CRES: 2004-001
DOES CORPORATE REAL ESTATE CREATE WEALTH FOR SHAREHOLDERS?
Kim Hiang LIOW and Joseph T.L. Ooi, Department of Real Estate, National University of Singapore
Corresponding author
Dr Kim Hiang LIOW
Associate Professor and Deputy Head (Academic)
Department of Real Estate
National University of Singapore
4 Architecture Drive, Singapore 117566
Tel: 65 68743420
Fax: 65 67748684
Email: rstlkh@nus.edu.sg
Acknowledgement
We would like to thank Mr. Chew Chee Song for providing valuable research assistance with data collection and
processing.
Abstract
This study examines the influence of corporate real estate (CRE)on shareholder value using two valuebased measures: economic value added (EVA) and market value added (MVA).We find that CRE has
impacted negatively on non-real estate firms’ EVA and MVA in the period 1997-2001. This happens for the
non-real estate corporations from different industries. Further, the higher the real estate asset intensity, the
greater the negative impact on the firms’ EVA and MVA. Our results have important implications for the
traditional notion that there is a competitive advantage in owning CRE by diversified conglomerates
.Specifically, more studies are needed to explore and compare the main reasons and motivations as to why
Asian non-real estate firms are still more involved with real estate activities than their counterparts in
Europe and USA even though ownership of CRE appears to destroy shareholders’ wealth. .
Keywords: corporate real estate, economic value added, market value added, real estate asset intensity
Introduction
Corporate real estate (CRE) refers to the land and buildings owned by companies not primarily in the real
estate business. In today’s business environment, many non-real estate firms are investing significantly in properties
that are used for operational and investment purposes. In some cases, real estate has become the corporations’
largest asset. From an international perspective, the ownership of significant amounts of real estate by corporations in
the United States (Johnson and Keasler, 1993; Seiler, Chatrath and Webb, 2001), UK (Liow, 1995), Singapore (Liow,
1999) and European countries (Laposa and Charlton, 2001) has been well documented. Additionally, many property
analysts have also noted that Asian companies are more deeply exposed to real estate than their counterparts
elsewhere in the world.
In this paper, we consider a broader perspective of CRE to comprise both business (operational) properties
and other non-business (i.e. investment) properties of a non-real estate company. This is an expansion of the definition
adopted by Manning and Roulac (1999) that defines CRE as “real property that house productive activities of a
traditional corporation”. There are four main reasons to adopt a broader definition of CRE. First, changing business,
property and capital market conditions have prompted many traditional non-real estate companies to re-examine their
property holdings, and in particular commercial real estate, both as an operating business and as an asset class.
Consequently, real estate has been increasingly viewed as having investment and operating characteristics that render
it both an asset class within the investment world and a distinct business area within the economy. Second, some
major non-real estate companies combine primary (non-property) business with property business in order to diversify
risk. CRE in these corporations comprises both operational and investment properties. Thirdly, the distinction between
operational and non-operational properties can be arbitrary. Because changing business needs may affect the
requirements for operational properties, the non-operational property holdings may be restructured when the
companies conduct regular reviews of their business strategies and real estate holdings. Even for non-real estate
companies that regard their CRE as simply an agent of production. The need for property can still change. For
instance, some operational properties might become non-operational properties because they are in excess of
business requirements (i.e. surplus). Analyzing the impact of CRE from both the operational (space) and nonoperational (investment) perspectives will help provide a complete picture of how real estate interacts with corporate
strategy in a non-real estate company context. Finally, the adoption of a broader definition of CRE supports our
empirical tests. This is because our CRE data used for the empirical tests do not separate net operating profit after tax
(NOPAT) and weighted average cost of capital (WACC) for operational and investment properties.
Given the prominence and significance of real estate in a corporation’s asset base, there is increasing
concern as to whether ownership of CRE is a “value-enhancing” asset. The concept of shareholder value provides a
direct capital market indicator to demonstrate to management how real estate affects the financial health of the
company (Louargand, 1999). In this regard, the adoption of alternative metrics to measure corporate performance such
as the Economic Value Added (EVA) and Market Value Added (MVA) has important implications on the attractiveness
of real estate as an asset class and as a business entity. As Ooi and Liow (2002) find that EVA tends to understate the
true economic performance of property investment and development companies, similar questions can be raised with
regard to the contribution of CRE to EVA and MVA in diversified non-real estate companies. Additionally, as a
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consequence of many corporate restructuring activities involving real estate in the USA in the 80’s, non-real estate
corporations would have to re-examine the tradition notion that there is a competitive advantage in CRE asset
ownership (Brueggeman, Fisher and Porter, 1990). However, there is little evidence on the possible impact of CRE,
whether as an asset or as a business unit, on the overall economic performance of diversified conglomerates with
property and non-property businesses. Hence these firms might not know whether their real estate assets are creating
value or destroying value within their business portfolio.
