Division of Building and Real Estate Economics
Department of Infrastructure
Royal Institute of Technology, Stockholm
Master of Science Thesis No: 291
Economies of Scale in Shopping Center
Industry
Author:
Qiong Wang
Supervisor:
Professor Stellan Lundström
Stockholm, April 2005
1
Master of Science Thesis
Title:
Economies of Scale in Shopping Center Industry
Author:
Qiong Wang
Department
Department of Infrastructure,
Division of Building and Real Estate Economics,
Royal Institute of Technology, Stockholm
Report reference: Thesis no. 291
Supervisor:
Stellan Lundström
Keywords:
Real estate, Economies of scale, Shopping centers, Cost, Size
Abstract
During recent decades the real estate industry has been undergoing tremendous
expansions, and the shopping center industry is also witnessed consolidations,
mergers and acquisitions of holding companies and size increasing of shopping
centers. Under the assumption that the inspiration of these activities is to pursue
efficiency benefits from economies of scale in larger shopping centers and holding
companies, this thesis uses different methods to test the existence of scale economies
in two shopping center holding companies and further proposes a method to
investigate scale economies for shopping centers through cost structure analysis.
The relationship between firm profitability and firm asset size is found to be direct in
regression analysis while to remain ambiguous in trend analysis due to some mixed
results. Additionally, the largest potential economy source is common area
maintenance expenses and the strongest sources are most likely to be the insurance
expenses or general and administrative expenses. Further researches on scale
economies for shopping centers could examine cost savings from the increasing
operation size and the externalities across stores (or anchors) and the hosting center.
2
Acknowledgement
I would like to thank my supervisor Professor Stellan Lundstöm for his helpful
inspiration and guidance. The teaching and administrative staff in Division of
Building and Real Estate Economics are highly appreciated and respected.
Great gratitude to Dr. Bers Martina and Dr. Thomas Springer for their advice
and direction in my thesis writing and their contribution to researches on scale
economies of real estate industry.
My thanks also to the respondents interviewed during the research who found
time to respond to my questions despite of their busy schedules and who
provided help for the collection of information: Stina Samuelsson at
Centrumutveckling; Christina Gustafsson at IPD; Mike Tubridy at ICSC; and
Kelvin Huang at SRE, Singapore.
In addition, I like to express my appreciation to my classmates and friends for
their encouragement, constructive suggestion and revision.
3
Table of Contents
1.
Introduction ..........................................................................................................................5
1.1.
1.2.
1.3.
1.4.
1.5.
2.
Background ...............................................................................................................5
1.1.1.
Overview of Real Estate Industry .....................................................................5
1.1.2.
Shopping Center Terms .....................................................................................6
Statement of problems and Objectives....................................................................11
Methodology ...........................................................................................................11
Limitations ..............................................................................................................11
Structure ..................................................................................................................11
Theoretical Background .................................................................................................13
2.1.
2.2.
Introduction to economies of scale..........................................................................13
Economies of scale in real estate industry-- Review of literature ...........................14
2.2.1.
Research on Real Estate Investment Trust .................................................14
2.2.2.
Methodology to test economies of scale .......................................................17
2.3.
Economies of Scale in Shopping Centre Industry...................................................18
2.3.1.
Internalities for Shopping Centres...............................................................19
2.3.2.
Externalities for Shopping Centres..............................................................19
3.
Analysis of Economies of Scale for Shopping Center Industry ..........................23
3.1.
3.2.
3.3.
Brief Presentation of Atrium and Westfield ............................................................23
Data .........................................................................................................................24
Methods...................................................................................................................25
3.3.1.
Regression analysis........................................................................................25
3.3.2.
Trend analysis................................................................................................26
3.4.
Translog cost model ................................................................................................27
3.4.1.
Data required.................................................................................................27
3.4.2.
Translog cost function...................................................................................31
4.
Results ..................................................................................................................................32
4.1.
4.2.
4.3.
5.
Results from regression analysis .............................................................................32
Results for Trend Analysis ......................................................................................33
Remarks of possible results for translog cost model...............................................37
Conclusion ...........................................................................................................................39
Reference
4
1.
Introduction
1.1. Background
1.1.1.
Overview of Real Estate Industry
Real estate companies has experienced tremendous expansion and consolidation over the last
decade, and the implication is that companies are in an effort to pursue the gains resulting from
any efficiency in production and operation with an increasing size under an assumption that
economies of scale occur in real estate industry. Most obvious evidences being observed are from
Real Estate Investment Trusts (REIT), where signals from real estate investment growth in income
producing properties and the ascending REITs capitalization are generally used to support the
argument of the existence of economies of scale.
From 1997 to 2004, REITs were seen an expansion of market capitalization from 1.5 billion
dollars to 308 billion dollars, and the number of REITs increases from 34 to 1921 It is explained
that REITs are excellent tools to capture economies of scale opportunities because there is no
equity capital constraint for REITs (Ambrose, Highfield and Linneman, 2004)
Following this trend, shopping center industry expands dramatically in different markets. Since
1986, the total number of US shopping centers has increase 65 per cent, the total area expanded
nearly 6 billion square feet, and more than 90 per cent of shopping centers are REIT occupied2. In
Sweden, tremendous expansion on the shopping centre market with store area increasing by
almost 50 per cent between 1995 and 2000 alone3.
Individual retail property companies and REITs have increased size by mergers, acquisitions and
consolidations. The Australia-based REIT Westfield Group created the largest shopping center
owner in the world by equity capitalization with total asset of 34,281 million dollars (at the end of
2004) by aggressive mergers and acquisitions, and now holds a portfolio of 124 shopping centers
and 36.5 billion dollars asset under management4. Taking it as an example to exploit economies of
scale, if we assume that Westfield Group is seeking efficiency benefits in operation and
management with the increasing size, we can get some implications of economies of scale by
simple analyses on various performance measures of retail property companies, whereas the
inaccuracy of simple analyses requires the sophisticated researches.
The debate over the existence of economies of scale has lasted for decades and three general
results about REIT economies being discussed and tested by variable methods processing data
from different sectors through different time periods. They are existence of economies of scale,
existence of diseconomies of scale and existence of economies of scale for certain REIT type.
1
Data from Historical REIT Industry Market Capitalization: 1972-2004
2
Data from ICSC (International Council of Shopping Centers)
3
Data from NCSC (Nordic Council of Shopping Centers)
4
Data from Westfield Group, www.westfield.com
5
Meanwhile, big shopping centers become even larger. For example in Sweden, individual
shopping centers increase significantly in square areas by refits and redevelopment due to limited
new supply of retail premises. Most of Swedish shopping centers now are experiencing expansion.
In the Stockholm region, Vällingby centre is expanding by further 18,000 M2, taking its total store
area to 46,000 M2 in 2005. Skärholmen centre is undergoing a refit and is expanding by 20,000 M2
to cover a total store over 80,000 square meters5.
One of the motivations of the expansion activities is that the inclusion of more entertainment and
food courts should take shopping centers more retail traffic and increase the sales of other retail
stores, though its effects are doubted by some researchers (Haynes, Joel B. and Salil Talpade, 1996)
that consumers who are drawn by entertainments are less likely to visit shops rather than food
courts.
1.1.2.
Shopping Center Terms
Shopping Center Definitions
The term “shopping center” has been evolving since its first presence in 1920s. As the shopping
center industry has grown and changed, more types of centers have evolved and the content of the
term has been enriched. There are various definitions for shopping center, whereas, the
internationally accepted term of shopping center is given by Urban Land Institute (ULI),
Washington D.D., USA.
