PSZ 19:16
UNIVERSITI TEKNOLOGI MALAYSIA
BORANG PENGESAHAN STATUS TESIS 
JUDUL: A STUDY OF MACRO ECONOMY IN DETERMINING THE COMMERCIAL
PROPERTY MARKET
SESI PENGAJIAN: 2005 / 2006
Saya
SIM BOON HUN
(HURUF BESAR)
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan Universiti
Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut:
1.
2.
3.
4.
Tesis adalah hakmilik Universiti Teknologi Malaysia.
Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan pengajian
sahaja.
Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
**Sila tandakan ( √ )
SULIT
(Mengandungi maklumat yang berdarjah keselamatan
atau kepentingan Malaysia seperti yang termaktub di
dalam AKTA RAHSIA RASMI 1972)
TERHAD
(Mengandungi maklumat TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
√
Disahkan oleh
(TANDATANGAN PENULIS)
Alamat Tetap : 7, TATAU NEW TOWN
EXTENSION PHASE II, 97200,
BINTULU, SARAWAK..
Tarikh : 31 DECEMBER 2005
CATATAN :
*
**

(TANDATANGAN PENYELIA)
PROF. ROSDI AB RAHMAN
Nama Penyelia
Tarikh : 31 DECEMBER 2005
Potong yang tidak berkenaan.
Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak
berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh 00
tesis ini perlu dikelaskan sebagai SULIT atau TERHAD.
Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana
secara penyelidikan, atau disertasi bagi pengajian secara kerja kursus dan
penyelidikan, atau Laporan Projek Sarjana Muda (PSM).
SUPERVISOR’S DECLARATION
“I/We* hereby declare that I/we* have read this thesis and in my/our*
opinion this thesis is sufficient in terms of scope and quality for the
award of the degree of Master of SCIENCE (PROPERTY MANAGEMENT)
(or Doctor of Philosophy or Doctor of Engineering(specialisation) )”
Tandatangan
: …………………………
Nama Penyelia
: PROF. ROSDI AB RAHMAN
Tarikh
: 30TH DECEMBER 2005
* Delete as necessary
PENGESAHAN
SEKOLAH
PENGAJIAN
SISWAZAH/FAKULTI/AGENSI
KERJASAMA
BAHAGIAN A – Pengesahan Kerjasama*
Adalah disahkan bahawa projek penyelidikan tesis ini telah dilaksanakan melalui
kerjasama antara _______________________ dengan ____________________________
Disahkan oleh:
Tandatangan : ………………………………………………...
Nama
: …………………………………………………
Jawatan
: …………………………………………………
Tarikh :……………
(Cop rasmi)
* Jika penyediaan tesis/projek melibatkan kerjasama.
===============================================================
BAHAGIAN B – Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah
Tesis ini telah diperiksa dan diakui oleh:
Nama dan Alamat Pemeriksa Luar :
……………………………………………..........
……………………………………………..........
……………………………………………..........
Nama dan Alamat Pemeriksa Dalam :
……………………………………………..........
……………………………………………..........
……………………………………………..........
Nama Penyelia Lain (jika ada) :
……………………………………………..........
……………………………………………..........
……………………………………………..........
……………………………………………..........
Disahkan oleh Penolong Pendaftar di SPS:
Tandatangan : ……………………………………
Nama
: ……………………………………
Tarikh :……………
A STUDY OF MACRO ECONOMY IN DETERMINING THE COMMERCIAL
PROPERTY MARKET
SIM BOON HUN
A project report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Science (Property Management)
Faculty of Geoinformation Science And Engineering
University Technology of Malaysia
DECEMBER 2005
ii
DECLARATION
I declare that this thesis entitled “A Study Of Macro Economy In Determining the
Commercial Property Market” is the result of my own research except as cited in the
references. The thesis has not been accepted for any degree and is not concurrently
submitted in candidature of any other degree.
Signature
: …………………………
Name
: SIM BOON HUN
Date
: 30TH DECEMBER 2005
iii
DEDICATION
“To my beloved father and mother, sisters and Ms Khoo”
iv
ACKNOWLEDGEMENT
The years I have spent at Faculty of Geoinformation Science & Engineering
taught me many valuable lessons, mainly through the interaction with lecturers who
gave advices and guided me.
I am thankful to my supervisor, Prof. Rosdi Ab Rahman for his insightful
comments and assistance on completion of this master’s project report. The same also
goes to the lecturer who have contributed in this report, especially Dr. Nor Abidah.
A word of thanks are also forwarded to Pusat Sumber of Property Management
department, PSZ, JPPH, Central bank and etcs for their co-operation in assisting me to
obtain the valuables data.
To the input of Eu As Properties, especially Mr. Cheah and Mr. Tan, thanks a
lot for the understanding and flexibility in allowing me to take leave while in the
completing this master project report.
My fellow course mates, colleagues and all my friends, thank you all for the
assistance rendered in so many occasions. Miss Khoo, the person whom I am to be
specified as her supporting and help all the time this thesis’s progression.
My parents, whose support and encouragement was always there when I
needed. Without their support and encouragement, indeed this project report are
unable to be completed.
v
ABSTRAK
Adalah tidak boleh dinafikan bahawa implikasi pembolehubah makro
ekonomi memberi pengaruh yang mendalam terhadap prestasi harta tanah serta
urusniaga harta tanah komersial. Ini memberikan inspirasi terhadap keperluan untuk
menjalankan satu kaedah penyelidikan, dimana penganalisaan pembolehubah makro
ekonomi tersebut dapat ditentukan, terutamanya dalam penentuan prestasi harta tanah
komersial,dan bilangan urus niaganya. Dengan ini, kajian ini memberi manfaat yang
besar dalam pelbagai penyelidikan yang dijalankan di Malaysia. Tujuan penyelidikan
ini adalah untuk menghasilkan hipotesis yang membolehkan pelbagai pembolehubah
makro ekonomi di Malaysia diuji dalam satu jangka masa. Seterusnya, pelbagai jenis
tren dihasilkan demi pengujian kualitatif. Disamping itu, pelbagai kaedah
penganalisaan seperti analisis korelasi dan regresi turut dijalankan untuk pengujian
secara kuantitatif. Penilaian yang dijalankan adalah berdasarkan kepada nisbah bagi
setiap pemboleh ubah makroekonomi, dan ia seterusnya digabungkan sebagai satu
model yang bertujuan untuk perjangkaan bilangan urus niaga hartanah komersial.
Penemuan dalam kajian ini menunjukkan terdapatnya kewujudan beberapa tren yang
mempengaruhi perkembangan urus niaga harta tanah komersial. Antara beberapa
pemboleh ubah makro ekonomi yang memberi impak berkesan terhadap makro
economi adalah Keluaran Dalam Negara Kasar (KDNK), kadar pinjaman dasar (pada
krisis ekonomi 1997), simpanan nasional serta pinjaman bank yang memberi pinjaman
kepada sektor harta tanah. Penemuan dalam kajian ini secara langsung mencadangkan
bahawa pengawalan pemboleh ubah makro ekonomi, akan berupaya meminimumkan
kegagalan dalam pelbagai pembangunan dan pembinaan projek baru, dan seterusnya
meningkatkan prestasi pasaran terutamanya dalam permintaan harta tanah komersial.
vi
ABSTRACT
The influence of macroeconomic variables toward real estate performance, as
well as it effects on commercial property transaction is inevitable. As a result, the
relationship propagates for this study in the endevor to understand and explain the
influence of these macroeconomic variables. This study is set out to ascertain the
commercial property performance, especially in its transaction volume. Hopefully, it
will serve as a complement to other related studies carried out. The main purpose of
this study is to develop a hypothesis for projecting trends of property transactions. It
will be based upon an analysis of the relationship between key macroeconomic
variables and property transaction. Several analytical methods such as correlation and
regression analysis were carried out. Evaluation based on proportion of each
macroeconomic variable was determined, and ultimately combined as a complete
model to predict in a more acceptable manner. In this finding, it obviously showed that
the existence of several trends were aimed to affect the commercial property
transaction volume. As presented in this research, some of the macroeconomic
variables varies in the way they influence the market. They significant ones include
Gross Domestic Product, Base Lending Rate (During the economic crisis in year
1997), national saving and bank loan allocated to the property sector. In a significant
way, this finding suggests that by monitoring the macroeconomic variables, the
commercial property market performance is predictable, especially in its demands on
the market. More Important, it will also contribute towards a sustainable development
of new projects in the coming future.
vii
TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
THESIS STATUS DECLARATION
SUPERVISOR’S DECLARATION
DECLARATION ON COOPERATION &
CERTIFICATION OF EXAMINATION
1
TITLE PAGE
i
DECLARATION PAGE
ii
DEDICATION PAGE
iii
ACKNOWLEDGEMENT
iv
ABSTRAK
v
ABSTRACT
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xii
LIST OF FIGURES
xvi
LIST OF SYMBOLS
xviii
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
4
1.3
Research Objective
4
1.4
Scope Of Study
5
1.5
The Importance Of Study
5
1.6
Methodology
6
1.7
Limitation Of Study
8
viii
2
1.8
Assumption
9
1.9
Outline Of Report
9
LITERATURE REVIEW
2.1
Introduction
10
2.2
Supply and Demand
10
2.2.1 The Law Of Demand
10
2.2.2 Effective Demand
11
2.2.3
11
2.3
2.4
2.5
Other Thing Being Equa1
2.2.4 The Work Of The Law Of Demand
12
2.2.5 Market Equilibrium
13
The Function Of Government In Macro
Economy
14
2.3.1
Provision of Public Goods
14
2.3.2
Transfer of Income
15
2.3.3
Regulation Of Private Businesses
16
2.3.4
Administration of Justice
16
2.3.5
Overlapping Functions
17
A Simple Economy
18
2.4.1 Stocks and Flows
19
2.4.2
National Income And Product
19
2.4.3
Saving and Investment
20
2.4.4
Aggregate Supply and Demand
21
Measuring National Income And Product
22
2.5.1
Gross National Product
22
2.5.1.1 Consumption
23
2.5.1.2 Investment
24
2.5.1.3 Government Purchase
25
2.5.1.4 Net Exports
25
2.5.2 Gross Versus Net National Product
26
ix
2.5.3 National Income
27
2.5.4 The Relationship between
National Income and GNP
2.6
2.5.5 Personal Income
30
Real Estate Cycle
31
2.6.1 The Concept
31
2.6.2
Characteristic of Real Estate Cycle
32
2.6.3
Real Estate Cycle - Regional And
Global
2.7
2.8
3
28
34
2.6.4 The "Malaysian Cycle”
34
2.6.5
35
The Dynamic Of Real Estate Cycle
Correlation And Regression
39
2.7.1
Correlation Coefficient
39
2.7.2
Looking At Data: Scatter Diagrams
40
2.7.3
Calculation Of The Correlation
Coefficient
43
2.7.4
Significance Test
45
2.7.5
Spearman Rank Correlation
49
2.7.6
The Regression Equation
50
2.7.7
More advanced methods
56
Summary
56
COMMERCIAL PROPERTY TRANSACTION
TREND AND ANALYSIS OF FACTOR
INFLUENCED
3.1
Introduction
3.2
Number Of Commercial Property
Transaction & Price Range
3.3
57
57
Number Of All Type Property Transaction
& Percentage Of Commercial Property
62
x
3.4
Annual Percentage Change In Number Of
Property Transaction
66
3.5
Value Of Commercial Property Transaction
70
3.6
Value Of All Type Properties Transacted
And Percentage Of Commercial Property
3.7
Annual Percentage Change In Value Of
Property Transaction
3.8
3.12
4
83
Analysis Of Commercial Property
Transaction By Graph
3.11
82
Quarterly Percentage Change In Value Of
Commercial Property Transaction
3.10
78
Quarterly Percentage Change In Number
Of Commercial Property Transaction
3.9
74
84
Others Graph (Plot From The Data
Obtained)
89
Summary
94
ANALYSIS IN DETERMINATION OF
MACRO ECONOMIC FACTORS
4.1
Introduction
96
4.2
Macroeconomic Data To Be Examined
96
4.3
Base Lending Rate
99
4.3.1
Base Lending Rate 1997-2003
Monthly (%)
4.3.2
Base Lending Rate 1997-2003
Quarterly (%)
4.4
99
99
4.3.3 Analysis Of BLR
100
Gross Domestic Product
103
xi
4.4.1
Gross Domestic Product (GDP)
1997-2003 (At Current Price)
4.5
Quarterly
103
4.4.2 Analysis Of GDP
104
National Saving
108
4.5.1
4.5.2
4.6
National Saving Outstanding
1997-2003 Quarterly
109
Analysis Of National Saving
109
Bank Loan To Commercial Property
Sector
4.6.1
113
Bank Loan To Commercial
Property Sector 1997-2003
4.6.2
5
Quarterly
113
Analysis Of Bank Loan
114
4.7
Model
117
4.8
Summary
120
CONCLUSION AND RECOMMENDATION
5.1
Economy Crisis
122
5.2
Finding
123
5.3
Recommendation
126
5.4
Gain From This Study
126
REFERENCES
127
xii
LIST OF TABLES
TABLE NO.
2.1
TITLE
PAGE
Nominal Gross National Product By Type Of
Expenditure(In RM Million)
23
2.2
Nominal National Income (in RM Million)
28
2.3
Relation Of National Income To GDP (RM Million)
30
2.4
National Income And Personal Income (RM Million)
31
2.5
Correlation between height and pulmonary anatomical
dead space in 15 children
42
2.6
Distribution of t (two tailed)
46
2.7
Derivation of Spearman rank correlation from data
of table 2.5
49
3.1
Number of commercial property transaction in 1997
58
3.2
Number of commercial property transaction in 1998
58
3.3
Number of commercial property transaction in 1999
59
3.4
Number of commercial property transaction in 2000
59
3.5
Number of commercial property transaction in 2001
60
3.6
Number of commercial property transaction in 2002
60
3.7
Number of commercial property transaction in 2003
61
3.8
Number of all type property transacted and percentage
of commercial property 1997
3.9
62
Number of all type property transacted and percentage
of commercial property 1998
63
xiii
3.10
Number of all type property transacted and percentage
of commercial property 1999
3.11
Number of all type property transacted and percentage
of commercial property 2000
3.12
69
Annual percentage change in number of property transaction
2002
3.21
68
Annual percentage change in number of property transaction
2001
3.20
68
Annual percentage change in number of property transaction
2000
3.19
67
Annual percentage change in number of property transaction
1999
3.18
67
Annual percentage change in number of property transaction
1998
3.17
65
Annual percentage change in number of property transaction
1997
3.16
65
Number of all type property transacted and percentage
of commercial property 2002
3.15
64
Number of all type property transacted and percentage
of commercial property 2002
3.14
64
Number of all type property transacted and percentage
of commercial property 2001
3.13
63
69
Annual percentage change in number of property transaction
2003
70
3.22
Value of commercial property transactions (RM Million) 1997
71
3.23
Value of commercial property transactions (RM Million) 1998
71
3.24
Value of commercial property transactions (RM Million) 1999
72
3.25
Value of commercial property transactions (RM Million) 2000
72
3.26
Value of commercial property transactions (RM Million) 2001
73
3.27
Value of commercial property transactions (RM Million) 2002
73
3.28
Value of commercial property transactions (RM Million) 2003
74
xiv
3.29
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 1997
3.30
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 1998
3.31
81
Annual percentage change in value of property transaction
2003
3.43
81
Annual percentage change in value of property transaction
2002
3.42
80
Annual percentage change in value of property transaction
2001
3.41
80
Annual percentage change in value of property transaction
2000
3.40
79
Annual percentage change in value of property transaction
1999
3.39
79
Annual percentage change in value of property transaction
1998
3.38
78
Annual percentage change in value of property transaction
1997
3.37
77
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 2003
3.36
77
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 2002
3.35
76
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 2001
3.34
76
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 2000
3.33
75
Value of all type properties transacted (RM Million) and
percentage of commercial property (%) 1999
3.32
75
82
Quarterly percentage change in number of commercial
property transaction (%)
83
xv
3.44
Quarterly percentage change in value of commercial
property transaction
84
4.1
Base Lending Rate 1997-2003 monthly (%)
99
4.2
Base Lending Rate 1997-2003 quarterly (%)
100
4.3
Number of commercial property transaction vs BLR
only in 97 Q1 until 99 Q4
4.4
Gross Domestic Product (GDP) 1997-2003 (at current
price) quarterly (RM Million)
4.5
109
Number of commercial property transaction vs national
saving in 97 Q1 until 03 Q4
4.8
107
National saving outstanding 1997-2003 by quarterly
(RM Million)
4.7
104
Number of commercial property transaction vs GDP in
00 Q1 until 03 Q4
4.6
102
112
Bank loan to commercial property sector 1997-2003
quarterly (RM Million)
114
xvi
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Methodology study flow chart
7
2.1
Idealised Real Estate Cycles
33
2.2
Correlation illustrated
40
2.3
Scatter diagram of relation in 15 children between
height and pulmonary anatomical dead space
2.4
43
Regression line drawn on scatter diagram relating height
and pulmonary anatomical dead space in 15 children
53
3.1
Number of commercial property transaction
85
3.2
Percentage share of commercial property transaction
86
3.3
Annual percentage change of commercial property
transaction (compared with same quarter)
3.4
87
Quarterly percentage change of commercial property
transaction
87
3.5
Value of commercial properties transaction (RM Million)
89
3.6
Percentage share for value of commercial property
transaction
3.7
Annual percentage change for value of commercial
property transaction (compared with same quarter)
3.8
3.9
89
90
Quarterly percentage change for value of commercial
property transaction
90
Number of all type properties transaction
91
xvii
3.10
Annual percentage change of all type properties
transaction (compared with same quarter)
3.11
91
Quarterly percentage change of all type properties
transaction
92
3.12
Value of all type properties transaction
92
3.13
Annual percentage change for value of all type
properties transaction (compared with same quarter)
3.14
Quarterly percentage change for value of all type
properties transaction
4.1
93
Graph number of commercial property transacted vs
Base Lending Rate 1997-2003 quarterly (%)
4.2
93
101
Graph percentage of commercial property transaction
vs percentage change of Base Lending Rate 1997-2003
quarterly (%)
101
4.3
Number of commercial property transacted vs GDP
105
4.4
Percentage change of commercial property transaction
vs percentage change of GDP
4.5
Number of commercial properties transaction vs
national saving
4.6
110
Percentage change of commercial property transacted
vs percentage change of national saving
4.7
106
110
Percentage change of commercial property transacted
vs percentage change of bank loan to commercial
property
4.8
115
Percentage change of commercial property transacted
vs percentage change of bank loan
116
xviii
LIST OF SYMBOLS
NEP
-
New Economy Policy
%
-
Percent
GDP
-
Gross Domestic Product
MNEAC
-
National Economic Action Council
RM
-
Ringgit Malaysia
CDRC
-
Corporate Debt Restructuring Committee
VCD
-
Video Compact Disk
GNP
-
Gross National Product
NNP
-
Net National Product
U.K
-
United Kingdom
NEP
-
National Economic Plan
BLR
-
Base Lending Rate
SPSS
-
Statistical Packages for Social Science
CHAPTER I
INTRODUCTION
1.1
Introduction
The New Economic Policy (NEP) to restructure the Malaysian economy
introduced in the 1970s had a positive effect on improving the property market sector.
Acting as a catalyst to help the recovery of the property market sector, provisions in
the NEP allowed active participation by both foreign and local property investors. The
periods from early 1990s until mid-1997 showed a growth of 8.0 percent per annum.
In late 1997, the rate of economic slowdown accelerated due to the financial
crisis in the Far East. In 1998, the gross domestic product (GDP) indicated a negative
rate of growth of between 2.8 to 6.8 percent while the economic growth had fallen to
about 7.4 percent. This negative growth rate indicated that Malaysia was facing a
financial meltdown in the economy and property market as a whole.
According to Bank Negara Malaysia (1998), the Malaysian Gross Domestic
Product showed a decrease of 6.1 percent in 1997 that influenced a drop of 26.5
percent of aggregate demand. As a result, inflation rate rose to 5.3 percent whereas
employment rate dropped by 3.4 percent. It was even more drastic when the foreign
2
exchange rate dropped by 40 percent and the construction sector also dropped by 24.5
percent in 1998 (Bank Negara Malaysia Annual Report, 1998).
However, due to the government's relentless effort to improve the economic
situation, the GDP grew by about 5 percent in 2001.
More specifically, the government has taken steps to regulate the formal
measures either financially or fiscally by establishing the National Economic
Action Council (NEAC) in 1999 to propose plans in improving the economy
institutionally. Steps taken by NEAC including the proposal to stabilize the value
of the Ringgit Malaysia (RM), to reestablish confidence of the market, to stabilize
the financial of the market, to strengthening the fundamentals of economy, to
continue the socio-economic and equinity agenda and to improve the weakened
sectors within the economy.
By doing so, the NEAC has taken radical approaches such as to improve the
credit control in the foreign exchange in order to stabilize the ringgit. In addition, the
Central Bank has imposed control on foreign capital to restrict the outflow. Moreover,
the Central Bank has injected about RM34 billion into the banking sector in order to
improve the low interest rate in 1997.
In 1998, the government established `Danaharta', the Asset Management Fund
Agency, to take over non-performing loans from the banking system. At the same
time, the capital of management fund agency was set up to inject funds into the
banking system. Apart from these measures, the Corporate Debt Restructuring
Committee (CDRC) was set up to restructure the banking system.
The government took steps to improve the property slump in the property sector
by establishing the National Property Information Centre (NAPIC) to provide up-todate and accurate information on property. Among its purposes, the NAPIC was
established to avoid the occurrences of oversupply in the property market.
3
Therefore, the economic measures such as fiscal and legal exemptions,
incentives and restrictions also affect land development and property investment
decisions. In early 1984, for example, a restriction on foreign land ownership was
imposed on the National Land Code (1965) due to an influx of overseas purchasers in
certain urban areas (The Star Metro, 16 August, 2002). The restriction was imposed on
certain types of property and a levy was chargeable for certain residential property.
However, the restrictions were repealed in 1987 during the recession and, were
amended again in 1991 when the economy recovered.
Due to the financial crisis in mid-1997, the levies on residential properties were
lifted again to boost the confidence of foreign and local investors in the property
market (Property Market Report, 1998).
