Influence of Occupiers’ Characteristics in Office Space Decision
Sing, Tien Foo*1, Ooi, Joseph T.L.*2, Wong, Ah Long@1, and Lum, Patrick K.K.@2
Date: 24 October 2004
Abstract:
In a unitary city system, firms choose to locate in the city centre that offers the most
efficient form of face-to-face communications with clients and suppliers. However,
firms are heterogeneous and they may have different considerations in their office
space decision, the assumption of the agglomeration economies may not, therefore, be
strictly binding. Extended along the behavioral agenda, this study evaluates the office
space preference of occupiers in Suntec City using a structured questionnaire survey.
Based on the mean score statistics, the sample firms ranked the image and prestige of
the office location and the accessibility by public transport as the two most important
factors in office space choice process. Proximity to competitors or firms in similar
business line, which was the proxy for agglomeration economies, was ranked the
second lowest among the 36 office space determinants. Using the principal
component methodology, the office determinants were reduced to 8 latent principal
factors, which explain more than 75% variances in the office space determinants for
the sample firms. When we examine the influence of the 8 principal factors on the
office space decision of the 4 homogenous clusters of firms using a multinomial
logistic regression model, it was found that firms that place significant importance on
face-to-face convenience and image and branding of the office location will likely be
those that have a flatter organization structure. These firms are more willing to pay a
rental premium to be close to the competitors, suppliers and clients. The pro-business
environment factor will appeal to firms that have already established a strong business
network in the building.
Keywords:
Agglomeration Economies, Office Space Choice Determinants;
Network effects
*1
Corresponding Author, Department of Real Estate, National University of Singapore, 4
Architecture Drive, Singapore 117566. Email: rststf@nus.edu.sg
*2 Department of Real Estate, National University of Singapore, 4 Architecture Drive, Singapore
117566. Email: rstooitl@nus.edu.sg
@1 Chief Executive Officer, Suntec City Development Limited,
@2 General Manager, Suntec City Development Limited.
We wish to thank NUS for the research grant and Suntec City Development Limitd for its support and
participation in the above study. We would also like to thank Joan Teo and her colleagues for their
assistance and support in the survey exercise. The research assistance of Chu, Yongqiang, Seah, Huili
Irene, and Ivy Loh and Pearl Lok are appreciated Comments are welcome.
Influence of Occupiers’ Characteristics in Office Space Decision
1.
Introduction
Historically, firms choose to cluster in a center that offers comparative location
advantages, such as proximity to labors and suppliers, accessibility to major
transportation systems and convenience of face-to-face contacts. The agglomeration
of firms gives rise to a nucleus city with high concentration of employments, which
evolves over time into a Central Business District (CBD) (Marshall, 1961; Krugman,
1991). The “history” of the CBD location as a result of the sunk costs and
infrastructure built-in creates the first-mover disadvantage and increases firms’ inertia
to relocate from their CBD premises (Rauch, 1993). However, when the CBD grows
and reaches a critical size, the agglomeration benefits of the CBD diminish as a result
of the growing costs of traffic congestion and the increased office density. The firms’
inertia of relocation is weakened and they are more ready to trade-off the
agglomeration economies for new office location in the fringe areas, which offer
lower density office space with newer facilities at lower costs. Subcenters are formed,
as a result, to accommodate the outward movement of firms from the existing CBD
(Richardson, 1978; DiPasquale and Wheaton, 1996). The decentralization process can
be accelerated with the advancement of the information and communications
technology (ICT), which breaks down the geographical barrier and reduces the
significance of face-to-face contacts in the CBD (Ball, Lizieri and MacGregor, 1998).
The issues of location determinants and inverse bid-rent gradient have been widely
researched in the urban economics literature (Clapp, 1980; Archer, 1981; Dunning
and Norman, 1987; Rauch, 1993; and Bollinger, Ihlanfeldt and Bowes, 1998). The
bid-rent function is invariably represented by hedonic specifications that consist
mainly of location characteristics of offices with the underlying assumption that there
exists only a unitary city. The heterogeneity of the office stocks is always omitted.
These simplified assumptions were increasingly challenged when evidence was found
to support the existence of subcenters (Dunse, Leishman and Watkins, 2001 and
2002). On the demand side, the assumptions that firms are rational and homogenous
and they have perfect information in their location choice decision were also
increasingly questioned by researchers in behavioral studies (Wyatt, 1999; Leishman,
Watkins, 2004; Leishman, Dunse, Warren and Watkins, 2003).
This paper aims to extend the research along the behavioral agenda by investigating
the office space choice decision of firms currently having offices in Suntec City,
Singapore. The research design adopted in this study is distinguished from the earlier
studies in two aspects. First, by selecting Suntec City as the case for analysis, the
location and office stocks heterogeneity can be controlled by the fact that the sample
firms in this study are selected from a single building. Second, the intra-firm
agglomeration effect at the building level can be empirically tested. The paper is
organized into five sections. Section 1 provides the background, motivations and
objectives of the studies. Section 2 reviews literature relating to office location choice,
both the agglomeration economies and behavioral literatures. Section 3 gives an
overview of the office submarkets at CBD and the Marina Center, and also the case
fact of the Suntec City. Section 4 describes the data collection and survey process, and
2
the empirical tests. The empirical design and results are discussed in Section 5.
Section 6 concludes the findings.
2.
