MACROECONOMIC POLICY AND REAL ESTATE, STOCK
MARKET IMPACT ANALYSIS
HO, Kim Hin / David *
rsthkhd@nus.edu.sg
MUHAMMAD Faishal bin Ibrahim**
rstmfi@nus.edu.sg
&
LIOW, Kim Hiang*
rstlkh@nus.edu.sg
Department of Real Estate
SCHOOL OF DESIGN AND ENVIRONMENT
NATIONAL UNIVERSITY OF SINGAPORE
Current Version: 2 November 2005
Abstract
This paper models a key aspect of real estate market uncertainty, i.e. the frequent
mismatch between office demand and supply under the impact of the macro economy,
macroeconomic policy and the domestic stock market. The dynamic interaction of this
aspect of the office sector’s market uncertainty is structured under the demand-side and
supply-side aspects. Prices in the form of office rents, total returns and CVs (capital
values) affect changing office investment. These prices are determined by office
demand and supply regardless of the speed of adjustment. Office demand in turn
depends on macroeconomic policies like economic growth policy, interest rate policy
and fiscal policy. Macroeconomic factors are envisaged to influence the movement of
office prices because private investors and developers tend to view the office sector as
an alternative to the domestic share market. This is attributed to balanced portfolios so
that the rate of return in the domestic share market moves in line with the office sector.
Equity-investment prices may well vary with office prices, proxied by a composite
private office price index. In examining the relationships between the private office
price index and related variables, the short-term future movements of the index may
well be modeled, that mirror the supply and demand determinants at work. In turn, the
index would be influenced by supply-side determinants (with a structural lag), which
include units completed and under construction for the office sector; and the price index
for building materials. The demand-side determinants would include rents, CVs and the
annual yields. This paper introduces system dynamics modeling in order to structure the
associated complex system aspects, as outlined above, involving the macro economy,
macroeconomic policy, the domestic stock market and the office sector. System
dynamics is concerned with the formation of causal feedback control loops, algebraic
equation and model formulation, model validation in terms of the ex post forecasts of
the relevant estimated equations, policy analysis as well as scenario planning that is
concerned with foreseeable ex ante outlooks.
_____________________________________________________________________
For correspondence, kindly forward to the first joint-author by email at rsthkhd@nus.edu.sg or
by mail at Department of Real Estate, National University of Singapore, 4 Architecture Drive,
Singapore 117566. *Associate Professor, ** Assistant Professor.
1.
Introduction
Singapore’s island-state economy remains sustainable at a moderate pace of growth
albeit maturing, with real estate being a fundamental sector in the economy (Liow,
2002). Real estate contributes significantly to wealth creation while office
accommodation remains a key revenue-generating sector of the economy. The
Singapore office sector is an important sector of the domestic real estate market for two
reasons. First, it provides the accommodation for firms and businesses in order to
conduct their activities and functions. Secondly, the office sector remains a key
investment asset class owing to its traditional role as the preferred real estate class of
institutional investors. Thus, an underlying research motive is to forecast rents and
capital values (CVs) for investors in office accommodation. While GDP expansion
typically provides employment for around 70% of Singapore's workforce, the output of
the financial, business and other services sector is equally important. This sector’s
output is observed to constitute close to 40% of total GDP in year 2001 in nominal
terms. Since office accommodation is a derived demand, the performance of the service
related sector affects office sector demand principally through the filtering effect of
multi-round economic demand expansion.
Real estate investment and development firms account for about 15% of Singapore’s
stock market capitalization. About 40 percent of the Singapore listed non-real estate
firms own at least 20 percent of the economy’s real estate (Liow, 1999). The
expectation is that changes in real estate prices impact changes in stock prices. One
important question arising from this expectation is the nature and extent of the
relationship between real estate prices and stock prices, both in the long- and short-run
in the presence of macroeconomic factors (Ross, 1976). Both the stock and property
markets are integral to the wider economy. The performance of the stock market reflects
the underlying corporate performance, while the performance of real estate reflects
property market performance. For many firms, real estate is both a factor of production
and an asset. In favorable economic conditions, corporate profitability growth (with
higher share prices) leads to corporate expansion, which further leads to rising rents,
given increased demand and short-run supply inelasticity. Rising rents lead to higher
CVs in the real estate markets and thus, raise the net asset values anticipated in common
stock prices. In an economic downturn, the reverse process happens. In the longer term,
2
rising rents and CVs of real estate may well increase the cost of capital of firms.
Coupled with other possible speculative development activities and bank lending on real
estate, one likely market outcome is that higher returns on real estate are associated with
low (negative) returns to the corporate sector and vice versa. Thus, real estate and the
stock market can be related both in the long-term and short-run in different manner.
The Research Issue
Nevertheless, there is limited primary and domestic research in modeling a key aspect
of real estate market uncertainty, i.e. the frequent mismatch between office demand and
supply under the impact of the domestic stock market and macroeconomic policy. The
dynamic interaction of such a key aspect of office market uncertainty can be structured
under the demand-side and supply-side aspects that in turn depend on macroeconomic
policies like economic growth policy, interest rate policy and fiscal. Thus, this paper
aims to investigate a structural and dynamic model for the office sector market-balance,
in conjunction with the impact analysis owing to the domestic stock market and
macroeconomic policy. This is because there exists an inherent relationship between the
Singapore economy, its office sector and the domestic stock market. For this purpose,
this paper adopts system dynamics modeling as the research methodology to
specifically model the office sector’s market uncertainty in terms of the dynamic
interaction between the office sector, the macro economy, macroeconomic policy and
the domestic stock market. Macroeconomic policy is investigated through testing the
sensitivity of the macro economy to changes in key macroeconomic factors, and the
resulting system dynamics model would conduct an impact analysis of the office sector
and the domestic stock market. The system dynamics model uniquely enables this paper
not only to make use of historical data but also to robustly take into account new
factors, and policy makers should also benefit from the paper’s findings.
The dynamics of the structural relationship among the factors would enable policy
makers to simulate the impact of their actions in affecting the office sector and the
domestic stock market. Bakken (1993) highlights the observation that interviews with
real estate developers have revealed that some developers realize that their judgments
are not always consistent. A bias in the historical data may be inevitable, and there is a
lack of adequate models to help decision makers understand the causes behind unstable
3
markets (e.g. commodity markets). He further observes that the degree to which
simulated decisions may remedy poor real world learning has been given scarce
attention. Thus, real estate developers would benefit from this paper, which draws on
system dynamics, a state-of-the-art discipline in cause-and-effect model building and
model simulation, which is originally developed at the Massachusetts Institute of
Technology (Roberts, 1978). By focusing on several key factors affecting Singapore’s
economic growth, the model enables users to develop a better understanding of the
economic and sector specific fundamental forces behind the office sector and the
domestic stock market. The resulting structural and system-modeling analysis enables a
closer examination of the implications for office-sector policy and the domestic stock
market in order that more informed investment decisions, owing to enhanced
understanding of office market uncertainty, can be made pertaining to the office sector,
the wider economy, macroeconomic policy and the domestic stock market.
As a result, this paper has the following objectives in mind:
ƒ
To investigate the dynamic and structural relationships among key
macroeconomic factors and sector-specific fundamentals of the office sector and
the domestic stock market in Singapore;
ƒ
To rigorously develop the required algebraic equations in order to investigate the
micro market relationships involving the office sector, the domestic stock
market and changing economic growth in Singapore.
This paper is organized along the following sections: section 1 provides the
introduction, which includes the background, the research issue and this paper’s
objectives. Section 2 provides the literature review that highlights the research gap.
Section 3 provides a brief real estate market analysis (REMA) of the private office
sector in Singapore. Section 4 discusses the research methodology while section 5
discusses the model estimation. Section 6 looks into the model validation while Section
7 discusses the post-model findings and results. Section 7 concludes the paper, with
some recommendations for further investigative research.
4
2.
Review of the Related Literature
The required algebraic equations for model building can be defined by prior expert
knowledge, prior experience or through econometric model analysis although there is
limited econometric research on Singapore, which models the dynamic and structural
interactions involving the macro economy, the office and the domestic stock market.
Nevertheless, the structural econometric model is a special stochastic model, which
includes one or more random variables, and represents a system by way of a set of
stochastic relationships among the causal variables of interest. Many econometric
models have been developed for forecasting rents in the real estate market, and the
seminal ones include those by Wheaton et. al. (1987 and 1997), Gardiner and
Henneberry (1991). Several variables are envisaged to play an important part of the
system, such as the stock of office space, service employment particularly in the FIRE
(finance, insurance and real estate) sector, the potential office supply, vacancy rates,
interest rates, money supply, GDP growth, CV (capital value) growth and CPI
(consumer price index) inflation. A more rigorous approach is to incorporate into the
single-system econometric model, a lagged structure involving several causal variables
that in combination would affect office rental movement. These lagged variables may
well include GDP growth lagged by two periods, and the level of the gross office stock
lagged by eight periods. Even the Box-Jenkins model may well be considered for
forecasting prime office rents in the short-run, involving the impact of vacancies and the
growth in output of the service related sector on rents.
Although there are limited studies on the dynamic relationship involving the macro
economy, the stock market and the office sector, there have been attempts to examine
the relationship between the real estate market and the stock market. Liow (2005)
identified three groups of studies under this category. The first group of research
involves examining the relationship between real estate and the stock market. One of the
earliest studies is that conducted by Liu, Hartzell, Greig and Grissom (1990). They find
that the US securitized real estate market is integrated with the stock market. However,
they further find that the US (direct) commercial real estate market is segmented from
the stock market. Okunev and Wilson (1997) detect a weak non-linear relationship
5
between the US securitized real estate market and the overall stock market, using a nonlinear mean reversion stock price model.
In the UK, Lizieri and Satchell (1997) find a strong contemporaneous correlation that
exists between property stock returns and the overall equity market returns. From an
international perspective, in an empirical study that covers 17 countries, Quan and
Titman (1999) report that, with the exception of Japan, the contemporaneous
relationship between the yearly real estate prices changes and stock returns is not
statistically significant. Finally, Tse (2001) finds that the unexpected changes in both
the Hong Kong residential and the office prices are important determinants of the
change in stock prices. The study uses impulse response function and an errorcorrection VAR (vector auto-regression) model to examine the dynamic relationship
between real estate and common stock prices.
The second group of research empirically investigates the relationships between direct
and indirect real estate markets in the context of price discovery. The usual argument is
that since the underlying assets of the two markets are real estate, they should be closely
related to each other. In general, a number of studies (by Giberto, 1990; Gyourko and
Keim, 1992; Myer and Webb, 1993; Clayton and Mackinnon, 2001, Barkham and
Geltner, 1995; Newell and Chau, 1996; Liow, 1998(a)) have detected strong positive
contemporaneous correlation and lead-lag linkages in the US, UK, Australia and
Singapore markets. However, there is also evidence of segmentation, with the Singapore
commercial real estate and property stock markets moving apart in recent years (Liow,
1998(b)).
