Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

A Linkage analysis of the real estate sector using the hypotheticalextraction method

By

Yu Song*, Chunlu LiiTf and Craig LangstoiT

Abstract
Based on the newest Organisation for Economic Co-operation and Development (OECD)
input-output database at constant prices, this research aims to measure and compare the total, backward, forward, internal and sectoral linkages of the real estate sector using the hy
pothetical extraction method over thirty years and explore the role of this sector in national
economies and the quantitative interdependence between the real estate sector and the remaining sectors from a new angle. Empirical results show an increasing trend of these link
ages, which confirms the increasing role of the real estate sector with economic maturity
over the examined period. On the other hand, the significant rank correlations in the link
ages imply that, the importance of real estate remained fairly stable among highly devel
oped economies over the examined period. This may supply a tool to signal the maturity ofan entire economy. Furthermore, the findings can aid governments making relative policies
and businesses choosing strategic partners and location strategies.

1 Introduction
A sector's relationships with the rest of the economy through its direct and indirect intermediate purchases and sales are described as the sector's linkages (Miller and Lahr,
2001). The sectors with the highest linkages should be possible to stimulate a more
rapid growth of production, income and employment than with alternative allocationsof resources. The importance of linkage lies in its tremendous influence on govern
ments, industries and enterprises. Firstly, information on these linkages is essential tounderstanding the structure of an economy, which is in turn important in formulating

industry policies for government (Cai and Leung, 2004). Governments can interfere ina sector by imposing on other sectors, which have high linkages with this sector, andvice versa. Secondly, linkage is one of the most important factors for gaining competi
tive advantage for industry. For example, the linkage can affect the sector's location
Received June 2006,final version received October 2006.

Xinhua School of Banking and Insurance, Zhongnan University of Economics and Law, Wuhan
430073, China.

** School of Architecture and Building, Deakin University, Geelong, Vic 3217, Australia
1 Contact author, email: chunlu@deakin.edu.au26

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

strategies because the transport cost can be cut, since locating with sectors that havehigh linkages. A high linkage between a supplier and a buyer may guarantee on timedelivery of inputs and the quality of the inputs. Moreover, when a sector successfully
enters a foreign market, it will be relatively easy for sectors that have high linkages
with this sector to gain access to the foreign market (Hoen, 2002). Thirdly, the linkage
can impact the diversity and investment strategies of enterprises and investors, who
may prefer to invest in industries that have high linkages with each other to guarantee
profits and avoid risks. More importantly, the linkage can indicate a sector's economic
pull and push because the direction and level of such linkages present the potential ca
pacity of each sector to stimulate other sectors (Bon, 2000). Hence, a historical per
spective of linkages is necessary and helps to better comprehend the relationship of asector with other economic sectors and the kind of role it has played at different stagesof the economic development.
By displaying all flows of goods and services within an economy, the input-out
put methodology has been considered in the literature as a main tool to determine, define, measure and assess the linkages between sectors (Miller and Blair, 1985; Lean,

2001; Miller and Lahr, 2001). With the linkage measures, two different countries or regions can be compared and the methods may even be used to analyse productivity,
technological and energy linkages (Pietroforte and Gregori, 2003; Su et al, 2003; Liuand Song, 2005). Measure methods of the linkages rooted in the input-output table

may be classified under two main categories, one refers to the traditional method andthe other is the hypothetical extraction method (HEM). The traditional methods mainly
focus on the calculations of the demand-driven model (Leontief model) developed by
Leontief (1936) and the supply-driven model (Ghosh model) proposed by Ghosh(1958).

The real estate sector in the input-output table refers to the flow of services
yielded during any period of time by real estate stock, which is playing an importantrole in the entire economy (Tse, 1994). The linkage measure using the traditional
method has a relatively short history within the field of real estate. Liu and Song
(2004) measured real estate productivity using the traditional method. Song et al.

(2004) compared the linkages between the construction and real estate sectors and Liu
et al. (2005) analyzed the linkages of the real estate sector and formulated a set of in

dicators to compare the linkages of the real estate sector in seven OECD countries.
However, the traditional calculation methods are being gradually ignored because they
do not capture much of the inherent complexity of an economy (Miller and Lahr,
2001).
On the other hand, linkage measures based on the HEM become increasingly influential (Miller and Lahr, 2001). The HEM has been applied to the agriculture sector(Cai and Leung, 2004), the water sector (Duarte et al., 2002), the construction sector(Song et al., 2006a) and some other sectors (Dietzenbacher and Van der Linden, 1997;
Yue and Andreosso-O'Callaghan, 2004). Even though the HEM studies have been ap

plied to many sectors, no real estate linkage research uses the HEM to the best of ourknowledge. In the literature, only some real estate research using the traditional method
can be found (Song and Liu, 2005; Song et al., 2005; Song et al., 2006b). It is there

fore necessary to fill this gap. Using the newest OECD input-output database1 at con
stant prices, this research aims to measure and compare the linkages of the real estate

A Linkage analysis of the real estate sector using the hypothetical extraction method

27

sector using the HEM and explore the role of this sector in national economies and thequantitative interdependence between the real estate sector and the remaining sectors
from a new angle. The rest of this paper comprises an introduction of the HEM, a data

description, an analysis of the empirical results, testing, discussion and finally the con
clusions of the research.

2 Hypothetical extraction method
The original idea of the HEM was to extract a sector hypothetically from an economic
system and examine the influence of this extraction on other sectors in the economy(Cella, 1984; Clements, 1990). Mathematically, the idea was to quantify how much an
economy's total output would decrease if the sector were extracted. Thus, by compar

ing the output levels for each of the remaining sectors before and after the hypotheticalextraction, the impact of the extracted sector can be assessed. The difference between
the output in the reduced case and in the original situation reflects the linkages be
tween the extracted sector and all other sectors in the economy. The linkage can be decomposed into total, backward, forward and internal linkage indicators according todifferent transformations.

