The Evolution of Chinese Office Markets: A Comparison of Beijing and Shanghai *Qiulin Ke and...
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![Page 1: The Evolution of Chinese Office Markets: A Comparison of Beijing and Shanghai *Qiulin Ke and **Michael White *Nottingham Trent University, Nottingham **Heriot-Watt.](https://reader035.fdocuments.net/reader035/viewer/2022062619/551911445503462f428b489d/html5/thumbnails/1.jpg)
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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
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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.
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Research ObjectivesCompare 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.
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Methodology
210
ERD Demand is a function
of rent and economic activity
Demand equals non vacant space in equilibriumSUvD )1(
)1(lnlnlnln 22100 vSUER
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Stages of Chinese Commercial Property Market
5
Stage 1 Experimental period 1980s to 1992
Laws and regulations regarding land transfer came into effect. Unavailability of internationally acceptable office property
Stage 2 Transformation period (1993-1996)
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
Stage 3 Oversupply period (1997-1999)
Low take-up rate High vacancy rate Falling rental values
Stage 4 Maturing period (2000-onward)
Substantially increasing demand for office property Moderate increase in supply Rising rent Entry of foreign investment and development companies
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GDP of Beijing and Shanghai to National GDP
6
4% 4% 4% 4% 4% 5% 5% 5%
4%
5%
4%
6%
4%
5%
4%5%
4%
3% 2% 2% 3% 3% 3% 3% 3% 3% 4% 4% 4% 4% 4% 4%3% 4%
0%
1%
2%
3%
4%
5%
6%
7%
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
GDP (Shanghai)
GDP (Beijing)
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FDI: China, Beijing, and Shanghai in ($ billions)
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Year FDI
National FDI
Beijing FDI
Shanghai % of Beijing and
Shanghai to nation 1993 275.15 11.17 31.75 16%
1994 337.67 13.72 39.89 16% 1995 375.21 10.80 52.98 17% 1996 417.26 15.53 75.10 22% 1997 452.57 15.93 63.45 18% 1998 454.63 21.68 48.16 15% 1999 403.19 19.75 59.99 20% 2000 407.15 16.84 53.91 17% 2001 468.78 17.68 74.10 20% 2002 527.43 17.25 50.30 13% 2003 535.05 21.91 58.50 15% 2004 606.30 30.84 65.41 16% 2005 603.25 45.52 68.50 19% 2006 630.21 35.26 71.07 17% 2007 747.68 50.66 79.20 17% 2008 924.00 60.82 100.66 17% 2009 900.30 61.20 103.18 18%
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Year National
Growth rate
(national) (%)
Office investment
(Beijing)
Growth rate
(Beijing) (%)
Office investment (Shanghai)
Growth rate
(Shanghai) (%)
Beijing and Shanghai to national (%)
1999 40.94 6.35 9.83 40% 2000 36.02 -12% 5.47 -14% 6.95 -29% 34% 2001 37.24 3% 8.71 59% 3.17 -54% 32% 2002 46.07 24% 11.77 35% 4.05 28% 34% 2003 61.47 33% 17.26 47% 8.06 99% 41% 2004 78.86 28% 22.72 32% 10.07 25% 42% 2005 92.27 17% 23.72 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%
2008 162.57 16% 24.93 -24% 27.23 27% 32%
2009 201.46 24% 24.37 -2% 27.65 2% 26%
Average 18% 17% 18% 36%
Office Property Investment and growth rate in Beijing and Shanghai (in billion RMB)
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9Source: DTZ, China
0
10
20
30
40
50
60
0
20
40
60
80
100
120
rr_sh
vr_sh
Real Rent and Vacancy Rates: Shanghai
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Source: DTZ, China
0
5
10
15
20
25
30
35
0
10
20
30
40
50
60
70
80
90
rr_bj
vr_bj
Real Rent and Vacancy Rates: Beijing
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Comparison of Office Rents in Beijing and Shanghai
0
20
40
60
80
100
120
140
rr_bj
rr_sh
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Comparison of Vacancy Rates in Beijing and Shanghai
0
10
20
30
40
50
60
vr_bj
vr_sh
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Estimated Models
ttttt uvStockGDPR )1(4310
Long Run Model
Short Run Adjustment
Also tested with FDI as an additional explanatory variable and with employment to represent demand instead of GDP
tttttt uvStockGDPR 143210 )1(
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Long Run Model: BeijingDependent 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 7.688020 0.404062 19.02685 0.0000
Real GDP 0.357042 0.110213 3.239563 0.0032 Stock -0.388848 0.056417 -6.892448 0.0000
1 - vacancy rate 0.167099 0.055704 2.999755 0.0057 R-squared 0.801199 Mean dependent var 3.535732
Adjusted R-squared 0.779110 S.D. dependent var 0.373180 S.E. of regression 0.175390 Akaike info criterion -0.523692 Sum squared resid 0.830567 Schwarz criterion -0.338662 Log likelihood 12.11723 F-statistic 36.27150 Durbin-Watson stat 0.970387 Prob(F-statistic) 0.000000