Similarly, as observed in the period from 1991 to mid-1997, the lending
regulations have played an important role in pushing up the cost of borrowing for land
developers and property borrowers. The aim was to restrict land development
activities and to avoid an oversupply of properties in the market.
The control on credit facilities and financial crisis were responsible for the
slowing down of construction activities since mid-1997 (Property Market Report,
1998). The government reviewed the existing financial and legal conditions imposed
on land developers and provided incentives to improve the property sector despite the
economic recession (Property Market Report, 1999).
As a result, these measures indicated positive signs of economic growth and a
recovery of the property sectors in 2000. This highlights the relationship between
economic indicators and real estate development activities.
4
1.2
Problem Statement
It seems obvious that researches concerning real estates in this country are
scarcely done by individuals, except those done by government departments, such as
JPPH, NAPIC and departments of Real Estate Management in local universities.
In this study, therefore the investigation focused on market research on profitoriented companies that is, developers and Real Estate agents. These companies often
overlook the effect and the consequences of the whole market economy on the real
estate market. There is also a lack of detailed analysis of the situation in the annual
reports from Bank Negara (Annual Report) or JPPH (Property Market Report). Thus
this research was undertaken to provide a more detailed analysis of the situation in the
report from government departments.
1.3
Research Objective
The following are the objectives of this study
1. to establish the relationship between macro economy and real estate
market and to understand how several factors mutually influenced these
two sectors
2. to determine the macroeconomic variables that will most influence the
commercial property market
3. to create an "ideal" model by combining several macroeconomic factors
which determined commercial property transaction
5
1.4
Scope Of Study
In order to achieve the research objectives, this study focused on
providing in-depth understanding of the property market situation. The scope
of the study is as follows:
1. the research focused on recognizing the factors that will most affect the real
estate market in Malaysia. This is done by combining several macroeconomic
variables.
2. several macroeconomic variables from 1997-2003 (7 years) were
determined and examined.
3. data used in this study were mainly those collected in Malaysia in the
period from 1997 through to 2003.
1.5
The Importance of Study
The aim of the study was to reinforce the market study of the real estate market,
which is seldom done by the Malaysian government. Therefore, findings from this
research would give a better understanding of the current real estate market situation.
Newspaper journalists, real estate agents and developers do conduct their own
market research on real estate and the economy and their relationship with the
macroeconomic performance. However, insufficient and undefined data variables and
research without the use of appropriate tools such as hedonic and regression models
might have rendered their research findings inconclusive. In addition, these studies
depended mostly on researches in or findings from developed countries. Since the
situation in each county varies, too much dependence on these findings will not assist
us in evaluating or observing our own situation and environment.
6
This study also aimed to assist developers, investors and purchasers in predicting
the most appropriate time to enter the real estate market. Macro economic situation
plays an important role in real estate marketing strategies. Any changes in macro
economic situation will result in a significant effect on real estate performance.
It is hoped that findings from this study will increase the awareness among
Malaysians of the factors that affect Malaysia's real estate scenario.Since the purchase
of real estate has been the largest form of investment/expenditure from most
Malaysians, it is hoped that this study would provide a detailed source of reference for
their decisions on real estate investments/expenditure.
1.5
Methodology
This study involved analysis of secondary data on several macro economic
variables such as mortgage interest rate, gross domestic products, population size,
income per capita, average property price and average age of population and other
variables.
Data for the research were mostly obtained from the internet, property market
report, data published by the Ministry of Finance, Department of Statistics Malaysia,
Bank Negara, newspaper archives, journals, book and other sources.
Data were collected, compiled and analysed. Correlation and regression
models (non-linear models) were used to analyse the data. Conclusions and summary
of the factors that affect the commercial property transaction were drawn from the
findings and generated equations.
7
Stage 1
Issues And Problems Of Study
Implementation Of Study
1.
2.
3.
4.
5.
Objective
Importance Of Study
Scope
Limitation
Methodology Of Study
Issues And Problems
1.
2.
Aim to study the commercial property
movement trend and find out the
macroeconomic factor that affected
the demand of the commercial
property transaction.
Aim by statistical tools, correlation &
regression analysis will carry out to
analyse the macroeconomic variables.
Stage 2
Theoretical Research
1.
2.
3.
4.
5.
6.
Stage 3
Supply and demand
The function of government in macro economy
The circular flow of income and product
Measuring national income and product
Real estate cycle
Correlation & regression analysis
References
1. Books
2. Journals
3. Magazines
4. Newspapers
5. Internet
Data Collection (Secondary data)
Stage 4
Study Analysis
1. Determination of macroeconomic variables
2. Graph analysis (Observation of graph and giving
subjective view)
3. Correlation & regression analysis (Statistical tools
and giving objective view)
Stage 5
Conclusion and Recommendation
Figure 1.1 Methodology study flow chart
8
1.7
The Limitation Of Study
The limitations of the study are as follows
1.
selection of the most influential macro economic variables on the real
estate situation had to be done to reduce the difficulty level of the research
2.
various types of properties (besides commercial property) were not
discussed in this study, such as residential, industrial, land and others were
considered as "other properties"
3.
since the supply is determined by developers, only the demand for
property was analysed
4.
data for this research was limited to those from 1997-2003 only. It is
recommended that to obtain a more comprehensive overview of the situation
data from 1957 should be analaysed.
1.8
Assumption
The assumptions of the study included the following
1.
All data acquired from secondary resources were considered true.
2.
The variables excluded in this study were considered unimportant and
thus have no effect upon the real estate market.
3.
Malaysia was considered as a whole body of research, that is, no
subdividing into different states was done for this study.
9
1.9
Outline Of Report
Chapter one (1) describes the Problem Statement, Research Objective, Scope
of study, limitation of study and assumptions of study. Literature review in chapter
two (2)describes the macro economy, and also real estate cycle. Commercial property
transaction trend and analysis of factor influencing transaction in chapter three (3)
provides the data for analyses. For a subjective view, factors that influence the
movement of the commercial property transaction is determined. Analysis in
determination of macro economic factors known as in chapter four (4)
discusses the macro economic factors that influence the commercial property
transaction by applying the Correlation and Regression analysis. Then, the equation
was generated when the relation between number of commercial property transacted
and the macro economy variables was made. Finally, the model used to forecast the
number of commercial property transaction between years 2004 to 2006 was created.
Finally, conclusion and recommendation in chapter five (5) provides an overall
summary of this research and some recommendations.
.
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
This chapter will look at some macroeconomic factors that are related to this
study.
2.2
Supply and Demand
2.2.1 The Law of Demand
The analysis begins with the law of demand, which says simply that, in the
market for any good, the quantity of that goods demanded by buyers tend to increase
as the price of the goods decreases and tends to decrease as the price increases, other
things being equal.
11
2.2.2 Effective Demand
First, what is meant by quantity demanded? It is important to understand that
the quantity demanded at a given means the effective demand – the quantity
purchasers are willing and able to buy at that price. The effective demand at a
particular price may be different from the quantity consumers want or need.
I may want a new car, but given my limited financial resources; I am not
willing actually to offer to buy such a car at it current price of RM 50,000. My want
does not count as part of the quantity demanded in the market for this car. Similarly,
I might need a dental surgery to avoid premature loss of my teeth, but I might be very
poor. If I were unable to pay and no others person or agency were willing to pay, my
need would not be counted as part of the quantity demanded in the market for dental
services.
2.2.3
Other Thing Being Equa1
Second, why is the phrase other things being equal attached to the law of
demand? The reason is that a change in the price of a product is not the only thing that
affects the quantity of that product demanded.
If people's incomes go up, they are likely to increase the quantities they
demand of great many goods, even if prices do not change. If people's basic tastes and
preferences change, the quantities of things they buy will change. If their expectations
future prices or their own future incomes change, they may change their spending
patterns even before those price and income changes actually take place.
Above all, in the law of demand, the "other things being equal" condition
indicates that the prices of other goods remain unchanged. What really counts
12
determining the quantity demanded of some good is its price relative to the other
goods.
If the price of gasoline goes up and consumers' incomes and the prices of all
other goods go up by the same proportion, the law of demand does not suggest any
change in the quantity of gasoline demanded. But if the price of gasoline goes up 10
percent while the price of everything else goes up 20 percent, an increase can be
expected in the quantity of gasoline demanded, because its relative price has fallen
(Edwin G. Dolan, 1992).
2.2.4
The Work of the Law of Demand
Why the laws of demand work? It can consider in 3 explanations:
First, when the price of a good falls while the prices of other goods remain
unchanged, we are likely to substitute some of that good for other things. For if the
price of fish falls while the price of meat remains the same, we would have to put fish
on the menu a few of the times when we would have used meat had the price of fish
not changed.
Second, when the price of a good changes, other thing s being equal, our
effective purchasing power changes even though our income measured in money term
does not. For example, if the price of clothing rises while nothing else changes, we
will feel poorer, very much as if a few dollars a year had been trimmed from our
paycheck or allowance. Feeling poorer, it is likely that we will buy a bit less of many
things, including clothing.
Third and this reason are not quite distinct from the other two-when the price
of a goods falls, new buyers who did not use a product at all before are drowning into
13
the market. There was a time, for example, when VCD (Video Compact Disk) were
play things for the rich or technical tools for businesses. Today, they can be bought
very cheaply. Rich people are not buying ten or twenty VCD apiece at the lower
prices, but sales have gone up ten- or twenty fold because many people are buying
them who never had entered that market at all before (Edwin G. Dolan, 1992).
2.2.5 Market Equilibrium
Commonly, large numbers of buyers and sellers formulate their mark plans
independently of one another. When buyers and sellers of some particular good
actually meet and engage in the process of exchange, some of them may find it
impossible to carry out their plans.
Perhaps the total quantity planned purchases will exceed the total quantity
of planned sales at the expected price. In this case, some of the would-be buyers
will find their plan, frustrated and will have to modify them. Perhaps, instead,
planned sales will exceed planned purchases. Then, some would-be sellers will be
unable to sell all they had expected to and will have to change their plans.
Sometimes no one will be disappointed. Given the information that market
prices have conveyed, the total quantity of the goods that buyers plan purchase may
exactly equal to the quantity that suppliers plan to sell. The separately formulated
plans of all market participants may turn out to mesh exactly when tested in the
marketplace, and no one will have frustrated expectations or be forced to modify
plans. When this happens, the market is said to be in equilibrium.
Market equilibrium mean a condition in which the separately formulated
plans of buyers and sellers of some goods exactly mesh when tested in the market
14
place, so that the quantity supplied is exactly equal to the quantity demanded at the
prevailing price (Edwin G. Dolan, 1992).
2.3
The Function Of Government In Macro Economy
Governments use the third to two-thirds of GNP that passes through their
hands to perform a wide variety of functions. These functions can be classified under
five general headings: provision of public goods, transfer of income, economic
stabilization, regulation of private businesses, and administration of justice.
2.3.1
Provision of Public Goods
The first function of government is to provide what economists call public
goods, that is goods or services having the properties that (1) they cannot be provided
to one citizen without being supplied also to that citizen's neighbors, and (2) once
provided for one citizen, the cost of providing them to others, is zero.
Perhaps the best example of a public good is national defense. One citizen
cannot very well be protected against invasion or nuclear holocaust without having the
protection "spill over" on neighbors. Also, it costs no more to protect a single
resident of a given area than to protect an entire city.
Public goods are traditionally provided by government because their special
properties make it hard for private business to market them profitably. Imagine
what would happen if someone tried to set up a commercially operated ballistic
missile defense system. If you subscribed, I would have no reason to subscribe too and
would instead play the "free rider," relying on the spillover effect for my protection.
15
But you would not subscribe, hoping that I would, so that you could be the free rider.
The missile defense company would soon go bankrupt (Edwin G. Dolan, 1992).
2.3.2
Transfer of Income
The second function of government consists of making transfers of income
and wealth from one citizen to another. Income or wealth is usually taken from
citizens by means of taxation; but sometimes, as in the case of the military draft or
jury duty, it is taken by conscription of services.
Benefits are distributed either in the form of direct cash payments or in the
form of the free or below-cost provision of goods and services. Among the more
familiar types of cash transfers are social security payments, welfare benefits, and
unemployment compensation. Goods. and services used for transfers include public
education, public housing, and fire protection. They are provided at low or zero cost
on the basis of political decision rather than at market prices on the basis of ability to
pay.
From the viewpoint of economic theory; the subsidized services used as
vehicle for income transfers are different from the true public goods discussed above.
They are consumed individually by selected citizens and do not share the two special
properties of public goods. It sometimes happens, though, that services provided
primarily as transfers may be public goods in part.
For example, consider the fraction of fire protection devoted to preventing
general fraction as opposed to putting out fires in individual private buildings or action
of public health services devoted to controlling epidemic diseases as opposed to
treating individual patients (Edwin G. Dolan, 1992).
16
2.3.3
Regulation Of Private Businesses
A third major function of government is the regulation of private businesses.
Regulatory control is exercised through a network of dozens of specialized agencies
and takes a variety of specific forms.
Some agencies set maximum prices at which certain products can be sold
whereas others set minimum prices. The Food. and Drug Administration and the
Federal Communications Commission exercise considerable control over what can be
produced by the firms they regulate.
Agencies such as the Occupational Health and Safety Administration and the
Environmental Protection Agency regulate how things are produced. Finally, the
Equal Employment Opportunity Commission exercises a major say over who will
produce which goods. Regulation is a subject of widespread research and
controversy (Edwin G. Dolan, 1992).
2.3.4 Administration of Justice
The fourth major function of government is administration of justice.
Usually, the police and courts are not thought of part of the economic area of
government; but their activities do, in fact, have important economic
consequences.
Consider what happens, for example, when a judge makes a decision in a
case involving an unsafe product, a breach of contract, or an automobile accident.
The decision has an immediate effect on resource allocation in the particular case,
17
because one party must pay damages to the other or make s ome other form of
compensation.
More importantly, other people will observed the outcome of the decision
and, as a result, may change the way they do things, if the courts say that buyers
can collect damages from the makers of unsafe products, firms are likely to design
their products differently. If certain standards are set for liability in automobile
accidents car makers, road builders, and insurance companies will take notice
(Edwin G. Dolan, 1992).
2.3.5 Overlapping Functions
The fifth major function of government is overlapping functions. The
classification of government activities by function helps provide a theoretical
understanding of the role of government in the economy, but it does not
correspond very well to any breakdown of government activities by program or
agency.
Particular programs and. agencies often perform a number of different
functions at the same time. For example, the main business of the Defense
Department appears to be the provision of a public good-nation defense, but it
performs other functions as well.
In wartime it performs a transfer function by shifting of the cost of wars
from the general taxpayer
to young lower-class males via the draft. In peacetime
it provides an instrument of economic stabilization through the way it administers
its huge budget for the purchase of goods and services (Edwin G. Dolan, 1992).
18
2.4
A Simple Economy
To see the circular flow in its most basic form, begin by imagining an economy
made up only of households and firms - an economy with no public sector at all. To
make things simpler still, assume that households live entirely from hand to mouth,
spending all of their income on consumer goods as soon as that income is received.
Similarly, assume that firms sell their entire output consumers as soon as it is
produced.
Two sets of markets link households to firms in this economy. Product markets,
which are markets where households purchase goods and services - bread,, television
sets, houses, dry cleaning services, entertainment - for their own direct consumption.
Factor markets, which are the markets in which households sell to firms the factors of
production they use in making the things in product markets.
Factors of production are traditionally classified as natural resources, labor,
and capital. Natural resources include everything useful as a productive input in its
natural state – agricultural land, building. sites, forests, and mineral sits, for example.
Labor includes the productive contributions made by people working with their minds
and muscles. Capital is all means of production created by people, including tools,
industrial equipment, structures, land, building sites, forests, and improvements to
land.
An return for the natural resources, labor, and capital that they buy from households, firms make factor payments in the form of rents, wages, salaries, and interest
payments. As a matter of accounting convention, when firms use land, labor, or capital
that they themselves own, they are counted as "purchasing” those factors from the
households that own the firms, even though no money changes hands and no explicit
factor payment is made. For purposes of macroeconomic analysis, profits are thus
19
considered an implicit factor payment from firms and the household that own them
(Edwin G. Dolan, 1992).
2.4.1 Stocks and Flows
T h e technical language of economics distinguishes carefully between
flows and stocks. A stock is an accumulated quantity of something existing at a
particular time. (The word stock in this general sense has nothing to do with the
stock market kind of stocks that are bought and sold on Wall Street)
For the illustration of the difference between stocks and flows, we can think of
a bathtub filling. When we talk about how fast the water is running, we are talking
about a flow, measure in liter per minute. Similarly, in the world of economics, we
might talk about the rate of housing construction in Johor Bahru , in term of new units
per month as distinct from the actual number of house in Batu Pahat as of 1th
December 2004 (the stock).
2.4.2
National Income And Product
National income has a meaning of the total of all wages, rents, interest
payments, and profits received by house holds. While national product is a
measure of the total value of the goods and services produced.
In this economy, national income and national product are equal, simply
because of the way they are defined. This equality can be verified in either of two
ways. First, consider household expenditures as a link between national income
and national product. Households are assumed to spend all of their income on
20
consumer goods as soon as they receive it, and firms are asst sell all of their output
to consumers as soon as it is produced. The payment made by buyers must equal
the payments received by sellers, so national product must equal national income.
Alternatively, consider factor payments as a link between national income
and national product. When firms receive money for the goods they sell, they use
part of it to pay the workers, natural resource owners, and other; contributed
factors of production to make the goods. Anything left over is profit. Factor
payments also account for all the money received by firms, so total factor
payments must be equal to national product. It again follows national income and
national product must be equal (Edwin G. Dolan, 1992).
2.4.3 Saving and Investment
The first change will be to drop the requirement that households
immediately spend all of their income to purchase consumer goods and to permit
instead to save part of what they earn. The rate of saving by households, under this
assumption, is simply the difference between national income and household
consumption expenditures.
The second change will be to drop the requirement that f i r m s immediately
sell all of their output to consumers. Instead, they will be permitted to sell some
products to other firms and let some accumulate in inventory before selling them to
anyone.
When firms buy newly produced capital goods, for example, production
machinery, newly built structures, or office equipment) from other firms, they are
said to engage in fixed investment. When firms increase the stocks of finished
21
product material that they keep on hand, they are said to engage in inventory
investment.
The rate of inventory investment can be less than zero in periods when
firms are decreasing their stocks of g o o d s or raw materials on hand. The sum of
fixed investor investment, and inventory investment will be called s i m p l y
investment (Edwin G. Dolan, 1992).
2.4.4
Aggregate Supply and Demand
The term aggregate supply refers to the grand total of all goods supplied by all
firms in the entire economy. There is already, another term another term for the same
thing: national product. Aggregate supply and national product are two names for the
total value of goods and services supplied by al firms.
Following the same terminology, aggregate demand can be used to mean the
grand total of all goods demanded for the whole economy. In defining aggregate
demand this way, though, care must be taken in the way "demand" is used.
The precise way of defining aggregate demand is to say that it means the total
planned, unplanned expenditures of all buyers in the economy (Edwin G. Dolan,
1992).
22
2.5
Measuring National Income and Product
2.5.1
Gross National Product
Of all economic statistics, perhaps the most widely publicized is the measure
of an economy’s level of total production called the gross national product (GNP).
This statistic represent the dollar value at current market prices (nominal value) of all
final goods and services produced annually by the nation’s economy.
Final goods and the services are goods and services sold directly for
household consumption, business investment, government purchase, or export.
Intermediate goods, such as the flour used to bake bread at commercial bakeries, are
not counted in GNP. To count both the value of the flour at its market price (an
intermediate good) and the value of the bread its market price (a final good) would
be to count the flour twice, because the value of the flour is included in the price of
the bread.
In principle, GNP could be measured directly by constructing a table that
shows the quantity of each final good and service produced-massages, apples,
submarines, housing units, and all the rest-multiplying these quantities by the prices
at which they were sold; and adding the resulting column of figures.
But that is not what national income accountants actually do. Instead, they
take a shortcut based on the equality of national product and total expenditure. In
practice, GNP is measured-summing the nominal expenditures of all economic units
on domestically produced final goods„and services. This way of measuring aggregate
economic activity is known as the expenditure approach. Table 2.1 provides an
illustration of how it works (Edwin G. Dolan, 1992).
23
Table 2.1 : Nominal Gross National Product By Type Of Expenditure(In RM Million)
Personal consumption expenditure
Durable goods
Nondurable goods
Services
1,340.1
197.5
430.3
616.2
Plus Gross private domestic Investment
Fixed investment
Change in business inventories .
345.6
329.6
16.0
Plus government purchases of goods and services
Federal
State and local
433.9
Plus net exports of goods and services
Exports
Less imports
153.8
280.2
-12.0
204.8
-216.8
Equal gross national product (GNP)
Less capital consumption allowance
2,107.6
-216.9
Equal net national product (NNP)
1,890.7
Source: U.S. Department of Commerce, Survey of Current Business, June 1989.
2.5.1.1 Consumption
Consumption expenditures by households and unattached persons fall into
three categories: durable goods, nondurable goods, and ser vices; In principle, goods
that do not wear out entirely in one year - automobiles, furniture, and household
appliances-are considered are considered durable goods. Goods that are used up in
less than a year-such as soap, food gasoline are considered nondurable goods. (In
practice, the classifications are arbitrary. All clothing, for example, is considered
nondurable, whether a pair of stockings; which may wear out in a matter of weeks,
or a woolen overcoat, which may be used for a decade).
24
The remaining item, services, includes things that are not embodied in any
physical object when sold, such as haircuts, legal advice, and education. No
distinction is made between services, that are durable and those that are nondurable
in their effect.
Both the goods and the services components of consumption contain items
that are produced but that do not actually pass through the marketplace on their
way to consumers. One such item is an estimate of the food produced and directly
consumed on farms. Another is an estimate of rental value of owner occupied
homes (Rental payments. on tenant occupied housing are automatically) (Edwin
G. Dolan, 1992).
2.5.1.2 Investment
The item called gross private domestic investment is the sum of purchases
of all firms purchased of newly produced capital goods (fixed investment) plus in.
business inventories. Fixed investment, in turn, is broken down into the durable
equipment of producers-such as machine tools, trucks, and office equipment, and
new construction-including both business structures and residential housing.
