KSF-CA Correlation Matrix for Probabilistic Cashflow Model on
Construction Project Financing in South Korea
Jin-hyuk Yoo, Dong-gun Lee and Hee-sung Cha
Department of Architectural Engineering, Ajou University, Suwon, South Korea
Keywords: Cash Account, Cash-flow Model, Correlation Matrix, Key Success Factor, Project Financing.
Abstract: In the construction industry, the main obstacle in successfully completing a project is a failure in identifying
and responding the project risk factors. Especially for construction project financing (PF), many project
practitioners are struggling in developing a cash-flow model by integrating the key risk factors for the
subject project. This study has identified key success factors (KSFs) of construction project financing (PF)
throughout an extensive literature review in collaboration with an industry survey. They have been further
derived from Factor Analysis technique and qualified using Fuzzy-AHP method. Throughout the evaluation
of the derived success factors in real building construction projects, a strong correlation has been identified
between the score of each PF success factor and the level of success and/or expected rate of return (ROR).
Using the result of this investigation, this study has been developing a correlation matrix for inter-relating
each KSF and its corresponding cash account in order to effectively measure the financial viability of PF
projects. With the help of this mechanism, the project stakeholders can reach more objective and transparent
decision-making process. The contribution of this study will help decision makers of the PF project make a
better decision and give a meaningful guidance in achieving more successful PF projects.
1 INTRODUCTION
1.1 Research Background
Project Financing (PF) is a type of project delivery
method which contributes to the development of
national economy and business through a private
sector investment. PF is also termed as Public-
Private Partnership (PPP). The various types of PF
have been further developed as Build-Operate-
Transfer (BOT), Build-Own-Operate (BOO), and
Build-Transfer-Lease (BTL). In many cases,
however, PF projects have been forecasted too
optimistically to financially succeed, lacking in
intensive consideration of various project-related
risk factors. As such, the recent financial crisis has
jeopardized most PF projects resulting in investment
shrinkage due to extremely conservative approach in
financial model evaluation (Ye and Tiong 2000).
Although many financially-viable PF projects are
recognized in these days, the private-sector investors
are reluctant to launch a new PF project because
there is no comprehensive risk evaluation model
which identifies various types of risk factors,
evaluates them, and recommends future cash-flow
profile based on the quantitative risk evaluation
approach (Lucko 2011).
Although many researchers have been
conducting a study in relation with project risk
evaluation in a qualitative approach, little research
has been conducted about a quantitative analysis
which links risk factors with cash account items in
developing a cash flow model on PF projects
(Odeyinka et al. 2008; 2012).
The objective of this study is to develop a
correlation matrix between risk factors and cash
accounts, by identify Key Success Factors of a PF
project. To achieve this objective, the authors have
identified Key Success Factors (KSF) on a
successful PF projects throughout an extensive
evaluation of various influential factors on a PF
project. The risk-cash correlation matrix contributes
to a more predictable cash flow analysis model for
various types of PF project on a quantitative
evaluation approach. In addition, it helps decision
makers to make better decisions in investing their
money to PF projects, resulting in a more reliable
foundation in predicting their financial models of PF
projects.
383
Yoo J., Lee D. and Cha H..
KSF-CA Correlation Matrix for Probabilistic Cashflow Model on Construction Project Financing in South Korea .
DOI: 10.5220/0005253903830388
In Proceedings of the International Conference on Operations Research and Enterprise Systems (ICORES-2015), pages 383-388
ISBN: 978-989-758-075-8
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
1.2 Research Methodology
This study has first induced key influential factors
from an industry survey (1
st
) in order to develop a
list of KSF for a PF project. In the 2
nd
survey, a list
of cash accounts (CA) has been developed. By
objectively evaluating the financial outcomes of
previous PF and then establishing the
appropriateness with the weights of the influential
factors, a total set of 23 KSF has been established as
the most significant factors to PF with an analysis of
the interviews from the industry experts.
Subsequently, this study derived Cash account
(CA) in order to define the cost/revenue elements
which influence the cash-flow model of PF projects.
From an industry survey (2
nd
) the CA elements
were justified. Finally, the matrix has been
established in order to integrate the KSF with the
CA. This Matrix was established from the result of
the industry survey (3
rd
) with PF practitioners, and
its implication has been defined as the magnitude of
interrelationship between the two factors, KSF and
the CA. The figure 1 below depicts research
progress.
