Energy Prices and Housing Property Demand in ...

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Energy Prices and Housing Property Demand in Shanghai, China G. L. Zou

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College of Geographical Sciences, Sichuan Normal University, Chengdu, China

To cite this article: G. L. Zou (2012): Energy Prices and Housing Property Demand in Shanghai, China, Energy Sources, Part B: Economics, Planning, and Policy, 8:1, 1-6 To link to this article: http://dx.doi.org/10.1080/15567249.2010.509081

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Energy Prices and Housing Property Demand in Shanghai, China G. L. Zou1 1

College of Geographical Sciences, Sichuan Normal University, Chengdu, China

This study investigates long-run relations among the prices of fuel and power, sales of new residential constructions, and total industrial output in Shanghai, China. Structural break and unit root tests show that these three variables each contain a unit root. Allowing for finite-sample biases, a trivariate cointegration system is suggested. Both housing property demand and industrial output are weakly exogenous for the equilibrium system, but energy prices are not. Therefore, the results imply that housing demand influences energy prices in the long run, but not vice versa. By decreasing the demand (or expected demand) for residential property, municipal authorities could expect to restrain or reduce energy use and prices, thereby facilitating the restructuring of the economy into a resource-saving one. Keywords: cointegration, energy price, finite sample, housing demand, industrial output, structural break, weak exogeneity

INTRODUCTION With an expanding economy and higher living standards, activities and services related to real estate are consuming increasing amounts of energy. For example, a large amount of energy is consumed for heating and cooling houses and offices (Hsueh and Gerner, 1993). Thus, an increase in sales of newly constructed property, in square feet, would usually be followed by an increase in energy use, which can induce even higher energy prices than usual in a competitive market (Quigley, 1984; Dinan and Miranowski, 1989). According to SHS (2010), during the period 2000–2008, the square footage of newly constructed commodity property sold in Shanghai, China, grew by 47.5%; during the same period (2001–2008), retail prices for fuel and power grew by 165.7%. We thus rationally argue that property is a determinant of energy prices. In Shanghai, the demand for housing property constitutes most of the property demand. For example, in 2008, in the commodity property market for new construction, residential sales amounted to 19.66 million square meters, accounting for 85.6% of the total; in the resale property market, residential sales constituted 78.3% of the total (SHS, 2010). Thus, an examination of the effect of housing demand on energy prices is worthwhile. Additionally, in 2008, the industrial output in Shanghai constituted 42.2% of the total gross domestic product output. Industry is too important a determinant of energy prices to be overlooked (Bassi et al., 2009; Linn, 2009). Address correspondence to Gao Lu Zou, College of Geographical Sciences, Sichuan Normal University, Jin An Road, Chengdu, Sichuan 610066 China. E-mail: [email protected]

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G. L. ZOU

The main purpose of this article is to investigate whether the demand for residential property in Shanghai has long-run effects on energy prices. While examining the effect of housing demand on energy prices, this study allows for the effect of industrial production on those prices and analyses the relevant data in terms of cointegration and weak exogeneity in a trivariate setting. There is a growing consensus that supports restructuring the urban economy to conserve resources and reduce carbon emissions; this study is thus expected to have some policy implications in this area. The remainder of this article is organized as follows: the second section describes econometric methods, the third section presents data and the definition of the three variables, the fourth section gives the econometric results, and the fifth section presents conclusions and proposes policy implications.

METHODOLOGY We investigated whether long-run equilibrium relations exist between energy prices, housing demand, and industrial output in a trivariate setting. Cointegration indicates long-term equilibrium between I(1) variables (Engle and Granger, 1987). When the variables each contained a unit root, we used the Johansen multivariate trace test (Johansen and Juselius, 1990) in a vector autoregressive model (VAR). Since the Johansen test may suffer from finite-sample biases, the critical-value corrections suggested by Cheung and Lai (1993) were needed. Next, if a cointegration vector was suggested, we conducted tests for weak exogeneity: on the basis of the coefficient matrix ….D ˛ˇ0 / estimated by the trace tests, we first imposed zero restrictions on the unrestricted cointegration vectors ˇO and then, holding the restricted ˇO fixed and using it as the parameter of interest, we imposed zero restrictions on the i th row of the estimated adjustment parameters ˛, O that is, H0 : ˛i D 0. Using 2 -tests as suggested in Johansen and Juselius (1990), an acceptance of this null hypothesis for ˛i indicated the presence of weak exogeneity. It is implied that a weakly exogenous variable has driven, but has not been conversely driven, by the long-run system (Hendry and Juselius, 2001). To increase test robustness, additional unit root analysis that used both the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were carried out. However, the results of these standard tests may be misleading where structural breaks exist in data. To identify structural shifts, we used Perron’s (1997) mixed innovational outlier model, by which t˛ -tests are driven; this method is termed the Perron test.