When thinking about investment, keep in mind the phrase newly produced
capital goods. The business person who buys a used machine is not engaging in
investment expenditure, according to the national income accountants. The
machine was already counted in some previous year.
Also people who speak of making investments in land or corporate bonds
are not using the word investment in the national income accountants' sense. Real
estate and securities are not capital goods. In fact, they are not even part of the more
general category, goods and services, with which the measure of GNP is concerned
25
(Edwin G. Dolan, 1992) .
2.5.1.3 Government Purchase
The contribution that government makes to GNP at the federal, state, and
local levels presents a special problem for national come accountants. Ideally, this
contribution should be measured in terms of value of the services that government
produces-education, national defense, police protection, and all the rest. However,
since very few government services are actually sold to consumers and businesses,
there are no market price in terms of which to value them. Instead, national income
accountants use government purchases of goods and services to approximate
contribution of government to GNP.
Government purchases of goods and services, include the wages and
salaries of all civilian and military personnel hired by government plus the
purchase of all the buildings, computers, paper clips, and so on used by those
employees. Presumably, all the government workers using all that equipment
produce an output at least as valuable as the same inputs could have produced in
the private sector. In any event, that is the assumption that justifies inclusion of
government purchases in GNP. Note that government transfer payments are not
included; since they do not represent expenditures made to purchase newly
produced goods or current services (Edwin G. Dolan, 1992).
2.5.1.4 Net Exports
The final item in GNP is exports - the difference between the nominal value
of goods and services imported abroad and the nominal value of goods and services
26
imported from abroad. Exported goods must be added in because they are products
produced in country, even though they are bought elsewhere.
Imports must be subtracted because some of the expenditures on consumer
goods, investment goods, and government purchases that have already been added
in were purchases of goods make abroad, and these goods should not be counted as
part of national product(Edwin G. Dolan, 1992).
2.5.2
Gross Versus Net National Product
Gross versus Net National Product “gross”? It is the fact that gross private
domestic investment is not a measure of the actual change in capital assets and
business inventories for a particular year. In the process of production, existing
buildings and equipment wear out o r lose their value through obsolescence.
As a result-, the actual increase in the stocks of capital goods and business
in the stocks of capital goods and inventories each year, called net private domestic
investment, is less than gross private domestic investment. Although depreciation
and obsolescence is difficult to measure accurately, national income accountants
make an approximation called the capital consumption allowance.
Investment that mere replaces plant and equipment that has worn out during
the year does not move the economy ahead but only keeps it standing in the same
place. Gross national product is thus, in a sense, an overstatement of how much the
country is getting out of the economy.
To arrive at a measure of national product that includes o n l y the actual
net increase in capital g o o d s and business inventories, the capital consumption
allowance is subtracted from GNP. The resulting figure is called net national
27
product. All told, net national product (NNP) is the sum of personal consumption
expenditure, net private domestic investment, government purchases of goods and
services, and net exports of goods and services (Edwin G. Dolan, 1992).
2.5.3 National Income
It is a different way of measuring what goes on in the circular flow: the
income approach to national income accounting. As name implies, the income
approach measures the overall nominal rated of the circular flow by adding up all
the different kinds of income earn by households.
This is done as shown in Table 2.2. The categories of income used by
national income accountants differ somewhat from the theoretical classification of
incomes into wages, rent, interest, and profit; and they deserve some explanation.
Compensation of employees includes not only wages and salaries but two
other items as well. The first is employer contributions for social insurance. The
second is other labor income, which includes various fringes, benefits received by
employees.
Rental income of person includes all income in the form of rent and
royalties received by owners of property. Net interest is equal to household interest
income minus consumer interest payment.
Corporate profits include all income earned by the owner (the stockholder)
of corporations, whether they actually receive that income or not. Dividends are
the part of the income that the owners actually receive. Another part go to pay
corporate profits taxes; and a third part, "undistributed corporate profits,” is
retained by the corporations to use for investment purposes.
28
The final component of national income, proprietors' income, is a sort of
grab bag of all income earned by self-employed, professionals and owners of
unincorporated businesses. National income accountants make no attempt to sort
out which parts of this income theoretically ought to be classified as wages, rent,
interest, or profit (Edwin G. Dolan, 1992).
Table 2.2 : Nominal National Income (in RM Million)
Compensation of employees
Wages and salaries
Employer contributions for social insurance
Other labor income
1,301.4
1101.0
94.5
105.9
Plus rental income of persons
23.4
Plus net interest
Plus corporate profits
Dividends
Corporate profits taxes
Undistributed corporate profits
106.3
159.5
49.3
83.9
68.8
Plus proprietors' income
Equal national income
113.2
1,703.8
Source: U.S. Department of Commerce, Survey of Current Business, June 1989.
2.5.4 The Relationship between National Income and GNP
In the simplified economy, national income and national product were
defined in such a way that they were exactly equal. In the real world, things do
not work out quite so neatly. Some adjustments must be made so that national
income as measured by the income approach fits GNP, as measured by the
expenditure approach. These adjustments are shown in table 2.3.
29
For one thing, net and gross national product must be distinguished from
other - a difference ignored in elementary theoretical discussions. The
investment expenditures made to replace worn-out or obsolete equipment are
counted as a part of the business expenses of firms, so they do not show up either
incorporate profits or in proprietors' income. The first step in going from GNP to
national income, then, is to subtract the capital consumption allowance, leaving
net national product.
Next, an adjustment must be made to reflect the fact that some of the
money firms receive from sales of their product is not "earned" by owners of
firms. Instead, it is taken directly by government in payment of so-called
indirect business taxes, which include sales taxes, excise taxes, and business
property taxes paid to federal, state, and local governments.
These taxes are treated differently from the corporate income tax, which is
considered to be earned by owners and then taken by government out of corporate
profits. Indirect business taxes are included in the prices of goods and services, so
they count as part of net national product; but they are not included in income, so
they must be subtracted when going from NNP to national income as shown in
table 2.3.
In principle, subtracting the capital consumption allowance and indirect
business taxes from GNP ought to give national income, but in practice there is one
further difficulty. GNP is estimated by the expenditure approach, using one set of data,
and national income is measured by the income approach, using an entirely different
set of data.
Inevitably, no matter how carefully the work is done, there are some errors and
omissions, so that the two sets of table do not quite fit. The difference between NNP
minus indirect business taxes on the one hand and national income on the other is
30
called the statistical discrepancy. The discrepancy has no theoretical significance; it is
simply a "fudge factor" that makes things balance (Edwin G. Dolan, 1992).
Table 2.3: Relation of National Income to GDP (RM Million)
Gross national product
Less capital consumption allowance
2,107.6
-216.9
Equal net national product
1,890.7
Less indirect business taxes
-185.2
Less statistical discrepancy
-1.8
Equal national income
1,703.7
Source: U.S. Department of Commerce, Survey of Current Business, June 1989.
2.5.5
Personal Income
National income, as mentioned several times, is a measure of income earned
b y households, whether or not those households ever to actually get t h e i r h a n d
o n t h e income. For some purposes, it is more important to measure what households
actually receive than what they earn. The total income actually received by household
is called personal income.
Table 2.4 shows the steps required to transform national income. First, three
items that are earned by households but not received by them are subtracted. These
items are contributions for social insurance (both employer and, employee),
corporate profits taxes, and undistributed corporate profits. Next, transfer payment –
payments received by households although not earned by them are added. The result
is person income.
31
One further income measure is shown at the table 2.4 disp o s a b l e personal
income (or disposable income for short). This income is what households have left of
their personal income after they pay personal taxes, of various kinds to federal, state,
and local governments (Edwin G. Dolan, 1992).
Table 2.4: National Income and Personal Income (RM Million)
National income
Less contributions for social insurance
Employer contributions
Employee contributions
1,703.7
-164.2
94.5
69.7
Less corporate profits taxes
-83.9
Less undistributed corporate profits
-68.8
Plus transfer payment
321.2
Equal personal income
Less personal taxes
Equal disposable personal income
1,708.0
-256.2
1,451.8
Source: U.S. Department of Commerce, Survey of Current Business, June 1989.
2.6
Real Estate Cycle
2.6.1 The Concept
Real estate cycle is an economic phenomenon which exhibits a “sinuous” or
“wavy” movement of economic variables, related to landed and asset properties, over
a certain period of time. This movement reflects “ups” and “downs” changes of
32
particular macro factors such as real estate aggregate demand and supply, price, rental,
and return on investment.
However, real estate cycle is not isolated from other business/ economic cycles
such as inflation and national income, since changes in these macro variables are
related to changes in the real estate variables. In this content, real estate cycle is
actually a form of business/economic cycle (Abdul Hamid Mar Iman, 2002).
2.6.2
Characteristic of Real Estate Cycle
An idealized model of real estate cycle is shown in Figure 2.1. According to
this model, the cycle has four main phases:
a) Rising period: In this phase, development takes place. Demands
accelerate and building responds. Occupancy is healthy and rent is rising.
Strong absorption requires new development to meet demand. This
development continues for four to five years. Land prices rise rapidly.
Later, demand peaks, near development remains strong, occupancy falls
and, then, rents flatten.
b) Building phase: In this phase, demand begins to decline just as building
peaks. Absorption starts to slow down. Occupancy and rent are weakening
further from their levels in the rising phase. Lenders and developers begin
cutting back starts.
c) Declining phase: In this phase, demand continues to drop and new
developments start to turn down. As there is symptom of overbuilding,
new starts plummet. Occupancy slides further and rent concessionaires
33
become widespread. Market players seem to be pessimistic about the
market prospect.
d) Bottom phase: This is an acquisition phase. Starts still decline, absorption
may drop to its lowest ever, excess supply is prevalent and, occupancy and
rents are bottoming.
1. Rising period
2. Building phase
Units
3. Declining phase
SS (Supply)
DD (Demand)
4.Bottom phase
Year
Figure 2.1: Idealized Real Estate Cycles
Real estate cycles can be identified as long and short cycles. Short cycles last
up to five years and are caused by shuts in some macro economic factors such as the
money markets, the availability of mortgage funds, and government policies on real
estate sector. Long cycles can last up to 18 years and are based on more complex
structural changes in the real estate market which are related to major macro economic
factors such as inflation, changes in supply and demand, and global economic
conditions (Abdul Hamid Mar Iman, 2002).
34
2.6.3
Real Estate Cycle - Regional and Global
Real estate cycle can be regional and global. There is some evidence for both.
Regional real estate cycle involves a particular country while global real estate cycle
involves a number of countries together over a defined time period. For example, the
first global real estate cycle was believed to have occurred during the 1985-1994
period in many parts of the world. During that time, a large number of countries
experienced strong real estate booms that peaked around 1989 followed by severe
asset price deflation and output contraction that lasted until 1994.
As for regional real estate cycle, the literature shows that several countries
have experience such a cycle for various property sub sectors at about the same time
period. The cyclic behaviour of the Greater London office market has occurred
during the 1972-1994. The U.K. commercial properties were evidenced to have been
on a cycle from 1967-1994. There was some evidence of cyclic behaviour of
apartment, vacant lands and condominium properties in Switzerland from 19701992. A cobweb cycle of residential prices had occurred in France during the 19841993 periods (New Strait Times, 15 May ,1999).
2.6.4 The "Malaysian Cycle”
As to believe that, there were two distinct long cycles in Malaysia as follows:
1965-77 (12 years); 1977-93 (18 years). The first long cycle marched its milestones
a" with a number of events of national importance such as formation of Malaysia
(1963), Singapore pulling out from the Federation (1985), racial uprising (1969),
world oil crisis (1974), and introduction of First Malaysia Plan (1975).
The second long cycle also carried with it a number of important national
events such as formulation of National Economic Plan (NEP) (1980), decade's worst
35
recession (1984-86), and country's best era of economic growth (1990-1995) (New
Strait Times, 15 May ,1999).
2.6.5
The Dynamic Of Real Estate Cycle
There are several stages of how the dynamics of real estate supply and demand
work in relation to real estate cycle as discussed as follows:
a) Stage 1
There is imbalance between demand and supply. Unemployment rate is high,
but the economy is expanding, government fiscal and monetary polices are
expansionary, inflation is moderate, and interest rate is low. Population and household
size are expanding; incomes are rising, and increasing number of employment
increases demand for real estate.
At this stage, real estate prices have not yet increased while construction starts
have been in the basement for sometimes. Therefore, the supply of real estate is
relatively fixed over a short run, with demolitions and removals offsetting completion
of new space.
When demand increases, vacancies decrease as the existing vacant homes are
sold, apartments, office and retail spaced rented. Prices of these produces then rise
sharply relative to development and operating costs. Developers are optimistic, profits
are thriving, and more and more construction takes places. At this stage also, there will
be favourable interest rates for both development and property purchase and
developers respond by providing more abundant medium-priced real estate products.
36
As subdivisions accelerated, vacant land in the urban periphery gradually disappears
and developments move to urban outskirts. Eventually, land prices start to hike.
b) Stage 2
Sales activity rise sharply and the market is very active. The selling prices are
increasing as are the costs of construction and businesses of all types are expanding.
Real estates areas sold very soon after they have been compiled and developers are
buildings actively in all price ranges.
Investors and speculator begin to enter the market to capitalize on the real
estate construction boom and the availability of ban at low rates. Housing projects
begin to take off rapidly, followed by increasing numbers of commercial and industrial
projects, which are developed to service the expanded residential areas.
The demand for all types of space is high, and supply is increasing rapidly to
meet the pent-up demand. All market indicators, rents and prices, mortgage
recordings, building permits, and deed recordings increase to record levels. Inflation
and interest rates are also rising, but building profits are still high and expectations are
still optimistic. Investors are actively buying existing properties of all types and
bidding up the selling prices of those properties.
c) Stage 3
Although demand increases at a steady rate, new construction tends to come
onto the market all at once. Too many developers getting the same Idea at the same
time eventually cause an oversupply of space as projects are completed.
37
Market saturation ( especially for housing) occurs, inventory builds up, profits
decrease, and developers begin advertising campaigns and offer additional amenities
and financial inducements-to buy at higher prices. Apartment and commercial
properties are completed in large numbers and appear on the market.
Inflation increases rapidly. Interest rates rise further. Credit controls reduce the
availability of supply of loan funds, and effective demand for homes is decreased.
With high prices, high interest rates, and greater difficulty in qualifying for loans, the
attitude toward ownership changes and rentals become preferable. Prices of older bulk
properties begin to fall; average time between listing and sales increase, and
developers become pessimistic.
Rising land and interest costs further decrease profit expectations and binding
feasibility. Over-exasperation of new apartment space results in higher vacancy rates,
and rental income levels off or decreases as landlords complete for tenants.
d) Stage 4
General business activity is curtailed as the Federal Reserve continue to use
monetary brakes to fight inflation. Real estate activity is beginning to decline,
although the supply of commercial space is still increasing at a relatively strong pace.
Developers are hawing trouble selling their properties and are taking second mortgage
and offering concessions to facilitate sales.
Holding costs are extremely high, as prime lending rates increase to record
levels. The apartment developers will overbuild the market; as a result, renters are
getting batter services as landlords compete to avoid vacancies and turnover costs.
Vacancies increase and in overbuilt locations, reach levels as high as 20-30%.
38
Cash flows and profitability decline, developer and owner have difficulty
meeting mortgage obligations, and foreclosures become more frequent. Lenders
become pessimistic and cease to make new permanent loans on properties, and interim
lenders demand repayment of loans with accrued interest.
e) Stage 5
Business activity slow down, unemployment increases, and inflation continues
at a record pace and causing real estate incomes to fail. Credit is tight, interim and
permanent mortgage interest rates are at record highs. Consumers and producers are
pessimistic.
The real estate cycle begins a rather sharp decline. Unemployment is high,
especially in the building trades, renters double up to wave money, and the rate of new
units slows clean. Effective demand for all types of space is decreasing while
substantial amounts of new space are being completed or are still under construction.
How far down the real estate cycle goes depends on the degree of overbuilding
that has taken place, any changes in restrictive monetary policies and lending
practices, the degree to which lenders will work with developers and property owners
to avoid foreclosure, and the degree to which real estate demand is decreased by the
general economic recession. Stage 5 will end and a new stage 1 will began only when
there is an improvement in Income and employment in the general business economy
and consumers become more optimistic.
As per above mentioned, hence, we can see that economy factor have affect
real estate cycle (Abdul Hamid Mar Iman, 2002).
39
2.7
Correlation and Regression
In statistical terms we use correlation to denote association between two
quantitative variables. We also assume that the association is linear, that one variable
increases or decreases a fixed amount for a unit increase or decrease in the other. The
other technique that is often used in these circumstances is regression, which involves
estimating the best straight line to summaries the association (Koop Gary, 2000).
2.7.1
Correlation Coefficient
The degree of association is measured by a correlation coefficient, denoted by
r. It is sometimes called Pearson's correlation coefficient after its originator and is a
measure of linear association. If a curved line is needed to express the relationship,
other and more complicated measures of the correlation must be used.
The correlation coefficient is measured on a scale that varies from + 1 through
0 to - 1. Complete correlation between two variables is expressed by either + 1 or -1.
When one variable increases as the other increases the correlation is positive; when
one decreases as the other increases it is negative. Complete absence of correlation is
represented by 0. Figure 2.2 gives some graphical representations of correlation.
40
r = -1
r = +1
r=0
Curved line
Figure 2.2: Correlation illustrated
2.7.2
Looking At Data: Scatter Diagrams
When an investigator has collected two series of observations and wishes to
see whether there is a relationship between them, he or she should first construct a
scatter diagram.
The vertical scale represents one set of measurements and the horizontal scale
the other. If one set of observations consists of experimental results and the other
consists of a time scale or observed classification of some kind, it is usual to put the
experimental results on the vertical axis. These represent what is called the "dependent
variable". The "independent variable", such as time or height or some other observed
classification is measured along the horizontal axis, or baseline.
The words "independent" and "dependent" could puzzled because it is
sometimes not clear what is dependent on what. This confusion is a triumph of
common sense over misleading terminology, because often each variable is dependent
on some third variable, which may or may not be mentioned.
41
It is reasonable, for instance, to think of the height of children as dependent on
age rather than the converse but consider a positive correlation between mean tar yield
and nicotine yield of certain brands of cigarette. The nicotine liberated is unlikely to
have its origin in the tar: both vary in parallel with some other factor or factors in the
composition of the cigarettes.
The yield of the one does not seem to be "dependent" on the other in the sense
that, on average, the height of a child depends on his age. In such cases it often does
not matter which scale is put on which axis of the scatter diagram. However, if the
intention is to make inferences about one variable from the other, the observations
from which the inferences are to be made are usually put on the baseline.
As a further example, a plot of monthly deaths from heart disease against
monthly sales of ice cream would show a negative association. However, it is hardly
likely that eating ice cream protects from heart disease! It is simply that the mortality
rate from heart disease is inversely related - and ice cream consumption positively
related - to a third factor, namely environmental temperature (Koop Gary, 2000).
2.7.3
Calculation of the Correlation Coefficient
A pediatric registrar has measured the pulmonary anatomical dead space (in
ml) and height (in cm) of 15 children. The data are given in table 2.5 and the scatter
diagram shown in figure 2.3. Each dot represents one child, and it is placed at the
point corresponding to the measurement of the height (horizontal axis) and the dead
space (vertical axis). The registrar now inspects the pattern to see whether it seems
likely that the area covered by the dots centers on a straight line or whether a curved
line is needed.
42
In this case the paediatrician decides that a straight line can adequately
describe the general trend of the dots. His next step will therefore be to calculate the
correlation coefficient.
Table 2.5: Correlation between height and pulmonary anatomical dead space in 15
children
Child number Height (cm) Dead space (ml), y
1
110
44
2
116
31
3
124
43
4
129
45
5
131
56
6
138
79
7
142
57
8
150
56
9
153
58
10
155
92
11
156
78
12
159
64
13
164
88
14
168
112
15
174
101
Total
2169
1004
Mean
144.6
66.933
43
When making the scatter diagram (figure 2.3) to show the heights and
pulmonary anatomical dead spaces in the 15 children, the paediatrician set out figures
as in columns (1), (2), and (3) of table 2.1. It is helpful to arrange the observations in
serial order of the independent variable when one of the two variables is clearly
identifiable as independent.
The corresponding figures for the dependent variable can then be examined in
relation to the increasing series for the independent variable. In this way we get the
Anatomical dead space (ml)
same picture, but in numerical form, as appears in the scatter diagram.
100
80
60
40
20
0
100
110
120
130
140
150
160
170
180
Height of children (cm)
Figure 2.3: Scatter diagram of relation in 15 children between height and pulmonary
anatomical dead space
The calculation of the correlation coefficient is as follows, with x representing
the values of the independent variable (in this case height) and y representing the
values of the dependent variable (in this case anatomical dead space). The formula to
be used is:
44
which can be shown to be equal to:
Calculator procedure:
1. Find the mean and standard deviation of x, as described in
, _
x = 144.6, SD (x) = 19.3769
2. Find the mean and standard deviation of y
, _
y = 66.93, SD(y) = 23.6476
3. Subtract 1 from n and multiply by SD(x) and SD(y),
(n - 1)SD(x)SD(y),
14 x 19.3679 x 23.6976 (6412.0609)
4. This gives us the denominator of the formula. (Remember to exit from "Stat"
mode.)
5. For the numerator multiply each value of x by the corresponding value of y,
add these values together and store them.
110 x 44 = Min
116 x 31 = M+
6. This stores
in memory. Subtract
MR - 15 x 144.6 x 66.93 (5426.6)
7. Finally divide the numerator by the denominator.
45
r = 5426.6/6412.0609 = 0.846.
The correlation coefficient of 0.846 indicates a strong positive correlation
between size of pulmonary anatomical dead space and height of child. But in
interpreting correlation it is important to remember that correlation is not causation.
There may or may not be a causative connection between the two correlated variables.
Moreover, if there is a connection it may be indirect.
A part of the variation in one of the variables (as measured by its variance) can
be thought of as being due to its relationship with the other variable and another part
as due to undetermined (often "random") causes. The part due to the dependence of
one variable on the other is measured by r2.
For these data r2 = 0.716 so we can say that 72% of the variation between
children in size of the anatomical dead space is accounted for by the height of the
child. If we wish to label the strength of the association, for absolute values of r, 00.19 is regarded as very weak, 0.2-0.39 as weak, 0.40-0.59 as moderate, 0.6-0.79 as
strong and 0.8-1 as very strong correlation, but these are rather arbitrary limits, and the
context of the results should be considered (Koop Gary, 2000).
