Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 1GDPt 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