Nevertheless, one main shortcoming exists in the previous HEM research, namely,

the internal linkage and sectoral linkages are not investigated well because the method
is used only to analyze the linkages between a specific sector and all other sectors.
Most of HEM research has focused on the effect of each sector on the economic sys
tem as a whole, which is not suited well for answering questions as to how the link

ages operate within a sector and between two specific sectors (Hoen, 2002). In this research, using the input-output tables of 36-sectors in seven OECD counties, four ex

traction structures are adopted to formulate the total, backward, forward and internal

linkage indicators according to Miller and Lahr (2001) and one structure is developed
further to formulate the sectoral linkage indicator of the real estate sector, which shows
the linkage between the real estate sector and a specific sector. Thus, linkages of thereal estate sector can be measured from all directions.

2.1 Total linkage indicator
In light of the basic ideal of HEM, it is assumed that the n-sector input-output techni
cal coefficient A has been partitioned into two groups: group one (gi) is the sectors thatare to be extracted from the economy and group two (g2, gi+g2=n) consists of all theremaining sectors of the economy. Now, g\ has been extracted hypothetically from theeconomy, using the same final demand vector F, the Leontief model X= (I-A)~lY9

and can be rewritten as X'= (I -A')~lY, where X and X' are the output before and af
ter extraction, A is the technical coefficients matrix (nxn) and Ay is a reduced technical

The newest OECD database 2002 edition is unpublished publicly and can be obtained on requestfrom OECD.

28

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

coefficient matrix ((n-l)x(n-1)), i.e. A'= I

L U A22 -I. The reduction in output can be

expressed as X-X', which reflects the linkage between gi and g2, given the technicalproduction process is held constant. Assuming gi=l and gi (sector 1) is hypotheticallyextracted entirely from the economy, then using the Leontief model, the total linkage(TL), can be expressed as:

TL = [h (H -I) +X2L2A21H] xr,+ [XiHAiJj^XaLnAiiHAiTln] xY2(1)

where A,i and X2 are column summation vectors for sector 1 and sector 2 respectively.
A12, An and A22 are the partitioned matrixes of the technical coefficient matrix A. H

equals (/-A11-A12L22A21)"1. / denotes the identity matrix. Ly is the ijth element of the
Leontief inverse matrix and Y\ and Y2 are the final demand of sector 1 and sector 2 respectively. Thus, the total linkage indicator can be obtained as:
77

Total linkage indicator = -j||b<100%(2)

where A, is a summation column vector.

2.2 Backward linkage indicator
By assuming that sector 1 purchases import goods only to substitute completely for the

local inputs, i.e. A'= \L U.n , the backward linkage (BL) can be decided.

A22 J

BL = [Xy (H -I) + ^2L22A2I//]xF,+[X, (H -I) A^i+XiU^xHAxd^^(3)

The backward linkage indicator can be obtained as:/?/

Backward linkage indicator = -tttx100%(4)

2.3 Forward linkage indicator
The measures of forward linkage are based on the extraction of the Ghosh model. The
corresponding forward linkage can be similarly obtained. It is assumed that sector 1 ishypothetically extracted, i.e. A'=

L A21 A22 J, the forward linkage (FL), can be obtainedas:

FL = V{x [(K-Gn) hJ+KBnG22X2]+V2x [G22 B2l (K-Gn) h'+G22B2lKBi2G22X2] (5)where A,i' and V are row summation vectors for sector 1 and sector 2 respectively. BJ2,
B21 and B22 are the partitioned matrixes of allocation coefficients matrix B. dj is theyth element of the Ghosh inverse matrix. K equals (/-Bn-tf^Ai)'1 and Vi and V2
are the value added of sector 1 and sector 2 respectively. The forward linkage indicator
can be obtained as:

A Linkage analysis of the real estate sector using the hypothetical extraction method

FT

Forward linkage indicator = -t7ttx100%

29

(6)

where A,' is a summation row vector.

2.4 Internal linkage indicator
The internal linkage indicator reflects the internal effects within a sector. Relative tohorizontal linkage, this indicator mirrors the vertical flows among sub-sectors of a sec

tor. Just extracting the internal effect of sector 1, i.e. A'= \ A

L An

,21

A22 J

, the internal

linkage (/L), can be obtained as:

IL=[h (H-6) +X2L22A2i(H-d)] xF,+ [h(H-d) A]2L22+X2L2A2i(H-6)Al2L22]xY2 (7)where 0 = (/ -AuLiAu)'1. So, the internal linkage indicator can be shown as:
Internal linkage indicator = -tttx100%(8)

2.5 Sectoral linkage indicator
The sectoral linkage indicator represents the linkage between any two sectors. Twoquestions must be resolved here: one is how to measure this linkage. The other is how
to distinguish the directions of the linkage, from sector i to sector j or from sector j tosector /.

(1) Sectoral linkage indicators I (from sector i to j)

It is assumed that the ^-sector input-output technical coefficient matrix A has been
partitioned into two groups: group 1 (gi) and group 2 (g2). The symbol g. is a groupthat consists of two sectors: sector / andy, which are to be extracted from the economy
and sector i has relationship with sector j. The symbol g2 consists of all the remaining

sectors of the economy. By extracting gx hypothetically from the economy, the firstquestion mentioned above can be resolved. Theoretically, in the Leontief model, thetechnical coefficient matrix A is also called direct input coefficient matrix. All column

elements of the matrix A represent the direct input from sector i to y, that is, the purchases of the j sector from the / sector per monetary unit. Moreover, all column ele
ments of the total input coefficient matrix L represent both direct and indirect flows
from sector 1 to j, that is, the effect of one monetary unit change in final demand of
the j sector on total output of the i sector. Hence, using the Leontief model to measure

the sectoral linkage, the linkage direction (from sector i to j) can be stated. According
to the analysis above, the Leontief model can be shown as

x.i

rAn Anirx.

where sub-matrices An and A2\ show the relationships between g\ and g2 in production.
An and A22 indicate the intra-sectoral connections of g\ and g2, X\ and X2 denote the30

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

outputs of gi and g2, and Y^ and Y2 denote the final demand of gi and g2 respectively.
Now, let A,,= 0, then

[x\-\

\ o

a12i

rxu

ry.i

The difference between Eq. (10) and (9) can be expressed as*,-*',]r^-(/-a12l22a21)-'[^-(/-ipX22A21r]A12L22

i

r^-i

2-X'2] iLA2i[fi-(i-Al2t*A2irl] L22A21[^-(/-A12Z22A21n AX7tn\x L%J

where ft = (I-An-i,^^)"1, and Ln =(/ -A22)~l. Then the sectoral linkage from sec
tor i to j (5Ly) can be expressed as,)"1) i12L22 + huAvT1) Al2L2] x %(12)