Additionally, a combination of cointegration and an error correction mechanism allows
for the long-run and short-term relationships between property stock and real estate
markets, to be simultaneously captured. A majority of the studies such as those by Ong
(1994), Wilson and Okunev (1996), He (1998), Wilson et al (1998) and Chaudhry et al
(1999), appear to support that the notion that the two markets are segmented (i.e. not
cointegrated). More recently, however, Tuluca, Myer and Webb (2000) find that the
price indices of capital and real estate markets (T-bills, bonds, stocks, public real estate
and private real estate) are cointegrated. Specifically, their system of the five asset
indices is governed by three common (non-stationary) factors since two cointegrating
6
vectors are present. The existence of this long-term equilibrium relationship has a
different effect on asset allocation, price discovery and predictability of returns.
Finally, the third group of research aims to uncover the relationships between asset
markets and the macro economy as implied under the APT (arbitrage pricing theory).
There is growing evidence that the expected variations in stock and bond returns are
related to the state of the economy, as reflected in key macroeconomic variables.
Examples of the systematic factors include the growth rate in real per capita
consumption, real Treasury-bill rate, term structure of interest rates and unexpected
inflation. Similarly, most studies that investigate the relationships between real estate
returns and the macro economy include factors that are based on previous studies of the
stock market returns. Examples include the studies by McCue and Kling (1994), Ling
and Naranjo (1997), Karolyi and Sanders (1998), Brooks and Tsolacos (1999) and Liow
(2000). These studies have documented linkages, of different degree, between the
commercial property market and key macroeconomic factors.
Liow (1999) attempts to integrate the three group of real estate empirical literature by
assessing the combined and relative impacts of real estate markets on the general stock
and property stock markets, from both the long-run and short term perspectives, after
controlling for changes in the macroeconomic conditions. A recent study by Kallberg,
Liu and Pasquariello (2002) (KLP) shows that the Asian stock and real estate markets
are related. They analyze how real estate and stock markets had reacted during the time
around the Asian financial crisis, and investigate whether the crisis had fundamentally
changed the relationship between the real estate and stock markets. Using the Granger
Causality test and BLS (Bayensian least-square) technique, they identify regime shifts
in time series of the monthly-securitized real estate returns and equity index returns as
well as volatilities in eight Asian countries.
This paper complements and differs from the KLP research in that this paper uses the
direct office real estate price indices (instead of the securitized real estate price indices).
Additionally, instead of raising the Granger Causality issue between the real estate and
stock markets, this paper takes the perspective that real estate prices drive stock market
prices and it deploys the ARDL (auto-regressive distributed lag) methodology in order
to examine their possible long-run relationship and short-term linkages in the economy.
7
Space and Capital Markets
In this paper, the office rent and its absorption rate are formulated within the structural
dynamics context of an expectation-augmented, autocorrelation-error corrected and
structural stock-flow adjustment model. Fisher (1992) invokes the importance of
recognizing two distinct but interrelated types of real estate markets - the space and
capital market. While the use decision is made in the space market, the investment
decision is made in the capital market. DiPasquale and Wheaton (1992, 1996) also
conceptualize a four-quadrant representation, which depicts two important links
between the two markets that are in long-run equilibrium. In the space market, the need
for tenants (demand) as well as the type and quality of buildings available (supply),
would determine the rent for real estate in the short run. Thus, when the demand for
space equals the supply of space, rent is determined. In the space market, the annual
flow of new construction adds to the existing stock in the long run. Still in the space
market, the new supply of real estate assets depends on the price of those assets relative
to the cost of replacing or constructing them. In the long run, the space market should
equate market prices with replacement costs.
In the asset market, when buildings are bought, sold and exchanged willingly between
investors, the investment-sale transactions occur and these determine the asset price of
space. Real estate is attractive simply because of its relatively low correlation with
stocks and bonds. In addition, it acts as an inflation-hedge. The players in the capital
market are the investors, who are keen in real estate as a vehicle for earning a
competitive risk-adjusted rate of return. In conjunction with the cap rate, the prevailing
yield in the market that investors demand in order to hold a real estate asset, then the
CV can be ascertained.
The Dipasquale and Wheaton conceptual model of both the space market and the asset
market is a long-run model, given that construction exists. However, the limitation of
the model is that it does not trace the intermediate steps as the market moves to its new
equilibrium, because the task of depicting the intermediate market adjustments would
require a dynamic system of equations that would complicate the analysis. As a result of
8
an enhanced understanding of the space and capital markets, the appropriate structural
behavior affecting the pattern and the movements of rents and prices can be further
investigated by way of the error correction model, the auto-regressive error approach
and the structural stock-flow model.
Several studies in the office sector have looked at the possible deployment of an error
correction model (ECM) but within a structural stock-flow model, in order to determine
cap (capitalization) rates. The ECM also helps to reduce the amount of residual errors.
There have been several ECM research works of interest and they include those by
Morales (1998), Sarantis and Stewart (2000), Martens et. al. (1998), Modeste and
Muhammad ( 1999). Ong et. al. (2002) attempts a panel co-integration test on the
existence of a long run initial yield for the office market in Singapore. The subsequent
results are nevertheless obtained from a limited number of time periods and office
buildings (ten office buildings in total). It is found that there is a long-run relationship
between rents and prices. The results seem to support the use of the capitalization rates
in appraisals.
3.
Singapore’s Office Sector
In terms of structural economic behavior, the service related sector sustains Singapore’s
robust economic expansion. The service related sector is not only a necessary
infrastructure that attracts multinational corporations (MNCs) but also a global financial
center in Asia. This sector is nurtured along an unconventional path, enhanced by
conducive economic circumstances, institutional arrangements and macroeconomic
policy objectives. Since the early 1990s, significant structural changes had taken place
in the service related sector, in particular the non-traditional financial activities - fund
management, off-balance sheet corporate financing, derivative trading and foreign
exchange hedging. In April 1985, the Singapore government set up the Economic
Committee to review Singapore’s development strategy, and formulate a master plan to
restructure the economy. A key strategic measure is to promote the service related
sector as actively as the manufacturing sector in order to be a growth stimulus in
reserve, if one sector of the economy should stagnate or be in decline. Although the
9
service related sector is observed to expand robustly by 9.6% p.a. in the period between
1986 and 1996, after the Asian economic crisis in 1997, growth did slow to 5.1% in the
period between 1996 and 2001, owing to the sharp fall in exchange rates and purchasing
power in the regional countries. Singapore’s (private) office sector is thus anticipated to
remain positive in the longer term because of the government’s effective pro-business
strategy in attracting MNCs to Singapore. The office sector is important because it had
helped Singapore to be one of Asia’s leading financial centres, given that the service
related sector comprises many major users of office accommodation, and that the office
sector offers substantial investment demand among institutional investors and
entrepreneurs.
Several key aspects of structural office demand behavior are duly noted for a relatively
healthy office sector, which would sustain the demand for higher quality office
buildings in the longer term.
•
Demand side pressure for office accommodation originating from the economy
is considered by way of growth in GDP, a common and imperative economic
measure that is an aggregation of the value-added for all economic sectors. In
periods of economic boom, consumer and investor confidence would spike and
stimulate demand for products and services. Firms then seek to expand and
require more office accommodation. If supply remains constant, then prices and
rents would rise. Giussani et. a.l (1993) and Dárcy et. al. (1997) had shown that
the change in real GDP is a strong determinant of real office rents in the
European office markets. Thus, GDP growth can be a selected variable for
model building. Fig 1 depicts GDP growth for the Singapore economy. Before
1994, there is low GDP growth volatility, and a noticeable decline is observed
around 1999. It is only in mid-2002 that Singapore’s economy started to
improve after stagnating in the last five quarters.
10
Fig 1.
Analysis of GDP
50000
$millions
40000
30000
20000
10000
1Q
19
1Q 88
19
1Q 89
19
1Q 90
19
1Q 91
19
1Q 92
19
1Q 93
19
1Q 94
19
1Q 95
19
1Q 96
19
1Q 97
19
1Q 98
19
1Q 99
20
1Q 00
20
1Q 01
20
02
0
Years(Quarters)
(Source: Authors; Department of Statistics, Ministry of Trade & Industry, 2005)
•
Sustainable growth in company turnover and profits for both the foreign
corporations and the domestic companies is imperative for stimulating the wider
economy of Singapore, in order to significantly sustain productivity and
competitiveness improvements for greater goods and services production. These
favorable outcomes would provide sustainable wage growth and levels. With
more production of goods and services, the economy grows in conjunction with
the growth in consumer confidence and spending as well as boosting domestic
inward investments and foreign direct investments.
•
In purchasing a real estate asset, whether for owner-occupation or investment,
most investors would take some form of borrowing. With a high cost of
borrowing, the purchasers may reconsider their investment decision. Thus, the
prime-lending rate would have a negative relationship with office demand.
DiPasquale and William (1992) had highlighted that the long-term interest rate
affects the office cap rate, which in turn affects the price through the annual
rents. The behavior of Singapore’s prime lending rate in Singapore is depicted in
Fig 2. Subject to the crisis of investor confidence, it is first observed to decline
significantly by some -6% by end-1993 in the aftermath of the 1990 Gulf War in
Iraq. Secondly, a significant decline in the prime-lending rate by end-1998 had
been precipitated by the 1997 Asian economic crisis, and in the aftermath of the
Singapore government’s introduction of its 1996 real estate anti-speculation
policy. The prime-lending rate did continue to remain moderately soft at around
11
5%, as a result of the post global downturn in the high technology, media and
telecommunications industries from 2001 and the prevailing global slow down
in international trade.
Fig.2
Analysis of Prime Lending Rate
%
10
8
6
4
2
1Q
19
1Q 8 8
19
1 Q 89
19
1Q 90
19
1Q 91
19
1Q 9 2
19
1 Q 93
19
1Q 94
19
1Q 95
19
1Q 9 6
19
1 Q 97
19
1Q 98
19
1 Q 99
20
1Q 00
20
1 Q 01
20
02
0
Years(Quarters)
(Source: Authors; Department of Statistics, Ministry of Trade & Industry, 2005)
•
Vacancy in office accommodation normally results from firms that seek to
relocate, upgrade, expand, to downsize their operations and consolidate their
office accommodation. It is commonly used in studies of office rental
determination, and can be another selected variable for model building.
DiPasquale and Wheaton (1996) had found that the relationship, between the
change in office rents and vacancy rates in San Francisco, to be a negative
relationship. Wheaton and Raymond (1988) had found the vacancy-rental
adjustment process in the US office market, tends to be such that office rents in
real terms move downward when the actual vacancies rise. Because vacancy can
be affected by more complicated relationships, it is the combination of the
amount of vacant space and the level of tenant activity that determines the
expected leasing time for vacant space. However, owing to the fact that it is hard
to estimate tenant-leasing mobility, then the net space absorption in an office
market would act as a close approximation. Singapore office sector’s vacancy
rate and the rate of office space absorption can be incorporated for office rental
model building. From Fig 3, it can be observed that the Singapore office sector’s
vacancy is decreasing island wide from 1988. Since 1989, it had been volatile
and reached its peak in mid-1999, reflecting weakness in office demand. Soon
12
after the terrorists’ attacks in New York on 11 September 2001, the Singapore
office demand is observed to dampen, with vacancies starting to rise in 2002.
Analysis of Vacancy Rate
Fig 3.