The principal task in this research is to evaluate the possible impact of ownership of CRE on shareholder
value using EVA and MVA metrics. The key question to address is: “Does CRE create wealth for shareholders?” This
is important since the question of whether stock markets are able to value CRE holdings adequately poses great
concern for corporate management. There is evidence in the literature to support the management claim of significant
“hidden value” in CRE that is not reflected in a company’s share price. For example, Brennan (1990) categorized real
estate as “latent assets” where the value of assets owned by a corporation might not be accurately reflected in its
share prices. If ownership of CRE decreases firm valuation, then there appears to have little incentive for non-real
estate firms to own properties. Based on the 1997-2001 EVA and MVA performance of listed non-real estate firms that
have significant property asset holdings on the benchmark stock market index in Singapore, the empirical evidence
suggests that many companies involved in property have struggled to create shareholder wealth – if they have not
destroyed it.. During this period, many Asian markets experienced severe recession following the financial crisis
experienced in the region. The Asian Financial Crisis has escalated the riskiness of real estate investments in East
Asia.
Conceptual Foundation of Shareholder Value, EVA and MVA
For a non-real estate firm with significant CRE, its shareholder value is derived from the profitability of its
primary business and the value of its real estate holdings. However, the contribution from real estate is often hidden.
This paper applies the concept of shareholder value to investigate the influence of CRE on wealth maximization.
The concept of shareholder value states that a company only creates value for its shareholders when the
return it achieves on its capital is greater than its opportunity cost. Modern corporations are obliged to maximize
shareholder value, i.e. an expectation of return is created with every dollar raised and invested. Traditional measures
of corporate performance such as accounting profits, returns on investment (ROI) and return on equity (ROE) have
been criticized as inconsistent with the goal of wealth maximization.
The conceptual foundation of EVA, one of the important shareholder value measures, is based on the
“residual income” concept. As defined by Stewart (1991), EVA is net operating profit minus an appropriate charge for
the opportunity cost of all capital in an enterprise, i.e.
EVA = operating profit – cost of capital employed ……………….. ……………………………………… (1); or
EVA = operating profit – (capital employed X average cost of capital)…………………………………….(2)
Equation (2) thus shows that the magnitude of EVA depends on: (a) the firm’s net operating profit after taxes
(NOPAT); (b) Its total capital invested to generate net income; and (c) Its weighted cost of capital (WACC).
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Economic profit is derived after subtracting the cost of capital employed. This includes equity as well as debt
capital. A positive EVA indicates that the operating profit is sufficient to cover the total cost of capital. The EVA
equation hence highlights four ways in which a firm can increase shareholders’ wealth. First, by utilizing existing
resources more efficiently to yield higher earnings; second, by injecting more capital into projects which are expected
to earn positive NPV; third, by withdrawing capital from activities and assets that yield unattractive returns; and finally,
by employing optimal capital structure and exploring alternative sources of finance to reduce its WACC.
Applying to the business unit level, EVA provides a powerful management tool for investors and corporate
managers to identify where value has been created or destroyed in a business organization. From the CRE
perspective, there are two main ways in which real estate might affect shareholder value. First, occupancy costs play a
very important role in determining the cost base and hence the net operating profit of the firm. The proportion of fixed
occupancy cost thus fundamentally affects the value of the business entity through the simple equation: profit =
revenue – cost. The second way is through its cost of capital. The presence of real estate on the balance sheet could
mean a higher cost of capital that includes a substantial risk premium to account for higher operating leverage arising
from ownership of fixed real estate. Another source of added risk comes from increased financial leverage as a result
of financing CRE using debt. Higher ownership of CRE normally suggests that the firm is likely to have a higher debt
ratio (i.e. high-geared). As debt financing has the effect of leveraging (positively or negatively) any changes in the
company’s returns, it then means that a high real estate / high-geared firm may be riskier than a low real estate / lowgeared firm and in turn will result in unfavorable stock market valuation.