So, the sectoral linkage indicator I (from the sector i to sector j) can be shown as
SLr

Sectoral linkage indicator I = -r^-xl00%(13)(2) Sectoral linkage indicators II (from sector j to i)

Similarly, using the Ghosh model to measure the sectoral linkage, the linkage direction(from sector j to /) can be confirmed. In the Ghosh model, the allocation coefficientmatrix B is also called direct output coefficient matrix. All row elements of the matrix
B represent the direct output from sector j to sector /, that is, the sales of the; sector
to the i sector per monetary unit. Moreover, all row elements of the total output coeffi
cient matrix G represent both direct and indirect flows from sector j to /, that is, theeffect of one monetary unit change in value added of the i sector on total output of thej sector. So, the Ghosh model can be expressed as:[X, X2)= [Z, &]x\6" f2l+[t>, fc]
L D2\

(14)

D22 J

where, ft and V2 denote the value added of gx and g2 respectively.
From the supply-side model, it is assumed that gx is hypothetically extracted, so let Bu= 0. Thus, Eq. (14) can be rewritten as:[X, fc]= [X, X2]x\ ° f'2l+[t>, n
L D2\

(15)

D22 J

The difference between Eq. (15) and Eq. (14) can be shown as:£-U -BnG22B^V] BnG22 1
2i[^_(/ _^^^-.j ^i2(?22 J(16)

where K-(I -Bn-BnGiiBu)1, Gu = (I -Z?,,)"1 and G22 = (I -B22)~l. Consequently, thesectoral linkage from sector j to i (SL/i) can be expressed as

A Linkage analysis of the real estate sector using the hypothetical extraction method

31

SL, = V,x [(£-(/ -BnG22B2i)1 X\+ V2 [622B2l (£-(/ - AaG^i)"1) A,1, +622Ai(£-(/ -BnG22B2,)x) Bl2d22X\](17)

So, the sectoral linkage indicator II (from the sector j to sector /) can be shown as

SL

Sectoral linkage indicator II = -p-rrxlOO%(18)

3 Date description
The OECD input-output database, which is published by the Economic Analysis andStatistics Division of the OECD, is a very useful empirical tool for economic researchand structural analysis at international level (OECD, 2004). Moreover, this is the mostcomprehensive database for comparing the real estate and construction sectors interna

tionally so far (Pietroforte and Gregori, 2003; Liu and Song, 2005). The early editionof OECD input output database (1995 edition) covered ten highly developed countries(Australia, Canada, Denmark, France, Germany, Italy, Japan, Netherlands, United Kingdom, and United States), five year points from the early 1970s through to the early

1990s. These were produced using an earlier system of national accounts (SNA68) andindustrial classification system (ISIC Revision 2), including 36 sectors. The newest edi

tion (2002 edition) is unpublished publicly and can be obtained on request from OECDand covered one or more years around the mid/late 1990s for eighteen OECD countries(Australia, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece,
Hungary, Italy, Japan, Korea, Netherlands, Norway, Poland, Spain, United Kingdom)

and two non-member OECD countries (Brazil and China). The tables are based on
ISIC Revision 3 industrial classifications, including 42 sectors.
Based on the 1995 edition database, the 2002 edition database has been aggre

gated into 36 sectors in order to facilitate comparisons over time in this study. The 36
sectors used in the OECD input-output table are shown in Appendix 1. For the same

reason, the countries which are not involving in the 1995 edition database are ignored.
Moreover, due to limited comparable and available data in the real estate sector, Ger
many, Italy and United Kingdom are not considered. The data of France are unavail
able before the early 1980s. The data from Australia are unavailable before mid-1980s

and the data from Netherlands is not available in the early-1990s. In addition, the ex
amined period is divided into six comparative periods as shown in Appendix 2: early1970s (1968-1972), mid/late- 1970s (1975-1978), early-1980s (1980-1982), mid-1980s(1985-1986), early-1990s (1989-1990) and mid/late-1990s (1995-1998).

4 Empirical results
Assuming that the real estate sector has been extracted hypothetically from the eco
nomic system, the total, backward, forward, internal and sectoral linkages of the real

32

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

estate sector are calculated, analyzed and compared in sequence. Thus, the importanceof the real estate sector and the quantities relationship between the real estate sector
and other sectors can be measured. The developing trends of the real estate sector indifferent countries can be compared. Based on a good understanding on these linkages,
government and businesses can develop their policies and strategies and create a favorable competitive position in the modern economy.

4.1 Total linkage indicators of the real estate sector
Total linkage indicator is one comprehensive measure of the real estate sector's importance to the economy because all connections (forward, backward and internal effects)

have been extracted completely. With the "disappearance" of the real estate sector, theremaining sectors in the economy would have to purchase real estate service fromoverseas and the real estate sector's final demand would have to be satisfied by imports
as well. The difference between the outputs before and after the extraction just reflects

the importance of the real estate sector. In other words, the total linkage indicator is an

integration of the forward, backward and internal effects. The total linkage indicators of
the real estate sector for the seven selected countries are generated from Eq. (2) and reported in Table 1, which shows the changes of the total real estate linkage relative toeach national economy.