%
16
14
12
10
8
6
2002Q1
2001Q3
2001Q1
2000Q3
2000Q1
1999Q3
1999Q1
1998Q3
1998Q1
1997Q3
1997Q1
1996Q3
1996Q1
1995Q3
1995Q1
1994Q3
1994Q1
1993Q3
1993Q1
1992Q3
1992Q1
1991Q3
1991Q1
1990Q3
1990Q1
1989Q3
1989Q1
1988Q3
4
2
0
Years(Quarters)
(Source: Authors; Urban Redevelopment Authority,Singapore, 2005)
•
The stock-flow model by Dipasquale and Wheaton (1996) suggests the
relevance of office stock level in determining rents and prices, from supply-side
considerations. Demand for office space at any point in time can be measured ex
post as the portion of office stock that is actually occupied, i.e. the occupied
stock. From Fig 4, the Singapore office stock and its occupied stock are
observed to follow quite closely in line with each other. At end-1993, the total
office stock had increased island wide while the occupied stock is observed to
follow suit a quarter later. After the government’s introduction of the 1996 real
estate anti-speculation policy, the occupied stock did increase at a slow rate,
resulting in a greater divergence between the pace of occupied stock and the
total stock. In conjunction with a spike in investor confidence, owing to the
“dot.com” boom in 2000, a sharp rise in the office total stock island wide is
observed in that year. Since then, the stock growth had been mild to stagnating.
13
Fig 4.
Analysis of Stock and Occupied Stock
Sqm
15000000
10000000
5000000
Q
4
Q
3
01
20
Q
2
00
99
19
Years(Quarters)
20
Q
1
Q
4
98
19
Q
3
96
19
19
95
Q
2
Q
1
94
19
Q
4
93
19
Q
3
91
19
Q
2
90
19
89
19
19
88
Q
1
0
Stock
Occupied Stock
(Source: Authors; Urban Redevelopment Authority,Singapore, 2005)
•
Net space absorption is defined as the change in the amount of occupied office
space from period to period. It is used by Dipasquale and Wheaton (1996) in the
eventual formation of rents, and for subsequent rental forecasting of the San
Francisco office market. Net space absorption is positive when existing vacant
space is leased, or when newly built office space, is leased without increasing
the vacancy of existing buildings. Net space absorption for office
accommodation island-wide in Singapore seems to be highly volatile from 1989
in Fig 5, reaching its peak by end-1993. This is the period when the demand for
office accommodation did rise sharply, owing to a combination of higher office
sector vacancies experienced towards the later part of 1993 and a lower office
stock level then. During the 1998 economic recession, the demand for office
accommodation is observed to be contracting significantly. Net space absorption
had been on a decline since then. After 1999, the Singapore economy started to
recover, and had led to an increase in demand for office space. However, this
recovery had lasted for some six quarters before the economy started to head
into shallow economic growth, and the office sector is subsequently deemed to
follow a shallow recovery.
As office demand continues to recover, the average vacancy is observed to decline from
a peak of 16.6% at the end of 2003 to 11.6% at the end of the first half of 2004 in the
CBD (central business district)’s prime Raffles Place area.
14
Fig 5.
Analysis of Net Space Absorption
Sqm
500000
400000
300000
200000
100000
0
2Q
19
2Q 88
19
2Q 89
19
2Q 90
19
2Q 91
19
2Q 92
19
2Q 93
19
2Q 94
19
2Q 95
19
2Q 96
19
2Q 97
19
2Q 98
19
2Q 99
20
2Q 00
20
2Q 01
20
02
-100000
Years(Quarters)
(Source: Authors; Urban Redevelopment Authority,Singapore, 2005)
Consequently, office rents are observed to bottom at the end of 2003, and had started to
recover in the first half of 2004 (owing to office accommodation expansion and
upgrading). Gross rent in the prime Raffles Place area averages S$5.05 psf pm (per sq ft
per month) at the end of the second quarter of 2004, relative to slightly lower rent of
$4.95 psf pm the first quarter at the end of 2004’s first quarter. New supply of the prime
CBD office accommodation is estimated to be 513,000 sq ft in 2004, as compared to a
much lower 410,000 sq ft in 2003, and to the historical average of 1.8 million sq ft per
year. This new supply is largely contributed by 1 George Street (the “Pidemco Centre
Redevelopment” owned by the German insurance company Ergo and Singapore’s
CapitaLand Ltd), a 25- storey office building that is anticipated for completion and
actually completed in the first half of 2005.
4.
Research Methodology
This paper adopts the system dynamics for model building and the required algebraic
relationships are obtained as far as possible from econometric model-analysis, but
inclusive of analytical models based on prior expertise and experience, to enable the
formation of the structural causal (cause and effect) loop framework for this paper. The
structural framework as such is concerned with the feedback process and the dynamic
behaviour of economic growth as well as several key macroeconomic, the expectational
15
determinants of the office real estate sector and the stock market in Singapore. Hence,
this paper is able to investigate how the macro economy affects the office sector and the
domestic stock market.
System Dynamics
The system dynamics model applies control theory to the analysis of industrial systems
(Richardson, 1985). It is a rigorous explanation based model, defining the dynamic
feedbacks among the cause-and-effect variables, that goes beyond a mere computable
simulation model. It is the pioneering work of Jay W. Forrester at the Sloan School of
Management, Massachusetts Institute of Technology. He is among the first to recognize
the lack of adequate models to supplement the quality of research that addresses
complex problems with feedback systems. With an invaluable hindsight, he mooted
“systems dynamics” and attempted to apply the seemingly simple concepts of “systems”
and “feedback” to human affairs and social systems, such as commercial operations,
schools and government bodies (Zemke, 2001). Over time, system dynamics has been
developed to analyze economic, social and environmental systems of all kinds. System
dynamics is a unique and advanced method to enhance the learning of complex systems,
and is widely deployed for developing management flight simulators in order to help
users learn about dynamic complexity and design best-fit solutions to counter-act
management problems.
Kummerow (1999) highlights the advantages of system dynamics models: (i) it is
relatively easy to incorporate qualitative mental and written information as well as
quantitative data; (ii) model simulations can even be conducted where data is inadequate
to support statistical methods or where change in the associated causal-effect processes
makes historical data misleading. As Kwak (1995) noted, one of the most powerful
features of system dynamics lies in its analytic capability. It provides an analytic
solution for complex and non-linear systems. It is appropriate to apply to the real estate
sectors as they are highly dynamic and involve multiple feedback processes, coupled
with management issues. As a modeling approach, system dynamics is well suited to
deal with the dynamic complexity involved in the office real estate market and domestic
stock market. By adopting system dynamics modeling, this paper seeks to examine the
16
economic influences of the office sector and the domestic stock market on the macro
economy in Singapore.
System dynamics involve six main components, namely, stocks, flows, converters,
connecters, time delays and feedback structures.
components.
Table 1 briefly explains these
System dynamics modeling generally comprises five main steps, as
explained by Kwak (1995):
Step One: System understanding
This is the process of deepening the modeler’s understanding of the system with
relevant information. The modeler would then define the problem and understand the
various relationships underlying the variables in the system.
Step Two: Conceptualization
In the system conceptualization step, the hypotheses are formulated and the conceptual
model structures are represented in the form of causal loop diagrams, to show the
relationships of the variables involved in the system.
Step Three: Model Formation
Kwak (1995) and Sterman (2000) define this step to include the identification of the
stock and flow structures, which characterize the state of the system and generate the
information upon which decisions and actions are based, by giving the system some
inertia and memory. The system variables of interest are then organized into a set of
algebraic equations (estimated or imposed by prior expertise and experience) that are
built into the causal loop diagrams. Numerical values are imputed into equations in
order to initialize the entire model as a workable prototype in practice.
Table 1. Components of System Dynamics
Components
Description
Stocks
Represent the accumulation of resources. Symbolized by rectangles, it
characterizes the state of the system and generates the information upon
which decisions and actions are based. Examples of stocks include
capital value, rent, retail space stock, retail space under construction and
gross domestic product.
Flows
Flows refer to the dispersal of resources. They are signified by pipes,
valve (flow regulators) with one or two arrowheads attached.
Generally, it represents the rates of increase. Examples, include, GDP
17
Converters
Connectors
Time Delays
Feedback
Structure
growth rate, capital value and rent growth rates, retail space start date,
construction and demolition rates.
They convert inputs into outputs. Converters hold values for constants,
define external inputs to the model, calculate algebraic relationships,
and serve as the repository for graphical functions. Examples include,
GDP and rental growth rate, construction rate, demand for retail space,
total vacancy, retail yield and expected cost.
They connect and provide linkages to model elements. They represent
inputs and outputs; they do not take on numerical values and cannot be
connected into a stock.
A process where the output lags behind its input. Delays are pervasive,
as it takes time for decisions to affect the state of a system. Delays in
feedback loops create instability and increase the tendency of the
system to oscillate
The feedback structure for a market is depicted by a loop in the causal
loop diagram. The feedback loop is a circle of cause-and-effect; the
arrows always run from a stock to a flow and back to a stock again. A
negative sign at the end of the arrow signifies a negative feedback loop,
and represents the counteracting loop. It serves to counteract or offset a
movement in conditions away from target conditions in order to
maintain in equilibrium. For the reinforcing loop (i.e. a positive
feedback loop), it is signified by a positive sign at the end of the arrow.
Source: Sterman (2000 and 2001) and Ho et al. (2003)
Step Four: Model validation
Model testing and validation are carried out in accordance with the purpose of the
model. The adoption of estimated econometric models offers the advantage of enabling
the ex post forecast of the main and relevant variables built into the system dynamics
model. This ensures that the system loop is “closed”.
Step Five: Policy analysis
Finally, the validated structural and integrated model is applied to resolve the given
problems or to forecast a few key scenarios (i.e. scenario planning for enabling policy
analysis).
5.
Model Estimation
System Conceptualization
18
This paper deploys some of the office structural characteristics, first mooted by Sterman
(2000) but the paper extends the Sterman’s model to incorporate additional
macroeconomic and expectational factors affecting Singapore’s office sector. This
paper’s resulting system dynamics model is a uniquely original urban growth model that
can rigorously investigate the economic impact analysis of the office sector and the
domestic stock market, due to the wider macro economy. On the whole, office
accommodation demand depends on internal and external demand, and economic
expansion in Singapore can be measured by GDP, which consists of the following
components for an open economy in eq (1). In other words, the (C+G+I) expression can
denote internal wing of the Singapore economy while the (X-M) expression can denote
the external wing of the economy.
GDP = C + G + I + X – M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .…..(1)
where C = consumer expenditure;
G = government expenditure;
I = investment;
X = exports; and
M = imports.