Literature Review
The classical urban economic literature on location choice of office is developed on
the assumption that there exists only one unitary urban centre where firms could enjoy
agglomeration economies by locating in the centre. The classical Alonso’s (1964) bidrent function declines with the distance away from the CBD, and firms will have to
trade-off accessibility for larger office space in the fringe location. Clapp (1980)
tested the rent-accessibility trade-off using 105 office buildings data in Los Angeles
and found significant evidence to support the negative rental function with respects to
the distance from the CBD and the commuting time. His results also supported the
importance of face-to-face interaction in the CBD. Using a more recent set of office
rental data in Greater Los Angeles from the same source (Coldwell Banker database),
Sivitanidou (1995) again found that the accessibility factors (distance to CBD,
distance to airport and number of interstate freeways) are significantly reflected in
variations of the office rental function. However, she found that the standard bid-rent
function is incomplete in explaining office bid-rent relationships unless other
variables like worker amenities, zoning and local institutional control are included in
the model.
Bollinger, Ihlanfeldt and Bowes (1998) estimated hedonic office rent models that
control for building characteristics and lease terms using office data in Atlanta region
over three different periods: 4Q1990, 3Q1994 and 1Q1996. They found evidence to
support the agglomeration economies of CBD and confirmed the earlier findings of
Clapp (1980). In their models, the face-to-face convenience, which was represented
by the concentration of professional workers and workers employed in FIRE and
repair service sectors, was found to be positively related to the office rent. Unlike the
three studies reviewed earlier that used building level data in their empirical analysis,
Archer (1981) attempted to study the intra-urban location of office using firm-level
data collected from questionnaire surveys of office space usage conducted in
Jacksonville, Florida and Hartford, Connecticut during the Summer of 1977. They
found that linkages, which represent the needs for face-to-face interaction among
offices, were not as significant as evidenced in Clapp (1980) and Bolligner, Ihlanfeldt
and Bowes (1998) in affecting the firms’ decision to locate in downtown. He also
showed that location of the market centroid is an important office location
determinant only for the market-oriented firms, which find access to clients to be very
important.
Some researchers believe that the advancement of ICT will diminish the
agglomeration economies of CBD. The use of ICT in conveying information replaces
the need for face-to-face interactions among firms in the city centre (Ball, Lizieri and
MacGregor, 1998). The ICT-enabled new working practices adopted by firms like
corporate downsizing, delayering, outsourcing, hot-desking would also influence the
trade-off of space for accessibility of CBD office buildings (Gibson and Lizieri, 2001;
Sing, 2002). However, Bollinger, Ihlanfeldt and Bowes (1998) found no empirical
evidence to support the hypothesis that face-to-face interaction can be substituted by
the use of ICT. The debate of substitutability of the face-to-face importance in a CBD
is though unresolved, but one likely phenomenon is that the agglomeration and
3
information costs will go down with the prevalent use of ICT. As a result of the
lowering of information costs coupled with increases in wage and commuting costs
when a CBD grows in size, subcenters with competitive cost structure and
agglomeration economies will be formed (DiPasquale and Wheaton, 1996, pages 110111). The evidence of subcenters or submarkets was found by Dunse, Leishman and
Watkins (2000 and 2002). They applied the empirical methodology developed by
Bourassa, Hamelink and Hoesli (1999) to office markets in Glasgow and Edinburgh
in the 1990s. The results showed that submarkets exist in Glasgow with clearly
distinguished spatial and property type characteristics, whereas the Edinburgh market
is unitary.
Another limitation of the classical location theory is the implicit assumption that the
agents who choose office space are homogenous and rational with prefect information.
The behavioral approach to analyzing office space determinants has advocated the
importance of studying the heterogeneity of firms and their decisions in the space
selection. Leishman and Watkins (2004) surveyed 119 office occupiers in the
Edinburgh office market. They classified the office properties of the sample occupiers
into four homogenous clusters based on the physical attributes of the properties, and
then they showed that the choice of the office type by the firms is dependent on the
characteristics of the firms such as their size, type of business and their market
coverage either locally, regionally or nationally.
At occupier side, surveys have also been conducted to find out the preference for
office space, and the importance of demand heterogeneity in affecting the
agglomeration effects. Leishman, Dunse, Warren and Watkins (2003) surveyed 61
firms in three main submarkets in Edinburgh, and found that the linkages in terms of
face-to-face contacts with other organizations in the similar or same line of businesses
were not ranked as important as the supply-side factors such as accessibility to
employees and space for future expansion. However, the linkage factors were ranked
higher by city centre occupiers in relation to other submarket occupiers. The CBD
location was more important for occupiers in the city centre and West-end than the
submarkets in Leith and South Gyle. The survey results imply that there exist not only
different subcenters with inherent urban and spatial characteristics; the occupiers in
these subcenters also show significantly different preference in their office space
choice decision. Wyatt (1999) further confirmed the demand-side heterogeneity
assumption in his survey covering 114 office occupiers in Bristol. He again found that
the agglomeration economies of being located close to workforce and complementary
businesses appear significant only in the trade-off decisions of financial and
professional firms.
3.
Office Submarkets and Suntec City Development
3.1.
History and Origin of CBD and Marina Center
In Singapore, urbanization process started at the mouth of Singapore River. The CBD,
which was created as the center for business and employment, took shape to the south
of the river stretching from the current Raffles Place to Shenton Way, Keppel Road,
Peck Seah Street and Telok Ayer Street (Urban Redevelopment Authority, 1995)
4
(Figure 1). The CBD with a land area of 82 hectares1 is served by two Mass Rapid
Transit (MRT) stations: Raffles Place and Tanjong Pagar, within which the most
preferred office location in town, known as the Golden Shoe, is located. Through the
private initiatives and the government land sale programs since 1967, the CBD has
been developed into one of the world class business and financial hub by 1980s.
