

these effects. For example, if a dwelling unit contained 500 square

feet of wall area, four doors and frames and six windows and frames,

adding another identical dwelling to the contract would increase trim

costs by approximately $9.00 while reducing wall costs by approximately

$4.00. Recall that this is for direct costs and not total costs which

include markup. Consequently, based on the Phase I cost experience,

although increasing the size of the contract produced some reductions

in wall costs more than compensating increases in trim costs are likely

to result. Thus it can not be claimed that reductions in direct cost

due to larger contract packages exist and are significant. Total costs

which include markup can be expected to follow this pattern.

As expected, this phenomena was also observed in Phase II where the

presence of trim deleading work strongly affects the markup applied to

the entire contract. Thus if only wall abatement were required the

best strategy would be to use larger contracts since a potential for

economies of scale does exist. Unfortunately, this does not reflect

the true nature of the lead paint problem. Dwelling units with hazardous levels of lead paint contain not only hazardous wall areas but substantial amounts of lead painted trim surfaces. In addition, the trim

surfaces have higher lead levels which would tend to place more emphasis

on their abatement.

Fortunately, the markup equation, presented in Table C.21, reveals that

there exists a potential for judiciously grouping dwelling units

together into contract packages so that the siim of the bid prices can

be minimized. This approach takes into explicit consideration the

potential for cost reductions due to increased amounts of wall work and

balances it against cost increases due to increased amounts of trim work.

The approach, explained in Section C.3.2, uses an iterative scheme which

results in a stable solution in that no cost reductions can be achieved

At the 10 percent level.

2 See Table 4.3 on page 49 in Robert E. Chapman, Economic Analysis of

Experiment Lead Paint Abatement Methods: Phase I .

3 Doublas R. Shier and William G. Hall, Analysis of Housing Data Collected in a Lead-Based Paint Survey in Pittsburgh, Pennsylvania

Part I .

4 Douglas R. Shier and William G. Hall, Analysis of Housing Data Collected in a Lead-Based Paint Survey in Pittsburgh, Pennsylvania

Part I .

85
by moving a dwelling unit from one contract to another. Contract packages of up to five dwelling units are allowed. However, a more rigorous test of the hypothesis that scale economies due to increasing the

size of the abatement contract do not exist is now in order. The

following experiment was performed. A distribution of hazardous wall

and trim surfaces based on the data presented in the Shier-Hall reports

and data from EHEP was formulated. A random sample of 50 hypothetical

dwelling units was selected from this distribution. These dwelling

units were grouped into five preliminary contracts of ten units each.

The data on each preliminary contract was then fed into the computer

program. Contract package sizes were then recorded and averaged.

The average contract package which resulted was 2.2 dwelling units.

Obviously contract package sizes of two were the most frequent. Thus

it seems likely that any gains due to economies of scale in wall work

are more than offset by the presence of trim work. Consequently, it can be asserted that no significant inter-unit scale economies exist.

D,4 POTENTIAL SAVINGS RESULTING FROM THE USE OF THE EHEP PHASE II

COST MODELS

In the report Guidelines for Cost-Ef fective Lead Paint Abatement , it was stated that "if the least-cost combination of abatement techniques

can be identified and installed, empirical results indicate that

savings of approximately $100 per dwelling unit can be achieved,"

The purpose of this section is to present those empirical results,

A first means of justifying this figure, and the most probable range

of savings from $80 to $120, is the assumption that cost estimates

would be based on a dwelling unit having 500 square feet of wall area,

3 doors and frames, 5 windows and frames and 50 linear feet of miscellaneous trim requiring deleading. Since assumptions may always be

questioned additional empirical evidence has been brought forward to

support this claim. In particular, two independent tests were conducted to determine if the $80 to $120 figure was supported by existing lead paint abatement cost data.

In the first test a random sample of dwelling units deleaded in the

Atlanta operation was selected. (Recall that in Atlanta the abatement

techniques were assigned on the basis of engineering judgment.) The

cost of each abatement technique was then estimated for each dwelling

unit. Actual deleading costs were then compared to the estimated costs.

Anticipated savings associated with the use of the least-cost technique

were then tabulated. In the event that the least-cost technique was

the one actually installed no cost savings were attributed to the use

of the EHEP procedure. The tabulated cost savings, including those

which were zero entries, were then averaged. These findings, based on

Robert E, Chapman and Joseph G, Kowalski, Guidelines for CostEf fective Lead Paint Abatement , page iv,

86
the Atlanta cost experience, indicate that a direct cost savings of

approximately $110 per dwelling would have resulted if the least-cost

abatement technique could have been identified and installed.

In the second test a random sample of the hypothetical dwelling units

used in the experiment discussed in Section D,3 was selected, (Recall

that in Section D.3 the existence of scale economics was tested.) In

this test the expected cost of each technique was estimated. The three

least costly wall techniques and the two least costly trim techniques

were then identified. Average wall costs and average trim costs were

then calculated for each dwelling unit. (Note that these averages are based on the three least costly wall and two least costly trim techniques.) The average savings in direct costs were $83 with a range

from $43 to $140. The average savings when markup was included were

$106 with a range from $53 to $181.

Both tests support the claim that substantial savings can be achieved

through the use of the EHEP procedure. Furthermore, this is true even

if some a_ priori knowledge can be used to reduce by one half the number

of abatement techniques under consideration. Given that the second

test is both more stringent and produces more conservative levels of

savings, a strong argument can be made to claim per dwelling unit

savings of between $80 and $120.

87
APPENDIX E

METHODOLOGY FOR PERFORMING HAND CALCULATIONS

E.l DESCRIPTION OF METHOD

Given that a program administrator faces the problem of deleading a few

dwelling units, the selection of the least-cost wall and/or trim techniques to use in each unit can be accomplished using hand calculations.

Four major tasks are required. These tasks are:

(1) Grouping dwelling units into contract packages;

(2) Identifying the least-direct-cost wall and/or trim

techniques for each unit;

(3) Computing the markup factor for each contract; and

(4) Estimating the minimum bid price for each contract.

The computational procedures for each of these tasks are described

below:

Task 1 : Grouping Dwelling Units Into Contract Packages

A program administrator faced with deleading 5 dwelling units conceivably could let five separate contracts containing specifications for one dwelling unit in each contract or could let one contract with specifications and scopes of work for all five dwelling units. Although

contract package size (the number of dwelling units per contract) and

the composition of the contract do themselves affect program costs,

including them as variables would pose serious computational problems

when relying on the hand calculations for estimating abatement costs.

(The reader interested in a detailed discussion of this issue is

referred to Section 3 of Appendix C.)

For the purpose of the hand calculations, contract packages should be

based on administrative convenience, geographic contiguity, and constraints on package size arising from the EHEP cost models. The following guidelines should be kept in mind when grouping dwelling units into

contract packages:

(1) No more than four dwelling units should be placed in

any one contract;

(2) Preference should be given to grouping dwelling units

which are locationally near one another;

See Section 3 of Appendix D for a discussion of the rationale behind

this requirement,

88
(3) Contract packages should be grouped so as to disperse

evenly the perceived risk of theft or vandalism;

(4) Contract packages should be organized in such a way

that contract monitoring can be effectively carried

out.

The first two guidelines above arise from assumptions which were incorporated in the Phase II EHEP cost models. In order to ensure that the

Phase II EHEP cost estimation models will be reliable, guidelines (1)

and (2) should therefore be followed. Guideline (3) is intended to

ensure that the maximum number of contractors will respond to any

requests for proposals; while guideline (4) is dependent upon the monitoring resources available to the lead-based paint abatement program.

Task 2 ; Identifying the Least-Direct-Cost Wall and/or Trim Techniques

for Each Dwelling Unit.

Given that the dwelling unit composition of the contract packages has

been determined in Task 1, the calculation of the direct costs of the

alternative abatement techniques for each dwelling unit can now be

undertaken. The data inputs needed to implement these calculations are discussed in Chapter 5 of the companion report Guidelines for Cost Effective Lead Paint Abatement . Worksheet E.l summarizes the data requirements needed to carry out these calculations. Diagram E.l traces the

flow of steps that ought to be taken once the data has been acquired

in order to estimate the direct costs of applying barrier materials.

In Diagram E.l some of the blocks are labeled with a capital letter.

These letters refer the reader to specific work sheets in this appendix

which contain the computational routine(s) which is (are) to be carried

out at that block, (The worksheets in this appendix can be photocopied

to provide the user with an adequate number of worksheets to complete

this task.) More than one computational routine may be applicable at

any particular step. For example, at block (D) there will be two computational tables describing the steps needed to calculate productivity.

One table is needed for cement-coated fiberglass and another for vinylcoated fabric. An index of the worksheets necessary to do the hand calculations is given in Table E.l. (Table E.l indexes worksheets related

to Diagrams E.l, E.2, and E.3.) As the values of the direct cost for

the wall and trim techniques for a particular dwelling unit are computed

they should be recorded on a summary sheet. Table E.2 serves as an example of a summary sheet.

The computation procedure for determining the direct costs of trim techniques are outlined by Diagram E.2, Table E,2 also includes space to

summarize the costs of the trim techniques.

Diagram E,l should be read carefully before starting the hand calculations.