The associated feedback strategy for the loops is depicted in Fig 6 where the positive
signs in the loop dominate in some occasions while the negative signs dominate in
others. In this manner, the principle of loop dominance is observed. A mix in between
can occur. Thus, the loop (i.e. a specific diagram within the causal loop framework)
illustrates the structure of the office sector’s market dynamic at work. Fig 6 effectively
depicts the conceptual causal loop diagram for the office sector and the wider macro
economy that concurrently represents this paper’s hypotheses, in which the demand for
office accommodation is influenced by several key factors. Exogenous demand includes
factors outside the office system itself (Bakken, 1993). Aggregate demand, the domestic
stock market and the prime lending rate are three such factors that positively affect
office demand. Nevertheless, apparent office demand is influenced by factors such as
changes in rents. As more office accommodation is needed, vacancy rates will start to
fall. When vacancy rates are low, the average effective rent start to rise, and higher rents
would lead to some reduction in demand as businesses make do with less space per
worker. However, the elasticity of the negative demand response feedback loop (B1) is
envisaged to be relatively low, and there is a delay involved in the feedback from
effective rent to the demand for office accommodation, primarily owing to
19
informational inefficiency. The non-linear counteracting loop works to maintain office
demand in line with target conditions.
On the supply side, rising effective rents will have a positive effect on expected office
rents. As expected costs are increasing at a slower rate than expected rents, owing to
lease renewal and rental reversion, the overall expected returns of office assets are still
on the rise. Capital values (CVs) of the existing assets are also high and rising, further
boosting the profitability for developers. The high profit margins in turn attract new
developers into the office sector, who have no difficulty finding financial assistance in
times of strong economic growth. Therefore, many new office developments are in the
pipeline and the office space start rate takes on a steadily increasing value. After a long
time lag of two to five years, when the office buildings are finally completed, the office
space stock rises sharply, causing a swell in the supply line of office developments. The
new surplus of office accommodation (space) stock then leads to the increase in total
vacancies, which causes effective rents to fall, thus lowering CVs and total returns of
the office developments. As profit margins are lowered, so does the office space start
rate. The vicious cycle continues and this creates the negative non-linear Supply
Response Feedback loop (B2) that is self-correcting. The central loop (B2) in Fig 8
counteracts to balance demand and supply so as to maintain a steady state.
Moving on to the non-linear Supply Line Control loop (B3), developers should forecast
the expected vacancy rate by predicting the future growth of demand and supply
conditions in the office sector. In particular, developers should take into consideration
the supply line of office buildings in the planning and construction stage when
estimating future supply. In this way, they are able to make better judgments as to when
to initiate office projects via inspecting the vacancy rate.
Hence, it is important to recognize the feedbacks among the supply of space, vacancies,
rents, profits and construction activities in the office sector. Sterman (2000) notes that
failure to account for the supply line would lead to overbuilding during booms, and
prevents investment from recovering early enough to prevent a tight real estate market
after the robust period. Hernandez (1990) and Thornton (1992) have interviewed several
selected developers to find out their understanding of market dynamics and whether the
developers account for the feedback structure and time delays of the market. The results
20
suggest that most developers conveniently ignore real estate cycles, time lags and
projects in the pipeline. They merely focus on current market conditions and merely
extrapolate from the historical trends. Even when the developers claim to account for
the supply line, they fail to close the feedbacks of the real estate market, (i.e. they did
not consider that increased supply would affect rental rates).
21
Expected
Income
Total GDP
+
Office Space
Start Rate
Stock
Market
+
Office Space under
Construction
Office Space Stock
Completion
Rate
Demo Rate
+
Aggregate
Prime
Lending Rate
+
+
Vacancy Rate
+
B3
Supply Line
Control
Expected
Vacancy Rate
+
Expectd
Returns +
-
B2
Supply
Response
-
Demand
Expected Office
Rent
+ +
Ave
Effective
Rent
B1
Demand
Response
+
Demand for
Office Space
-
Delay
Expected Costs
Fig 6. The Conceptual Causal Loop Diagram for the Office Sector & the Macroeconomy (Source: Authors, 2005)
22
The Data Source
The model estimation is much influenced by the availability of data. The data is
collated in the period between 1993Q3 and 2004Q4 at quarterly frequency. As such, this
paper covers the performance of Singapore’s economy and the office sector that is
confined to this limited time period. The required data comprise GDP, effective rents,
capital values (CVs), office accommodation (space) stock and vacancy rates.
For the estimation on the performance of the economy, the gross domestic product,
government expenditure, aggregate investment and aggregate consumption data are
required. High quality, quarterly time series data are collected and collated from the
following authoritative national and reliable private international sources: Singapore
statistics (SingStat) database, comprising the domestic universe economic indicators,
maintained by the Department of Statistics in the Ministry of Trade and Industry,
Singapore; the Property Market Information – Commercial and Industrial Properties and
its various quarterly real estate benchmark-universe price reports, maintained by the
Urban Redevelopment Authority of Singapore; the Data Stream on-line information
system; the Jones Lang LaSalle Real Estate Intelligence Service-Asia Research Data, a
popular prime real estate asset class research index of ten key Asian cities; and the
Jones Lang LaSalle Online Market Reports.
Gross Domestic Product (GDP)
GDP refers to the aggregate value of output produced by the Singapore economy. In
order to compare the real value of expenditure over time, it is essential to remove the
effect of inflation and this is achieved by selecting the price structure of 1990 as the
base year. In the non-linear model formulation of GDP, it is envisaged that office
demand depends not only on Keynesian aggregate demand, i.e. the demand of an
internal economy, but also on consumers’ expectations of their future income. Income
expectations, in turn, depend on the total income earned, which because of the inclusion
of the entire population is the total output of the economy (GDP). Thus, the model
finally envisages the total demand for an open economy, like Singapore, without
exception. The result is a positive feedback, represented by the consumption multiplier,
in which an increase in GDP would stimulate income and raise consumption; further
expanding aggregate demand with a short time lag. Consumer expectations of future
22
income would also adjust to the actual income (GDP), with a delay (i.e. time lag). Total
demand adjusts with a short delay to the rate of aggregate demand in the economy. A
first-order exponential smoothing is presumed in eq (2), a common autoregressive
assumption in many macroeconomic models. As expressed in the following equations
below, the initial value of GDP is set to its equilibrium value, the aggregate demand
(AD). When GDP=AD, then the change in GDP is zero. For clarity and simplicity, the
rest of algebraic equations, apart from the GDP equation, are expressed in the numerical
method notation.
Thus, GDP = ∫ (Change in GDP) e-ADt dt, which can be expressed under a numerical
method as
GDP = INTEGRAL(Change in GDP, AD)…………………………………………(2)
Change in GDP = (AD-GDP)/Time to Adjust Production………………………….(3)
Aggregate domestic demand is set to be the sum of consumption C, government
expenditure G, and investment I:
AD = C + I + G………………………………………………………………………(4)
Consumers spend a fraction of their expected income, the marginal propensity to
consume (MPC):
C = MPC * Expected Income……………………………………………………….(5)
From Keynesian demand theory, expected income is envisaged to be greater than zero
but less than one in regard to the least-square relationship between expected income and
the GDP. In other words, if the GDP is anticipated to increase by $1 in expected
income it would spend some but not all of the increase in aggregate consumer demand.
The MPC is estimated from the coefficient ao in the following least-square multiple
regression analysis (MRA) model of eq (6).
GDP = C + ao Consumption + a1 Consumption t-1 + GDP t-1 …………………….. (6)
23
Expected income adjusts to actual income, which in the aggregate is the GDP. The first
order exponential smoothing is again taken to model the adjustment process. The initial
value of expected income is set to the GDP, its equilibrium value i.e. $47,285,600,000
as at 2004 4Q.
Expected Income = INTERGAL(Change in Expected Income, GDP)………….. (7)
Change in
= (GDP - Expected Income)/Expectation Formation Time ……(8)
Expected Income
Demand for Office Space
The demand for office space also refers to the amount of occupied stock in the office
sector. The principal macroeconomic causal factors comprise aggregate demand, prime
lending rate, the domestic stock market and the average effective rent, which is the
gross rent after netting off the rent-free and other discounts on a net floor area basis.
Therefore, structural office demand behavior can be expressed via the least-square
MRA method in eq (9), on the basis of the MRA results in respect of the demand for
office space (NOFFICED, sqm) in Table 2. The right hand side of eq (9) reflects a speed
of adjustment factor of 0.15, implying an adjustment period of roughly 6 quarters for
office space demand itself in relation to its structural causal factors. In the syntax of the
system dynamics ‘ithink’ program, the ‘DELAY’ operator represents a causal factor in
its lagged form, t-n, where ‘t’ refers to the current quarter-period while ‘n’ refers to the
previous nth quarter. Table 2 summarizes the MRA results in conjunction with the
autoregression (AR) procedure, which corrects for the auto regressive error in the
equation system.
Demand for office space = 0.15 x [DELAY(-164.964 x Ave_Effective_Rent, 5) +
DELAY(1.540 x 10^(-5) x Aggregate_Demand, 2) + DELAY(-342026.0 x
Prime_Lending_Rate, 3) + DELAY(191.579 x Stock_Mkt_Index, 4) + 5042007.0]
……………………………………… . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (9)
, where Ave_Effective_Rent = office net effective rent, S$ per sqm p.a.
Aggregate_Demand = aggregate demand of the economy, at current market price, S$
Prime_Lending_Rate = nominal prime lending rate, % p.a.
Stock_Mkt_Index = all-share price index of the Singapore Stock Exchange, index units.
24
Table 2. MRA Model Estimation For Office Demand (NOFFICED)
D e p e n d e n t V a ria b le : N O F F I C E D
M e t h o d : L e a s t S q u a re s
D a t e : 0 4 / 0 8 / 0 5 T im e : 2 0 : 5 9
S a m p le (a d ju s t e d ): 1 9 9 3 Q 3 2 0 0 4 Q 4
I n c lu d e d o b s e rv a t io n s : 4 6 a ft e r a d ju s t m e n t s
C o n v e rg e n c e a c h ie v e d a ft e r 1 9 it e ra t io n s
V a ria b le
C o e ffic ie n t
S t d . E rro r
t -S t a t is t ic
C
N G D P (-2 )
E R E N T (-5 )
N P L R (-3 )
S G X I (-4 )
A R (1 )
5042007.
1 . 5 4 E -0 5
-1 6 4 . 9 6 3 7
-3 4 4 2 0 2 6 .
1 9 1 .5 7 8 6
0 .9 2 7 2 6 4
4 1 3 5 9 6 .3
7 . 7 3 E -0 6
1 5 0 .2 9 8 0
2630587.
1 6 4 .3 9 3 2
0 .0 1 7 2 2 8
1 2 .1 9 0 6 5
1 .9 8 9 6 5 4
-1 . 0 9 7 5 7 8
-1 . 3 0 8 4 6 3
1 .1 6 5 3 6 8
5 3 .8 2 2 8 8
R -s q u a re d
A d ju s t e d R -s q u a re d
S . E . o f re g re s s io n
S u m s q u a re d re s id
L o g lik e lih o o d
D u rb in -W a t s o n s t a t
0 .9 9 1 1 0 0
0 .9 8 9 9 8 7
5 2 4 4 9 .2 0
1 .1 0 E + 1 1
-5 6 1 . 9 6 6 3
1 .9 6 6 8 3 9
M e a n d e p e n d e n t va r
S .D . d e p e n d e n t va r
A k a ik e in fo c rit e rio n
S c h w a rz c rit e rio n
F -s t a t is t ic
P ro b (F -s t a t is t ic )
P ro b .