Table 1 : Total linkage indicators and ranks of real estate in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA
Average

Early-

Mid/Late-

Early-Mid-

Early-

1970s

1970s

1980s

1980s

1990s

1990s

Linkage

N/A

N/A

N/A

6.73%

7.26%

5.96%

Rank

N/A

N/A

N/A

2

2

6

Linkage

2.05%

2.33%

3.13%

3.36%

3.99%

5.92%

20

19

14

13

13

7

4.11%

4.81%

5.31%

6.00%

6.82%

8.71%

Rank

10

9

9

6

5

3

Linkage

N/A

N/A

6.75%

7.22%

9.95%

8.82%

Rank

Linkage

Mid/Late-

Rank

N/A

N/A

3

1

1

1

Linkage

4.17%

5.39%

5.26%

5.46%

6.36%

4.69%

12

8

9

5

4

3

1.86%

2.56%

2.85%

3.21%

N/A

3.34%

18

18

16

13

N/A

25

5.40%

5.28%

5.79%

6.38%

7.09%

2

4

2

1

1

1

3.52%

4.08%

4.85%

5.48%

6.91%

6.42%

Rank

Linkage
Rank
Linkage

Rank

7.53%

The total linkage indicators show two distinct groups of countries: Netherlands andCanada with a lower total linkage indicator and the remaining countries with higher
ones. In economic development, the real estate sector seems to play a more importantrole in Australia, Denmark, France, Japan and USA than in Netherlands and Canada. It
can be observed that the average values tend to increase over the examined period. In

A Linkage analysis of the real estate sector using the hypothetical extraction method33

the 1990s the pace of increase is significant in most of the countries. The reason may

be due to the increase in the price of real estate in these countries. However, it has tobe noted that the volumes of real estate service are still underestimated because some
private brokage are only partially captured by official statistics.
According to Eq. (2), the total linkages of 36 sectors are calculated and ranked forall seven countries, and the rankings of the real estate sector of these countries are reported in Table 1. Except for Canada and Netherlands and Japan, all values of the total

linkage of the real estate sector are ranked in the top ten over the examined period.

Specifically, the values ranked France and USA in the top five over the whole exam
ined period. Moreover, a trend of increase in the rankings is apparent and all rankings
are increasing between the initial and final stages of the examined period. The ranking
differences may be contributed by different industrial structures, relative prices, tech
nology changes and government policies in different countries.

4.2 Backward linkage indicators of the real estate sector
Assuming that all local product inputs of the real estate sector are extracted and all inputs will depend on imports (the forward and internal effects will remain), the backward linkage of a sector reflects this sector's dependence on local inputs that are pro
duced within the production process of the economy. A weak backward linkage sug
gests a strong sectoral independence. On the other hand, a lower value represents a

weak economic pull of the real estate sector to the remaining sectors. The backward
linkage induces growth through the process of derived demand because the remaining
sectors would have to face the losses without the purchase of the real estate sector.

More importantly, the backward linkage indicator is a measure of the degree of indus
trialization of the real estate production process and the national technology differencein terms of intermediate and valued added inputs composition (Pietroforte and Gregori,2003), because it is generally agreed that input-output tables reflect a general equilib

rium model of the economy where inputs are allocated according to technologicalavailability.

The backward linkage indicators of the real estate sector for the seven selected

countries are calculated from Eq. (4) and presented in Table 2. The values are scattered
at a low value between 0.5% and 4% over the examined period. The low backward
linkage indicator suggests a strong sectoral independence and a weak economic pull of
real estate. The relatively lower value is reasonable for the real estate sector because

this sector plays a fundamental connecting role in the value chain (Roulac, 1999).
Moreover, with a lower backward linkage indicator, the real estate sector represents low

industrialization and technology levels. However, a slightly upward trend over the entire study period can be seen. In any industry, the progress of technology cannot be
stopped. Compared with Canada and Netherlands, the Australian, Danish, French, Japa

nese and American real estate sectors show relatively weak economic independences,strong pull effects to the remaining sectors of the economy and higher technology lev
els.

Like the total linkages, the backward linkage ranks of the real estate sector are

listed in Table 2. Except for Australia and USA, most of countries have a relative lower
ranking. However, most of ranks have experienced a significant rise except for Austra-

34

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

lia and Netherlands. For example the rankings of Canada, Denmark, France and Japanrose from 25 to 16, 11 to 4, 9 to 5 and from 14 to 6 respectively between the initialand final stages of the examined period. The increasing trend represents a decreasing
sectoral independence of the real estate sector and means the real estate sector needs
support more and more from other sectors. On the other hand, it means that the realestate sector's ability to pull the rest of the economy was increasing over the examined
period.

Table 2: Backward linkage indicators and ranks of real estate in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA
Average

Linkage

Mid/Late-

Early-

Mid/Late-

Early-Mid-

Early-

1970s

1970s

1980s

1980s

1990s

1990s

N/A

N/A

N/A

3.50%

3.17%

3.04%

Rank

N/A

N/A

N/A

4

5

5

Linkage

0.80%

0.78%

0.95%

1.08%

1.31%

2.35%

25

23

23

21

21

16

2.39%

2.91%

3.13%

3.39%

3.64%

3.90%

11

10

8

7

7

4

Linkage

N/A

N/A

2.59%

2.87%

4.05%

2.84%

Rank

N/A

N/A

9

6

3

5

Linkage

2.24%

2.57%

2.53%

2.51%

3.16%

2.02%

Rank

Linkage
Rank

Rank

Linkage
Rank

Linkage
Rank

14

10

11

6

0.90%

1.22%

1.29%

1.33%

6
N/A
N/A

6
2.11%

20

17

15

17

2.08%

2.04%

1.95%

2.44%

2.60%

2.48%

6

8

8

5

5

5

1.68%

1.90%

2.07%

2.45%

2.99%

2.68%

23

4.3 Forward linkage indicators of the real estate sector
Assuming that the real estate sector just sells for export, except for deliveries to itself,
the difference between the outputs in the reduced case and in the original situation reflects the economic losses of the remaining sectors of the economy without the supplyof the local real estate sector. The forward linkage of a sector reflects the dependenceof the remaining sectors in the economy on this sector's supplies that are produced
within the production process. The forward linkage indicators of the real estate sector
for the seven selected countries are calculated from Eq. (6) and depicted in Table 3.

The value of the forward linkage indicators are stabilising at a higher value com
pared with the backward linkage. A strong forward linkage shows a weak sectoral independence and a strong economic push of the real estate sector. Moreover, the valueof the indicator reflects that the proportion of final demand of the real estate sector islarger than its intermediate demand in most selected countries. The main reason seems
to be that real estate has a major role in creating demand and attracting the buyer tothe distribution system. The arithmetic means of the forward linkage indicators divides
these countries into two distinct groups of countries: Denmark, Canada and Nether-

A Linkage analysis of the real estate sector using the hypothetical extraction method 35
lands, with a lower forward linkage indicator and the remaining countries with higher
ones. These differences can be explained in terms of the level of the intermediate demand in different countries. In Denmark, Canada and the Netherlands, the economic
push of real estate was weaker with a lower level of intermediate demand over the
study period. The reason seems to be most of the output of real estate flows into final
demand, that is, private domestic consumption and government consumption. For Aus

tralia, France, Japan and USA, the proportion between intermediate demand and final
demand tends to be equal. These countries' push strength to economic growth was rela
tively stronger.

