The contribution of the market versus policies on house prices in the People’s Republic of China

Qiang Li and Satish Chand

University of New South Wales, Australia, ACT2610

25th April, 2012

Abstract: House prices in China increased at an average annual nominal rate of 11 percent in the last

two decades to 2009. This increase in prices occurred during a period of rapid transition in ideology

(from the plan to the market), in income, urban population and with concomitant changes in policies to

manage the price boom. This paper uses the annual data from 29 provinces from 1998 to 2009 to

decipher the contribution of market fundamentals vis-à-vis policies to house prices in China. Our

findings show that market fundamentals of levels of income, construction costs, and marriages are the

primary determinants of house prices, followed by statistically significant but quantitatively small

negative effects of changes in real interests, land purchasing area and the housing stock. The VECM

model shows the area of land under release for building and available stock of housing resulted in

short-term dynamics of housing inflation. The half deviation of housing prices from fundamentals will

spend 15 years to come back to the equilibrium. The housing policies matter most for short terms

swings in the price level. For example, the introduction of land transfer method by “biding, auction and

listing” in 2002 has played a great role in the rapid growth of housing prices afterwards.

1. Introduction

Chinese house prices increased at an annual nominal rate of 11 percent in the two decades to 2009.

And the differences across the 29 provinces are stark. The increase over time and across space has

occurred during a period of rapid transition including an ideological shift from planning to the market

whilst without giving in fully to the former. Per capita disposable income in urban area has also grown

rapidly – at a rate of 9.91% per annum between 1998 and 2009. Accompanying the above has been a

rapid growth in urban population, from 416 million in 1998 to 621 million in 2009. The above have

had a direct impact on the demand for housing with concomitant supply response. Far from sitting on

the sidelines, the planners have intervened whenever deemed necessary: sometimes to ameliorate the

rate of increase in house prices, at other times to use construction as a means to managing domestic

demand. Furthermore, reforms to land use, tenure, and titling have had major ramifications for both

the demand for and supply of urban housing by the private sector.

China is not alone in having experienced rapid house price inflation. Amongst developed countries,

Australia leads with real house prices having more than doubled between 1990 and 2010 (Tumbarello

& Wang, 2010). The increase in the equilibrium price of housing was driven by strong fundamentals,

mainly an increase in the terms of trade and population. In Europe, the boom in the Swedish housing

market continued in 2010, with house prices up 5% according to Statistics Sweden. Both strong

economic growth and shortage of supply are attributed to having led to the rise in the price of housing

(Ball, 2010).

House prices in China have been driven by a mix of plan, population-growth, and economic prosperity.

In the context of economic transition to the market since the 1980s, China has pursued a more gradual

process of reforms and particularly when compared with the “Shock therapy” approach that was used

in USSR and Poland (McMillan & Naughton, 1996). Reforms to the housing market commenced in

1980, two years after the announcement of the policy of “reform and opening-up”, aimed at

transferring the socialist system into the market system (Huang & Clark, 2002). Several reforms

including to land tenure system, property taxes, and access to credit were undertaken to develop a

private housing market (Tolley, 1991). Furthermore, policies were used to manage growth of

construction as part of demand management. Along with the reform process, rapid growth of per capita

income within the coastal urban regions induced rural to urban migration. Data from the China Statistic

Bureau shows that in the span of 11 years to 2009, nearly 230 million rural workers moved to towns

and cities. As housing is a basic need, the demand for residential housing in the cities increased with

urbanisation. Houses, moreover, are an asset and one that has provided handsome returns to investors

in China (Chen & Zheng, 2008). Thus, the demand for housing was accompanied with increased

supply as prices and rents increased.

Here we decipher the contribution of policies, population growth, and economic prosperity on house

prices using a panel comprising data for 11 years from 29 provinces. The results show the presence of a

long-run equilibrium relationship between the levels of income, construction costs, and marriages and

house prices. Figure 1 illustrates our key findings for Beijing, one of the 29 provinces in our data set.

The solid line shows the time path of actual price of housing in Yuan (at 2002 prices) per square metre

of residential space while the broken line shows long-run prices as predicted by our model and

elaborated upon later. The estimates suggest that houses in Beijing were under-priced by 13 percent in

2002, at around par value in 2004 and over-priced by 5 percent in 2009. Furthermore, short-run

deviations in prices from the equilibrium persist but these deviations diminish, albeit slowly, with time.

In terms of the magnitude of the effect of fundamentals on equilibrium prices for the complete panel,

the elasticity of house prices to real GDP is 0.55; that is, ceteris paribus, long-term house prices rise at

approximately half the rate of increase in GDP. Construction costs and the number of marriages in the

following year are the next most important determinants with their elasticity being 0.31 and 0.25,

respectively. These factors combined account for nearly all of the change in the equilibrium price. The

analysis reveals that while population does not count in its impact on house prices, the number of

marriages in the following year do! This is explained by the commonly known ‘mother-in-law

phenomenon’; that is, mother in laws demand that the suitor has a house before being handed the hand

of her daughter. Policies in the form of interest rates and methods of acquisition of land for housing

have a statistically significant but quantitatively small impact: a one percentage point increase in

interest rates led to a fall of 0.4 percent in equilibrium prices while land reforms of 2002 boosted house

prices by 5 percent. Policy interventions in the form of changes in the supply of (State) land and

property taxes have an impact on short-run prices. Furthermore, any given short-run deviation of the

price from its equilibrium level is halved in approximately 15 years.

The rest of the paper is structured as follows. Section 2 reviews the extant literature on housing market

related to the topic. Section 3 provides the conceptual framework. Section 4 presents the data and

empirics. Conclusions and policy implications bring the paper to a close.

2. Literature review

Several variables have been used to proxy economic fundamentals in modelling house prices in the

extant literature. The most widely used factors include construction costs, population, and income

(Poterba, 1991; Case and Shiller, 1990; Poterba, 1996; Jud and Winker, 2002; Shen and Liu, 2004).