2.7.4
Significance Test
To test whether the association is merely apparent, and might have arisen by
chance use the t test in the following calculation:
The t in table 2.6 is entered at n - 2 degrees of freedom.
46
Table 2.6: Distribution of t (two tailed)
d.f.
Probability
0.5
0.1
0.05
0.02
0.01
0.00l
1
2
3
4
5
l.000
0.816
0.765
0.741
0.727
6.314
2.920
2.353
2.132
2.015
12.706
4.303
3.182
2.776
2.571
3l.821
6.965
4.541
3.747
3.365
63.657
9.925
5.841
4.604
4.032
636.6l9
31.598
12.941
8.610
6.859
6
7
8
9
10
0.718
0.711
0.706
0.703
0.700
1.943
1.895
l.860
l.833
l.812
2.447
2.365
2.306
2.262
2.228
3.l43
2.998
2.896
2.82l
2.764
3.707
3.499
3.355
3.250
3.169
5.959
5.405
5.04l
4.78l
4.587
11
12
13
14
15
0.697
0.695
0.694
0.692
0.69l
1.796
1.782
1.771
1.76l
l.753
2.201
2.179
2.160
2.145
2.13l
2.718
2.681
2.650
2.624
2.602
3.l06
3.055
3.012
2.977
2.947
4.437
4.3l8
4.221
4.l40
4.073
16
17
18
19
20
0.690
0.689
0.688
0.688
0.687
1.746
1.740
1.734
l.729
1.725
2.120
2.110
2.101
2.093
2.086
2.583
2.567
2.552
2.539
2.528
2.92l
2.898
2.878
2.861
2.845
4.015
3.965
3.922
3.883
3.850
21
22
23
24
25
0.686
0.686
0.685
0.685
0.684
1.721
1.717
1.714
1.711
1.708
2.080
2.074
2.069
2.064
2.060
2.518
2.508
2.500
2.492
2.485
2.831
2.819
2.807
2.797
2.787
3.8l9
3.792
3.767
3.745
3.725
26
27
28
29
30
0.684
0.684
0.683
0.683
0.683
1.706
1.703
1.701
1.699
l.697
2.056
2.052
2.048
2.045
2.042
2.479
2.473
2.467
2.462
2.457
2.779
2.771
2.763
2.756
2.750
3.707
3.690
3.674
3.659
3.646
40
60
120
0.681
0.679
0.677
0.674
l.684
1.671
1.658
1.645
2.021
2.000
l.980
1.960
2.423
2.390
2.358
2.326
2.704
2.660
2.617
2.576
3.551
3.460
3.373
3.291
For example, the correlation coefficient for these data was 0.846.
47
The number of pairs of observations was 15. Applying equation, we have:
Entering table 11.6 at 15 - 2 = 13 degrees of freedom we find that at t = 5.72,
P<0.001 so the correlation coefficient may be regarded as highly significant. Thus (as
could be seen immediately from the scatter plot) we have a very strong correlation
between dead space and height which is most unlikely to have arisen by chance.
The assumptions governing this test are:
1. That both variables are plausibly normally distributed.
2. That there is a linear relationship between them.
3. The null hypothesis is that there is no association between them.
The test should not be used for comparing two methods of measuring the same
quantity, such as two methods of measuring peak expiratory flow rate. Its use in this
way appears to be a common mistake; with a significant result being interpreted as
meaning that one method is equivalent to the other.
The reasons have been extensively discussed, but it is worth recalling that a
significant result tells us little about the strength of a relationship. From the formula it
should be clear that with even with a very weak relationship (say r = 0.1) we would
get a significant result with a large enough sample (say n over 1000).
2.7.5
Spearman Rank Correlation
48
A plot of the data may reveal outlying points well away from the main body of
the data, which could unduly influence the calculation of the correlation coefficient.
Alternatively the variables may be quantitative discrete such as a mole count, or
ordered categorical such as a pain score. A non-parametric procedure, due to
Spearman, is to replace the observations by their ranks in the calculation of the
correlation coefficient.
This results in a simple formula for Spearman's rank correlation,
.
where d is the difference in the ranks of the two variables for a given individual. Thus
we can derive table 2.7 from the data in table 2.5.
49
Table 2.7: Derivation of Spearman rank correlation from data of table 2.5
Child number Rank height Rank dead space d
1
1
3
2
4
2
2
1
-1
1
3
3
2
-1
1
4
4
4
0
0
5
5
5.5
0.5 0.25
6
6
11
5
25
7
7
7
0
0
8
8
5.5
-2.5 6.25
9
9
8
-1
1
10
10
13
3
9
11
11
10
-1
1
12
12
9
-3
9
13
13
12
-1
1
14
14
15
1
1
15
15
14
-1
1
Total
From this we get that:
60.5
50
In this case the value is very close to that of the Pearson correlation coefficient.
For n> 10, the Spearman rank correlation coefficient can be tested for significance
using the t test given earlier (Koop Gary, 2000).
2.7.6
The Regression Equation
Correlation describes the strength of an association between two variables, and
is completely symmetrical, the correlation between A and B is the same as the
correlation between B and A. However, if the two variables are related it means that
when one changes by a certain amount the other changes on an average by a certain
amount.
For instance, in the children described earlier greater height is associated, on
average, with greater anatomical dead Space. If y represents the dependent variable
and x the independent variable, this relationship is described as the regression of y on
x.
The relationship can be represented by a simple equation called the regression
equation. In this context "regression" (the term is a historical anomaly) simply means
that the average value of y is a "function" of x, that is, it changes with x.
The regression equation representing how much y changes with any given
change of x can be used to construct a regression line on a scatter diagram, and in the
simplest case this is assumed to be a straight line. The direction in which the line
slopes depends on whether the correlation is positive or negative.
When the two sets of observations increase or decrease together (positive) the
line slopes upwards from left to right; when one set decreases as the other increases
51
the line slopes downwards from left to right. As the line must be straight, it will
probably pass through few, if any, of the dots.
Given that the association is well described by a straight line we have to define
two features of the line if we are to place it correctly on the diagram. The first of these
is its distance above the baseline; the second is its slope. They are expressed in the
following regression equation:
With this equation we can find a series of values of
the variable that
correspond to each of a series of values of x, the independent variable. The parameters
and
have to be estimated from the data. The parameter
signifies the distance
above the baseline at which the regression line cuts the vertical (y) axis; that is, when
y = 0. The parameter (the regression coefficient) signifies the amount by which
change in x must be multiplied to give the corresponding average change in y, or the
amount y changes for a unit increase in x. In this way it represents the degree to which
the line slopes upwards or downwards.
The regression equation is often more useful than the correlation coefficient. It
enables us to predict y from x and gives us a better summary of the relationship
between the two variables. If, for a particular value of x, x i, the regression equation
predicts a value of y fit , the prediction error is
.
It can easily be shown that any straight line passing through the mean values x
and y will give a total prediction error
of zero because the positive and
negative terms exactly cancel. To remove the negative signs we square the differences
and the regression equation chosen to minimize the sum of squares of the prediction
errors,
We denote the sample estimates of
and
by a and b.
52
It can be shown that the one straight line that minimize , the least squares
estimate , is given by
and
It can be shown that
which is of use because we have calculated all the components of equation in the
calculation of the correlation coefficient.
The calculation of the correlation coefficient on the data in table 2.7 gave the
following:
Applying these figures to the formulae for the regression coefficients, we have:
53
Therefore, in this case, the equation for the regression of y on x becomes
This means that, on average, for every increase in height of 1 cm the increase
in anatomical dead space is 1.033 ml over the range of measurements made .
The line representing the equation is shown superimposed on the scatter
diagram of the data in figure 2.4. The way to draw the line is to take three values of x,
one on the left side of the scatter diagram, one in the middle and one on the right, and
substitute these in the equation, as follows:
If x = 110, y = (1.033 x 110) - 82.4 = 31.2
If x = 140, y = (1.033 x 140) - 82.4 = 62.2
If x = 170, y = (1.033 x 170) - 82.4 = 93.2
Although two points are enough to define the line, three are better as a check.
Anatomical dead space (ml)
Having put them on a scatter diagram, we simply draw the line through them.
100
x
80
x
60
40
x
20
0
100
110
120
130
140
150
160
170
180
Height of children (cm)
Figure 2.4 Regression line drawn on scatter diagram relating height and pulmonary
anatomical dead space in 15 children
54
The standard error of the slope SE(b) is given by:
where
is the residual standard deviation, given by:
This can be shown to be algebraically equal to
We already have to hand all of the terms in this expression. Thus
square root of
is the
The denominator is 72.4680.
Thus SE(b) = 13.08445/72.4680 = 0.18055.
We can test whether the slope is significantly different from zero by:
t = b/SE(b) = 1.033/0.18055 = 5.72.
Again, this has n - 2 = 15 - 2 = 13 degrees of freedom. The assumptions
governing this test are:
1. That the prediction errors are approximately normally distributed. Note this
does not mean that the x or y variables have to be normally distributed.
2. That the relationship between the two variables is linear.
55
3. That the scatter of points about the line is approximately constant - we
would not wish the variability of the dependent variable to be growing as the
independent variable increases. If this is the case try taking logarithms of both
the x and y variables.
Note that the test of significance for the slope gives exactly the same value of P
as the test of significance for the correlation coefficient. Although the two tests are
derived differently, they are algebraically equivalent, which makes intuitive sense.
We can obtain a 95% confidence interval for b from
where the t statistic from has 13 degrees of freedom, and is equal to 2.160.
Thus the 95% confidence interval is
l.033 - 2.160 x 0.18055 to l.033 + 2.160 x 0.18055 = 0.643 to 1.422.
Regression lines give us useful information about the data they are collected
from. They show how one variable changes on average with another, and they can be
used to find out what one variable is likely to be when we know the other - provided
that we ask this question within the limits of the scatter diagram.
To project the line at either end - to extrapolate - is always risky because the
relationship between x and y may change or some kind of cut off point may exist. For
instance, a regression line might be drawn relating the chronological age of some
children to their bone age, and it might be a straight line between, say, the ages of 5
and 10 years, but to project it up to the age of 30 would clearly lead to error.
Computer packages will often produce the intercept from a regression
equation, with no warning that it may be totally meaningless. Consider a regression of
56
blood pressure against age in middle aged men. The regression coefficient is often
positive, indicating that blood pressure increases with age. The intercept is often close
to zero, but it would be wrong to conclude that this is a reliable estimate of the blood
pressure in newly born male infants! (Koop Gary, 2000).
2.7.7
More advanced methods
More than one independent variable is possible - in such a case the method is
known as multiple regressions. This is the most versatile of statistical methods and can
be used in many situations. Examples include: to allow for more than one predictor,
age as well as height in the above example; to allow for covariates - in a clinical trial
the dependent variable may be outcome after treatment, the first independent variable
can be binary, 0 for placebo and 1 for active treatment and the second independent
variable may be a baseline variable, measured before treatment, but likely to affect
outcome (Koop Gary, 2000).
2.8
Summary
From the above various macroeconomic variables, demand on property, as
well as correlation and regression analysis, it is possible to make a link between each
of that. Thus, all of these will be discussed in further chapters.
CHAPTER 3
COMMERCIAL PROPERTY TRANSACTION TREND AND ANALYSIS OF
FACTOR INFLUENCING TRANSACTION
3.1
Introduction
Commercial property is a kind of property which influences Malaysia
properties market. Although the volume is not more than 10% in the overall Malaysia
property market, it transaction value is approximately one (1) over six (6) of total
value in Malaysia property market, which is about two times from the transaction
volume. Thus, its transaction volume and value are focused and then graph is plotted
to study its trend of movement (Property Market Report, 1997-2003).
3.2
Number of Commercial Property Transaction & Price Range
Table 3.1 demonstrates the number of transactions ranges from 1997 to 2003.
58
Table 3.1 : Number of commercial property transaction in 1997
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
224
4.5
170
3.4
232
4.6
360
7.2
523
10.5
704
14.1
529
10.6
1,455
29.1
498
10.0
309
6.2
5,004
100.0
Apr-Jun
Number
%
72
1.3
218
3.9
395
7.0
426
7.5
541
9.6
726
12.8
755
13.3
1668
29.5
546
9.7
311
5.5
5,658
100.0
Jul-Sept
Number
%
108
1.8
228
3.8
192
3.2
329
5.5
559
9.3
941
15.6
865
14.4
1,855
30.8
637
10.6
308
5.1
6,022
100.0
Oct-Dec
Number
%
111
2.2
278
5.6
194
3.9
302
6.1
432
8.7
723
14.6
624
12.6
1,563
31.6
486
9.8
235
4.7
4948
100.0
Annual
Number
%
515
2.4
894
4.1
1,013
4.7
1,417
6.6
2,055
9.5
3,094
14.3
2,773
12.8
6,541
30.2
2,167
10.0
1,163
5.4
21,632
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1997)
Table 3.2 : Number of commercial property transaction in 1998
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
114
3.3
219
6.3
192
5.5
196
5.7
287
8.3
446
12.9
466
13.5
1,066
30.8
356
10.3
118
3.4
3,460
100.0
Apr-Jun
Number
%
115
3.8
233
7.7
139
4.6
169
5.6
336
11.2
342
11.4
393
13.1
867
28.8
304
10.1
111
3.7
3,009
100.0
Jul-Sept
Number
%
111
3.9
219
7.7
186
6.5
198
6.9
347
12.1
373
13.0
299
10.5
771
27.0
279
9.8
76
2.7
2,859
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1998)
Oct-Dec
Number
%
132
4.8
141
5.1
156
5.7
172
6.2
352
12.8
362
13.1
308
11.2
779
28.3
256
9.3
99
3.6
2757
100.0
Annual
Number
%
472
3.9
812
6.7
673
5.6
735
6.1
1,322
10.9
1,523
12.6
1,466
12.1
3,483
28.8
1,195
9.9
404
3.3
12,085
100.0
59
Table 3.3 : Number of commercial property transaction in 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
766
22.1
184
5.3
190
5.5
168
4.8
281
8.1
410
11.8
329
9.5
771
22.3
264
7.6
101
2.9
3,464
100.0
Apr-Jun
Number
%
667
15.6
309
7.2
236
5.5
228
5.3
454
10.6
511
12.0
416
9.7
934
21.9
372
8.7
143
3.3
4,270
100.0
Jul-Sept
Number
%
222
5.8
163
4.2
183
4.8
272
7.1
445
11.6
523
13.6
447
11.6
946
24.6
493
12.8
155
4.0
3,849
100.0
Oct-Dec
Number
%
171
4.0
329
7.6
505
11.7
259
6.0
454
10.5
533
12.3
466
10.8
1,019
23.6
440
10.2
143
3.3
4319
100.0
Annual
Number
%
1,826
11.5
985
6.2
1,114
7.0
927
5.8
1,634
10.3
1,977
12.4
1,658
10.4
3,670
23.1
1,569
9.9
542
3.4
15,902
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1999)
Table 3.4 : Number of commercial property transaction in 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
206
5.2
177
4.5
341
8.6
329
8.3
483
12.2
523
13.2
437
11.0
969
24.5
360
9.1
134
3.4
3,959
100.0
Apr-Jun
Number
%
235
5.8
133
3.3
196
4.8
264
6.5
474
11.6
578
14.2
532
13.0
1,066
26.1
447
11.0
157
3.8
4,082
100.0
Jul-Sept
Number
%
169
3.8
215
4.9
310
7.0
270
6.1
569
12.9
580
13.2
512
11.6
1,150
26.1
439
10.0
189
4.3
4,403
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2000)
Oct-Dec
Number
%
313
7.7
303
7.5
188
4.6
250
6.1
469
11.5
490
12.1
448
11.0
1,064
26.2
405
10.0
136
3.3
4066
100.0
Annual
Number
%
923
5.6
828
5.0
1,035
6.3
1,113
6.7
1,995
12.1
2,171
13.1
1,929
11.7
4,249
25.7
1,651
10.0
616
3.7
16,510
100.0
60
Table 3.5 : Number of commercial property transaction in 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
141
4.1
179
5.2
192
5.5
208
6.0
422
12.2
464
13.4
371
10.7
1,009
29.1
375
10.8
108
3.1
3,469
100.0
Apr-Jun
Number
%
183
4.7
243
6.3
191
4.9
253
6.5
475
12.2
523
13.5
430
11.1
1,099
28.3
365
9.4
126
3.2
3,888
100.0
Jul-Sept
Number
%
201
4.6
359
8.2
222
5.1
334
7.7
553
12.7
560
12.8
445
10.2
1,143
26.2
408
9.4
136
3.1
4,361
100.0
Oct-Dec
Number
%
193
4.7
221
5.3
203
4.9
323
7.8
530
12.8
497
12.0
465
11.2
1,141
27.5
406
9.8
165
4.0
4144
100.0
Annual
Number
%
718
4.5
1,002
6.3
808
5.1
1,118
7.0
1,980
12.5
2,044
12.9
1,711
10.8
4,392
27.7
1,554
9.8
535
3.4
15,862
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2001)
Table 3.6 : Number of commercial property transaction in 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
114
3.0
207
5.5
233
6.2
240
6.4
451
12.0
610
16.3
366
9.8
1,013
27.0
374
10.0
137
3.7
3,745
100.0
Apr-Jun
Number
%
124
3.0
230
5.5
191
4.6
281
6.7
462
11.0
655
15.7
477
11.4
1,165
27.9
414
9.9
183
4.4
4,182
100.0
Jul-Sept
Number
%
174
3.5
215
4.3
363
7.3
315
6.4
523
10.6
700
14.1
542
11.0
1,395
28.2
491
9.9
231
4.7
4,949
100.0
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2002)
Oct-Dec
Number
%
228
5.5
530
12.7
229
5.5
254
6.1
512
12.3
437
10.5
491
11.8
1,009
24.2
361
8.7
121
2.9
4172
100.0
Annual
Number
%
640
3.8
1,182
6.9
1,016
6.0
1,090
6.4
1,948
11.4
2,402
14.1
1,876
11.0
4,582
26.9
1,640
9.6
672
3.9
17,048
100.0
61
Table 3.7: Number of commercial property transaction in 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number
%
140
3.7
210
5.5
215
5.6
242
6.3
470
12.3
555
14.5
427
11.2
1,038
27.2
351
9.2
170
4.5
3,818
100.0
Apr-Jun
Number
%
192
4.2
274
6.0
254
5.6
358
7.9
548
12.1
496
10.9
510
11.2
1,307
28.8
433
9.5
172
3.8
4,544
100.0
Jul-Sept
Number
%
112
2.2
239
4.8
248
5.0
360
7.2
551
11.1
656
13.2
646
13.0
1,455
29.2
518
10.4
200
4.0
4,985
100.0
Oct-Dec
Number
%
159
3.0
325
6.1
325
6.1
367
6.9
573
10.8
640
12.0
641
12.1
1,542
29.0
544
10.2
200
3.8
5316
Annual
Number
%
603
3.2
1,048
5.6
1,042
5.6
1,327
7.1
2,142
11.5
2,347
12.6
2,224
11.9
5,342
28.6
1,846
9.9
742
4.0
100.0
18,663
Number = Number of commercial property transacted
% = Percentage of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2003)
Based on the data obtained above, it shows that commercial property with a
price range RM 250k-500k had the highest number of transactions, contributed by the
transactions of two storey shop houses in Malaysia, either in urban or sub urban area.
Commercial property with the price range of RM 100k-150k, 150k-200k,
200k-250k recorded the second highest in transactions respectively. It was due to
contributions of transaction of one storey shop house, one and a half storey shop
house, as well as two storey shop house in rural area or inferior location in sub-urban
area.
However, transaction in term of price range is not important in this study, what
is more important is the total number of transactions, which represents the
affordability of nation in purchasing the commercial property.
100.0
62
3.3
Number of All Type Property Transaction & Percentage of Commercial
Property
In Malaysia, the transacted properties consist of six (6) categories, which can
be classified to residential, industrial, agricultural, development and commercial
property, as well as other types (besides the above-mentioned 5 type).
Thus, it is important to study the total figure of all types of properties and from
the figure; percentage of commercial property can be calculated. Below are the
number of all types of transacted properties & percentage of commercial properties,
which range from 1997 to 2003.