DATA The data used came from SHS (2010) in monthly series covering the period from 2006:01 to 2010:04. All series were seasonally adjusted using the same X12 procedure and taken in logs. Energy price (EP) was proxied by aggregate price indexes of fuel and power. In China, the price index of fuel and power measures changes in the prices of energy products. These products include coal, gasoline, diesel oil, natural gas, and electricity, and so on. This index is the only available index reflecting the prices of energy in Shanghai. The central government has often imposed impacts on the energy prices, so the prices of energy products are unable to change fully to the global market. Residential property demand (RES) represented the total square footage of sales of residential new construction. Because the sales figures were collected beginning in January, January values were missing, so all January values were given as the average of February values. Industrial output (IDU) was used to represent the total industrial output for designated size


ENERGY PRICES AND HOUSING DEMAND IN CHINA

FIGURE 1

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Changes in energy prices, residential sales, and industrial output in Shanghai.

enterprises, where the designated size enterprises in China include all state-owned enterprises and the non-state-owned ones that contributed an output of 5 million yuan and above each year. These three series were plotted in Figure 1. It is worth noting that both the residential and the industrial energy uses should be the factors influencing energy prices more directly than the variables used in this study. However, the time series data for these energy uses are not available in any sources in Shanghai.

EMPIRICAL RESULTS AND DISCUSSION The results of the ADF, PP, and Perron tests are reported together in Table 1. Both the ADF and PP tests suggest that all three of the variables are typically I(1) either at the 1% or 5% significant

TABLE 1 Unit Roots and Structural Break Tests Unit Roots

Structural Breaks

ADF Variable EP REI IDU

L 2.34 (1) 1.75 (0) 1.46 (1)

PP FD 3.63 (0) 6.87 (0) 10.83 (0)

L 1.61 (4) 2.02 (4) 2.56 (4)

Perron Test FD 3.58 (1) 6.95 (4) 10.73 (3)

˛O 0.35 (9) 0.03 (9) 0.01 (9)

tO ˛ 1.79 0.14 0.04

p

TOb

0.08 0.89 0.97

31 23 22

Note: EP, REI, IDU denote energy price, residential sales and industrial output, respectively; L denotes level, FD denotes first difference. Unit root test equations included both intercept and trend according to Hendry and Juselius (2000). Figures in parentheses are lag lengths; they were chosen by minimising Schwarz information criterion for ADF tests, and by using the Newey-West bandwidth method for PP tests; the lag orders for Perron tests were chosen by datadependent procedures (Ng and Perron, 1995). Only estimates for ˛O on yt 1 in the Perron (1997) model are reported. The fraction used in the Perron tests is 23% and so regressions were run from T D 12 to 40 (T is sample size). For the Perron tests, the finite-sample critical value for T D 70 was 5.29 at the 10% level.

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TABLE 2 Johansen Cointegration Trace Tests

r

k

Trace

5% O-L c.v.

5% C & L c.v.

MHM-p

Joint JB

Adj.Q

D0 51 52

4

56.38 22.23 4.83

42.92 25.87 12.52

54.42 32.81 15.87

0.00 0.13 0.62

16.6(0.01)

17.2(0.05)

Note: The 3-variable system tested was (EP, REI, IDU). The test equation included intercept and trend according to Hendry and Juselius (2001). Trace is Johansen and Juselius’ (1990) LR trace test statistic. r is the null hypothesis of at most r cointegration rank. k is lag length in the Johansen-type VAR and was chosen by making AIC as small as possible while having multivariate normality and serial correlations taken into account. O-L c.v., C & L c.v., and MHM-p denote asymptotical quantiles in Osterwald-Lenum (1992), finite-sample corrected quantiles in Cheung and Lai (1993), and asymptotical MHM p-values in MacKinnon, Haug and Michelis (1999), respectively. Joint JB is the statistic for joint multivariate normality. Adj.Q is finite-sample corrected Portmanteau multivariate Q statistic for the null of which there is no serial correlation.

level. The one-sided t˛ -statistics also imply that these variables each contain a unit root. Overall, these three variables are non-stationary I(1) processes. Table 2 reports the results of the Johansen tests. Allowing for the finite-sample corrections, the trace statistics at the 5% level were rejected for a cointegration rank of at most 0, accepted for a rank of at most 1, and rejected for a rank of at most 2, implying that a cointegration vector existed among the three variables. The normalized unrestricted cointegration vector ˇO is (EP, 0.1RES, 0.29IDU, 3.9t) where t is the time trend. The ADF statistic, in level, for this vector was 13.07 (regression contained only the intercept, and the lag length chosen by minimizing Schwarz’s information criterion was 0), definitely implying a stationary process. Thus, the industrial output had a positive effect on the energy prices; this is rational, as the industrial sectors had usually consumed a substantial amount of energy and thereby causing an increase in the energy prices. The sales of new residential constructions appeared to have negative effects on the prices; this may be attributable to the high vacancy rates of newly constructed commodity property in Shanghai. For example, as of the end of 2008, the vacancy rates for all the commodity property and the commodity housing property were 12% and 6.92%, respectively (SHYJ, 2009). A high vacancy rate implies that a large percentage of the property space that was sold had not supplied any services and so consumed little energy. In addition to these conventional interpretations for the Johansen’s estimates, further restricted weak exogeneity tests are still needed, since the unrestricted cointegration vector by itself may lack enough economic interpretations (Hendry and Juselius, 2001). Table 3 reports the results of the weak exogeneity tests. The three zero restrictions on ˇO were easily rejected at the 1% level, implying that the restricted vector should contain all three