A group of retail and other commercial establishments that is planned, developed and
managed as a single property. On-site parking is provided. The centre’s size and
orientation are generally determined by the market characteristics of the trade area
served by the centre.
There are four basic types of shopping centres, neighbourhood, community, regional and
superregional, classified according to major differences in size, number of anchors, and trade
area, which are typical characteristics of each type. As the shopping centre industry matures,
numerous types of shopping centres exist currently that go beyond the originally four basic
classifications. And all the centres remain in two basic configurations:
Mall: Malls are typically enclosed, with a climate-controlled walkway between two facing
strips of stores. The term represents the most common design mode for regional and
superregional centres and has become an informal term for these types of centres.
Strip Centre: A strip centre is an attached row of stores or service outlets managed as a
coherent retail entity, with one-site parking usually located in front of the stores. Open
canopies may connect the storefronts, but a strip centre does not have enclosed walkways
5
Data from Atriumfastigheter AB
6
linking the stores. A strip centre may be configured in a straight line, or have an “L” or “U”
shape.
Classifications of Shopping Centres in Different Countries
Following American ways to develop and operate shopping centers, professionals in most other
countries innovate management methods of shopping centers and adapt centers to the local society.
Different countries therefore categorize shopping centers a little differently according to
geographic conditions, demographic structures and social custom.
ICSC has defined eight principle shopping centre types in America, neighbourhood, community,
regional, superregional, fashion/specialty, power, theme/festival, outlet and lifestyle. The details
of definitions of these eight types are shown in Table 1.
As stated in the Centrumutveckling Shopping Center Directory 04/05, the definition of shopping
center by ICSC would exclude many of 320 Swedish shopping centers, since they consist of
different properties and have different owners and management. So the classifications of shopping
centers in Sweden are modified and adapted to the local situation and need, see table 2.
7
8
Table 2. Swedish Shopping Center Definitions: Centrumutveckling Shopping Center Directory 04/05
Type of shopping
Concept
center
Squaremet
Number
Number
of
ers (GLA)
of shops
inhabitants in
Type of stores/ services
market area
Neighbourhood
Convenience goods and personal services
3,000—
7--15
15,000
A large supermarket (anchor), a post office, a
/Convenience
bank, a limited number of durable goods.
center
7,000
Community/
Durable goods, convenience
7,000—
Municipality/
16--35
30,000-60,000
20,000
stores, a café shop, a post office, a bank, and a
Town center
state owned liquor store.
District Center in
Larger districts or groups of districts have
major cities
a District center
Regional Center
Power
One/two large supermarket, national chain
Center/
N/A
N/A
N/A
All retail categories
50,000--6
More than
More
Entertainments
0,000
50
10,000
8,000—15
From 4-5
A number of very large retail units in different
,000
and up
retail categories.
10,000--
N/A
Special type of out-of-town centers
Retail Park
Factory Outlets
Second-rated products at a discount price
As offered in Community centers
than
One/more department store like hypermarket,
large supermarkets, large national chain stores
N/A
Retail units owned and run by manufacturers
from manufacturers of e.g. textile, or
and designer companies.
glassware
Theme center, city
A
center/city galleries
merchandise
Retail
clusters/
Trading centers
narrow
but
with
deep
a
selection
specific
of
retail
A
few
15--
thousand
category, located at a density of visitors
upwards
Agglomeration of retail units without
N/A
More
50,000
than
Niche of retail market, retail category could be
fashion, home furnishing, leisure/sporting goods
or restaurant or entertainment.
N/A
N/A
Category-killers
original planning, have a large catchment
9
1.2. Statement of problems and Objectives
Economies of scale in real estate industry could explain the expansion trend of shopping center
industry. A cost continuum with the variable cost at the one extreme and the fixed costs at the
other is believed to exist in any operating company. The total cost for a shopping center can be a
hybrid cost taking on the characteristics of variable costs and fixed costs. If shopping centers
operate near to the fixed end of the cost continuum, they will have a large degree of potential
economies of scale associated with the total costs.
The objective of this study is to assess economies of scale in shopping center industry from the
following two respects:
* Do retail property firms (shopping center owners) improve profitability as the asset size
increasing?
* Analyzing the cost structure of shopping centers to identify the source of economies of
scale.
1.3. Methodology
Three research methods are adapted in this study in order to examine the existence of scale
economies for both retail property companies and shopping centers. Regression analysis is used
to investigate the statistic relationship between the performance and the size of a retail property
company and shopping centres. Trend analysis summarizes the information from historical
records in an attempt to describe the evolution of the retail property company and shopping
centers. And a shopping center cost structure analysis with the application of the translog cost
function is proposed to test economies of scale for the shopping centre industry.
1.4. Limitations
The scope of proposed research analyses is limited by data availability. Unlike publicly
published REIT database in the U.S., there is less existing database specially containing the
financial and cost information of retail properties in Sweden or other countries. In this sense, the
data cannot capture the true size of the industry studied. And the results of the study are solely
based on the data available by annual reports of Atrium Real Estate Company and Westfield
Holding Company.
1.5. Structure
This thesis comprises six parts. The first part presents objectives of the thesis and briefly
introduces the outline. The second part is the theoretical presentation of economies of scale,
including the literature review of REIT researches and shopping center researches and the third
part consists of shopping center definitions and classifications. Case studies with the application
of research methods proposed to test economies of scale in the shopping center industry are
established in the fourth part and results are discussed in the fifth part. The final part is the
conclusion of this thesis with further research recommendations.
11
2.
Theoretical Background
2.1. Introduction to economies of scale
Economies of scale refer to increasing returns to scale (IRS), which exist when the cost of
producing one unit of a product or service decreases as the volume of production increases.
In economics, economies of scale and diseconomies of scale have been often used. Though
the definitions of these concepts remain ambiguous. Here definitions given by Hanoch
(1975, p.492), and Takayama (1994, p.157) are briefly presented, and it is obvious that they
are used under different conditions and can be equal when certain requirements are met.
Hanoch’s definition is most widely used and goes as follows. IRS is said to prevail if output
increases more than k times when all factors are increase by k times (k > 1). Define
parameter α by
α(k,x)≡f(kx)/[kf(x)],
(1)
f(x) is the production function of input x, then IRS(α) is said to prevail, if
α(k,x) > 1 for all k >1 , and
α(k,x) < 1 for all k such that 0<k<1
(α(k,x)=1, when k=1),
According to Takayama’s textbook, IRS can be defined in terms of the cost elasticity of
output θ, which can be a measure of economies of scale,
θ(y)≡MC/AC=(dC/dy)/(C/y)
(2)
where y=f(x) and C(w, y) is the cost function obtained by the usual cost minimization procedure.
w is the price of the input factor x. MC is the marginal cost and AC is the average cost.
Therefore economies of scale occur, that is, IRS(θ) prevails, if θ<1. This means that one per
cent increase in output results from less than 1 percent increase in the total cost. This definition
will be used in the later part of this study to test the economies of scale for shopping center
industry.
There are many reasons for the rises of economies of scale. In large-scale producing industries, a
large operation allows labour specialization that improves the productivity and reduces the
labour switching time. Meanwhile, the increasing output volume makes it possible to use more
indivisible inputs with higher productivity and hence reduce the costs. Economies of scale tend
to happen for the capital-intensive industries like automobile industry and oil refining business,
which require a large number of investments to start up. These industries have a high fixed cost
while relatively lower variable costs, as the outputs increase, total costs will increase at a
decreasing rate, and economies of scale are exploited.
