MACROECONOMIC STUDY OF CONSTRUCTION FIRM'S PROFITABILITY USING CLUSTER ...

87 Pages·2012·1.62 MB·English

MACROECONOMIC STUDY OF CONSTRUCTION FIRM’S PROFITABILITY
USING CLUSTER ANALYSIS

A Thesis
by
PARTH ARORA

Submitted to the Office of Graduate Studies of
Texas A&M University
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE

August 2012

Major Subject: Construction Management
Macroeconomic Study of Construction Firm’s Profitability Using Cluster Analysis
Copyright 2012 Parth Arora
MACROECONOMIC STUDY OF CONSTRUCTION FIRM’S PROFITABILITY
USING CLUSTER ANALYSIS

A Thesis
by
PARTH ARORA

Submitted to the Office of Graduate Studies of
Texas A&M University
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE

Approved by:
Co-Chairs of Committee, Mohammed Haque
Kunhee Choi
Committee Member, Sarah Deyong
Head of Department, Joseph Horlen

August 2012

Major Subject: Construction Management
iii
ABSTRACT

Macroeconomic Study of Construction Firm’s Profitability Using Cluster Analysis.
(August 2012)
Parth Arora, B.E., Birla Institute of Technology and Sciences
(BITS), Pilani, Rajasthan, India
Co-Chairs of Advisory Committee: Dr. Mohammed Haque
Dr. Kunhee Choi

This research aims to identify important factors contributing to a construction
firm’s profitability and to develop a prediction model which would help in determining
the gross margin/profitability of a construction firm as a function of important
parameters. All the data used in the research was taken from U.S Census Bureau reports.
The novelty of the research lies on its focus at a state level, by dividing states into
pertinent clusters and then analyzing the trends in each cluster independently.
The research was divided into two phases. Phase 1 of the research focused on
identification of the most important factors contributing to gross margin of a
construction firm. The variables used were derived from the U.S Census Bureau data.
Based on the independent variables and gross margin, all the states were divided into
three clusters. Subsequently, a prediction model was developed for each cluster using
step-wise backward elimination, thus, eliminating non-significant variables.
Results of Model 1 gave impetus to developing Model 2. Model 1 clearly showed
that labor productivity was the most important variable in determining gross margin.
iv
Model 2 was developed to predict gross margin as a function of single most important
factor of labor productivity. Similar to Model 1, states were clustered based on their
labor productivity and gross margin values. Prediction model was developed for each
cluster.
In this study, an excel embedded decision support tool was also developed. This
tool would aid the decision-makers to view the state’s level of gross margin and labor
productivity at a glance. Decision support tool developed was in the form of color-coded
maps, each of which was linked to a spreadsheet containing pertinent data.
The most important conclusion of the research was that there exists a positive
linear relationship between labor productivity and gross margin at a state level in the
construction industry. The research also identified and quantified other important factors
like percent of rental equipment used, percent of construction work sub-contracted out
and percent of cost of materials, components and supplies which affect gross margin.
v
DEDICATION

Dedicated to the 3 most beautiful women in my life – my mother, my sister and my wife:
Monica Arora
Saumya Arora
Simran Arora
vi
ACKNOWLEDGEMENTS

I would like to extend my whole-hearted thanks to my committee chair and co-
chair, Dr. Mohammed Haque and Dr. Kunhee Choi, and my committee member, Dr.
Sarah Deyong for their guidance and support throughout the course of this research.
They were always there when I had any doubts or questions and this thesis report is a
result of constant motivation and direction I received from them.
I also would also like to thank my family for their endless love and support
throughout the course of my graduate degree.
vii
NOMENCLATURE

A/E/C Architecture Engineering Construction
ANOVA Analysis of Variance
BLS Bureau of Labor Statistics
CPI Consumer Price Index
PRESS Predicted Error Sum of Square
SSE Sum of Square of Errors
viii
TABLE OF CONTENTS

Page
ABSTRACT .............................................................................................................. iii
DEDICATION .......................................................................................................... v
ACKNOWLEDGEMENTS ...................................................................................... vi
NOMENCLATURE .................................................................................................. vii
TABLE OF CONTENTS .......................................................................................... viii
LIST OF FIGURES ................................................................................................... x
LIST OF TABLES .................................................................................................... xi
1. INTRODUCTION ............................................................................................... 1
1.1 Background .......................................................................................... 1
1.2 Definition of Terms .............................................................................. 3

2. RESEARCH SCOPE AND SIGNIFICANCE .................................................... 7
2.1 Problem Statement ............................................................................... 7
2.2 Research Objectives ............................................................................. 7
2.3 Research Hypotheses ............................................................................ 8
2.4 Limitations of the Research .................................................................. 8
2.5 Research Significance .......................................................................... 9

3. LITERATURE REVIEW

3.1 Quantifying Labor Productivity ........................................................... 10
3.2 Productivity Prediction Modeling ........................................................ 13

4. RESEARCH METHODS .................................................................................... 16
4.1 Data Collection ..................................................................................... 16
4.2 Missing Values in the Data .................................................................. 17
4.3 Adjusting the Values Based on Consumer Price Indexing (CPI) ......... 17
4.4 Cluster Analysis ................................................................................... 17
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Page

4.5 Statistical Analysis ............................................................................... 22
4.6 Decision Support Tool ......................................................................... 22

5. RESULTS AND INTERPRETATION ............................................................... 24
5.1 Model 1 ................................................................................................ 24
5.1.1 Cluster 1 .................................................................................... 25
5.1.2 Cluster 2 .................................................................................... 28
5.1.3 Cluster 3 .................................................................................... 32
5.1.4 Model -1 Summary .................................................................... 34
5.2 Model 2 ................................................................................................ 36
5.2.1 Cluster 1 .................................................................................... 37
5.2.2 Cluster 2 .................................................................................... 39
5.2.3 Cluster 3 .................................................................................... 41
5.2.4 Model -2 Summary .................................................................... 43
5.3 Decision Support Tool ......................................................................... 44

6. CONCLUSIONS ................................................................................................. 52
6.1 Summary of Findings ........................................................................... 52
6.2 Scope for Future Research ................................................................... 54

REFERENCES .......................................................................................................... 56
APPENDIX 1 ............................................................................................................ 61
APPENDIX 2 ............................................................................................................ 67
VITA ......................................................................................................................... 75