[Insert Figure 1]
In the 1970s, in anticipation of the potential diminishing effects of the agglomeration
economies of the CBD, lands surrounding the Marina Centre were reclaimed. The
first parcel of land was sold by the Urban Redevelopment Authority (URA)2 in 1978
for the development of Marian Square, which is an integrated development consisting
of a shopping mall and three hotels.3 More land parcels in the area were released
sequentially for comprehensive and integrated commercial projects. A mega scale
project, the Suntec City, was built on a land parcel bought in the URA’s tender sale in
1988. The five office towers in Suntec City together with the space in the neighboring
buildings such as Millennia and Centennial Towers and One Raffles Link form the
new sub-center to the North-Eastern fringe of the CBD (Figure 1).
The new center lacks the accessibility of the MRT system and the agglomeration
economies of the CBD. The East Coast Parkway (ECP) expressway is the main
connecting point for office buildings in the Marina Centre to the international airport
in the East and the seaport in the West of the island. Beset with the inherent location
disadvantages, office buildings in the Marina Centre are still able to defy the
gravitational force of CBD and lure away quality office tenants to the new centers.4
The Marina Centre has attracted a fair share of blue-chip financial companies and
banks, which include big names like UBS, Bankers Trust Co and Lehman Brothers in
Suntec City, Goldman Sachs and Credit Suisse First Boston in One Raffles Link, and
Citibank in the Millennia and Centennial Towers. The “migration” of these firms
away from the historical CBD has raised questions about the continued desirability of
Raffles Place and Shenton Way offices.5
3.2.
Suntec City6 – Case Facts
The idea of the “Suntec City” was conceived when a group of eleven tycoons from
Hong Kong were invited by the then Prime Minister of Singapore, Mr Lee Kuan Yew,
1
2
3
4
5
6
The land area of the existing CBD constitute only less than 0.12% of the total area of the island,
which is estimated to have a total land area of 77,050 hectares, based on the information capture in
the latest Master Plan 2003.
The Urban Redevelopment Authority (URA) is the national planning agency of Singapore, which
is responsible for the long-term physical and transportation planning of the island-state. It is also
the government agent administering the land sale program by tender for state-owned lands.
The Urban Redevelopment Authority (2003), “Marina Centre – The rise of a commercial and
cultural hub,” Skyline, January/February 2003, page 2-5.
Suntec City is one of the projects, which has received overwhelming response when completed in
1995. The 45-storey Tower 2 office floor space was fully snapped up within a single day when it
was put up for sale as multiple strata-titled floors. Rashiwala K. and Koh, E. (1995), “Suntec City
developers mull over selling second office tower,” Straits Times, 21 September 1995.
Rashiwala, K. (1999), “Migration to Marina area continues,” Straits Times, 18 July 1999.
Suntec City is not, as the name implies, a city. It is, however, the name of a single comprehensive
project comprising office, exhibition and convention halls, retail malls and entertainment centers,
developed on 11.7 hectares of reclaimed land at Marina Centre.
5
to attend the national day’s celebration function in 1984.7 In December 1988, they
tendered successfully and acquired a vacant plot of land at S$200.9 million via the
URA’s sale of site program. The success story of the Suntec City begins thereon.
Developed as a ‘city within a city,’ the S$2.3 billion Suntec City project offers 4.3
million square feet (sqf) of commercial space, which includes five office tower, a
retail mall, an entertainment centre and international conventional and exhibition
facilities, and the breakdown of space usage in Suntec City is summarized in Table 1.
[Insert Table 1]
With a supply of 2.3 million square feet of office space in five office towers, Suntec
City has the critical scale to seriously rival the office space in the existing CBD at
Raffles Place, which is just five minutes away by car. Despite the disadvantages in
Suntec’s location and the lack of agglomeration economies, Mr Wong Ah Long, the
Chief Executive Officer of Suntec City Development Limited, predicted with
confidence that “in five years’ time, we want (Marina Centre) to take over Raffles
Place.” 8 What are the “pull” factors for offices in the new center? Experts in the
Singapore’s office market offer some insights9:
“Some buildings (in Marina Centre area) have been positioned as
alternatives to those in Raffles Place. These buildings are also newer and
designed with a sense of space and ambience.”
Mr Tay Kah Poh, the Director at Knight Frank
“Buildings in Marina Centre meet the needs of corporations,
especially financial institutions, who prefer offices with large floor plates
so their workplaces are not spread over too many floors. Such buildings
are preferable over the office blocks in Raffles Place which, because they
were built on smaller land parcels, have smaller plates. Moreover, Marina
Centre has attracted “a critical mass” of financial institutions”
People will put the building before the location as long as the
building is in the hub. And that is why the Marina area, among the more
sought after buildings, rents are close to what Raffles Place buildings
command.”
Mr Chris Fossick, the Executive Director of CB Richard Ellis
The increase use of ICT among firms in the new economics, which reduces the firms’
needs to have face-to-face contacts, helps to accelerate the decentralization process of
firms from the CBD. Suntec City has effectively harnessed the ICT strategies by
providing broadband enabled work place and connectivity to its tenants and
occupiers. 10 The effort has borne fruits. Currently, IT firms constitute about 30
7
8
9
10
Wee, A. (1995), “SM Lee praises Suntec developers’ foresight in pursuing project in the 1980s,”
Business Times Singapore, 31 August 1995.
“Suntec City Looking for Tenants for Tower 4,” Business Times Singapore, 28 November 1996.
The following comments are extracted from the newspaper article written by Chew, Marissa,
(1999) “Office market – old vs new,” Business Times Singapore, 11 November 1999.
The four-part partnership between Suntec City, Origin Technology, StarHub and 1-Net was
formed to fulfil the e-BizHub@Suntec vision, as one stop center for new and existing tenants to
attain dot-com status. It was reported by Pang, J., “e-BizHub@Suntec to be one-stop centre for ebusiness companies,” Business Times Singapore, 27 January 2000.