13
Economies of scale can be classified into internal economies and external economies. The former
refers economies of scale existing within the scope of a firm, while the latter occurs outside of a
firm, within an industry. In urban economics, there are two classes of external economies of scale,
localization economies and urbanization economies.
Localization economies are external economies of scale occurring when firms producing the
same products locate to one another to benefits from sharing the supplier’s intermediate inputs,
sharing a pool of labour, and sharing information, therefore the production costs of individual
firms decreases as the total output of the industry increases.
Unlike economies from clustering at the same location, urbanization economies occur if the
production cost (in average) of an individual firm decrease as the total output of the urban area
increases and are economies of scale across industry boundaries. Economists think that if there
are urbanization economies, what matters is the size of the urban economy, not the size of a
particular industry, so expect larger cities to generate higher productivities, more plant births, and
faster employment growth.
2.2. Economies of scale in real estate industry-- Review of literature
After the worldwide saving and loan crisis in the late 1980s, real estate investors who used to
finance investments by debt turned to the public capital market. Since 1994, mergers,
acquisitions and consolidations have become a new trend in the industry, and these activities
make the real estate companies (or REIT size) much larger than ever. Whether the real estate
companies (funds or trusts) can benefit from big sizes becomes a hot topic for real estate and
finance researchers.
2.2.1.
Research on Real Estate Investment Trust
Researches on economies of scale in real estate industry reveal some interesting and mixed
results. This section briefly introduces researches on REIT from information perspective.
Though economies of scale are defined as situations of increasing outputs associated with
diminishing average costs, and thus can be measured, it is not an easy task to identify economies
of scale for real estate industry or REIT. According to Ambrose, Highfield and Linneman, (2004)
the difficulty arises for two main reasons.
First, given the relatively similar size of most REITs and their recent integration, the statistical
technology available to accurately measure economies of scale is not sufficiently precise to fully
capture across sectional variations.
Second, the effort requires by growing firms to capture scale economies is difficult and time
consuming, with the pain of integration generally occurring prior to the realization of the
benefits. Thus, exploring cross sectional variations in the presence of significant merger and
acquisition activity understates the benefits of scale.
14
Economists measure economies of scale for REITs by analyzing effects of merges, acquisition
and/or consolidation on security prices and growth of profits, investigating the cost structures
and inspecting performance measures for different classifications of REITs.
Merges, acquisitions and consolidations impact on investors’ expectation of efficiency of
integrated REITs, reflect on an increase or decline on share prices, and hence add value to
shareholders´ wealth. So it is stated that one of the main driving forces behind consolidation in
real estate industry is the belief that larger REITs should obtain some measure of efficiency
gained in improving profit margins by increasing revenues and lowering expenses (Ambrose,
Highfield and Linneman, 2004).
Allen and Sirmans (1987) measure the effects of REIT mergers on the wealth of the acquiring
trust's shareholders and find a significant increase in shareholder wealth, which is attributed to
the improved management of the acquired trust's assets, suggesting potential economies of scale.
Thinking of the limited sample size of A&S’s study, McIntosh, Officer and Born (1989) extend
the analysis and further testify the A&S’s result by increasing sample size.
The opposite results found by McIntosh, Liang and Tompkins (1991) indicate that the small
REITs earn higher returns with no more risk than large REITs during 1974 to 1988. This result
proves the small-firm effect in banking industry also works for REITs securities.
Ambrose, Ehrlich, Hughs and Watcher (2000) study whether or not there are gains to
consolidation due to economies of scale from size, brand imaging and informational gains from
geographic specialization. Using data on 41 multi-family equity REITs from 1990s, they
construct shadow portfolios that mimic each REIT’s exposure to changes in local market
conditions and separate the growth resulting simply from market growth. Results indicate that
net operating income (NOI) growth rates of small REITs exceed average growth rates in their
holding-property markets, and hence small REITs appear to be generating revenues and
operating under economies, which it is not the case for large REITs.
Since economies of scale occur if an increase in a firm’s inputs is less than directly proportional
to increase in its outputs, economists also explore scale economies in real estate industry from
the cost side of REITs.
Research by Capozza and Seguin (1996) analyses the effect of managerial style on firm value by
partitioning general and administrative (G&A) expenses in REIT industry into non-discretionary
“structural” component associated with the costs of asset and liability management and a
discretionary or “style” component. They find that the structural component of G&A expenses
increases with assets and liabilities under management at a decreasing rate, which suggests
economies of scale in managing.
Bers and Springer (1998) identify the source of the scale economies by applying the estimation
methodology, translog cost model (TLC), to various cost categories. By investigating the cost
15
categories—G&A expenses, management fees, operating expenses, interest expense and interest
expense as a percentage of total liability—as potential sources of economies of scale for REITs,
they find that the strongest sources of economies of scale are G&A expenses and management
fees, which are also the smallest components of total costs, while the largest cost components,
namely, operating expenses and interest expense, show either modest scale economies or
diseconomies of scale in the study. Therefore, it is suggested that the REITs seeking to merge
with or to acquire other REITs should seek out targets that have larger percentages of total
expense in G&A expenses and management fees.
The heterogeneity is the most important feature of real estate industry. Different characteristics
of different sectors in the real estate industry, as well as differences in size, are also introduced as
the possible reasons for economies of scale for REITs.
By using two-way analysis of variance to test for differences among size and property-type
categories of REITs from 1985-92, Capozza and Lee (1995) find that retail REITs trade at
significant premiums relative to the average REIT while warehouse/industrial REITs trade at
discounts and small REITs trade at significant discounts while large REITs trade at premiums.
But they do not explore the possible sources of the premiums and discounts.
Crober (1998) states that Equity Residential Trust’s biggest competitive advantage is the
ability to invest in high quality personnel, which results from of its size; and implies that
larger REITs, with higher quality management, will be better positioned to acquire
properties and position them for rent growth. He suggests that a key component of the
economies of scale argument is the idea that larger REITs are better able to identify
properties whose rents will grow faster than the overall market rent.
The working paper by Bers and Springer (1997) shows the existence of economies of scale for
REITs, and difference in leverage, management type and the degree of investment in mortgages
clearly affect the level of scale economies achieved by individual REITs. The study finds that the
impact of diversification on the estimated scale economies is inconsistent. Moreover, they find
there is an optimal size for REITs, which cannot be isolated because of its dependence on the
characteristics of the individual REITs and variance overtime.
A later study by Bers and Springer (1998) further extend their earlier research on the sources of
scale economies, by assessing differences in scale economies between various classifications of
REITs, which are categorized according their management, type of investment, capital structure,
the extent to which they diversify across property types, and dominant type. The translog cost
model is still adapted to derive the scale economy estimates. The study shows that REITs
experience scale efficiencies differently according to their characteristics: the externally
managed REITs, mortgage REITs, low-leverage REITs and well-diversities REITs have larger
estimated scale economies, compared to other classifications in their respective categories.
Ambrose and Linneman (2001) test the hypothesis that scale economies exist due to firm size by
regression analysis and get results that larger firms have higher profit margins and rental revenue
16
ratios, lower implied capitalization rates and lower costs of capital, but there is not a statistically
significant relationship between firm size and expense ratios.
Through examining growth prospects, revenue, and expense measures, profitability ratios,
systematic risk and capital costs, Ambrose, Highfield and Linneman (2004) investigate scale
economies in REITs using a sample of 187 equity REITs trading in U.S. markets from January
1990 to December 2001. They find that large REITs are increasing growth prospects while
succeeding at lowering costs, leading to a direct relationship between firm profitability and firm
size, which is coincide with Ambrose and Linneman’s findings (2001).