Another important value-creation measure, MVA, is closely linked to the EVA metric. It is defined as the
difference between a company’s total market value and invested capital and is used to assess if a company has
created shareholder value. If the MVA is positive, the company has created wealth for its shareholders; if otherwise,
the market value is less than the capital invested, and the company has destroyed shareholder value. In short:
Market Value Added (MVA) = Market Value of Equity (MV) – Book Value of Equity (BV)…………(3)
Conceptually, MVA is identical in meaning to the market-to-book value ratio (MV/BV). The only difference is
that MVA is an absolute measure while MV/BV is a relative measure. If MVA is positive, it means that MV/BV is more
than one. Conversely, a negative MVA means that the MV/BV is less than one. The next question is how is the MVA
linked to EVA, which measures the absolute wealth the company creates each year? A positive EVA indicates that the
operating profit is sufficient to cover the total cost of capital. MVA can be interpreted as the value the market places on
the future stream of annual EVAs. Generating big, positive EVAs year after year is the key to enriching investors. The
connection between MVA and EVA is therefore:
MVA = Present value of all future EVAs……………(4)
Corporate Real Estate in Singapore
The selection of Singapore CRE for this study was mainly due to practical reason. This was because timeseries data on real estate owned by business firms are not publicly available and has to be extracted manually from
annual accounts of Singapore listed firms. Most of the company accounts were available from the University library.
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Singapore is one of the strongest Asian economies and leading financial centre in Asia. The real estate
sector, in particularly commercial real estate, has played an important role in enhancing the Republic’s status and
attracting multi-national companies to establish their regional headquarters in Singapore. Another interesting
institutional factor is, compared with real estate market in the USA, Asia is characterized by land scarcity and high
population density, and thus real estate values are relatively high. This has made real estate the most favored
investment target within Asia. There is a strong desire by individuals and corporations to own real estate on further
belief that the Pacific-Asia region is potentially a hot spot in international real estate markets because of its rapid
urbanization and economic growth that have created enormous opportunities for land and property development. Of
the many countries in the region, Singapore real estate possess great potential to be one of the key investment targets
in years to come in view of increasing globalization in Asian property and stock markets.
We identified 109 listed business firms that were “real estate intensive”.1 A focus on “real estate intensive”
business firms would profile the companies better and reflect the growing strong feature of real estate in corporate
financial management. As there was no prior agreement in the literature as to the level of real estate asset holdings (in
both absolute and relative terms) that could be considered as “real estate intensive”, as in the previous studies (Liow,
1999), we defined real estate asset intensity (PPTY%) as the proportion of total tangible assets represented by
property in a non-real estate company’s asset structure and applied a 20 per cent cut-off point to identify “real estate
intensive” non real-estate firms. The choice of the cut-off point was guided by several research studies to the effect that
a benchmark portfolio should hold 20 per cent real estate (Firstenburg, Ross and Zisler, 1988).
Table 1 reports the distribution of the gross real estate asset holdings (PTYABS) and real estate asset
intensity (PPTY %) of the sample companies for the financial year 2001. Total real estate asset holdings were
approximately S$34.22 billion, and property made up about 43.24 per cent of a non-real estate corporation’s total
tangible assets. Additionally, Figure 1 reveals that 55 per cent of the “real estate intensive” companies had PPTY % of
between 20 per cent and 40 per cent. Only 8 companies were extremely “real estate intensive” with at least 80 per cent
of their resources invested in property.
(Table 1 and Figure 1 here)
The proportion of total tangible assets made up by real estate varied considerably between different business
segments. Specifically, the role of CRE varied from around 32.5% for manufacturing companies to around 73.4% for
hotels and restaurants in year 2001 (Table 2). Statistically the Chi-square values of 35.25 (absolute level) and 31.75
(PPTY %) (both significant at the 1% level) derived from non-parametric Kruskal Wallis tests indicated that there were
marked differences in the absolute and relative levels of CRE ownership and investment among the seven non-real
estate business segments. Hence CRE ownership is a function of industry.
(Table 2 here)
Over the period 1997-2001, absolute CRE holdings reported a decrease from S$410.46 million in 1997 to
S$313.95 million in 2001. However, the average PPTY % increased from about 40.3% to 43.2% over the same period.
As of December 2001, all 239 listed companies of the Singapore Stock Exchange Mainboard business sectors (multiindustry, manufacturing, commerce, transport/communication, construction, hotel /restaurant and services) were
included in the population. The total gross real estate holdings of these companies (balance sheet value) were
estimated at S$145.72 billion, and real estate made up about 24.5% of these companies’ total tangible assets.
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On average, the hotel / restaurant group was ranked the most “real estate intensive” with its PPPTY % ranged from
56.1% (1997) to 73.4% (2001). The multi-industry group was ranked the second most “real estate intensive” with an
average PPPTY % of 48.4% over the five-year period. Consistently at the lowest ends were the manufacturing and the
transportation/storage/communication groups, with annual PPTY % ranged between 26.6% and 36.9%.