6
percent of its existing tenants and it is also home to the government agency for ICT
development in Singapore, the Infocomm Development Authority. The success of the
ICT-centered strategy was lauded by the real estate expert, Dr Amy Khor, Director
(Research) at Knight Frank11,
“Suntec City in Singapore has been successful in luring IT firms to
the development, initially through offering broadband fibre-optic network
to occupiers and then through the networking opportunities available as a
result of the concentration of IT firms in the development”
The rule of “location, location, location’ in real estate, which is linked to the inherent
physical attributes of fixity, immobility and indivisibility of real estate, is no longer
binding in Suntec City case. Recognizing the location shortfall of the project, Mr
Wong Ah Long, the Chief Executive Officer of Suntec City Development Private
Limited, has been able to turn the odds to his favor by facilitating “connectivity,
connectivity, connectivity” for Suntec tenants and occupiers. From early 1999, a slew
of ICT and broadband initiatives, which include fibre optic broadband access, instant
networking, digital offices, common telecommunication connection, internet call
center, plug and play environment, 4G network, wireless broadband services, and
many others, has been implemented in phases in the so-called “Suntec IT Waves”
projects. The concerted effort in embracing ICT and broadband technology has earned
Suntec City the reputation of the “Asia’s Vertical Silicon Valley.”
However, the connectivity concept goes beyond merely providing the high-speed
broadband connection to the development to creating and supporting a networkintensive environment for business stakeholders in Suntec City. By connecting a large
number of tenants in complimentary and competing businesses within a building,
“network externality,” or agglomeration effect within a building, can be established,
which enhances the attractiveness of the building as a business center. New tenants in
the same business line will reap the positive benefits of the existing network by
locating in the building (Sing, Lee and Wong, 2001). In the connected network of
Suntec City, the landlord’s role is transformed into one that facilitates the partnerships
and links with new tenants. The strategy termed “Facilities Service Provider” has
been proven to be successful, and it has been patented by the management of Suntec
City as a new way of creating value in real estate projects in the digital age.12
4.
Survey Design & Data Analysis
4.1
Surveys and Data Collection
Data from occupiers of office space in Suntec City were collected via a mailed
questionnaire exercise carried out in the months from March to June 2004. The survey
covers all the firms, either tenants or owner-occupiers, with at least one office unit in
one of the Suntec City office towers. With the assistance of the management of
Suntec City, the firm’s roll consisting of 514 tenants and occupiers in the Suntec City
was obtained. After eliminating the duplications of firms leasing of occupying more
11
12
Khor, Amy, “Facing new economy challenges,” Business Times Singapore, 13 April 2000.
This FSP business concept has been successfully patented by Suntec City in Hong Kong and
Taiwan. The patent (No. 10/010/319) titled “System and Method for Increasing Perceived Value of
a Property to Tenants” has also been filed in the US.
7
than one office units in the towers and also taking out vacant units, we collated a full
sample list of 342 firms for our survey purposes. A survey approach including two
rounds of following-ups with separate pre-paid returned envelopes was adopted to
encourage occupiers’ participation in the exercise. A good response rate of 17.8%,
which is composed of 61 responses from the occupiers, was attained in the exercise.
The profiles of the responding firms and the users are respectively summarized in
Figures 2 and 3. Firms are grouped into five categories (Figure 2). The finance,
insurance, real estate and banking services (15%) and the information technology,
media, telecommunications and dot-com businesses (20%) constitute about 35% of
the total firms responding to the survey. The majority of the respondents are firms
with employment size of less than 10 employees (49%) and between 10 and 49
employees (43%). The large size firms represented by more than 50 employees
constitute only 8% of the sample respondents. In term of the office size currently
occupied by the firms, the distributions as shown in Figure 3 indicate that more than
75% of the responding firms occupy office space of not more than 5,000 sqf, and the
large-floor occupiers with more than 10,000 sqf office space constitute about 11.5%
of the total sample firms.
[Insert Figures 2 and 3]
4.2
Office Space Determinants
In the survey, 36 factors varying from location, lease contract and structure, building
design and space, landlord-tenant relationships, branding, broadband and wireless
facilities to surrounding amenities were listed. The sample firms were asked to
evaluate the importance of the factors in influencing their office space selection
decision. They were also asked to rate each of the factor on a 5-point scale between 1
to 5, where 5 denotes the most important factor and 1 denotes the least important
(preferred) factor. The mean scores and standard deviations for each of the listed
factors were computed and the results were summarized in the ascending order from
the most preferred (the highest mean-scored) factors to the least preferred (the lowest
mean-scored) factors in Table 2.
[Insert Table 2]
The average score and the standard deviation for the 36 factors were estimated at
3.566 and 0.365 respectively. The most imiportant factor considered by the sample
firms in the office space decision was the “image and prestige of the office location.”
The emerging of Marina Center as an important commercial node, as a result, has
created strong competition to the prime office buildings in the traditional Raffles
Place CBD. Other factors that were ranked highly by the sample firms include the
accessibility of the office location by public transport (3.983), the flexibility of lease
terms (3.982), the responsiveness of management and maintenance teams (3.967) and
the availability of ample parking lots (3.967). The broadband and wireless connection
was also found to be highly important by the sample firms.
The location theory that emphasizes on the agglomeration economies of firms was not
supported in the study. Firms did not rank the need to be close to clients and support
services (3.138) and close to firms in the similar business line (2.881) as important
8
factors in their office space decision. The findings were consistent with those in
Leishman, Dunse, Warren and Watkins (2003), which showed relatively weak
attraction of the central location for the sample firms. The increase in the use of ICT
may be one of the reasons for the decline in the need to have face-to-face contacts
between firms (Ball, Lizieri and MacGregor, 1998; Gibson and Lizieri, 2001). The
proximity to port and airport was considered by the sample firms as the least
important factor in their office space selection criteria.