In all, researches on economies of scale for REITs have two main strains. One demonstrates the
small-firm effect that larger economies occur for small REITs than for big ones; and this result
seems to be based on analyses of REITs before mid 80s. The other thinks that larger REITs can
generate economies of scale, while with diminishing returns. There are also diseconomies
existing for certain types of REITs.
2.2.2.
Methodology to test economies of scale
Two techniques are commonly used to test economies of scale for real estate industry; they are
translog cost model (TLC) and data envelopment analysis (DEA).
Translog Cost Model
Translog cost model is a regression-based technique. It is widely used in scale economy studies.
This technique is based on a translog cost function, which is applied when there are many
outputs and inputs. That is:
Outputs: y= (y1,….., ym);
Inputs: w=(w1,…..,wn )
Then,
m
n
ln C ( y, w) = a 0 + ∑ a i ln y i + ∑ bi ln wi
i =1
+
m
i =1
n
1
1
a
ln
y
ln
y
+
bij ln wi ln w j
∑∑ ij i j 2 ∑∑
2 i =1 j =1
i =1 j =1
m
n
m
(3)
n
+ ∑∑ g ij ln y i ln w j
i =1 j =1
C(y,w): the cost to produce outputs y by input w.
In real estate research, the outputs are usually total assets, and the inputs are various cost control
factors, such as financial leverage, management type and so on (Bers and Springer, 1997 and
17
1998). Economies of scale are measured by the elasticity of costs with respect to total assets,
which is calculated as the ratio of the percentage change in costs to a percentage change in total
assets accordingly. Scale economy estimator (SCE) is the reciprocal of the elasticity (average
elasticity for total costs), when it is higher than 1, economies of scale exists, which means that an
additional unit of input is associated with more than a unit of output, and vice versa.
While DEA modelling requires a set of sophisticated mathematics, it has advantages when
regression based techniques lead to model sensitivity and functional instability.
Data Envelopment Analysis
DEA is a non-parametric, linear programming technique used to construct an efficient cost
frontier to measure the relative efficiency. This technique is employed in researches on a broad
range of industries to measure performance and economies of scale.
When using DEA to detect economies of scale for real estate industry, the efficient frontier is
constructed by examining linear combinations of sample firms and determining the minimum
input usage necessary to achieve a given output level. The inputs can be proxy with measures of
various costs, specially operating costs, general and administrative cost, management fees, and
interest expenses, resulted from various activities involved in operating firms. Total assets are the
measure of the output. A firm with given level of output (total assets) with minimum input usage
(costs) is said to operate on the efficient cost frontier and is identified as X-efficient. If there is
any additional input, the firm is classified as X-inefficient, and this overall inefficiency is
decomposed into deviations from the efficient frontier as a function of poor input utilization and
failure to operate at constant returns, which is defined as scale inefficiency.
2.3. Economies of scale in shopping centre industry
Like for other sectors in real estate industry, economies of scale for shopping centres are detected
by whether there are reductions of average costs resulting from the increasing size, which can be
measured by market capitalization or be proxy with physical area, since some researchers believe
that the relationship between the area and value of a shopping centre is generally directly
proportional.
Retail leases, which require an overage rent that equals a portion of the difference between retail
sales of stores and the threshold sales and add this rent to the base rents, are designed differently
from the housing contracts and office leases, and the financial success of a shopping center is
tied to the ability of retailers to increase sales productivity. A center depends in large measures on
this increase to produce a continually growing rental income stream, so shopping centre owners
can benefit from economies of scale from different sources other than common ones such as
costs.
To get a comprehensive understanding of scale economies in shopping centre industry, this
section presents economies of scale both for property owners and retail tenants. According to
18
shopping centre literatures, economies of scales can be tested within the domain of a shopping
centre, the market area shared by competitive shopping centres or real estate companies holding
a shopping centre portfolio, and across the borders of shopping centres and retail stores.
2.3.1.
Internalities for Shopping Centres
Internal economies of scale can occur both for a single shopping centre and for a property
company that owns a number of shopping centres. The possible main sources of internalities are
cost reductions, which means the management quality and centre size play an important role in
the process of achieving internalities. As Sirman and Guidry (1993) demonstrate, shopping
centre area, age and anchor tenants increase shopping centres’ attractiveness and centre rents.
For shopping centers, it means that they have to deal with property taxes, lease accounting and
administration, tenant administration, the property’s long-term planning, annual budgeting and
ongoing forecasting, the monthly operating statement, the timely reporting to owners and
partners, the collection of receivables, payrolls and all the other concerns for management funds,
as well as maximizing the value of assets.
Like portfolio managers, shopping centre owners should decide on structure, which is the
number and categories of tenants contained in the centre, and stock, which is the amount of space
that will be allocated to each chosen tenants. Tenant mix together with the architectural design,
location selection and advertisement promotion create a unique retail image of the shopping
centre, which is the consumers’ perception of store attribute. The inputs to this process comprise
of development, construction, operation and maintenance, renovation, management, and
financing, while the outputs are rental incomes and market value of the shopping centres. So
internal economies of scale for shopping centres can be explored by owners’ active management.
For retail property companies, expenses can be saved from efficient operations and also enhance
the centre’s bottom-line that might achieve internalities. However, the research by Bers and
Springer (1998) shows that diseconomies exist for regional shopping center REITs due to the
heterogeneity.
2.3.2.
Externalities for Shopping Centres
As the supplier of store spaces where retail activities house, shopping centres are closely related
with retailers (tenants), this relationship was described as symbiosis, since shopping centres will
get a higher rental income generated from increasing retail sales, while retail stores gain more
customer visits attracted by shopping centre image (or anchor’s image as some researchers
argue). Shopping centres develop with the change of retail shopping behaviours and retailing
patterns. Even the shopping centre literature has followed the historic practices of retail
practitioners rather than leading them to a new horizon (Eppli and Benjamin, 1993).
The first study on externalities for retailers and centres is the Central place theory (Christtaller,
1920), which acclaims that heterogeneous stores tend to cluster in the centre of the market. Retail
19
agglomeration economies (Hotelling, 1929), based on central place theory and the principle of
minimum differentiation, and retail demand externalities, which is newly emerging these years,
offer a theoretical foundation for studies on economies of scale in shopping centre.
Hotelling’s model reveals that two competing firms selling a homogenous product will locate in
the centre of the market. Though many economists at that time thought that the cluster of
homogenous firms is socially wasteful and economically unstable for retailers, Hotelling’s
supporters found that consumers desire to do comparison shopping and the cluster is socially
useful (Eaton and Lipsey, 1979). Some later researches show that the retail cluster can reduce
consumers’ searching cost and transportation cost and consumers will bypass the closest
shopping alternatives to comparison shop at a more distant shopping center that houses a
sufficiently large number of similar stores.
Therefore, shopping externality is introduced and it occurs if the sales of one store are affected
by the location of other stores; these stores have two types of products, imperfect substitutes and
complements to generate shopping externality. Shopping centres provide consumers with a mix
of imperfect substitutes and complements, allowing both comparison shopping and one-stop
shopping. So the implication of this theory is that shopping centres should be well planed with a
desirable tenant mix to create agglomeration economies for all tenants by active management.
While retail agglomeration economies explain why retailers tend to cluster in the market centre
and how they could benefit from the clustering, the proponents of retail demand externalities
believe that in large shopping centres, low-order good retailer and smaller retailers receive
demand externalities from the additional traffic generated by high-order anchor retailers. The
benefits of demand externalities flow one-way from the latter to former, which is different from
the two-way benefit flow advocated by retail agglomeration economies. So they think that it is
the anchor retailers who create these externalities.
