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

Guidelines for Cost-Effective

Lead Paint Abatement
NATIONAL BUREAU OF STANDARDS

The National Bureau of Standards 1 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 fof 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.

THE NATIONAL MEASUREMENT LABORATORY provides the national system of

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

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

physical and chemical measurement throughout the Nation's scientific community, industry,

and commerce; conducts materials research leading to improved methods of measurement,

standards, and data on the properties of materials needed by industry, commerce, educational

institutions, and Government; provides advisory and research services to other Government

Agencies; develops, produces, and distributes Standard Reference Materials; and provides

calibration services. The Laboratory consists of the following centers:

Absolute Physical Quantities 2 — Radiation Research — Thermodynamics and

Molecular Science — Analytical Chemistry — Materials Science.

THE NATIONAL ENGINEERING LABORATORY provides technology and technical

services to users in the public and private sectors to address national needs and to solve national problems in the public interest; conducts research in engineering and applied science

in support of objectives in these efforts; builds and maintains competence in the necessary

disciplines required to carry out this research and technical service; develops engineering data and measurement capabilities; provides engineering measurement traceability services; develops test methods and proposes engineering standards and code changes; develops and

proposes new engineering practices; and develops and improves mechanisms to transfer

results of its research to the utlimate user. The Laboratory consists of the following centers:

Applied Mathematics — Electronics and Electrical Engineering2 — Mechanical

Engineering and Process Technology2 — Building Technology — Fire Research —

Consumer Product Technology — Field Methods.

THE INSTITUTE FOR COMPUTER SCIENCES AND TECHNOLOGY conducts

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

economy in Government operations in accordance with Public Law 89-306 (40 U.S.C. 759), relevant Executive Orders, and other directives; carries out this mission by managing the Federal Information Processing Standards Program, developing Federal ADP standards

guidelines, and managing Federal participation in ADP voluntary standardization activities; provides scientific and technological advisory services and assistance to Federal Agencies; and

provides the technical foundation for computer-related policies of the Federal Government.

The Institute consists of the following divisions:

Systems and Software — Computer Systems Engineering — Information Technology.

'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.
Guidelines for Cost-Effective

Lead Paint Abatement

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

national Bureau ot Mansaras MAR 2 3 1379

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fc ^-CCLk^ka LiXU. S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

\A NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued January 1979
National Bureau of Standards Technical Note 971

Nat. Bur. Stand. (U. S.), Tech. Note 971, 88 pages (Jan. 1979) CODEN. NBTNAE

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

Price $2.75 Stock No. 003-003-02014-1

(Add 25 percent additional for other than U.S. mailing).
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 for lead-based

paint abatement the cost figures collected during Phase II of the

Experimental Hazard Elimination Program (EHEP) and provides public

and private decision makers with a procedure for estimating the costs

of lead -based paint abatement.

Appreciation is extended to Drs. Harold E. Marshall and 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 and

Mr. Philip T. Chen, Cost Engineer, Applied Economics Program, who

critiqued the cost procedures presented in this paper. Special appreciation is extended to Ms. Barbara Cassard, formerly with the Applied

Economics Program, and Ms. Kimberly Hockenbery, Applied Economics

Program, for their valuable assistance in the data analysis phase of

this research effort.

iii
ABSTRACT

Public and private concern about tbe 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.

Through this program lead-based paint abatement techniques were tested

in field deleading operations conducted in Boston, Massachusetts. The

major focus of the program was on the collection of data on the direct

costs of labor, materials and special equipment associated with these

abatement techniques. Data were also collected on contractor's bids

so that markup ratios could be calculated.

This report provides an overview of the statistical analysis of these

direct cost data by abatement technique and building component (i.e.,

walls, doors and frames, windows and frames, and miscellaneous trim).

An overview of the statistical analysis of the markup ratio is also

included. Cost models for each abatement technique are developed which

identify the key factors which affect direct cost and markup. Guidelines

are given so that these models can be used by municipal officials and

building owners to estimate deleading costs as well as provide input

to policy evaluation and formulation.

Keywords: Abatement; applied economics; building economics; building

materials; economic analysis; housing; lead-based paint; lead poisoning.

IV
EXECUTIVE SUMMARY

Lead poisoning in children through the ingestion of lead-based paint

chips is a serious health problem in American housing. Interest in

how to eliminate the lead-based paint hazard from housing led to the

passage of the "Lead-Based Paint Poisoning Prevention Act" (PL 91-695)

in Janaury 1971. Through this Act, Congress delegated to the Department

of Housing and Urban Development (HUD) the leadership role in the

research and development of technologies by which the lead-based paint

poisoning hazard may be eliminated from the nation's housing. One part

of HUD's research role has been the formulation of the Experimental

Hazard Elimination Program (EHEP). This program is intended to aid in

the identification of potential lead-based paint abatement techniques

and to gather data on the costs associated with these techniques. An

economic model is needed to determine the efficiency of the alternative

lead-based paint abatement strategies.

This study provides an overview of the analysis of the direct cost and

contractors' bid price data collected in Boston, Massachusetts during

Phase II of EHEP. The Phase II direct costs associated with each abatement technique are analyzed by building components (walls, doors and

frames, windows and frames, and miscellaneous trim). The per unit direct

costs (per square foot, per item, per linear foot) are presented, as are results of statistical analyses of per unit direct costs at the dwelling

unit level. Cost models which include those variables having the

greatest impacts on direct cost are then formulated for each abatement

technique.

The cost models are then validated against an independent set of data

collected in Atlanta, Georgia during Phase I of EHEP. The validation

procedure was undertaken to test the adequacy of the cost models in

predicting per unit direct costs in another city at a different point

in time. Once the cost models had been validated, a procedure for predicting the contractor's markup for overhead and profit was developed.

An important result of the EHEP Phase II cost data analysis was to underscore the fact that there is no uniform least-costly abatement technique.

Thus the use of one technique exclusively is not economically efficient.

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. These savings depend on variations in direct costs and markup associated with different types of

housing, supply and demand conditions in the labor and materials market,

and differing contracting procedures.

The cost models presented in this report have been formulated in such

a manner that variations in per unit direct cost due to the quantity of

surface deleaded, prevailing wage rates, and the productivity of labor

are captured. Since this method of cost estimation addresses the major

sources of variation in direct cost, it also captures differences in
direct costs due to regional effects. Thus the cost models may be used

with confidence in most demographic regions of the nation as a means

of estimating lead-based paint abatement costs. To facilitate the use

of these cost models by public officials and building owners, a smallscale computer program was developed. This program permits the user to

input specific information on an anticipated contract package of dwelling

units. The output tells the user what the least-cost combination of

abatement techniques for each dwelling unit is; an estimate of the overall expected bid price for the contract is also provided.

Since many of the techniques used for lead-based paint abatement are useful in the rehabilitation on renovation of housing, the cost models

developed in this report can also provide a reliable procedure for

obtaining estimates for the direct costs of certain aspects of

rehabilitation.

Due to the quantity and level of detail in the cost data collected

during Phase II of EHEP and the degree of statistical analysis it

required, details of individual statistical analyses are not presented

in this report. This streamlining permits us to focus upon those results

which are of greatest importance. However, for those readers wishing to

verify that the results presented in this report have firm technical and

theoretical underpinnings, a companion report has been prepared. This

report, Lead Paint Abatement Costs: Some Technical and Theoretical Considerations, treats in detail the considerations and planning which went

into the development and analysis of the Phase II EHEP cost models.

VI
TABLE OF CONTENTS

Page

ABSTRACT iv

EXECUTIVE SUMMARY v

LIST OF FIGURES ix

LIST OF DIAGRAMS ix

SI CONVERSION UNITS . . 9 • • •

1. INTRODUCTION 1

1.1 The Experimental Hazard Elimination Program (EHEP) . . 5 1.2 Purpose 6

1.3 Scope and Approach 7 1.4 Need for EHEP-Type Cost Models 7

2. DESCRIPTION OF PHASE II EHEP 12

2.1 Site Selection 12

2.2 Dwelling Unit Selection 12

2.3 Abatement Technique Selection . 14

2.4 Format and Methodology for Cost Data Collection. ... 16

3. RESULTS OF COST DATA ANALYSIS 19

3.1 The Cost Concepts . 21

3.2 Techniques for LBP Hazard Abatement on Walls 23

3.2.1 Gypsum Wallboard. ... ....... 23

3.2.2 Plywood Paneling 24

3.2.3 Cementitious Coating and Veneer Plaster .... 24

3.2.4 Vinyl-Coated Fabric 25

3.2.5 Cement-Coated Fiberglass ...» 26

3.3 Techniques for LBP Hazard Abatement on Trim. & e . . . 27

3.3.1 Electric Heat Gun 29

3.3.2 Hand Scraping 29

3.3.3 Infra-Red Heating Device ..... 30

3.3.4 Solvent-Based Paint Remover 30

3.3.5 Component Replacement ..... 31

3.4 Finish Painting ...... 31

3.5 Summary of Cost Data Analysis 31

VI
Page

4. GENERALIZATION OF EHEP COST MODELS 32

4.1 Markup and Its Determinants , 35

4.2 Validation 36

4.2.1 Internal Validity 36

4.2.1.1 Experimental Design 37

4.2.1.2 Data Collection Procedure 40

4.2.1.3 Internal Reliability and Accuracy ... 41

4.2.2 External Validity 44

5. APPLICATIONS OF THE EHEP COST MODELS 47

5.1 Determining Data Needs 47

5.2 Obtaining the Necessary Data 49

5.3 Forecasting Lead Paint Abatement Costs 51

6. SUMMARY AND RECOMMENDATIONS FOR FURTHER RESEARCH 57

6.1 Summary 57

6.2 Recommendations for Further Research 58

7. REFERENCES 61

Appendix A. How to Use the Computerized Cost Estimating

Procedure 63

VI 11
LIST OF FIGURES

Page

2.1 Dwelling Unit Cost Data Form 18

LIST OF DIAGRAMS

1.1 Ways in which Environmental Sources of Lead Can Enter

the Body 2

5.1 Sample Floor Plan 50

5.2 Flowchart for Computer Time-Sharing Program 54

A.l Wage and Material Price Data 66

A.

2

Dwelling Unit Data Form f 67

LIST OF TABLES

2.1 Surface Abatement Criteria 13

2.2 Abatement Techniques Used in Phase II of EHEP 15

3.1 Average Direct Cost Per Square Foot for Applying Barrier

Materials 20

3.2 Average Direct Cost Per Linear Foot for Paint Removal . 28

3.3 Summary Analysis of Direct Costs Per Square Foot for

Applying Barrier Materials 33

3.4 Summary Analysis of Direct Cost Per Linear Foot for

Paint Removal 34

4.1 Experimental Design for the Trim Experiment 37

4.2 Experimental Design for Phase II of EHEP 39

4.3 Average Absolute Errors as a Percentage of Actual Direct

Cost for Barrier Materials 43

4.4 Estimating LBP Abatement Costs in Atlanta, Georgia Using

the Phase II EHEP Cost Models 46

5.1 Input Data Requirements 55

A.l Number of Dwelling Units by Type/Age Category 65

ix
SI CONVERSION UNITS

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

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

by the 11th General Conference on Weights and Measures which net 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

Area

1 in 2 = 6.4516* x 10"4meter 2

1 ft 2 = 0.0929 meter 2

Vo lume

1 in 3 = 1.639 x 10~5meter3

1 liter = 1.00* x 10~ 3meter 3

Mass

1 grain = 6.479 x 10 kilogram

1 ounce-mass (avoirdupois) = 2.835 x 10 kilogram

1 pound-mass (avoirdupois) = 0.4536 kilogram

Pressure or Stress (Force/Area)

o 1 inch of mercury (60°F) = 3377 newton/meter

9 1 pound-force/inch (psi) = 6894 newton/meter

Exactly
Energy

1 inch-pound force (in-lbf) = 0.1130 joule

Plane Angle

—2

1 degree (angle) = 1.745 x 10 radian

Power

1 watt = 1.000* x 10 erg/second

Temperature

°C = 5/9 (Temperature °F - 32)

XI

1. INTRODUCTION

Lead is a metal which has been used extensively throughout recorded

history. Currently it is used in pipes, tubing, flashing, sheathing

for cables, and in shielding against radioactive substances. Lead alloys

and lead compounds are used as casting alloys, gasoline additives, and

paint pigments. Thus, lead occupies an important role in the functioning

of modern industrialized societies, and is present in the environment in

many forms.

People who are exposed to lead in sufficient quantities risk developing

lead poisoning, or plumbism. Although it affects the entire body, lead

poisoning is usually concentrated in the nervous system, the gastrointestinal tract, and the blood forming tissues. Lead poisoning is most

acute among children due to their higher growth rates. They are therefore the most susceptible group to permanent brain damage by virtue of

lead's effect on the nervous system.

Although the environmental sources of lead are many, those which are potential sources of lead poisoning can be grouped into four basic

categories: (1) air, (2) dust, (3) soil, and (4) paint. The two paths

through which lead can enter the body are (1) the digestive system

and (2) the respiratory system.

Diagram 1.1 illustrates the ways in which the environmental sources

of lead can enter the body. In this study we treat the branch of

Diagram 1.1 which starts with lead-based paint (LBP) and ends with

lead poisoning.

Lead poisoning in children due to LBP is precipitated by ingestion of

contaminated paint chips. Children between the ages of one and seven

are most vulnerable to the disease, because they have a natural tendency

1

2

For an authoritative source on lead poisoning, see J.J. Chisolm, "Lead

Poisoning," Scientific American, Vol. 224, No. 2, February 1971.

This does not imply that the major source of lead poisoning in children

is directly attributable to LBP. In fact, a recent study (see Anthony J,

Yankel, Ian von Lindern, and Stephen D. Walter, The Silver Valley Lead

Study: The Relationship of Childhood Lead Poisoning and Environmental

Exposure , draft report) has shown that excess ambient air lead can cause very high levels of blood lead in children. Therefore, LBP abatement

decisions should not be made without considering other environmental

sources of lead, such as smelters.
DIAGRAM 1.1 WAYS IN WHICH ENVIRONMENTAL SOURCES OF LEAD CAN ENTER THE BODYJ

PAINT AIR

V

^DIGESTIVE

SYSTEM

RESPIRATORY

SYSTEM

'BLOOD LEAD

LEAD POISONING

This diagram is patterned after one presented in Anthony J. Yankel,

Ian von Lindern, and Stephen D. Walter, The Silver Valley Lead Study:

The Relationship of Childhood Lead Poisoning and Environmental Exposure ,
to mouth foreign objects. At this age children usually can not distinguish between harmful substances and ones that are not harmful. Poisoning may therefore occur without either the knowledge of the child or of

the other family members. Furthermore, the symptoms of lead poisoning

are similar to those of other childhood ailments so that the disease

may go undetected for long periods of time. Although mouthing may be

an important cause of LBP poisoning, the potential for lead poisoning

is more acute for children who suffer from pica, or the abnormal craving

for non-food substances. In these cases, children may consume massive

amounts of lead by eating LBP chips which they have peeled or pried

from painted surfaces or picked up from the floor.

To determine the degree to which the presence of LBP affects the lead

poisoning problem, it is first necessary to identify the age and type

of housing where it is most likely to occur. Recent studies indicate

that LBP is located primarily in housing constructed prior to 1950. *

» In this stock of housing, many interior paints contain lead pigments as their primary source of color and holding power. Since 1950, however,

lead oxides and carbonates in paints have largely been replaced by

titanium dioxide.

An analysis of the two surveys published by the National Bureau of

Standards, indicated that approximately 30 percent of all pre-1940 res- idential dwelling units contain LBP which is either peeling or located on a poor substrate. The survey conducted in Pittsburgh, Pennsylvania,

which involved a random sample of 3300 dwelling units, indicated that

approximately 1,900 dwelling units had a lead content (on at least one

2

3

4

5

Some estimates assert that as much as 1/4 of all confirmed LBP poisoning cases are due to mouthing contaminated surfaces which are in a tight or otherwise sound condition. See 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.

Selma J. Mushkin and Ralph Freiden, Lead Poisoning in Children: The

Problem in D.C. and Preventative Steps , Public Services Laboratory,

Georgetown University, Washington, D.C, 1971.

Judith F. Gilsinn, Estimates of the Nature and Extent of Lead Paint

Poisoning in the United States , National Bureau of Standards, Technical Note 746, December 1972.

Douglas R. Shier and William 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.

Substrate is the underlying material to which the paint film adheres.
1 9 wall) of 2.0 milligrams per square centimeter (mg/cm ) or more. It

also revealed that approximately 62 percent of all dwelling units con- structed before 1940, and that slightly over 31 percent of all dwelling

units constructed between 1940 and 1960, had lead contents of 2.0 mg/cm^

or more on at least one wall. LBP was also found in a significant

number (approximately 13 percent) of dwelling units constructed after

1960. The Pittsburgh survey looked at a cross section of all housing

and not just that deemed likely to contain LBP. Therefore, it provides

some measure of how widespread the use of LBP was in housing in the

Middle Atlantic geographical region.

A comparison of the results of the Pittsburgh survey with those of a smaller test program carried out in Washington, D.C. indicated that the

distribution and levels of LBP in the two cities were very similar.

Under the relatively strong assumption that the distribution and levels

of LBP in the nation's stock of housing are similar to those observed

in Pittsburgh, it becomes possible to estimate the total expenditure

required to delead the nation's stock of housing. Combining census data

and the results of the Pittsburgh survey with empirical cost data produces

a national cost estimate (in 1976 dollars) of between 28 and 35 billion

dollars depending on the minimum lead level above which a dwelling unit

is deemed to be "at risk." Furthermore, in analyzing these estimates it

1 9 A lead content of 2.0 mg/cm is used in this illustration due to

potential measurement inaccuracies of the lead detection instruments

at lead levels below 2.0 mg/cm .

2 Based on computer analysis conducted by William Hall, Mathematician,

Operations Research Division, Center for Applied Mathematics, National

Engineering Laboratory, National Bureau of Standards. Details are given in Douglas R. Shier and William 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.