Construction cost and income are commonly found to be the most important determinants of house

prices in the literature. The role of population is ambiguous. Poterba (1991), for example, shows that

real income and construction costs can explain changes in housing price, but population growth has no

explanatory power. Sometimes, the population variable is replaced by the unemployment rate as in

Quigley (1999) and Jacobsen and Bjorn (2005). Some studies have used both population and

unemployment rate (Clapp and Giaccotto, 1994; Shen and Liu, 2004). Moreover, the income variable is

often measured as GDP (Liang and Gao, 2007). Other factors such as interest rate, land prices and

housing vacancy are also included in some studies (Poterba, 1996; Quigley, 1999; Jud and Winkler,

2002; Jacobsen, 2005; Hannah, Kim and Mills, 1993; Peng and William, 1994; Shen and Liu, 2004;

Peng and Tam, 2008; Yu, 2010; Du, Ma and An, 2011). The contribution of each of these factors

depends on the different context. Liang and Gao (2007) for instance find that the price of housing in

eastern and western regions are highly affected by the credit policy while income is the main

determinant in the central region of urban China. Yu (2010) draws on panel data econometrics to reach

the conclusion that land supply, area of housing sold and vacancy levels each has a negative impact on

housing prices while mortgage rate has a positive effect. In sum, existing studies have drawn on

different permutations of variables constituting economic fundamentals in modeling price of housing.

The interaction between government policies and housing prices has also been discussed in several

studies. Yu & Lee (2010) pointed the reason for the government intervention in the housing market is

its imperfect and competitive characteristics. Government not only provides enough and accessible

housing to the general public, but stabilized housing prices when problems happened. In practice, a

majority of countries have employed policies to adjust the level of construction activity (Ronald,2010)

Such policies have included subsidizes, regulations on housing investment and interest rate controls.

Each of these interactions affect the present and future value of housing through their imposition on

costs (e.g., land use regulations, taxes, rent controls, building permits) and benefits (e.g., land subsidies,

tax relief, financial subsidies) on the activity of adjusting housing prices (Cheshire & Mills, 1999).

In empirical studies, the aggregate impact of housing policies on house prices is ambiguous. Malpezzi

(1999) drawing on data from the U.S., find the adjustment speed for the housing price to the long-run

equilibrium status is slower in more regulated environment. Lee and Ahn (2004) tested the

effectiveness of housing policy on housing price by categorizing the goal of housing policies into four

different kinds. They draw the conclusion that the housing policy issued by Korean government exerted

dramatic effect on the apartment and new dwelling house prices. However, Kim (2005) examined how

housing policies increase housing costs and found that real estate policies were ineffective in

decreasing the housing prices. More evidences can be found in Yu and Lee (2010) that housing policies

of the Roh Administration had no statistically discernible impact on the stabilization of the Korean

housing market by a statistical method. But macroeconomic variables such as the money supply,

corporate bond returns, and the number of permits for building constructions have a significant

association with housing prices. Other empirics focused on the specific policies. Land and financial

policies have been mostly discussed such as Yu (2010), Peng and Wheaton (1994), Egerd and Mihaljek

(2007), Du (2011) , Jarocinski & Smets (2008). Peng and Wheaton (1994) found that restricting the

release of new land will cause higher housing prices afterwards. Du (2011) examined the dynamic

impact of land policy on housing and land prices finding that the new land granting system in 2004

reduced the efficiency of the housing market due to the high land right fee collected in the process of

land transactions. For the long-run dynamics of housing prices, the institutional development of

housing market and housing finance are the most important factors in Europe (Egerd and Mihaljek,

2007). Jarocinski & Smets (2008) thought that easy monetary policy designed to stave off perceived

risks of deflation in 2002 to 2004 has contributed to a boom in the housing market in 2004 to 2005 in

European countries.

In sum, theoretical and empirical research on house price dynamics in China is not as many as that in

international countries. One finding from the literature is that existing studies have drawn on different

permutations of variables constituting economic fundamentals in modelling price of housing. However,

the primary determinants and their marginal effects on housing prices are not uniform across countries

or periods of time. Studies on the Chinese housing market has lacked theoretical motivations with

variables modelled drawn largely from similar studies in the West. A consistent finding from the extant

literature is that policy intervention has resulted in the dramatic impact on housing prices, especially

the land policies which have been mostly discussed in the literature. However, the impact of specific

policies is dependent on the context.

Our study builds on the existing literature and extends the conclusion in several ways. First, we

construct a panel comprising the 29 provinces and compile annual data on house prices and economic

fundamentals from 1998 to 2009. The data is drawn from published sources and provides the maximum

degrees of freedom for statistical analysis. Second, variables constituting economic fundamentals are

drawn from the conceptual framework first proposed by DiPasquale and Wheaton (1992) which

provides predictions on the qualitative impact of each of the explanatory variables on house prices.

Third, we add a new variable as a context specific fundamental: the number of marriages which in

itself is the first such attempt and found to have significant explanatory power. The motivation for the

inclusion of this variable in our model has been the fact that most couples in the urban cities purchase a

house before being married and society now expects the above for couples contemplating marriage.

The inclusion of the number of marriages in modeling house prices is supported by Tiwari and Prikh

(1999) who find that number of married couples increases the expenditure on housing because of the

requirement for a larger space. Henceforth we refer to the significance of the number of marriage on

the price of housing as “the mother-in-law” phenomenon. Fourth, the impact of land policy was

detected directly by the dummy variable in the model. And other policies’ influences are discussed by

the analysis of the error term from the model.

3. The conceptual framework

We draw on the conceptual framework first proposed by DiPasquale and Wheaton (1992) in the form

of their four quadrant model and expand on it using context specific information from studies on China.

DiPasquale and Wheaton (1992) use their model to explain the impact of changes in economic

fundamentals and policies on the real estate market. They do this by dividing the real estate market into

two inter-related markets; namely, those for real estate space (property market) and for real estate asset

(asset market).The property market is underscored by the housing stock and rent level, while the asset

market by the housing price level and construction activities (shown in figure 2).

The two right quadrants in figure 1 describe the property market while the two-left quadrants represent

the asset market. Rent is determined in the property market for space as shown in Quadrant I. The price

of housing is determined in the asset market as depicted in Quadrants II and III. The property market is

in equilibrium when the demand for residential space is equal to its supply while the asset market is in

equilibrium when prices reflect capitalisation rate for real estate (depicted by the ray emanating out of

the origin in quadrant II) and where new construction equals losses due to depreciation (shown in

Quadrant IV) such that total stock of housing is constant. Three critical assumptions are made in the

model. First, the capitalisation rate is taken as given. Second, the interest rate (i) is considered to be an

exogenous variable, thus P = R / i. Third, construction per unit of residential space increases with

building activity as shown in Quadrant III. In equilibrium, the asset price is proportionate to

construction costs: P = f (C). The last quadrant shows that the change in housing stock (ΔS) is given

by the difference between the new construction and losses due to depreciation (at rate d): ΔS = C- dS.