Table 3 presents a summary of CRE of the companies by three asset subtypes.2 As noted, about 72.4% of
the real estate held by the non-real estate firms was used as fixed properties (i.e. operational properties). Of the 109
firms, 55 per cent of them have no real estate interest other than owner-occupied factory and office premises (Group
1). Some other companies are willing to take limited property market risk in holding investment properties (Group 2 –
14 companies) or development properties (Group 3 – 15 companies). Finally, another 18 companies (16.5 per cent)
hold real estate in fixed properties, investment properties and development properties. Over time, real estate has
become the largest asset in these firms. In some cases the corporations’ real estate spins off as a separate business
altogether as they diversify. The corporate scene in Singapore has witnessed a number of such developments in
recent years.
(Table 3 here)
The contribution of CRE to the companies’ bottom lines (sales turnover and net profit before tax –NOPAT) is
summarized in Table 4. Additionally, Figure 2 displays the absolute NOPAT derived from the CRE by industry. The
information was derived from the segmental analysis provided by 44 of the 109 non-real estate companies (40.4%).
The breakdown is: multi-industry (10 firms); manufacturing (12 firms); hotels/ restaurants (8 firms), commerce (6 firms);
construction (3 firms); transport /storage/communication (3 firms), and service (2 firms). On average, CRE contributed
to about 21.8% and 40.8% of total sales turnover and NOPAT respectively. Additionally, there were wide variations
with regard to the degree of contribution that real estate can make to total sales turnover and NOPAT. For example,
construction firms emerged as the top performers in their CRE contributions to sales turnover (58.9%). On the other
hand, multi-industry firms derived 78.4% contribution to NOPAT from their CRE assets and outperformed other
sectors.
(Table 4 and Figure 2 here)
Finally, in term of segmental NOPAT per unit of PPTY%, Table 5 shows that firms in the multi-industry were
again the top performer. On average, every 1% of PPTY was associated with NOPAT of S$0.73 million. This compares
favorably with firms in the hotel industry, although have the highest PPTY %, only ranked sixth in term of segmental
profit per PPTY %. Additionally, Figure 3 reveals that 20 companies (45.4%) derived negative segmental profit per
PPTY %. Only 5 companies (11.3%) were able to generate at least NOPAT of S$1million per PPTY %. To the extent
The Singapore Accounting Standards allow CRE to be classified in three groups. First, properties held under fixed
assets are tangible assets that are used by an enterprise in the production of goods and services and are not intended
for sale. Most owner-occupied properties are part of tangible fixed assets. Second, an investment property is an
investment in land or buildings that are not occupied substantially for use by, or in the operations of, the investing
enterprise or another enterprise in the same group as the investing enterprise. This category only includes completed
properties held by non-real estate companies for generating rental income. Finally, development properties are
properties in the process of construction. They can appear in the balance sheets as long-term investments or current
assets. To qualify as long-term investments, there must be both the intention and the ability of the firm to hold the
development properties for more than a year. On the other hand, development properties classified under current
assets have to be readily realizable in nature and not intended to be held for more than a year.
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that business firms indeed commit their real estate assets to the highest and best use in order to minimize costs and/or
maximize earnings is therefore one important issue in strategic corporate real estate asset management.
(Table 5 and Figure 3 here)
Empirical Procedures
The primary motivation of this paper was to evaluate the CRE impact of EVA and MVA. Our empirical
investigation was carried out in two parts. In the first part, the annual performance (both EVA and MVA) for the 109
non-real estate firms were measured and analyzed. All the relevant accounting items were obtained from Datastream
(DS). Net interest expense (DS code 2408) was added back to cash earnings (DS code 2260) to derive the firm’s
NOPAT. It did not take into account unrealized capital gains. Current debt (DS code 308) was added to total capital
employed (DS code 322) to derive the firm’s invested capital at the end of each year. To compute the company’s rate
of return on capital employed, we divided NOPAT by the average capital employed for the particular year (i.e. the
beginning capital plus ending capital divided by two). The average balance was employed (instead of the beginning
balance as proposed by EVA proponents) to compute the rate of return because new funds might be added back to the
existing capital base throughout the year. The cost of debt and cost of equity were weighted based on the firm’s capital
structure to derive the WACC for each firm using the popularly known Capital Asset Pricing Model (CAPM). The EVA
of a company was computed by deducting the cost of capital (total capital investment multiplied by the WACC) from the
firm’s NOPAT. Similarly, the MVA of a company is computed by deducting its total shareholders’ equity (BV) from its
market value (MV).