Table 3 Forward linkage indicators and ranks of real estate in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA
Average

Mid/Late-

Early-

Mid/Late-

Early-Mid-

Early-

1970s

1970s

1980s

1980s

1990s

1990s

Linkage

N/A

N/A

N/A

4.75%

5.56%

3.13%

Rank

N/A

N/A

N/A

1

1

5

Linkage

1.68%

2.08%

2.87%

2.89%

3.50%

4.26%

14

12

7

7

6

3

3.49%

4.09%

5.80%

Rank

Linkage

Rank
Linkage

2.34%

2.57%

3.00%

8

8

7

3

3

1

N/A

N/A

5.73%

5.93%

7.89%

5.93%

Rank

N/A

N/A

1

1

1

1

Linkage

3.64%

5.23%

5.13%

5.19%

5.20%

4.05%

7

4

4

1

1

2

1.20%

1.61%

1.93%

2.20%

N/A

1.49%

13

10

9

7

N/A

16

5.21%

4.99%

5.92%

4.51%

5.94%

6.56%

2

2

1

1

1

1

2.81%

3.30%

4.10%

4.14%

5.37%

4.46%

Rank

Linkage
Rank

Linkage
Rank

The forward linkage indicator's rankings of the real estate sector for these countries are also presented in Table 3. The forward linkages have the highest ranking com

pared with the backward linkages. It seems that the higher rankings in the forward
linkage are the main reasons for the higher rankings in the total linkage. Except for

Canada and Netherlands in the early-1970s and the mid/late-1970s, all values of theforward linkage of the real estate sector were ranked in the top ten. Especially, the val
ues ranked Denmark, France and USA first in the mid/late-1990s. Compared with the
backward linkage indicators, the higher rankings reflect the strength of the push to eco
nomic growth is larger than that of the pull in the real estate sector. It also demon
strates that developing a national economy by promoting the real estate industry is not

as effective as developing real estate through promoting the national economy (Liu etal., 2005).

4.4 Internal linkage indicators of the real estate sector

36

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

Assuming that the real estate sector's intra-sectoral shipments are eliminated, the internal linkage of a sector reflects the interrelationship of sub-sectors. According to Eq.
(8), the internal linkage indicators are described in Table 4, which displays three characteristics. Firstly, the real estate sectors have low internal linkage indicators, which are
all under 1.6% relative to the entire economy. The real estate sector is usually divided
into two sub-sectors, namely residential and commercial real estate services. The resi
dential real estate sub-sector supplies living accommodation for the commercial subsector, whereas the commercial sub-sector supplies few services for the residential realestate sub-sector. The relationships between these two sectors are relatively loose witha lower internal linkage indicator.

Secondly, all values present an increasing pattern, which may be due to increasing

prices over the examined period. Thirdly, the differences among countries are enor
mous. For example, France had an extremely high value whereas Netherlands had avery low value. The differences may be attributed to different economic developmentlevels, relative prices and government policies in different countries. The relative prices
and government policies differences in different economic developing stages definitely

affects the interflow between the residential and commercial real estate sub-sectors.
Obviously, the internal flows between the sub-sectors are weak even relative to this
sector in seven countries. However, in some developing countries, the internal linkage
indicator may be higher than in developed countries. One reason is the dramatically increasing commercial real estate market in developing countries may need more residen
tial services than in developed countries. As expected, the internal linkages have a low
ranking as reported in Table 4. Compared with other sectors, the low ranking just reflects the industry characteristics of the real estate sector in developed countries.

Table 4: Internal linkage indicators and ranks of real estate in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA
Average

Linkage

Early-

Mid/Late-

Early-Mid-

Early-

1970s

1970s

1980s

1980s

1990s

Mid/Late1990s

N/A

N/A

N/A

0.48%

0.75%

1.46%

Rank

N/A

N/A

N/A

9

6

1

Linkage

0.12%

0.16%

0.22%

0.25%

0.32%

0.38%

25

22

17

17

17

17

Rank

0.15%

0.21%

0.27%

0.49%

0.63%

0.68%

Rank

20

18

15

11

11

9

Linkage

N/A

N/A

0.96%

1.02%

1.58%

2.00%

Rank

N/A

N/A

9

8

4

1

Linkage

0.13%

0.56%

0.29%

0.48%

0.58%

0.33%

Linkage

Rank

Linkage
Rank
Linkage
Rank

23

11

17

12

12

13

0.03%

0.04%

0.05%

0.06%

N/A

0.08%

27

27

28

29

N/A

34

0.68%

0.61%

0.75%

0.97%

1.07%

1.24%

8

8

8

3

2

3

0.22%

0.32%

0.42%

0.54%

0.82%

0.88%

A Linkage analysis of the real estate sector using the hypothetical extraction method

37

4.5 Sectoral linkage indicator between the real estate and construction sector
Assuming that the flows between the real estate sector and a specific sector are elimi
nated, the sectoral linkage indicator reflects the interrelationship of the real estate sec

tor and the specific sector in an economy. The real estate sector has a very tight relationship with the construction sector. The construction sectors mainly consist of new

construction and maintenance and repair construction, whereas real estate plays a fun
damental connecting role in the value chain (Roulac 1999). The real estate sectors pro

vide services for the construction sectors. Accordingly, most of intermediate goods andservices produced by the maintenance and repair construction sub-sector pour into thereal estate sector (Bon 2000). The construction sector is not only the supplier but also
the user of the real estate sector in the whole value chain. Moreover, both the construction and real estate sectors have been considered vital productive drivers for the eco
nomic development. As one of the largest consumers of the construction sector, theinter-sectoral flows between real estate and construction sectors are varied and complex
and it is difficult to determine the quantitative relationships between them in modern
economics. Considering the directions, the sectoral linkage indicator can be divided

into two groups: one is the linkage from the construction sector to the real estate sec
tor, which is calculated from Eq. (13) and described in Table 5. The other is the link
age from the real estate sector to the construction sector, which is from Eq. (18) and illustrated in Table 6.