5.
Empirical Models for Firms’ Office Space Determinants
The empirical tests of the determinants of office space for occupiers in different
business operations and with different expectations are designed using the approach
that is commonly used in submarket literature (Bourassa, Hamelink and Hoesli 1999;
Dunse, Leishman and Watkins, 2001 and 2002). It involves three empirical
methodologies: principal component analysis, cluster analysis and discrete choice
model.
5.1
Principal Component Extraction of Office Space Determinants
The set of 36 office determinants rated by the sample firms in Table 2 were reduced to
a manageable and representative set of determinants using the principal component
data reduction technique, which is also generally known as factor analysis technique.
We a-priori fixed the number of principal components for extraction to eight, instead
of using the eigenvalue of more than 1 as the selection criterion in the analysis.13 The
results show that the eight principal components/factors account for more than 75% of
the cumulative variability of all the original variables. The statistic of the KaiserMeyer-Olkin (KMO) measures of sampling adequacy of 0.617 indicates that the
factor analysis is robust. The Bartlett’s test of sphericity also supports the model
based on the eight-principal components at less than 1% significant level.
The Varimax rotational technique is adopted to redistribute the variance more evenly
among the eight principal components without affecting the explanatory power of the
components. The original 36 office space determinants were reduced into 8 latent
factors, which are grouped according to their factor loadings in Table 3. “Pro-business
environment,” “branding and image,” “broadband and automation system,” “lease
structure,” “workplace quality,” “accessibility,” “international connectivity,” and
“agglomeration economies” are the 8 important factors that explain the variations in
the sample firms’ office choice decisions. The 8 latent factors would be used in the
discrete choice model in the subsequent section to further identify the relevance of the
factors in the office choice decision among firms, which are controlled for different
characteristics.
The extraction of the above eight latent variables does not, however, imply that the
excluded variables are insignificant in the office space decision. Attributes such as
building space, architectural features, surrounding amenities, parking facilities and
13
Based on the eigenvalue of more than 1 criterion, 10 principal components were extracted, but the
incremental variance explained by the two additional principal components was marginal.
Therefore, eight principal components are appropriate to capture a substantial portion of the
variances in all the 36 factors.
9
others were not significant enough in differentiating the preference of the sample
occupiers in their office space decision.
5.2.
Clustering of Firms by the Network Connectivity Criteria
The sample firms are heterogeneous in various aspects, and the preference for office
space will vary from firms to firms depending on the firms’ characteristics. In order to
better understand the relationships between the firms’ characteristics and their office
space choice decisions, we employ a two-step cluster analysis procedure to reduce the
61 respondent firms into homogeneous sub-groups, known as clusters. Based on a set
of a-priori selected variables consisting of three categorical variables that are business
type (cbus), size of existing office space (size), the length of occupancy in the current
office (length), two binary dummy variables indicating the firms’ preference to locate
close to firms/organizations in the same business line (line) and the willingness to pay
a rental premium to enjoy agglomeration economies by being close to competitors and
complimentary suppliers and services (prem), and a continuous variable measuring
the ratio of professional and managerial staff to total staff in the firm (RMP2emp), the
firms can be grouped into four discernible clusters. The cluster distributions and
profiles by each variable are summarized in Tables 4(a) and (b). Cluster 2 with
30.36% of the combined sample firms is the largest cluster, and the distributions of
firms in the other three clusters range between 21% and 25%.
The four firm clusters and their respective characteristics are summarized in Table 5.
Compared to other firms, firms in cluster 1 are composed of those that prefer strong
agglomeration economies within the office building, where they could reap the
benefits of network externalities14 by being able to establish face-to-face contacts with
competitors and complementary firms and clients. They are also willing to pay a
premium in office rents for the network effects. These firms are likely to have
relatively flatter organization structure with a high proportion of managerial and
professional staff in the office. The other three clusters are mainly differentiated by
the length of occupation of firms in the existing office premises. Firms that have been
in the current office premises for more than 6 years are also likely to be those with a
relatively larger group of support and clerical staff. The firm clusters variable as
represented by K = [1, 2, 3 and 4] was included as the dependent variable in the
multinomial discrete choice model in the next section to further examine the
relationships between the office space determinants and the company clusters.
5.3.
Discrete Office Space Choice Model for Suntec’s Occupiers
After reducing the 36 office attributes into 8 principal components, and classifying the
firms into 4 clusters using a-priori selected criteria, the next step is to identify the
factor choice of firms in different clusters. Using the firm clusters as the dependent
variable, we analyse the office space determinants that are deemed to be important in
the office space selection decision for different firms using a multinomial logistic
regression model with the following specification:
14
Network externality, in real estate context, can be referred to the agglomeration effects created by
the presence of a large number of firms in complementary or competing lines of business, which
collectively enhance the attractiveness of the building as a preferred office location.
10
J
z ik = ∑ β kj Fkj + ε ik ,
j =1
where
zik =
Dependent variable in the multinomial regression, which measures the
“probability” of the sample i-th observation being classified into one of
the k clusters of firms as defined in Table 5;
Fkj =
Factor score of the j-th principal component for the i-th firm, where
Factor 1= Pro-business environment
Factor 2= Branding & Image
Factor 3= Broadband & automations system
Factor 4= Lease features
Factor 5= Workplace quality
Factor 6= Accessibility
Factor 7= International connectivity
Factor 8= Agglomeration Economies
Regression coefficient for firms in the k-th unobserved cluster
βkj =
k = [1, 2, 3] indicate number of firm cluster. Cluster 4 is used as referenced
cluster in the model; and
j = [1, 2, .. , J], where [J = 8] represents the 8 latent principal factors derived in
the principal component analysis.
