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

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

1.2 Research Methodology

This study has first induced key influential factors

from an industry survey (1

) in order to develop a

list of KSF for a PF project. In the 2

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

) 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

) 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.

KSF-CACorrelationMatrixforProbabilisticCashflowModelonConstructionProjectFinancinginSouthKorea

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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

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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