There are several studies about externalities for shopping centres. Bruecker (1993), Gatzlaff et.at
(1994), Eppli and Shilling (1995), Miceli and Sirman (1995), Pashigan and Gould (1999) and
Mejia Eppli (2003) all argue that the presence of anchor stores benefits non-anchor stores in
enclosed shopping centres by creating more custom traffic, that is, customer spillover benefit.
Since non-anchor stores free-ride the benefits, the owners should internalize externalities
generated by anchors, and the common means in practice and academics is to charge
non-anchors rent premiums and offer rent subsidies to anchors. As estimated by Pashigian and
Gould (1999), anchors receive a per foot subsidy of no less than 72 per cent that non-anchors
pay.
How to benefit from externalities remains a problem for both researchers and owners. Bruecker
(1993) creates a model where inter-store externalities generated by anchor stores and increase in
a store‘s spaces are both factors giving rise to the store’s sales, and analyze the relationship
between sales and rents under a number of different behavioral assumptions; he suggests that
developers should take the externalities into account and allocate space to the various stores to
maximize the shopping centres’ net operating income. Mejia and Eppli (2003) also suggests that
20
resource constrained developers need to assess the impact of competition on their investment
when allocation capital to anchor store physical assets (space) and intangible assets (image).
However, there is no commonly accepted methodology on how to proxy for demand externalities.
It is difficult to quantify exactly the effect and to identify how lower (or higher) the premiums
are.
Based on these researches on economies of scale for REITs and retail property industry, this
thesis is continued with case studies to test the existence of scale economies for individual real
estate companies and shopping centers.
21
3.
Analysis of Economies of Scale for Shopping Center Industry
As presented in Part 2, economies of scale for shopping centres are composed of externalities
and internalities. The symbiotic nature of relationship between retailing and shopping centre
industry makes it necessary to study them together. Studies show that retail tenants’, especially
anchors’, mergers, consolidations, bankruptcies and liquidations impact shopping centre’ rental
income a lot. Based on US data from 1990 to 1997, Sampson (1998) makes a conservative
estimate of total operating income losses for shopping centre owners, and finds that the
profitability of shopping centre dropped 10 per cent or less.
Though that would be interesting to do further research on externalities, it will not be the main
topic of this study due to limited accessibility of necessary datasets and information. In the spirit
of the previous researches on economies of scale for real estate industry (or REIT) and shopping
centre industry, this study will formulate three research methods to estimate respectively the
effect of firm size on profitability, and the effect of shopping centre size across multiple
dimensions of operating costs.
Regression analysis and trend analysis are used respectively to investigate the existence of scale
economies for two retail property companies, Atrium Fastigheter AB, Sweden, and Westfield
Group, Australia, and a shopping center cost analysis with the application of the translog cost
model is proposed to test economies of scale for the shopping centre industry.
3.1. Brief Presentation of Atrium and Westfield
Atrium Fastigheter AB has the second largest retail property portfolio with sixteen Swedish
shopping centers, which qualifies it as the largest shopping center owner in Sweden. The
company is a result from a merger in 1999 by two property companies Brogatan and Stadsgården.
The owners are Cooperative Society of Stockholm, Cooperative Pension Fund and Cooperative
Pensions Friendly Society. The company focuses on retail property sector, with lower portions of
office and residential premises, and the geographical concentration is on Great Stockholm Area.
In 2003, the company’s total asset amounted to 6.45 billion Swedish Kronor, with 80 properties,
which had a total market value of MSEK 8, 200 at the end of the year. Due to the limited supply
of shopping centers in Sweden nowadays, Atrium pursues a larger share of Swedish retail
property market by refits and development of existing premises.
Different from the very Swedish retail property company, Westfield Group is the largest
shopping center owner in the world by equity capitalization with total asset of 34,281 million
dollars (at the end of 2004) and now holds a portfolio of 124 shopping centers cross four
countries and 36.5 billion dollars asset under management. Westfield Group enlarges operation
scale through a series of mergers and acquisitions, construction and development and finally
attained the world No. One by the merger of Westfield Holdings, Westfield Trust and Westfield
America Trust in 2004. Although its REIT ownership and big size can provide real estate
researchers more evidence to support or to deny the argument of the existence of scale
economies, this study selects Westfield Holding Limited as the research subject to investigate
23
existence of scale economies by analyzing the trend of this property management company.
3.2. Data
Data in regression analysis is from annual reports of Atriumfastigheter AB, Sweden, covering the
most important financial data of the corporate from 1999 to 2003. Although the short time period
of the sample might limit the accuracy of the coefficients, the relationship between dependent
variables and independent variables can be postulated from coefficients. One observation
confines the possibility of estimating scale economies for the retail property industry. Data in
trend analysis is from Westfield holding’s annual reports from 1996 to 2004.
To clarify the use of variable measurement, I should present definitions of variables in this study.
First, total asset is defined as the sum of short-term investments, cash and bank balances and
total current assets. It measures the firm size.
Besides the total assets, other independent variables such as debt ratio, inventory turnover, and
receivable turnover are defined here. Interest-bearing liabilities divided by total assets yields the
debt ratio, which tells how much the company is debt financed. According to Modigliani and
Miller’s proposition 2, the expected rate of return on the common stock of a levered firm
increases in proportion to the debt-equity ratio, that is, a firm with a higher debt ratio can benefit
its shareholders more. Since the interest expenses can be used to deduct income taxes, a higher
debt ratio results in a lower weighted average cost of capital (WACC) for the firm. However, a
high financial leverage increases the probability of financial distress, whose costs will take a
substantial bite out of firm value. Thus, the optimal mix of debt for a firm involves a tradeoff
between the benefits of leverage and possibility of financial distress. In the existence of scale
economies, the firms that have a lower WACC tend to borrow more, which results in a higher
financing cost. However, the firms with higher financing costs do not certainly achieve scale
economies.
The inventory turnover measures a firm’s management of its inventory, and is calculated by
dividing sales by inventory. In general, a higher inventory turnover ratio indicts better
performance since the firm's inventories are being sold more quickly. However, if the ratio is too
high then the firm may be losing sales to competitors due to inventory shortages. While the
receivable turnover is indicative of the firm’s credit policy, measuring how fast it collects its
account receivables. The receivable turnover is defined as the ratio of sales to account
receivables; when it is higher, it implies that the firm can collect its account receivables sooner.
To measure the profitability of the company, this study includes net income (profit for the year in
Atriumfastighter’s annual reports), return on asset (ROA) and net profit margin (NM) as
dependent variables. Net income equals the company’s net operating income minus financial
expenses, depreciation, and taxes. As a ratio of net income to total assets, return on asset will
increase providing a unit increase of total assets leads to no less than a unit of profit
enhancement. Generally speaking, the higher ROA a company has, the more profitable it is. Net
profit margin is defined as net income as a percentage of rental income. It is an indicator of a
24
company’s effectiveness of cost control; and it is also a good means of comparing companies in
the same industry, which are subject to the similar business conditions. The higher the net margin
is, the more effective the company is at converting revenue into actual profit. In the presence of
economies of scale, NI, ROE and NM should provide the similar results, which is the
profitability increasing with size.
3.3. Methods
I test economies of scale for the retail property company and the shopping centre by examining
growth prospects, revenue and expense measures, profitability ratios. Methods in this case study
are regression analysis, trend analysis, and translog cost model.
3.3.1.