The regression statistics of the multinomial logistic model are summarized in Table 6.
The model fitting information shows that the final model outperforms the null model15
in fitting the observation at a less than 5% level of significance. The Pearson and
Deviance goodness of fit statistics do not reject the null hypothesis, which implies that
the final model fit the data adequately. The pseudo r-square as measured by Cox and
Snell, Nagelkerke and McFadden statistics show that the model explains a relatively
large proportion of the data variations ranging between 33% and 64%. The likelihood
ratio tests show that three principal factors: factor 1, factor 2 and factor 8 are
significant in explaining the probability of sample firms falling into either one of the
three clusters at 10% level.
Using cluster 4 as the reference group, the relative effects of the 8 principal factors on
the three firm clusters are summarized in Table 7. The results show that firms in
cluster 1 and cluster 2 place significant importance on the branding and image of the
office building, but they are less critical about the pro-business environment in the
building, compared to firms in cluster 4, which consist of firms with a higher ratio of
support and clerical staff. These firms in cluster 4 with longer presence in the building
may have established strong network and strategic alliance with other occupiers, and
therefore, they would be the group that favors the need for a pro-business
environment in the office building. The agglomeration economies factor is the third
factor that significantly differentiates the office space preference of firms in cluster 1
and clusters 3 vis-à-vis the cluster-4 firms. The cluster-1 firms, which indicate strong
preference to be close to businesses in the same line and willingness to pay a premium
for the benefits, choose to locate in a building that provides them with the
15
The null model is the base model with all the parameters set to zero.
11
convenience to interact with the competitors, suppliers and support services. However,
for the medium sized firms with offices in the range of 2,501 sqf to 5,000 sqf, and
occupy the existing premises from 4 to 6 years, the need to establish face-to-face
contacts with competitors and clients in the same building was relatively smaller.
Compared to cluster-4 firms, these firms may rely heavily on ICT in establishing
contacts with their clients and support services.
6. Conclusion
In a unitary city system, firms choose to locate in the city center that offers the most
efficient form of face-to-face communications with clients and suppliers. As a result,
the bid rent function is a monotonic inverse curve with expensive office space
concentrated in the center of a city. The agglomeration economies and the monotonic
bid-rent curve assumptions are distorted when the city growth does not emanate
uniformly from a nucleus city. Several sub-centers around the fringe of the city center
emerge to pull firms away from the CBD because of the diminishing agglomeration
economies caused by obsolete office buildings and increasing congestion in the CBD.
The emergence of Marina Center located at the North-Eastern fringe of Singapore
CBD is an example of the shift of the CBD centroid. The process of decentralization
can be expedited with firms increasingly adopting ICT-enabled new work practices.
The assumption of the agglomeration economies is not strictly binding, because firms
are heterogeneous and they place different priorities in the office space choice
decision. The behavioral literature has been advocating the need to understand the
factors that are important in influencing firms’ office space decisions. Extended along
the behavioral agenda, this study examined the office space preference of occupiers in
Suntec City via a structured questionnaire survey. In the mean score approach that
does not differentiate the firms’ characteristics, the image and prestige of the office
location and the accessibility by public transport are the two most highly ranked
factors by the firms. Proximity to competitors or firms in similar business line, which
was a proxy for agglomeration economies, was ranked the second lowest among the
list of 36 office space determinants.
By classifying the 36 office determinants into 8 latent principal factors using the
principal component methodology, the location and building factors were dropped out
from the list. Instead, factors like pro-business environment, branding and image
office, broadband and automation system, lease features, workplace quality,
accessibility, international connectivity and agglomeration economies were significant
in differentiating the firms’ choice of office space. We further cluster the sample firms
into 4 homogeneous clusters, and examine the office space preference of these firms
in the respective clusters. Firms that place significant importance on the face-to-face
convenience and image and branding of the office location will likely be those that
have a flatter organization structure. These firms are more willing to pay a rental
premium to be close to the competitors, suppliers and clients. The pro-business
environment factor appeals to firms that have already established a strong business
network in the building. These firms are likely to be those that have been in the same
premises for more than 6 years, and they have broader layered organization structure.