89
(A)

Compute

MC/SF

Compute

DC/SF

Compute

MC/SF

Compute

DC/SF

(C) + (E)

Multiply

DC/SF by No. of Gross SF

Select a wall Tech

Select a DU from

Contract

Package

(D)

Compute I Productivity

Compute

Average Labor

Cost/SF

KEY:

DC = Direct Cost

DU = Dwelling Unit

Fab= Vinyl-Coated Fabric

,Fib= Cement-Coated Fiberglass

Gyp= Gypsum Wall board

MC = Material Cost

/SF = Per Square Foot

Tech = Technique

Wks = ''lorksheet

Enter Value

on Wks

(Table E.l)

Compute

Painting

Cost/SF

Multiply (F, by No. of

Gross SF T

Enter Value

on Wks

Table E.l)

C

Go to Next DU

DIAGRAM E.l

HOW TO CALCULATE DIRECT COSTS

FOR WALL TECHNIQUES

90
TABLE E.l INDEX OF THE WORKSHEETS NEEDED TO PERFORM HAND CALCULATIONS

Diagram

Reference Block

Number Letter Worksheet Title and Worksheet Number

n.a. n.a. INPUT DATA REQUIREMENTS E.l

n.a n.a CALCULATING AVERAGE WAGE RATES E.2

E.l B DIRECT COST:: GYPSUM WALLBOARD E.3

E.l A MATERIAL COST: PLYWOOD PANELING E.4

E.l B DIRECT COST: PLYWOOD PANELING E.5

E.l A MATERIAL COST: CEMENTITIOUS COATING E.6

E.l B DIRECT COST: CEMENTITIOUS COATING E.7

E.l A MATERIAL COST: VENEER PLASTER E.8

VENEER PLASTER E.9

CEMENT-COATED FIBERGLASS E.IO

VINYL-COATED FABRIC E.ll

WALL PAINTING E.l

2

INFRA-RED (DOORS OR WINDOWS) E.l

3

E.l B DIRECT COST

E.l C,D,E DIRECT COST

E.l C,D,E DIRECT COST

E.l F DIRECT COST

E.2 A,B DIRECT COST

E.2 C DIRECT COST INFRA-RED OR SOLVENT STRIP

(BASEBOARDS) E.14

E.2 A,B DIRECT COST: SOLVENT STRIP (DOORS OR WINDOWS) E.l

5

E.2 A, 6 DIRECT COST: HEAT GUN (DOORS OR WINDOWS) E.16

E.2 A,B,C DIRECT COST: HEAT GUN (BASEBOARDS) E.17

E.2 B DIRECT COST: HAND SCRAPING (DOORS, WINDOWS, OR BASEBOARDS) E.l

8

E.2 B DIRECT COST: COMPONENT REPLACEMENT (DOORS) E.19

E.2 B DIRECT COST: COMPONENT REPLACEMENT (DOOR FRAMES) E.20

E.2 A DIRECT COST: COMPONENT REPLACEMENT

(WINDOWS AND FRAMES) E.21

E.2 D DIRECT COST: PAINTING TRIM (DOORS AND FRAMES) E.22

E.2 D DIRECT COST: PAINTING TRIM (WINDOWS AND FRAMES) E.23

E.2 D DIRECT COST: PAINTING TRIM (BASEBOARDS) E.24

n.a. n.a. CONTRACT DATA REQUIREMENTS E.25

E.3 C MARKUP CALCULATION E.2

6

E.3 G RECOMPUTING MARKUP E.27

91
Select a Trim

Technique

a: Compute

DC/LF for

Window Trim

(B)

Compute

DC/LF for

Door Trim

k:

Compute

DC/LF for

Baseboard

/^ Go to \.

V Next DU y

Select a DU from

Contract

Package

Mul ti ply

DC/LF by No of LF

Enter Value

on Wks

(Table E.l)

Compute

Painti ng Cost/LF

Multiply ( by No. of

LF

Enter Val ue on Wks

(Table E-1)

KEY:

DC = Direct Cost

DU = Dwelling Unit

/LF = Per Linear Foot

Wks = Worksheet

No /^Return to ^

VStart J

DIAGRAM E.2

HOW TO CALCULATE DIRECT COSTS

FOR TRIM TECHNIQUES

92
TABLE E.2 DWELLING UNIT SUMMARY OF DIRECT COSTS

Contract ID# Dwelling Unit ID# Room ID#

Total Total Sum of Direct

Direct Painting Costs and

Level # Technique Name Cost Cost Painting

Wall Methods

1 Gypsum Wallboard

2 Plywood Paneling

3 Cementitious Coal

4 Veneer Plaster

5 Cement-Coated Fil

6 Vinyl-Coated Fabi

Doors & Frames

7 Infra-Re

d

8 Solvent Strip

9 Hand Scraping

10 Heat Gun

11 Replacement

Windows & Frames

12 Infra-Red

13 Solvent Strip

14 Hand Scraping

15 Heat Gun

16 Replacement

Baseboards

17 Infra-Red

18 Solvent Strip

19 Hand Scraping

20 Heat Gun

21 Replacement

93
Task 3 : Computing the Markup Factor for Each Contract.

Direct costs for each technique are determined at the dwelling unit

level. The level of markup is determined at the contract level.

Furthermore, the contractors' familiarity with the technique affects

the level of markup. Specifically, two techniques, gypsum wallboard

and and plywood paneling (because of their familiarity and thus because

of the greater potential competition from other contractors), if listed

in the scope of the work of the contract, could cause contractors to

lower their bid price. Thus, even though a particular wall technique

may be identified as the lowest-direct-cost technique for a particular

dwelling unit, it is necessary to determine if substituting gypsum wallboard or plywood paneling (whichever of the two is the lower cost technique in that dwelling unit/room) for the identified least-direct-cost

technique, due to its impact on markup, would lead to a lower overall

expected bid price for that contract. If the overall expected bid price

would fall, then the more expensive technique (in terms of direct cost)

should be substituted for the least-direct-cost technique. Thus the

purpose of this step is to present an orderly method by which this

question can be answered and which, once answered, will lead to the

computation of the minimum markup factor for a particular contract.

As was done with Task 2, the solution to this step is outlined in a flow

diagram. Diagram E,3. If a computational block is labeled with a capital

letter, there will exist a corresponding table in Section E,2 which identifies the required data and computation procedures to be followed at

that block, (Table E,l indexes the required tables.)

Task 4 ; Estimating the Minimum Bid-Price for Each Contract.

The final result of Task 3 is the calculation of the predicted level of

markup for a given contract. The markup factor computed after going

through all the dwelling units at either Block C or Block G in Diagram E,3

will be the markup factor to use for the contract under consideration.

Hence, the least-costly (in terms of direct costs) wall and trim techniques have been identified. Gypsum wallboard or plywood paneling have

replaced the least-direct-cost techniques in a particular dwelling unit/ room observation if it was found that their overall impact on bid price

would justify substitution. Thus for each dwelling unit in a contract,

wall and trim techniques have been specified, their direct costs estimated,

and the overall markup factor has been estimated. The predicted bid price

will simply be the product of the contractor's markup factor times the sum

of the direct costs for the wall and trim techniques of the dwelling units

in the contract package.

The procedure outlined in these four steps provides: (1) a means of

determining which abatement techniques should be employed in a particular

dwelling unit, (2) a means of estimating direct costs and bid prices, and

(3) a method through which a program administrator can evaluate the causes

of any divergences between estimated and actual bids.

94
Yes

Select

Contract withN

Min DC Tech

.For Ea. DU

7

Sum TDC of

DU's in

Contract

M.

I

(b)

Select One

DU Wall Tech

From Contract

Compute

Contract

Markup

I (d;

Identify Min

DC of Gyp/ Ply

I

(e)

Subtract DC

of Min Cost

Tech from (d)

I (f)

Add Diff in

DC to TDC

(f = e + a)

KEY : DC = Direct Cost

Diff = Difference

DU = Dwelling Unit/ Room

Gyp = Gypsum Board

Min = Minimum

Ply = Plywood Panel

TDC = Total Direct Cost

Tech = Technique

Yes

1

Keep Original

Wall Tech

Compute Con

tract Markup

using Gyp/Ply

I

Find Diff

In Markups

(h = C - G)

Ihl

I (i)

Compute Gain

of Switch

(i = f • h)

I lii

Compute Cost

of Switch

(J = e . C)

I

Find Diff

between Techs

(k = i - j)

No 1

Substitute

Gyp/Ply for

Original Tech

/TbTunless^

VLast Tech J

DIAGRAM E.3

HOW TO CALCULATE MINIMUM BID PRICE

95
E.2 HAND CALCULATION WORKSHEETS

The worksheets that are provided in this section are based on the key

factor tables presented in Appendix C.3, In a number of instances a worksheet will differ slightly in form or content from its corresponding

key factor table. Such changes have been made in order to simplify the

computational procedure and/or the data collection requirements.

Most of the worksheets require that an average wage rate be entered.

The appropriate wage rate for a given technique will be found in the

calculations relating to average wage rates (see Worksheet E.2). Also

note that if a resulting computation of direct cost takes on an implausible value, for example a negative direct cost, Table D.l should be

consulted for guidance. The number of worksheets and the implied time

demands of using them may seem excessive but with the use of a hand

calculator the calculations will go very quickly. Finally note that

the worksheets are indexed in Table E.l of this appendix.