Regression analysis
Regression analysis in this study estimates the effect of firm size and cost control factors across
multiple dimensions of profitability factors. In order to capture the possibility that firm cost and
profitability factors follow the traditional U-shaped curve with respect to firm size, the study
includes the logarithm of total capitalization into regression analysis.
Using net profit to one of the measures a company’s profitability, the relationship between net
profit and variables such as total assets, debt ratio is investigated by regression.
NPi= a + blnAi+c(D/A)i+ei
OAi= a + blnAi+c(D/A)i+ei
NMi= a + blnAi+c(D/A)i+ei
(4)
(5)
(6)
Relationship between another profitability measure, return on assets, and the variables like total
assets, debt ratio and inventory turnover is examined by regression too.
NPi= a + blnAi+c(D/A)i+ d(S/IT)i + ei
ROAi= a + blnAi+c(D/A)i+ d(S/IT)i + ei
NMi= a + blnAi+c(D/A)i+ d(S/IN)i + ei
(7)
(8)
(9)
The last regression model adapted in this study investigates the relationship between the net
profit margin and variables like total assets, debt ratio and receivable turnover, to test whether
these variables impact on the efficiency of a firm’s cost control.
NPi= a + blnAi+c(D/A)i+ d(S/ AR)i + ei
ROAi= a + blnAi+c(D/A)i+ d(S/ AR)i + ei
NMi= a + blnAi+c(D/A)i+ d(S/AR)i + ei
(10)
(11)
(12)
Where NPi: net profit for a firm at year i
lnAi: logarithm of total assets of a firm or a shopping centre at year I
(D/A) I: debt ratio of a firm or shopping centre.
25
ROAi: return on assets for a firm in year i
NMi: net profit margin for a firm in year i
(S/IN)i : inventory turnover for a firm in year i
(S/AR)i : receivable turnover for a firm in year I
3.3.2.
Trend analysis
Trend analysis is a technical analysis practiced by stock market analysts to estimates future
changes in value by investigating past market behaviours. Real estate analysts frequently use
statistics to draw inferences about a general class of phenomena, studying data from the recent
past to predict future occurrences. (Gaylon E. Greer and Michael D. Farrell, Investment analysis
for real estate decisions, 2nd ed, New York: The Dryden Press, 1988, 520 and 81)
Generally, trend analysis is on the inferred basis that the performance of the subject property will
follow the past performance of other properties in its class. It might be misleading if the market
is not stable due to random changes of demand or supply. Market potentials of the subject
property such as its location and design are also important to lead to a right estimation.
Although trend analysis is not a flawless method, I adapt it in this study because:
1.
2.
3.
The research objective is not a collective of properties, but an individual property or firm.
The time period is selected from 1996 to 2004, covering the main consolidations of
Westfield Holding. (It would be better if the information of the market performance can be
available at the corresponding period, since it might be inferred that the out-/underperformance of the subject is partly contributed by economies or diseconomies of scale.
The estimation of the subject’s future development is not the final aim, while the focus is on
how the subject performed in the past years due to the increasing size.
Therefore, using time-series data from Westfield Holding’s annual reports, trend analysis here
examines the trends on several measures to roughly estimate the past development pattern. These
measures include size measures, performance measures and financing measures.
For firms, total assets and shareholder’s equity are considered as size measures, and the asset
under management is a measure of the firm’s operation size. While in shopping centre level, the
size can be measured by both the market value of properties, gross leasable areas, number of
tenants and number of anchor tenants. Here GLA follows the definition from Urban Land
Institute, The portion of Total Floor Area designed for tenants' occupancy and exclusive use,
including storage areas, department stores, and outparcel buildings owned by and leased from
the center. It is the area that produces rental income. Another important term GFA should be
distinguished. GFA refers to the total area of the covered floor space measured between the
centerline of party walls, including the thickness of external walls but excluding voids.
Performance measures for Westfield Group used in this study are the net profit, return on equity,
earnings per share and dividend. In a corporative firm, one of the management tasks is to
26
maximize the shareholders’ return, so the return on equity defined as equity earnings as a
proportion of the book value of equity shows that how well the managers fulfill this task.
To observe how firms finance by debt and equity, I include debt ratios and debt/equity ratios in
trend analysis, from which how a firm utilizes its leverage to magnify return on equity can be
estimated.
3.4. Translog cost model
As sources of scale economies, various costs associated with shopping center operation can be
saved to serve the purpose of the shopping centers operating efficiency. The cost structure of
shopping center industry is critical to explore scale economies. A cost can be divided into fixed
costs and variable costs. An industry with a higher percentage of fixed cost tends to have higher
degree of potential scale economies associated with costs.
The fact that a cost could follow a stepwise pattern to increase as the size of shopping centers
increases implies that the cost might increase dramatically after a certain threshold attained and
the increasing return to scale disappears therefore decreasing return prevails or diseconomies of
scale even occur further. As the cost elasticity with respect to output can be estimated to measure
scale economies, a cost function should be specified. For shopping centers, the inputs are the
total costs and the output is gross rents. In this study, a translog cost function is employed, and
the total cost elasticity with respect to gross rents is calculated according to Takayami’s
definition of IRS.
3.4.1.
Data required
The translog cost model is an input-output model running regressions cross a sample of
observations. To get the reliable statistic results, data about the costs and gross revenues from
shopping centers should be run in the model. Yearly publications Shopping Center Operations,
Revenues and Expenses (SCORE) by ICSC include data since 2002 on operating expenses and
rental income of 660 US shopping centers, of which 498 are open-air centers and 162 enclosed
malls. The age of the shopping centers is also available.
More than 50 enclosed malls should be included in a sample to run the regression of the translog
cost model. Since the samples of centers in SCORE each year are not directly comparable to the
samples in other years, observations in the sample should be selected with caution to have
consecutive records of operating expenses and revenues cross a certain time period, say, from
2002 to 2006.
Gross revenues
Gross revenues are defined by ICSC as the total amounts billed to tenants, along with
miscellaneous income, for the most recent 12-month period. Gross rents, which consist of the
base rental charge and overage rental charge, and the expense recovery from tenants are the main
27
components of gross revenues. Other revenues such CAM management fees, marketing funds
from tenants, and incomes from public telephone, vending machines or packing are also
included.
In this study, gross revenues are proxied as the output of the translog cost function for several
reasons. First, profits for shopping center owners are derived from leasing space in the properties.
Second, the market value or book value of shopping centers might be appraised at different time
points or by different methods. The last reason is that GLA or GFA measures the physical output
of shopping centers, but the heterogeneity such as quality, location and product type among
shopping centers and within a shopping center is not recognized. For instance, location plays a
vital role in determining the rent level of a shopping center; the city galleries can have as twice
as regional center average rents.
Gross revenues, GLA and GFA can be measures of property size. For retail property firms, gross
revenues refer to the revenue generation capability, and GFA and GLA demonstrate a shopping
center’s potential to generate revenues. In the shopping center industry, GFA and GLA are one of
the critical standards to classify shopping centers, for instance, regional centers should have a
GLA larger than 450,000 square foot, as is mentioned in Table 1.
Cost structure of shopping centers
Operating costs are the cost categories being analysed to investigate scale economies for
shopping centers in this study, including common area maintenance expenses, utility expenses,
general and administrative expense, insurance expenses, tax expenses and other expenses. As the
size of shopping centers increases these costs respond to the change in different degrees.
1. Common area maintenance expenses (CAM)
In shopping centers, about 50 per cent of the gross floor area is designed as common area,
which is the commonly shared, public area within a shopping center such as hallways,
vestibules, rest rooms, lobbies and atriums. CAM is the cost to maintain the shared interior
areas and exterior area of a shopping center and it includes the costs of payrolls, employee
benefits (taxes, workers’ compensation, pension contributions, etc.), service contracts and
maintenance materials and supplies purchased for the center.