2 IDENTIFYING KEY
INFLUNTIAL FACTORS ON
SUCCESSUFL PROJECT
FINANCING
This study conducted an in-depth data collection of
influential factors on PF projects from the previous
research. The influential factors on PF projects were
also induced from the interviews with PF
practitioners on the basis of the aforementioned
influential factors in the previous research
(Odeyinka et al. 2008). Under the assumption that
the key influential factors play a pivotal role in
achieving the performance of a particular PF project,
a total set of 104 factors were identified and they
were classified into five categories: project
participants, development plan, business plan,
project site, and financial performance.
Each category was further broken down into
detail-level classes. For example, “project
participants” category has three sub-categories,
including participant job performance, financial
status of participants, and reliability of a
construction company. Likewise, the five categories
have 15 sub-categories. The detailed sub-categories
are provided in Figure 2, which shows the
interrelationship of each influential factor under the
relevant sub-or main categories.
Figure 1: Research progress.
3 QUANTIFICATION ANALYSIS
OF KEY SUCCESS FACTORS
3.1 Overview of Factor Analysis
This study verified the existing factors by
quantifying the level of importance in representing
their influence on the performance of PF projects.
The total of 104 influential factors was induced from
the previous research; some of them were duplicated
and interrelated. Thus, it is essential for the authors
to recognize the factors with duplicated meaning
and/or low-impact. This study employed a factor
analysis technique in identifying the
overlapped/low-impact influential factors. With the
factor analysis, the authors effectively re-organize
the key factors and restructured the influential
factors into a few meaningful groups.
3.2 Factor Analysis Results
3.2.1 Data Collection
Factor analysis is a statistical method to extract a set
of meaningful variables by processing large number
of or massive data. This analysis is a type of
statistical analysis methodology explaining the
characteristics of the entire data by extracting the
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Figure 2: Influence Diagram of Key Success Factor in PF Project.
common variable innate into the variables using the
interrelation among the full set of variables.
Accordingly, this study conducted factor analysis
on 104 influential factors induced from the previous
research. By reviewing the interviews of the
previous research and adding additional
questionnaire to interview data established, factor
analysis was made by performing 28 person-to
person interviews in total.
Respondent interviewees consist of contractors
(28.57%), developers (7.14%), financial institutions
(14.29%), and academic researchers (21.43%). The
industry working experience ranges from 3 years
(18.52%), 3-5 years (22.22%), 5-10 years (33.33%),
to over 10 years (25.93%).
3.2.2 Data Reliability Check
This study conducted credit analysis in order to
increase credibility of questions in the interview
before carrying out fact analysis. Credit analysis
shows how similar the evaluated result are, therefore
performed to evaluate continuity and preciseness of
the interview result.
One of the credit analysis methods is Cronbach's
Alpha analysis method. Cronbach's Alpha Credit
coefficient represents the relationship among
multiple questions, having the range of value from -
1 to +1, which can be interpreted as; values closer to
0 having little relation, values closer to ±1 having
significant relationship.
This Study made use of SPSS 12.0 to perform
credit analysis. The result of credit analysis reads;
0.886 for "Evaluation for A project participant",
0.952 for "Master Plan Evaluation for B Project",
0.942 for "C site evaluation", 0.887 for "Financial
Performance evaluation for E project", meaning that
significant level of credibility over 0.6, moreover the
entire factor evaluation also reads 0.947, a value of
significance.
In order words, influencing factors verified
through the credit analysis under this study found
out to be structured in order for the interviewee to
apply comparatively significant level of credit.
Table 1: Result of analysis reliability.
Category Cronbach's Alpha Number of Item
A 0.886 13
B 0.952 31
C 0.942 22
D 0.887 10
E. 0.963 28
Total 0.974 104
3.2.3 Factor Analysis
This study performed factor analysis on the previous
influential factors on PF projects in order to group
the factors with higher relationship. The method of
factor analysis used was Principal Component
Analysis (PCA) and Varimax Method for rotation of
factors. Items with factor covariance (factor load
value) of 0.5 or higher were grouped at this moment.
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Factor covariance, which represents the relationship
among the factors with variance, is an index
showing how close they are interrelated. With the
range between 0 and 1, the value closer to 1 can be
defined as the most significant in terms of
relationship among factors (Li and Zou 2011).
As a result of factor analysis, 104 items in total
were integrated into 23 factors by this analysis. The
table 2 below represents 23 KSF in a successful PF
project.
Table 2: Result of factor analysis.