96
Data Requirement

WORKSHEET E.l

INPUT DATA REQUIREMENTS

Nature of Data

Wage

Carpenter

Painter

Plasterer

Paper Hanger

Apprentice Carpenter

Laborer

Average Hourly

Wage Rate

Material

Gypsum Wallboard

Plywood Paneling

Vinyl-Coated Fabric

Flat Latex Wall Paint

Semi-Gloss Enamel

(Oil Base)

If Needed:

Unfinished Door

Unfinished Door Frame

Unfinished Window and Frame

Dwelling Unit

Gross Sq, Ft. of Wall Area

Linear Ft. of Doors and Frame

Linear Ft. of Windows and Frames

Linear Ft. of Miscellaneous Trim

Occupancy

Wainscotting

Substrate Condition

Pantry Work

Wallpaper on Walls

If Needed:

Number of Doors to Replace

Number of Door Frames to Replace

Number of Windows and Frames to Replace

Price per 4' x 8' Sheet

Price per Square Yard

Price per Gallon

Price per Gallon

Price for One, New

Price for Both, New

Observed Value in the Particular Dwelling Unit

Square Feet

Linear Feet

1 If Occupied

If Unoccupied

Percent of Wall Area

1 If Poor

If Satisfactory or Better

1 If Needed

If Not

1 If 3 or More Layers

If 2 or Less

Numbe r

Address

XRF Readings

Address as Specified Average or Separate

for each Trim Type

97
WORKSHEET E.2

CALCULATING AVERAGE WAGE RATES

Line Techniques Labor Skill Wage Rate^ X 1kJeight =

1 Gypsum Carpenter X 0.6 ss 2 Wallboard A.pprentice Carpenter X 0.4 =

3 Average Wage = (sum of line 1 & 1:Lne 2)

4 Plywood Carpenter X 0.82 =

5 Paneling Apprentice Carpenter X 0.18 =

6 Average Wage = (sum of line 4 & 1 ine 5)

7 Cementitious Carpenter X 0.24 =

8 Coating Apprentice Carpenter X 0.16 =

9 Painter X 0.60 = 10 Average Wage

(sum of lines 7, 8 & 9)

'

11 Plaster Carpenter X 0.20 = 12 Veneer Labor X 0.12 = 13 Painter X 0.39 = 14 Plaster X 0.29 = 15 Average Wage = (sum of lines 11, 12, '.L3 & 14)

16 Cement-Coated Carpenter X 0.50 = 17 Fiberglass Painter X 0.14 = 18 Paperhangei X 0.36 = 19 Average Wage

(sum of lines 16, 17 & 18)

20 Vinyl- Painter X 0.26 =

21 Coated Paperhanger X 0.48 =

22 Fabric Carpenter X 0.26 = 23 Average Wage

(sum of lines 20, 21 & 22)

24 All Laborer X 0.5 =

25 Trim Apprentice Carpenter X 0.5 =

26 Techniques Average Wage = (sum of lines 24 & 25)

Enter average hourly wage rate for each skill.

98
WORKSHEET E.3

DIRECT COST: GYPSUM WALLBOARD

Contract ID# Dwelling Unit ID#

Step Enter

Data Operation

Weighting

Factor

Enter

Resultant

1.

2.

3.

4.

Gross Square Feet

Average Wage Rate

Price per 4' x 8'

Sheet

Pantry Work?

Yes = 1

No =

Divide into 393.06

Times 0.062

Times 0.501

Times 0.27

5.

Sum of Steps 1-4

Minus 1.770

Direct Cost/SF

99
WORKSHEET E.4

MATERIAL COST: PLYWOOD PANELING

Contract ID// Dwelling Unit ID#

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.08

Price per 4' x 8'

Sheet

2. Times -0.023

Average Wage Rate

3. Times 0.083

Sound Walls?

Yes = No =1

4. Times 1.0

Sum of Steps 1-3

100

Material Cost/SF
WORKSHEET E.5

DIRECT COST: PLYWOOD PANELING

Contract ID# Dwelling Unit ID#

Step Enter

Data Operation

Weighting

Factor

Enter

Resultant

1.

2.

Gross Square Feet

Average Wage Rate

Times 0.00042

Times 0.0748

3.

Material Cost/SF*

Times 1.2602

4.

Sum of Steps 1-3

Minus 0.3245

Direct Cost/SF

*Enter Resultant from Step 4, Worksheet E,4

101
WORKSHEET E.6

MATERIAL COST: CEMENTITIOUS COATING

Contract ID# Dwelling Unit ID//

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.174

Sound Walls?

Yes = No = 1

2. Plus 0.136

Result from Step 1

Material Cost/SF

102
WORKSHEET E,7

DIRECT COST: CEMENTITIOUS COATING

Contract ID# Dwelling Unit ID#

Step

#

Enter

Data Operation

Weighting

Factor

Enter

Resultant

1.

2.

Gross Square Feet

Average Wage Rate

Times -0.0004

Times 0,11

3. Times 3.86

Material Cost/SF*

4. Times 0.14

Wall Paper Stripping?

If 3 or More Layers = 1

If 2 or Less =

5. Minus 0.80

Sum of Steps 1-4

Direct Cost/SF

*Enter Result from Step 2 Worksheet E.6.

103
WORKSHEET E.8

MATERIAL COST: VENEER PLASTER

Contract ID# Dwelling Unit ID#

Step Enter

# Data

Weighting

Operation Factor

Enter

Resultant

1. Times 0.178

Sound Walls?

Yes = No = 1

2. Plus 0.056

Enter Result from

Step 1 Material Cost/SF

104
WORKSHEET E.9

DIRECT COST: VENEER PLASTER

Contract ID# Dwelling Unit ID#

Step Enter

Data

Weighting Enter

Operation Factor Resultant

1.

2.

Gross Square Feet

Average Wage Rate

Times -0.0004 =

Times 0.11

3.

Material Cost/SF*

Times 3.86

4. Times 0.14

Wallpaper Stripping?

If 3 or More Layers = 1

If 2 or Less Layers =

5. Plus 1.01

Sum of Steps 1-4

Direct Cost/SF

*Enter Resultant from Step 2, Worksheet E.8

105
WORKSHEET E.IO

DIRECT COST: CEMENT-COATED FIBERGLASS

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Data Operation Factor Resultant

Times -45.5

Percent Wall Area

Wainscotted

2„ Times -3.42

Sound Walls?

Yes = No = 1

3. Times 9.06

D.U. Occupied?

Yes = No = 1

4. Plus 17.39

Sum of Lines 1-3

5. Divided by

Average Wage Rate Resultant

from Line 4

6, Times 1.05

Resultant from

Line 5

7. Plus 0.437

Resultant from

Line 6

Plus

Resultant from Resultant

Line 7 From Line 5 Direct Cost/SF

106
WORKSHEET E.ll

DIRECT COST: VINYL-COATED FABRIC

Contract ID# Dwelling Unit ID#

Step

#

Enter

Data

Weighting

Operation Factor

Enter

Resultant

1.

2.

3.

4.

5.

6.

Price per Square Yd.

Percent Wall Area

Wainscotted

Percent Wall Area

Wainscotted

Average Hourly Wage

Gross Square Feet

Sum of Lines 3-5

Times 0.10

Times 0.54

Times -24,

Times 1.29

Times 0.022

Minus 8.00

7.

Average Hourly Wage

Divided by

Result of

Line 6

8. Plus

Sum of Lines 1-2 Result of

Line 7 Direct Cost/SF

107
WORKSHEET E.12

DIRECT COST: WALL PAINTING

Contract ID# Dwelling Unit ID#

Step

#

Enter Weighting

Data Operation Factor

Enter

Resultant

1. Times -0.000296 = Gross Square Feet

2. Times 0.1736

Ratio of Net-toGross SF

3. Times 0.0106

Average Wage Rate

4. Times 0.0257

'

Paint Price

5. Times -0.000296 = Gross SF Ceiling

Area

6.

Sum Steps 1-4, Enter

Painting Cost per Square

Foot for Techniques

Which Need Painting

Painting Cost/SFWalls

7.

Sum Steps 3-5, Enter

Painting Cost per Square

Foot for Techniques

Where Ceilings Are to

Be Painted.

Painting Cost/SF

Ceilings

108
WORKSHEET E.13

DIRECT COST: INFRA-RED (DOORS OR WINDOWS)

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Date Operation Factor Resultant

1. Times 0.2903

Average Wage Rate

2. Times -0.0007

XRF X Linear Feet

3. Divided by ,92 Doors

Sum of Steps 1-2 .89 Windows

Labor Cost/LF

Direct Cost/LF

109
WORKSHEET E.14

DIRECT COST: INFRA-RED OR SOLVENT STRIP (BASEBOARDS)

Contract ID# Dwelling Unit ID#

Step Enter

Data Operation

Weighting

Factor

Enter

Resultant

1.

2.

Average Wage Rate

Is It Infra-Red?

Yes = 1

No =

Times

Times

0.1354

-0.4913

3.

Sum of Steps 1-2

Labor Cost/LF

Divided by .80 InfraRed

.82 Solvent

Strip

Direct Cost/LF

110
WORKSHEET E.15

DIRECT COST: SOLVENT STRIP (DOORS OR WINDOWS)

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.5727

Average Wage Rate

2. Times -0.0013

XRF X Linear Feet

3. Minus 1.3499

Sum of Steps 1-2 Labor Cost/LF

4. Times .88

Step 3 Result

Direct Cost/LF

111
WORKSHEET E.16

DIRECT COST: HEAT GUN (DOORS OR WINDOWS)

Contract ID# Dwelling Unit IIH'f

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.2775

Average Wage Rate

2. Times -0.00861

Linear Feet

3. Plus 0.1769 Doors

Sum of Steps 1-2 0.4735 Windows

Direct Cost/LF

112
WORKSHEET E.17

DIRECT COST: HEAT GUN (BASEBOARDS)

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.1372

Average Wage Rate

2. Times -0.0073

Linear Feet

3. Plus 0.1406

Sum of Steps 1-2

Direct Cost/LF

113
WORKSHEET E.18

DIRECT COST: HAND SCRAPING (DOORS, WINDOWS, OR BASEBOARDS)

Contract ID# Dwelling Unit ID#

Step

// Enter

Data

Weighting

Operation Factor

Enter

Resultant

1.

2.

Linear Feet

Average Wage Rate

Times -0.0213

Times 0.3262

3.

Sum of Steps 1-2

Plus 0.9468

Direct Cost/LF

(Doors or Windows)

(For Baseboard)

1.