William Hall, T. Ayres, and D. Doxey, Survey Plans and Data Collection

and Analysis Methodologies; Results of a Pre-Survey for the Magnitude

and Extent of the Lead-Based Paint Hazard in Housing , National Bureau

of Standards, Interagency Report 74-426, January 1974.

Douglas R. Shier and William 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.

The $28 billion figure is based on a minimum lead level of 2.0 mg/cm . The $35 billion figure is based on a minimum load level of 1.5 mg/cm . Details of the national cost estimates are given in Appendix A of the

3

4

(continued on next page)
was revealed that between 40 and 45 percent of the national deleading

cost burden must be borne by the eight States in the Middle Atlantic

and East North Central regions.

1.1 THE EXPERIMENTAL HAZARD ELIMINATION PROGRAM (EHEP)

The extent to which LBP is distributed throughout the nation's stock of

housing and the potential it has for poisoning young children has generated a great deal of interest and concern among private individuals,

community groups, and public officials. It has also posed some difficult

legal questions regarding the responsibilities of building owners and

occupants (i.e., who should bear the burden of the abatement costs). » As a result, the "Lead-Based Paint Poisoning Prevention Act" (PL 93-695) was enacted by Congress on January 13, 1971 and amended (PL 93-151) on on November 9, 1973. These acts provided for Federal participation,

including grants to local governments for detection, treatment, and prevention of LBP poisoning.

Through the "Lead-Based Paint 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 effectively deal with

the LBP poisoning problem and which will promote economic efficiency in

the allocation of resources to eliminate high levels of LBP in housing.

HUD in return has requested the Center for Building Technology (CBT) of

the National Bureau of Standards to provide technical support in attaining these goals through a detailed analysis of LBP abatement technologies,

Past studies conducted by CBT for HUD have led to the formulation of the

Experimental Hazard Elimination Program (EHEP). EHEP is intended to

(continued from previous page)

companion report Lead Paint Abatement Costs: Some Technical and Theoretical Considerations . It is important to point out that the figures

cited above do not include the costs of administering a nationwide

lead paint abatement program. It is shown in Appendix A. 4 of the companion report that including administration costs in the national

estimate will increase the cost figures cited above by approximately

30 percent. In addition, no estimate was made on the cost of abating

the lead-based paint hazard from the exterior surfaces of a dwelling

unit. This was 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.

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.
contribute to the accomplishment of HUD's responsibilities 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 accuracy and ease of application.

EHEP consists of two phases. Phase I of EHEP involved LBP hazard abatement operations in 110 dwelling units. Thirty dwellings initially underwent LBP hazard abatement in Washington, D.C.; the remaining 80 underwent

LBP hazard abatement in Atlanta, Georgia shortly thereafter. Information

on the technical and engineering aspects of the Washington and Atlanta

portions of Phase I is given in The Demonstration of Experimental Lead

Paint Hazard Abatement Methods in Washington, D.C. , and The Demonstration of Experimental Lead Paint Hazard Abatement Methods in Atlanta,

Georgia . Information on the costs experienced in Phase I of EHEP and

how they impinge on abatement decisions is given in Economic Analysis

of Experimental Lead Paint Abatement Methods: Phase I . The cost information collected during Phase I of EHEP was also used as a data base

against which procedures for estimating abatement costs were validated.

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.

1.2 PURPOSE

The purpose of this report is to present an analysis of the costs of

experimental abatement techniques based on an evaluation of cost data

collected in Boston, Massachusetts during Phase II of EHEP. The analysis

of these data will aid in the identification of those LBP abatement

techniques which promote economic efficiency at the dwelling unit level.

1

2

3

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.

Thomas H. Boone, 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.

Robert E. Chapman, Economic Analysis of Experimental Lead Paint

Abatement Methods: Phase I , National Bureau of Standards, Technical

Note 922, September 1976.
Cost estimating procedures based on the Phase II of EHEP data are proposed that (1) identify those variables which have the greatest impact on direct costs and contractor markup, (2) show how the least-cost

abatement technique may be identified, and (3) provide guidelines for

estimating abatement costs at the dwelling unit level or for a major

program.

1.3 SCOPE AND APPROACH

The focus of this study is on Phase II of HUD's Experimental Hazard

Elimination Program. Cost data from field deleading operations undertaken in Boston, Massachusetts are analyzed. The analysis of these data

is directed at the formulation of a cost model for estimating LBP abatement costs at either the dwelling unit level or the program level.

Cost data are analyzed by building component (i.e., by walls, doors and

frames, windows and frames, and miscellaneous trim), whenever possible

a cost model is developed for each abatement technique. This approach

was chosen because it permits a wider variation in costs to be explained

and facilitates cost comparisons with other abatement techniques. The

individual cost models are validated against the cost data collected

during Phase I of EHEP. Suggestions for further work, including the

preparation of a guide for municipal officials, conclude the report.

In its focus on cost estimation, this study does not attempt to develop

a methodology for the definition and quantification of benefits to be

derived from LBP hazard abatement. Nor does it attempt to rigorously

estimate future costs or life-cycle costs resulting from maintenance

or repair necessitated by lack of long-term durability of an abatement

technique. In the absence of information on the potential benefits to

be derived from abatement and expected future costs, no attempt was made

to identify the economically optimal abatement technique at either the

dwelling unit level or the national level. Similarly, incentives for

large-scale abatement programs such as cost sharing and low-interest

loans are not treated.

1.4 NEED FOR EHEP-TYPE COST MODELS

If the least-costly abatement techniques can be identified and used,

results from EHEP indicate that, on the average, a reduction in costs

of from $80 to $120 per dwelling unit can be achieved. If these

figures are projected for a city-wide abatement program, it can be seen that the results of EHEP indicate that there is a great potential

for reducing program costs.

These figures assume that the quantity of abatement work is given by

the set of figures listed as "optimal" in the surface abatement criteria in Section 2.2. Empirical evidence also supporting the $80

to $120 savings per dwelling unit claimed above is presented in

Appendix D.4 of the companion report Lead Paint Abatement Costs

:

Some Technical and Theoretical Considerations.
We are now in a position to see how the objectives of EHEP relate to

the dual requirement of keeping program costs down and carrying out an agressive city-wide program. One of the major objectives of EHEP was

to produce a cost estimation procedure which was flexible, reliable, and

straight-forward in its application. Attaining this objective permits

abatement technique costs to be forecast with enough accuracy that they

could be carefully compared and the least-cost techniques identified.

Several important policy implications can also be derived from the use

of the EHEP procedure. First, by reducing the uncertainty in cost estimation and identifying those abatement techniques which are least costly,

a policy strategy that results in more abatement per dollar spent can

be initiated and carried out. Second, a system of priorities can be

established that would tend to maximize the number of injuries averted

per dollar spent. Finally, it assists decision makers in both policy

formulation and evaluation since it reduces the risks of either selecting a policy strategy that wastes scarce tax dollars or rejecting (or

abandoning) a policy strategy that is economically sound.

At this stage, it is only natural to ask, "What are the steps that can

be taken to bring the results of an experimental program like EHEP into

a form where they can be used in programs outside the context of EHEP?"

The importance of this question cannot be overstressed. To answer this

question we shall rely heavily on the concepts and methods of the discipline referred to as operations research. By applying the methods

of operations research it becomes possible, through four basic steps,

to bridge the gap between the experimental data collected in EHEP and

a cost estimation procedure suitable for policy analysis. These four

steps are : (1) problem definition, (2) model formulation, (3) model

validation, and (4) implementing and controlling the solution. In order

to better understand the connection between each of the four steps, we shall examine them individually.

Clearly the first step which must be taken is defining the problem.

But before the problem can be defined in enough detail to permit a meaningful solution, we must identify a suitable measure of performance,

what constraints, if any, are applicable to the controlled variables,

1

2

3

Each of the policy implications just outlined would be appropriate for

building owners since it would permit them to achieve the maxium amount

of abatement per dollar spent. This in turn would reduce the chance

of litigation due to LBP poisoning cases in their properties.

Operations research may be defined as the application of the scientific

method to the management of organized systems.

An example of a controlled variable is the quantity of surface to be

deleaded. Other controlled variables are given in Table 2.1 (Surface

Abatement Criteria) in Section 2.2.

8
and what are the uncontrolled variables in the experiment. Since we

are. concerned with cost estimation, it is natural to choose as a measure

of performance, the cost of a given level of hazard abatement. In this

case, the level of hazard abatement could either be in the present or over the long term. However, since we do not have an adequate measure

of the long-term durability of the alternative abatement techniques, we shall not attempt to differentiate between the levels of hazard abatement

over the life-cycle of the alternative techniques. Therefore, it will

be assumed that each technique has an equal level of hazard abatement.

Thus, the relevant costs to be considered in defining the measure of

performance are initial costs, in particular the bid price for the

abatement work. We may now define the problem.

Problem Definition ; To identify that abatement technique, which, for a given set of building considerations, minimizes the expected bid prices 9

Explicit in this definition is a procedure for estimating the direct

costs and markup for each abatement technique.

The second step in the approach is formulating a model which is consistent with the definition of the problem. This step relies not only on knowing which mathematical methods are appropriate for analysis of any

data collected during the abatement process, but also on how the abatement process operates. The initial step in this phase of the project

involves the development of a theoretical model. The theoretical model

usually differs from the final model in that it is based on operational

knowledge of the abatement process rather than on empirical data. The

main purpose of the theoretical cost model is to identify the most

efficient experimental design; in this case for Phase II of EHEP. As

data arrive from the experiment, they are compiled and statistics on individual abatement technique costs are computed. Costs may then be

1

2

3

A

An example of an uncontrolled variable is work stoppage due to lockouts.

The measure of performance may now be chosen as the cost per square

foot or the cost per linear foot.

Knowledge of the abatement process is based on construction experience

in the rehabilitation field. An illustration of how this operational

knowledge con elements data analysis can be seen in the format of the

Dwelling Unit Cost Data Form presented in Section 2.4.

It should be noted that the development of the theoretical model used

in Phase II of EHEP also relied on experience gained in analyzing the

Phase I cost data.
analyzed using the analysis of variance to see if significant differences result as a function of abatement technique, occupancy status,

or substrate condition. Further tests are then performed using multiple regression to see if relationships between abatement cost and

certain key factors can be identified and quantified. An iterative

process using multiple regression is used so that all of the key factors

which affect abatement costs can be identified and their relationships

quantified. This process is followed for each abatement technique.

Additional details on the formulation of the individual cost models are

given in Chapter 3; a discussion of how the theoretical considerations

in model building relate to EHEP is given in Appendix C of the companion

report Lead Paint Abatement Costs: Some Technical and Theoretical Considerations .

The third step in the approach is validating the model against an independent set of data. Validation is the important link which assures us that the structure of the cost model is consistent with the way the

abatement process operates in general, rather than just a description

of a specific set of data. To validate the Phase II EHEP cost models,

cost data from the Phase I of EHEP are used. The Phase I EHEP cost

data are used for four basic reasons. First, they were collected independently of the Phase II cost data. Second, they provide data on all

but one abatement technique used in Phase II of EHEP. Third, the data

base has a greater degree of detail than other available sources. And

finally, the reliability of the data is very high when compared to

other available sources. Using the Phase I data base for validation was very fruitful, because it stimulated feedback between the formulation

and implementation phases of the project. Thus, it helped both to generalize and to simplify the cost models. It is thought that this will

2

Analysis of variance permits the hypothesis that observed differences

in abatement technique costs are due to chance to be tested against

the hypothesis that observed differences are indicative of actual differences in abatement technique costs. 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.

The substrate is the underlying material to which the paint film

adheres.

Multiple regression may be defined as a statistical procedure for

investigating and quantifying the relationship between the dependent,

or response, variable and two or more independent, or explanatory,

variables. For a general discussion of multiple regression, see K.A. Brownlee, Statistical Theory and Methodology in Science and

Engineering , John Wiley and Sons, Inc., 1960. For an indepth analysis,

see N.R. Draper and H. Smith, Applied Regression Analysis . John Wiley

and Sons, Inc., 1966.

10
help promote the use of the EHEP models since they can now be applied

both more effectively and more efficiently by decision makers. More

information on the mechanics of the validation procedure is given in

Chapter 4.

The final step in the approach is to implement and control the solution

of the problem by using the model's results. Of crucial importance here

is that decision makers recognize the advantages that the EHEP cost estimation procedure has over other methods of cost estimation. Although

there are many methods of estimating construction costs, none is specifically directed at LBP hazard abatement. Similar skills and materials

are involved in LBP hazard abatement, but the level of activity at which

LBP hazard abatement is carried out is inconsistent with that assumed

in the construction cost estimating guides. This almost always causes

the cost estimates based on simple averages to be well below those

costs actually expected or experienced in field deleading operations.

Consequently, the way in which the key factors are related may cause

their impacts on costs to differ. Furthermore, other procedures are not flexible enough to include ways of modifying labor costs (e.g., the

productivity of labor) or material costs without relying heavily on judgmental decisions which can neither be confirmed nor denied. On the

other hand, the EHEP procedure minimizes the degree to which judgmental

decisions must be made, and concentrates on basing decisions on facts

which can be easily observed or measured. Uncertainty is therefore

reduced significantly. Another benefit of the EHEP procedure is that it

is structured in such a way that the cost estimation phase of a large

abatement program complements the dwelling unit screening phase. In this

way, very specific dwelling unit information can be collected while lead

readings are being taken at little or no extra cost to the overall

program. To facilitate the application of the EHEP procedure, a computer

program written in the BASIC language has been prepared. This program

2

3

4

Examples of cost estimating guides are Building Construction Cost Data

1977 , Robert Snow Means Company, Inc., Duxbury, Mass.; Dodge Manual for

Building Construction Pricing and Scheduling , McGraw-Hill Information

Systems Company, New York; Home-Tech. Estimator , Home-Tech Publications,

Bethesda, Maryland.

Joseph G. Kowalski, Cost Estimation in Residential Alteration and

Repair Construction: An Economic Analysis , National Bureau of Standards, Interagency Report (in preparation).

In the terminology of economic theory, the production function for LBP

hazard abatement is not the same as the one in new construction or in

major rehabilitation activities.

BASIC is an acronym for Beginner's All-purpose Symbolic Instruction

Code. A good introduction to BASIC is given in D. Spencer, A Guide to

BASIC Programming: A Time-Sharing Language , Addison-Wesley Publishing

Company, Inc., Reading, Mass., 1970.

11
will permit costs to be estimated rapidly and accurately either at the

dwelling unit level or at the program level. These applications are explored in more detail in Chapter 5.

2. DESCRIPTION OF PHASE II EHEP

2.1 SITE SELECTION

The selection of a candidate city for field deleading operations

reflected both the desire for the collection of meaningful cost information and the constraints under which this portion of EHEP had to

operate. Since the allocation of EHEP resources did not involve LBP

hazard abatement in privately-owned dwellings, the availability of

housing owned by the Federal government or local housing authorities

had to be considered in the selection of a candidate city. Thus, the

interest and willingness of local housing authorities as well as HUD

regional offices to participate in EHEP had to be assessed. Lead

poisoning incidence data for cities were then reviewed to determine

the frequency with which elevated blood lead levels were reported.

This approach was taken since it was hypothesized that the incidence

of lead poisoning would provide an indication of the number of dwelling

units which contain high levels of lead in paint. Implicit in these

considerations was that the city should have housing representative

of those types and ages of dwellings deemed likely to contain hazardous

levels of LBP.

A preliminary review of available data for cities indicated that Boston,

Massachusetts had a relatively high incidence of children with elevated

blood lead levels. Further investigations of census tract data showed

that Boston also has a varied stock of housing types which are deemed

likely to contain hazardous levels of LBP. Another important reason for

selecting Boston was that it had an existing lead paint hazard abatement

program. Thus, it was possible to work closely with health officialc

and housing officials to select the areas of the city for surveys and

to facilitate the survey process. Another result of the city's lead

paint abatement program was that there were a large number of contractors

experienced in deleading operations. Boston also had a large stock of

HUD acquired properties which were available for use in the program.

2.2 DWELLING UNIT SELECTION

Implicit in the selection of dwelling units for abatement was the requirement of the overall program to collect meaningful cost information.

In order to be able to retrieve the most usable information from the

experiment, it was necessary to select enough dwelling units so that

statistical testing would be possible. To do this efficiently, a rigorous experimental design was required to identify how many dwelling

units were needed to measure variations in costs associated with a given

attribute or combination of attributes, such as substrate condition or occupancy status. The reliability of statistical comparisons is enhnaced

12
when constraints are placed on variables such as the geographical distribution of the dwelling units and the maximum and minimum surface which

can be deleaded. The constraints placed on observations made during

Phase II of EHEP were designed to reflect (1) the values which are expected to be encountered in practice, and (2) the desire to maintain

a high degree of comparability within the data base. This was accomplished through the use of surface abatement criteria which set guidelines on the amount of abatement work which could take place. These

criteria provided guidelines not only on the optimum level of abatement

work but also on the minimum and maximum levels of abatement work for

inclusion in the analysis. Dwelling units which did not meet one or more of the abatement criteria were dropped from the sample. The surface abatement criteria used in Phase II of EHEP are shown in Table 2.1.

TABLE 2.1

SURFACE ABATEMENT CRITERIA

Optimum Minimum Maximum

System Surface Surface Surface

Walls 3 500 s.f. 400 s.f. 700 s.f.

Door/Frame 3 each 3 each 5 each

Window/Frame 5 each 5 each 9 each

Trimb50 l.f. 50 l.f. 100 l.f.

a

b

1

Surface measures are in square feet (s.f.).

Surface measures are in linear feet (l.f.).

Another reason for placing constraints on the values of key variables

was to make the data base generated in Phase II of EHEP compatible with

that generated in Phase I. This facilitated validation of the Phase II

cost models against the Phase I data base.

It should be noted that in the absence of such criteria, the ability

to estimate abatement costs is unnecessarily complicated. In fact, if

relevant constraints are removed, the potential sources of variation

increase rapidly and may confound the data so that the confidence with

which statistical tests can be applied and considered relevant is significantly reduced.

