Download The Evolution of Chinese Office Markets: A Comparison of

The Evolution of Chinese Office Markets:
A Comparison of Beijing and Shanghai
*Qiulin Ke and **Michael White
*Nottingham Trent University, Nottingham
**Heriot-Watt University, Edinburgh
1
Motivation for Research
Global investors have been searching for higher
returns beyond their local markets.
Emerging markets in Chinese cities have been
increasingly targeted for investment opportunities.
Beijing and Shanghai (Tier 1 cities (JLL, 2008)) have
the largest investable real estate assets in China and
are the most transparent markets in China.
Due to the emergent status of these markets,
empirical studies on Chinese office markets are rare.




2
Research Objectives
 Compare
and contrast rental adjustment
in the Beijing and Shanghai;
 Examining the amplitude of fluctuation in
rents and vacancy rates in the process of
market adjustment;
 Testing the role played by foreign direct
investment.
3
Methodology

1
D  0 R E
2
D  (1  v) SU

Demand is a function of
rent and economic activity
Demand equals non
vacant space in
equilibrium
ln R   0 ln 0   1 ln E   2 ln SU   2 (1  v)
4
Stages of Chinese Commercial Property Market
5
Stage 1
Experimental
period
1980s to 1992
Stage 2
Transformation
period (1993-1996)
Stage 3
Oversupply period
(1997-1999)
Stage 4
Maturing period
(2000-onward)
Laws and regulations regarding land
transfer came into effect.
Unavailability of internationally
acceptable office property
Entry of domestic investment and
development companies
Entry of foreign companies through joint
venture
Commencement of commercial real
estate development in large scale
Substantial increase in supply
High demand
High rental growth
High capital growth
Low take-up rate
High vacancy rate
Falling rental values
Substantially increasing demand for
office property
Moderate increase in supply
Rising rent
Entry of foreign investment and
development companies
GDP of Beijing and Shanghai to National GDP
7%
6%
6%
5%
5%
4% 4%
4%
4% 4%
5%
5% 5%
4%
4%
2%
1%
3% 2% 2%
3%
3% 3%
4%
4%
5%
4%
4%
3%
5%
3% 3%
3%
4% 4%
4% 4% 4%
4%
3% 4%
GDP (Shanghai)
GDP (Beijing)
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
0%
6
FDI: China, Beijing, and Shanghai in ($ billions)
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
7
FDI
National
275.15
337.67
375.21
417.26
452.57
454.63
403.19
407.15
468.78
527.43
535.05
606.30
603.25
630.21
747.68
924.00
900.30
FDI
Beijing
11.17
13.72
10.80
15.53
15.93
21.68
19.75
16.84
17.68
17.25
21.91
30.84
45.52
35.26
50.66
60.82
61.20
FDI
Shanghai
31.75
39.89
52.98
75.10
63.45
48.16
59.99
53.91
74.10
50.30
58.50
65.41
68.50
71.07
79.20
100.66
103.18
% of Beijing and
Shanghai to nation
16%
16%
17%
22%
18%
15%
20%
17%
20%
13%
15%
16%
19%
17%
17%
17%
18%
Office Property Investment and growth rate in Beijing and
Shanghai (in billion RMB)
Year
1999
2000
2001
2002
2003
2004
2005
Office
investment
(Beijing)
6.35
5.47
8.71
11.77
17.26
22.72
23.72
Growth
Growth
rate
rate
Office
Beijing and
(Beijing) investment (Shanghai) Shanghai to
(%)
(Shanghai)
(%)
national (%)
9.83
40%
-14%
6.95
-29%
34%
59%
3.17
-54%
32%
35%
4.05
28%
34%
47%
8.06
99%
41%
32%
10.07
25%
42%
4%
12.36
23%
39%
2006
118.83
29%
27.75
17%
15.91
29%
37%
2007
140.63
18%
32.91
19%
21.40
34%
39%
162.57
16%
24.93
-24%
27.23
27%
32%
2009 201.46
Average
24%
18%
2%
18%
26%
36%
2008
8
Growth
rate
(national)
National
(%)
40.94
36.02
-12%
37.24
3%
46.07
24%
61.47
33%
78.86
28%
92.27
17%
24.37
-2%
17%
27.65
Real Rent and Vacancy Rates: Shanghai
120
60
100
50
80
40
60
30
rr_sh
vr_sh
9
40
20
20
10
0
0
Source: DTZ, China
Real Rent and Vacancy Rates: Beijing
90
35
80
30
70
25
60
50
20
rr_bj
40
15
30
10
20
5
10
0
Source: DTZ, China
10
0
vr_bj
Comparison of Office Rents in Beijing and Shanghai
140
120
100
80
rr_bj
rr_sh
60
40
20
0
11
Comparison of Vacancy Rates in Beijing and Shanghai
60
50
40
30
vr_bj
vr_sh
20
10
0
12
Estimated Models