A number of comparative static experiments can be conducted with this simple model. We consider

the impact of changes in economic prosperity, policies, and housing stock on house prices as illustrated

in figures 2, panels B, C, D and E. The discussions provide the theoretical basis for the inclusion of

variables comprising market fundamentals and policies that are relevant in modelling house prices.

Panel B shows the impact of increased income on housing price. An increase in economic activity, all

else being equal, shifts the demand (D curve) to the right as shown by the darkened arrows. Since

construction takes time and depletion due to depreciation is slow (DiPasquale and Wheaton, 1992), the

stock of housing cannot change immediately. Consequently, increased economic activities drives up

house prices and more in the short run due to stickiness in supply than in the long-run where prices still

rise due to rising marginal cost of construction. Panel C depicts the impact of an increase in interest

rate on house prices. A hike in the interest rate, all else equal, lowers house price which in turn

decreases construction activity and lowers stock of housing in the long run. And Panel D shows that an

exogenous increase in the stock of housing, say from release of land under state ownership, will

decrease house prices. Finally, Panel E shows the impact on house prices from a rise in construction

costs which may materialise from an increase in the price of building materials. Increased cost of

construction depresses the supply of housing which then, for a given demand, raises the price of

housing. Adam and Fuss (2010), Riddel (2004) have extended the analyses of DiPasquale and

Wheaton (1992) and Fair and Jeffee (1972), while Deng, Ma & Chiang (2009) highlights the role of

land prices in determining house prices in China. Our modelling of the determinants of house prices in

urban China takes into account the contribution of all major variables noted in the extant literature.

4. The empirical study

4.1 Data

Annual data from 1998 to 2009 for 29 provinces (except Tibet and Chongqing which have been left out

due to due to missing observations for several years) in urban China is used for this study. Variables

used for empirics that follows and sources of data for each of variables are listed in Table 1. Housing

data is systematically collected and recorded by the China Statistic Bureau and published in the China

Statistic Yearbooks. At present, the available historical data only includes the average sale prices of

houses. Therefore, data on the mean housing prices is used as the dependent variable in the model.

Because there are no records at the level of the provinces on land price and rental rates, we use land

area and rental area as substitutes given that they are the quantity counterparts to their prices: that is,

area under rent and land area under residential construction reflects rental rates and land prices. Data on

the number of marriages per year for the period of study is collected from the China Statistic Yearbook

1999-2010. This variable is new to the literature on the determinants of house price, but as shown later

a significant factor for China. The motivation for the inclusion of this variable in our model has been

the fact that most couples in the urban cities purchase a house before being married and society now

expects the above for couples contemplating marriage1. The data on housing stock, construction costs,

land purchase and renting area found in the China Real Estate Statistics Yearbook from 1998 to 2010.

Data on interest rate is for 5-year mortgage rate and collected from China Statistics Yearbook and

annual reports from the Central Bank. The interest rate, construction cost, GDP and housing prices are

deflated by the CPI (to base year 1998) to covert these to their real (inflation-adjusted) counterparts.

And all variables, except the interest rate, are transformed into their natural logarithms to permit

interpretation of the estimated coefficients as elasticities. The analysis is conducted using R statistics

software (R version 2.14.0).

Table 2 presents summary statistics on each variable used for the subsequent analysis. There is a

maximum of 348 observations (i.e. 29 provinces each with annual observations from 1998 to 2009) for

the following variables: House price index (P), per capita income (Y), construction cost (C), real

interest rate (Ri), and land area purchased for residential construction. The variable to be explained is

the price of housing which is measured in RMB per square metre and has an average value of ￥2,615,

a standard deviation of ￥1,808, a minimum of ￥742 (for Jiangxi province) and a maximum of

￥14,111 (for Beijing). Figure 3 shows housing affordability, measured as the ratio of house price to

income, in the top ten cities in China. The base is the average ratio in 2001 with the value of 100.

Amongst the ten largest cities, Beijing is the least affordable with average house prices for the decade

to 2010 being 11 time average income while Chongqing is the most affordable with a corresponding

ration of 3.5 (see table 3). Besides, the ratio in Beijing, Shanghai, Shenzhen, Xiamen and Hangzhou is

more volatile with the standard deviation of 3.24 for Beijing, 2.71 for Shanghai, and 3.83 for Shenzhen,

2.95 for Xiamen and 3.47 for Hangzhou while low for Tianjin (2.29), Wuhan (1.45), and Chongqing

(1.48). In terms of the sources of the variation in the data, the ANOVA table shows over-time

difference in house prices is greater than the cross-city variation with the F-value of 19.1 and 26.2,

respectively (shown table 4).

4.2 Estimation model

We employ a simple demand-supply analysis of the forms:

Dt = a X1t + b Pt + µ1 (1); and,

St = c X2t + d Pt+ µ2 (2)

where, the price level (P) is assumed to have a negative impact on demand (i.e. b<0) and an opposite

effect on supply (d>0). X refers to factors other than price influencing demand (D) and supply (S).

In this study, applying the general model to the housing market, the housing demand shifter includes

income (Y), population (N), interest rate (i), rent (R), and housing price (P). Therefore, equation (1)

can be rewritten as follows:

1 http://www.economist.com/node/21542777

Dt = D (Y, N, i, P, R) (3) ;

while the supply of housing is assumed to be the function of housing price (P), land purchasing area (L),

construction cost (C) and housing stock (S).

St = S (P, L, C, S) (4);

Equilibrium in the housing market implies supply equating demand with the market clearing house

price from combining equations (4) with (5) given by:

Pt* = f (Y, N, C, R, i, L, S) (5);

Equation (5) shows the (long-run) equilibrium price of housing with any short-term fluctuations

modelled as a dynamic adjustment to this equilibrium. The adjustment of short-term prices to the long-

run equilibrium is modelled through a vector error correction model (VECM) of the form:

(6);

where et-1 is the lagged error term from the estimation of the equilibrium relationship in equation (5),

‘X’ is a vector comprising the set of exogenous variables impacting on the short-run dynamics in P,

and ‘j’ denotes the lag length. The VECM assumes the existence of a long-run (cointegrating)

relationship as depicted in equation (5) and for < 0 has prices converging to the long-run equilibrium

(Malpezzi, 1999).