13
The contract management firm, the Boeing Aerospace Company, while

working in close coordination with the HUD regional office, established

a pool of dwelling units which could be surveyed for the presence of

LBP. 1

Two three-man teams were then assembled to conduct the survey.

Each team was equipped with a portable x-ray fluorescent (XRF) lead

analyzer with which lead level readings were taken and recorded. The

physical measurements of the dwelling unit and any other important

attributes were also taken and recorded at that time. If the dwelling

unit was found to meet the surface abatement criteria, it was put in

a reserve pool until specific abatement techniques could be assigned.

In all, 119 dwelling units were surveyed. Of these, LBP hazard abatement operations were carried out in 71. All dwelling units inspected were from HUD's stock of acquired properties.

2.3 ABATEMENT TECHNIQUE SELECTION

The experimental abatement techniques tested in Phase II of EHEP were selected on the basis of recommendations which resulted from an extensive,.

2 laboratory testing program conducted at the National Bureau of Standards.

Construction experience indicates that the effective abatement of LBP may

be accomplished through (1) paint removal, (2) component replacement,

and (3) installation of barrier materials. The method actually used will

depend upon the building component to be deleaded.

To facilitate the discussion of the individual abatement techniques and

their applications, building components may be conveniently grouped into

1 n A lead content of 2.0 mg/cm or more was used as the criteria for

inclusion in the sample due to potential measurement inaccuracies

of the lead detection instruments at lead levels below 2.0 mg/cm .

2 Detailed descriptions of the testing procedures, the performance criteria, and the experimental abatement techniques tested are available

in David Waksman, John B. Ferguson, McClure Godette, and Thomas Reichard,

Potential Systems for Lead Hazard Elimination: Evaluations and Recommendations for Use , National Bureau of Standards, Technical Note 808,

December 1973.

3 Component replacement was not used in Phase II of EHEP due to its

excessive cost. This method was used in Phase I of EHEP and was found to be appropriate only in cases where the component was severely

damaged or deteriorated. The cost estimation procedures presented in

in Section 3.3.5 for component replacement are based on Phase I EHEP

cost information and are patterned after those procedures presented

in Robert E. Chapman, Economic Analysis of Lead Paint Abatement

Methods: Phase I , National Bureau of Standards, Technical Note 922,

September 1976.

14
two categories: (1) planar surfaces (walls and ceilings), and (2) trim

(doors and frames, windows and frames, baseboards, and other miscellaneous trim surfaces). A breakdown of the abatement methods used in

Phase II of EHEP into specific techniques and the building components

applicable to those techniques is given in Table 2.2. A brief summary

of the abatement techniques used in both phases of EHEP is given in

Appendix B of the companion report Lead Paint Abatement Costs: Some

Technical and Theoretical Considerations . Detailed requirements for

the preparation, installation and finishing work as well as construction

specifications of the individual abatement techniques used in Phase I of EHEP may be found in the two reports by Boone et al .

TABLE 2.2

1

ABATEMENT TECHNIQUES USED IN PHASE II OF EHEP

Method Technique

Applicable Building

Component

Barrier

Materials

Gypsum Wallboard

Plywood Paneling

Cement itious Coating

Cement-Coated Fiberglass

Veneer Plaster

Vinyl-Coated Fabric

Walls

Walls

Walls

Walls

Walls

Walls

Paint

Removal

Electric Heat Gun

Infra-Red Heating

Device

Solvent Stripping

Hand Scraping

All Trim Components

All Trim Components

(Used in Unoccupied

Units Only)

All Trim Components

All Trim Components

The Demonstration of Experimental Lead Paint Hazard Abatement Methods

in Washington, D.C., pp. 71-95, and The Demonstration of Experimental

Lead Paint Hazard Abatement Methods in Atlanta, Georgia, pp. 78-111.

15
Nine of the ten LBP abatement techniques used "in Phase II of EHEP were

also used in Phase I. In all, there were 17 abatement techniques used

in Phase I of EHEP. Eight were dropped because of excess cost, potential fire hazards, logistical problems, or similarities with other

techniques that were used in Phase II of EHEP.

This replication should be emphasized* in that it facilitates the identification and determination of variations in costs or quality of workmanship resulting from regional differences, different housing conditions

and types, the availability of local labor, the prevailing labor rates,

and the types of contractors performing the abatement work.

2.4 FORMAT AND METHODOLOGY FOR COST DATA COLLECTION

Once an available pool of dwelling units had been established, the individual abatement techniques were assigned to the stock of dwelling units.

The dwelling units were then grouped together into contract packages.

The contract packages consisted of either two or four dwelling units.

They were designed so that the number of different abatement techniques

used in any one contract was kept to a minimum. Emphasis was also placed

on locating all dwelling units in the same contract in as small an area

as possible.

Explicit in the cost experiment was that expected abatement costs for two

different ownership categories be analyzed. The two ownership categories

analyzed were (1) "privately owned" dwelling units and (2) "acquired

properties" owned by HUD. In both cases the dwelling units were owned

by HUD. However, the approach taken in soliciting bids from contractors

differed between the two categories.

For the cases involving the "privately owned" dwelling units, a representative from the contract management firm posed as a landlord who

was anticipating deleading work in some of their rental properties.

The representative met with contractors and provided information on the type of dwelling unit, its location, the scope of the work and

what abatement techniques were to be used. Bids were then solicited

from the contractors.

For the cases involving the "acquired-properties" owned by HUD, the

contract management firm provided technical scopes of work and technical specifications for each dwelling unit in the contract package.

HUD then used its approved list of contractors, from which ten were

selected for bid solicitation. As under HUD normal practice, performance and payment bonds, as well as Davis-Bacon wages, were required

Thirty-six dwelling units were deleaded in the "privately owned"

category.

' Thirty-five dwelling units were deleaded in the "acquired-property"

category.

16
for all contracts in excess of $2000. Since some of the deleading

contractors who were low bidders, but bid over $2000, were unfamiliar

with obtaining performance and payment bonds, excessive delays resulted.

In several cases the contract award had to be withdrawn and given to

the next lowest bidder.

The contract management firm negotiated a price for recording all of the

contractor's direct costs for both the "privately owned" and "acquired

property" cases after the abatement contracts had been awarded. It

should be noted that the direct costs to the contractor are generally

less than the bid price. Direct costs represent the basic costs to the

abatement contractor of labor, materials, and any special equipment,

whereas the bid price includes a markup for overhead and profit.

The collection of direct cost data outlined above was accomplished

through the use of the Dwelling Unit Cost Data Form, a sample of which

is shown in Figure 2.1. The form is used to collect information about

the contract, the address of the dwelling unit, the time elapsed between

the start and finish of the abatement work, the substrate condition,

the surface deleaded, and the abatement techniques used. This format

was chosen because it facilitates access to cost and other data for

indepth analysis. The form is divided so that both direct labor use

and material and equipment use can be identified for each sequence in

the abatement process. Information on labor includes skill, hours

expended, and hourly rate. Information on materials and equipment

includes the type (e.g., gypsum wallboard) , unit size (e.g., 4* x 8' x 1/2"), quantity, unit cost, and equipment purchase or rental price.

The type of abatement work being performed (i.e., the sequence in the

process) is divided into seven basic tasks including preparation, repair,

installation or paint removal, and finish painting. The type of work

being done also took into consideration such tasks as cleanup and waste

disposal, down-time (e.g., lock-outs), and other activities (e.g., transportation to and from the job site). Thus, the form improved the efficiency with which data on each technique could be collected. It also

aided in the identification of those portions of the abatement process

which were major sources of variations in direct costs.

As mentioned earlier, abatement contractors were required to report

direct cost figures to the contract management firm. The method of

recording cost figures from abatement contractors, however, differed

between Phase II and Phase I of EHEP. This resulted from a desire to

improve both the efficiency and accuracy with which cost figures were

reported.

Those contracts which were under $2000 were issued under purchase

orders and had no requirement for bonds or Davis-Bacon wage rates.

17
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18
In Phase II of EHEP, costs were reported to the contract management firm

on a daily basis. This was accomplished by having a representative of

the contract management firm visit the job site daily, check the work

progress, and record the figures for labor, material, and equipment

usage. A great deal of confidence can therefore be placed in the accu- racy of the data reported. Furthermore, contracts were kept smaller

than in Phase I of EHEP so that more control could be exercised over

data collection as well as give more contractors an opportunity to bid on the limited number of dwelling units.

In Phase I of EHEP the collection of cost data differed between Washington and Atlanta. This was largely due to the problems experienced in

the Washington portion of the program.

In Washington, cost data was collected after all abatement work was completed. Unfortunately, the length of time between the start and finish

of the contract was long enough so that the accuracy with which cost

figures were reported may have suffered, especially if adequate records were not kept during the abatement work. Furthermore, all abatement

work in Washington was done by the same contractor so that less variation in wage rates, material costs, equipment costs, and productivity

was experienced than would be expected with more than one contractor.

The major changes in cost data collection between Washington and Atlanta

involved the reduction of the size of the contract packages and the

requirement that cost data figures be reported each week. With a variety

of contractors it became possible to measure the effects which variations

in wage rates and productivity had on direct costs. The collection of

cost figures each week strongly encouraged good record keeping and hence

improved the reliability of the figures recorded.

3. RESULTS OF COST DATA ANALYSIS

The cost data collected in Phase II of EHEP show a rather wide variation.

For example, the direct cost per square foot for cementitious coating

runs from $0.22 per square foot to $1.34 per square foot; gypsum wallboard, a material which is widely used, ranged from $0.35 per square foot

to $1.20 per square foot. Sample data of this sort is usually summarized

by computing average values. Summarizing the Phase II EHEP cost data by

computing average values is quite straightforward, and it provides a means of comparing the costs of the different abatement techniques.

Table 3.1 presents the average direct cost per square foot for the

techniques which were used to abate the LBP hazard on walls (barrier

materials). Additionally, Table 3.1 presents an estimate of a confidence

If any work stoppage resulted from the visit of the representative of

the contract management firm, it was logged on the Dwelling Unit Cost

Data Form,

19
interval about these average figures. These figures are preceded by +; this means that the reported average figure plus or minus the number in

the second column represents the interval within which we can safely

say that average costs will fall. In the case of gypsum wallboard,

based on data collected from 13 dwellings units, the average direct

cost per square foot will reliably fall between 65 cents and 93 cents

per square foot (the average of 78 cents per square foot + 15 cents).

Cement coated fiberglass on the average, costs 18 cents more per square

foot than gypsum wallboard (see Table 3.1). However, because the confidence interval for cement-coated fiberglass (between $0.82 and $1.10)

overlaps that of gypsum wallboard, one cannot say with very much certainty that gypsum wallboard is less costly. Care must be excercised

in drawing conclusions based on averages of data.

TABLE 3.1

AVERAGE DIRECT COST PER SQUARE FOOT FOR APPLYING BARRIER MATERIALS

Abatement

Method

Average Direct

Cost per Square

Foot ($)

Width of

Confidence

Band Around

the Average ($)

Gypsum Wallboard

Plywood Paneling

Cementitious

Coating

0.78

1.04

0.79

+ 0.15

+ 0.14

+ 0.20

Veneer Plaster

Cement-Coated

Fiberglass

Vinyl-Coated

Fabric

0.82

0.96

1.19

+ 0.22

+ 0.14

+ 0.41

A confidence interval shows the width of the band of uncertainty which

surrounds the estimated average cost figures in Table 3.1.

The average cost figure used here is the average of abatement costs in

many dwelling units. Abatement costs in a particular dwelling unit may

therefore be well above or well below this average cost figure.

20
In many cases it may be quite satisfactory to use the reported average

figures as a basis for estimating the costs of abating the LBP hazard.

However, additional problems exist with the use of average figures. How

does one modify the costs experienced in Boston for gypsum wallboard to

reflect the labor market conditions of, say, Columbus, Ohio? In other

words, if the data is to be useful it must include a procedure, or method, which will allow for conditions different from those experienced

in Boston to enter into the calculation of predicted costs.

Deriving a method of cost estimation which is based on real world experience and which allows local conditions to register their influence on deleading costs is a difficult problem of statistical measurement. The core conceptual premise which enables this to be achieved is the assumption that costs are dependent on (i.e., are made to vary by) a small

handful of identifiable key factors. In other words, changing the values

of the key factors will cause changes to occur in the average direct

costs. The problems involved in statistical measurement require us to

identify those key factors and then to measure their impact on costs.

Economic theory and engineering practice help identify a list of candidate key factors for each abatement technique. Statistical analysis

provides the tools for measuring the relative importance (or unimportance) of the candidate key factors. Using such an approach considerably

lowers the level of uncertainty. It was mentioned above that the range

of reliability for gypsum wallboard was 78 cents + 15 cents. After

introducing five key factors as causal agents in explaining the cost per

square foot of gypsum wallboard, the confidence interval of prediction

about the average has been reduced to + 7 cents. This represents a 53

percent reduction in the possible error of estimation that would be made

if we relied only upon the simple average figure.

In this chapter, after reviewing our definitions of costs, we shall

discuss the cost models for each technique for deleading walls and for

deleading trim. An explanation of the variations in painting costs will

then be presented. Finally, at the end of this chapter, we shall sum- marize the results of our data analysis. For readers wishing to pursue

the more technical details of estimation, a short theoretical discussion

is provided in Appendix C of the companion report Lead Paint Abatement

Costs: Some Technical and Theoretical Considerations .

3.1 THE COST CONCEPTS

In this report the definitions of costs being used are fairly conventional. However, to insure understanding, it is necessary to define

the terms which will be used throughout the report. For any particular

This chapter does not attempt to explain in any detail how to use these cost models to estimate costs. Applications of the material

presented in this chapter are presented in Chapter 5. This chapter

and Chapter 4 form the basis for the procedures discussed in

Chapter 5.

21
lead paint abatement task, say deleading 360 gross square feet in a bedroom with gypsum wallboard, a contractor will experience a total

cost of undertaking that particular task. The total cost will include

payments to labor, payments for material, and any overhead costs that

should in principle be assigned to that task. The difference between

the bid price, i.e., the contract amount for which the contractor

agreed to do the work, and the total labor, materials, and overhead

costs represents the contractor's pretax profits.

Those costs that the contractor would not incur if he had not undertaken

the specific job (i.e., certain labor, material and other job specific

costs) are called direct costs or variable costs. Those costs that

the contractor or firm would incur regardless of whether the firm undertook a specific job (i.e., certain rental payments, debt service payments,

payments for equipment, payments for clerical and secretarial labor, and

payments for management) are lumped in the category of overhead costs.

The amount of overhead costs and profits which accrue to any specific

job are a function of many factors over and above a particular job.

Since these factors may be separated from those which are related to a specific technique, they will be dealt with in Chapter 4. Our focus in

this chapter will therefore be on the direct costs of the alternative

abatement techniques.

Direct cost can be discussed either in total terms or in per unit terms.

If we divide the total direct costs of deleading 360 square feet of

bedroom wall area with gypsum wallboard by the number of square feet, we are then discussing direct costs per square foot (i.e., per unit

direct cost). In this report our discussions of costs are presented on

a per unit basis. Note that per unit direct cost is always equal by

definition to the sum of per unit labor costs, per unit material costs,

and any per unit special equipment costs.

Also of interest is the size of the markup factor. Total costs per unit

(the sum of per unit direct costs and the overhead and profit residual

per unit) divided by per unit direct costs yields the markup factor.

The product of per unit direct cost, times the markup factor, times the

number of units, e.g., square feet of wall area or linear feet of trim,

will yield the total cost of a particular deleading job.

Finally, it should be carefully noted that in all our discussions of

direct costs we separate the direct costs for finish painting from those

for installing the abatement technqiue. Some abatement techniques,

e.g., plywood paneling, need not be painted. Some may or may not be

painted, e.g., cementitious coating. Some require painting, e.g.,

gypsum wallboard. By keeping the two cost figures separate, we are

"Direct" and "variable" can be used synonymously. In this report we shall use "direct."

22
insuring that variations in painting costs are not interpreted as causing

variations in the installation cost for a particular abatement technique.

3.2 TECHNIQUES FOR LBP HAZARD ABATEMENT ON WALLS

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 the six techniques

for deleading walls: gypsum wallboard, plywood paneling, cementitious

coating, veneer plaster, cement-coated fiberglass, and vinyl-coated

fabric.

In Section 3.3 we shall present the results of our cost analysis for the

trim methods.

For each technique the key factors affecting direct costs are identified.

An indication of whether direct costs rise or fall as the value of the

key factor is increased is also presented. For those readers who wish

to calculate abatement technique costs by hand, rather than use the

computer program presented in Chapter 5, a separate set of tables and

worksheets is provided. These tables include statistical estimates of

the weighting factors associated with each key factor. The weighting

factor tells us what quantitative impact a unit change in the value of

the associated key factor has on per unit direct cost. The methodology

for performing LBP abatement cost estimates by hand is described in

Appendix E of the companion report Lead Paint Abatement Costs: Some

Technical and Theoretical Considerations . Statistical estimates of the

weighting factors are found in Appendix C.3 of the same report.

3.2.1 Gypsum Wallboard

The results of our data analysis indicate that the per unit direct cost

of gypsum wallboard will

2 o fall as job size increases;

o increase with higher wage rates and material prices; and

o be more sensitive to increases in material prices than to

increases in wage rates.

1

2

A 10 percent level of significance is assumed in the discussion which

follows.

Job size is defined here as the square feet o^ wall area deleaded.

For~ paint removal methods, i.e., for trim surfaces, it is defined

as the linear feet of trim from which LBP was removed.

23
3.2.2 Plywood Paneling

The direct costs of plywood paneling will:

o increase with higher wages and material costs per square foot;

o increase with larger job sizes; and

2 o be sensitive to management competence and experience.

Separate estimates were made of the factors influencing material costs

per unit. Here it was found that material costs will:

o increase with higher sheet prices for paneling;

o increase if the wall surfaces are in an unsound condition; and

o fall with higher wage rates, because they proxy higher skills.