Long Run Model
Rt  0  1GDPt  3 Stockt   4 (1  v)t  ut

Short Run Adjustment
Rt  0  1GDPt  2 Stockt  3(1  v)t  4ut 1   t

Also tested with FDI as an additional explanatory variable
and with employment to represent demand instead of
GDP
13
Long Run Model: Beijing
Dependent Variable: Real Rent in Beijing
Method: Least Squares
Sample (adjusted): 1994S2 2009S2
Included observations: 31 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
Real GDP
Stock
1 - vacancy rate
7.688020
0.357042
-0.388848
0.167099
0.404062
0.110213
0.056417
0.055704
19.02685
3.239563
-6.892448
2.999755
0.0000
0.0032
0.0000
0.0057
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.801199
0.779110
0.175390
0.830567
12.11723
0.970387
14
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
3.535732
0.373180
-0.523692
-0.338662
36.27150
0.000000
Short Run Adjustment Model: Beijing
Dependent Variable: Change in Real Rent Beijing
Method: Least Squares
Sample (adjusted): 1995S1 2009S2
Included observations: 30 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-0.029392
Change in Real GDP
0.183792
Change in Stock
-0.115772
Change in 1 – vacancy
rate
0.155456
Error Correction
-0.456206
0.037716
0.240670
0.134279
-0.779283
0.763668
-0.862172
0.4431
0.4522
0.3968
0.080057
0.172693
1.941806
-2.641714
0.0635
0.0140
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
15
0.309759
0.199321
0.143739
0.516525
18.35929
1.759204
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
-0.046662
0.160637
-0.890619
-0.657086
2.804811
0.047338
Long Run Model: Shanghai
Dependent Variable: Real Rent Shanghai
Method: Least Squares
Sample (adjusted): 1994S1 2009S2
Included observations: 32 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
Real GDP
Stock
1 – vacancy rate
9.896558
0.838141
-0.660881
0.141858
0.629211
0.215823
0.104411
0.067835
15.72853
3.883458
-6.329621
2.091220
0.0000
0.0006
0.0000
0.0457
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.848843
0.832647
0.206416
1.193006
7.222118
1.069108
16
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
3.638322
0.504575
-0.201382
-0.018165
52.41247
0.000000
Short Run Adjustment Model: Shanghai
Dependent Variable: Change in Real Rent Shanghai
Method: Least Squares
Sample (adjusted): 1994S2 2009S2
Included observations: 31 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.008053
Change in Real GDP
0.036936
Change in Stock
-0.377527
Change in 1 – vacancy
rate
0.101235
Error Correction
-0.362089
0.032972
0.178647
0.180023
0.244236
0.206754
-2.097106
0.8090
0.8378
0.0459
0.046353
0.139992
2.184012
-2.586499
0.0382
0.0156
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
17
0.428195
0.340225
0.134032
0.467082
21.03910
1.807704
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
-0.040289
0.165011
-1.034781
-0.803492
4.867507
0.004587
Demand as Measured by Employment
Long Run Model
Constant
Employment
Stock
1 – vac rate
Adjusted R2
DW
Prob F-stat
Beijing
Coefficient
t-stat
-0.147311
-0.049782
1.236858
2.519298
-0.239426
-8.317784
0.187655
3.172698
0.751635
0.648748
0.000000
Shanghai
Coefficient
t-stat
-3.020281
-1.382777
2.089298
5.311956
-0.383121
-10.42833
0.255826
5.898448
0.871751
0.980526
0.000000
Short Run Adjustment Model
Δ Constant
Δ Employment
Δ Stock
Δ (1 – vac rate)
Error Correction
Lagged Real Rent
Adjusted R2
DW
18 F-stat
Prob
Beijing
Coefficient
t-stat
-0.018766
-0.622194
2.100893
3.081486
-0.075361
-0.616327
0.126830
1.616287
-0.435108
-2.660060
0.249517
1.350247
0.298417
2.033541
0.016877
Shanghai
Coefficient
t-stat
-0.006113
-0.181039
1.483364
1.950571
-0.307941
-1.374379
0.106886
2.170878
-0.392571
-2.140457
0.166120
0.874992
0.294540
2.078689
0.015453
Long Run Model
Constant
GDP
Stock
1 – vac rate
FDI
Adjusted R2
DW
Prob F-stat
Beijing
Coefficient
t-stat
7.772463
12.19926
0.326860
1.579508
-0.385600
-6.381277
0.168214
2.946231
0.035955
0.173610
0.770880
0.950901
0.000000
Shanghai
Coefficient
t-stat
8.962443
14.55174
0.609395
2.561209
-0.577014
-5.850340
0.168620
2.439706
0.409071
1.875720
0.841171
1.023482
0.000000
Short Run Adjustment Model
Δ Constant
Δ GDP
Δ Stock
Δ (1 – vac rate)
Error Correction
Δ FDI
Adjusted R2
DW
Prob F-stat
19
Beijing
Coefficient
t-stat
-0.029654
-0.770827
0.168484
0.683109
-0.124697
-0.889656
0.152262
1.859653
-0.445198
-2.511585
0.062719
0.376463
0.168037
1.739466
0.091158
Shanghai
Coefficient
t-stat
0.012709
0.388560
-0.004643
-0.032806
-0.401471
-2.749871
0.112456
3.706956
-0.379538
-2.361945
0.075907
0.438992
0.333377
1.789915
0.008372
FDI
Elasticities
Price Elasticity
Income Elasticity
20
Beijing
-2.577
0.920
Shanghai
-1.513
1.268
Market Structure and Vacancy Rates

Following Voith and Crone (1988), and Grenadier (1995)
vit  vit*   it
vit*   i  f (t )
vit  (1   ) i  itti  vit 1   it

The final model permits testing hypotheses of city specific
(α), time specific (β) and market specific shocks (ρ) to the
vacancy rate.
21
Impact of City, Time, and Market
City Component Time Component
Market
Component
Beijing
10.74749**
0.00104
0.79947***
Shanghai
7.32099**
0.01142
0.73091***



The time component is insignificant in both cities.
City and market components are significant
The market component suggests slow adjustment to
shocks
22
Conclusions
•




Cointegration tests support evidence of a valid long
run relationship in Beijing and Shanghai office
markets.
The error correction coefficient implies adjustment to
market imbalance in both markets.
Shocks show evidence of persistence
Quite large difference in price elasticity of demand
for space.
Unlike previous study of Shanghai office market, FDI
is insignificant for both Beijing or Shanghai in both
the long and short run.
23