Average Wage Rate

Times 0.1146

Direct Cost/LF

(Baseboards)

114
WORKSHEET E.19

DIRECT COST: COMPONENT REPLACEMENT (DOORS)

Contract ID# Dwelling Unit ID//

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times -4.5476 = Average Wage Rate

2. Times 0.7604 = Door Price

3. Plus 95.2023 = Sum of Steps 1-2

Direct Cost/Unit

115
WORKSHEET E.20

DIRECT COST: COMPONENT REPLACEMENT (DOOR FRAMES)

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Date Operation Factor Resultant

1. Times 5.702

Average Wage Rate

2. Times 1.699

Door Frame Price

3.

Sum of Steps 1-2

Direct Cost/Unit

116
WORKSHEET E.21

iJiRECT COST: COMPONENT REPLACEMENT (WINDOWS AND FRAMES)

Contract ID# Dwelling Unit ID#

Step Enter

Data Operation

Weighting

Factor

Enter

Resultant

1.

2.

Average Wage Rate

Purchase Price

Times 6.740

Times 1.1016

3.

Sum of Steps 1-2

Plus 25.213

Direct Cost/Unit

117
WORKSHEET E.22

DIRECT COST: PAINTING TRIM (DOORS AND FRAMES)

Contract ID# Dwelling Unit ID#

Step Enter

Data

Weighting

Operation Factor

Enter

Resultant

1.

2.

3.

Average Wage Rate

of Painter

Paint Price

(Semi-Gloss)

Number Doors

and Frames

Times 0.7517

Times 0.9343

Times -0.8776

4.

Sum of Steps 1-3

Direct Cost/Unit

118
WORKSHEET E.23

DIRECT COST: PAINTING TRIM (WINDOWS AND FRAMES)

Contract ID# Dwelling Unit ID#

Step

#

Enter

Date

Weighting Enter

Operation Factor Resultant

1. Times 2.5942

Average Wage Rate

of Painter

Paint Price

(Semi -Gloss)

Number Windows

and Frames

Times 1.5865

Times -3.8199

4.

Sum of Steps 1-3

Direct Cost/Unit

119
WORKSHEET E.24

DIRECT COST: PAINTING TRIM (BASEBOARDS)

Contract ID# Dwelling Unit ID#

Step Enter Weighting Enter

# Data Operation Factor Resultant

1. Times 0.0388

Average Wage Rate

of Painter Direct Cost/LF

120
WORKSHEET E.25

CONTRACT DATA REQUIREMENTS

Line Item Entry

1. Number of DUS/rooms in Contract

2. Total Net Square Feet of Wall Area

3. Total Linear Feet of Trim

4. Net SF of (Tech)/DU ID#'s

a) Gypsum Wallboard

b) Plywood Paneling

c) Ceraentitious Coating

d) Veneer Plaster

e) Cement-Coated Fiberglass

f) Vinyl-Coated Fabric

5. Sum of 4(a) plus 4(b)

6. Final value of Markup Factor

121
WORKSHEET E.26

MARKUP CALCULATION

Contract ID# Dwelling Unit ID#

Step Enter

# Data

Weighting Enter

Operation Factor Resultant

1.

100' s of NSF

Times -0.01

2.

3.

lOO's of LF

Fraction Gyp or Plywood

4.

Sura of Steps 1-3

Times

Plus

0.05

Times -0.2144 =

1.2972 =

Markup Factor

122
WORKSHEET E.27

RECOMPUTING MARKUP

Contract ID# Dwelling Unit ID#

Step Enter

Data

Weighting

Operation Factor

Enter

Resultant

1.

NSF of Wall Area

This DU/Room

Divided by

Net SF Wall

Area in

Contract

2.

Enter Result of

Step 2

Times 0.214

3.

Initial Markup

Factor

Minus

Result of

Step 2 New Markup

Factor

123
APPENDIX F

LISTING OF COMPUTER PROGRAM

00(5iSlREMP20CHAIN RUNNH : J0003***

00010REMS141. DESCRIPTION

00012REMS01

00014REMS62 J0003 WILL ANALYZE THE COSTS OF THE ALTERNATIVE METHODS FOR

00016REMS01

0IS018REMS62 ELIMINATING THE LEAD PAINT HAZARD FROM A DWELLING UNIT. THE

00020REMS01

00022REMS60 PROGRAM PERMITS THE USER TO INPUT SPECIFIC INFORMATION ON

00024REMS01

00026REMS62 ANTICIPATED CONTRACT PACKAGES OF DWELLING UNITS. THE LEAST00028REMS01

00030REMS61 COST COMBINATION OF ABATEMENT TECHNIQUES FOR EACH DWELLING

00032REMS01

00034REMS63 UNIT IS IDENTIFIED. DWELLING UNITS ARE GROUPED TOGETHER INTO

00036REMSO1

00038REMS63 CONTRACT PACKAGES SO THAT THE SUM OF THE EXPECTED BID PRICES

0004|OrEMS01

00042REMS60 IS MINIMIZED. THE EXPECTED BID PRICE FOR EACH CONTRACT IS

00044REMS01

00046REMS60 GIVEN. EXPECTED CONTRACT COSTS FOR EACH DWELLING UNIT ARE

00048REMS01

00j550REMSl4 ALSO GIVEN.

00052REMS01

00054REMS142. LIMITATIONS

00056REMS01

00058REMS62 J0003 WILL HANDLE ANTICIPATED CONTRACT PACKAGES OF UP TO 10

0006OREMS01

00062REMS60 DWELLING UNITS. DATA FOR EACH DWELLING UNIT IS ENTERED IN

00064REMS01

00066REMS37 RESPONSE TO INQUIRIES AT RUN TIME.

00068REMSO1

00070REMS073. DATA

00072REMS0I

00074REMS58 TWO TYPES OF DATA ARE INPUT, CONTRACT SPECIFIC DATA AND

00076REMS01

00078REMS63 DWELLING UNIT SPECIFIC DATA. INPUT DATA FOR EACH ANTICIPATED

0008OREMS01

00082REMS38 CONTRACT CONSISTS OF THE FOLLOWING:

00084REMS01

00086REMS27 A. CONTRACT SPECIFIC DATA

00088REMS01

0009OREMS09 WAGE

00092REMS54 AVERAGE HOURLY

00094REMS51 CARPENTER WAGE RATE

00096REMS01

00098REMS48 PAINTER

124
0|D10(QREMS01

00102REMS48

00104REMS01

00106REMS48

0O108REMS01

00110REMS48

00112REMS01

00114REMS48

00116REMS01

00118REMS13

00120REMS01

00122REMS63

00124REMS01

00126REMS54

00128REMS01

00130REMS61

00132REMS01

00134REMS56

00136REMS01

00138REMS48

00140REMS18

00142REMS01

00144REMS15

00146REMS01

00148REMS58

00150REMS01

00152REMS51

00154REMS01

0ID156REMS59

00158REMS01

00160REMS23

00162REMS01

00164REMS51

00166REMS01

00168REMS51

00170REMS01

00172REMS46

00174REMS01

00176REMS46

00178REMS01

0£)180REMS61

00182REMS63

0I5184REMS48

00186REMS01

00188REMS43

00190REMS01

00192REMS61

00194REMS58

00196REMS63

00198REMS60

00200REMS48

PLASTERER

PAPER HANGER

APPRENTICE CARPENTER

LABORER

MATERIAL

GYPSUM WALLBOARD

PLYWOOD PANELING

VINYL-COATED FABRIC

LATEX FLAT WALL PAINT

SEMI-GLOSS ENAMEL

(OIL BASE)

IF NEEDED:

UNFINISHED DOOR

UNFINISHED DOOR FRAME

UNFINISHED WINDOW AND FRAME

B. DWELLING UNIT DATA

GROSS SQ. FT. OF WALL AREA

LINEAR FT. OF DOORS AND FRAMES

LINEAR FT. OF WINDOWS & FRAMES

LINEAR FT. OF MISCELLANEOUS TRIM

OCCUPANCY

WAINSCOATING

SUBSTRATE CONDITION

PANTRY WORK

WALLPAPER ON WALLS

IF NEEDED:

125

PRICE PER 4' X 8' SHEET

PRICE PER SQUARE YARD

PRICE PER GALLON

PRICE FOR ONE, NEW

It

PRICE FOR BOTH, NEW

SQUARE FEET

LINEAR FEET

PERCENT OCCUPIED OR 1 IF OCCUPIED

IF UNOCCUPIED

PERCENT OF WALL AREA

PERCENT UNSOUND OR 1 IF POOR

IF NOT

PERCENT NEEDING IT OR 1 IF NEEDED

IF NOT

PERCENT HAVING >2 LAYERS, 1 IF 3 OR

MORE LAYERS IF

NONE
NUMBER OF DOORS TO BE REPLACED

NUMBER OF DOOR FRAMES TO REPLACE

NUMBER OF WINDOWS AND FRAMES

TO REPLACE

XRF READINGS

DU/AGE CATEGORY

AVERAGE OR SEPARATE OR

FOR EACH TRIM TYPE

ADDRESS AS SPECIFIED

00202REMS01

00204REMS36

0j8206REMS01

00208REMS38

0021OREMS01

00212REMS3 4

00214REMS17

00216REMS01

00218REMS60 ADDRESS

00220REMS01

00222REMS42

00224REMS38

10000 FILES VJAGE; MAIL; Aim

10005 DM D(10,10),F(10,10),L(10,10),P(10,10)

10010 DIM W(10,10),Y(10,10),Z(5,10),X(45,3)

10015 DDI Z$(10),PS(10),YS(10),Q$(10)

10020 REM READ NAMES OF WALL TECHNIQUES INTO VARIABLES

10025 READ A$ ,BS , CS ,DS ,ES , FS

10030 DATA GYPSUM WALLBOARD, PLYWOOD PANELING, CEMENTITIOUS COATING

10035 DATA VENEER PLASTER , VINYL-COATED FABRIC, CEMENT-COATED FIBERGLASS

10040 REM READ NAMES OF TRIM TECHNIQUES INTO VARIABLES

10045 READ GS,H$,I$,J$

10050 DATA INFRA-RED DEVICE, SOLVENT STRIP , ELECTRIC HEAT GUN

10055 DATA HAND SCRAPING

10060 MAT READ F(7,6)

10065 DATA 0.6,0,0,0,0.4,0,0.82,0,0,0,0.18,0,0.24,0.6,0,0,0.16,0

10070 DATA 0.20,0.39,0.29,0,0,0.12,0.26,0.26,0,0.48,0,0

10075 DATA 0.50,0.14,0,0.36,0,0,0,0,0,0,0.5,0.5

10080 READ K$,L$

10085 DATA NONE , COMPONENT REPLACEMENT ONLY

10090 REM WAGE RATES STORED AS W(I,1) FOR MATRIX MULTIPLICATION

10095 PRINT "WAGE RATE INFOR>IATION"

10100 PRINT

10105 MAT W=ZER(6,1)

10110 PRINT "INPUT WAGE RATE PER HOUR FOR CARPENTER"

10115 INPUT W(l,l)

10120 PRINT "INPUT WAGE RATE PER HOUR FOR PAINTER"

10125 INPUT W(2,l)

10130 PRINT "INPUT WAGE RATE PER HOUR FOR PLASTERER"

10135 INPUT W(3,l)

1014£) PRINT "INPUT WAGE RATE PER HOUR FOR PAPERHANGER"

10145 INPUT W(4,l)

10150 PRINT "INPUT WAGE RATE PER HOUR FOR APPRENTICE CARPENTER"

10155 INPUT W(5,l)

10160 PRINT "INPUT V/AGE RATE PER HOUR FOR LABORER"

10165 INPUT W(6,l)

10170 MAT L=ZER(7,1)

10175 MAT L=F*W

10180 SCRATCH #1

10185 SCRATCH #3

10190 MAT \miTE ^^ ,W

I

126
10195 MAT WRITE #3,L

10200 REM MATERIAL PRICES STORED AS M(I)

10205 PRINT

10210 PRINT

10215 PRINT "MATERIAL PRICE INFORMATION"

10220 PRINT

10225 SCRATCH #2

10230 PRINT "INPUT PRICE OF 4 FT BY 8 FT SHEET OF GYPSUM WALLBOARD"

10235 INPUT M(l)

1024£) PRINT "INPUT PRICE OF 4 FT BY 8 FT SHEET OF PLYWOOD PANELING"

10245 INPUT M(2)

10250 PRINT "INPUT PRICE PER SQUARE YARD OF VINYL-COATED FABRIC"

10255 INPUT M(3)

10260 PRINT "INPUT PRICE PER GALLON OF LATEX FLAT WALL PAINT"

10265 INPUT M(4)

10270 PRINT "INPUT PRICE PER GALLON OF SEMI-GLOSS ENAMEL (OIL BASE)"

10275 INPUT M(5)

10280 FOR L=l TO 5 10285 WRITE #2,M(L)

10290 NEXT L

10295 PRINT "TYPE 1 IF THERE ARE ANY DOORS, DOOR FRAMES OR WINDOWS"

10300 PRINT "AND FRAMES THAT NEED TO BE REPLACED, IF NOT"

10305 INPUT Q

10310 IF Q=0 THEN 10345

10315 PRINT "INPUT PRICE OF NEW, UNFINISHED DOOR"

10320 INPUT M(6)

10325 PRINT "INPUT PRICE OF NEW, UNFINISHED DOOR FRAME"

10330 INPUT M(7)

10335 PRINT "INPUT PRICE OF NEW, UNFINISHED WINDOW AND FRAME"

10340 INPUT M(8)

10345 PRINT

10350 REM COMPUTE AVERAGE WAGE RATES FOR EACH WALL TECHNIQUE AND FOR TRIM

10355 PRINT "IF PROGRAM COST ESTIMATES ARE DESIRED, TYPE 1, IF"

10360 PRINT "CONTRACT COST ESTIMATES ARE DESIRED, TYPE 0"

10365 INPUT P 10370 IF P=l THEN 10395

10375 PRINT "INPUT NUMBER OF DWELLING UNITS TO BE DONE (MUST BE <= 10)"

10380 INPUT N

10385 LET N9=N

10390 GO TO 10400

10395 LET N=l

10400 PRINT

10405 PRINT "STOP FOR A MINUTE AND CHECK THE DATA YOU HAVE JUST INPUT"

10410 PRINT "IF ANY ERROR WAS MADE IN ENTERING IT, YOU MAY TYPE 1"

10415 PRINT "TO REPEAT INPUT STATEMENTS; IF NOT, TYPE TO CONTINUE"

10420 INPUT Z 10425 IF Q701 THEN 10435

10430 IF Z=l THEN 10295

10435 IF Z=l THEN 10095

10440 REM N2 IS NUMBER OF CONTRACTS, N3 IS NUMBER OF PAIRS

10445 IF INT(N/2)=N/2 THEN 10465

127
10450

10455

10460

10465

10470

10475

10480

10485

10490

10495

10500

10505

10510

10515

10520

10525

10530

10535

10540

10545

10550

10555

10560

10565

10570

10575

10580

10585

10590

10595

10600

10605

10610

10615

10620

10625

10630

10635

10640

10645

10650

10655

10660

10665

10670

10675

10680

10685

10690

10695

10700

DWELLING UNIT INFORMATION"

DWELLING UNIT NUMBER"

J

LET N2=(N+l)/2

LET N3=N2-1

GO TO 10475

LET N2=N/2

LET N3=N2

MAT F=ZER(N,10)

REM INPUT DU INFORMATION THAT MUST BE STORED FOR COMPUTING

REM MARKUP AND BID PRICE

FOR J=l TO N

IF P=0 THEN 10510

PRINT " GO TO 10525

PRINT

PRINT

PRINT " PRINT

PRINT "TYPE IN AN IDENTIFYING ADDRESS OR DU TYPE FOR THIS DWELLING UNIT"

INPUT Q$(J)

IF P=0 THEN 10560

PRINT "FOR POLICY ESTIMATES INPUT AVERAGES FOR SQUARE FEET, LINEAR FEET,"

PRINT "XRF, ETC.; AND PERCENTAGE OF SAMPLE NEEDED FOR THOSE VARIABLES"

PRINT "REQUIRING A 1 OR 0"

PRINT "INPUT GROSS SQUARE FEET OF WALL AREA"

INPUT D(1,J)

REM D(2,J) IS NET SQUARE FEET OF WALL AREA

IF D(1,J)<400 THEN 10590

LET D(2,J)=77+0.64*D(1,J)

GO TO 10595

LET D(2,J)=0.83*D(1,J)

PRINT "INPUT LINEAR FEET OF DOORS PLUS LINEAR FEET OF DOOR"

PRINT "FRAMES REQUIRING PAINT REMOVAL"

INPUT D(3,J)

REM D8 IS NUMBER OF DOORS AND FRAMES TO BE DELEADED

LET D8=D(3,J)/8

PRINT "INPUT LINEAR FEET OF WINDOWS PLUS LINEAR FEET OF"

PRINT "WINDOW FRAMES REQUIRING PAINT REMOVAL"

INPUT D(4,J)

REM D7 IS NUMBER OF WINDOWS AND FRAMES TO BE DELEADED

LET D7=D(4,J)/7

PRINT "INPUT LINEAR FEET OF MISCELLANEOUS TRIM REQUIRING'

PRINT "PAINT REMOVAL"

INPUT D(5,J)

LET D(7,J)=D(3,J)+D(4,J)+D(5,J)

PRINT "TYPE 1 IF UNIT IS OCCUPIED, IF NOT"

INPUT D(6,J)

REM INPUT OTHER DU RELATED DATA NECESSARY FOR CALCULATIONS

IF D(1,J)=0 THEN 10735

PRINT "INPUT PERCENT OF WALL AREA THAT IS WAINSCOATED"

PRINT "(TYPE AS A DECIMAL, E.G., 0.25 FOR 25 PERCENT)"

INPUT E(3)

PRINT "TYPE 1 IF SUBSTRATE CONDITION IS POOR, IF NOT"

128
10705 INPUT E(4)