0 .0 0 0 0
0 .0 5 3 5
0 .2 7 9 0
0 .1 9 8 2
0 .2 5 0 8
0 .0 0 0 0
4925935.
5 2 4 1 5 5 .6
2 4 .6 9 4 1 9
2 4 .9 3 2 7 0
8 9 0 .8 4 5 9
0 .0 0 0 0 0 0
(Source: Authors; Eviews program, 2005)
NB. C = a constant
ERENT(-5) = office net effective rent lagged by 5 quarters
NGDP(-2) = aggregate demand of the economy lagged by 2 quarters
NPLR(-3) = nominal prime lending rate lagged by 3 quarters
SGX(-4) = all-share price index of the Singapore Stock Exchange lagged by 4 quarters.
From Table 2, it is observed that the NOFFICED model is rigorously estimated with a
very good fit (R-squared being 0.99), and with virtually no problematic serial error
correlation. This is because the Durbin-Watson test statistics is close to 2.0, and the
Akaike as well as the Schwarz criteria, which test for model selection on the basis of
striking a balance between goodness-of-fit and parsimony, are not excessive (as low
values are preferred).
Prime Lending Rate
In terms of structural office demand behavior, a key causal factor is the prime lending
rate (PLR). In this paper, the prime-lending rate is plotted against the changing level of
GDP in a graphical non-linear relationship, as an alternative to a least-square multiple
regression analysis relationship. Intuitively and from prior experience and expert
knowledge, the PLR is deemed to rise in tandem with expanding GDP in Fig 7 until the
cost of credit becomes excessive, as part of the credit rationing process, and eases off
subsequently in order to facilitate GDP expansion.
25
Fig 7. Graphical Relationship Between PLR & Changing GDP
(Source: Authors; ‘ithink’ program, 2005)
Domestic Stock Market
Another key causal factor of structural office demand behavior is the domestic stock
market, which is represented by the price index of the Stock Exchange of Singapore
(SGX). The relationship between the stock market and the changing level of GDP can
be expressed via the least-square MRA method in eq (10). Table 3 summarizes the
MRA econometric results in conjunction with the autoregression (AR) procedure. From
Table 3, it is observed that the NOFFICED model is robustly estimated with a very
good fit (R-squared being 0.83), and with not much of a serial-error-correlation
problem. This is because the Durbin-Watson test statistics of 1.76 is not too far from
2.0, and the Akaike as well as the Schwarz criteria are not excessive (albeit much
smaller than those criteria of Table 2)
Stock_Mkt_Index = 1.39 x 10^(-8) x Change_in_GDP + 407.25……………………(10)
, where Stock_Mkt_Index = price index of the SGX
Change_in_GDP = Changing level of the gross domestic product, S$.
It is noteworthy to highlight that although the specific and singular relationship between
the stock market price index and the changing GDP level is a linear regression, the
wider relationship is essentially non-linear where this stock index is an integral part of
the non-linear negative feedback control-loop in Fig 6, involving GDP, the prime
lending rate and the demand for office space.
26
Table 3. MRA Model Estimation For The Stock Market Price Index (SGXI)
D e p e n d e n t V a r ia b le : S G X I
M e th o d : L e a s t S q u a re s
D a t e : 0 4 / 0 8 / 0 5 T im e : 1 9 : 4 6
S a m p le ( a d ju s t e d ) : 1 9 8 1 Q 3 2 0 0 4 Q 4
I n c lu d e d o b s e r v a t io n s : 9 4 a ft e r a d ju s t m e n t s
C o n v e r g e n c e a c h ie v e d a ft e r 1 5 it e r a t io n s
V a r ia b le
C o e ffic ie n t
S td . E rro r
t - S t a t is t ic
P ro b .
C
D (N G D P )
A R (1 )
4 0 7 .2 5 3 9
1 .3 9 E -0 8
0 .9 0 3 6 5 3
4 7 .0 5 5 2 1
4 .8 9 E -0 9
0 .0 4 5 7 4 3
8 .6 5 4 8 1 1
2 .8 3 7 4 9 0
1 9 .7 5 5 1 8
0 .0 0 0 0
0 .0 0 5 6
0 .0 0 0 0
R -s q u a re d
A d ju s t e d R - s q u a r e d
S . E . o f r e g r e s s io n
S u m s q u a r e d r e s id
L o g lik e lih o o d
D u r b in - W a t s o n s t a t
0 .8 2 6 1 5 2
0 .8 2 2 3 3 2
4 3 .7 4 2 5 2
1 7 4 1 2 0 .1
-4 8 7 .0 1 7 9
1 .7 5 9 4 2 6
M ean d ep e n d e n t va r
S .D . d e p e n d e n t va r
A k a ik e in fo c r it e r io n
S c h w a r z c r it e r io n
F - s t a t is t ic
P r o b ( F - s t a t is t ic )
4 0 7 .1 7 7 7
1 0 3 .7 7 6 4
1 0 .4 2 5 9 1
1 0 .5 0 7 0 8
2 1 6 .2 2 3 6
0 .0 0 0 0 0 0
(Source: Authors; Eviews program, 2005)
NB. D(NGDP) = first-order difference of the GDP at market prices, S$
AR(1) = first-order autoregressive error process
C = constant term as in Table 2
Rents and Capital Values
In this paper, rents and capital values are two of the main dependent variables that are to
be anticipated with economic expansion. Office net effective rents are gross rents net of
outgoings and rent-free periods on a net floor area basis. Under the asset market
(DiPasquale and Wheaton, 1996), the rent is directly related to capital appreciation. If
rental yield increases, the desirability of an office asset would increase, leading to a
price (CV) appreciation of the asset. In the office sector for the model, the series of rents
and CVs are constructed on the basis of an updated basket of prime office in
Singapore’s Central Business District as at 2005Q4. Rents are measured in Singapore
dollars per square metre per annum (S$ psm p.a.). The rental growth is assumed to grow
at 1.5% per quarter compounded. For the CV, the equation is expressed as:
CV growth = CV (t) – CV (t-1) x 100%
CV (t-1)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (11)
, where t = current quarter period
Yield
27
The annual office (rental) yield, also sometimes known as the office capitalization rate
(in contrast to the larger office investment capitalization rate of rate) is a fundamental
input in the analysis of real estate markets as they are used to estimate CVs. The office
capitalization rate is on the whole the property capitalization rate and it represents the
all-risk yield composed of the office market-wide risk and the specific risk, which in
turn is associated with a particular office asset (for e.g, leasing risk, location risk,
project management risk and property management risk). The market risk component
can be explained by the office-sector’s market analysis conducted periodically by the
international real estate consultants, and subject to the prevailing risk-free rate in order
to bring the office investor into the risk-neutral world of real estate investments. The
specific risk can be managed at the portfolio level within the economy. The relationship
between office CV and office rent is commonly expressed as the office rental yield, or
simply the “office yield” in short, that is defined in eq (12).
Office Yield = (Rent/ Capital Value) x 100% . . . . . . . . . . . . . . . . . . . . . ……….... ...(12)
However, in the office sector for the model building purposes, the office yields along a
time line (of over 40 quarters, i.e. 10 years) are to be investigated. This helps to
determine CV growth and the expected returns subsequently. Thus, when the yield rises,
the associated CV would slow down its growth or decline, owing to high-perceived risk
adversely affecting the CVs. Office yields in the prime office sector vary from 3.0% to
5.6% p.a.
Vacancies
Vacancies ought to be accounted for in every office development. In this model, total
vacancy is taken to be total stock minus occupied stock, as expressed in the following
equation:
Total Vacancy = Office Space Stock – Demand for Office Space . . . . . . . . . . ……. (13)
Expected Vacancy (in space terms) is deemed to lag behind total vacancy by 3 quarters,
as the time lag is about the typical time taken for office space under construction to
come into the total office stock. The conception of expectation in this paper’s contextual
use follows from the consistent theories of rational expectation, where continual
28
feedback from past outcomes facilitates the formation of current expectations, and of
weak-form capital market efficiency.
Expected Vacancyt = Office Space under Constructiont + DELAY(Total Vacancy,
3)…………………………………………………………………………………….(14)
Expected Vacancy Rate =
Expected Vacancy x 100% . . . . . . . . . . . . ………(15)
Total Office Stock
Expected Costs
Expected costs are outgoings in the day-to-day operations of the office assets. They are
accounted as part of effective rents and are necessary for the smooth running of the
office assets. Expected costs include expenses for property tax, utilities bill, routine
maintenance (for e.g. servicing of lifts, escalators, air-conditioning, air-handling units,
water tanks etc.) and payment to security guards, to contract staff for maintenance of
landscape and toilets. It should not account for more than 20% of the effective rents,
and for the office model, it is assumed to grow at 1% per quarter compounded.
Expected Costs = % of Average Effective Rent x Average Effective Rent x
CGROWTH(1)………………………………………………………………………..(16)
Expected Office Rent and Expected Returns
Using the integrated system dynamics and econometric office model, the expected
office rents (in annualized terms) and the corresponding returns can be anticipated for
the next 40 quarters. This information is vital to the developers and investors alike, as it
enables them to estimate the profitability of office space. The expected office rent is
formed from the expected cost over and above the actual (effective rent) on average, and
adjusted by the associated occupancy rate. The latter is represented by the expression
(1-expected vacancy rate). Thus, the expected office rent can be expressed in eq (17),
while the expected returns can therefore be expressed in eq (18), where the expected
return is formed from the sum of the CV appreciation and the expected office returns of
the office asset. Eq (19) then re-expresses eq (18) on an annualized basis instead of on a
per quarter basis.
Expected Office Rent = (1-Expected Vacancy Rate) x (Ave Effective Rent +
Expected Costs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .(17)
29
Expected Returns =
(Expected Office Rent) + CV growth per qtr. . . . . . . . . . . . (18)
CV
=
[(Expected Retail Rent) + CV growth x 4 p.a. . . . . . . . . . (19)
CV
, where CV = capital value in current quarter period.
Office Space Start Rate
The annualized office space start refers to the office space start rate, and it is correlated
to the expected return and the demolition rate encountered in the office sector. In
Singapore’s office sector, it is the industry practice to take about 2 years for the
approval to construct until the completion of the office development. Hence, the data
series for total completions are adjusted 2 years in advance, in order to estimate the
subsequent correlation with the expected returns. The office space start rate can
therefore be expressed in eq (20).
Office Space Start Rate = b1Expected Returns Rate – Demolition rate. . . . . . . . . . . . (20)
On the completion factor, it is deemed to be a rate that is typically growing at 5% per
quarter compounded for a maturing office sector, like Singapore’s, in the steady state. In
the case of the demolition factor, it is deemed to be the demolition rate that typically
grows at 2.5% per quarter compounded for the office sector.
6
Model (Ex Post) Validation
The validation of the resulting system dynamics model is conducted with respect to the
ex post forecasts of the main and relevant variables that are built into the system
dynamics model, in order to close the overall system loop. These variables denote the
dependent variables and their corresponding explanatory variables that are robustly
estimated from the required MRA models, solved by the least-square method. The
variables concerned are mainly defined in eq (9) for office space demand and eq (10) for
the domestic stock market price index. All the variables are statistically significant,
rectified for serial correlation error, correctly specified in their functional forms and
30
achieved high goodness-of -fit. These are appropriately reflected in the ex post model
plots of Fig 8 and Fig 9.