In the second part, we sought to isolate the possible impact of CRE on the firm’s EVA and MVA by pooling all
the cross-sectional observations over the sample period. Specifically, the EVA (and MVA) of the individual firms in
each year were regressed against the set of specific variables using a panel regression format shown in equations (5)
and (6).
EVA it = a i + a 1* WACC it + a 2 * PP it + a 3* PPTY % it +
µ it ……………(5)
MVA it = b i + b 1* WACC it + b 2 * PP it + b 3* PPTY % it +
µ it ……………(6)
with the i and t subscripts denoting the cross-sectional and time-series dimensions respectively. The dependent
variables, EVA and MVA, are the annual economic value added and market value added respectively. WACC
represents the yearly weighted average cost of capital (cost of debt and cost of equity) of each firm; PP represents the
net operating profit generated by the real estate assets and was derived from the segmental analysis in the annual
report; PPTY% is real estate asset intensity (gross property asset value / total tangible asset value) each year to proxy
for additional income or capital gain. The main idea behind equations (5) and (6) is that they first estimated an
unlevered beta for the company (i.e. beta that is adjusted to exclude financial risk). Then, two CRE variables were
included (PP and PPTY%) to assess the possible impact of CRE on the firm’s EVA (and MVA).3 Of specific interest in
this study was whether the coefficients a3 and b3 were statistically different from zero and their respective signs. The
It may appear that EVA (equation 5) and MVA (equation 6) are identical with the same predictors. This is certainly not
the case. Instead, our intention was to investigate the influence of CRE (represented by PPTY%) on EVA and MVA in
the same multivariate context (i.e. WACC, PP and PPTY%).
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results would reveal whether CRE has a significant positive (negative) impact on shareholders’ wealth. We further
repeated the regressions on firms classified by industry and by PPTY%.
Finally, the advantage of adopting a panel specification format for equations (5) and (6) is that it facilitates
the identification of effects that are simply not detectable in pure cross-section or pure time-series studies. In the above
estimation models, we specified the error term
µ i to be fixed for each company over the analysis period 1997-2001.
This error term represents the effects of omitted variables unique to each company and stays constant over time. The
fixed effect model provides a common set of partial regression coefficients while allowing a different intercept for each
of the cross-sectional units.
Results and Discussion
The estimated annual EVA and MVA results for the 109 firms (in the seven industries) over the financial
years 1997 to 2001 are reported in Tables 6-7 and displayed in Figure 4. First, the 5-year average EVA for the full
sample was about S$ 24.96 million. In addition, firms in the multi-industries were the top performer registering a
positive EVA of S$74.26 million. On the other hand, the construction sector performed badly in registering a negative
average EVA of S$3.46 million. On a year-to-year basis, the majority of the non-real estate firms derived positive EVA
with average EVA reported a decrease from 1997 to 1999 before picking up in the year 2000.
Average MVA for the entire period was a positive of S$39.87 million. This suggests non-real estate firms
were still able to create wealth for their investors in spite of the unfavorable market environment during the period.
Further industry analysis indicates that the “multi-industries” and “hotel/restaurant” sectors were the two worst MVA
performers (MVA of negative S$0.28 billion and negative S$0.20 million respectively). On the other hand, despite
reporting a negative EVA, the construction sector stood up in achieving an average MVA of S$2.3 million.
(Tables 6-7 and Figure 4 here)
Table 8 provides the main results from estimating models 5 and 6. The focus was on the coefficients
attached to the PPTY % variable. For the full sample, the results indicate that both coefficients (a3 on EVA and b3 on
MVA) were both negative and statistically significant at the 1% and 5% level respectively. These results suggest that
higher PPTY% was associated with lower EVA and MVA. Hence non-real estate firms would be better off without
owning properties. The sector results (Part B) were broadly consistent. All the industry sectors did not perform well
under the EVA and MVA metrics with their CRE. Finally, the firms were classified into two portfolio based on their
PPTY% level (Part C). Again, CRE has an adverse effect on EVA and MVA.
(Table 8 here)
One likely quest of the quest for shareholder value is that as in the case of property companies (Ooi and
Liow, 2002), CRE investment and ownership would be viewed unfavorably because of its adverse effect on EVA and
MVA. In particular, if investment decisions would be made on the basis whether they would yield positive EVA and
MVA or not, then non-real estate firms would have no incentive at all to own properties! Under these circumstances, it
seems strategically wise for non-real estate firms to refocus its energies and resources on their core businesses where
they have competitive advantage. Hence a review of the CRE asset strategy would be timely to evaluate the
comparative advantages of owing real estate. Essentially, this review looks at ways in which EVA and MVA can be
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improved through rationalizing, liquidating, or curtailing investments in real estate operations that cannot generate
returns greater than the cost of capital. For some companies, if the pride of real estate ownership is less significant,
divestment of non-core real estate assets may become a viable option to boost share prices and increase shareholder
wealth.