Table 5: Sectoral linkage indicator I from construction to real estate in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA

Average

Early-

Mid/Late-

Early-Mid-

Early-

Mid/Late-

1970s

1970s

1980s

1980s

1990s

1990s

Linkage

N/A

N/A

N/A

1.02%

1.09%

2.05%

Rank

N/A

N/A

N/A

12

17

14

Linkage

0.70%

0.85%

1.38%

1.11%

1.20%

1.30%

Rank
Linkage
Rank

13

7

6

8

7

13

1.57%

1.91%

2.03%

2.34%

2.52%

2.78%

5

4

3

3

1

3

Linkage

N/A

N/A

1.84%

1.78%

2.65%

3.55%

Rank

N/A

N/A

11

10

8

5

Linkage

1.05%

1.56%

1.14%

1.30%

1.46%

1.08%

8

5

9

10

11

10

2.13%

2.26%

2.18%

2.18%

N/A

2

2

3

3

N/A

2

1.50%

1.49%

1.54%

1.75%

1.93%

2.16%

Rank

Linkage
Rank
Linkage
Rank

3.98%

8

9

10

11

8

4

1.39%

1.62%

1.68%

1.64%

1.81%

2.41%

As expected, the linkages from construction to real estate are larger than that fromreal estate to construction. The real estate sector supplies various kinds of services forthe construction sector, such as brokerage, plant location, layout and lease, procurement

38

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

decisions, and so on. One of the main assignments of the real estate sector is to makedecisions for plant location of construction businesses concerning the country, region,
submarket and site. Another is the size of facility, layout, lease or buy decision andbrokerage. What is more, the real estate sector also influences construction manufactureaccess including the location of the manufacturer's showrooms, access to displays of
construction merchandise, and catalogues. Generally, the real estate sector as supplier
just plays a service delivery role in the value chain of the construction sector. On theother hand, the construction sector is the one of largest suppliers for real estate andmost intermediate goods and services produced by the maintenance and repair con
struction sub-sector go to the real estate sector. This explains why linkages from con
struction to real estate are larger than that from real estate to construction.

According to the input and output directions of the real estate sector, all sectoral
linkages from the other sectors to the real estate sector and from the real estate to theother sectors are calculated and ranked respectively. The rankings of the sectoral link
age between the real estate and construction sectors are also reported in Tables 5 and 6.

The sectoral linkage from construction to real estate has a higher ranking than thatfrom real estate to construction. This just reflects that construction contributes more to
real estate. However, it can be stated that the economic development in a developed
country has been characterized by two main trends: the decreasing economic importance of the construction industry and the progressively increasing services of the realestate sector. Interestingly, the two main trends are just reflected by the rankings of thesectoral linkage indicators in Tables 5 and 6. All rankings of the sectoral linkage indicator II (from real estate to construction) showed an increasing pattern (Australia, Canada, Denmark, France and USA were increasing and Japan and Netherlands kept con
stant), whereas, only three countries had an increasing ranking of the sectoral linkage
indicator I (from construction to real estate).
Table 6: Sectoral linkage indicator II from real estate to construction in each country

Australia

CanadaDenmark

FranceJapan

Netherlands

USA

Average

Early-

Mid/Late-

Early-Mid-

Early-

Mid/Late-

1970s

1970s

1980s

1980s

1990s

1990s

Linkage

N/A

N/A

N/A

1.07%

1.26%

1.39%

Rank

N/A

N/A

N/A

15

21

13

Linkage

0.54%

0.72%

1.16%

0.98%

1.11%

0.94%

Rank

14

10

6

9

12

8

1.24%

1.45%

1.62%

1.97%

2.23%

1.75%

7

4

4

5

4

4

N/A

N/A

1.85%

1.79%

2.61%

2.52%

Rank

N/A

N/A

12

13

11

5

Linkage

0.90%

1.65%

1.12%

1.37%

1.44%

0.84%

9

8

10

10

14

9

1.49%

1.54%

1.48%

1.51%

N/A

1.19%

Linkage
Rank
Linkage

Rank

Linkage
Rank

Linkage
Rank

3

4

3

4

1.82%

1.74%

1.95%

1.55%

2.46%

2.11%

11

11

12

11

11

7

1.20%

1.42%

1.53%

1.46%

1.85%

1.53%

N/A

3

A Linkage analysis of the real estate sector using the hypothetical extraction method 39

According to the data of the early-1990s, a detailed ranking of the sectoral linkage

indicators I and II are reported in Appendixes 3 and 4 respectively, which present the

rankings of the sectoral linkages both from other sectors to real estate and from realestate to other sectors. As can be seen, in different countries, the rankings of the sec

toral linkage indicators vary in the early-1990s. For example, in Australia, the sectoral
linkages between the wholesale and retail trade and real estate sectors are ranked first.
However, in France, the sectoral linkages between the finance and insurance and realestate sectors are ranked first. In fact, the interrelationship between the real estate sec
tor and other sectors is determined by the characteristics of real estate in different
countries. Based on the rankings of the sectoral linkage indicators I and II in the early1990s, all relative sectors are re-ranked based on the average rankings of every sector.

The top ten sectors that have the highest average sectoral linkage with the real estate
sector are reported in Table 7. As can be seen, the top ten sectors that have the sectoral
linkage with the real estate sector are by and large similar. To some extent this means
the constituents of the input/output of real estate are analogous. The finance and insur
ance, manufacturing, transport and storage, wholesale and retail trade and construction
sectors are the main suppliers and users of the real estate sector. Except for the com
munity, social and personal service sectors, the remaining sectors in the national econ
omy are easily affected by the real estate sector, and these sectors have a significant ef
fect on the real estate sector as well for all selected countries. The intrinsic characteristic of linkages can aid the governments in formulating industry policies and businesses
in choosing strategic partners and location strategies. Governments can interfere in thereal estate sector by imposing on other sectors, which have high linkages with it, andvice versa. The businesses can cut their costs, improve productivities by adopting ap
propriate integrative and location strategies, and therefore gain competitive advantage.
Table 7: Re-ranked sectors of the sectoral linkage indicators I and II in the early-1990s
Sectoral linkage indicator II

Sectoral linkage indicator I
Rank

Sector

Sector

SectorNo.