Fig 8. Ex Post Model Plots For Space Office Demand (NOFFICED)
6000000
5500000
5000000
4500000
4000000
300000
3500000
200000
100000
0
-1 0 0 0 0 0
1994
1998
1996
R e s id u a l
2000
A c tu a l
2004
2002
F itte d
(Source: Authors; Eviews program, 2005)
Corresponding explanatory variables consist of:
ERENT(-5) = office net effective rent lagged by 5 quarters
NGDP(-2) = aggregate demand of the economy lagged by 2 quarters
NPLR(-3) = nominal prime lending rate lagged by 3 quarters
SGX(-4) = all-share price index of the Singapore Stock Exchange lagged by 4 quarters.
Fig 9. Ex Post Model Plots For The Stock Market Price Index (SGXI)
700
600
500
400
200
300
100
200
0
-1 0 0
-2 0 0
1985
1995
1990
R e s id u a l
A c tu a l
2000
F it t e d
(Source: Authors; Eviews program, 2005)
Corresponding explanatory variable consist of :
D(NGDP) = first-order difference of the GDP at market prices, S$
31
7
Post-Model (Ex Ante) Findings and Analysis
The finalized version of the structural and dynamic system dynamics model for the
office sector can be represented as a causal loop flow diagram in Fig 10. In general, the
model is a uniquely original urban growth model that can investigate the impact
analysis on the office sector, owing to the wider macro economy, macroeconomic
policy and the domestic stock market in Singapore. The causal loop flow diagram
interprets the conceptual causal loop diagram of Fig 6, and casts it in a programmable
form, in order to facilitate subsequent computable simulation runs and planned scenario
analyses. The planned scenario analysis represents the foreseeable ex ante outlook,
incorporating forward-looking expectations, and involves two planned scenarios:
•
a “High Economic Growth Scenario” and
•
a “Low Economic Growth Scenario”.
The computable model-outlook results are to be presented in graphs and tables. The
time horizon for the forecasted results is a period of 40 quarters or 10 years from the
present (i.e. time t = 0). The associated sets of equations for the developed model are
provided in programmable syntax format, as detailed in Appendices 1 and 2 for
reference purposes.
Scenario Analysis from the Overall Dynamic Model – Office Sector
First Scenario – High GDP Growth
In the first scenario, high GDP growth is represented a series of significantly large step
increases every 4 quarters into the future in government expenditure and investment
demand in the aggregate. This can be readily observed in eq (21) and eq (22). In line
with a broad based economic recovery, resulting in substantially improved investor
confidence and consumption confidence, the marginal propensity to consume is
adjusted upwards to a level of 0.95. As a consequence, the expected income is initially
32
set higher at 10 percent more than the GDP’s equilibrium value of. $47,285,600,000 as
at 2004 4Q.
Government_Expenditure =
19244000000+step(1000000,4)+step(5000000,8)+step(5000000,12)+step(2500000,16)+
step(2000000,20)+step(1000000,24)+step(500000,28)+step(100000,32)+step(250000,3
6)+step(1000000,40) ………………………………………………………………..(21)
Investment Demand =
16886000000+step(800000,4)+step(800000,8)+step(1000000,12)+step(1500000,16)+st
ep(1500000,20)+step(1500000,24)+step(2000000,28)+step(2000000,32)+step(1000000,
36)+step(1000000,40)……………………………………………………………….(22)
The graphs of Fig 11 depict the high GDP growth of the first scenario. The domestic
stock market is anticipated to strengthen sharply in the short run, tapering off and
downwards gradually only after about 3 quarters into the future.
33
GDP
Completions
Expectation Formation
Production Adjustment Time
completion factor
GDP
Change in GDP
demo factor
Change in Expected Income
exp returns
office space under construction
office space stock
Expected Income
office space start rate
completion
demolition
~
stock mkt index
Prime Lending rate
Aggregate Demand
Vacancy
total vacancy
Consumption
demand for office space
Government Expenditure
Investment
exp vacancy
Marginal Propensity to Consume
exp vac rate
demand for office space
Rent
rent factor
Capital Value
Ave Effective Rent
exp returns
cv growth
CV
rent growth
exp office rent
exp office rent
~
office yield
yieldm1
rentm1
exp costs
exp vac rate
Fig 10. The Causal Loop Flow Diagram for the Structural System Dynamics
Model for the Office Sector in Singapore.
(Source: Authors; ithink program, 2005)
34
Fig 11. First Scenario Results – High GDP Growth
1: Government …
19270000000.00
1:
16905000000.00
2:
400000000000.00
3:
350000000000.00
4:
600.00
5:
2: Investment
3: Consumption
4: GDP
5: stock mkt index
4
4
1
3
1
2
19255000000.00
1:
16895000000.00
2:
200000000000.00
3:
200000000000.00
4:
500.00
5:
3
5
4
5
5
3
5
1
3
19240000000.00
1:
16885000000.00 1
2:
0.00
3:
50000000000.00
4:
400.00
5:
1.00
2
4
2
2
10.75
20.50
Graph 22 (Untitled)
30.25
Quarters
40.00
04:26
09 Apr 2005
Key:
1 – Government Expenditure (S$)
2 – Investment Demand (S$)
3 – Consumption Demand (S$)
4 – Gross Domestic Product (S$)
5 – Price Index of the Stock Exchange of Singapore (Index units)
(Source: Authors; iThink program, 2005)
Fig 12. First Scenario Results (Continued) – High GPD growth
1:
2:
3:
4:
1: CV
11300.50
0.05
0.20
800.00
2: office yield
3: exp returns
4: exp office rent
1
1:
2:
3:
4:
3
11300.25
0.05
0.10
500.00
4
2
4
1
4
2
3
3
4
1
2
3
1:
2:
3:
4:
11300.00
0.05
2
0.00
200.00 1
1.00
10.75
Graph 23 (Untitled)
20.50
Quarters
Key:
1 – Office Capital Value (S$ per sqm)
2 – Office Yield (% p.a.)
3 – Expected Office Total returns (% p.a.)
4 – Expected Office Rent (S$ per sqm p.a.)
(Source: Authors; iThink program, 2005)
35
30.25
04:26
40.00
09 Apr 2005
Fig 13.
1:
2:
3:
4:
First Scenario Results (Continued) – High GPD growth
1: demand for office …
6000000.00
800.00
3000000.00
2: Ave Effective Rent
3: total vacancy
4: exp vacancy
2
1
1:
2:
3:
4:
1
4500000.00
600.00
1500000.00
1
2
3
4
2
3
4
3
1:
2:
3:
4:
3000000.00
400.00
4
3
4
2
1
0.00
1.00
10.75
Graph 2 (Untitled)
20.50
Quarters
30.25
04:26
40.00
09 Apr 2005
Key:
1 – Demand for Office Space ( sqm)
2 – Average Effective Rent (S$ per sqm p.a.)
3 – Total Vacancy (sqm)
4 – Expected Vacancy ( sqm)
(Source: Authors; iThink program, 2005)
Fig 12 and Fig 13 depict the resultant impact on the office sector, and indicate that when
rents steadily grow faster than inflation, the demand for office space remains stable in
the short run for about 3 quarters into the future. Thereafter, the demand for office space
rises sharply, to be followed by a gradual and steady expansion in office space demand,
even though office space demand has negatively signed relationships with office rents
and the prime-lending rate in eq (9). In tandem with robust GDP expansion, the
structural causal factors of interest i.e. the office yield, expected office total return and
the office capital value (CV), are all seen to be moderately fluctuating around a broadly
upward trend. Office space stock is growing at a decreasing rate, owing to the
decreasing trend of office space under construction. This trend is reasonably anticipated
as office space has reached a saturation point in the prime office; thus the only way to
expand vertically is by increasing its stock through refurbishment, alterations and
extension, or demolishing old office assets and building new office developments.
As a result, expected total returns for the office sector generally rise and strengthen in
the long run (the next 40 quarters) in the range between 3% to 13% p.a. on annualized
basis in Table 4. These returns dip to the bottom around 2006 1Q, owing to high
vacancies. However, the office expected total returns peak at about 13% in 2006 3Q and
36
2014 1Q. Other related factors of interest, namely, office space demand, office space
stock (Off Sp Stk), effective rent, rental growth and vacancies are forecasted up to 2014
3Q in order to visually project a sense of future trends. However, in the dynamic setting
of market forces, accurate forecasting becomes increasingly difficult. Hence, the
forecasted figures on the whole may well be an indication of market performance in the
medium term. A summary of these forecasts under the ‘First Scenario’ is presented in
Table 4, corresponding to the graphs in Fig 11, 12 and 13.