Another main implication arising from the quest for value is that it will put greater pressure on corporate
management to boost returns through effective corporate real estate asset management. Hence, asset management
will take on a more important and pro-active role. This means that existing real estate assets would be subject to more
frequent and rigorous evaluation to justify their continual inclusion in the firm’s asset portfolio. In particular, CRE
performance would be benchmarked against the cost of capital which should be optimized to support the business
strategy and real estate strategies.
Finally our evidence that CRE has an adverse impact on shareholder wealth during the 1997-2001 periods
should not be taken as the case that CRE has a negative impact on non-real estate firms for any other time period.
Since the Asian financial crisis it would appear that holding real estate assets as part of investment portfolio would lead
to lower returns and greater risk. This was in great contrast with the situations in the late 1980s and early 1990s where
many Asian countries (including Singapore) reported remarkable growth in their property markets. In consequence,
many non-real estate firms made “extremely healthy profits” from their CRE and the ownership of CRE on shareholder
wealth might have been a positive one. Hence, our main finding that CRE has a negative impact on shareholder value
would be much more convincing if we could have chosen a more neutral (or balanced) over return period for the
empirical tests. However, the choice of other sample periods earlier than 1997 would result in much smaller sample
size for the empirical tests.
Conclusion
The major contribution of this research is an extension of previous research on the relationship between CRE
and shareholder wealth. Panel regressions were conducted to shed lights on how ownership of CRE, as represented
by the real estate asset intensity (PPTY%), affected EVA and MVA of non-real estate corporations that own significant
properties. Although CRE affects shareholder value through its impact on net operating earnings and cost of capital,
the value of CRE is usually hidden from investor and, therefore, not fully reflected in share prices. What is even of
great concern is that this study has found that ownership of CRE appear to destroy shareholders wealth under the EVA
and MVA metrics. The negative impact of CRE on EVA (and MVA) exists for non-real estate firms from the different
industries. Further, the higher the real estate asset intensity, the greater the negative impact on the firms’ EVA and
MVA.
Our results have at least two important implications for corporate management. First, a negative EVA (and
MVA) associated with real estate serves as a red flag that the CRE holding strategy and business of the firm may need
to be reviewed for its competitive advantages of owing real estate. Under this circumstance, divestment of non-core
real estate assets may become a viable option for many business firms to boost share prices. On the other hand, EVA
(and MVA) is just one dimension of corporate performance measure. Other factors such as the long-term sustainable
growth of the company are equally important. Hence, conglomerates with diverse business units should not divest
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themselves of their property business based solely on EVA and /or MVA. The second implication is related to the
unresolved question of why some non-real estate firms still own properties even though the inclusion of real estate in a
business portfolio is likely to decrease shareholder wealth. Further research is definitely necessary to explore and
compare the main reasons and motivations as to why non-real estate companies own properties in Asia, Europe and
USA. In all, given that real estate is neither the core business nor the only business at many non-real estate firms, it is
interesting to find out why so many corporations (particularly Asian firms) are still hanging on to ownership of CRE.
Table 1
Real Estate Asset Holdings of the “Real Estate Intensive” Companies*
Size of PTYABS (S$
Number of companies
million)
Mean PTYABS (S$
Mean PPTY% 2
million)1
PTYABS > 1000
7
3012.19
61.71
100 ≤ PTYABS ≤ 1000
34
311.44
54.69
50 ≤ PTYABS ≤ 100
18
66.36
40.10
0 ≤ PTYABS ≤ 50
50
27.03
34.00
All
109
313.95
43.24
* For
Financial Year 2001
PTYABS (S$m) = gross property asset value reflected on balance sheets (million)
2 PPTY% (real estate asset intensity) = [gross property asset value (PTYABS) / gross total asset value] x 100%
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Source: Derived from Company Accounts
Figure 1 Distribution of Real Estate Asset Intensity (PPTY%)
60
50
40
Number 30
20
10
0
PPTY%> 80%
60%<PPTY%<80%
40%<PPTY%<60%
20%<PPTY%<40%
PPTY%
Source: Derived from Company Accounts
Table 2
CRE Asset Holdings of Singapore Non-real Estate Firms1
Business segments
Number of
Companies
Average
PTYABS(S$
million)2
Average PPTY% 3
Multi-industry
10
1431.72
46.77
Manufacturing
45
204.30
32.45
Commerce
17
212.61
47.30
Transportation / Storage /
Communication
7
67.64
36.86
Construction
14
78.97
48.67
Hotel /Restaurant
11
430.19
73.35
Services
5
156.80
47.05
11
Overall
109
Chi-square value
4
313.95
43.24
35.25*
31.75*
For the Financial year 2001
Average PTYABS (S$m) = gross property asset value (million)
3 Average PPTY% = [gross property asset value (PTYABS) /gross total asset value] x 100%
4 Derived from non-parametric Kruskal-Wallis tests
* Indicates two-tailed significance at the 1% level
1
2
Source: Derived from Company Balance Sheets and Datastream
Table 3
Group2
Analysis of CRE by Asset Subtypes1
Number of
companies
Average
PPTY% (%)
Proportion of real estate held as
Fixed assets
Investment properties
Development
properties
1
60
36.86
1
-
-
2
14
36.42
0.558
0.442
-
3
15
48.47
0.526
-
`0.474
4
18
60.85
0.180
0.583
0.237
5
2
84.90
-
-
1
Mean
109
43.24
0.724
0.153
0.123
1 Properties in corporate balance sheet are analyzed by three major asset subtypes: fixed assets, investment properties
and development properties.