Category Key Success Factors
A. Project
participants
A1. Participant job performance
A2. Financial Status of Participants
A3. Reliability of a Construction
Company
B.
Development
Plan
B1. Business management strategy
B2. Safety of development plan
B3. Business risk management
strategy
B4. Marketing strategy
B5. Project site surroundings
B6. Incentive to the development
B7. Growth potential of the
development profit
B8.Diversification Plan
C. Project
Site
C1. Project site obtainments plan
C2. Project site procure rate
C3. Project site conditions
C4. Adequacy of the purchase price
for the project site
C5. Infrastructure maturity
D. Business
Process
D1. construction consent management
D2. Business cash management
E. Financial
Performance
E1. Adequacy of income forecast
E2. Adequacy of financial planning
E3. Adequacy of return investment
E4. Adequacy of sales rate
E5. Adequacy of business profits rate
4 CASH ACCOUNT (CA) OF PF
PROJECTS
Literature review and Professional Interview were
performed to induce the Cash Account which Cash-
flow comprises.
From a PF project for a mixed-purpose building
of residential and commercial uses, the induced Cost
Account was largely subdivided into revenue and
expenses. Items for revenue were sub-categorized
into the revenue from sales and rent, whereas items
for expenditure were deduced the expense for land,
construction, design and CS, sales fee, registration,
utilities and shares, incidentals, others, PF etc. Each
of Cost Account items deduced are being explained
in the Table 3.
Table 3: Cash Accounts of PF Project.
Revenue
Sales
Housing for Sale, Long-
term Key-money rental,
Commercial buildings
Rents
Office rent,
commercial building rent
Expense
Land
Land purchase, Tax, legal
fees etc.
Constrct’n
Surface Construction,
underground construction,
Excavation, Various
incoming
Prepayment/arrearages
Design &
Customer
Service
Design Contract, C.S.
contract, Geological and
topographical survey etc.
Marketing
Sales
M/H construction, M/H Site
rent,
M/H operation, Sales fee,
PR etc.
Registrat’n Registration tax and fee
Utilities
and Shares
Contribution for
Transportation, water/
sewage, Construction
permit bond, Infrastructure,
Integrated Land tax etc.
Fees
Trust fees, Sales Guarantee
fees, Management,
Authorization
Incidentals
Contingency, Customer
claim, Unsold stock
Management etc.
Financial
Service
Financing Expense, PF fee
5 ESTABLISHING KSF-CA
MATRIX
This study established the matrix that has the items
for PF expenditures (CA) as X(vertical axis) and 23
KSFs induced from the factor analysis as Y
(horizontal). Also, industry practitioners in the field
work were asked to be interviewed on the subject of
the given matrix. This interview was focused on
asking for reviews on the interrelationship of the two
items; the items on the X and the blanks made by the
items on the Y crossing the X. The level of
interrelation was described as; blanks if there's no
relationship, whereas the range was given from 1 to
5 according to the level of relationship they have so
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Figure 3 KSF-CA Matrix.
as to be used as an Input.
Then respective expert interview results were
used to revise the matrix by putting the mean value
of the input already established in each cell. For
example, “A.1. Participants job performance” in the
category A, has an influence on “Housing Sales,”
“Long term Key-money Rental Sales (SHIFT),” and
“Commercial Building Sales,” and also has an
influence on the items of expenditure such as “Land
(Site) Purchase,” “'Taxes for Land Acquisition” and
“Legal fees.” By using this matrix, one can easily
identify which risk factors have higher influence on
the revenue-expenditure structure on the cash-flow,
and also it is possible for one to develop cash-flow
model predicting the future with the consideration
for the risk structures of a particular PF project.
Figure 4 presents the conceptual model for cash flow
analysis using this matrix.
6 CONCLUSIONS
This study has demonstrated that it is possible to
ascertain how significantly the key factors in the PF
projects can influence on certain CAs by
establishing the matrix on KSF-CA relationships.
Although the Matrix developed in this study is in
the process of verification, the authors firmly believe
that it will be surely of help to the decision makers
in the process of investment or project development
by preventing PFs from expended with indiscretion
as well as offering helps to discern sound projects,
and also providing more reliable prediction for PF
cash-flows in an objective way.
Figure 4: Concept model of Forecasting Cash flow.
ACKNOWLEDGEMENTS
This research was supported by the Basic Science
Research Program, through the National Research
Foundation of Korea (NRF), which is funded by the
Ministry of Education (No. 2012R1A1B3001009).
This work was also partially supported by the Ajou
University Research Fund.
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