37
Table 4
Period
4Q2004
1Q2005
2Q2005
3Q2005
4Q2005
1Q2006
2Q2006
3Q2006
4Q2006
1Q2007
2Q2007
3Q2007
4Q2007
1Q2008
2Q2008
3Q2008
4Q2008
1Q2009
2Q2009
3Q2009
4Q2009
1Q2010
2Q2010
3Q2010
4Q2010
1Q2011
2Q2011
3Q2011
4Q2011
1Q2012
2Q2012
3Q2012
4Q2012
1Q2013
2Q2013
3Q2013
4Q2013
1Q2014
2Q2014
3Q2014
First Scenario Forecasts for the Office Sector under the Overall
Dynamic Model for High GDP Growth
Office Sp Dem,
sqm
3,062,030
3,062,030
3,062,030
3,081,398
4,468,097
4,480,270
4,490,026
4,498,629
4,506,482
4,513,763
4,520,522
4,526,821
4,532,692
4,538,179
4,543,280
4,548,037
4,552,467
4,556,603
4,560,446
4,564,024
4,567,352
4,570,455
4,573,333
4,576,008
4,578,492
4,580,803
4,582,940
4,584,923
4,586,757
4,590,165
4,951,929
5,313,486
5,657,067
5,937,111
6,165,481
6,326,942
6,390,055
6,309,016
6,218,873
6,018,142
Effective
Off Sp Stk, Rent, S$
psqm pa
sqm
5,167,000
426
5,214,473
432
5,253,690
439
5,286,088
445
5,312,852
452
5,334,962
459
5,353,227
466
5,368,316
473
5,380,781
480
5,391,079
487
5,399,585
494
5,406,613
502
5,412,418
509
5,417,214
517
5,421,176
525
5,424,449
533
5,427,153
541
5,429,387
549
5,431,232
557
5,432,756
565
5,434,015
574
5,435,056
582
5,435,915
591
5,436,625
600
5,437,211
609
5,437,696
618
5,438,096
627
5,438,426
637
5,438,700
646
5,438,925
656
5,439,111
666
5,439,265
676
5,439,393
686
5,439,498
696
5,439,584
707
5,439,656
717
5,439,715
728
5,439,764
739
5,439,804
750
5,439,838
761
Expected
Rental
Total
return
Growth
2.0%
11.0%
6.4%
8.0%
6.5%
8.0%
6.6%
3.0%
6.7%
3.0%
6.8%
12.0%
6.9%
13.0%
7.0%
7.0%
7.1%
7.0%
7.2%
12.0%
7.3%
12.0%
7.4%
9.0%
7.5%
9.0%
7.6%
8.0%
7.8%
8.0%
7.9%
8.0%
8.0%
8.0%
8.1%
8.0%
8.2%
9.0%
8.4%
10.0%
8.5%
10.0%
8.6%
12.0%
8.7%
12.0%
8.9%
11.0%
9.0%
11.0%
9.1%
10.0%
9.3%
10.0%
9.4%
10.0%
9.6%
10.0%
9.7%
7.0%
9.8%
7.0%
10.0%
9.0%
10.1%
9.0%
10.3%
9.0%
10.4%
9.0%
10.6%
12.0%
10.8%
13.0%
10.9%
11.0%
11.1%
11.0%
11.3%
(Source: Authors; iThink program, 2005)
Scenario Analysis from the Overall Dynamic Model – Office Sector
Second Scenario – Low GDP Growth
38
Total
Vacancy, Vacancy
sqm
Rate
2,056,363 45.0%
2,103,835 44.0%
2,143,053 43.0%
2,144,507 42.0%
775,760
42.0%
776,535
42.0%
776,241
42.0%
773,763
16.0%
769,129
16.0%
762,612
15.0%
754,558
15.0%
745,244
15.0%
734,922
15.0%
723,785
14.0%
712,033
14.0%
699,791
14.0%
687,182
14.0%
674,291
13.0%
661,216
13.0%
648,011
13.0%
634,733
13.0%
621,416
12.0%
608,112
12.0%
594,840
12.0%
581,628
11.0%
568,487
11.0%
555,444
11.0%
542,507
11.0%
529,685
10.0%
516,980
10.0%
504,411
10.0%
491,975
10.0%
479,678
10.0%
467,517
9.0%
455,502
9.0%
443,631
9.0%
431,905
9.0%
420,321
8.0%
408,884
8.0%
397,592
8.0%
In the second scenario, while no change from the first scenario is anticipated in
government expenditure and investment demand in the aggregate, low GDP growth is
represented by shallow economic recovery that undermines investor confidence and
consumption confidence, the marginal propensity to consume is adjusted to a lower
level of 0.75. As a consequence, the expected income is initially set lower at 80 percent
of the GDP’s equilibrium value of. $47,285,600,000 as at 2004 4Q.
The graphs of Fig 14 depict the low GDP growth of the second scenario. The domestic
stock market is anticipated to peak much more quickly and sharply in the short run of
over the next two quarters, followed by a more sharp decline into the long run.
However, consumption and GDP are anticipated to be growing much more slowly over
the long run.
Fig 14. Second Scenario Results – Low GDP Growth
1: Government …
1:
19270000000.00
2:
16905000000.00
105000000000.00
3:
140000000000.00
4:
5:
475.00
2: Investment
3: Consumption
5: stock mkt index
3
3
3
5
4
4
1
4
1
2
1:
19255000000.00
2:
16895000000.00
3:
65000000000.00
100000000000.00
4:
5:
440.00
3
1:
19240000000.00
2:
16885000000.00
3:
25000000000.00
4:
60000000000.00
5:
405.00
4: GDP
4
5
1
2
5
2
1
5
2
1.00
10.75
Graph 22 (Untitled)
20.50
Quarters
Key:
1 – Government Expenditure (S$)
2 – Investment Demand (S$)
3 – Consumption Demand (S$)
4 – Gross Domestic Product (S$)
5 – Price Index of the Stock Exchange of Singapore (Index units)
(Source: Authors; iThink program, 2005)
39
30.25
04:30
40.00
09 Apr 2005
Fig 15. Second Scenario Results (Continued) – Low GPD growth
1:
2:
3:
4:
1: CV
11300.50
0.05
0.20
900.00
2: office yield
3: exp returns
4: exp office rent
4
1
1:
2:
3:
4:
3
11300.25
0.05
0.10
550.00
1
4
11300.00
0.05
2
0.00
200.00 1
1.00
4
2
3
1:
2:
3:
4:
2
3
4
1
2
3
10.75
20.50
Graph 23 (Untitled)
30.25
Quarters
40.00
04:30
09 Apr 2005
Key:
1 – Office Capital Value (S$ per sqm)
2 – Office Yield (% p.a.)
3 – Expected Office Total returns (% p.a.)
4 – Expected Office Rent (S$ per sqm p.a.)
(Source: Authors; iThink program, 2005)
Fig 16. Second Scenario Results (Continued) – Low GPD growth
1:
2:
3:
4:
1: demand for office … 2: Ave Effective Rent
7000000.00
800.00
3000000.00
3: total vacancy
4: exp vacancy
2
1:
2:
3:
4:
5000000.00
600.00
1000000.00
3
4
3
2
4
1
3
4
1
1
2
1: 3000000.00
2
2:
400.00
3:
1
4: -1000000.00
1.00
4
3
10.75
Graph 2 (Untitled)
20.50
Quarters
Key:
1 – Demand for Office Space ( sqm)
2 – Average Effective Rent (S$ per sqm p.a.)
3 – Total Vacancy (sqm)
4 – Expected Vacancy ( sqm)
(Source: Authors; iThink program, 2005)
40
30.25
04:30
40.00
09 Apr 2005
Fig 15 and Fig 16 depict the resultant impact on the office sector, and indicate that when
rents steadily grow faster than inflation, the demand for office space remains stable in
the short run (of over the next 2 quarters). Thereafter, office space demand rises sharply
and stabilizes over an extended period (until the 30th quarter into the future), to be
followed by a strong growth before tapering off by the end of the 40th quarter. The other
structural causal factors of interest i.e. the office yield, expected office total return and
the office capital value (CV), are all seen to be mildly fluctuating around a broadly
upward trend in line with maturing GDP expansion.
As a result, expected total returns for the office sector generally rise and strengthen in
the long run (the next 40 quarters) in the range between 3% to 13% p.a. on annualized
basis in Table 5. These returns dip to the bottom around 2006 1Q, owing to high
vacancies. However, the office expected total returns peak at about 13% in 2006 3Q and
at about 12% in 2010 3Q. Other related factors of interest, namely, office space demand,
office space stock (Off Sp Stk), effective rent, rental growth and vacancies are
forecasted up to 2013 2Q in order to visually project a sense of future trends. These
forecasted figures on the whole do provide an indication of the most likely market
performance in the medium term. A summary of these forecasts under the ‘Second
Scenario’ is presented in Table 5, corresponding to the graphs in Fig 14, 15 and 16.
8
Conclusion
This paper has developed a unique, rigorous and structural system dynamics model for a
key aspect of real estate market uncertainty, i.e. the frequent mismatch between office
demand and supply under the impact of the domestic stock market, the macro economy
and macroeconomic policy. The dynamic interaction of such a key aspect of office
market uncertainty can be structured under the demand-side and supply-side aspects that
are influenced by the wider economy and depend on macroeconomic policies like
economic growth policy, interest rate policy and fiscal. Prices in the form of office
rents, total returns and CVs affect changing office investment. These prices are
determined by office demand and supply regardless of the speed of adjustment. Office
demand in turn depends on macroeconomic policies like economic growth policy,
interest rate policy and fiscal policy. Macroeconomic factors are envisaged to influence
41
the movement of office prices because private investors and developers tend to view the
office sector as an alternative to the domestic share market. This is attributed to
balanced portfolios so that the rate of return in the domestic share market moves in line
with the office sector. Equity-investment prices may well vary with office prices,
proxied by a composite private office price index. The algebraic relationships among
the cause-and-effect variables are achieved through analytical relations based on prior
experience or expert knowledge and robust econometric model estimations where
possible, owing to data availability. System dynamics modeling itself involves the
insightful adoption of feedback control loops and the principle of loop dominance. The
overall system dynamics model is validated within the context of the Singapore
economy, inclusive of the domestic stock market, and the office sector.
A sufficiently long data set over the period between 1993 and 2004 at quarterly
frequency, and from several domestic authoritative sources, has been utilized. This
paper validates the research hypothesis through the formulation of the conceptual causal
(cause and effect) loop diagram for the overall structural dynamic model. Economic
expansion is imperative in influencing the growth or moderation of the office sector
endogenously, through several key macroeconomic variables and expectational factors.
The scenario analysis that is conducted from the overall structural dynamic model has
enhanced the understanding of the structural causal factors and their relationships at
work between Singapore’s office sector, its domestic stock market and the developing
as well as the maturing phases of economic expansion. The office sector is on the whole
susceptible to GDP growth policy that affects both the GDP expansion in actual terms
and the expectations for office rents and returns.
The econometric model estimates in relation to the wider economy and the office sector
are reliant on the data set and the period from 1993 to 2004. Although stable for the
period concerned, the model estimates may change along a time line, going forward.
They would need to be re-estimated as more and new data becomes available. The
corresponding initialization of the overall structural dynamic model would also need to
be re-calibrated in the process. Hence, empirical validation of this overall model does
matter for its continued and accurate use. In addition, the structure of the underlying
42
causal relationships may well be different in different cities, apart from the island-state
of Singapore in the Southeast Asian region.
This paper can even be extended to develop the planned scenario analysis that
represents the ex post outlook, incorporating the Singapore office sector, the domestic
stock market as well as the wider macro economy, and another similar kind of structural
system dynamics model that would involve the non-office real estate sectors and the
macro economy. The latter model can be realized through a combination of system
dynamics modeling and advanced econometric modeling, in order to understand the
dynamics of the structural causal relationships in the different real estate sectors.