2 Classification of companies based on property asset subtype:
Group 1 All properties are held as fixed assets
Group 2 Properties are held as fixed assets and investment properties
Group 3 Properties are held as fixed assets and development properties
Group 4 Properties are held as fixed assets, investment properties and development properties
Group 5 Properties are classified as development properties only
Source: Derived from Company Balance Sheets
Table 4
Contribution of CRE to Sales Turnover and Net Profit before Tax (NOPAT)
MI
28.2
26.2
21.9
19.2
24.5
24.0
Percentage contribution of CRE to sales turnover (%)
Industry
MU
CO
TR
CN
HO
3.9
19.0
1.8
57.8
17.3
2.6
14.4
6.5
57.3
22.1
7.0
10.8
10.3
53.0
17.2
8.9
13.1
13.7
65.6
10.8
6.9
15.6
17.1
60.8
24.0
5.9
14.6
9.9
58.9
18.3
SEE
14.1
19.8
24.6
24.5
23.1
21.2
MI
75.4
93.2
Percentage contribution of CRE to Net Profit before tax (NOPAT)
Industry
MU
CO
TR
CN
HO
5.8
20.2
-1.9
52.5
46.3
10.3
62.7
42.0
44.2
48.1
SEE
45.9
58.7
Year
1997
1998
1999
2000
2001
5-yr
average
Year
1997
1998
Average
20.3
21.3
20.7
22.3
24.5
21.8
Average
34.9
51.3
12
1999
2000
2001
5-yr
average
51.1
45.5
127.1
78.4
-64.6
68.6
-11.8
1.6
53.4
83.5
72.7
58.5
59.6
40.4
10.7
30.2
55.4
90.0
54.6
59.4
-128.2
-573.0
593.6
-2.6
60.4
56.7
77.6
59.9
12.4
-26.9
132.1
40.8
Note
MI
MU
CO
TR
CN
HO
SEE
:
:
:
:
:
:
:
Multi-industry
Manufacturing
Commerce
Transport / storage / communication
Construction
Hotel / restaurant
Service
Figure 2 Net Profit before Tax (NOPAT) due to CRE
1200000
1000000
SGD
800000
600000
400000
200000
0
1997
1998
1999
2000
2001
-200000
Year
whole
MI
MU
CO
TR
CN
HO
SE
Table 5 Net Profit before Tax from CRE (NOPAT- CRE): 1997-2001
Industry
MI
CO
MU
CN
HO
TR
SE
Average NOPAT – CRE
(S$’000)
34780.282
24341.742
12164.565
7628.966
4512.209
2181.094
2155.881
PAI
47.39%
52.06%
33.61%
64.10%
60.75%
27.41%
47.59%
(NOPAT-CRE) per PAI
733.916
467.571
361.933
119.017
74.275
79.573
45.301
Rank
1
2
3
4
6
5
7
13
Figure 3
Net Profit before tax from CRE (NOPAT-CRE) per PPTY % : 1997-2001
5
9
10
20
SGD('000) per PAI < 0
0 < SGD('000) per PAI < 200
200 < SGD('000) per PAI < 1000
SGD('000) per PAI > 1000
Table 6 Average EVA results (S$’000) of non-real estate firms: 1997-2001
Industry
Multi- industry (MI)
Manufacturing (MU)
Commerce (CO)
Construction (CN)
Hotel (HO)
Services (SE)
Transport (TR)
Total
Industry
5-year average
Ranking
Firms
10
46
17
14
11
5
6
109
MI
74,261
1
MU
24,717
4
1997
103,795
30,175
28,418
2,633
22,043
8,851
14,251
34,070
CO
13,982
5
1998
98,758
2,0628
9,107
-23,508
15,800
16,096
18,559
24,788
CN
-3,464
7
1999
-19,893
15,383
-796
378
4,156
8,712
11,578
5,921
HO
31,366
3
2000
94,897
29,470
17,294
4,521
25,721
25,145
16,471
29,563
SE
7,763
6
TR
15,339
2
2001
93,746
27,927
15,889
-1,342
89,109
-19,987
15,833
30,476
All
24,964
NA
14
Note
MI
MU
CO
TR
CN
HO
SEE
:
:
:
:
:
:
:
Multi-industry
Manufacturing
Commerce
Transport / storage / communication
Construction
Hotel / restaurant
Service