3.2.3 Cementitious Coating and Veneer Plaster

The cost data for these two methods of deleading walls were combined

into one analytical unit. It was found that direct costs per square

foot will:

o rise when either wage rates or material prices rise;

3

The impact of this factor is less certain than for wage rates or material prices since it is significant only at the 20 percent level.

The impact of experience (familiarity) is less certain than for wage

rates, material prices, or management competence since it is significant only at the 20 percent level.

An unsound wall is defined as any wall which exhibits one or more of

the following characteristics: (1) crumbling plaster, (2) bulging,

(3) efflorescence, (4) declamation from lath or structural members,

and (5) holes greater than four inches in diameter. If none of these

five characteristics are present, then the wall is defined as sound.

This combination was necessary because separate cost analyses of each

technique were not successful due to the lack of a sufficient number of

observations. Since both techniques are coatings with similar product

characteristics, and since there was no statistically significant difference ifi their average direct cost per square foot (only 3 cents in

realized cost), it was decided to analyze their cost characteristics

jointly. Although some prior objections could be advanced for combining

these data sets, the accuracy of the estimated results in predicting

the experienced costs of each of these techniques justified the merger

of the data sets.

24
o fall with larger job sizes;

o increase if the removal of wallpaper is necessary; and

o be significantly lower per square foot if cementitious coating

is used rather than veneer plaster, everything else held equal.

Separate estimates were made for the material costs of these techniques.

It was found that material costs will:

o be sensitive to room dimensions and configurations;

o be increased by poor wall conditions;

o be lower for veneer plaster than for cementitious coating.

3.2.4 Vinyl-Coated Fabric

In our previous discussions of barrier materials for LBP abatement, our primary focus has been on explaining variations in direct cost per square

foot. For vinyl-coated fabric and, as we shall see later, for cementcoated fiberglass, our statistical measurements of costs follow a slightly different sequence. This is because suitable results for

direct costs could not be immediately obtained. Instead it was necessary to first develop statistical measurements of the key factors which

explained labor productivity. These measures can than be used to find

labor cost per square foot. Next it was necessary to develop statistical

measurements of the key factors which determined material cost per square

foot. The sum of labor and material cost per square foot is equal to the

direct cost per square foot.

The calculation, or prediction, of direct costs per square foot thus

involves five steps:

(1) The determination of the average wage rate;

(2) The determination of the expected productivity of labor;

1 The impact of this factor is less certain than for wage rates or material prices since it is significant only at the 20 percent level.

The above sta ;ement should be very carefully qualified. The significant difference is a reflection of the fact that material prices

for cementitious coating tend to be higher than those for veneer

plaster. It does not necessarily imply that significant differences

in per unit direct costs exist per se. Since per unit direct cost is

very sensitive to small changes in material prices, an allowance had

to be made for cases involving veneer plaster.

25
(3) The division of the wage rate by labor's productivity to

determine labor cost per square foot;

(4) The determination of material cost per square foot; and

(5) The summing of labor cost with material cost to determine

direct cost per square foot.

It was found that the productivity of labor in the installation of vinylcoated fabric will:

o decrease if wainscotting requires removal;

o be higher for larger jobs and jobs which involve less cutting

and trimming of the material; and

2 o increase with higher paid labor.

Material costs per square foot will increase with higher prices per

square yard of vinyl-coated fabric and by the requirement that wainscotting be removed.

3.2.5 Cement-Coated Fiberglass

Labor's productivity in the installation of cement-coated fiberglass

will be:

3 o raised when the ratio of net-to-gross square feet is lowered;

o lowered by wainscotting removal;

1 Two key factors are involved in the above statement. The first,

job size, is significant at the 10 percent level. The second, a proxy for the amount of cutting and trimming of material, is significant only at the 30 percent level.

The impact of higher wage rates on productivity is less certain

than job size or the removal of wainscotting since it is significant only at the 20 percent level.

The gross square feet of wall area is defined as the total wall area including openings for windows, doors, and cabinets. It is equal to

the perimeter of the room times the ceiling height. The net square

feet of wall area is defined as the wall area remaining after the area

for all openings, such- as doors, windows and cabinets has been subtracted from the gross square feet of wall area. For a detailed discussion of this result, see Appendix D in the companion report Lead

Paint Abatement Costs: Some Technical and Theoretical Considerations.

26
° raised in unoccupied units; and

o lowered by unsound substrates.

Material costs per square foot increase with higher net square feet to

gross square feet ratios and with higher labor costs per square foot.

3.3 TECHNIQUES FOR LBP HAZARD ABATEMENT ON TRIM

Identifying low cost and effective methods of deleading LBP found on windows, doors, baseboards and other trim surfaces is as important as identifying cost effective methods of deleading walls. Trim surfaces

cannot be considered any less accessible to the pica child than wall

surfaces. In addition, trim surfaces contain higher quantities of

lead than do wall surfaces.

Trim surfaces exhibit substantially higher lead levels than do

walls, for all indicated combinations of occupancy class, room

type, and age category.

Four methods of removing LBP from trim surfaces were tested in the

Boston phase of EHEP. Two additional methods were tested in Atlanta.

In this section we present the cost characteristics of five of these

six methods: the infra-red heating device, solvent stripping, the

heat gun, hand scraping, and component replacement.

Note that in Table 3.2, which presents the average direct costs per

linear foot of paint removal, the estimates of the width of the confidence intervals about these average figures are quite large. Recall

that these figures, preceded by a + sign, represent the interval within

which we can safely say that average costs will fall. Using the costs

1

2

3

4

5

The impact of substrate condition on productivity is less certain

than the other key factors since it is significant only at the 30

percent level.

Douglas R. Shier and William G. Hall, Analysis of Housing Data Col - lected in a Lead Based Paint Survey in Pittsburgh, Pennsylvania Part I , National Bureau of Standards, Interagency Report 77-1250, March 1977,

p. 43.

The sixth method, the diptank, is described in Appendix B of Lead Paint

Abatement Costs: Some Technical and Theoretical Considerations .

A confidence interval shows the width of the band of uncertainty which

surrounds the estimated average cost figures in Table 3.2.

The averge cost figure used here is the average of abatement costs in

many dwelling units. Abatement costs in a particular dwelling unit

may therefore be well above or well below this average cost figure.

27
TABLE 3.2

AVERAGE DIRECT COST PER LINEAR FOOT FOR PAINT REMOVAL

Abatement Method

and Surface Type

Average Direct

Cost per Linear

Foot ($)

Width of Confidence

Band Around the

Average ($)

Heat Gun

Doors

Windows

Trim

Inf ra-Red

Doors

Windows

Trim

Hand Scraping

Doors

Windows

Trim

Solvent Stripping

Doors

Windows

Trim

1.64

2.20

0.57

1.93

2.27

0.54

2.53

2.23

0.81

3.80

3.44

1.26

± 0.87

± 0.89

± 0.19

± 1.43

± 1.24

± 0.80

±1.01

± 1.15

± 0.53

± 1.18

± 1.24

± 0.79

28
for the electric heat gun to remove LBP from doors based on data collected in nine dwelling units as an illustration, we can see that the

average direct cost per linear foot will reliably fall between $0.77

and $2.51 (the average of $1.64 per linear foot + 87 cents). The magnitude of the variations in per unit direct costs for the electric heat

gun are large but are also indicative of those of the three other paint

removal methods. Keeping this in mind, we may now go on to see how the use of the key factor method permits us to reduce the width of the band

of uncertainty about per unit direct cost to an acceptable level.

3.3.1 Electric Heat Gun

Direct costs per linear foot were analyzed for the electric heat gun

method of paint removal. The direct costs of using the heat gun to remove LBP from door and window surfaces were combined into one analytical unit while the direct costs of using the heat gun to remove LBP

from baseboard trim was made a separate analytical unit. Our analysis

indicates that the direct cost of paint removal using the electric

heat gun will:

o fall with larger job sizes;

o rise with higher wage rates;

o be higher if non-woodworking contractors perform the work; and

o be lower for doors than for windows and are lowest for baseboard

trim.

3.3.2 Hand Scraping

The direct cost of removing paint by hand scraping is sensitive to two

variables. Direct costs will:

o rise as the wage rate increases; and

2 o fall as the number of linear feet deleaded is increased.

A 10 percent level of significance is assumed in the discussion

which follows.

The impact of job size on direct costs is less certain than wage

rates since it is significant only at the 20 percent level.

29
3.3.3 Infra-Red Heating Device

For the infra-red heating device we found that an analysis of labor

cost per linear foot rather than direct cost per linear foot gave us much more reliable measures of the underlying key factors. Material

costs represent a small and relatively constant proportion of direct

costs per linear foot for this method. Furthermore, the fraction of

direct costs per linear foot associated with labor costs was also

relatively constant. Therefore, it was decided to factor up labor

costs by that fraction in order to determine direct costs per linear

foot.

Two key factors each for windows, doors and baseboard trim determine per

unit labor costs for this technique. The wage rate has, as one would

expect, a strong and very reliably measured impact on labor costs. Job

size expresses itself as a key factor in an unusual manner. Rather than

appearing simply as a measure of linear feet, job size is the product of

two physical measurements — linear feet multiplied by the XRF reading,

the lead content in the paint film covering the trim surface. Labor

costs per linear foot fall as the product of this key factor increases.

Neither linear feet nor XRF readings by themselves show up as measurable

key factors affecting labor costs per unit. This compound factor probably measures the joint effects of job size and difficulty of removal

of the paint. Paint technologists have speculated that the XRF readings

(lead content of the paint) may be related to the strength of the bond

between the paint film and the substrate in this way. As paint films

become very old, they suffer significant loss of bond strength. The

older paints encountered in housing quite likely have a significantly

higher lead content than newer paints. Thus, in general, the paints

with high lead levels, being older, should be more easily and quickly

removed.

3.3.4 Solvent-Based Paint Remover

The labor costs per linear foot for this technique respond to the same key factors that affect the costs of removing paint with an infra-red

heating device. It was found that labor costs will:

o rise with wage rate increases;

o fall as the product of the XRF reading and linear feet rises,

and;

o be lower for baseboard trim than for windows and doors.

1 The infra-red heating device was used only in unoccupied dwelling

units in Phase II of EHEP because it produced smoke which may have

contained lead fumes.

30
3.3.5 Component Replacement

The per unit (i.e., per door, per door frame, or per window and frame)

direct cost of component replacement is primarily sensitive to two

key factors:

o the wage rate; and

o the price per unit of the component.

3.4 FINISH PAINTING

In the introduction of this chapter we stated that painting costs were

not included in our analysis of the direct costs for the various techniques. This was done in order to keep variations in painting costs

from falsely influencing the cost data for the installation of barrier

materials or paint removal methods. It was found that wall painting

costs will:

° fall with larger job sizes;

rise when either wage rates or material prices rise; and

° rise when the ratio of net-to-gross square feet rises.

It was found that painting costs for doors and frames and windows and

frames will:

° fall with larger job sizes; and

° rise when either wage rates or material prices rise.

Baseboard painting costs were found to be sensitive only to changes in

wage rates. In particular, baseboard painting costs will rise as wage

rates rise.

3.5 SUMMARY OF COST DATA ANALYSIS

This chapter has presented our attempts to identify and measure the

impacts that relative changes in the value of key factors have on

Component replacement was not used in Phase II of EHEP due to its

excessive cost. This method was used in Phase I of EHEP and was found to be appropriate only in cases where the component was severely

damaged or deteriorated. The cost estimation procedures presented in

Section 3.3.5 for component replacement are based on Phase I EHEP

cost information and are patterned after those procedures presented

in Robert E. Chapman, Economic Analysis of Lead Paint Abatement

Methods: Phase I , National Bureau of Standards, Technical Note 922,

September 1976.

31
direct costs. The salient result of our statistical analysis has been

the measurement of the role played by job size (square feet or linear

feet) in the determination of costs. For all of the techniques, except

component replacement, job size in some form is an important key factor.

This result may seem surprising to some because of the relatively small

sizes of. the jobs that were undertaken in each dwelling unit. Furthermore, it should be noted that in nine of the ten techniques analyzed,

increasing job size causes costs per unit to fall. From a cost effectiveness point of view, this implies that it will be less expensive to

delead 1,000 square feet of wall area in one dwelling unit than deleading

500 square feet of wall area in each of two dwelling units.

In all of the wall models the wage rate and material cost and/or prices

are statistically significant key factors. In all of the trim models

wage rates are significant key factors. These results are in full accord

with our hypothesized expectations. The structure of the Phase II experimental design emphasized isolating the effect of substrate condition.

For all of the wall models, except gypsum wallboard, poor substrate con- dition was found to add significantly to direct cost per square foot.

It also bears pointing out, that even though LBP abatement operations

are conducted in an environment which contains many unknowns and unpredictable events, it is possible to explain the level and behavior of

costs with a relatively small handful of key factors. Using the key

factor approach to estimate, i.e., predict costs, will result in much

more accurate predictions than those based on averages of historical

data.

It was stressed in the introduction of this chapter that caution must

be exercised when making use of average figures. The selection of one technique on the basis of its average cost for deleading walls or trim

(as is done in some cities) and its uniform application in every dwelling

unit may not be cost effective.

Tables 3.3 and 3.4 summarize the cost data that was collected in the

Boston phase of EHEP. If our qualifications are remembered, these tables

should provide a useful portrayal of the structure of costs for each of

the different abatement techniques.

4. GENERALIZATION OF EHEP COST MODELS

In order to effectively use the information on direct costs presented

in Chapter 3, two additional steps must be taken. First, we must know

how direct costs are associated with total cost, so that an estimate of

the contractor's bid price can be made. Second, we must be able to

generalize our conclusions about direct costs to programs outside the

See Section 4.2.1.1 of this report.

32
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34
context of EHEP. In other words, we must be satisfied that the statistical measurements made in Boston, Massachusetts under Phase II of EHEP

will also be valid in Chicago, Illinois or Coos Bay, Oregon. These two

topics are the subject of this chapter.

4.1 MARKUP AND ITS DETERMINANTS

Every buyer of LBP abatement services is presented with a total cost

figure from the abatement contractor which is referred to as the bid

price. The bid price includes not only the contractor's anticipated

direct costs (labor, materials, and equipment) associated with doing

the job, but also a contribution to overhead and a residual which may

be referred to as profit. If the buyer agrees to the contractor's bid

price, then after the job has been completed the difference between the

bid price and direct costs will be the the contractor's overhead and

profit. The ratio of bid price (total cost) to direct cost represents

the markup ratio. This relationship may be expressed by the following

simple identity:

BID PRICE = MARKUP RATIO x DIRECT COST 4.1

In Chapter 3, a means of determining direct cost was presented. In this

section we shall see how certain job specific factors combine to produce

an expected markup ratio.

It is important to point out here that, if the decision is made to operate

in a cost plus fixed fee contracting mode, direct costs may be factored

up by the agreed upon fixed fee rate to arrive at total costs. The

decision-maker faced with a large amount of abatement work thus might

want to weigh the advantages of the cost plus fixed fee contracting mode

against those of the bid price. In general it would be expected that

abatement contractors would prefer to operate in a cost plus fixed fee

mode since uncertainties due to varying housing conditions are shifted

from the contractor to the buyer of his services. This would probably

stimulate more competition among abatement contractors for the contracts

being let. On the other hand, the buyer loses a certain amount of fiscal

control in going from the relatively firm figure represented by the bid

price to the cost plus fixed fee arrangement.

The Phase II EHEP data used to analyze markup involved 19 separate contracts and 58 dwelling units. About 28,000 net square feet of wall area had barrier materials applied, and LBP was removed from over 3200 linear

feet of trim. The total bid price of these 19 contracts was $63,784.

This was 17.6 percent higher than the actually experienced direct costs

Although 24 contracts were let during Phase II of EHEP, five contracts, were excluded from the data base used to analyze markup. Four contracts involving two contractors were excluded because their data

was proven to be unreliable. The fifth contract was eliminated because

the building was seriously damaged in a fire.

35
(i.e., the average markup ratio was 1.176). The actual markup ratio on

the 19 individual contracts ranged from 0.94 to 1.49.

Three key factors were found to explain 77.3 percent of the variations

in the markup ratio. From the results of our analysis, the following

can be inferred:

o increasing the net square feet of wall area to be deleaded tends

to lower the markup ratio;

o increasing the number of linear feet of paint removal raises tne

markup ratio; and

o contracts using abatement techniques with which the contractors

are familiar (e.g., gypsum wallboard or plywood paneling) are

subject to markup discounts.

4.2 VALIDATION

The findings of this empirical study can be generalized to cities other

than Boston and to times other than 1976. This conclusion is based on several considerations. First, the key factors associated with each

technique will reflect important differences which exist between regions.

Second, EHEP has been structured so as to facilitate the generalization

of its results.

Four major research tasks have been built into EHEP to insure that its

results can be generalized. Three of these tasks relate to the internal

validity and consistancy of the experiment. The fourth task relates to

a test of the ability of the EHEP Phase II models to predict direct costs

for different regions and times. This task is one test of EHEP's external

validity. In this section we shall assess the results of Phase II of EHEP

both in terms of its internal validity and in terms of its external validity.

Certain portions of the discussion which follows are of a more technical

nature than in previous sections. Therefore, the reader who is primarily

interested in applying the cost estimation procedure presented in

Chapter 3 is directed to Chapter 5.

4.2.1 Internal Validity

If empirical findings are to be useful for prediction (i.e. , if they are

to be externally valid), they first must be internally valid. Three

dimensions comprise the elements of internal validity:

(1) the experimental design;

(2) the data collection procedures; and

(3) the degree of reliability and accuracy within the data set.

36
Each of these three dimensions will now be discussed to show how the

experiment was constructed, how it was controlled, and why its results

exhibit a high degree of internal validity.

4.2.1.1 Experimental Design

To insure internal validity, an experiment must be designed so that a degree of control is introduced over the various factors that affect

the outcome of the experiment. This is necessary in order to identify

the main causative factors.