Table 3.8: Number of all type property transacted and percentage of commercial
property in 1997
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
9875
2.3
8860
1.9
9876
2.3
8182
4.4
8633
6.1
5494 12.8
3255 16.3
6351 22.9
2211 22.5
2235 13.8
64,972
7.7
Apr-Jun
Number %
10,172
0.7
9,163
2.4
10,912
3.6
8,806
4.8
9,692
5.6
6,672 10.9
3,796 19.9
7,154 23.3
2,199 24.8
1,694 18.4
70,260
8.1
Jul-Sept
Number %
9,389
1.2
9,753
2.3
10,518
1.8
9,153
3.6
9,255
6.0
6,896 13.6
4,252 20.3
7,609 24.4
2,112 30.2
1,503 20.5
70,440
8.5
Oct-Dec
Number %
9,932
1.1
9,306
3.0
9,927
2.0
9,324
3.2
10,019
4.3
6,676 10.8
3,864 16.1
6,656 23.5
1,794 27.1
1,579 14.9
69,077
7.2
Annual
Number
%
39,368
1.3
37,082
2.4
41,233
2.5
35,465
4.0
37,599
5.5
25,738 12.0
15,167 18.3
27,770 23.6
8,316 26.1
7,011 16.6
274,749
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 1997)
7.9
63
Table 3.9: Number of all type property transacted and percentage of commercial
property 1998
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
9,767
1.2
7,378
3.0
7,062
2.7
6,729
2.9
6,818
4.2
4,478 10.0
2,483 18.8
3,968 26.9
1,040 34.2
636 18.6
50,359
6.9
Apr-Jun
Number %
8,697
1.3
7,201
3.2
6,707
2.1
6,231
2.7
6,267
5.4
3,649
9.4
2,103 18.7
3,428 25.3
957 31.8
486 22.8
45,726
6.6
Jul-Sept
Number %
9,728
1.1
7,072
3.1
6,526
2.9
5,752
3.4
5,860
5.9
3,564 10.5
1,934 15.5
3,224 23.9
803 34.7
426 17.8
44,889
6.4
Oct-Dec
Number %
9,209
1.4
7,439
1.9
6,386
2.4
5,367
3.2
5,972
5.9
3,810
9.5
2,048 15.0
3,552 21.9
795 32.2
525 18.9
45,103
6.1
Annual
Number
%
37,401
1.3
29,090
2.8
26,681
2.5
24,079
3.1
24,917
5.3
15,501
9.8
8,568 17.1
14,172 24.6
3,595 33.2
2,073 19.5
186,077
6.5
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & by same quarter)
(Source: Property Market Report, 1998)
Table 3.10: Number of all type property transacted and percentage of commercial
property 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
13308
5.8
7739
2.4
6040
3.1
5190
3.2
5901
4.8
3727 11.0
2135 15.4
3534 21.8
870 30.3
439 23.0
48,883
7.1
Apr-Jun
Number %
13002
5.1
8540
3.6
7837
3.0
7119
3.2
7610
6.0
4641 11.0
2764 15.1
4542 20.6
1230 30.2
526 27.2
57,811
7.4
Jul-Sept
Number %
11823
1.9
8878
1.8
8391
2.2
7749
3.5
8282
5.4
5211 10.0
2888 15.5
5011 18.9
1418 34.8
654 23.7
60,305
6.4
Oct-Dec
Number %
10209
1.7
9639
3.4
7955
6.3
7741
3.3
8250
5.5
5050 10.6
2876 16.2
5065 20.1
1408 31.3
709 20.2
58,902
7.3
Annual
Number
%
48,342
3.8
34,796
2.8
30,223
3.7
27,799
3.3
30,043
5.4
18,629 10.6
10,663 15.5
18,152 20.2
4,926 31.9
2,328 23.3
225,901
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 1999)
7.0
64
Table 3.11: Number of all type property transacted and percentage of commercial
property 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
9,524
2.2
8,298
2.1
6,983
4.9
6,863
4.8
7,651
6.3
5,002 10.5
2,629 16.6
4,723 20.5
1,226 29.4
639 21.0
53,538
7.4
Apr-Jun
Number %
9,579
2.5
8,123
1.6
8,398
2.3
7,981
3.3
8,621
5.5
5,545 10.4
3,058 17.4
5,037 21.2
1,332 33.6
656 23.9
58,330
7.0
Jul-Sept
Number %
11,042
1.5
9,386
2.3
9,998
3.1
8,788
3.1
10,174
5.6
6,415
9.0
3,303 15.5
5,808 19.8
1,587 27.7
796 23.7
67,297
6.5
Oct-Dec
Number %
10,882
2.9
8,568
3.5
8,278
2.3
7,673
3.3
9,272
5.1
5,656
8.7
2,953 15.2
5,285 20.1
1,522 26.6
732 18.6
60,821
6.7
Annual
Number
%
41,027
2.2
34,375
2.4
33,657
3.1
31,305
3.6
35,718
5.6
22,618
9.6
11,943 16.2
20,853 20.4
5,667 29.1
2,823 21.8
239,986
6.9
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 2000)
Table 3.12: Number of all type property transacted and percentage of commercial
property 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
8,222
1.7
7,531
2.4
8,075
2.4
7,484
2.8
8,495
5.0
5,407
8.6
2,764 13.4
4,907 20.6
1,288 29.1
579 18.7
54,752
6.3
Apr-Jun
Number %
9,433
1.9
8,162
3.0
9,046
2.1
8,844
2.9
9,206
5.2
5,560
9.4
3,144 13.7
5,115 21.5
1,343 27.2
612 20.6
60,465
6.4
Jul-Sept
Number %
10,215
2.0
9,125
3.9
9,747
2.3
9,775
3.4
10,500
5.3
6,175
9.1
3,097 14.4
5,252 21.8
1,399 29.2
615 22.1
65,900
6.6
Oct-Dec
Number %
8,995
2.1
8,828
2.5
8,216
2.5
8,918
3.6
9,910
5.3
6,041
8.2
3,196 14.5
5,319 21.5
1,420 28.6
674 24.5
61,517
6.7
Annual
Number
%
36,865
1.9
33,646
3.0
35,084
2.3
35,021
3.2
38,111
5.2
23,183
8.8
12,201 14.0
20,593 21.3
5,450 28.5
2,480 21.6
242,634
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 2001)
6.5
65
Table 3.13: Number of all type property transacted and percentage of commercial
property 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
8,319
1.4
8,495
2.4
7,455
3.1
8,225
2.9
9,304
4.8
5,734 10.6
2,686 13.6
4,733 21.4
1,394 26.8
624 22.0
56,969
6.6
Apr-Jun
Number %
8,753
1.4
9,307
2.5
8,646
2.2
9,139
3.1
9,369
4.9
5,982 10.9
3,150 15.1
5,311 21.9
1,474 28.1
758 24.1
61,889
6.8
Jul-Sept
Number %
9,440
1.8
9,232
2.3
8,652
4.2
8,685
3.6
9,622
5.4
6,366 11.0
3,547 15.3
6,339 22.0
1,619 30.3
892 25.9
64,394
7.7
Oct-Dec
Number %
8,428
2.7
7,486
7.1
5,884
3.9
6,011
4.2
7,159
7.2
4,589
9.5
2,531 19.4
4,230 23.9
1,220 29.6
604 20.0
48,142
8.7
Annual
Number
%
34,940
1.8
34,520
3.4
30,637
3.3
32,060
3.4
35,454
5.5
22,671 10.6
11,914 15.7
20,613 22.2
5,707 28.7
2,878 23.3
231,394
7.4
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 2002)
Table 3.14: Number of all type property transacted and percentage of commercial
property 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Number %
8,854
1.6
7,618
2.8
6,703
3.2
6,598
3.7
7,942
5.9
5,154 10.8
2,923 14.6
5,032 20.6
1,400 25.1
770 22.1
52,994
7.2
Apr-Jun
Number %
8,449
2.3
8,988
3.0
7,676
3.3
8,538
4.2
9,632
5.7
5,729
8.7
3,164 16.1
5,743 22.8
1,658 26.1
819 21.0
60,396
7.5
Jul-Sept
Number %
9,255
1.2
9,097
2.6
8,148
3.0
7,965
4.5
9,812
5.6
6,620
9.9
3,445 18.8
6,574 22.1
1,717 30.2
931 21.5
63,564
7.8
Oct-Dec
Number %
10,202
1.6
9,540
3.4
8,350
3.9
8,222
4.5
9,751
5.9
6,710
9.5
3,799 16.9
6,914 22.3
1,925 28.3
1,009 19.8
66,422
8.0
Annual
Number
%
36,760
1.6
35,243
3.0
30,877
3.4
31,323
4.2
37,137
5.8
24,213
9.7
13,331 16.7
24,263 22.0
6,700 27.6
3,529 21.0
243,376
Number = Number of all type property transacted
% = Percentage of commercial property transacted per all type of property transacted (in same price
range & same quarter)
(Source: Property Market Report, 2003)
7.7
66
From the data obtained above, it could be observed that commercial property
with the price range of RM500k-1000k was the highest among all price range. It was
due to the characteristics of higher price ranged of commercial property if compared
with other types of properties.
However, the total number of all types of transaction and percentage for total
commercial property transacted are the important indicators to make further analysis.
3.4
Annual Percentage Change (In Number) of Property Transaction
For analysis purpose, annual percentage change (in number) of property
transaction is calculated. Annual percentage change (in number) of property
transaction means comparison is done between this quarter and the same quarter of the
year before.
In this calculation, percentage change of commercial property and all types of
properties are calculated between 1997-2003. This is to analyse the percentage
movement in commercial property and all types of properties.
67
Table 3.15: Annual percentage change in number of property transaction 1997
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
%
All %
-32.5
-30.9
-58.0
-13.2
-34.1
2.3
18.8
0.5
-12.1
6.5
-6.5
14.4
-5.4
9.5
15.8
21.1
-5.7
11.4
32.6
53.3
-5.9
-2.8
Apr- Jun
%
All %
-59.1
-12.0
-32.7
-7.4
64.6
4.1
19.0
4.4
3.2
21.7
-0.4
47.6
36.5
42.1
31.2
41.2
17.4
25.2
38.2
-5.6
16.3
9.5
Jul-Sept
%
All %
-40.3
-29.1
-40.0
-9.0
-42.0
-7.2
6.8
-9.9
-9.8
-1.5
24.1
25.3
32.3
32.1
22.3
16.7
30.0
-2.2
24.2
-20.0
9.8
-5.0
Oct-Dec
%
All %
19.4
-5.9
48.7
7.5
-33.6
-5.6
-6.8
8.5
-30.8
10.3
-4.9
21.6
11.2
31.1
28.1
14.8
-1.8
-8.0
-11.3
-14.9
2.6
5.6
Annual
%
All %
-34.1
-20.7
-31.0
-6.1
-16.6
-1.8
9.6
0.4
-13.0
8.8
3.1
26.7
19.2
28.4
24.3
22.7
9.6
5.9
19.8
0.4
5.6
1.6
% = Obtained by number of commercial property transaction once quarter /year minus number of property
transaction one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter /year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one year
before
(Source: Property Market Report, 1997)
Table 3.16: Annual percentage change in number of property transaction 1998
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
-49.1
-1.1
28.8
-16.7
-17.2
-28.5
-45.6
-17.8
-45.1
-21.0
-36.7
-18.5
-11.9
-23.7
-26.7
-37.5
-28.5
-53.0
-61.8
-71.5
Apr- Jun
%
All %
59.7
-14.5
6.9
-21.4
-64.8
-38.5
-60.3
-29.2
-37.9
-35.3
-52.9
-45.3
-48
-44.6
-48
-52.1
-44.3
-56.5
-64.3
-71.3
Jul-Sept
%
All %
2.8
3.6
-4.0
-27.5
-3.1
-38.0
-39.8
-37.2
-37.9
-36.7
-60.4
-48.3
-65.4
-54.5
-58.4
-57.6
-56.2
-62.0
-75.3
-71.7
Oct-Dec
%
All %
18.9
-7.3
-49.3
-20.1
-19.6
-35.7
-43.1
-42.4
-18.5
-40.4
-49.9
-42.9
-50.6
-47.0
-50.2
-46.6
-47.3
-55.7
-57.9
-66.8
Annual
%
All%
-8.4
-5.0
-9.2 -21.6
-33.6 -35.3
-48.1 -32.1
-35.7 -33.7
-50.8 -39.8
-47.1 -43.5
-46.8 -49.0
-44.9 -56.8
-65.3 -70.4
Total
-30.9
-46.8
-52.5
-44.3
-44.1
-22.5
-34.9
-36.3
-34.7
-32.3
% = Obtained by number of commercial property transaction once quarter/ year minus number of property
transaction
one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one year
before
(Source: Property Market Report, 1998)
68
Table 3.17: Annual percentage change in number of property transaction 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
571.9
36.3
-16.0
4.9
-1.0
-14.5
-14.3
-22.9
-2.1
-13.4
-8.1
-16.8
-29.4
-14.0
-27.7
-10.9
-25.8
-16.3
-14.4
-31.0
Apr- Jun
%
All %
480.0
49.5
32.6
18.6
69.8
16.8
34.9
14.3
35.1
21.4
49.4
27.2
5.9
31.4
7.7
32.5
22.4
28.5
28.8
8.2
Jul-Sept
%
All %
100.0
21.5
-25.6
25.5
-1.6
28.6
37.4
34.7
28.2
41.3
40.2
46.2
49.5
49.3
22.7
55.4
76.7
76.6
104.0
53.5
Oct-Dec
%
All%
29.6
10.9
133.3
29.6
223.7
24.6
50.6
44.2
29.0
38.1
47.2
32.5
51.3
40.4
30.8
42.6
71.9
77.1
44.4
35.0
Annual
%
All%
287
29.3
21
19.6
66
13.3
26
15.5
24
20.6
30
20.2
13
24.5
5
28.1
31
37.0
34
12.3
Total
0.1
-2.9
41.9
26.4
34.6
34.3
56.7
30.6
32
21.4
% = Obtained by number of commercial property transaction once quarter/ year minus number of property
transaction one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one year
before
(Source: Property Market Report, 1999)
Table 3.18: Annual percentage change in number of property transaction 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
-73.1
-28.4
-3.8
7.2
79.5
15.6
95.8
32.2
71.9
29.7
27.6
34.2
32.8
23.1
25.7
33.6
36.4
40.9
32.7
45.6
Apr- Jun
%
All %
-64.8
-26.3
-57.0
-4.9
-16.9
7.2
15.8
12.1
4.4
13.3
13.1
19.5
27.9
10.6
14.1
10.9
20.2
8.3
9.8
24.7
Jul-Sept
%
All %
-23.9
-6.6
31.9
5.7
69.4
19.2
-0.7
13.4
27.9
22.8
10.9
23.1
14.5
14.4
21.6
15.9
-11.0
11.9
21.9
21.7
Oct-Dec
%
All %
83.0
6.6
-7.9
-11.1
-62.8
4.1
-3.5
-0.9
3.3
12.4
-8.1
12.0
-3.9
2.7
4.4
4.3
-8.0
8.1
-4.9
3.2
Annual
%
All %
-50
-15.1
-16
-1.2
-7
11.4
20
12.6
22
18.9
10
21.4
16
12.0
16
14.9
5
15.0
14
21.3
Total
14.3
9.5
-4.4
0.9
14.4
11.6
-5.9
3.3
4
6.2
% = Obtained by number of commercial property transaction once quarter/ year minus number of property
transaction one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one year
before
(Source: Property Market Report, 2000)
69
Table 3.19: Annual percentage change in number of property transaction 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
-31.6
-13.7
1.1
-9.2
-43.7
15.6
-36.8
9.0
-12.6
11.0
-11.3
8.1
-15.1
5.1
4.1
3.9
4.2
5.1
-19.4
-9.4
Apr- Jun
%
All %
-22.1
-1.5
82.7
0.5
-2.6
7.7
-4.2
10.8
0.2
6.8
-9.5
0.3
-19.2
2.8
3.1
1.5
-18.3
0.8
-19.7
-6.7
Jul-Sept
%
All %
18.9
-7.5
67.0
-2.8
-28.4
-2.5
23.7
11.2
-2.8
3.2
-3.4
-3.7
-13.1
-6.2
-0.6
-9.6
-7.1
-11.8
-28.0
-22.7
Oct-Dec
%
All %
-38.3
-17.3
-27.1
3.0
8.0
-0.7
29.2
16.2
13.0
6.9
1.4
6.8
3.8
8.2
7.2
0.6
0.2
-6.7
21.3
-7.9
Annual
%
All %
-22.2
-10.1
21.0
-2.1
-21.9
4.2
0.4
11.9
-0.8
6.7
-5.8
2.5
-11.3
2.2
3.4
-1.2
-5.9
-3.8
-13.1
-12.2
Total
-12.4
2.3
-4.8
3.7
-1.0
-2.1
1.9
1.1
-3.9
1.1
% = Obtained by number of commercial property transaction once quarter/ year minus number of property transaction
one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one year
before
(Source: Property Market Report, 2001)
Table 3.20: Annual percentage change in number of property transaction 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
-19.1
1.2
15.6
12.8
21.4
-7.7
15.4
9.9
6.9
9.5
31.5
6.0
-1.3
-2.8
0.4
-3.5
-0.3
8.2
26.9
7.8
Apr- Jun
%
All %
-32.2
-7.2
-5.3
14.0
0.0
-4.4
11.1
3.3
-2.7
1.8
25.2
7.6
10.9
0.2
6.0
3.8
13.4
9.8
45.2
23.9
Jul-Sept
%
All %
-13.4
-7.6
-40.1
1.2
63.5
-11.2
-5.7
-11.2
-5.4
-8.4
25.0
3.1
21.8
14.5
22.0
20.7
20.3
15.7
69.9
45.0
Oct-Dec
%
All %
18.1
-6.3
139.8
-15.2
12.8
-28.4
-21.4
-32.6
-3.4
-27.8
-12.1
-24.0
5.6
-20.8
-11.6
-20.5
-11.1
-14.1
-26.7
-10.4
Annual
%
All %
-10.9
-5.2
18.0
2.6
25.7
-12.7
-2.5
-8.5
-1.6
-7.0
17.5
-2.2
9.6
-2.4
4.3
0.1
5.5
4.7
25.6
16.0
Total
8.0
4.0
7.6
2.4
13.5
-2.3
0.7
-21.7
7.5
-4.6
% = Obtained by number of commercial property transaction once quarter/ year minus number of property
transaction one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one
year before
(Source: Property Market Report, 2002)
70
Table 3.21: Annual percentage change in number of property transaction 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
22.8
6.4
1.4
-10.3
-7.7
-10.1
0.8
-19.8
4.2
-14.6
-9.0
-10.1
16.7
8.8
2.5
6.3
-6.1
0.4
24.1
23.4
Apr- Jun
%
All %
54.8
-3.5
19.1
-3.4
33.0
-11.2
27.4
-6.6
18.6
2.8
-24.3
-4.2
6.9
0.4
12.2
8.1
4.6
12.5
-6.0
8.0
Jul-Sept
%
All %
-35.6
-2.0
11.2
-1.5
-31.7
-5.8
14.3
-8.3
5.4
2.0
-6.3
4.0
19.2
-2.9
4.3
3.7
5.5
6.1
-13.4
4.4
Oct-Dec
%
All %
-30.3
21.0
-38.7
27.4
41.9
41.9
44.5
36.8
11.9
36.2
46.5
46.2
30.5
50.1
52.8
63.5
50.7
57.8
65.3
67.1
Annual
%
All %
-5.8
5.2
-11.3
2.1
2.6
0.8
21.7
-2.3
10.0
4.7
-2.3
6.8
18.6
11.9
16.6
17.7
12.6
17.4
10.4
22.6
Total
1.9
-7.0
8.7
-2.4
0.7
-1.3
27.4
38.0
9.5
5.2
% = Obtained by number of commercial property transaction once quarter/ year minus number of property
transaction one year before (with the same quarter) & divide by number of property transaction one year before
All % = Obtained by all type number of property transaction once quarter/ year minus all type number of property
transaction one year before (with the same quarter) & divide by number of all type property transaction one
year before
(Source: Property Market Report, 2003)
3.5
Value of Commercial Property Transaction
Value of transacted commercial property can also acts as an indicator to
analyse the trend of demand for commercial property. It can be compared with the
number of transacted property by studying its movement from 1997-2003.
The tables on provided below illustrate the value of commercial properties
transacted in each year and quarter.