4.3 Econometric issues and estimates

The existence of a long-run relationship implies that variables in equation (5) when estimated in levels

are cointegrated. It is now standard practice in time series analysis to subject the data in levels and first

differences to a battery of tests to establish the order of integration. The test for the presence of a

cointegrating relationship involves testing for the order of integration for each variable in levels and

their differences. In the case of unit roots in levels, the error terms arising from the estimate of equation

(5) is then tested for unit roots. The specific case where the variable in levels display unit root while the

error terms arising from the estimated long-run relationship are stationary provides evidence of

cointegration. Panel unit root test is employed to determine the order of integration. Recent

econometric literature has proposed several methods for testing the presence of a unit root under panel

data setting (Hasio, 2003). Four different tests are used, namely those of Levin, Lin & Chu (2002) ,Im,

Pesaran and Shin W-stat(1997), Fisher-ADF and Fisher PP tests proposed by Maddala & Wu (1999).

Results of panel unit root tests are presented in tables 5 and 6. The tests reveal that the null hypothesis

of a unit root cannot be rejected for all the level variables at the 1% significance level: a result that gets

flipped around when a time trend is included. The result of model with time trend can be found in the

Appendix A1. Furthermore, the first differences of variables lead to the rejection of the null hypothesis

at the 1% significance level. Given the low power of these tests when the time series is short as is the

case here, we conclude that the variables in levels are integrated of order one (i.e. I(1)).

The next challenge is to choose between the use of fixed versus random effects modelling for equation

(5). The fixed-effect model assumes that slope coefficients are constant for all cross-sectional units

while the intercept varies over individual cross-section units but is time-invariant. Furthermore, the

different intercepts are assumed to take account of the heterogeneity resulting from unobserved

variables that may differ across the cross-section units (M.Wooldridge 2000). The random effects

model, in contrast, assumes that slope coefficients are constant for all cross-section units, but the

intercept is a random variable (M.Wooldridge, 2000). Hausman’s test supports the choice of a fixed-

effects model (test statistic reported in Table 7).

A residual based multivariate ADF test is used for the presence of cointegration for estimate of

equation (5). The presence of cointegration is examined using the analysis developed by Pedroni for

the panel data (1999, 2000). The Pedroni heterogeneous panel test is feasible and more appropriate for

the dynamic panel data with a large cross-section dimension (Mahadevan & Asafu-Adjaye, 2007)

which allows for the heterogeneity across individual members of the panel. Pedroni (1999) developed

two groups of test statistics, both of which are based on pooling the resulting residuals obtained from

the regressions. The first group contains four tests: panel v-statistic, panel ρ-statistic, panel PP-statistic

and the panel ADF-statistic under the assumption that these four tests are all distributed as being

standard normal. In addition, the residuals obtained are within-group. The second group also assumes

the standard normal distribution but is based on the residuals from the between-group including the

group ρ-statistic, group PP-statistic and the group ADF-statistic. Table 8 presents panel cointegration

test results that with the exception of panel v/ ρ and group v –statistic reject the null hypothesis that

there is no cointegration among variables. Since the panel ADF and the group ADF are considered

superior tests of cointegration for small samples (Pedroni, 1999), the evidence is taken as supporting

cointegration. In sum, the estimates support the proposition of a long-run equilibrium relationship as

depicted in equation (5) and that the VECM is appropriate in estimating the short-run dynamics of

house prices.

Results of estimates of equation 5 are reported in Table 9. The second columns of the table 9 (i.e.

Model 1) shows result of fixed-effects estimates of equation 5. The coefficient for renting area and

urban population is statistically insignificant. The insignificance of renting area as a determinant of

house prices in China is also supported by Zheng (2011). This observation, moreover, accords with the

commonly held view in China that houses are seldom purchased to rent except in large and well

established cities such as Beijing and Shanghai. The insignificant coefficient on population, a variable

found to be significant in modeling house prices in industrial countries (Tumbarello & Wang, 2010),

does not lend itself to an easy explanation. It could be a statistical artifact given the high correlation

with GDP (simple correlation coefficient of 0.73). However, replacing population with the number of

marriages gives significant results. We add data on the number of marriages for the year and a year lag

as substitutes for the population variable. Model 2 reports parameter estimates when rent (R) is

dropped and the number of marriages (M) is added in place of population. The parameter estimate on

marriages for the year is statistically indifferent from zero: dropping this variable lead to the estimates

reported for Model 3 where now all coefficient estimates are significant2 . While the coefficient

estimates for variables that are significant does not differ much across the three models, the discussion

that follows draws on the estimates from model 3.

Drawing on the parameter estimates from Model 3, changes in income have the largest impact on house

prices: a one percent increase in income raises house prices by 0.55 percent. Next is the contribution of

construction costs where the corresponding figure is 0.31, followed by the number of marriage in the

subsequent year with a corresponding figure of 0.24. These three factors combined account for nearly

all of the change in house prices, suggesting that market fundamentals have a dominant impact in

determining house prices. The parameter estimates for remaining variables; namely, interest rates,

housing stock, and land area are all statistically significant and all have the expected signs. The land

purchasing area has the greatest negative impact with a 1 percent changes leading to 0.05% decrease in

the price of housing. Moreover, a one percentage point increase in the real interest rate and a one

percent increase in housing stock leads to 0.004% and 0.05% decrease in housing prices, respectively.

The model is able to explain 78 percent of the variation in house prices. Model 4 includes a dummy

variable D2002 to estimate the contribution of the reforms to land transferring system that took place in

Year 2002 and elaborated upon in the next subsection. While the parameter estimate on D2002 is

significant and comparable in magnitude to that for land release, the rest of the coefficient estimates are

statistically indifferent to those from Model 3. In sum, the contribution of economic fundamentals of

income and construction costs are comparable to those found in Australia, Canada, New Zealand, and

the USA (Tumbarello & Wang, 2010) and (Malpezzi, 1999) but with an additional Chinese

fundamental of the number of marriages in place of the size of the population.

Short run dynamics are modelled using a VECM, the results of which are reported in Table 11. The lag

length for each variable has been selected on the basis of Akaike Information Criterion (AIC) and

Schwarz Bayesian Criterion (SBC) (results reported in table 10). The coefficient on the error correction

term, that is the estimate of in equation (6), is negative and statistically significant at 0.1 percent. It

shows that any given deviation of the price level from its equilibrium value is halved in approximately

15 years. The Chinese housing market adjusts to its long-run equilibrium at approximately half the

speed of their counterparts in Australia, Canada, and New Zealand as suggested by estimates reported

in Tumbarello and Wang (2010). The other variables that have statistically significant coefficients

include the one year lagged value of land release and one year lagged value of housing stock. The

magnitude of their impact on house-price inflation, however, is small with elasticity in the range of 0.1

percent. Interestingly, income, construction costs, and the number of marriages determine equilibrium

prices while short-run fluctuations in the price of housing is driven by the extent of disequilibrium in

prices, and the levels of land release and available stock of housing.