The collection of meaningful cost information from the dwelling units

selected for LBP abatement hinged on an experimental design capable of

identifying and measuring those factors which cause variations in abatement technique cost. Of crucial importance in the measurement of variations in abatement technique costs is the ability to identify the ways

in which variations in the individual abatement technique's costs are introduced. In order to be able to identify the sources of variation

in costs, it was necessary to specify where the data were to be collected

in terms of occupancy status of the dwelling unit, the condition of the

substrate, the functional uses of the rooms, the ownership charcteristics

of the dwelling units, and how the abatement techniques were to be

assigned to dwelling units.

The experimental design is described in part by referring Table 4.1,

which shows the structure of the experiment for making cost comparisons

among trim abatement techniques.

TABLE 4.1

EXPERIMENTAL DESIGN FOR THE TRIM EXPERIMENT

Occupied Unoccupied

Abatement Technique Abatement Technique

ABCD ABCD

5055 5555

37
Table 4.1 is divided into two parts; one part for occupied dwelling

units and one part for unoccupied dwelling units. The columns (letters

A through D) identify the specific abatement technique. The number under

each letter specifies the number of dwelling units the experiment requires

for that technique, For example, if the dwelling unit is occupied, and

abatement technique A is used to remove paint from all trim surfaces,

then five dwelling units were required. Note that each cell but one in Table 4.1 has the same number of observations.

The complete experimental design consisted of three separate tests to measure variations in costs as a function of abatement technique, occupancy status and surface type or condition. This third test category was further divided into three parts. These parts were concerned with

assessing the variation in relative costs as a function of (1) wet or dry rooms, (2) sound or unsound wall surfaces, and (3) trim surfaces.

The overall experimental design is shown in Table 4.2. Note that the

numbers in each cell of Table 4.2 are divided into two parts by a slash. The desired number of observations comes before the slash.

The actual number of observations is shown after the slash. The fact

that the desired number of observation is not always equal to the actual

number of observations is a reflection of the complexity of controlling

an experiment of this size. (Recall that the complexity of the experiment has already been significantly reduced as a result of providing

surface abatement criteria.) An additional problem resulted in that

no abatement work was undertaken in any of the local housing authority's

stock of dwelling units. This portion of the program was abandoned due

to the difficulty experienced in finding lead levels which were comparable to those in HUD acquired properties. An important part of the

Phase II EHEP experimental design was the assignment of abatement techniques to the stock of dwelling units. A procedure was developed so

2

Technique B, the infra-red heating device, was not used in occupied

dwelling units because it produced smoke which may have contained

leaded fumes.

Wet rooms refer to bathrooms and kitchens; all other rooms were

assumed to be dry.

3 An unsound wall is defined as any wall which exhibits one or more of

the following characteristics: (1) crumbling plaster, (2) bulging,

(3) efflorescence, (4) declamation from lath or structural members,

and (5) holes greater than four inches in diameter. If none of these

five characteristics are present, then the wall is defined as sound.

4 The surface abatement criteria used in Phase II of EHEP were discussed

in Section 2.2.

5 2 The same criteria for lead content, 2.0 mg/cm or more, was used in

screening dwelling units owned by the local housing authority as was used in screening the HUD acquired properties.

38
TABLE 4.2

EXPERIMENTAL DESIGN FOR PHASE II OF EHEP

Building Occupied Unoccupied

Component ABCDEF ABCDEF

Walls3 (wet) 5/4 5/7 5/5 5/5

(dry) 5/4 5/3 5/3 5/2

; (sound) 5/4 5/7 5/5 5/5 5/4 5/4 5/4 5/3 5/2 5/2 5/2 5/3

(unsound) 5/4 5/3 5/3 5/2 5/2 5/3 5/4 5/4 5/3 5/4 5/5 5/1

Trimb 5/6 0/0 5/3 5/4 5/3 5/3 5/4 5/3

a

A

B

C

D

E

F

b

A

B

CD

Techniques for LBP Hazard Abatement on Walls

Gypsum Wallboard

Plywood Paneling

Cementitious Coating

Cement-Coated Fiberglass

Veneer Plaster

Vinyl-Coated Fabric

Techniques for LBP Hazard Abatement on Trim (Doors & Frames, Windows &

Frames, and Miscellaneous Trim)

Electric Heat Gun

Infra-Re d Heating Device

Solvent-Based Paint Remover

Hand Scraping and Sanding

39
that the assignment of abatement techniques to the stock of dwelling

units was a random process. This minimized ' the changes of systematic

bias entering into the experiment.

The assignment of the abatement techniques to the stock of dwelling

units may now be explained by referring once again to Table 4.1. To

begin with, a dwelling unit is selected from those which satisfied

the surface abatement criteria, and the criteria for lead content.

Assuming this dwelling unit is unoccupied, it has an equal chance of

being assigned to each of the four trim abatement techniques. To determine which abatement technique to which it is assigned a random number

is drawn. (Picking a random number is analagous to flipping a coin or rolling a die.) The random number is selected in such a way that it

may be characterized as a proportion between zero and one. Similarly,

the four abatement techniques may be characterized by ranges of proportions between zero and one. For example, Technique A is characterized

by all proportions between and 1/4, B by all proportions between 1/4

and 1/2, C by all proportions between 1/2 and 3/4, D by all proportions

between 3/4 and 1. If the random number indicates that the proportion

is 3/10, then this corresponds to the range of values assigned to Technique B. Thus Technique B is assigned to that dwelling unit. There

are now only four observations required to fill the cell unoccupied —

Technique B. Similarly, there are only 19 more observations needed to

complete the trim experiment in unoccupied dwelling units. The proportions are now recalculated for each of the four trim abatement techiques,

Technique A is now characterized by all proportions between and 5/19,

B by all proportions between 5/19 and 9/19, C by all proportions between

9/19 and 14/19, and D by all proportions between 14/19 and 1. The

process is continued until all cells have been filled. A similar procedure was used to assign abatement techniques to dwelling units with

walls requiring deleading work. For those readers who are interested in

the decision model used in Phase II of EHEP, a mathematical discussion

is provided in Appendix C of the companion report Lead Paint Abatement

Costs: Some Technical and Theoretical Considerations .

4.2.1.2 Data Collection Procedure

Measurement errors pose serious problems to the internal validity of an experiment. For example, the statistical analysis of data with serious

measurement errors involving the key factors requires the use of complicated statistical procedures. As a consequence, very strong assumptions

about the nature of the measurement errors must be made. Thus the administration of the experiment and the actual collection and recording of

the data is an important element affecting the internal validity of the

experiment.

The collection of the data in the field was under the direction of a team from the Special Projects Group of the Boeing Aerospace Company.

40
The project manager and the project engineer from Boeing are both

experts in the fields of remodeling construction. With their extensive

background in the administration of construction contracts, they were

able to effectively monitor and record accurately the field collected

data. In Phase II of EHEP, costs were reported to the Boeing representatives on a daily basis. This was accomplished by having a representative of the contract management firm visit the job site daily, check

the work progress, and record the figures for labor, material, and

equipment usage. A great deal of confidence can therefore be placed

in the accuracy of the data reported. Smaller contracts in Phase II

than in Phase I of EHEP also permitted more control to be exercised

over data collection. Smaller contracts also stimulated more competition since it gave more contractors an opportunity to bid on the limited

number of dwelling units.

4.2.1.3 Internal Reliability and Accuracy

Probably the best check of an experiment's internal validity is provided

by the quality of the experiment's statistical measurements. Realiable

and accurate statistical measurements will result when the experiment

has successfully identified the basic structure of the process being

analyzed and has developed procedures which accurately quantify the factors which interplay to cause variations in the process being studied.

Tests of the reliability of statistical measurements involve, in some form or other, asking "how well do the statistical measures explain

the data set from which they were derived?" Three major tests have

been used to examine the EHEP results. First, a descriptive statistic,

the average absolute percentage error, was used. Second, a statistic

relating the overall ability of the model to explain the variations

that occurred in the dependent variable was computed. Third, an indicator of the relative (statistical) importance of each of the key factors

was calculated. With regard to these three statistics and their values,

it is possible to assert that EHEP Phase II results exhibit a high degree

degree of internal validity.

The Average Absolute Percentage Error

The Average Absolute Percentage Error is a descriptive statistic that

measures how close, in percentage terms, that predicted (estimated)

costs come to actual (experienced) costs. It is analagous to the cost

estimator's concept of "percent error in the estimate."

The project manager heading EHEP administration was Lee Gaylor of the

Special Project group, Logistics Support and Services Division, Boeing

Aerospace Company. The project engineer, Ben Brown, was responsible

for field management of EHEP. Close cooperation existed between HUD,

Boeing and NBS.

41
As an example, let us look at a particular case involving gypsum wallboard. In one dwelling unit in the Dorchester Section of Boston, a contract was awarded to a contractor which involved the installation

of 327 net square feet of gyspum wallboard in a living room. The wage

rates and crew composition dictated a $10.26 per hour average wage rate.

The contractor paid $2.40 per sheet for wallboard. Using these key

factors results in a predicted direct cost of $1.01 per square foot.

This predicted value compares with the actually experienced value of

92 cents per square foot. The error that results is 9 cents per square

foot, and amounts to 9.8 percent of the actual value.

For each of the 13 dwelling units where gypsum wallboard was installed,

we have computed the error that results when the key factor method of

cost estimation was used. Summing the absolute values of these errors

and dividing the sum of the absolute errors by the sum of the actual

values yields a measure referred to as the "Average Absolute Percentage

Error" (AAPE). In the case of gypsum wallboard, the average absolute

error associated with the use of the key factor method of estimating the

direct costs of gypsum wallboard is 8.8 percent. Table 4.3 presents the

average absolute errors associated with the predictions of direct cost

per unit presented in Section 3.2.1 through 3.2.5 of this report. The

median of the AAPE's for the six wall techniques is 6.7 percent.

The average absolute error percentages associated with trim's key factors

ranged from a low of 5.4 percent for the heat gun (doors and windows) to

a high of 32.7 percent for hand scraping (baseboards only). The median

error percentage was 15 percent. The error percentages for trim methods were higher than for wall methods for three reasons. First, linear foot

measurements are not as reliable as square feet measurements for job size,

This is because trim varies not only in length, but also in width and in

degree of ornateness. These two characteristics were not measured. As

such, an element of inherent variability is introduced in our measures of

direct cost. Second, because of the fundamental simplicity of the trim

methods, the number of candidate key factors and consequently the number

of utilized key factors are fewer than for the wall techniques. Third,

the total labor hours devoted to deleading trim in a dwelling unit were

substantially less than total labor hours involved in applying barrier

materials to walls. (As few as four labor hours were spent deleading

trim in one dwelling unit). Small errors of measurements or rounding

in the recording process would therefore produce greater impacts on measured per unit costs for trim methods in comparison to wall methods.

The average absolute percentage error associated with using the key

factor approach to project the markups in the Boston data is 5.1 percent.

If the average markup of 1.176 was used uniformly in every contract, the

resulting error would be almost twice as large as the error associated

with predicting markups using the key factor method.

The Coefficient of Determination

42
TABLE 4.3

AVERAGE ABSOLUTE ERRORS AS A PERCENTAGE OF

ACTUAL DIRECT COST FOR BARRIER MATERIALS

Average Absolute

Technique Percentage Errors

Gypsum Wallboard 8.8

Plywood Paneling 5.6

Cementitious Coating 5.1

Veneer Plaster 11.5

Vinyl-Coated Fabric 6.1

Cement-Coated Fiberglass 7.7

Another statistic which is widely used to evaluate the quality of statistical measurements is the coefficient of determination. This

statistic measures the ratio of the explained or predicted variation in

a set of sample data to the total or actual variation in the data. This

statistic can vary between (the model does nothing to help explain the

variation in the dependent variable) and 1 (all variations are explained

by the model). The lowest coefficient of determination for the barrier

techniques is 0.66 while the highest is 0.96.

Although the trim methods of cost estimation did not perform as well as

the wall methods of cost estimation in terras of error percentages, they

did as well in terms of their ability to explain the inherent variation

in the trim sample data. The median value of the trim techniques coefficient of determination was 0.85 with all of the estimated coefficients

of determination for the trim methods being over 0.79. This is a very

encouraging result. The coefficient of determination for the estimated

markup relationship is 0.77.

Significance 1 of the Statistical Measurements

A 5 percent level of significance is assumed in the discussion which

follows.

43
Lastly, another way of evaluating these statistical measurements involve

examining the significance of our measurements of the individual impacts

of each of the key factors. In the analysis of barrier materials presented in Chapter 3, there are 43 key factors identified. Twenty-nine

(67%) of these key factors are statistically significant. In terms of

the significance of the statistical measurements of the impacts of the

key factors for the trim techniques, 25 of the 35 statistical measurements (71%) are statistically significant. All of the key factors for

the markup relationship are statistically significant.

4.2.2 External Validity

The cost data generated in the Atlanta portion of Phase I of EHEP was

used to test the ability of the Boston results to predict costs in a different city at a different time. The accuracy of the key factor

method in estimating (i.e., predicting) direct costs in Atlanta provides an indication of the level of confidence which we can have in

its external validity. In addition to assessing the predictive accuracy of the key factor method, it would also be useful to know if in

fact the EHEP procedure does better than a "naive" or less complicated

method of prediction. Furthermore, it would be useful to know how much

better in numerical terms our predictive mechanism does relative to

such a "naive" method. To illustrate this point we may draw on an

analogy to a betting system. Assume that one has developed a method

for predicting the expected point difference in professional football

games. It would be useful here to know how close (in percentage terms)

this method came to predicting the actual point spread over some sample

of games. Furthermore, since the method of prediction may be costly to

implement, it would be useful to know what improvement this system

offers relative to a "naive" system. The "naive" system could be

predicting a future point spread on the basis of the point spread in the

last game played between two teams. Atlanta data provides us with an opportunity to make such tests.

An estimate of the EHEP procedures' accuracy is provided by calculating

the Average Absolute Percentage Error (AAPE) associated with predicting

direct costs per dwelling unit in Atlanta when compared to the direct

costs per dwelling unit actually experienced. In order to perform this

test, the key factor models were used to predict direct costs for each

of the deleading tasks required in a sample of Atlanta dwelling units.

The predicted costs of these tasks were summed to obtain a predicted

One naive method of cost estimation is to use historical cost figures

that are adjusted to reflect differences in time and location.

44
total direct cost for that particular dwelling unit. Table 4.4 lists

in the third column the predicted dwelling unit costs for nine dwelling

units.

The first column of Table 4.4 lists the techniques used in a particular

dwelling unit. Column two reports actual direct costs. The magnitude

of the error (predicted value minus actual value) is reported in the

fourth column. This number divided by the actual value yields the percentage error. Summing the absolute values of these errors and dividing

by the number of observations yields 8.4 percent. This means that the

average absolute percentage error associated with the use of the EHEP

models in predicting direct costs at the dwelling unit level in Atlanta

is 8.4 percent. When the sum over the nine units of the predicted

dwelling unit cost (column 3) is divided by the sum of the actual

reported costs (column 2), this error is reduced to 3.8 percent.

(Notice that the estimated total is below that of the direct costs

actually experienced.) Earlier in this section we stated that it would

be useful to compare the EHEP procedure to a "naive" method of forecasting costs. This requires a more complete definition of a "naive" method.

For the purposes of this comparison we assumed as our naive method the

following: that per unit predicted costs for a subtask be based on the

simple adjusted averages reported in Tables 3.3 and 3.4.

This assumes, "naively," that Atlanta will be like Boston on the average

with respect to the direct costs of deleading.

3 Based on a procedure developed by Henri Thiel, the following assertion

can be made: Using EHEP key factor models to predict direct costs per

The nine dwelling units used in the assessment of the Phase II EHEP

cost models represent approximately 11 percent of the total population

of dwelling units deleaded in the Atlanta portion of Phase I of EHEP.

Several criteria were used to select candidates for inclusion in the

assessment of the Phase II EHEP cost models. These criteria included:

(1) similarity with a technique used in Phase II; (2) the dwelling

unit contained both trim and wall work; (3) the observations were

independent. Dwelling units which satisfied these criteria were then

selected at random for the validation exercise.

2 The average per unit labor and material costs experienced in Boston

were deflated by relative price indicies (Atlanta relative to Boston).

The indicies, based on city cost indicies for labor and materials

found in Building Construction Cost Data 1975 and 1977 , R. S. Means,

editor, were used to adjust the Boston data to reflect the differences

in labor market and material market conditions that existed between

Atlanta in January of 1975 and Boston in May of 1976.

3 Thiel, Henri, Applied Economic Forecasting North-Holland Publishing

Company, Amsterdam (1966), pp. 15-36.

45
TABLE 4.4

ESTIMATING LBP ABATEMENT COSTS IN ATLANTA, GEORGIA

USING THE PHASE II EHEP COST MODELS

Methods Used in the Dwelling Unit

Actual3 Dwelling Unit

Direct Cost

Predicted*

Dwelling Unit

Direct Cost

Error3 Predicted

Minus Actual

Direct Cost Percent Error

(1)

Wall Trim

(2) (3) (4) (5)

Gypsum Heat Gun + +

Painting Painting 500.68 529.86 29.18 5.8

Melamine Solvent +

Painting 1059.61 1172.73 113.12 10.7

Veneer Replacement

Plaster Solvent + +

Painting Painting 775.94 758.39 -17.55 -2.3

Gypsum

+

Painting

Replacement

Heat Gun +

Painting 662.52 684.95 22.43 3.4

Gypsum Replacement + +

Painting Painting 687.35 633.95 -53.4 -7.8

Filled

Paint + Painting

Solvent

+ Painting 444.87 466.41 21.54 4.6

Gypsum

+

Painting

Replacement

Solvent

+ Painting 1558.11 1323.83 -234.28 -15.0

Veneer

Plaster +

Painting

Replacement

Heat Gun +

Painting 501.88 422.24 -79.64 -15.9

Filled

Paint

Solvent +

Painting 636.06 571.72 -64.34 -10.1

Entries are in dollars per dwelling unit.