71
Table 3.22: Value of commercial property transactions (RM Million) 1997
Price Range
Jan-Mar
Value
%
Apr-Jun
Value
%
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jul-Sept
Value
%
Oct-Dec
Value
%
Annual
Value
%
NA
1,994.20
100.0
2,434.21
100.0
2,196.58 100.0 2,138.49 100.0 8,763.48
NA = No data avaiable for each price range
100.0
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1997)
Table 3.23: Value of commercial property transactions (RM Million) 1998
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
1.57
0.1
6.87
0.5
12.11
0.9
17.57
1.4
37.57
2.9
79.96
6.3
108.03
8.4
368.15
28.8
244.43
19.1
402.70
31.5
1,278.96
100.0
Apr-Jun
Value
%
1.97
0.2
9.20
0.8
8.80
0.8
15.24
1.4
43.81
4.0
60.39
5.5
90.34
8.2
301.31
27.3
203.08
18.4
369.50
33.5
1,103.64
100.0
Jul-Sept
Value
%
1.57
0.1
8.01
0.7
12.11
1.1
17.52
1.5
43.11
3.8
66.36
5.9
68.44
6.0
274.04
24.2
183.88
16.2
456.85
40.4
1,131.89
100.0
Oct-Dec
Value
%
1.57
0.1
5.42
0.4
10.12
0.7
15.04
1.1
45.36
3.2
65.39
4.7
70.84
5.1
279.11
19.9
175.28
12.5
734.48
52.4
1,402.61
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1998)
100.0
Annual
Value
%
6.68
0.1
29.50
0.6
43.14
0.9
65.37
1.3
169.85
3.5
272.10
5.5
337.65
6.9
1,222.61
24.9
806.67
16.4
1,963.53
39.9
4,917.10
100.0
72
Table 3.24: Value of commercial property transactions (RM Million) 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
14.57
0.9
7.14
0.5
12.19
0.8
14.59
0.9
36.18
2.3
73.03
4.7
76.49
5.0
272.38
17.7
185.60
12.0
849.69
55.1
1,541.86
100.0
Apr-Jun
Value
%
13.27
0.9
10.81
0.7
15.62
1.1
20.33
1.4
57.87
4.0
92.21
6.3
96.68
6.6
325.65
22.3
261.85
17.9
568.47
38.9
1,462.76
100.0
Jul-Sept
Value
%
4.24
0.2
6.44
0.4
12.00
0.7
24.26
1.4
56.28
3.2
93.65
5.3
103.31
5.8
334.78
18.8
338.19
19.0
809.78
45.4
1,782.93
100.0
Oct-Dec
Value
%
2.70
0.2
12.48
0.9
31.34
2.2
23.22
1.6
57.98
4.1
96.03
6.8
107.00
7.6
355.38
25.1
303.24
21.4
426.96
30.1
1,416.33
100.0
Annual
Value
%
34.78
0.6
36.87
0.6
71.15
1.1
82.40
1.3
208.31
3.4
354.92
5.7
383.48
6.2
1,288.19
20.8
1,088.88
17.6
2,654.90
42.8
6,203.88
100.0
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 1999)
Table 3.25: Value of commercial property transactions (RM Million) 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
2.06
0.1
6.88
0.4
22.39
1.4
29.92
1.9
62.56
4.0
94.21
6.0
100.38
6.4
342.41
21.8
246.79
15.7
664.88
42.3
1,572.48
100.0
Apr-Jun
Value
%
3.06
0.2
5.14
0.3
12.69
0.8
23.38
1.4
62.06
3.8
104.60
6.5
122.25
7.6
372.23
23.0
305.44
18.9
606.00
37.5
1,616.85
100.0
Jul-Sept
Value
%
2.35
0.1
8.39
0.5
20.10
1.2
23.92
1.4
73.48
4.2
104.26
6.0
116.69
6.7
404.17
23.3
305.96
17.6
677.82
39.0
1,737.14
100.0
Oct-Dec
Value
%
5.06
0.3
11.35
0.8
11.99
0.8
22.28
1.5
60.40
4.0
87.04
5.8
103.15
6.8
372.87
24.6
276.08
18.3
562.47
37.2
1,512.69
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2000)
100.0
Annual
Value
%
12.53
0.2
31.76
0.5
67.17
1.0
99.50
1.5
258.50
4.0
390.11
6.1
442.47
6.9
1,491.68
23.2
1,134.27
17.6
2,511.17
39.0
6,439.16
100.0
73
Table 3.26: Value of commercial property transactions (RM Million) 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
2.19
0.1
6.72
0.5
12.27
0.8
18.52
1.3
53.75
3.6
82.66
5.6
85.91
5.8
351.76
23.8
259.98
17.6
604.23
40.9
1,477.98
100.0
Apr-Jun
Value
%
2.74
0.2
8.93
0.5
12.42
0.7
22.80
1.3
60.50
3.5
93.00
5.4
98.54
5.7
383.68
22.3
253.58
14.8
781.54
45.5
1,717.73
100.0
Jul-Sept
Value
%
2.52
0.2
13.50
0.8
14.45
0.9
29.52
1.8
70.85
4.4
100.21
6.2
102.69
6.4
398.30
24.7
282.06
17.5
601.12
37.2
1,615.23
100.0
Oct-Dec
Value
%
3.59
0.2
8.12
0.5
13.31
0.8
29.13
1.8
68.00
4.2
89.29
5.5
106.78
6.6
406.39
25.2
280.21
17.3
611.02
37.8
1,615.84
100.0
Annual
Value
%
11.04
0.2
37.27
0.6
52.45
0.8
99.97
1.6
253.10
3.9
365.16
5.7
393.92
6.1
1,540.13
24.0
1,075.83
16.7
2,597.91
40.4
6,426.78
100.0
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2001)
Table 3.27: Value of commercial property transactions (RM Million) 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
1.44
0.1
7.87
0.6
14.43
1.1
21.44
1.6
58.69
4.4
109.36
8.3
84.18
6.4
355.08
26.8
260.40
19.7
412.30
31.1
1,325.19
100.0
Apr-Jun
Value
%
1.99
0.1
8.65
0.5
12.26
0.7
25.30
1.4
60.22
3.3
117.52
6.5
109.63
6.1
405.08
22.4
287.59
15.9
779.17
43.1
1,807.41
100.0
Jul-Sept
Value
%
2.23
0.1
7.97
0.4
23.40
1.2
27.89
1.4
67.87
3.4
126.86
6.3
125.57
6.2
481.18
23.9
338.19
16.8
814.71
40.4
2,015.87
100.0
Oct-Dec
Value
%
4.08
0.3
21.41
1.7
14.66
1.1
22.75
1.8
66.12
5.1
78.04
6.0
113.71
8.8
351.53
27.1
249.12
19.2
373.98
28.9
1,295.40
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2002)
100.0
Annual
Value
%
9.74
0.2
45.90
0.7
64.75
1.0
97.38
1.5
252.90
3.9
431.78
6.7
433.09
6.7
1,592.87
24.7
1,135.30
17.6
2,380.16
37.0
6,443.87
100.0
74
Table 3.28: Value of commercial property transactions (RM Million) 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 1,000,000
1,000,001 & above
Total
Jan-Mar
Value
%
2.06
0.1
6.88
0.4
22.39
1.4
29.92
1.9
62.56
4.0
94.21
6.0
100.38
6.4
342.41
21.8
Apr-Jun
Value
%
3.06
0.2
5.14
0.3
12.69
0.8
23.38
1.4
62.06
3.8
104.60
6.5
122.25
7.6
372.23
23.0
Jul-Sept
Value
%
2.35
0.1
8.39
0.5
20.10
1.2
23.92
1.4
73.48
4.2
104.26
6.0
116.69
6.7
404.17
23.3
Oct-Dec
Value
%
5.06
0.3
11.35
0.8
11.99
0.8
22.28
1.5
60.40
4.0
87.04
5.8
103.15
6.8
372.87
24.6
Annual
Value
12.53
31.76
67.17
99.50
258.50
390.11
442.47
1,491.68
%
0.2
0.5
1.0
1.5
4.0
6.1
6.9
23.2
246.79
664.88
15.7
42.3
305.44
606.00
18.9
37.5
305.96
677.82
17.6
39.0
276.08
562.47
18.3
37.2
1,134.27
2,511.17
17.6
39.0
1,572.48
100.0
1,616.85
100.0
1,737.14
100.0
1,512.69
100.0
6,439.16
100.0
Value = Value of commercial property transacted
% = Percentage value of commercial property transacted per total (by quarterly basis)
(Source: Property Market Report, 2003)
3.6
Value of All Type Properties Transaction and Percentage of Commercial
Property
It is important to study the total value of all types of properties and from the
figure; percentage value of commercial property is calculated.
The figures as provided below illustrate all type of transacted properties &
percentage of commercial property ranging from year 1997 to 2003.
75
Table 3.29: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 1997
Price Range
Jan-Mar
All
Value
%
Apr-Jun
All
Value
%
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jul-Sept
All
Value
%
Oct-Dec
All
Value
%
Annual
All
Value
%
NA
12,834.66
15.6
14,051.10
17.3 13,860.93 15.9 12,470.62 17.2
NA = No data avaiable for each price range
53,217.31
16.5
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & by same quarter)
(Source : Property Market Report, 1997)
Table 3.30: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 1998
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
All Value
%
135.42
1.2
270.14
2.5
446.28
2.7
587.14
3.0
857.63
4.4
781.94 10.2
561.39 19.2
1,330.22 27.7
712.54 34.3
2,405.67 16.7
Apr-Jun
All Value
%
128.81
1.5
273.67
3.4
419.25
2.1
547.03
2.8
778.79
5.6
632.12
9.6
474.47 19.0
1,141.46 26.4
633.18 32.1
1,557.82 23.7
Jul-Sept
All Value %
135.59
1.2
265.98
3.0
411.59
2.9
500.38
3.5
737.24
5.8
624.32 10.6
438.07 15.6
1,094.09 25.0
532.62 34.5
1,675.72 27.3
Oct-Dec
All Value
%
128.69
1.2
275.18
2.0
400.40
2.5
474.94
3.2
762.56
5.9
624.02 10.5
464.87 15.2
1,213.25 23.0
545.18 32.2
1,931.75 38.0
Annual
All Value
%
528.51
1.3
1,084.97
2.7
1,677.52
2.6
2,109.49
3.1
3,136.22
5.4
2,662.40 10.2
1,938.80 17.4
4,779.02 25.6
2,423.52 33.3
7,570.96 25.9
Total
8,088.37 15.8
6,586.60 16.8
6,415.60 17.6
6,820.84 20.6 27,911.41
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & same quarter)
(Source : Property Market Report, 1998)
17.6
76
Table 3.31: Value of all types properties transacted (RM Million) and percentage of
commercial property (%) year 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
All Value
%
204.3
7.1
295.58
2.4
383.67
3.2
461.03
3.2
753.27
4.8
652.99 11.2
485.11 15.8
1208.12 22.5
597.41 31.1
2277.09 37.3
7318.57
Apr-Jun
All Value
%
196.83
6.7
331.97
3.3
499.54
3.1
634.45
3.2
970.43
6.0
815.81 11.3
631.78 15.3
1540.34 21.1
847.54 30.9
1761.43 32.3
21.1
8230.12
17.8
Jul-Sept
All Value %
168.45
2.5
343.22
1.9
534.02
2.2
688.28
3.5
1050.83
5.4
914.22 10.2
660.11 15.7
1698.59 19.7
976.07 34.6
2650.81 30.5
9684.60
18.4
Oct-Dec
All Value
%
124.33
2.2
370.08
3.4
504.95
6.2
687.81
3.4
1045.59
5.5
888.04 10.8
656.92 16.3
1723.11 20.6
970.17 31.3
2218.33 19.2
9189.33
15.4
Annual
All Value
%
693.91
5.0
1,340.85
2.7
1,922.18
3.7
2,471.57
3.3
3,820.12
5.5
3,271.06 10.9
2,433.92 15.8
6,170.16 20.9
3,391.19 32.1
8,907.66 29.8
34,422.62
18.0
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & same quarter)
(Source : Property Market Report, 1999)
Table 3.32: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
All Value
%
120.76
1.7
318.95
2.2
443.04
5.1
608.78
4.9
972.16
6.4
876.16 10.8
599.80 16.7
1,601.54 21.4
842.89 29.3
2,708.36 24.5
9,092.44
17.3
Apr-Jun
All Value
%
118.50
2.6
314.50
1.6
531.54
2.4
705.87
3.3
1,093.09
5.7
973.59 10.7
694.15 17.6
1,705.94 21.8
911.79 33.5
2,083.60 29.1
9,132.57
17.7
Jul-Sept
All Value %
144.43
1.6
356.87
2.4
638.78
3.1
774.75
3.1
1,294.65
5.7
1,126.99
9.3
747.95 15.6
1,961.59 20.6
1,084.63 28.2
2,533.94 26.7
Oct-Dec
All Value
%
131.60
3.8
325.85
3.5
526.24
2.3
675.83
3.3
1,176.26
5.1
992.62
8.8
669.90 15.4
1,793.88 20.8
1,039.06 26.6
2,974.68 18.9
Annual
All Value
%
515.29
2.4
1,316.17
2.4
2,139.60
3.1
2,765.23
3.6
4,536.16
5.7
3,969.36
9.8
2,711.80 16.3
7,062.95 21.1
3,878.37 29.2
10,300.58 24.4
10,664.58
10,305.92
39,195.51
16.3
14.7
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & by quarterly basis)
(Source : Property Market Report, 2000)
16.4
77
Table 3.33: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
All Value
%
110.73
2.0
287.71
2.3
516.52
2.4
658.84
2.8
1,083.16
5.0
944.10
8.8
629.87 13.6
1,657.93 21.2
888.39 29.3
2,352.24 25.7
9,129.49
Apr-Jun
All Value
%
127.86
2.1
314.25
2.8
580.35
2.1
775.95
2.9
1,171.49
5.2
974.19
9.5
714.53 13.8
1,729.55 22.2
929.02 27.3
2,342.88 33.4
16.2
9,660.07
17.8
Jul-Sept
All Value %
138.13
1.8
348.71
3.9
626.03
2.3
856.00
3.4
1,334.38
5.3
1,080.53
9.3
703.57 14.6
1,764.55 22.6
956.28 29.5
1,830.76 32.8
9,638.94
16.8
Oct-Dec
All Value
%
125.58
2.9
341.33
2.4
526.45
2.5
785.57
3.7
1,257.96
5.4
1,054.66
8.5
726.39 14.7
1,806.42 22.5
972.35 28.8
2,609.73 23.4
Annual
All Value
%
502.30
2.2
1,292.00
2.9
2,249.35
2.3
3,076.36
3.2
4,846.99
5.2
4,053.48
9.0
2,774.36 14.2
6,958.45 22.1
3,746.04 28.7
9,135.61 28.4
10,206.44
38,634.94
15.8
16.6
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & same quarter)
(Source : Property Market Report, 2001)
Table 3.34: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
All Value
%
112.90
1.3
324.06
2.4
478.33
3.0
721.65
3.0
1,190.57
4.9
1,001.23 10.9
609.91 13.8
1,601.85 22.2
956.31 27.2
2,272.30 18.1
9,269.11
14.3
Apr-Jun
All Value
%
120.55
1.7
354.69
2.4
556.02
2.2
798.59
3.2
1,192.35
5.1
1,047.26 11.2
717.62 15.3
1,789.32 22.6
1,018.46 28.2
2,796.20 27.9
Jul-Sept
All Value %
131.59
1.7
356.25
2.2
555.91
4.2
761.95
3.7
1,222.58
5.6
1,115.04 11.4
808.32 15.5
2,153.71 22.3
1,108.44 30.5
2,901.83 28.1
10,391.06
11,115.62
17.4
18.1
Oct-Dec
All Value
%
115.63
3.5
286.30
7.5
377.20
3.9
527.25
4.3
910.19
7.3
802.84
9.7
575.92 19.7
1,446.54 24.3
839.10 29.7
1,986.53 18.8
7,867.50
16.5
Annual
All Value
%
480.67
2.0
1,321.30
3.5
1,967.46
3.3
2,809.44
3.5
4,515.69
5.6
3,966.37 10.9
2,711.77 16.0
6,991.42 22.8
3,922.31 28.9
9,956.86 23.9
38,643.29
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & same quarter)
(Source : Property Market Report, 2002)
16.7
78
Table 3.35: Value of all type properties transacted (RM Million) and percentage of
commercial property (%) 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
All Value
%
120.76
1.7
318.95
2.2
443.04
5.1
608.78
4.9
972.16
6.4
876.16 10.8
599.80 16.7
1,601.54 21.4
842.89 29.3
2,708.36 24.5
9,092.44
17.3
Apr-Jun
All Value
%
118.50
2.6
314.50
1.6
531.54
2.4
705.87
3.3
1,093.09
5.7
973.59 10.7
694.15 17.6
1,705.94 21.8
911.79 33.5
2,083.60 29.1
9,132.57
17.7
Jul-Sept
All Value %
144.43
1.6
356.87
2.4
638.78
3.1
774.75
3.1
1,294.65
5.7
1,126.99
9.3
747.95 15.6
1,961.59 20.6
1,084.63 28.2
2,533.94 26.7
Oct-Dec
All Value
%
131.60
3.8
325.85
3.5
526.24
2.3
675.83
3.3
1,176.26
5.1
992.62
8.8
669.90 15.4
1,793.88 20.8
1,039.06 26.6
2,974.68 18.9
Annual
All Value
%
515.29
2.4
1,316.17
2.4
2,139.60
3.1
2,765.23
3.6
4,536.16
5.7
3,969.36
9.8
2,711.80 16.3
7,062.95 21.1
3,878.37 29.2
10,300.58 24.4
10,664.58
10,305.92
39,195.51
16.3
14.7
All value = Value of all type property transacted
% = Percentage value of commercial property transacted per all type value of property transacted (in same price
range & same quarter)
(Source : Property Market Report, 2003)
3.7
Annual Percentage Change in Value of Property Transaction
For analysis purpose, annual percentage change in property transaction value is
calculated as well. Annual percentage change in property transaction value is known
as the comparison of percentage between a quarter and the same quarter of the year
before.
In this calculation, the percentage change in commercial property value and all
type of properties are calculated between 1997-2003.
16.4
79
Table 3.36: Annual percentage change in value of property transaction 1997
Price Range
Jan-Mar
%
All %
Apr- Jun
%
All %
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jul-Sept
%
All %
Oct-Dec
%
All %
%
Annual
All %
NA
10.1
13.7
22.6
-0.9 16.6
12.3 22.4
3.1 17.7
NA = No data available for each price range
7.0
% = Obtained by value of property transaction once quarter minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
Table 3.37: Annual percentage change in value of property transaction 1998
Price Range
Jan-Mar
%
All %
Apr- Jun
%
All %
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
NA
-35.9
-37.0
-54.7
Jul-Sept
%
All %
Oct-Dec
%
All %
Annual
%
All %
25.60
-23.1
-9.10
-20.5
-30.30
-30.4
-41.60
-27.3
-32.80
-31.0
-48.10
-38.3
-46.30
-39.9
-44.60
-46.5
-44.20
-53.3
-43.80
-61.9
-53.1 -48.5
-53.71 -34.5
-45.3 -43.90
NA = No available data of year 1997 to be calculated
-47.8
% = Obtained by value of property transaction once quarter/ year minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter/ year minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
80
Table 3.38: Annual percentage change in value of property transaction 1999
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
%
All %
828.0
50.9
3.9
9.4
0.7
-14.0
-17.0
-21.5
-3.7
-12.2
-8.7
-16.5
-29.2
-13.6
-26.0
-9.2
-24.1
-16.2
111.0
-5.3
20.6
-9.5
Apr- Jun
%
All %
573.6
52.8
17.5
21.3
77.5
19.2
33.4
16.0
32.1
24.6
52.7
29.1
7.0
33.2
8.1
34.9
28.9
33.9
53.8
13.1
32.5
25.0
Jul-Sept
%
All %
170.1
24.2
-19.6
29.0
-0.9
29.8
38.5
37.6
30.5
42.5
41.1
46.4
50.9
50.7
22.2
55.3
83.9
83.3
77.3
58.2
57.5
51.0
Oct-Dec
%
All %
72.0
-3.4
130.3
34.5
209.7
26.1
54.4
44.8
27.8
37.1
46.9
42.3
51.0
41.3
27.3
42.0
73.0
78.0
-41.9
14.8
1.0
34.7
Annual
%
All %
420.7
31.3
24.9
23.6
64.9
14.6
26.1
17.2
22.6
21.8
30.9
22.9
13.6
25.5
5.4
29.1
35.0
39.9
35.2
17.7
26.2
23.3
% = Obtained by value of property transaction once quarter minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
Table 3.39: Annual percentage change in value of property transaction 2000
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
%
All %
-85.9
-40.9
-3.6
7.9
83.7
15.5
105.1
32.0
72.9
29.1
29.0
34.2
31.2
23.6
25.7
32.6
33.0
41.1
-21.8
18.9
2.0
24.2
Apr- Jun
%
All %
-76.9
-39.8
-52.5
-5.3
-18.8
6.4
15.0
11.3
7.2
12.6
13.4
19.3
26.4
9.9
14.3
10.8
16.6
7.6
6.6
18.3
10.5
11.0
Jul-Sept
%
All %
-44.6
-14.3
30.3
4.0
67.5
19.6
-1.4
12.6
30.6
23.2
11.3
23.3
13.0
13.3
20.7
15.5
-9.5
11.0
-16.3
-4.4
-2.6
10.1
Oct-Dec
%
All %
87.4
5.8
-9.1
-12.0
-61.7
4.2
-4.0
-1.7
4.2
12.5
-9.4
11.8
-3.6
2.0
4.9
4.1
-9.0
7.1
31.7
34.1
6.8
12.2
Annual
%
All %
-64.00
-25.7
-13.80
-1.8
-5.60
11.3
20.70
11.9
24.10
18.7
9.90
21.4
15.40
11.4
15.80
14.5
4.20
14.4
-5.40
15.6
3.80
13.9
% = Obtained by value of property transaction once quarter/ year minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter/ year minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
81
Table 3.40: Annual percentage change in value of property transaction 2001
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
%
All %
6.2
-8.3
-2.3
-9.8
-45.2
16.6
-38.1
8.2
-14.1
11.4
-12.3
7.7
-14.4
5.0
2.7
3.5
5.3
5.4
-9.1
-13.1
-6.0
0.4
Apr- Jun
%
All %
-10.5
-7.9
73.7
-0.1
-2.1
9.2
-2.5
9.9
-2.5
7.2
-11.1
0.1
-19.4
2.9
3.1
1.4
-17.0
1.9
29.0
12.4
6.2
5.8
Jul-Sept
%
All %
7.0
-4.4
61.0
-2.3
-28.1
-2.0
23.4
10.5
-3.6
3.1
-3.9
-4.1
-12.0
-5.9
-1.5
-10.0
-7.8
-11.8
-11.3
-27.8
-7.0
-9.6
Oct-Dec
%
All %
-29.1
-4.6
-28.5
4.8
11.0
0.0
30.7
16.2
12.6
6.9
2.6
6.2
3.5
8.4
9.0
0.7
1.5
-6.4
8.6
-12.3
6.8
-1.0
Annual
%
All %
-12.00
-2.5
17.40
-1.8
-21.90
5.1
0.50
11.3
-2.10
6.9
-6.40
2.1
-11.00
2.3
3.20
-1.5
-5.20
-3.4
3.50
-11.3
-0.20
-1.4
% = Obtained by value of property transaction once quarter/ year minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter/ year minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
Table 3.41: Annual percentage change in value of property transaction 2002
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Jan-Mar
%
All %
-34.4
2.0
17.1
12.6
17.6
-7.4
15.8
9.5
9.2
9.9
32.3
6.1
-2.0
-3.2
0.9
-3.4
0.2
7.6
-31.8
-3.4
Total
-10.3
1.5
Apr- Jun
%
All %
-27.2
-5.7
-3.1
12.9
-1.4
-4.2
11.0
2.9
-0.5
1.8
26.4
7.5
11.3
0.4
5.6
3.5
13.4
9.6
-0.3
19.3
5.2
7.6
Jul-Sept
%
All %
-11.2
-4.7
-41.0
2.2
62.0
-11.2
-5.5
-11.0
-4.2
-8.4
26.6
3.2
22.3
14.9
20.8
22.1
19.9
15.9
35.5
58.5
24.8
15.3
Oct-Dec
%
All %
13.6
-7.9
163.7
-16.1
10.1
-28.4
-21.9
-32.9
-2.8
-27.6
-12.6
-23.9
6.5
-20.7
-13.5
-19.9
-11.1
-13.7
-38.8
-23.9
-19.8
-22.9
Annual
%
All %
-10.9
-5.2
18.0
2.6
25.7
-12.7
-2.5
-8.5
-1.6
-7.0
17.5
-2.2
9.6
-2.4
4.3
0.1
5.5
4.7
25.6
16.0
7.5
% = Obtained by value of property transaction once quarter/ year minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter/ year minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
-4.6
82
Table 3.42: Annual percentage change in value of property transaction 2003
Price Range
25,000 & below
25,001 -50,000
50,001 -75,000
75,001- 100,000
100,001 -150,000
150,001 - 200,000
200,001 - 250,000
250,001- 500,000
500,001 -1,000,000
1,000,001 & above
Total
Jan-Mar
%
All %
-85.9
-40.9
-3.6
7.9
83.7
15.5
105.1
32.0
72.9
29.1
29.0
34.2
31.2
23.6
25.7
32.6
33.0
41.1
-21.8
18.9
2.0
24.2
Apr- Jun
%
All %
-76.9
-39.8
-52.5
-5.3
-18.8
6.4
15.0
11.3
7.2
12.6
13.4
19.3
26.4
9.9
14.3
10.8
16.6
7.6
6.6
18.3
10.5
11.0
Jul-Sept
%
All %
-44.6
-14.3
30.3
4.0
67.5
19.6
-1.4
12.6
30.6
23.2
11.3
23.3
13.0
13.3
20.7
15.5
-9.5
11.0
-16.3
-4.4
-2.6
10.1
Oct-Dec
%
All %
87.4
5.8
-9.1
-12.0
-61.7
4.2
-4.0
-1.7
4.2
12.5
-9.4
11.8
-3.6
2.0
4.9
4.1
-9.0
7.1
31.7
34.1
6.8
12.2
Annual
%
All %
-5.8
5.2
-11.3
2.1
2.6
0.8
21.7
-2.3
10.0
4.7
-2.3
6.8
18.6
11.9
16.6
17.7
12.6
17.4
10.4
22.6
9.5
% = Obtained by value of property transaction once quarter/ year minus value of property transaction one year
before (with the same quarter) & divide by value of property transaction one year before
All % = Obtained by all type value of property transaction once quarter/ year minus all type value of property
transaction one year before (with the same quarter) & divide by value of all type property transaction one year
before
3.8
Quarterly Percentage Change in Number of Commercial Property
Transaction
The annual percentage change in number of property transaction was observed.