4.4 Policy interventions and house prices

2 We also test the number of marriages for a year ahead which shows insignificance in the model.

4.4.1 Policy interventions to reduce short-run fluctuations

House prices in China have undergone rapid change. Figure 4 shows movement in the house price

index (HPI) from 1st season in 2001 to the end of 2009, demarking the period into four phases

comprising the development period (< 2001), growth period (2002-2004), adjustment period (2005-

2007), recession period (2008) and recovery period (2009). Policies were used at first to develop a

housing market which included improvements in access to credit and interest rate subsidies for home

loans. In each period, polices were targeted at stabilizing prices. In 2001, for example, expansionary

fiscal and monetary policies were used to stimulate housing demand. The period of transition to the

market was accompanied with price instability: a surge was followed by a precipitous fall. Prices

resurged from 2002 to 2004, peaking at 10 percent above the base period of 2001.The first round of

policies to contain house-price inflation was implemented during this period. Reforms to the means to

acquiring land for construction commenced in 2002 and were complete by 2004. These included the

transition to use of listing, bidding and auctions instead of state allocation of land to the builders.

Nationwide controls commenced in 2005 when a majority of policies were issued to contain house

price inflation. The HPI remained stable from 1st season in 2005 to the end of 2007 after which it fell

by approximately 12 percentage points in the following year: at least part of this fall was due to the

Asian Financial Crisis, but policies were once again switched around to reduce the slump. The HPI

bottomed out in 1st quarter of 2009 and had rebounded by 20 percentage points to the last quarter.

One distinctive fature of policy intervention in China has been that policies have been switched around

quickly. Policymakers have been active in using the housing market: supporting prices when in a slump

and doing the opposite when there was evidence of house-price inflation. For example, the government

lowered interest rates and eased on credit requirements in 2008 to boost the economy following the

global finacial crisis. But other limits were put in place on the purchase of housing for investment.

Furthermore, decisive steps have been taken by the government to manage short-term fluctuations in

house prices. Examples include the release of land for building and the introduction of new taxes

including taxes on capital gains.We next use event analysis to corroborate the impact of sharp changes

in policies and their impact on house-price inflation. The discussion complements the econometric

analysis but by its nature shows correlations rather than the cause-effect relationships. Furthermore,

note needs to be made of the fact that policies were switched around quickly to mitigate the impact of

the prevailing conditions so as to stabilize house prices. Thus, any observed changes in house prices

are net of the impact of these policy interventions. We next measure the size of the deviation of house

prices from fundamental ones and then corroborate the contribution of policies to the magnitude of the

deviation.

4.4.2 The price deviation and impact of policies

The deviation of house prices from fundamental ones is represented by the difference between the

actual and predicted prices which is reflected by error terms from model 3 (results reported in the

figure A2 in the appendix). The magnitude of the deviation is interpretted as being net of all the

mitigating measures taken by the policymakers.

The magnitude and time profile of the deviation are broadly similar across the 29 provinces. Around

2004, there are obvious tuning points in the deviation in Beijing, Shandong, Xinjiang. The price

deviation switches from being negative to positive after 2004 in some provinces. For example, in

Beijing, the house price deivation fell to approximately 13% below its equlibrium value in 2002 and

peaked at approximately 15% and 11% above its equilibrium value respectively in 2005 and 2007.

Interestingly, the difference between actual and predicted prices was eliminated by 2004 and have

remained above par since. While we can not be definite, it appears as though policies issued during

2002 to 2005 played a significant role in the rapid growth of prices in the Beijing housing market. Yu

(2011) contends that the tuning point in 2004 is due to the impact of policies implemented then. It is

noted that in 2003 and 2004, the government changed its objective from developing affordable housing

to increasing the proportion of commericial residential. Additionally, the government took greater

control on the supply of land for construction and reformed the land transferring method started in

2002 and ended in 2004. All these changes strenghened ownership rights to residential property which

would have had a positive impact on the price of housing. From 2008 to 2009, the deviation deflated in

more than half of the provinces. Sixteen of the twenty-nine provinces in our data set show actual prices

as of 2009 being lower than predicted with prices in Ningxia being 15 percent below par. During this

period, the financial crisis caused the slow economic development which caused the slow devleopment

of the housing market.

The panel stucture offers the opportunity to identify the difference of house price development among

provincial groups with the similar development level. The 29 provinces are divided into three areas

according to their GDP level: eastern area3, western area4 and middle area5 (Liang and Gao, 2008).

Figure 2 shows that in the eastern area, only three out of ten provinces (Shanghai, Zhejiang and Fujian)

present the small deviation in housing prices and two out of seven provinces in the middle area (Jiangxi

and Hubei). Among the twelve provinces in the western area, Xinjiang Province is the only one that

shows sign of large deviation in housing prices. Liang and Gao (2008) posit that income growth, large

scale of urbanization and economic prosperity caused the above and thus explain the large deviation in

the eastern and middle area. While in the western area, the limitted number of population migration and

undeveloped economy prevented house prices from increasing rapidly. One exception is Xijiang

province which presents large deviation since 2005, although its price level is comparatively low

relative to the whole country. According to China Statistic Bureau report 2010-2012, Urumqi, the

captial of Xijiang province, experienced the largest growth of housing price since April, 2011 and

continued to be ranked in the top three untill Feburary 2012. Table 12 presents information on the

years of large price deviation in the middle, western and eastern provinces. The impact of the most

3 The eastern area includes Beijing, Tianjin, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan

with GDP more than 20,000 yuan.

4 The western area includes Jiangxi, Hunan, Sichuan, Guangxi, Guizhou, Yunnan, Shanxi, Gansu, Qinghai, Ningxia, Xinjiang,

and Inner Mongolia with GDP between 12,000 and 20,000 yuan.

5 The middle area includes Hebei, Shanxi, Jilin, Heilongjiang, Anhui, Henan, Hubei with GDP less than 12,000 yuan.

significant reform on land transferring has been presented as Model 4 (in table 9). It shows that these

reforms raised equilibrium prices by approximately 5 percent.