SectorNo.

1

31

Finance and insurance

T

2

T

Industrial chemicals

3*Food, beverages and tobacco

36*Paper, paper products and printing

31

Finance and insurance

4

29

Transport and storage

6*

Paper, paper products and printing

527

Wholesale and retail trade4*

Textiles, apparel and leather

63*

Food, beverages and tobacco

21*

Motor vehicles

7

26

Construction

29

Transport and storage

8

1

Agriculture, forestry and fishery

27

Wholesale and retail trade

9

33

i

Agriculture, forestry and fishery

10

12*

26

Construction

Community,
service
Iron and steelsocialandpersona]1

Industrial chemicals

Sectors 3 to 24 are categorized into the manufacturing sector according to the OECD classification.

40

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006
Table 8: Descriptive statistics of the real estate linkages

Linkages

Periods

N

Total

Early-1970s

50.03540.0186

0.05400.03520.0152linkage

Mid/Late-1970s

50.03060.0233indicator

0.05390.0408

Early-1980s

0.0151

6

0.03900.02850.06750.0485Mid-1980s

0.015470.04010.03210.07220.0548

Early-1990s

0.01606

0.05960.03990.09950.06910.0191

Range

Minimum

Maximum

Mean

Std. Deviation

Mid/Late-1990s

7

0.05480.03340.08820.06420.0205

Early-1970s

50.01600.00800.02390.01680.0077linkage

Mid/Late-1970s

50.02130.00780.02910.0190indicator

0.0089

Early-1980s

6

0.02190.00950.03130.02070.0084Mid-1980s

70.02420.01080.03500.02450.0094

Early-1990s

6

0.02740.01310.04050.02990.0096

0.02680.0065

Backward

Mid/Late-1990s

7

0.01880.02020.0390Forward

Early-1970s

50.04010.01200.05210.02810.0162linkage

Mid/Late-1970s

50.03620.0161

0.05230.03300.0169indicator

Early-1980s

60.03990.01930.05920.04100.0170Mid-1980s

70.03730.02200.05930.04140.0133

Early-1990s

6

0.04380.03500.07890.05370.0154

Mid/Late-1990s

70.05070.01490.06560.04460.0179Internal

Early-1970s

50.00650.00030.00680.00220.0026linkage

Mid/Late-1970s

50.00570.00040.00610.00320.0025indicator

Early-1980s

60.00910.00050.00960.00420.0035Mid-1980s

70.00960.00060.01020.00540.00350.0045

Early-1990s

6

0.01260.00320.01580.0082

Mid/Late-1990s

7

0.01920.00080.02000.00880.0070

Sectoral

Early-1970s

50.01430.00700.02130.01390.0054linkage

Mid/Late-1970s

50.01410.00850.02260.01620.0052indicator I

Early-1980s

60.01050.01140.02180.01680.0040Mid-1980s

70.01310.01020.02340.01640.0051

Early-1990s

60.01560.01090.02650.01810.00670.0109

Mid/Late-1990s

7

0.02900.01080.03980.0241

sectoral

Early-1970s

50.01270.00540.01820.01200.0050linkage

Mid/Late-1970s

50.01020.00720.01740.01420.0041indicator II

Early-1980s

60.00830.01120.01950.01530.0034Mid-1980s

70.00990.00980.01970.01460.0036

Early-1990s

60.01500.01110.02610.01850.0066

Mid/Late-1990s

7

0.01680.00840.02520.01530.00625 statistical testing
The linkage characteristics are worthy to be tested statistically. A descriptive statistic is
conducted over the examined period. Table 8 reports the sample numbers, ranges, mini
mum and maximum values, mean, and standard deviations of the linkages respectively.

As expected, all mean of linkages show an increasing trend, which confirms the increasing role of the real estate sector with economic maturity in all selected country
over the examined period. Furthermore, in order to investigate the consistency of all

A Linkage analysis of the real estate sector using the hypothetical extraction method 41

linkage indicators of the real estate sector among all selected countries over the exam
ined period, the Spearman Rank Correlation Coefficient (Rs) is used to test if there are
any notable differences in rankings of total, backward, forward, internal and sectoral
indicators among all selected countries.

These indicators are ranked according to the studying periods respectively and theRs of every two periods are worked out. If the Rs is significant at the level of probability<0.05, the consistency of these linkages between the two periods being com

pared is evidenced. A two-tailed test is adopted due to the small sample. As seen in Ta

ble 9, the significant rank correlations imply that the economic pull and push and internal effects of real estate keep constant amongst the seven OECD countries. In otherwords, the importance of real estate remained fairly stable among highly developed
economies over the examined period. This may supply a tool to signal the maturity ofan entire economy.

Table 9: Spearman rank correlation coefficients
Early-1970s

Early-1970s

Mid/Late-1970s

Early-1980s

Mid-1980s

Early-1990s

Mid/Late-1990s

1.000

Mid/Late-1970s

.974O

1.000

Early-1980s

.963O

.957O

1.000

Mid-1980s

.962O

.967O

.982O

1.000

Early-1990s.979(**).972(**).9700*)

.979O

1.000

Mid/Late-1990s

.842D

.818O

.857C).826C)

.825O

1.000

** Correlation is significant at the 0.01 level (2-tailed).

6 Conclusions
This research has measured and compared the linkages of the real estate sector using
the hypothetical extraction method based on the OECD input-output database. Four ex
traction structures are adopted to formulate the total, backward, forward and internal
linkage indicators. One structure is developed further to formulate the sectoral linkage
indicator of the real estate sector, which indicates the linkage between the real estateand construction sectors.

Empirical results show an increasing trend of these linkages in real estate, which con
firms the increasing role of the real estate sector with economic maturity over the ex
amined period. In economic development, the real estate sector seems to play a more
important role in Australia, Denmark, France, Japan and USA than in Netherlands andCanada.

The backward linkage indicators are scattered at a low value, which suggests a
strong sectoral independence and a weak economic pull of the real estate sector to theremaining sectors. The forward linkage indicators are stabilising at a higher value,
which show a weak sectoral independence and a strong economic push of real estate.