43
Table 5
Period
4Q2004
1Q2005
2Q2005
3Q2005
4Q2005
1Q2006
2Q2006
3Q2006
4Q2006
1Q2007
2Q2007
3Q2007
4Q2007
1Q2008
2Q2008
3Q2008
4Q2008
1Q2009
2Q2009
3Q2009
4Q2009
1Q2010
2Q2010
3Q2010
4Q2010
1Q2011
2Q2011
3Q2011
4Q2011
1Q2012
2Q2012
3Q2012
4Q2012
1Q2013
2Q2013
First Scenario Forecasts for the Office Sector under the Overall
Dynamic Model for High GDP Growth
Office Sp Dem,
sqm
3,062,030
3,062,030
3,062,030
3,081,398
4,468,097
4,480,270
4,490,026
4,498,629
4,506,482
4,513,763
4,520,522
4,526,821
4,532,692
4,538,179
4,543,280
4,548,037
4,552,467
4,556,603
4,560,446
4,564,024
4,567,352
4,570,455
4,573,333
4,576,008
4,578,492
4,580,803
4,582,940
4,584,923
4,586,757
4,590,165
4,951,929
5,313,486
5,657,067
5,937,111
6,165,481
Effective
Off Sp Stk, Rent, S$
psqm pa
sqm
5,167,000
426
5,214,473
432
5,253,690
439
5,286,088
445
5,312,852
452
5,334,962
459
5,353,227
466
5,368,316
473
5,380,781
480
5,391,079
487
5,399,585
494
5,406,613
502
5,412,418
509
5,417,214
517
5,421,176
525
5,424,449
533
5,427,153
541
5,429,387
549
5,431,232
557
5,432,756
565
5,434,015
574
5,435,056
582
5,435,915
591
5,436,625
600
5,437,211
609
5,437,696
618
5,438,096
627
5,438,426
637
5,438,700
646
5,438,925
656
5,439,111
666
5,439,265
676
5,439,393
686
5,439,498
696
5,439,584
707
Expected
Rental
Total
return
Growth
2.0%
11.0%
6.4%
8.0%
6.5%
8.0%
6.6%
3.0%
6.7%
3.0%
6.8%
11.0%
6.9%
13.0%
7.0%
7.0%
7.1%
7.0%
7.2%
12.0%
7.3%
12.0%
7.4%
9.0%
7.5%
9.0%
7.6%
7.0%
7.8%
7.0%
7.9%
8.0%
8.0%
8.0%
8.1%
8.0%
8.2%
8.0%
8.4%
10.0%
8.5%
10.0%
8.6%
12.0%
8.7%
12.0%
8.9%
11.0%
9.0%
11.0%
9.1%
9.0%
9.3%
10.0%
9.4%
10.0%
9.6%
10.0%
9.7%
7.0%
9.8%
7.0%
10.0%
9.0%
10.1%
9.0%
10.3%
9.0%
10.4%
Total
Vacancy, Vacancy
sqm
Rate
2,104,970 46.0%
2,152,443 45.0%
2,191,660 44.0%
2,204,690 43.0%
844,755
43.0%
854,692
43.0%
863,201
43.0%
869,687
17.0%
874,299
17.0%
877,316
17.0%
879,063
17.0%
879,791
17.0%
879,726
17.0%
879,036
17.0%
877,896
17.0%
876,412
17.0%
874,686
16.0%
872,784
16.0%
870,786
16.0%
868,733
16.0%
866,664
16.0%
864,600
16.0%
862,582
16.0%
860,616
16.0%
858,719
16.0%
856,893
16.0%
855,155
16.0%
853,504
16.0%
851,942
16.0%
848,760
16.0%
487,183
16.0%
125,780
16.0%
-217,674
16.0%
-497,613
9.0%
-725,897
2.0%
(Source: Authors; iThink program, 2005)
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Appendices
Appendix 1 – High GDP Growth Programmed Model Equations for the Office Sector
(ithink program)
Capital Value
CV(t) = CV(t - dt) + (cv_growth) * dt
INIT CV = 11300
INFLOWS:
cv_growth = (Ave_Effective_Rent/office_yield-rentm1/yieldm1)/(rentm1/yieldm1)
exp_returns = (exp_office_rent/CV)+cv_growth*4
rentm1 = DELAY(Ave_Effective_Rent,1)
yieldm1 = DELAY(office_yield,(1))
office_yield = GRAPH(TIME)
(0.00, 0.0483), (2.00, 0.0476), (4.00, 0.0478), (6.00, 0.049), (8.00, 0.0483), (10.0, 0.0488),
(12.0, 0.0484), (14.0, 0.0487), (16.0, 0.0493), (18.0, 0.0498), (20.0, 0.0502), (22.0, 0.0503),
(24.0, 0.05), (26.0, 0.0499), (28.0, 0.0502), (30.0, 0.0505), (32.0, 0.0515), (34.0, 0.0522), (36.0,
0.0529), (38.0, 0.0527), (40.0, 0.0531)
Completions
office_space_stock(t) = office_space_stock(t - dt) + (completion - demolition) * dt
INIT office_space_stock = 5167000
47
INFLOWS:
completion = office_space_under_construction*completion_factor
OUTFLOWS:
demolition = demo_factor
office_space_under_construction(t) = office_space_under_construction(t - dt) +
(office_space_start_rate - completion) * dt
INIT office_space_under_construction = 273000
INFLOWS:
office_space_start_rate = 0.11*exp_returns-demolition
OUTFLOWS:
completion = office_space_under_construction*completion_factor
completion_factor = CGROWTH(20)
demo_factor = CGROWTH(10)
GDP
Expected_Income(t) = Expected_Income(t - dt) + (Change_in_Expected_Income) * dt
INIT Expected_Income = 1.10*47285600000
INFLOWS:
Change_in_Expected_Income = (GDP - Expected_Income)/Expectation_Formation_Time
GDP(t) = GDP(t - dt) + (Change_in_GDP) * dt
INIT GDP = Aggregate_Demand
INFLOWS:
Change_in_GDP = (Aggregate_Demand - GDP)/Production_Adjustment_Time
Aggregate_Demand = Consumption+Government_Expenditure+Investment
Consumption = Expected_Income*Marginal_Propensity_to_Consume
demand_for_office_space = (DELAY(-164.9637*Ave_Effective_Rent,5)+DELAY(1.54*10^(5)*Aggregate_Demand,2)-DELAY(3442026*Prime_Lending_rate,3)+DELAY(191.5786*stock_mkt_index,4)+5042007)*0.15
Expectation_Formation_Time = 2
Government_Expenditure =
19244000000+step(1000000,4)+step(5000000,8)+step(5000000,12)+step(2500000,16)+step(2
000000,20)+step(1000000,24)+step(500000,28)+step(100000,32)+step(250000,36)+step(1000
000,40)
Investment =
16886000000+step(800000,4)+step(800000,8)+step(1000000,12)+step(1500000,16)+step(150
0000,20)+step(1500000,24)+step(2000000,28)+step(2000000,32)+step(1000000,36)+step(100
0000,40)
Marginal_Propensity_to_Consume = .95
Production_Adjustment_Time = 2
stock_mkt_index = 1.39*10^(-8)*Change_in_GDP+407.25
Prime_Lending_rate = GRAPH(Change_in_GDP)
(1e+008, 4.17), (1.9e+008, 5.22), (2.8e+008, 6.10), (3.7e+008, 7.68), (4.6e+008, 9.04),
(5.5e+008, 10.0), (6.4e+008, 10.4), (7.3e+008, 9.78), (8.2e+008, 8.87), (9.1e+008, 7.82),
(1e+009, 6.84)
Rent
Ave_Effective_Rent(t) = Ave_Effective_Rent(t - dt) + (rent_growth) * dt
INIT Ave_Effective_Rent = 426
INFLOWS:
rent_growth = Ave_Effective_Rent*rent_factor
exp_costs = 0.3934*426*CGROWTH(1)
exp_office_rent = (1-exp_vac_rate)*(Ave_Effective_Rent+exp_costs)
rent_factor = CGROWTH(1.5)
Vacancy
48
exp_vacancy = office_space_under_construction+DELAY(total_vacancy,3)
exp_vac_rate = exp_vacancy/office_space_stock
total_vacancy = office_space_stock-demand_for_office_space
Appendix 2 – Low Economic Growth Programmed Model Equations for Office Sector
(ithink program)
Capital Value
CV(t) = CV(t - dt) + (cv_growth) * dt
INIT CV = 11300
INFLOWS:
cv_growth = (Ave_Effective_Rent/office_yield-rentm1/yieldm1)/(rentm1/yieldm1)
exp_returns = (exp_office_rent/CV)+cv_growth*4
rentm1 = DELAY(Ave_Effective_Rent,1)
yieldm1 = DELAY(office_yield,(1))
office_yield = GRAPH(TIME)
(0.00, 0.0483), (2.00, 0.0476), (4.00, 0.0478), (6.00, 0.049), (8.00, 0.0483), (10.0, 0.0488),
(12.0, 0.0484), (14.0, 0.0487), (16.0, 0.0493), (18.0, 0.0498), (20.0, 0.0502), (22.0, 0.0503),
(24.0, 0.05), (26.0, 0.0499), (28.0, 0.0502), (30.0, 0.0505), (32.0, 0.0515), (34.0, 0.0522), (36.0,
0.0529), (38.0, 0.0527), (40.0, 0.0531)
Completions
office_space_stock(t) = office_space_stock(t - dt) + (completion - demolition) * dt
INIT office_space_stock = 5167000
INFLOWS:
completion = office_space_under_construction*completion_factor
OUTFLOWS:
demolition = demo_factor
office_space_under_construction(t) = office_space_under_construction(t - dt) +
(office_space_start_rate - completion) * dt
INIT office_space_under_construction = 273000
INFLOWS:
office_space_start_rate = 0.11*exp_returns-demolition
OUTFLOWS:
completion = office_space_under_construction*completion_factor
completion_factor = CGROWTH(20)
demo_factor = CGROWTH(10)
GDP
Expected_Income(t) = Expected_Income(t - dt) + (Change_in_Expected_Income) * dt
INIT Expected_Income = 0.8*47285600000
INFLOWS:
Change_in_Expected_Income = (GDP - Expected_Income)/Expectation_Formation_Time
GDP(t) = GDP(t - dt) + (Change_in_GDP) * dt
INIT GDP = Aggregate_Demand
INFLOWS:
Change_in_GDP = (Aggregate_Demand - GDP)/Production_Adjustment_Time
Aggregate_Demand = Consumption+Government_Expenditure+Investment
Consumption = Expected_Income*Marginal_Propensity_to_Consume
demand_for_office_space = (DELAY(-164.9637*Ave_Effective_Rent,5)+DELAY(1.54*10^(5)*Aggregate_Demand,2)-DELAY(3442026*Prime_Lending_rate,3)+DELAY(191.5786*stock_mkt_index,4)+5042007)*0.15
Expectation_Formation_Time = 2
49
Government_Expenditure =
19244000000+step(1000000,4)+step(5000000,8)+step(5000000,12)+step(2500000,16)+step(2
000000,20)+step(1000000,24)+step(500000,28)+step(100000,32)+step(250000,36)+step(1000
000,40)
Investment =
16886000000+step(800000,4)+step(800000,8)+step(1000000,12)+step(1500000,16)+step(150
0000,20)+step(1500000,24)+step(2000000,28)+step(2000000,32)+step(1000000,36)+step(100
0000,40)
Marginal_Propensity_to_Consume = .75
Production_Adjustment_Time = 2
stock_mkt_index = 1.39*10^(-8)*Change_in_GDP+407.25
Prime_Lending_rate = GRAPH(Change_in_GDP)
(1e+008, 4.17), (1.9e+008, 5.22), (2.8e+008, 6.10), (3.7e+008, 7.68), (4.6e+008, 9.04),
(5.5e+008, 10.0), (6.4e+008, 10.4), (7.3e+008, 9.78), (8.2e+008, 8.87), (9.1e+008, 7.82),
(1e+009, 6.84)
Rent
Ave_Effective_Rent(t) = Ave_Effective_Rent(t - dt) + (rent_growth) * dt
INIT Ave_Effective_Rent = 426
INFLOWS:
rent_growth = Ave_Effective_Rent*rent_factor
exp_costs = 0.3934*426*CGROWTH(1)
exp_office_rent = (1-exp_vac_rate)*(Ave_Effective_Rent+exp_costs)
rent_factor = CGROWTH(1.5)
Vacancy
exp_vacancy = office_space_under_construction+DELAY(total_vacancy,3)
exp_vac_rate = exp_vacancy/office_space_stock
total_vacancy = office_space_stock-demand_for_office_space
50