10710 PRINT "TYPE 1 IF PANTRY WORK IS NECESSARY, IF NOT"

10715 INPUT E(5)

10720 PRINT "TYPE 1 IF 3 OR MORE LAYERS OF WALLPAPER ARE ON WALLS,"

10725 PRINT "0 IF NOT"

10730 INPUT E(6)

10735 IF Q=0 THEN 10770

10740 PRINT "INPUT NUMBER OF DOORS TO BE REPLACED"

10745 INPUT E(7)

10750 PRINT "INPUT NUMBER OF DOOR FRAMES TO BE REPLACED"

10755 INPUT E(8)

10760 PRINT "INPUT NUMBER OF WINDOWS & FRAMES TO BE REPLACED"

10765 INPUT E(9)

10770 IF D(1,J)<>0 THEN 10810

10775 LET Z1=0

10780 LET C8=0

10785 LET C9=0

10790 LET B8=0

10795 LET B9=0

10800 LET B(1)=0

10805 GO TO 10815

10810 LET Zl=9

10815 IF D(7,J)=0 THEN 10905

10820 PRINT "TYPE 1 IF SEPARATE XRF READINGS ARE AVAILABLE FOR DOORS,"

10825 PRINT "WINDOWS AND MISCELLANEOUS TRIM, IF ONLY AN AVERAGE"

10830 PRINT "IS AVAILABLE"

10835 INPUT Y

10840 IF Y=l THEN 10875

10845 PRINT "INPUT AVERAGE XRF READING"

10850 INPUT X

10855 LET X1=X

10860 LET X2=X

10865 LET X3=X

10870 GO TO 10905

10875 PRINT "INPUT XRF READING FOR DOORS AND DOOR FRAMES"

10880 INPUT XI

10885 PRINT "INPUT XRF READING FOR WINDOWS AND FRAMES"

10890 INPUT X2

10895 PRINT "INPUT XRF READING FOR MISCELLANEOUS TRIM"

10900 INPUT X3

10905 PRINT "STOP AND CHECK THE DATA FOR THIS DV7ELLING UNIT"

10910 PRINT "IF THERE IS AN ERROR, TYPE 1 TO REPEAT INPUT STATEMENTS"

10915 PRINT "IF NOT, TYPE 0"

10920 INPUT Z 10925 IF Z=l THEN 10520

10930 REM COMPUTE PAINTING COST FOR WALLS AND CEILINGS

10935 IF Z1=0 THEN 11025

10940 LET C8=1.35*(D(l,J)/39.95)+2

10945 LET C9=D(1,J)+C8

10950 REM B8 IS PAINTING COST FOR ALL TECHNIQUES BUT GYP AND VENEER PLASTER

10955 LET B8=-0.000296*C8+0.0106*W(2,1)+0.0257*M(4)+0.1736*D(2,J)/D(1,J)

129
10960 IF B8>=0.08 THEN 10970

10965 LET B8=0.08

10970 REM B9 IS PAINTING COST FOR GYP AND VENEER PLASTER

10975 LET B9=-0.000296*C9+0.0106*W(2,1)+0.0257*M(4)+0.1736*D(2,J)/D(1,J)

10980 IF B9>=0.08 THEN 10990

10985 LET B9=0.08

10990 LET B(l)=-1. 7700+306. 5874/D(2,J)+0.0620*L(l,l)

10995 LET B=0.5009*M(1)+0.2740*E(5)

11000 REM GYPSUM WALLBOARD

11005 LET B(1)=B(1)+B

11010 IF B(l)>=0.25 THEN 11020

11015 LET B(l)=0.25

11020 LET B(1)=B(1)*D(1,J)+B9*C9

11025 LET C(J)=B(1)

11030 LET G(J)=B(1)

11035 LET Z$(J)=A$

11040 LET P$(J)=A$

11045 IF Z1O0 THEN 11080

11050 LET Z$(J)=K$

11055 FOR K=2 TO 6

11060 LET B(K)=0

11065 NEXT K

11070 GO TO 11340

11075 REM PLYWOOD PANELING

11080 LET A(2)=0.07995*M(2)-0.0231*L(2,1)+0.0829*E(4)

11085 LET B(2)=-0. 3245+0. 00054*D(2,J)+0.0748*L(2,1)+1.2602*A(2)

11090 IF B(2)>=0.77 THEN 11100

11095 LET B(2)=0.77

11100 LET B(2)=B(2)*D(1,J)+B8*C8

j 11105 IF C(J)<=B(2) THEN 11130

11110 LET C(J)=B(2)

11115 LET G(J)=B(2)

11120 LET Z$(J)=B$

11125 LET P$(J)=B$

11130 REM CEMENTITIOUS COATING

11135 LET A(3)=-0. 5002+0. 6872*D(2,J)/D(l,J)+0. 1037+0. 1741*E(4)

11140 LET B(3)=-0. 8817-0. 0004*D(1,J)+0.1154*L( 3, l)+3.8636*A(3)+0. 141 6*E(6)

11145 IF B(3)>=0.22 THEN 11155

11150 LET B(3)=0.22

11155 LET B(3)=B(3)*D(1,J)+B8*C8

11160 IF C(J)<=B(3) THEN 11175

11165 LET C(J)=B(3)

11170 LET Z$(J)=C$

11175 REM VENEER PLASTER

11180 LET A(4)=A(3)-0.0847

11185 LET B(4)=0.8817-0.0004*D(1,J)+0.1154*L(4,1)+3.8636*A(4)+0.1416*E(6)+0.2O6

11190 IF B(4)>=0.35 THEN 11200

11195 LET B(4)=0.35

11200 LET B(4)=B(4)*D(1,J)+B9*C9

11205 IF C(J)<=B(4) THEN 11220

11210 LET C(J)=B(4)

3 130
11215 LET Z$(J)=D$

11220 REM VINYL- COATED FABRIC

11225 LET R(5)=-25.2456+)O.0276*D(2,J)+22.0745*D(2,J)/D(l,J)

11230 LET R=1.2933*L(5,1)-24.8364*E(3)

11235 LET R(5)=R(5)+R

11240 LET A(5)=0.0958*M(2)+0.5447*E(3)

11245 LET B(5)=L(5,1)/R(5)+A(5)

11250 IF B(5)>=0.84 THEN 11260

11255 LET B(5)=0.84

11260 LET B(5)=B(5)*D(1,J)+B8*C8

11265 IF C(J)<=B(5) THEN 11280

11270 LET C(J)=B(5)

11275 LET Z$(J)=E$

11280 REM CEMENT-COATED FIBERGLASS

11285 LET R(6)=43. 5809-33. 6107*D(2,J)/D(1,J)-45.5105*E(3)

11290 LET R=-3.4202*E(4)+8.1578

11295 LET R(6)=R(6)+R

11300 LET A(6)=0.5600*D(2,J)/D(1,J)+1.0900*L(6,1)/R(6)

11305 LET B(6)=L(6,1)/R(6)+A(6)

11310 IF B(6)>=0.66 THEN 11320

11315 LET B(6)=0.66

11320 LET B(6)=B(6)*D(1,J)+B8*C8

11325 IF C(J)<=B(6) THEN 11340

11330 LET C(J)=B(6)

11335 LET Z$(J)=F$

11340 REM FIND XRF TIMES LINEAR FEET FOR TRIM

11345 LET Y1=X1*D(3,J)

11350 LET Y2=X2*D(4,J)

11355 LET Y3=X3*D(5,J)

11360 REM FIND MINIMUM DIRECT COST TECHNIQUE FOR TRIM

11365 REM S(I)=DOORS T(I)=WINDOWS U(I)=TRIM V(I)=TOTAL

11370 REM INFRA-RED HEATING DEVICE

11375 IF D(6,J)=1 THEN 11445

11380 LET S(l)=(0.2903*L(7,l)-0.0007*Yl)/0.92

11385 IF S(l)>=1.05 THEN 11395

11390 LET S(l)=1.05

11395 LET T(l)=(0.2903*L(7,l)-0.0007*Y2)/0.89

11400 IF T(l)>=1.05 THEN 11410

11405 LET T(l)=1.05

11410 LET U(l)=(0.l354*L(7,l)-)5.A913)/0.80

11415 IF U(l)>=0.29 THEN 11425

11420 LET U(l)=0.29

11425 LET V(1)=S(1)*D(3,J)+T(1)*D(4,J)+U(1)*D(5,J)

11430 LET H(J)=V(1)

11435 LET Y$(J)=G$

11440 GO '0 11455

11445 LET H(J)=100000

11450 LET V(1)=0

11455 REM SOLVENT-BASED PAINT REMOVER

11460 LET S(2)=(-l. 3499+0. 5727*L(7,l)-0.0013*Yl)/0. 88

11465 IF S(2)>=1.25 THEN 11475

L31
1147jO LET S(2)=1.25

11475 LET T(2)=(-l. 3499+0. 5727*L(7,l)-0.0jZ)l3*Y2)/0. 88

1148£) IF T(2)>=1.25 THEN 11490

11485 LET T(2)=1.25

1149JD LET U(2)=0.1354*L(7,l)/0.82

11495 IF U(2)>=0.51 THEN 11505

11500 LET U(2)=0.51

11505 LET V(2)=S(2)*D(3,J)+T(2)*D(4,J)+U(2)*D(5,J)

11510 IF H(J)<=V(2) THEN 11525

11515 LET H(J)=V(2)

11520 LET Y$(J)=H$

11525 REM ELECTRIC HEAT GUN

11530 LET S(3)=0.2775*L(7,1)-0.00861*D(3,J)+0.1769

11535 IF S(3)>=0.58 THEN 11545

11540 LET S(3)=0.58

11545 LET T(3)=0.2775*L(7,l)-0.0)9861*D(4,J)+0.4735

11550 IF T(3)>=0.58 THEN 11560

11555 LET T(3)=0.58

11560 LET U(3)=0.1372*L(7,1)-0.0073*D(5,J)+0.1406

11565 IF U(3)>=0.17 THEN 11575

11570 LET U(3)=0.17

11575 LET V(3)=S(3)*D(3,J)+T(3)*D(4,J)+U(3)*D(5,J)

11580 IF H(J)<=V(3) THEN 11595

11585 LET H(J)=V(3)

11590 LET Y$(J)=I$

11595 REM HMD SCRAPING

11600 LET S(4)=0. 9468-0. 0213*D(3,J)+0. 3262*1(7,1)

11605 IF S(4)>=l.i6 THEN 11615

11610 LET S(4)=1.16

11615 LET T(4)=0. 9468-0. 0213*0(4, J)+0. 3262*1(7,1)

11620 IF T(4)>=1.16 THEN 11630

11625 LET T(4)=1.16

11630 LET U(4)=0. 1146*1(7,1)

11635 IF U(4)>=0.45 THEN 11645

11640 LET U(4)=0.45

11645 LET V(4)=S(4)*D(3,J)+T(4)*D(4,J)+U(4)*D(5,J)

11650 IF H(J)<=V(4) THEN 11665

11655 LET H(J)=V(4)

11660 LET Y$(J)=V$

11665 REM PAINTING COSTS FOR TRIM

11670 LET S(9)=(0.7517*W(2,1)+0.9343*M(5)-0.8776*D8)*D8

11675 LET T(9)=(2.5942*W(2,1)+1.5865*M(5)-3.8199*D7)*D7

11680 LET U(9)=(0,0388*W(2,1)*D(5,J)

11685 LET V(9)=S(9)+T(9)+U(9)

11690 REM V(9) IS TOTAL COST FOR PAINTING TRIM-ALL TECHNIQUES \ 11695 IF V(9)>=0.09*D(7,J) THEN 11705

11700 LET V(9)=0.09*D(7,J)

11705 REM COMPONENT REPLACEMENT COST

11710 LET S(8)=(0.7517*W(2,1)+0.9343*M(5)-0.8776*E(7))*E(7)*0.7

11715 LET T(8)=(0.7517*W(2,1)+0.9343*M(5)-0.8776*E(8))*E(8)*0.7

11720 LET U(8)=(2.5942*W(2,1)+1.5865*M(5)-3.8199*E(9))*E(9)

132

,
11725 LET V(8)=S(8)+T(8)+U(8)