CAM is always the largest component of operating costs and is customarily reimbursed by
tenants as a part of rent payments. So shopping center managers are required to create a
win-win situation for owners and tenants as the store margins are tighten and retail property
profits decline.
2. Utility expenses
Energy crisis soars the utility costs nowadays, and makes it to be one of the most expensive
elements of shopping center’s costs. Retail tenants are required to purchase energy and
28
utilities such as electricity and energy for heating, ventilating and air conditioning (HVAC)
directly from landlords in the form of part of rental payments, and thus create utility costs as
a profit source for landlords.
3. General and administrative (G&A) expenses
G&A expenses include all expenses related to the management of the shopping center, office
staff, office supplies, office equipment rental expenses, management fees and professional
services.
4. Tax expenses
Expenses related to the taxation of shopping centers are defined as tax expenses here. Taxes
based on payroll and income taxes are also included. Tenants recover parts of tax expenses
of shopping centers.
5. Insurance expenses
Shopping centres are exposed to risks of suffering catastrophe property lines like wind,
storm, earthquakes, and being affected by terrorism. Insurance helps landlords and tenants
minimize or transfer the risks of loss. The insurance can help shopping center owners and
tenants to minimize or transfer risks of suffering catastrophe property lines like wind and
earthquakes, and terrorism.
Besides the insurance for the shopping center structure and equipment, which is always
recovered by tenants, costs associated with insurance covering public liability, bonding of
employees and insurance consultation are components of insurance expenses.
6. Other expenses
The other cost elements that cannot be categorized into the above five cost components.
They also have a big proportion of the total operating expenses.
Concepts of breakdowns of the cost of American enclosed centers related to operation cost are
shown in table 3.
29
Table 3 Percentage of cost breakdowns
Items
Cost
breakdowns
Common
maintenance
expenses
Landscaping, housekeeping, system equipment maintenance, parking lot
cleaning, sweeping and repairs, snow removal, trash removal, painting or
minor repairs, roof repairs, security, elevators and other expense.
Utility expenses
Common area HVAC energy, tenant space HVAC energy, common area
other utilities, and tenant space other utilities.
G&A expenses
Professional services, legal and audit expenses, on-site-payroll and benefits,
management fee, bad debt allowance and other miscellaneous G&A
expenses.
Insurance
expenses
All premiums and costs incurred for insurance covering structures, public
liability, rental values, equipment and bonding of employees and the cost of
an insurance consultant.
Tax expenses
Real property taxes, personal property taxes, franchise taxes and the costs of
real estate tax consultants.
Other expenses
Food court expenses, leasing fees and commissions, capital expenditures
and annual attributions to Marketing or Media Fund.
Factors affecting shopping center operation
As one member of the retail property family, shopping centers are characterized as
heterogeneous. Factors such as age of shopping centers, retail mix, proportions of anchor
occupied areas, types of management and the ratio of GLA to GFA affect costs of shopping
centers and can be included as variables in the translog function being introduced below.
Retail mix defines a shopping center’s image and attracts the catchment population to the center.
Selection of discount and fashion anchor retailers, the allocation of space to different
merchandise categories, and the balance between franchise and independent tenants are main
tasks of retail mixing. A variable accounting for the retail mix is included in the model, which is
calculated as Hirschman-Herfindahl index (HHI) of the mixture of retail stores. HHI is a
standard measure of market concentration in economics, and is widely used to analyse the
diversification of property portfolios in real estate researches. A portfolio with higher
diversification of property types has lower HHI value. HHI is calculated as following:
2
Retailindex=ΣSj ,
Sj is the proportion of the GLA allocated to the merchandise category j in a shopping center.
Heterogeneous space allocation tends to create higher merchandise category sales than
homogeneous space allocation. Therefore a shopping center with large retailers combined with
small retailers tent to get more overage rents than one with retailers of relatively equal size.
30
There are three ways to manage a shopping center. Self-management and affiliate-management
of properties are categorized as internal management, which is believed to generate more
revenue than external management in REIT researches. Whereas, external management of
shopping centers can avoid the losses resulting from a lack of comprehensive knowledge in both
retail and retail property management in some less developed markets.
3.4.2.
Translog cost function
To correspond to a well-behaved production structure, the cost function must satisfy the following
regularity conditions: continuity, symmetry, linear homogeneity in prices, monotonicity in prices
and outputs, and concavity in prices.
Satisfying the regularity conditions, the cost for a shopping center is written as a function of its
gross revenues. The translog cost function can be approximated as following:
2
LnC = β0+βRlnR + 1/2βRR(lnR) + ΣβjXj + e
(13)
Where C is the total costs for a shopping center;
R is the gross revenues of a shopping center;
βis the regression coefficients;
X is the characteristics of a shopping center that might impact the costs; and
e is a random error.
The cost elasticity with the respect to gross revenues is calculated according to the definition
given by equation (2),
Ec,R=MC/AC=(dC/dR)/(C/R)=(dC/C)/(dR/R)=d(lnC)/d(lnR)
(14)
Taking the first derivative of equation (7), and the cost elasticity with respect to gross revenuss is
calculated,
Ec,R= βR +βRR(lnR)
(15)
If the cost elasticity with respect to gross revenues is higher than one, it means that the costs
increase faster than gross revenues, so diseconomies of scale exist. If the scale economies
estimator, the reciprocal of the average elasticity cross the individual shopping centers, exceeds
one, a scale economy exists, and vice versa.
31
4.
Results
4.1. Results from regression analysis
The regression results for Atrium are reported in Table 4, Table 5 and Table 6. All three
regression models show that profitability is directly related to size, larger total assets bring more
profits for the subject company. A statistically significant negative relationship between
profitability and leverage is also found. The possible explanation is that a higher interest expense
lessens the cooperate net profit in the same year, and also Atrium sets a equity-to-asset ratio no
less than 0.3 as one of the corporate strategies to reduce the financial distress risk form a high
debt ratio.
Table 4. Regression results for Atrium Real Estate
Dependent
variables
NP
t Stat
ROA
t Stat
NPM
t Stat
a
lnA
D/A
-4340,15**
687,17**
-23,57**
-2,80
3,74
-4,41
-0,91**
0,15**
-0,01*
-2,85
3,88
-4,84
-4,12***
0,72***
-0,03**
-1,71
2,52
-3,74
Adj, R2
0,86
0,88
0,79
* Statistical significance at p=0.01, **Statistical significance at p=0.05,
***Statistical significance at p=0.10
Data limitations pose limitations on model building to include both inventory turnover and
receivable turnover together other descriptive variables in one model. It should be noticed that
the inclusion of either inventory turnover or receivable turnover generally improves R-square
value of the models.
Table 5. Regression results for Atrium Real Estate
Dependent
variables
NP
t Stat
ROA
t Stat
NPM
t Stat
a
lnA
D/A
S/IN
-3987.29***
634.33**
-23.45**
3.26
-2.29
3.02
-4.03
0.83
-0.80*
0.13*
-4.98
6.75
-9.84
2.72
-3.43
0.62**
-0.03**
0.01
-1.65
2.46
-4.43
1.36
-0.01* 0.001***
Adj,. R2
0.84
0.97
0.85
* Statistical significance at p=0.01, **Statistical significance at p=0.05,
***Statistical significance at p=0.10
Table 5 shows results from the regression model with lnA, D/A ratio and inventory turnover as
independent variables. This regression finds that ROA and inventory turnover are positively
32
related, indicating that the real estate corporate has lower inventories enjoy higher return on
assets, while inventory turnover rate is not statistically significant when it comes to dependent
variables NP and NPM. Positive coefficients for receivable turnover are found statistically
significant in Table 6, which implies that a higher accounting receivable management helps
improve the profit margin. Also higher R-square value got in this regression model tend to
indicate the necessity of addition of receivable turnover, but this explanation should be cautioned
due to the limited sample size and short time series.