5. Conclusion and policy implications

We use data on a panel for 29 provinces from 1998 to 2009 to investigate the determinants of

equilibrium house prices and factors driving the short-run dynamics in house-price inflation. We find

that market fundaments comprising the level of income, the cost of construction, and the number of

marriages that are to take place in the subsequent year are the key determinants of house prices in the

long term. Our results show that market fundamentals, including the mother-in-law phenomenon in

that a house is purchased before the hands of the daughter are given to the groom, overwhelm the

contribution of policies in determining equilibrium prices. Policies, however, have a significant role in

driving the short-run dynamics. In other words, house prices in urban China are largely determined by

the economic fundamentals of income, construction costs and number of marriages which are then

followed by statistically significant but quantitatively small negative effects of changes in real interest

rates, land purchasing area and the housing stock. We then use a vector error corrections model

(VECM) to decipher the factors underscoring short-term dynamics in house-price inflation. The

determinants of short term fluctuations in prices include the prevailing level of disequilibrium in the

market, the area of land under release for building, and the available stock of housing. Furthermore,

short-run dynamics are stable in that a given deviation of house prices from the equilibrium is halved in

approximately 15 years.

The contribution of fundamentals to house prices in China is similar to that of three industrialised

countries where similar estimates have been made. The elasticity of income of 0.55 for China is in the

mid-range of the elasticity of terms of trade, a measure closely correlated with the level of income, for

Australia, Canada, and New Zealand (Tumbarello & Wang, 2010). The contribution of the mortgage

rate at less than 0.5 percent is approximately one twentieth of the corresponding figure for the three

industrial nations. Rents, moreover, do not have a discernible impact on equilibrium house prices in

China. The relatively low elasticity of interest rates may be due to an immature financial market while

the absence of an impact of rents on house prices may reflect a cultural bias against renting of property.

It is also evidence in support of the proposition that the asset market for residential housing is yet to

mature. Finally, the housing market in China adjusts to its long-run equilibrium at a rate half that of its

industrial country counterpart. This suggests that the housing market in China is still some distance

from reaching the fluidity of its industrial country cousins and that the influence of the ‘mother-in-law’

may be a drag on the transition.

These results provide a number of lessons for policymakers. First, it says that market fundamentals, in

so far as house prices in the Peoples’ Republic are concerned, rule. Second, policies matter in driving

short-run fluctuations in house prices. Last, land release has a significant impact on house prices after

one to two years of its implementation. Given the rapid transition to the market in China, these findings

may have applicability to other transition economies wrestling with house-price inflation.

Tables

Table 1: Variables definitions and data sources

Variable Name description Source

P House prices in urban China(RMB/sq.m) China Statistics Yearbook

Y Gross domestic product(10,000yuan) China Real Estate Statistics Yearbook

C Construction Cost(yuan/sq.m) China Statistics Yearbook

S Housing Stock(sq.m ) China Real Estate Statistics Yearbook

i Real Interest rate for 5 years(%) China Statistic Yearbook

L Land purchasing area (L) (sq.m) China Real Estate Statistics Yearbook

R Residential rent area (R) (sq.m) China Real Estate Statistics Yearbook

M Marriage number China Statistic Yearbook

Note: Natural logarithm and real forms of each variable are denoted as: LP, LY, LC, LS, Li, LL, LM and i.

Table 2: Summary statistics of variables used

Variables Observations Mean St.dev Min Max

P 348 2615.425 1808.487 742.9768 14110.92

Y 348 7328.171 7082.499 243.6831 42673.55

C 348 1382.527 537.5361 623.0819 3094.533

S 261 2,745,127 2,785,996 36,828.00 13,546,921

Ri 348 6.683120 2.673058 0.007238 11.87136

L 348 11,492,591 8,977,710 278,409 41,271,810

R 319 170232.2 289375.1 150.0000 1536306

M 248 584,817 385,534 52,000 1,662,422

Table 3: Ratio of housing prices to average income in the 10 largest cities in urban China

Year Beijing Shanghai Guangzhou Shenzhen Hangzhou

max 17.40 15.40 9.90 15.60 14.70

min 7.70 6.70 5.00 5.40 3.50

mean 11.09 10.63 7.48 9.35 8.41

median 9.65 10.40 7.25 8.25 8.40

std.dev 3.24 2.71 1.73 3.83 3.47

Year Xiamen Wuhan Chongqing Tianjin Shenyang

max 13.20 8.9 7.8 10.6 8.3

min 5.20 5.1 3.1 3.8 6.5

mean 9.09 7.14 4.7 7.35 7.06

median 9.65 7.35 4.25 8.15 7

std.dev 2.95 1.45 1.48 2.29 0.51

Data source: http://news.dichan.sina.com.cn/2011/04/01/297910_all.html

Table 4: Analysis of Variance on cross-city and over-time (ANOVA) for 10 cities

Df Sum Sq Mean Sq F value Pr(>F)

City 9 105690 11743.3 19.138 < 2.2e-16 ***

Year 9 144725 16080.6 26.207 < 2.2e-16 ***

Residuals 81 49702 613.6

Table 5: Results of the unit root tests for all the variables in levels

Individual intercept Individual intercept and trend None

Statistic Prob. Statistic Prob. Statistic Prob.

Levin, Lin & Chu t* 3.87925 0.9999 -47.5680 0.0000 12.9786 1.0000

Im, Pesaran and Shin W-stat 3.49364 0.9998 -11.6658 0.0000

ADF - Fisher Chi-square 654.623 0.0000 761.005 0.0000 129.336 1.0000

PP - Fisher Chi-square 494.591 0.0017 913.207 0.0000 117.505 1.0000

Note: Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

Table 6: Results of the unit root tests for all the variables in the first difference

Individual intercept Individual intercept and trend None

Statistic Prob. Statistic Prob. Statistic Prob.

Levin, Lin & Chu t* -73.5949 0.0000 -79.7616 0.0000 -19.2907 0.0000

Im, Pesaran and Shin W-stat -31.0457 0.0000 -10.5733 0.0000

ADF - Fisher Chi-square 1356.20 0.0000 1010.99 0.0000 1631.70 0.0000

PP - Fisher Chi-square 1402.47 0.0000 1554.51 0.0000 1571.59 0.0000

Note: Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality

Table 7: Results of the Hausman test

Chisq p-value 150.9908 < 2.2e-16

Table 8: Results of the Pedroni Residual cointegration test for all variables

Individual intercept Individual intercept and trend

Statistic Prob. Statistic Prob.