The annual percentage change in number of transacted property was known as the
comparison between these quarters with the same quarter of a year before.
However, the comparison was done between quarter and quarter, in another
words, it was the comparison between this quarter and quarter before, but not with the
same quarter of the previous year.
5.2
83
Table 3.43: Quarterly percentage change in number of commercial property
transaction (%)
Year
1997
1998
1999
2000
2001
2002
2003
Jan-Mar
%
Number Change
5,004
3,460
3,464
3,959
3,469
3,745
3,818
/
-30.1
25.6
-8.3
-14.7
-9.6
-8.5
Apr-Jun
%
Number Change
5,658
3,009
4,270
4,082
3,888
4,182
4,544
13.1
-13.0
23.3
3.1
12.1
11.7
19.0
Jul-Sept
Number
6,022
2,859
3,849
4,403
4,361
4,949
4,985
Oct-Dec
%
Change
Number
6.4
-5.0
-9.9
7.9
12.2
18.3
9.7
4,948
2,757
4,319
4,066
4,144
4,172
5,316
%
Change
-17.8
-3.6
12.2
-7.7
-5.0
-15.7
6.6
Number = Number of commercial property transacted
% Change = Percentage change in commercial property transacted (compared quarterly)
Example calculation : In 2000,Jan-Mar.
Number of commercial property transacted = 3959
In 1999, Oct-Dec.
Number of commercial property transacted = 4319
Thus, percentage change of commercial property transacted =
(3959-4319)/4319 X 100 = -8.3
The number shown above in the table is the total number of commercial
property transaction in each quarter. Analysis would be carried out after plotting the
graph.
3.9
Quarterly Percentage Change in Value of Commercial Property
Transaction
Below is the table (tabulation) which illustrates quarterly percentage change in
value of commercial property transaction.
84
Table 3.44: Quarterly percentage change in value of commercial property transaction
Year
Jan-Mar
Value
1997
1998
1999
2000
2001
2002
2003
%
Change
1994.2
1278.96
1541.86
1572.48
1477.98
1,325
1,572
/
-40.2
9.9
11.0
-2.3
-18.0
21.4
Apr-Jun
%
Change
Value
2434.21
1103.64
1462.76
1616.85
1717.73
1807.41
1616.85
22.1
-13.7
-5.1
2.8
16.2
36.4
2.8
Jul-Sept
%
Change
Value
2196.58
1131.89
1782.93
1737.14
1615.23
2015.87
1737.14
-9.8
2.6
21.9
7.4
-6.0
11.5
7.4
Oct-Dec
%
Change
Value
2138.49
1402.61
1416.33
1512.69
1615.84
1295.4
1512.69
-2.6
23.9
-20.6
-12.9
0.0
-35.7
-12.9
Value = Value of commercial property transacted (in RM Million)
% Change = Percentage change in value of commercial property transacted (compared quarterly)
Example calculation : In 2000,Jan-Mar.
Value of commercial property transacted = 1572.48M
In 1999, Oct-Dec.
Value of commercial property transacted = 1416.33M
Thus, percentage change of commercial property transacted =
(1572.48-1416.33)/1416.33 X 100 = 11.0 %
3.10
Analysis of Commercial Property Transaction by Graph
Graph 3.1 provides a full overview for quantity of overall transaction. In fact,
this kind of analysis explains the amplitude as well as the movement trend of each
quarter. Therefore, several graphs are plotted for the purpose of presenting these
analysis studies. On the other hand, from observation and comparison, subjective
comments were given.
85
Number Of Commercial Property Transaction
The highest number
7000
6000
Number
5000
4000
3000
The lowest number
2000
1000
0
Quarter
Graph 3.1: Number of commercial property transaction
From Graph 3.1, we can observe that the highest number of transaction was
obtained in year 1997, Quarter 3. However, lowest numbers of transaction
immediately after five (5) quarters were recorded.
In fact, as the time of economic crisis started at the 1st of July 1997, the
highest number was obtained. Moreover, the effect of economic crisis is shown just
after 1 quarter. It definitely occurred faster than expected by experts; real estate
market always has a lag from economy market at least in 2 quarters.
The number of commercial property transaction continued to fall until lower
than one-half from it peak, right after 5 quarters period. Regardless, the overall
transaction still increased onward. However, until the last quarter of 2003, transaction
is still lower than 6 years before as shown above.
86
Percentage Share Of Commercial Property Transaction
10
9
Percentage Share (% )
8
7
6
5
4
3
2
1
0
Quarter
Graph 3.2: Percentage share of commercial property transaction
From Graph 3.2, the percentage share of commercial property transaction to all
properties transaction types was observed to maintain at the same level. In most of the
time, it varied from 6% to 7% or 7% to 8% showing only a 1% different.
Thus, commercial property was proved to be moving parallel with the overall
properties movement. However, if we look into the details, it could be observed that
after the peak transaction in 1997 Quarter 3, the percentage share of commercial
property transaction shows a decline in movement until 1998 Quarter 4. It was exactly
the same with the movement in Graph 3.1. Thus, it obviously illustrated that in time of
economic crisis, commercial property transaction is more affected than the overall
properties transaction.
Beginning from 2001, it was observed that the percentage of commercial
property transaction was leading the overall properties transaction after the effect of
economic crisis. The same situation occurred before the economic crisis, whereby the
percentage share of commercial property transaction was increasing at every quarter.
87
Annual Percentage Change Of Commercial Property Transaction (Compared
With Same Quarter)
80
Percentage Change (% )
60
40
20
0
-2 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-4 0
-6 0
Quarter
Graph 3.3: Annual percentage change of commercial property transaction (compared
with same quarter)
Quarterly Percentage Change Of Commercial Property Transaction
30
Percentage Change (% )
20
10
0
-1 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-2 0
-3 0
-4 0
Quarter
Graph 3.4: Quarterly percentage change of commercial property transaction
From Graph 3.3, started from year 2000; it could be observed that there was
only one continuous increment for the three years in the same period (quarter) which
88
occurred at Quarter 4 of year 2001 to 2003. For the rest of the period, a two year
continuous increment recorded only at 2002 & 2003 Quarter 1, as well as 2002 &
2003 Quarter 2.
It can be concluded that the commercial property show an increment year on
year especially after the economic crisis. In year 1999, it had shown a high increment
in annual percentage change of commercial property transaction. In fact, it increment
was to show a recovery from the economic crisis. Thus, it was useless to use the data
of year 1999 to determine it continuous increment.
From Graph 3.4, it can be observed that for the 4 year continuously from year
2000, the commercial property transaction was increasing at every Quarter 1.
Following the increment in every Quarter 1, Quarter 2 shows an increment for every
year. The second increment is then recorded in every Quarter 3.
89
3.11
Others Graph (Plot from the Data Obtained)
Value Of Commercial Properties Transacted
3000
Value (RM Million)
2500
2000
1500
1000
500
0
Quarter
Graph 3.5: Value of commercial properties transaction (RM Million)
Percentage Share For Value Of Commercial Property Transaction
25
Percentage Share (% )
20
15
10
5
0
Quarter
Graph 3.6: Percentage share for value of commercial property transaction
90
Annual Percentage Change For Value Of Commercial Property Transaction
(Compared With Same Quarter)
80
Percentage Change (% )
60
40
20
0
-2 0
-4 0
-6 0
-8 0
Quarter
Graph 3.7: Annual percentage change for value of commercial property transaction
(compared with same quarter)
Quarterly Percentage Change For Value Of Commercial Property Transaction
50
40
Percentage Change (% )
30
20
10
0
-1 0
97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-2 0
-3 0
-4 0
-5 0
Quarter
Graph 3.8: Quarterly percentage change for value of commercial property transaction
91
Number Of All Type Properties Transaction
80000
70000
Number
60000
50000
40000
30000
20000
10000
0
Quarter
Graph 3.9: Number of all type properties transaction
Annual Percentage Change Of All Type Properties Transaction (Compared
With Same Quarter)
50
40
Percentage Change (% )
30
20
10
0
-1 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-2 0
-3 0
-4 0
-5 0
Quarter
Graph 3.10: Annual percentage change of all type properties transaction (compared
with same quarter)
92
Quarterly Percentage Change Of All Type Properties Transaction
25
20
Percentage Change (% )
15
10
5
0
-5
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-1 0
-1 5
-2 0
-2 5
-3 0
Quarter
Graph 3.11: Quarterly percentage change of all type properties transaction
Value Of All Type Properties Transacted
16000
Value (RM Million)
14000
12000
10000
8000
6000
4000
2000
0
Quarter
Graph 3.12: Value of all type properties transaction
93
Annual Percentage Change For Value Of All Type Properties Transacted
(Compared With Same Quarter)
60
Percentage Change (% )
40
20
0
-2 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-4 0
-6 0
Quarter
Graph 3.13: Annual percentage change for value of all type properties transaction
(compared with same quarter)
Quarterly Percentage Change For Value Of All Type Properties Transacted
30
Percentage Change (% )
20
10
0
-1 0
97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-2 0
-3 0
-4 0
Quarter
Graph 3.14: Quarterly percentage change for value of all type properties transaction
94
Graph 3.5 to Graph 3.14 represents a series that show the value, number of all
properties types, and value for all properties types. However, the analysis in market
transaction was unclear especially in value aspect. For example, a transaction of
commercial properties in 20 units that are worth more than RM 150 Million (5 storey
shop lot in city area) would probably push out the magnitude of graph to a higher
level.
3.12
Summary
Economic crisis have great impact to commercial property transaction, as well
as on all properties transaction. Commercial property transaction, however was more
affected than the overall (all types) properties transaction.
The commercial property show an increment year on year especially after the
economic crisis. In year 1999, it had shown a high increment in annual percentage
change of commercial property transaction. In fact, strong increment in year 1999 was
only to show a recovery from the economic crisis.
In quarterly percentage change, quarter 1 showed a decrement in each year,
whereas Quarter 2 shown a increment in each year. The second increment was then
recorded in every Quarter 3.
There was one continuous increment for 3 years only as the same period
(quarter) started from year 2000, which occurred at Quarter 4 of year 2001 to 2003.
For the rest of the period, two years of continuous increment was recorded just only at
2002 & 2003 Quarter 1, as well as 2002 & 2003 Quarter 2. The highest number of
commercial property transaction was recorded at Quarter 3 at each year since it having
a second increment of the year only.
95
In the analysis of property transaction value, however, the value aspect
analysis failed to illustrate the real situation regarding to the transaction of market.
CHAPTER 4
ANALYSIS IN DETERMINATION OF MACRO ECONOMIC FACTORS
4.1
Introduction
In Chapter 3, the macroeconomic factors were determined subjectively and
their correlation with real data was analysed using the correlation regression analysis
which examined every lag in the number of commercial property transaction.
Thus, the macroeconomic factors giving the higher R square with strong
correlation was considered and the equation to relate this to the number of commercial
property transaction relation was formulated.
4.2
Macroeconomic Data to Be Examined
Several data were examined by SPSS regression analysis. After examining the
whole macroeconomic variables, ultimately only four of the macroeconomic variables
were selected for further discussion. They are Base Lending Rate, Gross National
Product, national saving and bank loan.
97
The data examined at the early stages are shown below:
1.
Money Reserve.
2. Monetary Aggregation. (M1, M2 and M3)
3. Loans of Banking System
4. Loans Approved by Banking System Sector
5. External Assets and Liabilities of Banking System
6. Statement of Assets by Commercial Banks
7. Statement of Liabilities by Commercial Banks
8.
Loan Provisions and NPLs
9. Constituents of Capital
10. Funds Monitored by Bank Negara Malaysia
11. Credit Card Operations in Malaysia
12. Other Financial Intermediaries
13. Employees Provident Fund
14. National Savings
15. Interest Rates (Base Lending Rate)
16. Rates Return to Depositors
17. Interest Rates of Interbank Money Market
18. Malaysian Ringgit exchange rate.
19. Volume of Interbank Transactions in Interbank Money Market
20. Funds Raised in the Capital Market
21. Kuala Lumpur Stock Exchange
22. National Accounts
98
23. Gross Domestic Product of Expenditure Components
24. Gross Domestic Product of Economic Activity at Constant Prices
25. Gross Domestic Product of Economic Activity at Current Prices
26. Industrial Production index.
27. Construction Indicators
28. Private Consumption Indicators
29. Private Investment Indicators
30. Consumer Price Index
31. Consumer Price Indicators
32. Producer Price Indicators
33. House Price Indicators
34. Labour Market Indicators
35. Federal Government Finance
36. Gross Exports of Manufactured Goods
37. Gross Imports by Economic Function
38. Country Investment
4.3
The Base Lending Rate
The Base Lending Rate is one of the macroeconomic variable that if it exists,
affect the number of commercial property transaction.
99
4.3.1
Monthly Base Lending Rate from 1997-2003 (%)
Table 4.1 shows monthly Base Lending Rate from 1997 to 2003:
Table 4.1: Monthly Base Lending Rate from 1997-2003 (%)
1997
1998
1999
2000
2001
2002
2003
January
9.19
10.44
8.04
6.79
6.79
6.39
6.39
February
9.20
11.08
8.04
6.79
6.79
6.39
6.39
March
9.24
11.96
8.04
6.79
6.79
6.39
6.39
April
9.25
12.16
7.64
6.78
6.79
6.39
6.39
May
9.27
12.21
7.24
6.75
6.79
6.39
6.00
June
9.50
12.27
7.24
6.75
6.79
6.39
6.00
July
9.58
12.07
7.24
6.75
6.79
6.39
6.00
August
9.61
11.70
6.79
6.76
6.79
6.39
6.00
September
9.61
8.89
6.79
6.76
6.39
6.39
6.00
October
9.53
8.49
6.79
6.76
6.39
6.39
6.00
November
10.07
8.04
6.79
6.76
6.39
6.39
6.00
December
10.33
8.04
6.79
6.78
6.39
6.39
6.00
Year
Month
4.3.2
The Quarterly Base Lending Rate from 1997-2003 (%)
Base on the monthly BLR, the Base Lending Rate was taken in 3 months
average as shown in Table 4.2:
100
Table 4.2: Quarterly Base Lending Rate from 1997-2003 (%)
Jan-Mar
Apr-Jun
%
Jul-Sept
%
Oct-Dec
%
%
Year
%
Change
%
Change
%
Change
%
Change
1997
9.21
/
9.34
1.41
9.60
2.78
9.98
3.96
1998
11.16
11.82
12.21
9.41
10.89
-10.81
8.19
-24.79
1999
8.04
-1.83
7.37
-8.33
6.94
-5.83
6.79
-2.16
2000
6.79
0.00
6.76
-0.44
6.76
0.00
6.77
0.15
2001
6.79
0.30
6.79
0.00
6.66
-1.91
6.39
-4.05
2002
6.39
0.00
6.39
0.00
6.39
0.00
6.39
0.00
2003
6.39
0.00
6.13
-4.07
6.00
-2.12
6.00
0.00
4.3.3 Analysis of BLR
The Number of Commercial Property Transaction compared with the Base
Lending Rate 1997-2003 in quarterly (%) was plotted to observe the trend and their
relation.
101
Number Commercial Properties Transacted Vs BLR (x1000)
14000
Number
12000
BLR(X1000)
Number / %
x1000
10000
8000
6000
4000
2000
0
Quarter
Graph 4.1: Graph Number of Commercial Property Trannsacted vs. Base Lending
Rate year 1997-2003 in quarterly (%)
Percentage Change Of Commercial Property Transacted vs Percentage Change
Of BLR
30
Percentage Change (% )
20
1. BLR increase
4. Cause property transacted
increase the most
10
0
-1 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Quarter
-2 0
-3 0
-4 0
2. One quarter
lag in property
transaction
3. Deduction of BLR
the most
Percentage Change In Commercial
Property Transacted
Percentage Change Of BLR
Graph 4.2: Graph Percentage of Commercial Property Transaction vs. percentage
change of Base Lending Rate year 1997-2003 in quarterly (%)
102
From Graph 4.2 - Percentage Change of Commercial Property Transaction vs.
Percentage Change of BLR, we can observe that the changes in percentage of BLR
result in an inverse change in property transaction. Meaning that it has a negative
correlated, where the result can be seen after 1 quarter (graph 4.2 No. 1-4). Although
at the time period, most of the transaction was affected by economic crisis, however,
influence of BLR could not be neglect.
From the graph above, we can see that the biggest influence in commercial
property transaction is the Base Lending Rate. It was only 97 Q1 until 99 Q4, by
running the SPSS, the most significance correlation coefficient was also obtained in
this period. So, by eliminating the others and retain only 97 Q1 to 99 Q4, data as
shown below was obtained.
4.3: Number of Commercial Property Transaction vs. BLR only in 97 Q1 until 99 Q4
Quarter
Number Of Commercial
Property Transaction
Base Lending Rate
Quarter
Number Of Commercial
Property Transaction
Base Lending Rate
97 Q1
97 Q2
97 Q3
97 Q4
98 Q1
98 Q2
98 Q3
98 Q4
5004
9.21
5658
9.34
6022
9.6
4948
9.98
3460
11.16
3009
12.21
2859
10.89
2757
8.19
99 Q1
99 Q2
99 Q3
99 Q4
3464
8.04
4270
7.37
3849
6.94
4319
6.79
Result after 1 quarter lag of number of commercial property transaction:
Correlation coefficient, r = -0.4125 (the most significance correlation coefficient)
R square = 0.1702
From Regression analysis by SPSS,
y = 6657.321-278.01x
103
where y is Number of commercial property transaction
x is Base Lending Rate
This equation is only obtained using 97 Q1 to 99 Q4 figures. So, the equation
could be rearranged to:
t = 99 Q4 – 1Q
y=Σ
6657.321-278.01x
t = 97 Q1 – 1Q
One quarter was minus to show that one quarter lag of commercial properties
transaction after the new BLR was applied. Equation obtained is a negative correlated,
it meant that the higher the BLR, the lower the commercial property transaction. Note
that -1Q meant BLR was 1 quarter leading.
4.4
The Gross Domestic Product
The Gross Domestic Product (GDP) is also one of the factor that affect the
commercial properties transaction. If compared with other factors, GDP affect also on
the transaction.
4.4.1
Quarterly Gross Domestic Product (GDP) year 1997-2003 (At Current
Price)
Below are the GDP value collected since year 1997-2003.
104
Table 4.4: Quarterly Gross Domestic Product (GDP) year 1997-2003 (at current
price) (RM Million)
Month
Jan-Mar
Apr-Jun
%
Jul-Sept
%
Oct-Dec
%
%
Year
Value
Change
Value
Change
Value
Change
Value
Change
1997
64994
/
67790
4.30
71854
5.99
77157
7.38
1998
70779
-8.27
70218
-0.79
71976
2.50
70271
-2.37
1999
67576
-3.84
73737
9.12
78080
5.89
81373
4.22
2000
81260
-0.14
84949
4.54
87786
3.34
88162
0.43
2001
82422
-6.51
83332
1.10
84760
1.71
84075
-0.81
2002
83349
-0.86
88582
6.28
93867
5.97
95799
2.06
2003
93622
-2.27
95134
1.62
99546
4.64
103710
4.18
Value = Total GDP for 3 month (Quarterly)
% Change = Percentage change in BLR (compared quarterly)
Example calculation: In 1999,Jan-Mar . GDP = 67576
In 1998, Oct- Dec . GDP = 70271
Thus, percentage change of GDP for 1999 , Jan- Mar =
(67576 - 70271) / 70271 X 100 = -3.84 %
4.4.2
Analysis of GDP
Observations on the movement trend of GDP in comparison with the
commercial property transaction are illustrated in Graph 4.3.
105
Number Commercial Properties Transacted Vs GDP (1/10)
Number / Value(RM Million)
12000
Number
10000
GDP (1/10)
8000
6000
4000
2000
0
Quarter
Graph 4.3: Number of Commercial Property Transacted vs. GDP
For the comparison purpose, the value of graph were divided to 10 times from
it original value. From the graph 4.3, we can see that the movement of GDP is similar
to the number of commercial property transaction, except in year 1997-1999 due to the
economic crisis. However, from the theory suggested by expert, the economic
movement supposes to have a lead to the real estate trend, thus the movement shall be
revised by observing the percentage movement.
106
Percentage Change Of Commercial Property Transacted vs Percentage Change
Of GDP
30
Percentage Change (% )
20
10
0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-1 0
Quarter
-2 0
Percentage Change In Commercial
Property Transacted
-3 0
Percentage Change Of GDP
-4 0
Graph 4.4: Percentage Change of Commercial Property Transaction vs Percentage
Change of GDP
From the graph percentage change above, we can observe that from year 2000
onward, the percentage change of commercial property transacted are nearly the same
as the GDP movement, without any lag. It means that the percentage change in
commercial properties transaction always had the same trend as the percentage change
of GDP.
To strengthen this hypothesis, a Regression Analysis by SPSS was run
whereby 1 quarter lag of commercial properties with GDP was compared and there is
no lag of commercial properties transaction with GDP. Then, the result below was
obtained.