What is more, a low internal linkage indicator means the relationships between these
two sectors are relatively loose and the sectoral linkage indicators from construction to42

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

real estate are larger than that from real estate to construction. The intrinsic characteristic of linkages can aid the governments in formulating industry policies and businesses

in choosing strategic partners and location strategies. Governments can interfere in thereal estate sector by imposing on other sectors, which have high linkages with it, andvice versa. The businesses can cut their costs, improve productivities by adopting ap
propriate integrative and location strategies, and therefore gain competitive advantage.

Moreover, the significant rank correlations in the linkages imply that the importance ofreal estate remained fairly stable among highly developed economies over the exam
ined period. This may supply a tool to signal the maturity of an entire economy.
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Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006

Appendix 1: OECD sectoral classification
No.
1

SectorAgriculture, forestry and fishery

2

Mining and quarrying

3

Food, beverages and tobacco

4

Textiles, apparel and leather

5

Wood products and furniture

6

Paper, paper products and printing

7

Industrial chemicals

8

Drugs and medicines

9

Petroleum and coal products

10

Rubber and plastic products

11

Non-metallic mineral products

12

Iron and steel

13

Non-ferrous metals

14

Metal products

15

Non-electrical machinery

16

Office and computing machinery

17

Electric apparatus

18

Radio, TV and communication equipment

19

Shipbuilding and repairing

20

Other transport

21

Motor vehicles

22

Aircraft

23

Professional goods

24

Other manufacturing

25

Electricity, gas and water

26

Construction

27

Wholesale and retail trade28

Restaurants and hotels

29

Transport and storage

30

Communication

31

Finance and insurance

32

Real estate and business services

33

Community, social and personal service

34

Producers of government services

35

Other producers

36

Statistical discrepancy(Source: OECD, 1995)

A Linkage analysis of the real estate sector using the hypothetical extraction method 45

Appendix 2: OECD input-output table coverage
Early

Mid/Late

Early

Mid

Early

Mid/Late-1970s-1970s-1980s-1980s-1990s-1990sAustralia

N/A

N/A

N/A

1986

1989

1995

Canada

1971

1976

1981

1986

1990

1997

Denmark

1972

1977

1980

1985

1990

1997

France

N/A

N/A

1980

1985

1990

1995

Japan

1970

1975

1980

1985

1990

1997

Netherlands

1972

1977

1981

1986

N/A

1998

USA

1972

1977

1982

1985

1990

1997

(Source: OECD, 1995)

46

Journal of Applied Input-Output Analysis, Vol. 11 & 12, 2006
Appendix 3: Ranks of the sectoral linkage indicator I(from other sectors to real estate) in the early-1990s
Australia

CanadaDenmark

FranceJapan

Netherlands

USA

1989

1990

1990

1990

1990

1986

1990

1

11

5

7

7

16

8

6

2

8

19

21

3

25

13

9

3*

9

9

3

9

12

1

11

Sector No.

4*

19

11

13

18

17

9

22

5*

16

17

14

26

21

14

23

6*

6

21

2

2

4

4

5

7*

4

4

5

4

2

2

4

8*

34

32

30

33

33

26

29

9*

22

25

11

20

19

19

19

10*

20

27

19

19

13

17

25

11*

21

26

33

34

34

20

34

12*

10

13

18

10

1

7

15

13*

15

14

22

12

10

32

16

14*

13

15

9

13

22

15

24

15*

25

16

8

21

14

11

20

16*

32

18

32

23

23

23

17

17*

24

23

17

24

15

6

26

18*

18

10

20

17

8

33

13

19*

31

33

24

28

30

24

32

20*

30

28

26

29

29

30

31

21*

14

2

28

14

5

18

10

22*

28

22

34

25

31

22

21

23*

26

30

27

32

27

29

27

24*

27

31

25

27

26

28

28

25

3

24

15

16

18

21

12

26

17

7

1

8

11

3

8

27

1

8

12

6

7

12

2

28

23

29

23

22

20

27

14

29

5

3

4

5

6

16

7

30

12

20

16

15

24

25

18

31

2

1

6

1

3

5

1

33

7

12

10

11

9

10

3

34

29

34

29

30

32

31

30

35

33

6

31

31

28

34

33

36**

-

-

-

-

-

-

-

Sectors 3 to 24 are categorized into the manufacturing sector according to the OECD classification.
Sector 36 (the statistical discrepancy sector) is not involved in the rankings.

A Linkage analysis of the real estate sector using the hypothetical extraction method 47

Appendix 4: Ranks of the sectoral linkage indicator II(from real estate to other sectors) in the early-1990s
Australia

CanadaDenmark

FranceJapan

Netherlands

USA

1989

1990

1990

1990

1990

1986

1990

1

14

8

9

8

18

11

8
14

Sector No.

2

13

22

30

4

32

19

3*

4

5

1

5

7

1

7

4*

7

10

6

9

11

5

10

5*

16

19

11

22

19

12

21

6*

8

6

3

3

6

6

5

7*

5

4

2

2

3

2

6

8*

23

29

22

27

31

23

28

9*

25

25

12

25

23

18

22

10*

20

24

19

23

16

17

26

11*

22

26

23

34

24

22

31

12*

12

16

21

13

2

8

20

13*

17

13

27

12

13

33

17

14*

15

17

8

14

20

14

24

15*

24

14

7

17

10

10

18

16*

34

7

34

18

17

21

16

17*

19

20

15

24

15

3

25

18*

11

3

16

15

5

34

12

19*

32

33

24

30

33

25

33

20*

31

27

17

29

27

27

30

21*

9

1

32

6

1

7

4

22*

27

21

31

16

29

13

15

23*

28

30

25

28

26

30

27

24*

29

31

26

32

28

26

29

25

2

28

18

20

21

24

13

26

21

12

4

11

14

4

11

27

1

15

14

10

9

16

3

28

26

32

28

26

22

28

19

29

10

9

5

7

4

20

9

30

18

23

20

21

25

29

23

31

3

2

10

1

8

9

2

33

6

18

13

19

12

15

1

34

33

34

33

33

34

32

34

35

30

11

29

31

30

31

32

36**

-

-

-

-

-

-

-

Sectors 3 to 24 are categorized into the manufacturing sector according to the OECD classification.
Sector 36 (the statistical discrepancy sector) is not involved in the rankings.