Panel v-Statistic -6.293667 1.0000 -7.117589 1.0000

Panel rho-Statistic 7.402851 1.0000 8.710816 1.0000

Panel PP-Statistic -8.477849 0.0000 -14.12127 0.0000

Panel ADF-Statistic -3.343177 0.0004 -2.125198 0.0168

Individual intercept Individual intercept and trend

Statistic Prob. Statistic Prob.

Group rho-Statistic 9.785806 1.0000 10.81175 1.0000

Group PP-Statistic -14.38456 0.0000 -24.21495 0.0000

Group ADF-Statistic -4.789679 0.0000 -5.359054 0.0000

No individual intercept and trend No individual intercept and trend

Statistic Prob. Statistic Prob.

Panel v-Statistic -5.941906 1.0000 Group rho-Statistic 8.630572 1.0000

Panel rho-Statistic 5.837809 1.0000 Group PP-Statistic -11.23893 0.0000

Panel PP-Statistic -5.439471 0.0000 Group ADF-Statistic -4.892402 0.0000

Panel ADF-Statistic -3.920570 0.0000

Table 9: Results of the fixed effect regression model

Model1 Model2 Model3 Model4 Independent variables

Estimate P-Value Estimate P-Value Estimate P-value Estimate P-value

LY 0.6099845 < 2.2e-16 *** 0.5492781 < 2.2e-16 *** 0.5537243 < 2.2e-16 *** 0.5684130 < 2.2e-16 ***

LC 0.3194094 2.635e-05 *** 0.3066975 5.475e-06 *** 0.3143190 2.721e-06 *** 0.3203307 1.203e-06 ***

i -0.0044539 0.0012455 ** -0.0041974 0.0008374 *** -0.0041029 0.001042 ** -0.0032029 0.0109809 *

LS -0.0517694 0.0032530 ** -0.0491214 0.0020094 ** -0.0494742 0.001854 ** -0.0510870 0.0010897 **

LL -0.0627379 0.0001408 *** -0.0482150 0.0017658 ** -0.0473541 0.002076 ** -0.0515121 0.0007044 ***

LLM . . 0.1962433 0.0086043 ** 0.2417039 1.826e-05 *** 0.2466380 8.911e-06 ***

LM . . 0.0624337 0.3576205 . . . .

LR -0.0069006 0.3531724 . . . . . .

LN -0.2383854 0.3398507 . . . . . .

D2002 . . . . . . 0.0459812 0.0026869 **

Adjusted R-squared: 0.77018 0.78312 0.7863 0.78594

F-statistic: 297.855 318.819 372.062 331.814

p-value: < 2.22e-16 < 2.22e-16 < 2.22e-16 < 2.22e-16

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

Table 10: Lag length selection by AIC and SC

Lag length AIC SC

1 -2.298031e+01 -2.210046e+01

2 -2.377834e+01 -2.221415e+01

3 -2.402128e+01 -2.177276e+01

4 -2.408402e+01 -2.115117e+01

5 -2.427901e+01 -2.066183e+01

*Calculated by R statistic software 2.13.

Table 11: Results of panel VECM model

Variables Estimate Pr(>|t|)

ect1 -0.045733 6.45e-06 ***

constant -0.741302 6.64e-06 ***

LY.dl1 0.227413 0.212317

LL.dl1 -0.230371 0.000959 ***

LC.dl1 0.300319 0.289406

LS.dl1 -0.218457 0.000805 ***

i.dl1 0.006274 0.436937

LLM.dl1

0.310255

0.059411 .

LP.dl1 -0.192254 0.396080

Adjusted R-squared 0.3415

F-statistic 15.92

p-value < 2.2e-16

*Calculated by R statistic software 2.13. --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

Table 12: The year of large price deviation in the eastern, middle and western provinces

Eastern Start time Middle Start time Western Start time

Beijing 2005-2009 Shanxi 2004-2007 Guangxi 2005-2007

Tianjin 2002, 2007-2009 Innormogolia 2002, 2007,2008 Sichuan 2002, 2005-2007

Hebei 2006-2009 Jilin 2006-2009 Guizhou 2003, 2006,2008,2009

Shanghai 2002,2007-2009 Heilongjiang 2006-2009 Yunnan 2002, 2004, 2005

Jiangsu 2002,2008,2009 Anhui 2004-2007 Shaanxi 2005-2009

Zhejiang 2002,2004-2008 Jiangxi 2002, 2007 Gansu 2002, 2005-2007

Fujian 2002, 2004,2006, 2007,2008 Henan 2004-2007,2009 Qinghai 2002, 2003

Shandong 2005-2007 Hubei 2002, 2004, 2005,2007 Ningxia 2002-2004,2006

Guangdong 2002-2005 Hunan 2004-2007 Xinjiang 2005-2009

Hainan 2002, 2005,2006

Liaoning 2002-2004

Data source: Author calculation.

Figures

Figure 1: Actual and predicted prices of housing in Beijing from 2002 to 2009

Data source: Author calculation.

Figure 2: The conceptual framework

A. the asset market and property market B. the property and asset markets: Macroeconomic shifts

C. the property and asset markets: interest rate shift D. the property and asset markets: stock shift

E. the property and asset markets: asset cost shifts

Figure 3: The affordablility level in the top 10 major cities in China from 2001 to 2010

Data source: http://news.dichan.sina.com.cn/2011/04/01/297910_all.html and Author calculations.

Figure 4: The development of housing prices and housing policies in each period

Data source: China Statistic Yearbook 2008-2010. The index was adjusted by using the first season of 2001 as the basic period.

References

Adams, Z., & Füss, R. (2010). Macroeconomic determinants of international housing markets. Journal of Housing Economics, 19(1), 38-50.

Ahn, J.-K. L. a. K.-H. (2004). Categorization and effectiveness of Korean Housing Policy-An analysis of effectiveness and price impact. Paper presented at the The Asian Real Estate Society 2004 Conference.