11730 LET V(5)=E(7)*(95. 2023-4. 5476*L(1,1)+|5.7604*M(6))

11735 LET V(5)=V(5)+E(8)*(5.702*L(1,1)+1.699*M(7))

11740 LET V(5)=V(5)+E(9)*(6.740*L(1,1)+1.1O16*M(8)+25.213)

11745 LET V(5)=Q*(V(5)+V(8))

11750 LET H(J)=H(J)+V(9)+V(5)

11755 FOR M=l TO 4

11760 LET V(M)=V(M)+V(9)

11765 NEXT M

11770 IF H(J)=0 THEN 11790

11775 IF D(7,J)<>0 THEN 11795

11780 LET Y$(J)=L$

11785 GO TO 11795

11790 LET Y$(J)=K$

11795 IF POl THEN 11935

11800 LET B(7)=(B(l)+B(2)+B(3)+B(4)+B(5)+B(6))/6

11805 IF V(1)=0 THEN 11820

11810 LET V(6)=(V(l)+V(2)+V(2)+V(3)+V(4))/4+V(5)

11815 GO TO 11825

11820 LET V(6)=(V(2)+V(3)+V(4))/3+V(5)

11825 LET P2=1.16-0.0003*D(2,J)+0.0015*D(7,J)

11830 IF P2<1.1 THEN 11850

11835 IF P2<1.5 THEN 11855

11840 LET P2=1.5

11845 GO TO 11855

11850 LET P2=l.l

11855 LET P3=P2*B(7)

11860 LET P4=P2*V(6)

11865 PRINT

11870 LET P5=P3+P4

11875 PRINT

11880 PRINT USING 11885, Q$(l)

11885 :

• CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC

11890 PRINT

11895 PRINT "WALL COST", "TRIM COST", "TOTAL COST" , "MARKUP RATIO"

11900 PRINT P3,P4,P5,P2

11905 PRINT

11910 PRINT

11915 PRINT "IF YOU WISH TO DO MORE COST ESTIMATES TYPE 1, IF NOT TYPE 0' 11920 INPUT P6

11925 IF P6=l THEN 10355

11930 GO TO 13300

11935 REM F IS BASIC DATA MATRIX

11940 LET F(J,1)=C(J)+H(J)

11945 LET F(J,2)=D(2,J)

11950 LET F(J,3)=D(7,J)

11955 IF Z$(J)=P$(J) THEN 11970

11960 LET F(J,4)=0

11965 GO TO 11975

11970 LET F(J,4)=D(2,J)

11975 LET F(J,5)=C(J)

133
11980 LET F(J,6)=G(J)

11985 LET F(J,7)=G(J)-C(J)

11990 NEXT J

11995 LET ¥1=0

12000 AT Y=ZER(N,10)

12005 MAT Y=F

12010 IF N<>1 THEN 12025

12015 LET Y(l,8)=l

12020 GO TO 12490

12025 REM PAIR DWELLING UNITS

12030 REM L IS DIFFERENTIAL MARKUP MATRIX

12035 MAT L=ZER(N,N)

12040 FOR 1=1 TO N

12045 FOR K=l TO N

12050 IF Y(I,2)<>0 THEN 12070

12055 IF Y(K,2)<>0 THEN 12070

12060 LET L(I,K)=10

12065 GO TO 12075

12070 LET L(I,K)=-0.000079*Y(I,2)+0.00052*Y(I,3)-0.2144*(Y(I,4)/(Y(I,2)+Y(K,2))

12075 NEXT K

12080 NEXT I 12085 FOR 1=1 TO N

12090 LET L(I,I)=9

12095 NEXT I 12100 REM P IS DIRECT COST MATRIX

12105 MAT P=ZER(N,N)

12110 FOR J=l TO N i 12115 LET P(J,J)=Y(J,1)

12120 NEXT J

12125 REM D IS TEST MATRIX

12130 MAT D=ZER(N,N)

12135 MAT D=L*P

12140 REM W IS TRIANGULAR TEST MATRIX

1 2145 MAT W=ZER(N,N)

12150 FOR K=l TO N-1

12155 LET I=K+1

12160 LET W(I,K)=D(I,K)+D(K,I)

12165 LET 1=1+1

12170 IF KN THEN 12160

12175 NEXT K

12180 LET Nl=N*(N-l)/2

12185 MAT X=ZER(N1,3)

12190 LET J=l

12195 FOR K=l TO N-1

12200 LET I=K+1

12205 LET X(J,1)=W(I,K)

12210 LET X(J,2)=I

12215 LET X(J,3)=K

12220 IF J>=N1 THEN 12250

12225 LET 1=1+1

12230 LET J=J+1

134
12235 IF I>N THEN 12245

12240 GO TO 12205

12245 NEXT K

12250 REM SORT X MATRIX

12255 FOR L=l TO Nl-1

12260 FOR M=l TO Nl-1

12265 LET Q1=X(M,1)

12270 LET Q2=X(M+1,1)

12275 LET R1=X(M,2)

12280 LET R2=X(M+1,2)

12285 LET S1=X(M,3)

12290 LET S2=X(M+1,3)

12295 IF Q1<=Q2 THEN 12330

12300 LET X(M,1)=Q2

12305 LET X(M+1,1)=Q1

12310 LET X(M,2)=R2

12315 LET X(M+1,2)=R1

12320 LET X(M,3)=S2

I 12325 LET X(M+1,3)=S1

' 12330 NEXT M

12335 NEXT L

12340 IF Fl=l THEN 12545

I 12345 REM Y IS PRELIMINARY CONTRACT PACKAGE MATRIX

I 12350 LET J=l

' 12355 LET K=l

12360 LET B2=X(J,2)

12365 LET B3=X(J,3)

12370 IF F(B2,10)=1 THEN 12430

12375 IF F(B3,10)=1 THEN 12430

12380 FOR M=l TO 7 12385 LET Y(K,M)=F(B2,M)+F(B3,M)

12390 NEXT M

12395 LET Y(K,8)=B2

12400 LET Y(K,9)=B3

12405 LET F(B2,10)=1

12410 LET F(B3,10)=1

\ 12415 IF K=N2 THEN 12490

12420 IF K=N3 THEN 12440

12425 LET K=K+1

12430 LET J=J+1

12435 GO TO 12360

12440 LET K=K+1

12445 FOR 1=1 TO N

, 12450 IF F(I,10)=0 THEN 12460

I 12455 NEXT I 12460 LET B2=I

12465 FOR M=l TO 7 12470 LET Y(K,M)=F(B2,M)

12475 NEXT M

12480 LET Y(K,8)=B2

12485 LET Y(K,9)=0

135
1249^ IF N>2 THEN 1253£)

12495 LET N6=l

12500 FOR M=l TO 7 12505 LET Z(1,M)=Y(1,M)

12510 NEXT M

12515 LET Z(l,8)=l

12520 LET Z(l,9)=0

12525 GO TO 12935

12530 LET Fl=l

12535 LET N=K

12540 GO TO 12025

12545 REM Z IS FINAL CONTRACT PACKAGE MATRIX

12550 LET J=l

12555 LET K=l

12560 IF INT(N/2)=N/2 THEN 12580

12565 LET N4=(N+l)/2

12570 LET N5=N4-1

12575 GO TO 12590

12580 LET N4=N/2

12585 LET N5=N4

12590 FOR J=l TO (N-l)*N/2

12595 IF X(J,1)>0 THEN 12685

12600 LET B2=X(J,2)

12605 LET B3=X(J,3)

12610 IF Y(B2,10)=1 THEN 12670

12615 IF Y(B3,10)=1 THEN 12670

12620 FOR M=l TO 7 12625 LET Z(K,M)=Y(B2,M)+Y(B3,M)

12630 NEXT M

12635 LET Z(K,8)=B2

12640 LET Z(K,9)=B3

12645 LET Y(B2,10)=1

12650 LET Y(B3,10)=1

12655 IF K=N4 THEN 12930

12660 IF K=N5 THEN 12680

12665 LET K=K+1

12670 NEXT J

12675 GO TO 12685

12680 LET tC=K+l

12685 IF N2=N3 THEN 12805

12690 FOR 1=1 TO N

12695 IF Y(I,9)=0 THEN 12705

12700 NEXT I 12705 IF Y(I,10)=1 THEN 12790

12710 LET B2=I

12715 LET Nl=N*(N-l)/2

12720 FOR 1=1 TO Nl

12725 IF X(I,2)=B2 THEN 12740

12730 IF X(I,3)=B2 THEN 12750

12735 NEXT I 12740 LET B3=X(I,3)

136
12745 GO TO 12755

12750 LET B3=X(I,2)

12755 IF Y (B3,10)=l THEN 1288jO

1276£) FOR M=l TO 7 12765 LET Z(K,M)=Y(B2,M)4-Y(B3,M)