12
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14
Table 1: Breakdown of Space Usage in Suntec City
Name of Building
Use
NLA (sq ft)
Suntec City Mall
Suntec City One
Suntec City Two
Suntec City Three
Retail
Office
Office
Office
889,000
484,000
484,000
484,000
Date of
Completion
1994/95
1995
1995
1997
Office
Office
Exhibition &
Convention
484,000
394,000
1,076,000
1997
1994
1995
Total
4,295,000
Suntec City Four
Suntec City Five
Suntec International
Exhibition & Convention
Centre (SIECC)
Status
Rental basis
Strata sold
Strata sold
30% Strata
sold
Rental basis
Rental basis
Rental basis
15
Table 2: Ranking of Office Space Determinants
Ranking
Office Choice Factors
Mean
Score
Standard
Deviation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Image & prestige of office location
Accessibility by public transports
Flexible lease terms
Responsive management & maintenance teams
Ample parking lots
Broadband connection & wireless access
Food & beverage outlets
Competitive rents
Connected to major transportation nodes
Efficient mechanical, electrical and fire systems
Prime office space
Security & CCTV surveillance
Prestigious business address
Flexibility in space layout & large floor-plate
Orientation of office space
Prof-business management & promoting strategic
alliances
Low service charge
Forward looking & innovative landlord
Proximity to CBD
Effective communication & publicity
Availability of space for future expansion
Good tenant mix strategy
Presence of prominent companies / organizations
Quality architectural design & building finishes
Building automation & energy management system
Positioned as "The Business Capital of Asia"
Branded as "Asia's Vertical Silicon Valley"
Surrounding hotel, shopping & conventional facilities
Greenery & landscape
Good geomency
Business referrals/ introduction services
Proximity to major clients & support
services/suppliers
Sport & recretional facilities
Networking activities
Proximity to competitor/firms in similar business line
Proximity to port and airport
Average
Standard Deviation
4.082
3.983
3.982
3.967
3.967
3.918
3.902
3.895
3.885
3.836
3.831
3.803
3.797
3.787
3.705
3.705
0.843
0.900
0.876
0.856
1.016
0.936
0.926
0.859
0.896
0.969
0.723
1.014
0.805
0.777
0.760
5.714
3.702
3.672
3.557
3.525
3.508
3.508
3.508
3.492
3.475
3.466
3.368
3.350
3.344
3.230
3.148
3.138
1.034
0.906
1.057
0.942
0.977
0.942
0.960
0.722
0.976
0.977
0.957
1.087
0.964
1.283
1.062
0.926
3.033
2.902
2.881
2.533
3.566
0.365
0.983
0.995
1.001
1.096
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
16
Factor Loading
Table 3: Results of Principal Component Extraction
o Business referrals/ introduction services
o Networking activities
o Prof-business management & promoting
strategic alliances
o Effective communication & publicity
0.909
0.854
0.812
o Positioned as "The Business Capital of
Asia"
o Proximity to CBD
o Branded as "Asia's Vertical Silicon Valley"
o Prime office space
0.754
0.719
0.716
0.674
o Image & prestige of office location
0.649
o Efficient mechanical, electrical and fire
systems
o Security & CCTV surveillance
o Building automation & energy management
system
o Broadband connection & wireless access
0.850
0.796
0.783
o Flexible lease terms
0.894
o Low service charge
o Competitive rents
0.822
0.815
o Presence of prominent companies /
organizations
o Greenery & landscape
0.674
o Sport & recretional facilities
0.612
Accessibility
o Connected to major transportation nodes
o Accessibility by public transports
International
connectivity
Agglomeration
Economies
Branding &
Image
Broadband &
automation
system
Lease features
Workplace
quality
Cumulative
Variance
( %)
Pro-business
environment
Variance
(%)
Office Space Attributes
Total
Loading
Principal
Factor
Rotation Sums of
Squared Loadings
5.284
14.677
14.677
4.302
11.951
26.628
3.947
10.965
37.593
3.028
8.411
46.004
2.835
7.875
53.879
0.894
0.885
2.740
7.612
61.491
o Proximity to port and airport
0.732
2.687
7.463
68.954
o Proximity to major clients & support
services/suppliers
o Proximity to competitor/firms in similar
business line
0.838
2.253
6.258
75.212
0.717
0.612
0.614
0.810
17
Table 4(a): Results of Cluster Analysis – Cluster Distribution
Cluster
1
2
3
4
Combined
Excluded
Cases
Total
Number of
Observation
12
17
13
14
56
5
61
% of
Combined
21.429
30.357
23.214
25.000
100.000
% of Total
19.672
27.869
21.311
22.951
91.803
8.197
Significant Factor(s) in each
cluster at 5% level
Line, Prem, RMP2emp
Length
Length, Size
Length, RMP2emp
100
18
Table 4(b): Results of Cluster Analysis – Cluster Profile
1
2
Cluster
3
4
Combined
a) Continuous Variable*1:
(i) Ratio of Management & professional staff to total staff (RMP2emp)
Mean
0.755
0.559
0.810
0.547
(0.14)
(0.21)
(0.27)
(0.17)
b) Categorical Variable*2:
0.669
(0.23)
(i) Business Type (Cbus)
o Finance, insurance, real
3.000
2.000
3.000
1.000
9.000
estate and banking services
(33.33)
(22.22) (33.33) (11.11)
(100%)
o Information technology,
4.000
3.000
4.000
0.000
11.000
media, telecommunications
(36.36)
(27.27) (36.36)
(0.00)
(100%)
and dot-com business
o Professional srvices
0.000
1.000
0.000
2.000
3.000
(accounting, medical &
(0.00)
(33.33)
(0.00)
(66.67)
(100%)
legal)
o Trading, wholesales, retail
1.000
5.000
0.000
8.000
14.000
& delivery services
(7.14)
(35.71)
(0.00)
(57.14)
(100%)
o others (consultancy, oil,
4.000
6.000
6.000
3.000
19.000
pharmaceutical)
(21.05)
(31.58) (31.58) (15.79)
(100%)
(ii) Size of existing office space (Size)
o Below 2,500 sqf
3.000
10.000
0.000
9.000
22.000
(13.64)
(45.46)
(0.00)
(40.91)
(100%)
o Between 2,501 sqf and
5.000
5.000
10.000
1.000
21.000
5,000 sqf
(23.81)
(23.81) (47.62)
(4.76)
(100%)
o Between 5,001 sqf and
1.000
2.000
0.000
4.000
7.000
10,000 sqf
(14.29)
(28.57)
(0.00)
(57.14)
(100%)
o Above 10,000 sqf
3.000
0.000
3.000
0.000
6.000
(50.00)
(0.00)
(50.00)
(0.00)
(100%)
(iii) Length of occupancy in existing office (length)
o Less than 3 years
9.000
0.000
0.000
26.000
17.000
(34.62)
(65.38)
(0.00)
(0.00)
(100%)
o 4 to 6 years
2.000
0.000
8.000
5.000
15.000
(13.33)
(0.00)
(53.33) (33.33)
(100%)
o More than 6 years
1.000
0.000
5.000
15.000
9.000
(6.67)
(0.00)
(33.33) (60.00)
(100%)
(iv) Importance of being close to businesses in similar lines (Line)
o No
0.000
16.000
13.000
14.000
43.000
(0.00)
(37.21) (30.23) (32.56)
(100%)
o Yes
1.000
0.000
0.000
13.000
12.000
(92.31)
(7.69)
(0.00)
(0.00)
(100%)
(v) Willingness of firms to pay a rental premium for agglomeration economies from
competitors and complimentary firms (Prem)
o No
4.000
17.000
11.000
14.000
46.000
(8.70)
(36.96) (23.91) (30.44)
(100%)
o Yes
0.000
2.000
0.000
10.000
8.000
(80.00)
(0.00)
(20.00)
(0.00)
(100%)
*1 – The statistics in the column are mean values and standard deviations (in parentheses) for the continuous variables
*2 – The frequency and percentage of combined observations (in parentheses) are given for the categorical variables.