46
dwelling unit reduces the root mean squared error to only 24.3 percent

of that which would result if the "naive adjusted average" method was used to predict direct costs per dwelling unit. That is, the "naive

adjusted average" method resulted in root mean squared errors that were approximately four times as large as those experienced with the key

factor method.

It is our belief that both the predictive accuracy of the EHEP results

in Atlanta and its performance relative to a "naive" method suggest that

the key factor approach should prove externally reliable in its application to other cities in future periods.

5. APPLICATIONS OF THE EHEP COST MODELS

Thus far we have seen EHEP described, how cost models based on data

collected in Phase II of EHEP were developed, and how the results of

these models were tested against the data collected in Phase I of EHEP.

The purpose of this chapter is to show how the cost models presented in

Chapters 3 and 4 can be used to calculate abatement costs for specific

needs. Two specific cases which we shall deal with are:

(1) forecasting abatement costs for an individual dwelling unit;

and

(2) forecasting abatement costs for a major program.

In the final section of this chapter a computer program will be described

which permits these abatement costs to be calculated.

5.1 DETERMINING DATA NEEDS

The first step in applying the EHEP cost models involves determining

the minimum data requirements to exercise the models. Through experience gained in EHEP, we have found that data needs can be divided into

three basic categories. These categories are:

(1) data on the dwelling unit;

(2) data on wage rates; and

(3) data on material prices.

These three categories reflect job specific requirements as well as prevailing supply and demand conditions in the labor and product markets.

(Within each category there may be some variation in the amount of data

required to exercise the models for different abatement techniques due

to differences in the key factors affecting technique cost.)

These data requirements may be viewed as "excessive" by some. The

greater flexibiliy and increased accuracy gained through the key factors

approach, however, will make the extra effort worthwhile. We are now

47
ready to examine the type of data associated with each of the three

categories.

Dwelling unit specific data may be divided into two basic types: those

concerned with the deleading of walls and ceilings, and those concerned

with the deleading of trim surfaces. Specific dwelling unit data which

are needed to perform deleading cost estimates for wall areas include:

° the gross square feet of wall area;

2 ° the net square feet of wall area;

° the square feet of ceiling; and

° if the wall is in an unsound condition; or if cabinet work,

pantry work, or wallpaper stripping is to take place.

Dwelling unit data required to perform deleading cost estimates for trim

surfaces include:

° window sill height and width;

the number of windows and frames requiring deleading;

° the number of doors and frames requiring deleading;

o

° the height to which deleading will take place;

° the linear feet of miscellaneous trim requiring deleading; and

° the number and type of units requiring replacement.

Data on wage rates include the hourly wage for laborers, carpenters,

apprentice carpenters, painters, wallpaper hangers, and plasterers. The

data on material prices needed for the wall techniques are the purchase

prices per square foot of barrier materials. No data on material prices

are needed for the paint removal methods.

The gross square feet of wall area is defined as the total wall area including openings for windows, doors, and cabinets. It is equal to

the perimeter of the room times the ceiling height.

2 The net square feet of wall area is defined as the wall area remaining

after the area for all openings, such as doors, windows and cabinets

has been subtracted from the gross square feet of wall area.

3 Experience in EHEP has shown that there is a considerable amount of

variation in the height to which LBP is removed. For example, in

Washington, D.C., LBP was removed to a height of five feet; in Atlanta

and Boston LBP was removed only to a height of four feet.

48
5.2 OBTAINING THE NECESSARY DATA

Once all of the factors needed to make the desired cost estimate have

been identified, it is possible to map out a strategy for systematically

and efficiently measuring their values.

One major source of data which should not be overlooked is the dwelling

unit survey. Surveys are almost always conducted at the start of an abatement program to determine the incidence of LBP in housing. Published information on conducting an LBP survey provides us with valuable

insight into how the survey can complement the cost estimation effort.

The importance of collecting data at this time can not be overstressed

since it permits very detailed information on specific characteristics

of the dwelling unit to be documented at little or no extra expense to

the overall program. Experience gained during EHEP indicated that a three man team could rapidly survey a dwelling unit and make measurements which greatly enhanced the quality and accuracy of the cost estimates. One exercise which was particularly useful was the preparation

of a floor plan for the dwelling unit being surveyed (see Diagram 5.1).

It was found that using a piece of graph paper helped the team member

sketch the floor plan accurately. One of the other team members then

assisted the artist in measuring the size of the rooms, the height of

the ceiling, the size and location of all doors and windows, window

sill height and information on cabinets. It was found that these

elements could easily be placed on the floor plan using the standard

architectural notations. Another benefit of the LBP survey is that it

helps to identify potential problem areas, such as, unsound substrates,

deteriorated trim or nonfunctional doors or windows. Careful documentation of this information during the survey permits an estimate of the

required repairs and component replacements to be made.

The "job specific" information collected during the dwelling unit survey

is a valuable tool in the key factors approach to LBP abatement cost

estimation. However, it does not provide all of the data necessary to

make the estimate. Information on the prevailing wage rates and material

prices mentioned earlier must also be obtained. Fortunately, this information can be based on costs experienced in the past. Most municipal

officials and building owners have had experiences in remodeling. These

figures can be used as a basis for estimating wage rates and material

2

A detailed description of the organization and implementation phases

of a dwelling unit survey is given in William G. Hall and Lillian T.

Slovic, Survey Manual for Estimating the Incidence of Lead Paint in

Housing , National Bureau of Standards, Technical Note 921, September

1976.

It is anticipated that the members of the survey team can learn enough

about what to look for during the survey as part of the inspector

training period described in William G. Hall and Lillian T. Slovic,

Survey Manual for Estimating the Incidence of Lead Paint in Housing .

49
fc^R*

DIAGRAM 5.1 SAMPLE FLOOR PLAN

50
prices. (More up-to-date figures on material prices can usually be

obtained from local building supply firms.) Furthermore, these factors

should remain constant for many dwelling units.

5.3 FORECASTING LEAD PAINT ABATEMENT COSTS

It was mentioned in Section 5.2 that a survey for determining the incidence of LBP in the housing stock was a valuable source of information

for making cost estimates. We shall now explore this topic in more

depth, with a special emphasis on how the survey results can aid policy

makers and municipal officials in forecasting the costs of the overall

program as well as for individual dwelling units and contracts. We

shall first focus on how this information simplifies the task of making

program cost estimates.

There exists within most urban areas a varied stock of housing. This 12 3 point has been underscored in previous housing surveys. ' ' In particular, these surveys have shown that differing amounts of LBP are found

in different types and ages of housing. Therefore, in order to establish

a credible baseline cost estimate, it seems reasonable that forecasts

for budgeting, scheduling and other programmatic purposes should take

these differences into consideration as much as possible.

Probably the most important step is to divide the housing stock into

several well defined types. For example, the following housing types

might be used: multifamily low rise, multifamily high rise, single

family attached, single family detached one story, single family detached

two or more stories. Associated with each housing types would then be an age group. Commonly used age groups are: Pre 1940, 1940 to 1960, and

1960 to the present. All dwelling units could therefore be grouped into

one of these 15 type/age categories. As the survey progresses, information collected on individual dwelling units can be used to establish

estimates (averages) of the expected amounts of deleading work to take

place in any dwelling unit by category (i.e., type and age). These

"statistical" dwelling units can then be used to generate baseline cost

estimates for the program. For example, if there were 2000 pre 1940

single family attached dwelling units and the expected bid price for

1

2

3

Selma J. Mushkin and Ralph Freiden, Lead Poisoning in Children: The

Problem in D.C. and Preventative Steps , Public Services Laboratory,

Georgetown University, Washington, D.C, 1971 e

William Hall, T, Ayres, and D. Doxey, Survey Plans and Data Collection

and Analysis Methodologies: Results of Pre-Survey for the Magnitude

and Extent of Lead-Based Paint Hazard in Housing , National Bureau of

Standards, Interagency Report 74-426, January 1974,

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

in Lead-Based Paint Survey in Pittsburgh, Pennsylvania: Part I , National Bureau of Standards, Interagency Report 77-1250, May 1977.

51
deleading the "statistical" pre 1940 single family attached dwelling

unit was $1250, then $2.5 million would be a good indicator of the

expected costs for that portion of the program. The use of "statistical" dwelling units to produce baseline program cost estimates should

give program administrators, and other public officials, some insight

into the magnitude of the anticipated program, as well as facilitate

the scheduling of the actual deleading work. These estimates should

also provide the community with a means of gauging the impact that the

program will have on the community and, to some extent, the local

housing market.

However, when dealing with contract cost estimates, it is important to

point out that although the use of "statistical" dwelling units for

forecasting overall program costs is desirable, it is not satisfactory

for estimating the costs of a "particular" dwelling unit. This stems

from the fact that by grouping large numbers of dwelling units into

characteristic (type and age) categories, we are able to nullify some

of the adverse effects that variations in the expected amount of deleading work cause. On the other hand, when we deal with individual dwelling

units, we wish to take into account these variations so that we can more effectively measure changes in costs and hence identify that technique

which is least costly. From a statistical viewpoint, the use of dwelling

unit specific data is necessary because we are no longer working with

large numbers of observations so that any variations in the level of

deleading activities which affect costs will probably not cancel out

each other. What this means is that different programmatic needs will

require similar but slightly different sets of information. Therefore,

in an attempt to reduce the additional work caused by these input

requirements, a procedure which is both quick and easy-to-use is needed.

Two procedures were developed through Phase II of EHEP to meet both of

these programmatic needs. The first procedure uses a computer program

written in the BASIC language which can be referenced through the Federal

Agencies' Computer Time-Sharing System (FACTS) library of programs. The

FACTS library is part of the Remote Access Multi-User System (RAMUS), a nationwide interactive time-sharing computer and telecommunications network developed and operated by the General Services Administration (GSA).

The RAMUS and FACTS systems are available to all Federal agencies and

their authorized contractors. The second procedure is aimed at those

program offices which do not have access to a computer time-sharing

system. This hand calculation procedure uses worksheets, cost equations,

1 For a detailed description of FACTS, see Users' Manual for the Federal

Agencies ' Computer Time-Sharing System , National Research Council,

Building Research Advisory Board, Federal Construction Council, Technical Report 68, 1976.

52
and a schematic diagram, for estimating costs and identifying those techniques which are least costly. In this report, we shall focus on the

first procedure.

As a first step in seeing what the program does and how to use it, we shall refer to Diagram 5.2. Diagram 5.2 is a flowchart of the computer

program; it shows the basic input requirements, the sequence in which

calculations take place, and the program outputs. Table 5.1 summarizes

the input data requirements which were discussed in Sections 5.1 and

5.2. Notice that with the exception of dwelling unit specific data,

the input data requirements are the same for estimating costs at the

program level and for estimating costs at the contract level. Therefore, only after the user has already input the required data on wage

rates and material costs will the computer ask the use if they wish to

estimate costs at the program or contract level. If they wish baseline

program costs, then they shall enter a 1 in response to the computer's

question; if contract costs estimates are desired, then a should be

entered.

Suppose the user enters a 1 in block A (see Diagram 5.2), indicating

that they wish a program cost estimate. The computer will then request

information (see Table 5.1) on the "statistical" dwelling unit. Once

all data have been entered, the computer 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 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. The user may then use this figure to determine the costs of

that portion of the deleading program. Once the cost information has

been printed out, the user has the option of either stopping or performing cost estimates for other categories (building type/age).

In the event that the user wished to make contract cost estimates, the

computer program would operate in a slightly different manner. However,

before explaining this section of the flowchart, it is important to

recognize several constraints. First, the user should place the dwelling units into groups of from one to ten dwelling units. Second, all

1

2

Details of the hand calculation procedure are given in the companion

report, Lead Paint Abatement Costs: Some Technical and Theoretical

Considerations .

The computer will not accept information on more than ten dwelling

units at any one time. Thus if 50 dwelling units required deleading,

at least five groups of ten dwelling units would have to be formed.

Generally speaking, eight dwelling units is a good number to work

with at any one time.

53
Input Wage

& Material

Cost Data

Contract

DU- Dwelling Unit

Tech-Technique

Calculate

Average

Direct Costs

1

Calculate

Expected

Bid Price

Input Data

Particular

DUsI

7-i

Calculate

Minimum

Direct Cost

Group into

Contract

Packages

Calculate

Minimum

Bid Price

DIAGRAM 5.2

FLOWCHART FOR COMPUTER TIME-SHARING PROGRAM

54
TABLE 5.1

INPUT DATA REQUIREMENTS

Nature of Data as a Function of the Type

of Cost Estimate Desired

Data Requirement Program Contract

Wage

Carpenter

Painter

Plasterer

Paper Hanger

Apprentice Carpenter

Laborer

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 Frames

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

Address

XRF Reading

Average Hourly

Wage Rate

Price per 4' x 8' Sheet

Price per Square Yard

Price per Gallon

Price per Gallon

Price for One, New

Price for Both, New

Based on "Statistical" Dwelling Unit

Square Feet

Linear Feet

Percent Occupied

Percent of Wall Area

Percent Unsound

Percent Needing It

Percent Having > 2 Layers

Numbe r

DU/Age Category

Average or Separate

for Each Trim Type

55

Average Hourly

Wage Rate

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 as Specified

Average or Separate

for Each Trim Type
dwelling units within a group should be relatively close to each other

so that the distance between dwelling units will not enter as a complicating factor in making up contract packages.

Let us now return to the flowchart. In response to the computer's question in block A, a is entered, indicating that contract cost estimates

are desired. (Recall from the previous discussion that information on wage rates and material prices would have been entered prior to reaching

block A.) The computer then asks for how many dwelling units the user

will be inputting data. Suppose the user has eight dwelling units for

which he wants cost estimates. The computer first requests information

on all the dwelling unit specific key factors listed in Table 5.1 for

dwelling unit number one. The cost of each abatement technique is then

calculated and the one which minimizes direct costs is identified. (This

procedure is followed for both wall and trim surfaces.) The computer

then returns to the user and requests information on the second dwelling

unit

.

This input loop continues until all data regarding specific dwelling

units have been input. As a next step, the computer groups the dwelling

units into "preliminary" contract packages based on the values of the

key factors factors and other decision criteria implicit in the program

(e.g., technique familiarity and occupancy status). The objective here

is to reduce the expected markup ratio to as small a number as possible.

The computer program then tests to see if the sum of the expected bid

prices for the "preliminary" contract packages can be reduced by removing

a dwelling unit from one contract package and placing it in another.

Once this search routine finds that no further reductions in the overall

bid price can be achieved, the dwelling units are assigned to contract

packages and the program enters the output routine. The output routine

first prints the least-cost technique for both wall and trim surfaces

for each dwelling unit and gives expected cost. This is done for each

contract. For example, the first contract might have dwelling units

number 1, 3 and 7; the second dwelling units 2 and 8; and the third

dwelling units 4, 5 and 6. The expected markup ratio for each contract

is also given in order to facilitate policy analysis by program decision

makers. This information is useful for policy analysis because if

markup ratios are consistently high, it may be possible to achieve a reduction in program costs by operating in a cost plus fixed fee con- tracting mode.

The discussion in this section has been aimed at explaining what tasks

the computer program performs and how it accomplishes these tasks. In

terms of using the program and how will be given in this section. The

reader wishing more details on running the program is referred to

Appendix A. The discussion here is limited to those details concerned

This approach was taken since it permits the computer to assign the

dwelling units to contract packages in such a way that the sum of

the expected bid prices for the contracts is minimized.

56
with establishing a computer account and identifying office terminal

needs.

Before the computer program can be referenced, the agency (or authorized

contractor) must subscribe to the RAMUS service. An agency or authorized

contractor wishing to subscribe to the RAMUS service should initiate the

administrative procedure by writing to:

Agency Services Coordination Division

ADTS, GSA

1776 Peachtree Street, N.W.

Atlanta, Georgia 30309

This letter should include the following information:

1. The date the agency wishes to begin RAMUS service;

2. The number and type of terminals which will be used;

3. The average number of hours per day of terminal connect time;

4. The estimated program storage requirements;

5. The name, address, and telephone number of the agency, the

person to be contacted for information pertaining to RAMUS

usage; and

6. The billing address and appropriate fund citation.

Item number 2 in the list above requests information about the type of

office terminal to be used. In order to use RAMUS, the terminal should

be either the Automatic Send /Receive Model 33 or 35 Teletypewriter,

or any fully compatible equipment. The office terminal communicates

with RAMUS by means of a telephone (voice grade) circuit; either FTS,

WATS, or leased lines may be used. Once the user has received an identification code and password, they enter the FACTS library via RAMUS.

(This procedure is analogous to checking a book out of a library and

using the information in the book to solve a particular problem.)

Details, including sample computer runs, of how the user establishes

a file and references it in order to run their program are given in

Apendix A.

6. SUMMARY AND RECOMMENDATIONS FOR FURTHER RESEARCH

6.1 SUMMARY

The potential of lead poisoning through the ingestion of lead-based

paint (LBP) chips is a threat to the health and well-being of young

children. The potential and the magnitude of the problem have stimulated public and private interest in eliminating the hazards of LBP

from the nation's housing. A method of estimating the costs of LBP

abatement is required to insure that the resources devoted to eliminating the hazards of LBP will be used efficiently. A major goal

of the Experimental Hazard Elimination Program (EHEP) was to produce

such a method.

57
Based on results from EHEP, this study develops and validates a series

of cost models for the estimation of the direct costs of LBP abatement.

A cost model for predicting the contractor's markup for overhead and

profit is also developed. Direct costs were analyzed at the dwelling

unit level by building component (walls, doors and frames, windows and

frames, and miscellaneous trim) and by abatement technique. They were compared on the basis of per unit direct cost (i.e., the direct cost

per square foot, per linear foot, or per item). Each abatement technique was then analyzed statistically so that the key factors which

accounted for variations in per unit direct cost could be identified.

Estimates of the relative impact that the key factors had on per unit

direct costs were then developed. The key factors and the weighting

factors were then combined in such a way that the resulting cost models

permitted the prediction of the per unit direct cost for each abatement

technique.