Table 7 Average MVA results (S$’000) of non-real estate firms: 1997-2001
Industry
Multi- industry (MI)
Manufacturing (MU)
Commerce (CO)
Construction (CN)
Hotel (HO)
Services (SE)
Transport (TR)
Total
Industry
5-year average
Ranking
Firms
10
46
17
14
11
5
6
109
MI
-281,547
7
1997
18,798
203,073
252,824
27,114
-78,953
31,360
133,066
129,046
MU
174,529
1
CO
62,212
4
1998
-524,634
86,863
-21,427
-2,764
-204,588
-20,993
55,164
-55,313
CN
23,817
5
1999
-102,276
270,530
125,250
52,507
-188,032
172,543
110,203
115,829
HO
-203,930
6
2000
-374,558
202,452
-18,350
29,557
-168,128
142,376
23,489
34,621
SE
113,358
2
2001
-425,065
109,727
-27,239
12,673
-379,948
241,502
-94
-24,823
TR
64,366
3
All
39,872
NA
Note
MI
MU
CO
TR
CN
HO
SEE
:
:
:
:
:
:
:
Multi-industry
Manufacturing
Commerce
Transport / storage / communication
Construction
Hotel / restaurant
Service
Table 8 EVA and MVA Analysis: Summary of Regression results on PPTY% Coefficients (a3 and b3)
Full sample
MI
MU
CO
HO
Others
Portfolio 1
Portfolio 2
Dependent variable
EVA (a3)
MVA (b3)
-25,927*
-68, 511,238**
Part B: By Industry
-36,645*
171,000,000
-12,709*
-45,800,571
-53,120*
-269,000,000**
-1,128
-116,000,000*
-380
-62,584,355
Part C: By Real estate asset intensity (PPTY%)
-22,411**
-98,592,678**
-6,793
-32,231,375
Sample PAI (5-year
average)
46.54%
47.39%
33.61%
52.06%
60.75%
46.56%
65.29%
27.78%
Notes
Two regression models are estimated: (Model 5) EVA it = a i + a 1* WACC it + a 2 * PP it + a 3* PPTY% it +
µ it;
15
(Model 6) MVA it = b i + b 1* WACC it + b 2 * PP it + b 3* PPTY% it + µ it. We focus on the coefficients of PPTY %
in both equations and assess whether a3 (on EVA) and b3 (on MVA) are statistically different from zero and their
respective signs. The results will suggest whether corporate real estate has a significant positive (negative) impact
on shareholders’ wealth. We perform the regressions on the full sample and on the firms classified by industry and
by level of property asset intensity respectively. We use fixed effect panel regression methodology that provides a
common set of partial regression coefficients while allowing a different intercept for each of the cross-sectional
units.
NB:
Full sample: 44 firms x 5 years = 220 observations
MI
:
Multi-industry
MU
:
Manufacturing
CO
:
Commerce
HO
:
Hotel / restaurant
Others :
Construction, services, transportation/storage/communication
*, **
:
Indicates two-tailed significance at the 1% and 5% level respectively
Figure 4
200,000
Comparison of 5-Year Average EVA and MVA Performance among Sectors
SGD('000)
150,000
100,000
50,000
0
-50,000
-100,000
-150,000
-200,000
-250,000
-300,000
MI
MU
EVA
74,261
24,717
13,982
-3,464
31,366
MVA
-281. 54 174,529
62,212
23,817
-203,93
CO
CN
SE
TR
Overall
7,763
15,339
24,964
113,358
64,366
39,872
HO
Sector
Note
MI
MU
CO
TR
CN
HO
SEE
:
:
:
:
:
:
:
Multi-industry
Manufacturing
Commerce
Transport / storage / communication
Construction
Hotel / restaurant
Service
16
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