Statistics highlighted in bold are significant at 95% level of confidence
19
Table 5: Clustering of Firms and the Distinguishing Characteristics
Cluster
1
2
3
4
Description of firm characteristics
Firms in this cluster in general have higher proportion of managerial,
executive and professional staff in the office, and they are firms that
find it highly important to be located close to businesses in the similar
line. They are willing to pay a premium to get connected to a network
of competing and complementary firms and enjoy the agglomeration
economies.
Majority of the firms are relatively new and they have moved into the
current office premises for less than 3 years
Firms occupying medium size office space with floor area between
2,501 sqf and 5,000 sqf, and majority of them have been in the
existing space between 4 to 6 years.
Firms with the longest occupancy history. Most of them have been
occupying the existing office premises for more than 6 years, and they
are firms with the lowest proportion of managerial, executive and
professional staff in the office
20
Table 6: Model Statistics for the Multinomial Logistic Regression
A) Model Fitting Information
Model
-2 Log Likelihood
Null
Final
127.539
85.238
Chi-Square
Degree of
freedom (d.f.)
Significance
(sig.)
42.301
24
0.012
Degree of
freedom
114
114
Significance
Chi-Square
Degree of
freedom
3
3
3
3
3
3
3
3
B) Goodness-of-Fit
Chi-Square
Pearson
Deviance
99.409
85.238
0.833
0.980
C) Pseudo R-Square
Cox and Snell
0.601
Nagelkerke
0.641
McFadden
0.332
D) Likelihood Ratio Tests
Effect
-2 Log Likelihood
of Reduced Model
Factor 1
101.345
Factor 2
92.020
Factor 3
87.268
Factor 4
85.332
Factor 5
86.217
Factor 6
86.460
Factor 7
88.234
Factor 8
108.108
16.107
6.783
2.030
0.094
0.979
1.222
2.996
22.870
Significance
0.001
0.079
0.566
0.992
0.806
0.748
0.392
0.000
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced
model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is
that all parameters of that effect are 0.
21
Table 7: Parameter Estimates of Individual Effects of Principal Factors
Firm
Cluster
Principal
Factor
β
Std.
Error
Wald
Factor 1
-1.828**
0.884
4.280
Factor 2
1.330*
0.787
2.860
Factor 3
0.769
0.726
1.123
Factor 4
-0.226
0.739
0.094
Factor 5
0.335
0.686
0.239
Factor 6
-0.524
0.663
0.624
Factor 7
-0.796
0.766
1.079
Factor 8
2.343**
1.013
5.355
Factor 1
-2.646***
0.916
8.337
2
Factor 2
1.641**
0.780
4.430
Factor 3
0.789
0.666
1.402
Factor 4
-0.155
0.734
0.045
Factor 5
0.647
0.677
0.913
Factor 6
-0.525
0.688
0.583
Factor 7
-1.171
0.802
2.133
Factor 8
1.096
0.959
1.306
3
Factor 1
-0.390
0.624
0.390
Factor 2
0.161
0.482
0.111
Factor 3
0.763
0.595
1.647
Factor 4
-0.073
0.612
0.014
Factor 5
0.239
0.469
0.260
Factor 6
0.143
0.631
0.051
Factor 7
-0.026
0.662
0.001
Factor 8
-1.172*
0.707
2.745
The reference category is: 4.
Exp(β) is the odd ratio
*** significance at 1% level
** significance at 5% level
1
d.f.
Sig.
Exp(β)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.039
0.091
0.289
0.760
0.625
0.429
0.299
0.021
0.004
0.035
0.236
0.833
0.339
0.445
0.144
0.253
0.532
0.739
0.199
0.906
0.610
0.821
0.969
0.098
0.161
3.782
2.159
0.797
1.399
0.592
0.451
10.416
0.071
5.162
2.201
0.856
1.909
0.591
0.310
2.992
0.677
1.174
2.145
0.930
1.270
1.154
0.975
0.310
* significance at 10% level
22
Figure 1: Suntec City and the Central Business District (CBD)
Suntec City
Source: URA & Suntec City Development Limited
Figure 2: Profile of Responding Firms in the Survey
Information
T echnology, Media,
T elecommunication
& Dot-com businesses
20%
Finance, Insurance,
Real Estate &
Banking Services
15%
Professional Services
(Accounting, Medical
and Legal)
5%
T rading, Wholesales,
Retail & Delivery
Services
25%
Others (Consultancy,
Oil, Pharmaceutical,
Government)
35%
23
Figure 3: Office Size Distributions for Sample Responding Firms
7
More than 10,001 sqf
Between 5,001, sqf and
10,000 sqf
8
Between 2,500 sqf and
5000 sqf
21
25
Below 2,500 sqf
0
5
10
15
20
25
30
24