To provide a measure of the confidence with which the EHEP cost models

can be used, a statistical estimate of how complete the model is at

explaining variations in per unit direct cost and the markup ratio was presented. These statistics took on values which ranged from 0.66 to

0.91 for the twelve separately estimated cost models; the average value was 0.81. That is, on the average, 81 percent of the variation in per

unit direct cost was explained by a linear cost model. It should be

noted that the estimate of how complete the model was at explaining

variations in per unit direct cost exceeded 0.80 for nine of the twelve

models. The results of the cost models were then tested against an independent set of data. This validation procedure was undertaken to

test the adequacy of the cost models to predict abatement costs in

another city at a different point in time. The results of the validation procedure were encouraging, underscoring the advantages of the

key factor approach over a simple average. With these considerations

in mind, it seems likely that most of the cost models developed in

this study provide a reliable procedure for estimating the costs of

LBP abatement. Finally, through validation it was found that most of

the cost models developed in this study are of general enough nature

that they have captured potential differences in per unit direct cost

due to regional effects.

Guidelines for using the EHEP cost models as a management tool were discussed. A computer program written in the BASIC language was developed

which permitted decision makers to make cost estimates via a time-sharing

terminal. Specific applications of the computerized cost models in estimating the least-cost combination of abatement techniques for a contract

package of one or more dwelling units were explored as were potential

applications of the procedure as a policy tool for making baseline estimates for planned or on-going programs.

6.2 RECOMMENDATIONS FOR FURTHER RESEARCH

In order to expand the cost models developed in this study to include

future expenditures, to determine the optimal level of LBP hazard

58
abatement, and to gain a fuller knowledge of what policy options are most likely to, be both economically and socially acceptable, further

research on several topics would be useful.

It was noted earlier that the hazard abatement quality of a barrier

material was a function of how well it prevented access to the LBP

which remained beneath. The durability of the product is therefore an important factor which should be considered in its use. However, due

to the lack of information on the durability of the various barrier

materials, it was not possible to assess the effect that future repair

costs would have on the choice of the least costly barrier material.

The impact that future costs, which are not always seen with certainty, can have on investment decisions has been demonstrated in other studies.

Since it is unlikely that the failure of a barrier material can be known

with certainity, it appears that, at least at the program level, some allowance for future costs due to technique failure should be made.

It was shown in the introduction of this report that there are environmental sources of lead capable of causing lead poisoning other than LBP.

This study in its analysis of deleading costs has not focused on the

benefits which would result from these deleading operations. The establishment of what benefits are associated with LBP abatement and their

quantification is essential to the identification of the optimal level

of protection against LBP poisoning and the optimal combination of LBP

abatement techniques. Furthermore, it is important to recognize that

the estimation of total project benefits may also be used as a policy

tool in the development of alternative LBP abatement strategies at the

national or city level. Developing such estimates in future research

will reduce the probability of pursuing an LBP abatement strategy which

wastes either scarce tax dollars or irreplaceable human resources.

It is important that the material presented in this report be placed in

the hands of municipal officials and building owners. A simplified

guidebook to LBP abatement which focuses on the cost estimation procedure

would seem most appropriate. This guide could include the additional

research topics discussed above so that the most efficient allocation

of funds could be achieved.

It might also be useful to investigate incentives programs which could

be used to eliminate the LBP hazard. Such incentives could include

financing LBP abatement through low-interest loans or some form of

cost sharing. Analyses such as these could provide public and private

decision makers with the information necessary to make the efficient

choice among competing policy alternatives.

Donovan Young and Luis Contreras, "Expected Present Worths of Cash

Flows Under Uncertain Timing," The Engineering Economist , Volume 20,

Number 4, Summer 1975.

59
Finally, the success that this procedure has had in estimating the

costs of LBP abatement indicates that a similar aproach may be very

useful in estimating the costs of housing rehabilitation. The current

lack of cost information in the housing rehabilitation industry may

be a deterrant to investments in the rehabilitation of central city

housing. Should reliable cost information be made available to municipal officials and potential homeowners, a more meaningful and comprehensive approach to housing conservation might be possible.

60
7. REFERENCES

Boone, Thomas H. , 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.

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., 1960.

Building Construction Cost Data 1977 , Robert Snow Means Company, Inc.

,

Duxbury, Mass., 1977.

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, Lead Paint Abatemnt Costs ; Some Technical and Theoretical Considerations , National Bureau of

Standards, Technical Note 979 (In Press).

Chisolm, J. J., "Lead Poisoning," Scientific American , Vol. 224, No. 2,

February 1971.

Dodge Manual for Building Construction Pricing and Scheduling , McGrawHill Information Systems Company, New York.

Draper, N. R. and H. Smith, Applied Regression Analysis , John Wiley and

Sons, Inc., 1966.

Gilsinn, Judith F., Estimates of the Nature and Extent of Lead Paint

Poisoning in the United States , National Bureau of Standards, Technical

Note 746, December 1972.

Hall, William, T. Ayres and D. Doxey, Survey Plans and Data Collection

and Analysis Methodologies: Results of a Pre-Survey for the Magnitude

and Extent of the Lead-Based Paint Hazard in Housing , National Bureau

of Standards, Interagency Report 74-426, January 1974.

Hall, William G. and Lillian T. Slovic, Survey Manual for Estimating the

Incidence of Lead Paint in Housing , National Bureau of Standards,

Technical Note 921, September 1976.

Home-Tech Estimator, Home-Tech Publications, Bethesda, Maryland.

61
Kowalski, Joseph G. , Cost Estimation in Residential Alteration and

Repair Construction: An Economic Analysis , National Bureau of

Standards, Interagency Report (In Preparation).

Mushkin, Selma J. and Ralph Freiden, Lead Poisoning in Children: The

Problem in D.C. and Preventative Steps , Public Services Laboratory,

Georgetown University, Washington, D.C, 1971.

Sarb, Thomas B., "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.

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

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

in a Lead-Based Paint Survey in Pittsburg, Pennsylvania Part I , National Bureau of Standards, Interagency Report 77-1250, May 1977.

Spencer, D„, A Guide to BASIC Programming: A Time-Sharing Language , Addi son-Wesley Publishing Company, Inc., Reading, Mass., 1970.

Tepper, R. Bruce, Jr., "Lead Paint Poisoning: The Response in Litigation,

St. Louis University Law Journal , Vol. 19, Winter 1974.

Thiel, Henri, Applied Economic Forecasting , North-Holland Publishing

Company, Amsterdam, 1966.

Users' Manual for the Federal Agencies 1 Computer Time-Sharing System , National Academy of Sciences, Building Research Advisory Board, Federal

Construction Council Technical Report No. 68, 1976.

Waksman, David, John B. Ferguson, McClure Godette, and Thomas Reichard,

Potential Systems for Lead Hazard Elimination: Evaluations and Recommendations for Use , National Bureau of Standards, Technical Note 808,

December 1973.

Yankel, Anthony J., Ian von Lindern, and Stephen D. Walter, The Silver

Valley Lead Study: The Relationship of Childhood Lead Poisoning and

Environmental Exposure , draft report.

Young, Donovan and Luis Contreras, "Expected Present Worths of Cash Flows

Under Uncertain Timing," The Engineering Economist , Volume 20, No. 4,

Summer 1975.

62
APPENDIX A:

HOW TO USE THE COMPUTERIZED COST ESTIMATION PROCEDURE

In Section 5.3, background information was given that identified the

inputs required to use the computerized cost estimation procedure.

Recall that these inputs were in part a function of whether the costs

desired were for policy analysis or for contract cost estimation. To

make efficient use of the time saving potential that the computerized

cost estimation procedure offers, some planning on the part of the

individual(s) using the program will be necessary. For example, to do

program cost estimates, a table similar to Table A.l would be useful.

Table A.l shows the number of dwelling units in each type/age category.

Each category can then be broken down into data on the "statistical"

dwelling unit. Diagram A.l shows how a simplified worksheet can be

used to record data on wages and material costs. (Recall that the same information on wages and material prices can be used for both

program analysis and contract cost estimation.) In entering dwelling

unit specific data, a worksheet similar to that shown in Diagram A.

2

was found to be very efficient. (Diagram A. 2 illustrates such a breakdown for single family attached dwellng units.) If these basic

guidelines are followed for keeping input data at hand, the cost estimation work should proceed both smoothly and rapidly.

We are now ready to see how, through the FACTS library of programs,

a user file can be established. Once you have established your file,

it is then possible to run your program without going through the FACTS

executive system. (Recall that establishing a file is like checking

out a book from a library. You use it until your need is satisfied

and then return it.)

As a first step, the user must call RAMUS. (The telephone number by

which you can reach RAMUS will be provided when you establish your subscription to the service.) When you reach RAMUS, the computer will

request your user ID, your secret password, and the type and name of

When entering data into the computer program, it is not necessary

to use a dollar, ($) sign. Thus, if the wage rate for a carpenter

was $9.00 per hour the proper input would be 9.00 or 9. The same argument holds for material prices, e.g., 2.50 not $2.50.

63
the program you wish to use. A sample of the type of information

requested is shown below. Notice that the computer prints over your

password so that it can not be deciphered and used by someone else.

GSA 440 TIME-SHARING SYSTEM 1 ON AT - 11:23 PN:04 TTY:60

12/12/77.

USER ID— _ PASSWORD?

TYPE OLD OR NEW: OLD: FACTS***

The computer is now ready to run FACTS. In response to the computer's

READY signal, please type RUNNH. The computer will then ask what level

of prompting you wish; in response to the ***?, enter HE as in the sample

be 1 ow

READY

RUNNH

FACTS RESPONSING. INDICATE LEVEL OF PROMPTING AT ***? PROMPT

ENTER TO GET

AB ABBREVIATED PROMPTING

FL FULL PROMPTING

HE FULL PROMPTING + SYSTEM EXPLANATION

At the end of each statement, e.g., B310, you must press the nonprinting carriage return character. This tells the computer to

proceed. If you make a mistake , e.g., typing B315 instead of B310,

you can correct by using the character «-. To change B315 to B310,

you would type * once and then 0. The line would then look like

B315-H). In the event that you do not have enough space to make

corrections using the character «-, you can press the escape key

(ESC). This action will cause the input for that line in the computer program to be repeated a second time. If you get in a real

bind, hit the BREAK character. This disconnects you from RAMUS.

64
TABLE A.l

NUMBER OF DWELLING UNITS BY TYPE /AGE CATEGORY

DWELLING DWELLING UNIT AGE

MULTIFAMILY

LOW RISE

UNIT PRE 1940- POST

TYPE 1940 1960 1960

MULTIFAMILY

HIGH RISE

SINGLE FAMILY

ATTACHED

SINGLE FAMILY

DETACHED j ONE STORY

SINGLE FAMILY

DETACHED

TWO OR MORE

STORIES

65
DIAGRAM A.l WAGE AND MATERIAL PRICE DATA

AVERAGE HOURLY

WAGE RATE

CARPENTER

PAINTER

PLASTERER

PAPER HANGER

APPRENTICE CARPENTER

LABORER

MATERIAL PRICES

GYPSUM WALLBOARD - (4' x 8' sheet)

PLYWOOD PANELING - (4' x 8' sheet)

VINYL-COATED FABRIC - (SQUARE YARD)

FLAT LATEX WALL PAINT - (GALLON)

SEMI-GLOSS ENAMEL (OIL BASE )-( GALLON)

IF NEEDED

UNFINISHED DOOR - (NEW-EACH)

UNFINISHED DOOR FRAME - (NEW-EACH)

UNFINISHED WINDOW AND FRAME - (NEW-EACH)

66
DIAGRAM A. 2 DWELLING UNIT DATA FORM^

DU/AGE CATEGORY PRE 40 SINGLE FAMILY ATTACHED

WALL AREA - (GROSS SQUARE FEET)

DOORS AND FRAMES -(LINEAR FEET)

WINDOWS AND FRAME S-( LINEAR FEET)

MISCELLANEOUS TRIM-( LINEAR FEET)

OCCUPANCY-( PERCENT OCCUPIED)

WAINSCOTTING-( PERCENT WALL AREA)

SUBSTRATE CONDITION-( PERCENT UNSOUND)

PANTRY WORK-( PRECENT NEEDING IT)

WALLPAPER REMOVAL-( PERCENT HAVING _> 2 LAYERS)

XRF READINGS - (DOORS)

XRF READ INGS-( WINDOWS)

XRF READINGS-(MISCELLANEOUS)

IF NEEDED

REPLACEMENT DOORS-( NUMBER)

REPLACEMENT DOOR FRAMES -(NUMBER)

REPLACEMENT WINDOWS AND FRAME S-( NUMBER)

(a) v y For making cost estimates at the program level,

67
***? HE

The computer will then print the following details on FACTS.

FACTS CONSISTS OF AN EXECUTIVE SYSTEM AND A LIBRARY OF FULLY TESTED

ENGINEERING AND SCIENTIFIC COMPUTER PROGRAMS.

THE FACTS EXECUTIVE SYSTEM ALLOWS THE USER TO FULLY UTILIZE THE FACTS

LIBRARY OF ENGINEERING AND SCIENTIFIC COMPUTER PROGRAMS BY (1) PROVIDING

LIBRARY VISIBILITY, (2) CONSTRUCTING COMMANDS PROGRAMS FOR THE USER WHICH

TAKE CARE OF ALL SYSTEM DETAILS THAT ARE NECESSARY FOR SETTING UP AND

RUNNING FACTS PROGRAMS AND (3) PROVIDING USER LIBRARY MANAGEMENT. THESE

FUNCTIONS ARE ACCOMPLISHED WITHIN SUBLEVELS OF THE EXECUTIVE SYSTEM.

SUBLEVEL STATUS IS ACHIEVED THROUGH THE USE OF COMMANDS GIVEN TO THE

COMPUTER IN RESPONSE TO THE ***? PROMPT PRINTED IN THE EXECUTIVE SYSTEM

COMMAND LEVEL. THE THREE PRINCIPAL FUNCTIONS OF THE FACTS EXECUTIVE

SYSTEM ARE AS FOLLOWS:

(1) LIBRARY VISIBILITY IS ACHIEVED THROUGH THE LIST AND BRIEF SUBLEVELS

/LIST SUBLEVEL/ -PROVIDES A LIST OF COMPUTER PROGRAMS CURRENTLY AVAILABLE

IN THE FACTS LIBRARY. THE FACTS LIBRARY, WHICH IS CONSTANTLY EXPANDING,

IS DIVIDED INTO SUBAREAS THAT ARE IDENTIFIED BY EITHER A SINGLE ALPHABET

LETTER OR BY A LETTER AND A NUMBER. A LIST OF THE SUBAREAS AND THE NUMBER

OF FACTS PROGRAMS IN EACH SUBAREA IS AVAILABLE AS AN OPTION WITHIN THE

LIST SUBLEVELS OF THE EXECUTIVE SYSTEM.

/BRIEF SUBLEVEL/ -PROVIDES A SHORT DESCRIPTION OF THE CAPABILITIES AND

DATA REQUIREMENTS FOR EACH PROGRAM IN THE FACTS LIBRARY.

(2) CONSTRUCTION OF COMMANDS PROGRAMS IS ACHIEVED THROUGH THE CONSTRUCT

SUBLEVEL.

/CONSTRUCT SUBLEVEL/ -FOR EACH FACTS LIBRARY PROGRAM THAT THE USER

WANTS TO RUN, THE COMPUTER CREATES A SMALL COMMANDS PROGRAM TO HANDLE

ALL THE DETAILS REQUIRED TO MAKE THE PROGRAM OPERATIONAL. THIS SMALL

PROGRAM IS STORED IN THE USERS OWN LIBRARY BY THE COMPUTER AND ITS

NAME RECORDED IN A DIRECTORY FILE THAT THE COMPUTER GENERATES FOR THE

USER THE FIRST TIME IT CREATES A COMMANDS PROGRAM FOR HIM. EACH TIME

THE USER CREATES ANOTHER COMMANDS PROGRAM IT IS ALSO STORED IN HIS LIBRARY

AND ITS NAME IS ADDED TO HIS DIRECTORY FILE. THUS, THE USERS DIRECTORY

FILE CONTAINS A CURRENT LIST OF THE COMMANDS PROGRAMS THAT ARE IN HIS

LIBRARY. COMMANDS PROGRAMS ARE CREATED BY THE FACTS EXECUTIVE SYSTEM

BUT THEY ARE RUN UNDER RAMUS IN THE SAME MANNER AS ANY OTHER COMPUTER

PROGRAM ON THE RAMUS SYSTEM. A COMMANDS PROGRAM, ONCE GENERATED, CAN

BE USED OVER AND OVER AGAIN WITHOUT RETURNING TO THE FACTS EXECUTIVE

SYSTEM.

68
(3)MANAGEMENT OF THE USERS LIBRARY OF COMMANDS PROGRAMS IS HANDLED

THROUGH THE DELETE, DIRECTORY AND MESSAGE SUBLEVELS.

/DELETE SUBLEVEL/ -PROVIDES THE MECHANISM FOR LISTING THE CONTENT OF

THE USERS DIRECTORY FILE(S) AND FOR DELETING COMMANDS PROGRAMS FROM

HIS LIBRARY. THE NAMES OF DELETED COMMANDS PROGRAMS ARE REMOVED FROM

THE USERS DIRECTORY FILE. THUS, THE DIRECTORY IS ALWAYS CURRENT.

/DIRECTORY SUBLEVEL/ -ALLOWS THE USER TO LIST THE CONTENT OF HIS

DIRECTORY FILE(S).

/MESSAGE SUBLEVEL/ -ALLOWS THE USER TO MAKE COMMENTS ON FACTS DURING

THE ACTUAL USE OF THE SYSTEM. MESSAGES ARE REVIEWED AT FREQUENT

INTERVALS AND THE USER IS NORMALLY CONTACTED IF ADDITIONAL INFORMATION

IS REQUIRED.

NOTE: COMMANDS AND RESPONSES SHOULD BE ENTERED ONLY IN RESPONSE TO PROMPTS:

OTHERWISE IT CONFUSES THE COMPUTER. ALSO, THINK BEFORE YOU RESPOND TO A

COMPUTER PROMPT, A LITTLE THOUGHT WILL PAY-OFF.