Table 6. Regression Results for Atrium Real Estate
Dependent
variables
NP
t Stat
ROA
t Stat
NPM
t Stat
a
lnA
D/A
S/RE
-3779.73**
573.14**
-17.55**
23.08**
-3.82
4.56
-4.00
2.07
-0.78*
0.12*
-0.004*
0.01*
-22.27
27.21
-25.52
13.35
-3.17**
0.53*
-0.02*
0.04*
-4.37
5.74
-6.51
4.77
Adj,. R2
0.95
0.999
0.98
* Statistical significance at p=0.01, **Statistical significance at p=0.05,
***Statistical significance at p=0.10
4.2. Results for Trend Analysis
Graph 1 shows the trends in total assets and equity, both size measures rose about threefold in
similar growth patterns respectively from 975 million dollars in 1996 to 3,021 million dollars in
2004 and from 374 million dollars to 1,347 million dollars. The tremendous ascendance
witnessed in 2000 can be partly attributable to the acquisitions of shopping centers in UK. The
average number of shops in each shopping center increased from 144 to 163 between 1997 and
2004, and the average square meters of shopping centers under Westfield Holding’s management
expanded from 60,606 M2 to 79032 M2 with an asset value spread of 116 million dollars in the
same time span. It is indicative of an economic concentration in shopping center industry that
both the asset value and area of shopping centers tend to be larger than ever before. A message
from Table 7 that the average store area is increasing tells a trend that retailers seek higher sales
from a large store area.
Westfield Holding strengthens the profitability steadily and net profits increased from 18.2
million dollars in 1988 to 328.5 million dollars in 2004 (Graph 3). Year 2001 saw an upswing of
earnings net profits, which is coincident with the tremendous expansion of its total assets in 2001.
Graph 2 indicates that earnings per share and dividend per share have registered the similar
growth pattern with the net profits. Shareholders receive a steadily increasing stream of
dividends. Overall, these results suggest that the company’s profits have generally kept pace with
increase in size variables over the time span studied.
Graph 4 shows that ROE has experienced fluctuations since 1998, and seems to indicate that the
company has an instable performance, partly due to the irregular increase of equity during the
33
time period studied. Simply considering ROE, the company has not achieved higher returns for
shareholders as it is getting larger. The financing leverage has fluctuated around 0,6 since 1996
(Graph 5), indicating Westfield follows a relatively stable financing policy as the firm size
increases.
Table 7. Trends of average size measures
Of each shopping center.
Average square Average No. of Average
Year
meters
tenants
Average store area
asset SQM
value
1997
60606
144
179
421
1998
69231
146
192
474
1999
70000
149
209
471
2000
68966
148
241
465
2001
69767
153
280
455
2002
75229
151
289
497
2003
75652
152
276
497
2004
79032
163
294
485
Graph 1- Trend analysis
Size measures
3500
3000
Million dollars
2500
2000
Total aseet
Equity
1500
1000
500
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
Year
34
Graph 2- Trend Analysis
Performance measures
EPS and DIV
70
60
Cents
50
40
EPS
DIV
30
20
10
0
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Year
Graph 3- Trend Analysis
Performance measures
Net profits
350
Million dollars
300
250
200
150
100
50
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
35
Graph 4-Trend Analysis
Performance measures
ROE and ROA
30,00
Per cent
25,00
20,00
ROE
ROA
15,00
10,00
5,00
0,00
1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Graph 5-Trend analysis
Financing measures
2,5
Ratio
2
1,5
D/A
D/E
1
0,5
0
1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
36
4.3. Remarks of possible results for translog cost model
The scale economy estimator for the shopping center industry can be calculated as the reciprocal
of the average elasticity with respect to gross revenues of each sample shopping center.
For individual shopping centers, the cost elasticity with respect to gross revenues can be broken
down into various cost elasticity components; they are CAM cost elasticity with respect to gross
revenues, utility expense elasticity with respect to gross revenues, tax expense with the respect to
gross revenues, insurance expense elasticity with respect to gross revenues, and other expense
elasticity with respect to gross revenues.
CAM is the largest operating expense and is customarily recovered by tenants, but these
expenses are not simply a pass-through for the landlords, it requires the shopping center owners
to control CAM and utility expenses that are also reimbursed by tenants to make a win-win
situation. A stronger bargain power of a larger shopping center to negotiate better deals such as
cleaning service could lead to scale economies. If the CAM expense elasticity with respect to
gross revenues is lower than one, shopping centers could achieve benefits by efficient CAM and
utility management.
G&A expenses are likely to follow a stepwise increasing pattern as shopping centers expand. For
instance, a shopping center may not need an additional employee until its size attains certain
point, the same would happen to office spaces and professional fees paid to professional services.
Bers Martina and Thomas Springer (1998) find that G&A expense are the best source of scale
economies for REITs. Though it remains unknown for shopping centers due to few researches in
this respect.
Insurance expenses are very likely to be the source of economies of scale for big shopping
centers. Generally speaking, a larger shopping center has a better statue to negotiate for favorable
insurance contracts, and thus it will take benefits as the center size increase.
It is difficult to estimate whether there are scale economies associated with the savings of other
expenses without statistical results. For example, marketing, a miscellaneous aggregation of this
category, is not only regarded as a cost of operating shopping centers but also believed as an
investment with measurable returns to affect leasing, to increase market share and to expand
market.
37
5.
Conclusion
One of the main driving forces behind the consolidation in the real estate industry is the belief
that larger operating companies are able to improve profit margins by increasing revenues and
lower expenses. Real estate researchers debate around whether economies of scale occur for the
real estate industry, and the literatures revealed some mixed results.
Regression analyses of financial ratios of Atrium Real Estate Company provide evidence that the
individual company could make more profits and be more efficient in management as its size
increase. Trend analyses of data from Westfield Holding have some mixed results about the
effect of the size on the profitability of the subject company, and at the meanwhile reveal that the
average asset value and area of shopping centers increase a lot from 1997 to 2004. The average
store area increases by about 60 square meters in eight years, implying that higher retail
turnovers could be generated from larger business areas.
Finally, the remarks of translog cost analysis indicate that CAM expenses are the largest potential
source of scale economies for shopping centers, and larger shopping centers are most likely to
gain benefits from insurance expense savings.
Further researches on economies of scale for shopping centers could be conducted from the
following two respects:
(1) Research within the confine of property management of shopping centers. To investigate
economies of scale occurring for shopping center owners as a result of the increasing
operating size, the cost structure analysis and revenue analysis could be adopted, and the
translog cost function and the data envelopment analysis are the alternative techniques.
(2) Research across borders of the property sector and retail sector. A widely accepted
measure of a shopping center’s performance is how much the retail revenues are
generated within the shopping center. By enhancing the retail performance of shopping
centers by the reasonable retail mix and promoting retail image, shopping center owners
could increase profit margins through the overage rental leasing with non-anchor tenants.
To examine whether the increasing shopping center size, anchor size, as well as the
individual store size could create a shopping spillover effect is another interesting topic
on economies of scale in shopping centers.
39
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