(i) For 1 quarter lag of commercial properties with GDP , Correlation
coefficient, r = 0.1305 , R square = 0.058
(ii) For same trend (no lag) of commercial properties transacted with GDP,
Correlation coefficient, r = 0.2376 , R square = 0.627 (higher than 0.5)
107
Table 4.5: Number of commercial property transaction vs. GDP in 00 Q1 until 03 Q4
Quarter
Number Of Commercial
Property Transaction
Gross Domestic Product
00 Q1
00 Q2
00 Q3
00 Q4
01 Q1
01 Q2
01 Q3
01 Q4
3959
81260
4082
84949
4403
87786
4066
88162
3469
82422
3888
83332
4361
84760
4144
84075
Quarter
Number Of Commercial
Property Transaction
Gross Domestic Product
02 Q1
02 Q2
02 Q3
02 Q4
03 Q1
03 Q2
03 Q3
03 Q4
3745
83349
4182
88582
4949
93867
4172
95799
3818
93622
4544
95134
4985
99546
5316
103710
In comparison, it proved that there was no lag for both GDP and commercial
property transacted. Then the equation could be derived as below:
y = -870.447 + 0.05737x
where y is Number of Commercial Property Transaction
x is Gross Domestic Product
The equation is then rearranged as below:
t = 03 Q4
y=Σ
t = 00 Q1
-870.447 + 0.05737x
Note that the above equation are positively correlated, which means that if the
Gross Domestic Product increase, the number of commercial property transaction will
also increased.
108
4.5
National Saving
One of the factors that were looked at in this study is national saving. Actually,
in the study of macro economy, the variable of population and the income of
household should be included.
It can be related with the following equation:
n = + ∞, j = + ∞
National saving = Σ (p1 Incj – Epd)+ (p2 Incj – Epd)+ … (pn Incj – Epd)
n=1,j=-∞
where n = number population
j = value of income
Inc = income
p = individual
Epd = others expenditure
The table below indicates the total national saving outstanding recorded at
every end period of each quarter, which consist of :
National saving outstanding = amount of saving deposits outstanding + amount
outstanding of Premium Savings Certificate + amount of fixed deposits outstanding +
Federal Government Securities + other Malaysian investments
109
4.5.1
Quarterly National Saving Outstanding Year 1997-2003
Table 4.6: National Saving Outstanding 1997-2003 By Quarterly (RM Million)
Month
Mar
Jun
%
Sept
%
Dec
%
%
Year
Value
Change
Value
Change
Value
Change
Value
Change
1997
6235.5
/
6195.8
-0.64
6271.0
1.21
6747.3
7.60
1998
6874.7
1.89
7255.1
5.53
7315.2
0.83
7410.7
1.31
1999
7377.8
-0.44
7926.2
7.43
8540.9
7.76
6187.1
-27.56
2000
8183.4
32.27
9111.0
11.34
9424.8
3.44
10258.8
8.85
2001
10725.9
4.55
10303.9
-3.93
10956.8
6.34
10737.2
-2.00
2002
10597.9
-1.30
9936.4
-6.24
9718.9
-2.19
9225.2
-5.08
2003
9174.3
-0.55
9480.2
3.33
10299.4
8.64
9517.3
-7.59
Value = Total national saving at end period (Quarterly)
% Change = Percentage change in national saving (compared quarterly)
Example calculation: In 1999, Mar. National saving =
7377.8
In 1998, Dec . National saving = 7410.7
Thus, percentage change of national saving for 1999 , Mar =
(7377.8 - 7410.7) /7410.7 X 100 = -0.44 %
4.5.2
Analysis of National Saving
Based on the data obtained, Number of Commercial Properties vs. National
Saving is plot.
110
Number Commercial Properties Transacted Vs National Saving
Number / Value(RM Million)
12000
10000
8000
Number
National Saving
6000
4000
2000
0
Quarter
Graph 4.5: Number of Commercial Properties Transaction vs. National Saving
From graph 4.5, we need to determine also if there is any lag and how the two
variables are correlated. Then, another graph which represents the percentage change
of commercial property transacted vs. percentage change of national saving is plot.
Percentage Change Of Commercial Property Transacted vs Percentage Change
Of National Saving
40
Percentage Change (% )
30
20
10
0
-1 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
-2 0
-3 0
-4 0
Quarter
Percentage Change In Commercial
Property Transacted
Percentage Change Of National
Saving
Graph 4.6: Percentage Change of Commercial Property Transacted vs. Percentage
Change of National Saving
111
This type of graph 5.6 is hard to determine the correlation by observation due
to its low correlation. However, a regression analysis by SPSS is done and the
following result is obtained:
a) With quarter lag of commercial properties transacted from national saving
from 1997 -2003
(i)
No quarter lag : Correlation coefficient, r = -0.1066, R square is
0.011
(ii)
1 quarter lag : Correlation coefficient, r = -0.0019, R square is 0
(totally no correlated)
(iii)
2 quarter lag : Correlation coefficient, r = 0.1327, R square is
0.018
(iv)
3 quarter lag : Correlation coefficient, r = 0.3725, R square is
0.139 (the highest)
(v)
4 quarter lag and above : Correlation coefficient, r = 0.1182, R
square is 0.015
b) With quarter lag of commercial properties transacted from national saving
from 2000 – 2003 ( by neglect the effect of economic crisis)
(i)
No quarter lag : Correlation coefficient, r = -0.1110, R square
is 0.012
(ii)
1 quarter lag : Correlation coefficient, r = -0.1130, R square is
0.013
(iii)
2 quarter lag : Correlation coefficient, r = -0.1871, R square is
0.035
(iv)
3 quarter lag : Correlation coefficient, r = -0.1432, R square is
0.0452
(v)
4 quarter lag and above : Correlation coefficient, r = -0.1183, R
square is 0.01399
112
From the above analysis, hypothesis is made that there are 3 quarters lag of
commercial properties transacted from national saving, its effect is predominant as the
effect of economic crisis in year 1997. This shows that in economic crisis, people will
be more careful to use their capital and investment. The transaction for property would
happen only if they have sufficient capital.
Table 4.7: Number of commercial property transaction vs. National Saving in 97 Q1
until 03 Q4
Quarter
Number Of Commercial
Property Transaction
National Saving
Quarter
Number Of Commercial
Property Transaction
National Saving
Quarter
Number Of Commercial
Property Transaction
National Saving
Quarter
Number Of Commercial
Property Transaction
National Saving
97 Q1
97 Q2
97 Q3
97 Q4
98 Q1
98 Q2
98 Q3
98 Q4
5004
6235.5
5658
6195.8
6022
6271.0
4948
6747.3
3,460
6874.7
3009
7255.1
2859
7315.2
2757
7410.7
99 Q1
99 Q2
99 Q3
99 Q4
00 Q1
00 Q2
00 Q3
00 Q4
3464
7377.8
4270
7926.2
3849
8540.9
4319
6187.1
3959
8183.4
4082
9111.0
4403
9424.8
4066
10258.8
01 Q1
01 Q2
01 Q3
01 Q4
02 Q1
02 Q2
02 Q3
02 Q4
3469
10725.9
3888
10303.9
4361
10956.8
4144
10737.2
3745
10597.9
4182
9936.4
4949
9718.9
4172
9225.2
03 Q1
03 Q2
03 Q3
03 Q4
3818
9174.3
4544
9480.2
4985
10299.4
5316
9517.3
Although the R square not much high due to exist of other factors. However,
the equation can be generated as follow:
Correlation coefficient, r = 0.3725
R square = 0.139
y = 2787.107 + 0.147x
where y is Number of commercial property transacted
113
x is National saving outstanding
The equation is then rearranged as follow:
t = 03 Q4 - 3Q
y = Σ 2787.107 + 0.147x
t = 97 Q1- 3Q
From the equation, we can see that the two variables are positively correlated,
which means that as the national saving outstanding occure, number of commercial
property transacted increased also. -3Q mean that national saving outstanding are 3
quarters leading to commercial properties transacted.
4.6
Bank Loan to Commercial Property Sector
Bank loan to the commercial property sector is also one of the factor that
ensure the direct correlated to the commercial property transaction. So, what we
examine here is how close the variables are related.
4.6.1
Bank Loan to Commercial Property Sector 1997-2003 Quarterly
Below is the table that indicates the bank loan allocated to commercial
property sector and it percentage change.
114
Table 4.8: Bank loan to commercial property sector 1997-2003 quarterly (RM
Million)
Month
Jan-Mar
Apr-Jun
Year
Value
%
Change
1997
1998
1999
2000
2001
2002
2003
6,210.0
9,886.4
10,929.2
11,076.4
11,457.7
12,130.0
12,594.0
/
6.92
1.47
0.29
0.75
0.50
0.73
Jul-Sept
Value
%
Change
7,214.8
10,564.5
11,009.7
11,081.7
11,953.5
12,228.5
12,860.0
16.18
6.86
0.74
0.05
4.33
0.81
2.11
Oct-Dec
Value
%
Change
Value
%
Change
8,071.6
10,813.3
10,693.5
11,360.0
11,972.0
12,350.7
13,182.9
11.88
2.35
-2.87
2.51
0.15
1.00
2.51
9,246.8
10,771.0
11,044.9
11,372.5
12,069.7
12,503.1
13,581.7
14.56
-0.39
3.29
0.11
0.82
1.23
3.03
Value = Total bank loan to commercial property for 3 month (quarterly)
% Change = Percentage change in total bank loan (compared quarterly)
Example calculation : In 1999,Jan-Mar . Total bank loan = 10929.2
In 1998, Oct- Dec . Total bank loan = 10771.0
Thus, percentage change of total bank loan for 1999 , Jan- Mar =
(10929.2-10771.0) / 10771.0 X 100 = 1.47 %
4.6.2
Analysis of Bank Loan
From the data, graph is plot to compare the total bank loan (released to
commercial property for 3 months) and the number of commercial property transacted.
115
Number Commercial Properties Transacted Vs Bank Loan
16000
Number
Number / Value(RM Million)
14000
Bank Loan
12000
10000
8000
6000
4000
2000
0
Quarter
Graph 4.7: Percentage change of commercial property transacted vs. percentage
change of bank loan to commercial property
By observing the graph above, the bank loan saw an increase quarter by
quarter. However, the revise is necessary to see the differentiation between both
trends. So, graph of percentage change of commercial property transacted vs.
percentage change of bank loan is plotted to examine it purpose.
116
Percentage Change Of Commercial Property Transacted vs Percentage Change
Of Bank Loan
30
Percentage Change (% )
20
10
0
-1 0
97 97 97 97 98 98 98 98 99 99 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03 03
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Quarter
-2 0
Percentage Change In Commercial
Property Transacted
-3 0
Percentage Change Of Bank Loan
-4 0
Graph 4.8: Percentage Change of Commercial Property Transacted vs Percentage
Change of Bank Loan
There is no lag or lead between these two variables due to the property
transacted is recorded while the loan is released. So what to be examine is how close
the relation between these two variables is.
It can be observe from the graph that between 1997 Q4 to 1998 Q4, there are
negative correlation of these two variables, in the economic crisis period, the bank
loan has increase much.
By running the Regression analysis, the following data are obtained:
(i)
1997 - 2003, Correlation coefficient, r = -0.2594, R square is 0.067
(ii)
1999 – 2003, Correlation coefficient, r = 0.6314, R square is 0.401 (the
highest, moderate correlated)
So, the equation can be generated as follow base on the time period 1999 Q1 to
2003 Q4, by eliminate the effect from 1997 Q1 to 1998 Q4.
117
Correlation coefficient, r = 0.6314
R square = 0.401
y = -205.325 + 0.371x
where y is Number of commercial property transaction
x is Bank loan to commercial property sector
The equation is then rearranged as follow:
t = 03 Q4
y = Σ -205.325 + 0.371x
t = 99 Q1
From the equation, we can see that the two variables are positively correlated,
that mean as bank loan to commercial property sector increase, number of
commercial property transacted increased also. There is no any lag between these two
variables.
4.7
Model
Apart from the above factors, another factor that give a strong impact to the
number of commercial property transaction, is the government policy toward market
property. However, these policies are unstable until it increases the difficulty to
predict the market.
Nevertheless, from the above equation generated, the equation can be joined
and with some correction, the volume of commercial property transaction still can be
predicted.
118
We can see that among the 4 variables, Base Lending Rate was the one
variable that did not give impact to the transaction starting from year 2000 due to it
constant rate of around 6 %. However, the other 3 were long-lasting until 2003. Here,
by altering the definite time of 2003 to 2006 (for predict the volume of transaction
after 3 years). It equation can be generated as follow:
By altering the algebra for GDP = G, national saving = S, and Loan = L ,
number of commercial property transaction still = y. Then the 3 equation is combined
as one until:
t = 06 Q4
t = 06 Q4 - 3Q
t = 06 Q4
y = {Σ -870.447 + 0.05737 G }+ {Σ 2787.107 + 0.147 S }+{Σ -205.325 + 0.371 L}
t = 00 Q1
t = 00 Q1 - 3Q
t = 00 Q1
Now, we have to determine the proportion of each variable, we can recall all
the R square which represent the degree of regression. The calculation of percentage
of each proportion as follow:
For GDP, R square = 0.627
For national saving, R square = 0.139
For bank loan, R square = 0.401
Total = 0.627 + 0.139 + 0.401 =1.167
Thus, percentage of each proportion can as follow:
% G = 0.627/1.167
% S = 0.139/1.167
% L = 0.401/1.167
= 53.7%
= 11.9%
= 34.4%
Now each proportion can be multiply with each variable, hence:
119
t = 06 Q4
t = 06 Q4 - 3Q
y = 0.537{Σ -870.447 + 0.05737 G } + 0.119{Σ 2787.107 + 0.147 S } +
t = 00 Q1
t = 00 Q1 - 3Q
t = 06 Q4
0.344 {Σ -205.325 + 0.371 L}
t = 00 Q1
t = 06 Q4
t = 06 Q4 - 3Q
t = 06 Q4
= { Σ -467.430 + 0.0308 G } + {Σ 331.665 + 0.0175 S} + { Σ -70.632 + 0.128 L}
t = 00 Q1
t = 00 Q1 - 3Q
t = 00 Q1
Due to GDP and bank loan have no lag, then these two variable can be
combined. Then the equation can be simplified as follow:
t = 06 Q4
t = 06 Q4 - 3Q
y = { Σ 0.0308 G + 0.128 L} + {Σ 0.0175 S} – 206.397
t = 00 Q1
t = 00 Q1 - 3Q
This model is used for testing the data of year 2000 to 2003 and is proven to be
acceptable. From this model it implied that the more macroeconomic variables are
included, the higher the accuracy in determining the volume of transaction, because
many factors can effect it.
In this research, some of the macroeconomic variable is neglected due to it
small effect if determined independently. However, if we total up the whole variables,
for sure it will give a big proportion, may be 30%, because macroeconomic variables
in much will effect the property transaction.
If there is an existence of macroeconomic variable that is 100% correlated with
property transaction in the market, with R square = 1. For sure, it is pointless to carry
out such a research.
120
4.8
Summary
More than 30 macroeconomic indicators were examined. The indicators as
stated above is known as factors, due to the number of property transaction affected
after these factors was applied.
Base Lending Rate (BLR) was one of the factors that influence the number of
commercial property transaction. Base Lending Rate influenced the number of
commercial property transaction only in 97 Q1 until 99 Q4. The equation of BLR with
the number of commercial property transaction was:
t = 99 Q4 – 1Q
y=Σ
6657.321-278.01x
t = 97 Q1 – 1Q
Gross Domestic Product is also one of the factor that affect the number of
commercial property transaction. There were no lag observed from 2000 onward,
percentage change of commercial property transaction is nearly as same as the GDP
movement, without any lag. Note that GDP acquired the highest correlation, it
Correlation Coefficient, r = 0.2376 and R square = 0.627 (higher than 0.5, high
correlated). The equation of GDP against the number of commercial property
transaction rearranged as below, and the equation are positively correlated.
t = 03 Q4
y=Σ
t = 00 Q1
-870.447 + 0.05737x
National saving is also one of the factors that affected the number of commercial
property transaction. National saving was the function of individual and income minus
total expenditure. National saving had 3 quarters leading to commercial properties
transacted. The equation was derived as follow:
121
t = 03 Q4 - 3Q
y = Σ 2787.107 + 0.147x
t = 97 Q1- 3Q
Bank loan is determined as one of the factor affect the commercial property
transaction. There were no lag or lead between these two variables due to the property
transacted is recorded while the loan is released. The equation is derived as follow,
with moderate high R square.
t = 03 Q4
y = Σ -205.325 + 0.371x
t = 99 Q1
Model that combines the macroeconomic factor was established. This model has been
tested for the data from year 2000 to 2003 and is acceptable. The equation can be
simplified as follow:
t = 06 Q4
t = 06 Q4 - 3Q
y = { Σ 0.0308 G + 0.128 L} + {Σ 0.0175 S} – 206.397
t = 00 Q1
t = 00 Q1 - 3Q
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1
The Economic Crisis
The economic crisis in mid year 1997 definitely had a negative impact on
commercial property transaction .It long-lasting affects were even felt in late 2003, six
and a half years after the crisis where the number of commercial property transaction
could not achieve the expected transaction volume.
The negative impact of commercial property transaction in economic crisis
was greater than the overall property transaction. This can be seen five quarters after
the crisis; while the percentage of commercial property market share was decreased
continuously, in comparison with the overall volume of property transaction.
However, in 1999, the market recovered strongly after the economic crisis. It
was estimated that the property market would recover approximately 50 percent as
viewed from the property transaction volume.
In this study, it could be seen that the Base Lending Rate was the
macroeconomic variables that had the largest influence on the number of commercial
property transaction. This was followed by national saving. It could be observed that
123
during this critical period, Base Lending Rate varied almost in every quarter and acted
as a government tool to handle the crisis. However, the government favours to lower
the BLR in order to stimulate the property market transaction.
5.2
Finding Of The Study
There were several findings obtained in this study.
By observing the graph of the number of commercial property transactions, our
country had still not yet achieved the volume of transaction as before 1997 although
its percentage share had increased year by year after the economic crisis.
From the findings, it can be observed that the annual percentage change of
commercial property transaction showed a continuous increment only for three years
in the same period (quarters) starting from 2000, and it occurred again at the fourth
quarter from 2001 to 2003. Implicitly, it means that for other quarters, every increment
in commercial property transaction must be followed by a decrement or no increment
totally for three years continuously.
In quarterly percentage change, quarter 1 showed a decline in each year,
whereas, in contrast, Quarter 2 showed an increment for every year .On the other
hand, the second increment is then recorded in every Quarter 3.The phenomenon of
decline in Quarter 1 was highly probably influenced by festivals, such as Hari Raya
Puasa and Chinese New Year, two of the most popular festivals in Malaysia.
However, the highest commercial property transaction was recorded at Quarter 3 each
year since it had a second increment by the year only.
In this study, the value of commercial property transaction was neglected due
to the difficulty in estimating the real situation of property transaction, especially in
124
determining the macroeconomic factor that influenced the commercial property
transaction.
In fact, in determining the macroeconomic variables, the commercial property
transactions data used was the transformation of macroeconomic indicator used by the
government, which aimed to illustrate the economy of our nation. The indicators were
used as a factor due to the leading feature of these macroeconomic variables toward
commercial property transaction, except for the Gross National Product variable and
Property Loan, as illustrated in this model.
In this study, it was found that there were more than 30 macroeconomic
variables to be examined; each one was examined thoroughly for its viability as a
factor in influencing the commercial property transaction volume. Basically, the
factors considered should have a lead or equality to the property examined. Then, the
leads of several macroeconomic variables toward the number of transaction was taken.
However, the data found in the moment of economic crisis were sometimes neglected.
Also, the data on economic crisis was taken occasionally, depending on the graph
observed. Finally, by running on the regression analysis in SPSS software, the
equation was generated.
The Base Lending Rate (BLR) was determined as one of the factors that had
the most influenced on the number of commercial property transaction. The BLR
influenced the number of commercial property transaction in 1997 Q1 only and
continued until 1999 Q4 due to BLR stability situation after this period. Thus, the
equation of BLR with the number of commercial property transaction was:
t = 99 Q4 – 1Q
y=Σ
6657.321-278.01x
t = 97 Q1 – 1Q
Gross Domestic Product was also one of the factors that affected the number of
commercial property transaction. It was a significant finding that there were no lags
125
observed since 2000 onwards, and the percentage change of commercial property
transacted was nearly the same as the GDP movement, without any lags. The equation
of GDP against the number of commercial property transaction could be rearranged as
below, while the equation was positively correlated. It must be stressed that GDP has
the highest correlation with the property transaction among all variables, which is R
square = 0.627 (higher than 0.5, high correlated)
t = 03 Q4
y=Σ
t = 00 Q1
-870.447 + 0.05737x
National saving also served as one of the factors that influenced the number of
commercial property transaction. The equation of population (individual), income and
expenditure with the national saving was generated, whereas national saving was the
function of individual and income minus total expenditure.
n = + ∞, j = + ∞
National saving = Σ (p1 Incj – Epd)+ (p2 Incj – Epd)+ … (pn Incj – Epd)
n=1,j=-∞
From the analysis, national saving had three quarters leading to commercial
properties transacted. The equation was derived as follows:
t = 03 Q4 - 3Q
y = Σ 2787.107 + 0.147x
t = 97 Q1- 3Q
Bank loan was also determined as one of the factors that affects the
commercial property transaction. There were no lags or leads between these two
variables because the property transacted was recorded while the loan was released.
The equation was derived as follows, with moderate high R square.
t = 03 Q4
y = Σ -205.325 + 0.371x
t = 99 Q1
126
It seems obvious that there were more than 30 macroeconomic variables to be
examined. For the most, the R square obtained zero value or less than 0.1. Thus means
that there was a very weak correlation or no correlation between the macroeconomic
variables and the number of commercial property transaction.
5.3
Recommendation
In a significant way, this type of analysis is beneficial to the developer,
especially for those who can further develop this research. They can limit the research
to a smaller range of area, such as the city or suburban area, to which the location they
wish to develop. This kind of research also can correspond with other kind studies,
such as real estate market research, feasibility study and others.
On the other hand, in depth analysis for the government should be conducted.
For JPPH or NAPIC, it is pointless to just publish the data in their annual report only.
Instead, they should also provide the analysis in the journal they published.
5.4
Gains from This Study
In the course of this research, various written sources and texts were used as
references. These references provided significant inputs in alleviating many
undocumented facts and situations to a more comprehensible understanding of the
actual property market situation.
In retrospect, we can comprehend this study as an in-depth guide to the
importance of various tools used vis-à-vis the property market growth and decline rate
both by the government and private sectors alike.
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