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Short Run Adjustment Model: BeijingDependent 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 0.037716 -0.779283 0.4431
Change in Real GDP 0.183792 0.240670 0.763668 0.4522 Change in Stock -0.115772 0.134279 -0.862172 0.3968
Change in 1 – vacancy rate 0.155456 0.080057 1.941806 0.0635
Error Correction -0.456206 0.172693 -2.641714 0.0140 R-squared 0.309759 Mean dependent var -0.046662
Adjusted R-squared 0.199321 S.D. dependent var 0.160637 S.E. of regression 0.143739 Akaike info criterion -0.890619 Sum squared resid 0.516525 Schwarz criterion -0.657086 Log likelihood 18.35929 F-statistic 2.804811 Durbin-Watson stat 1.759204 Prob(F-statistic) 0.047338
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Long Run Model: ShanghaiDependent Variable: Real Rent Shanghai Method: Least Squares Sample (adjusted): 1994S1 2009S2 Included observations: 32 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 9.896558 0.629211 15.72853 0.0000
Real GDP 0.838141 0.215823 3.883458 0.0006 Stock -0.660881 0.104411 -6.329621 0.0000
1 – vacancy rate 0.141858 0.067835 2.091220 0.0457 R-squared 0.848843 Mean dependent var 3.638322
Adjusted R-squared 0.832647 S.D. dependent var 0.504575 S.E. of regression 0.206416 Akaike info criterion -0.201382 Sum squared resid 1.193006 Schwarz criterion -0.018165 Log likelihood 7.222118 F-statistic 52.41247 Durbin-Watson stat 1.069108 Prob(F-statistic) 0.000000
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Short Run Adjustment Model: ShanghaiDependent 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 0.032972 0.244236 0.8090
Change in Real GDP 0.036936 0.178647 0.206754 0.8378 Change in Stock -0.377527 0.180023 -2.097106 0.0459
Change in 1 – vacancy rate 0.101235 0.046353 2.184012 0.0382
Error Correction -0.362089 0.139992 -2.586499 0.0156 R-squared 0.428195 Mean dependent var -0.040289
Adjusted R-squared 0.340225 S.D. dependent var 0.165011 S.E. of regression 0.134032 Akaike info criterion -1.034781 Sum squared resid 0.467082 Schwarz criterion -0.803492 Log likelihood 21.03910 F-statistic 4.867507 Durbin-Watson stat 1.807704 Prob(F-statistic) 0.004587
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Demand as Measured by EmploymentLong Run Model Beijing Shanghai Coefficient t-stat Coefficient t-stat Constant -0.147311 -0.049782 -3.020281 -1.382777 Employment 1.236858 2.519298 2.089298 5.311956 Stock -0.239426 -8.317784 -0.383121 -10.42833 1 – vac rate 0.187655 3.172698 0.255826 5.898448 Adjusted R2 0.751635 0.871751 DW 0.648748 0.980526 Prob F-stat 0.000000 0.000000 Short Run Adjustment Model Beijing Shanghai Coefficient t-stat Coefficient t-stat Δ Constant -0.018766 -0.622194 -0.006113 -0.181039 Δ Employment 2.100893 3.081486 1.483364 1.950571 Δ Stock -0.075361 -0.616327 -0.307941 -1.374379 Δ (1 – vac rate) 0.126830 1.616287 0.106886 2.170878 Error Correction -0.435108 -2.660060 -0.392571 -2.140457 Lagged Real Rent 0.249517 1.350247 0.166120 0.874992 Adjusted R2 0.298417 0.294540 DW 2.033541 2.078689 Prob F-stat 0.016877 0.015453
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FDILong Run Model Beijing Shanghai Coefficient t-stat Coefficient t-stat Constant 7.772463 12.19926 8.962443 14.55174 GDP 0.326860 1.579508 0.609395 2.561209 Stock -0.385600 -6.381277 -0.577014 -5.850340 1 – vac rate 0.168214 2.946231 0.168620 2.439706 FDI 0.035955 0.173610 0.409071 1.875720 Adjusted R2 0.770880 0.841171 DW 0.950901 1.023482 Prob F-stat 0.000000 0.000000 Short Run Adjustment Model Beijing Shanghai Coefficient t-stat Coefficient t-stat Δ Constant -0.029654 -0.770827 0.012709 0.388560 Δ GDP 0.168484 0.683109 -0.004643 -0.032806 Δ Stock -0.124697 -0.889656 -0.401471 -2.749871 Δ (1 – vac rate) 0.152262 1.859653 0.112456 3.706956 Error Correction -0.445198 -2.511585 -0.379538 -2.361945 Δ FDI 0.062719 0.376463 0.075907 0.438992 Adjusted R2 0.168037 0.333377 DW 1.739466 1.789915 Prob F-stat 0.091158 0.008372
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Elasticities
Beijing Shanghai Price Elasticity -2.577 -1.513 Income Elasticity 0.920 1.268
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Market Structure and Vacancy Rates
21
Following Voith and Crone (1988), and Grenadier (1995)
The final model permits testing hypotheses of city specific (α), time specific (β) and market specific shocks (ρ) to the vacancy rate.
ititit vv *
)(* tfv iit
ititiitiit vtv 1)1(
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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
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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.