TABLE 3 Weak Exogeneity Tests O D0 H0 : ˇ

2

DF

p-Value

H0 : ˛ O D0

2

DF

p-Value

ˇ11 D 0 ˇ12 D 0 ˇ13 D 0

9.10 16.68 16.71

1 1 1

0.00 0.00 0.00

˛11 D 0 ˛21 D 0 ˛31 D 0

9.22 0.72 2.44

1 1 1

0.00 0.40 0.12

Note: DF is degree of freedom. Both DF and p-values are estimated based on Johansen and Juselius (1990).

ENERGY PRICES AND HOUSING DEMAND IN CHINA

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variables. Then, applying the ˇO estimated by the unrestricted cointegration test, we directly drove zero-restriction tests for the adjustment coefficients .˛/: O ˛11 D 0 was rejected at the 1% level, while both ˛21 D 0 and ˛31 D 0 were accepted even at the 10% level, indicating that the variables O whereas the variable EP RES and IDU were weakly exogenous for the parameters of interest ˇ, was not.

CONCLUSIONS The results of the ADF and PP tests suggest that the three variables, RES, IDU, and energy price EP, are I(1) and no structural shifts are found using the Perron tests. Allowing for finitesample corrections, a cointegration relation for these three variables is suggested. By imposing zero restrictions on the long-run relation, this relation is found to be a trivariate system. Weak exogeneity tests suggest that this long-run relation is driven by both housing property demand and industrial output. These findings are economically important since they show that in the long run, both the demand for new residential constructions and the total industrial output drive the equilibrium, and therefore influences energy prices; however, the converse does not hold. Thus, if the demand for housing property decreases, the energy use may also decrease and so energy prices may fall. To facilitate restructuring of the economy into one that conserves resources, the municipal authorities should divert from an industrial policy that focuses on property and make efforts to restrain or reduce housing property demand.

ACKNOWLEDGMENTS The research described herein was supported by grants from Sichuan Normal University and the Southwestern University of Finance and Economics, Chengdu, China. The author is grateful to the anonymous reviewers for helpful comments. The author is solely responsible for any opinions or errors.

REFERENCES Bassi, A. M., Yudken, J. S., and Ruth, M. 2009. Climate policy impacts on the competitiveness of energy-intensive manufacturing sectors. Energ. Policy 37:3052–3060. Cheung, Y.-W., and Lai, K. S. 1993. Finite-sample sizes of Johansen’s likelihood ratio tests for cointegration. Oxford B. Econ. Stat. 55:313–328. Dinan, T. M., and Miranowski, J. A. 1989. Estimating the implicit price of energy efficiency improvements in the residential housing market: A hedonic approach. J. Urban Econ. 25:52–67. Engle, R. F., and Granger, C. W. J. 1987. Co-integration and error correction: Representation, estimation and testing. Econometrica 55:251–276. Hendry, D. F., and Juselius, K. 2000. Explaining cointegration analysis: Part I. Energy J. 21(1):1–42. Hendry, D. F., and Juselius, K. 2001. Explaining cointegration analysis: Part II. Energy J. 22:75–120. Hsueh, L.-M., and Gerner, J. L. 1993. Effect of thermal improvements in housing on residential energy demand. J. Consum. Aff. 27:87–105. Johansen, S., and Juselius, K. 1990. Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bull. Econ. Stat. 52:169–210. Linn, J. 2009. Why do energy prices matter? The role of interindustry linkages in U.S. manufacturing. Econ. Inq. 47:549– 567. Mackinnon, J. G., Haug, A. A., and Michelis, L. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. J. Appl. Econom. 14:563–577.


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Ng, S., and Perron, P. 1995. Unit root tests in ARMA models with data dependent methods for the selection of the truncation lag. J. Am. Stat. Assoc. 90:268–281. Osterwald-Lenum, M. 1992. A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics. Oxford B. Econ. Stat. 54:461–472. Perron, P. 1997. Further evidence on breaking trend functions in macroeconomic variables. J. Econometrics 80:355–385. Quigley, J. M. 1984. The production of housing services and the derived demand for residential energy. Rand J. Econ. 15:555–567. Shanghai Bureau of Statistics (SHS). 2010. Shanghai Statistics, Statistical Data. Available at: http://www.stats-sh.gov.cn Shanghai Yiju Real Estate Real Estate Research Institute (SHYJ). 2009. Research of the commodity property vacancy in China. Shanghai, China: Shanghai Yiju Real Estate Research Institute.