THE FACTS LIBRARY CONSISTS OF A NUMBER OF FULLY TESTED ENGINEERING AND

SCIENTIFIC COMPUTER PROGRAMS. THESE PROGRAMS ARE REALLY SUBROUTINES

AND EACH CONTAINS A PREAMBLE WHICH DEFINES THE PURPOSE OF THE PROGRAM,

THE DATA REQUIRED TO MAKE IT OPERATIONAL AND OTHER INFORMATION NECESSARY

FOR ITS EFFICIENT OPERATION. SINCE THESE PROGRAMS ARE SUBROUTINES, THEY

MAY BE LINKED TOGETHER FOR SOLVING EXTREMELY COMPLEX PROBLEMS WHERE

A MULTIPLICITY OF OPERATIONS ARE REQUIRED. THE FACTS EXECUTIVE SYSTEM

CAN READ AND INTERPRET THE CONTENT OF THE PREAMBLE OF FACTS PROGRAMS AND

FROM THIS ANALYSIS, CREATE A COMMANDS PROGRAM CONTAINING THE ELEMENTS THAT

ARE NECESSARY FOR MAKING THE FACTS SUBROUTINE FULLY OPERATIONAL.

YOU ARE NOW AT COMMAND LEVEL (PROMPT SYMBOL***).

ENTER NEXT FACTS COMMAND AT ***? PROMPT.

VALID ACCEPTABLE

COMMANDS ABBREVIATION

ABBREVIATE AB

BRIEF BR

CONSTRUCT CO

DELETE DE

DIRECTORY DI

FULL FL

HELP HE

LIST LI

MESSAGE ME

RAMUS RA

STOP STOP

PROMPT

j>YMBOL ***

$$$

%%%

&&&

•kitie @@@

###

<«

COMMAND

ACTION_

PRINT ABBREVIATED PROMPTING

PRINT BRIEF DESCRIPTION OF PROGRAMS

CONSTRUCT COMMANDS PROGRAMS

DELETE COMMANDS PROGRAMS

LIST OF PROGRAMS IN USER'S DIRECTORY

PRINT FULL PROMPTING

PRINT FACTS SYSTEM DETAILS

LIST OF FACTS PROGRAMS

MAKE SYSTEM COMMENTS

PRINT RAMUS SYSTEM DETAILS

RETURNS USER TO RAMUS SYSTEM

In respose to the ***? question, please enter RA. This will tell

the computer to print the following details on RAMUS.

69
***? RA

<<< ALL PROGRAMS, INCLUDING THE FACTS EXECUTIVE SYSTEM, ARE RUN UNDER THE

CONTROL OF RAMUS (REMOTE ACCESS MULTI-USER SYSTEM). IN ORDER TO RUN A

COMMANDS PROGRAM DEVELOPED WITHIN FACTS, THE USER MUST EXIT TO RAMUS.

THIS CAN BE DONE AT ANY TIME BY TYPING AN S AND THEN HITTING A CARRIAGE

RETURN OR WHEN AT COMMAND LEVEL THE USER MAY TYPE STOP. AT RAMUS LEVEL

THE USER MAY RUN ANY PROGRAM BY TYPING "RUN OPTION: PROGRAM NAME" FOR

EXAMPLE, IF THE USER WANTED TO RUN A COMMANDS PROGRAM NAMED RLR01 AND

ITS RUN OPTION IS RUN, THE USER WOULD TYPE RUN: RLR01 AND THEN HIT A

CARRIAGE RETURN. (FOR GREATER DETAIL OF THE RAMUS SYSTEM AND ITS OPTIONS,

THE USER MUST OBTAIN A COPY OF THE OPERATING SYSTEM MANUAL THROUGH GSA).

YOU ARE NOW AT COMMAND LEVEL (PROMPT SYMBOL***).

ENTER NEXT FACTS COMMAND AT ***? PROMPT.

VALID ACCEPTABLE PROMPT

COMMANDS ABBREVIATION SYMBOL

ABBREVIATE AB AAA

BRIEF BR $$$

CONSTRUCT CO +++

DELETE DE %%%

DIRECTORY DI &&&

FULL FL AAA

HELP HE @@@

LIST LI ###

MESSAGE ME + + + RAMUS RA <«

STOP STOP

COMMAND

ACTION_

PRINT ABBREVIATED PROMPTING

PRINT BRIEF DESCRIPTION OF PROGRAMS

CONSTRUCT COMMANDS PROGRAMS

DELETE COMMANDS PROGRAMS

LIST OF PROGRAMS IN USER'S DIRECTORY

PRINT FULL PROMPTING

PRINT FACTS SYSTEM DETAILS

LIST OF FACTS PROGRAMS

MAKE SYSTEM COMMENTS

PRINT RAMUS SYSTEM DETAILS

RETURNS USER TO RAMUS SYSTEM

In response to ***?, please type LI; this will get you to the LIST

level of FACTS. When the computer prints ###?, please type *; this

will tell the computer to list all the functional codes, the number

of programs in each area : and a description of the functional code.

Typing LI in response to the first question, and * in response to

the second will cause the following output to be printed.

***? LI

YOU ARE NOW AT LIST LEVEL (PROMPT SYMBOL ###).

ENTER ONE FUNCTIONAL AREA CODE (E.G. J) IN RESPONSE TO EACH PROMPT.

ENTER AN * FOR LIST OF CODES. ENTER WORD END TO RETURN TO COMMAND

LEVEL.

70
FUNCTIONAL PROGRAMS DESCRIPTION

AREA CODE IN AREA

E 1 ELECTRICAL

H

l

8 HYDRAULICS-SP ILLWAYS

H

2

11 HYDRUALICS-OUTLET WORKS

H

3

H

6

2 HYDRAULICS-GATES & VALUES

23 HYDRAULICS-OPEN CHANNELS

H

7 5 HYDRAULICS-SPECIAL PROBLEMS

I 4 SOILS

J 3 ECONOMICS

L 2 MECHANICAL

M 1 MATHEMATICAL

U 1 GEODESORY & SURVEYING

X 21 STRUCTURAL

Z 1 OTHER

After the computer has printed information on the functional codes, it

will ask the user if a listing of individual programs is desired. Since

the program on deleading costs is stored in the functional area code J,

the user should enter J in response to ###. The listing will then identify J0003 as the desired program. (Note, as of the date of the printing

of this publication, the computerized cost estimation program had not

been placed in the FACTS library. Thus, the listing of all programs

in the J, economic, functional area code is necessary to determine if

J0003 is the correct FACTS program number.)

The user should then type END and BR in response to the prompting, ***?,

question from the computer so that a brief of program J0003 can be

obtained (see below).

***? br

YOU ARE NOW AT BRIEF LEVEL (PROMPT SYMBOL $$$).

ENTER ONE PROGRAM NAME (E.G. H2010) IN RESPONSE TO EACH PROMPT.

TYPE WORK END TO RETURN TO COMMAND LEVEL.

$$$ ? J0003

I. DESCRIPTION

J0003 WILL ANALYZE THE COSTS OF THE ALTERNATIVE METHODS FOR

ELIMINATING THE LEAD PAINT HAZARD FROM A DWELLING UNIT. THE

PROGRAM PERMITS THE USER TO INPUT SPECIFIC INFORMATION ON ANTICIPATED CONTRACT PACKAGES OF DWELLING UNITS. THE LEAST-COST

COMBINATION OF ABATEMENT TECHNIQUES FOR EACH DWELLING UNIT IS

IDENTIFIED. DWELLING UNITS ARE GROUPED TOGETHER INTO CONTRACT

PACKAGES SO THAT THE SUM OF THE EXPECTED BID PRICES IS MINIMIZED.

THE EXPECTED BID PRICE FOR EACH CONTRACT IS GIVEN. EXPECTED

CONTRACT COSTS FOR EACH DWELLING UNIT ARE ALSO GIVEN.

71
II. LIMITATIONS

J0003 WILL HANDLE ANTICIPATED CONTRACT PACKAGES OF UP TO 10 DWELLING

UNITS. DATA FOR EACH DWELLING UNIT IS ENTERED IN RESPONSE TO QUESTIONS

AT RUN TIME.

III. DATA

TWO TYPES OF DATA ARE INPUT, CONTRACT SPECIFIC DATA AND DWELLING UNIT

SPECIFIC DATA. INPUT DATA FOR EACH ANTICIPATED CONTRACT CONSIST OF

THE FOLLOWING:

A. CONTRACT SPECIFIC DATA

WAGE

AVERAGE HOURLY

WAGE RATE

CARPENTER

PAINTER

PLASTERER

PAPER HANGER

APPRENTICE CARPENTER

LABORER

MATERIAL

GYPSUM WALLBOARD

PLYWOOD PANELING

VINYL-COATED FABRIC

FLAT LATEX WALL PAINT

SEMI-GLOSS ENAMEL

(OIL BASE)

PRICE PER 4' x 8' SHEET

PRICE PER SQUARE YARD

PRICE PER GALLON

PRICE PER GALLON

IF NEEDED:

UNFINISHED DOOR

UNFINISHED DOOR FPAME

UNFINISHED WINDOW AND FRAME

B. DWELLING UNIT DATA

GROSS SQ. FT. OF WALL AREA

PRICE FOR ONE, NEW

PRICE FOR BOTH, NEW

SQUARE FEET

NET SQ. FT. OF WALL AREA

LINEAR FT. OF DOORS AND FRAME

LINEAR FT. OF WINDOWS & FRAMES

LINEAR FT. OF MISCELLANEOUS TRIM

LINEAR FEET

OCCUPANCY PERCENT OCCUPIED OR 1 IF OCCUPIED

IF UNOCCUPIED

72
WAINSCOTTING PERCENT OF WALL AREA

SUBSTRATE CONDITION PERCENT UNSOUND

PANTRY WORK PERCENT NEEDING IT

OR

OR

1 IF POOR

IF SATISFACTORY OR BETTER

1 IF NEEDED

IF NOT

WALLPAPER ON WALLS PERCENT HAVING > 2 LAYERS ! IF 3 0R M0RE LAYERS

IF NOT

IF NEEDED:

NUMBER OF DOORS TO REPLACE NUMBER

NUMBER OF DOOR FRAMES TO REPLACE

NUMBER OF WINDOWS AND FRAMES

TO REPLACE

ADDRESS

XRF READINGS

FINISH

$$$? END

DU/AGE CATEGORY OR ADDRESS AS SPECIFIED

AVERAGE OR SEPARATE

FOR EACH TRIM TYPE

The user has now identified the computerized cost estimation program and

is now ready to construct a file to store a "duplicate" of the FACTS

program. To do this, type END in response to $$$, and then CO in response

to the prompting, ***?, question. Study the following steps carefully.

First, when the computer prints -H-+? , hit the nonprinting carriage return

character. Second, type J0003 (if the FACTS program number for the

computerized deleading program was different, please enter that number)

when the computer prints +++? . Third, type FINIS. Fourth, enter three

letters (for example, your initials). The FACTS executive system then

automatically establishes a directory file and assigns you a program name.

You should record this information where you can easily refer to it.

Fifth, type END. Sixth, type STOP. These six steps should generate an output similar to that which follows.

***? co

YOU ARE NOW AT CONSTRUCT LEVEL (PROMPT SYMBOLS +++)

ENTER DIRECTORY FILE NAME? IF YOU DON'T HAVE ONE, HIT A CARRIAGE RETURN.

73
ENTER ONE PROGRAM NAME (E.G. H2010) IN RESPONSE TO

EACH PROMPT. AFTER ALL NAMES HAVE BEEN ENTERED, TYPE FINIS AFTER

S LIB SEQUENT PROMPT.

+-H- ? J0003

+++ ? FINIS

ENTER # CHARACTERS (E.G. YOUR INTIALS) TO BE USED AS A PREFIX

IN NAMING YOUR NEW DIRECTORY FILE.

+++ ? KAH

THE FOLLOWING PROGRAMS ARE ADDED TO YOUR DIRECTORY FILE KAH100

(MON 12/12/77)

COMMANDS PROGRAM NAME FACTS PROGRAM USED RUN OPTION

KAHOO J0003 RUNNH

THESE PROGRAMS WILL BE SAVED UNTIL YOU DELETE THEM. THEY CAN BE RUN AT

RAMUS LEVEL BY ENTERING RUN OPTION: COMMENTS PROGRAM NAME (E.G. RUNNH:

SRHOO).

*** ? STOP

The computer has now returned you to RAMUS. You may now run your program

by typing OLD: (your program name) and then RUNNH after the computer

prints READY.

OLD: KAHOO

READY

RUNNH

KAHOO 13:30 ATL MON 12/12/77

WAGE RATE INFORMATION

INPUT WAGE RATE PER HOUR FOR CARPENTER

? 8.20

INPUT WAGE RATE PER HOUR FOR PAINTER

?. 8.50

INPUT WAGE RATE PER HOUR FOR PLASTERER

? 7.70

INPUT WAGE RATE PER HOUR FOR PAPEPHANGER

? 7.30

INPUT WAGE RATE PER HOUR FOR APPRENTICE CARPENTER

? 5.00

INPUT WAGE RATE PER HOUR FOR LABORER

? 5.50

74
MATERIAL PRICE INFORMATION

INPUT PRICE OF 4 FT BY 8 FT SHEET OF GYPSUM WALLBOARD

? 2.20

INPUT PRICE OF 4 FT BY 8 FT SHEET OF PLYWOOD PANELLING

? 5.90

INPUT PRICE PER SQUARE YARD OF VINYL-COATED FABRIC

? 4.10

INPUT PRICE PER GALLON OF LATEX FLAT WALL PAINT

? 6.50

INPUT PRICE PER GALLON OF SEMI-GLOSS ENAMEL (OIL BASE)

? 7.70

TYPE 1 IF THERE ARE ANY DOORS, DOOR FRAMES OR WINDOWS

AND FRAMES THAT NEED TO BE REPLACED, IF NOT

?

IF PROGRAM COST ESTIMATES ARE DESIRED, TYPE 1, IF

CONTRACT COST ESTIMATES ARE DESIRED, TYPE

? 1

STOP FOR A MINUTE AND CHECK THE DATA YOU HAVE JUST INPUT

IF ANY ERROR WAS MADE IN ENTERING IT, YOU MAY TYPE 1

TO REPEAT INPUT STATEMENTS: IF NOT, TYPE TO CONTINUE

?

DWELLING UNIT INFORMATION

TYPE IN AN IDENTIFYING ADDRESS OR DU TYPE FOR THIS DWELLING UNIT

? PRE 40 SINGLE

FOR POLICY ESTIMATES INPUT AVERAGES FOR SQUARE FEET, LINEAR FEET,

XRF, ETC.: AND PERCENTAGE OF SAMPLE NEEDED FOR THOSE VARIABLES

REQUIRING A 1 OR

INPUT GROSS SQUARE FEET OF WALL AREA

? 450

INPUT LINEAR FEET OF DOORS PLUS LINEAR FEET OF DOOR

FRAMES REQUIRING PAINT REMOVAL

? 33

INPUT LINEAR FEET OF WINDOWS PLUS LINEAR FEET OF

WINDOW FRAMES REQUIRING PAINT REMOVAL

? 30

INPUT LINEAR FEET OF MISCELLANEOUS TRIM REQUIRING

PAINT REMOVAL

? 110

TYPE 1 IF UNIT IS OCCUPIED, IF NOT

? .975

INPUT PERCENT OF WALL AREA THAT IS WAINSCOATED

(TYPE AS A DECIMAL, E.G., 0.25 FOR 25 PERCENT)

? TYPE 1 IF SUBSTRATE CONDITION IS POOR, IF NOT

? .25

75
TYPE 1 IF PANTRY WORK IS NECESSARY, IF NOT

? .10

TYPE 1 IF 3 OR MORE LAYERS OF WALLPAPER ARE ON WALLS,

IF NOT

? .05

TYPE 1 IF SEPARATE XRF READINGS ARE AVAILABLE FOR DOORS,

WINDOWS AND MISCELLANEOUS TRIM, IF ONLY AN AVERAGE

IS AVAILABLE

? INPUT AVERAGE XRF READING

? 3.47

STOP AND CHECK THE DATA FOR THIS DWELLING UNIT

IF THERE IS AN ERROR, TYPE 1 TO REPEAT INPUT STATEMENTS

IF NOT, TYPE

?

PRE 40 SINGLE

WALL COST TRIM COST TOTAL COST MARKUP RATIO

597.572 209.972 807.545 1.31

IF YOU WISH TO DO MORE COST ESTIMATES TYPE 1, IF NOT TYPE

?

RUNNING TIME: 13.7 SECS I/O TIME: 50.3 SECS

READY

BYE

OFF AT 14:20

76
rasrnx r* e v . -7-7 si

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBS TN 971

2. Gov't Accession

No.

3. Recipient's Accession No.

4. TITLE AND SUBTITLE

Guidelines for Cost-Effective Lead Paint Abatement

5. Publication Date January 1979

6. Performing Organization Code

7. AUTHOR(S)

Rnhprt. F. Chapman and ,1n«;pph £ KnwalcH

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

8. Performing Organ. Report No.

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 ol 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. Through this program lead-based paint

abatement techniques were tested in field deleading operations conducted in Boston,

Massachusetts. The major focus of the program was on the collection of data on the

direct costs of labor, materials and special equipment associated with these

abatement techniques. Data were also collected on contractors' bids so that markup

ratios could be calculated.

This report provides an overview of the statistical analysis of these direct

cost data by abatement technique and building component (i.e., walls, doors and

frames, windows and frames, and miscellaneous trim). An overview of the statistical

analysis of the markup ratio is also included. Cost models are developed for each

abatement technique which identify the key factors which affect direct cost and

markup. Guidelines are given so that these models can be used by municipal officials

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; economic analysis; housing;

lead-based paint; lead poisoning

18. AVAILABILITY [j£ Unlimited

I For Official Distribution. Do Not Release to NTIS

ft-XJ Order From Sup. of Doc, U.S. Government Printing Office Washington, D.C. 20402, Stock No. 003-003-

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88

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