1277)5 NEXT M

12775 LET Z(K,8)=B3

12780 LET Z(K,9)=B2

12785 LET Y(B2,10)=1

12790 LET Y(B3,10)=1

12795 IF K=N4 THEN 12805

12800 LET K=K+1

12805 FOR 1=1 TO N

12810 IF Y(I,10)=1 THEN 12870

12815 FOR M=l TO 7 12820 LET Z(K,M)=Y(I,M)

12825 NEXT M

12830 LET Z(K,8)=I

12835 LET Z(K,9)=0

12840 LET Y(I,10)=1

12845 FOR L=l TO N

12850 IF Y(L,10)=0 THEN 12865

12855 NEXT L

12860 GO TO 12875

12865 LET K=K+1

12870 NEXT I 12875 GO TO 12930

12880 FOR 1=1 TO N

12885 IF Z(I,8)=B3 THEN 12900

12890 IF Z(I,9)=B3 THEN 12900

12895 NEXT I 12900 LET Z(I,10)=B2

12905 FOR M=l TO 7 12910 LET Z(I,M)=Z(I,M)+Y(B2,M)

12915 NEXT M

12920 LET Y(B2,10)=1

12925 GO TO 12805

12930 LET N6=K

12935 MAT W=ZER(N6,5)

12940 FOR K=l TO N6

12945 LET Z8=Z(K,8)

12950 LET W(K,1)=Y(Z8,8)

12955 LET W(K,2)=Y(Z8,9)

12960 IF Z(K,9)=0 THEN 12995

12965 LET Z9=Z(K,9)

12970 LET W(K,3)=Y(Z9,8)

12975 LET W(K,4)=Y(Z9,9)

12980 LET Z9=Z(K,10)

12985 IF Z9=0 THEN 12995

12990 LET W(K,5)=Y(Z9,8)

12995 NEXT K

137
13£l£)£) FOR K=l TO N6 13005 PRINT

13010 PRINT

13015 PRINT " CONTRACT "K 13020 PRINT

13025 LET 1=1 13030 LET M(K)=-0.000079*Z(K,2)+0.00052*Z(K,3)-0.2144*(Z(K,4)/Z(K,2))

13035 LET M(K)=M(K)+1.2972

1304£) REM W5 IS DWELLING UNIT NUMBER

13045 LET W5=W(K,I)

13050 IF W5<>0 THEN 13075

13055 LET 1=1+1

13060 IF I>5 THEN 13165

13065 IF W(K,I)=0 THEN 13165

13070 LET W5=W(K,I)

13075 IF F(W5,2)=0 THEN 13120

13080 LET M9=-0.2144*(F(W5,4))/Z(K,2)

13085 LET M8=M9*Z(K,1)+M(K)*F(W5,7)

13090 IF M8>=0 THEN 13120

13095 LET M(K)=M(K)+M9

13100 LET Z$(W5)=P$(W5)

13105 LET F(W5,1)=F(W5,1)+F(W5,7)

13110 LET Z(K,1)=Z(K,1)+F(W5,7)

13115 LET Z(K,4)=Z(K,4)+F(W5,4)

13120 PRINT " DWELLING UNIT "W5,Q$(W5)

13125 PRINT

13130 PRINT " WALL TECHNIQUE 'Z$(W5)

13135 PRINT " TRIM TECHNIQUE "Y$(W5)

13140 PRINT " DIRECT COST $"F(W5,1)

13145 PRINT

13150 IF W5=0 THEN 13165

13155 LET 1=1+1

13160 IF I<=5 THEN 13045

13165 IF M(K)<1.1 THEN 13185

13170 IF M(K)<=1.5 THEN 13190

13175 LET M(K)=1.5

13180 GO TO 13190

13185 LET M(K)=1.1

13190 LET Z(K,1)=M(K)*Z(K,1)

13195 PRINT " CONTRACT PRICE $"Z(K,1)

13200 PRINT " MARKUP RATIO "M(K)

13205 PRINT

13210 IF Q=0 THEN 13205

13215 PRINT "THIS INCLUDES THE COMPONENT REPLACEMENT YOU REQUESTED"

13220 NEXT K 13225 PRINT "IF YOU WISH TO DO MORE COST ESTIMATES TYPE 1, IF NOT TYPE 13230 INPUT Q7

13235 IF Q7=0 THEN 13300

13240 RESTORE #1

13245 RESTORE #2

13250 RESTORE #3

13255 MAT W=ZER(6,1)

13260 MAT L=ZER(7,1)

13265 MAT READ #1,W

13270 MAT READ //3,L

13275 FOR L=l TO 5 13280 READ //2,M(L)

13285 NEXT L 13290 MAT D=ZER(10,10)

13295 GO TO 10295

13300 END

138
REFERENCES

Boeing Aerospace Company, Experimental Hazard Elimination Program

Dwelling Unit Report; Phase II , Part II B General Data, December

1976, (unpublished report),

Boone, Thomas H., Harvey W, Berger, A, Philip Cramp, Herbert A, Jackson,

The Demonstration of Expeimental Lead Paint Hazard Abatement Methods

in Washington, D.C ., National Bureau of Standards, Interagency

Report 75-761, June 1975.

Boone, Thomas H., Harvey W. Berger, A. Philip Cramp, Herbert A, Jackson^

The Demonstration of Experimental Lead Paint Hazard Abatement Methods

in Atlanta, Georgia , National Bureau of Standards, Interagency

Report 75-974, December 1975.

Brownlee, K. A., Statistical Theory and Methodology in Science and

Engineering , John Wiley and Sons, Inc., New York, 1960.

Chapman, Robert E., Economic Analysis of Experimental Lead Paint Abatement Methods: Phase I , National Bureau of Standards, Technical

Note 922, September 1976.

Chapman, Robert E. and Joseph G. Kowalski, Guidelines for Cost-Ef fective

Lead Paint Abatement , National Bureau of Standards, Technical Note

1971, (in press).

Dixon, W. J. and F. J. Massey, Introduction to Statistical Analysis , McGraw-Hill Book Company, New York, 1957.

Draper, N. R. and H. Smith, Applied Regression Analysis , John Wiley and

Sons, Inc., New York, 1966.

Fisz, Marek, Probability Theory and Mathematical Statistics , Third

Edition, John Wiley and Sons, Inc., New York, 1963.

Godfrey, Robert S., ed. , Building Construction Cost Data, 1977 , Robert S. Means, Inc., Duxbury, Massachusetts, 1976.

Hogben, David, Sally T. Peavy, Ruth N. Varner, OMNITAB II User's

Reference Manual , National Bureau of Standards, Technical Note 552,

October 1971.

Johnston, J., Econometric Methods , Second Edition, McGraw-Hill Book

Company, New York, 1972.

Musgrave, Gerald L. and Robert H. Rasche, "Estimation of Cost Functions," TTie Engineering Economist, Volume 22, Number 3, Spring 1977.

139
Natrella, Mary, Experimental Statistics , National Bureau of Standards,

Handbook 91, August 1963.

Scheffe, H, , The Analysis of Variance , John Wiley and Sons, Inc.,

New York, 1959.

Shier, Douglas R. and William G. Hall, Analysis of Housing Data Collected

In a Lead-Based Paint Survey In Pittsburgh, Pennsylvania Part I , National Bureau of Standards, Interagency Report 77-1250, May 1977.

Shier, Douglas R. and William G. Hall, Analysis of Housing Data Collected

In a Lead -Based Paint Survey In Pittsburgh, Pennsylvania Part II , National Bureau of Standards, Interagency Report 77-1293, June 1977.

Snedecor, G. W. , Statistical Methods , Fifth Edition, Iowa State University

Press, 1956.

U.S. Bureau of the Census, Census of Housing: 1970 Detailed Housing Characteristics Final Report HC(1)-B1, United States Summary , Table 22.

U.S. Bureau of the Census, Census of Housing: 1970 Metropolitan Housing

Characteristics Final Report HC, United States Summary , Table B-6.

U.S. Bureau of the Census, Statistical Abstracts of the United States;

1976, U.S. Department of Commerce, 97th ed., July 1976.

140
NBS-114A (REV. 7-73)

3. Recipient's Accession No. U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBS TN 979

2. Gov't Accession

No.

4. TITLE AND SUBTITLE

LEAD PAINT ABATEMENT COSTS: SOME TECHNICAL

AND THEORETICAL CONSIDERATIONS

5. Publication Date February 1979

6. Performing Organization Code

7. AUTHOR(S)

Robert E. Chapman and Joseph G, Kowalski

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP)

Office of Policy Development and Research

Department of Housing and Urban Development

Washington, D.C. 20410

13. Type of Report & Period Covered

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.)

Public and private concern about the potential for lead poisoning in children

due to the ingestion of lead-based paint chips has resulted in a Federally sponsored

program to develop technologies by which the lead-based paint hazard may be

eliminated from the nation's housing. The nature and design of the Experimental

Hazard Elimination Program (EHEP) is thought to be unique in that it permitted the

costs of the alternative lead paint abatement techniques to be rigorously analyzed.

The focus of this report is on the design, implementation and analysis of

EHEP and the cost information -it produced. Statistical analyses which permitted the

development of econometric models capable of estimating abatement technique costs

and expected contractor markup are described. Structural equations relating changes

in the values of certain key factors to variations in direct cost and contractor

markup are also presented. Guidelines, including a national deleading cost estimate,

are given so that these econometric models can be used by municipal officals and

building owners to estimate deleading costs as well as provide input to policy

evaluation and formulation.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons)

Abatement; building economics; building materials; cost estimation; econometric

models; economic analysis; housing; lead-based paint; lead poisoning.

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