Ball, M. (2010). Europe housing market review: Royal Institute of Chartered Surveyors. Case, & Shiller. (1990). Forecasting prices and excess returens in the housing market. Areuea Journal,

18(3), 253-273. Chen, J., & Zheng, T. (2008). The analysis of the role of foreign investments in housing market

development in urban China Price world, 36-37. Cheshire, P., & Mills, E. S. (1999). handbook of regional and urban economics: applied urban

economics. Chi-man Hui, E., & Sze-mun Ho, V. (2003). Does the planning system affect housing prices? Theory

and with evidence from Hong Kong. Habitat International, 27(3), 339-359. Clapp, J. M., & Giaccotto, C. (1994). The Influence of Economic Variables on Local House Price

Dynamics. Journal of Urban Economics, 36(2), 161-183. Deng, C., Ma, Y., & Chiang, Y.-M. (2009). Dynamic Behavior of Chinese Housing prices. International

Real Estate Review, 12(2), 121-134. DiPasquale, & Wheaton. (1992). The markets for real estate assets and space: A conceptual

framework. Journal of American Real Estate and Urban Economics Association, 20(1), 181-197.

Du, H., Ma, Y., & An, Y. (2011). The impact of land policy on the relation between housing and land prices: Evidence from China. The Quarterly Review of Economics and Finance, 51(1), 19-27.

Egerd, & Mihaljek. (2007). Determinants of house prices in central and eastern Europe. Fair, R. C., & Jaffee, D. M. (1972). Methods of Estimation for Markets in Disequilibrium.

Econometrica, 40(3), 497-514. Hannah, L., Kim, K. H., & E.S.Mills. (1993). Land use controls and house prices in Korea. Urban Studies,

30, 147-156. Huang, Y., & Clark, W. A. V. (2002). Housing tenure choice in transitional urban China: a multilevel

analysis. Urban studies 39(1), 7-32. Im, K., Pesaran, M.H.,, & Shin, Y. (1997). Testing for Unit Roots in Heterogeneous Panels. Jacobsen, D. H., & Bjorn, E. N. (2005). What drives house prices? . Economic Bulletin, 76(1), 29-41. Jarocinski, M., & Smets, F. (2008). House prices and the stance of monetary policy. Paper presented

at the 32nd Annual Economic Policy Conference of the Federal Reserve Bank of St.Louis. Jud, & Winker. (2002). The dynamics of metropolitan housing prices. journal of real estate research,

23(1/2), 29-45. Kim, w. (2005). A study on positive analysis of housing policies effect: focus on effect of housing price

changes. The university of Kookmin, Seoul. Levin, A., Lin, C. F., , & Chu, C. J. (2002). Unit Root Tests in Panel Data: Asymptotic and Finite-Sample

Properties. journal of Econometrics, 108, 1-24. Liang, Y., & Gao, T. (2007). Empirical analysis on real estate price fluctuation in different provinces of

China. Economic Research, 42(8), 133-142. M.Wooldridge, J. (2000). Introductory Econometrics: a modern approach: South-western College

Publishing. Maddala, G. S., & Wu, S. (1999). A Comparative Study of Unit Root Tests with Panel Data and New

Simple Test. Oxford Bulletin of Economics & Statistics, 61, 631-652.

Mahadevan, R., & Asafu-Adjaye, J. (2007). Energy consumption, economic growth and prices: a reassessment using panel VECM for developed and developing countries. Energy Policy, 35, 2481-2490.

Malpezzi, S. (1999). A Simple Error Correction Model of House Prices. [doi: 10.1006/jhec.1999.0240]. Journal of Housing Economics, 8(1), 27-62.

Mankiw, N. G., & D.Weil. (1989). The baby boom, the baby bust, and the housing market. Regional Science and Urban Economics, 19, 235-258.

McMillan, J., & Naughton, B. (1996). Reforming Asian Socialism: The Growth of Market Institutions: The University of Michigan Press.

Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multipule regressors. Oxford Bulletin of Economics & Statistics(61), 653-678.

Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Advances in Econometrics, 15, 93-130.

Peng, W., & Tam, D. C. (2008). Property market and the macroeconomy of mailand China: a cross region study. Pacific Economic Review, 13(2), 240-258.

Pollakowski, H. O., & Wachter, S. M. (1990). The Effects of Land-Use Constraints on Housing Prices. [Article]. Land Economics, 66(3), 315.

Poterba. (1996). Explaining intermetropolitan variation in housing prices, rents and land prices. Real Estate Economics, 24(2), 219-245.

Poterba, & Shiller. (1991). Housing price dynamics: the role of tax policy and demography:comments and discussion. Bookings papers on economic activity, 143-148.

Quigley, J. M. (1999). Why Should the Government Play a Role in Housing?: A View from North America. [Article]. Housing, Theory & Society, 16(4), 201-203.

Riddel, M. (2004). Housing-market disequilibrium: an examination of housing-market price and stock dynamics 1967-1998. Journal of Housing Economics, 13(2), 120-135.

Ronald, & Richard. (2010). Housing Policy Realignment in East Asia. [Article]. Housing Finance International, 24(4), 37-43.

Shen, Y., & Liu, H. (2004). Housing prices and Economic Fundamentals: A cross city analysis of China for 1995-2002. Economic Research, P78-86.

Tiwari, P., Parikh, K., & Parikh, J. (1999). Effective housing demand in Mumbai metropolitan region. Urban Studies, 36(10), 1783-1809.

Tolley, G. S. (1991). Urban Housing Refrom in China: An Economic Analysis. World Bank. Tumbarello, & Wang. (2010). What drives house prices in Australia? A cross-country approach. IMF

working paper. Wheaton, R. P. a. W. C. (1994). Effects of restrictive land supply on housing in HongKong: An

econometric analysis. Journal of Housing Research 5(2), 263-290. Yu, H. (2010). China's house price: affected by ecnonomic fundamentals or real estate policy.

Front.Ecno.China, 5(1), 25-51. Yu, H. J., & Lee, S. (2010). Government housing policies and housing market instability in Korea.

Habitat International, 34(2), 145-153. Zheng, W. (2011). Study on the determinants and relationship between China's urban housing prices

and rents. Zhejiang. Zhou, J. (2005). Monetary policy, bank loan and house prices: Evidence from 4 Municipalities of

China. Finance and Trade(5), 22-27.

Appendix:

Table A1: Estimate of model 3 with a time trend

Model 3 with time trend Independent variables

Estimate P-Value

LY 0.1388598 0.12546

LC 0.3154213 7.953e-07 ***

i -0.0026263 0.03116 *

LS -0.0356508 0.01979 *

LL -0.0656047 1.669e-05 ***

LLM 0.2812794 2.776e-07 ***

Trend 0.0282958 1.183e-06 ***

Adjusted R-squared: 0.79073

F-statistic: 356.262

p-value: < 2.22e-16

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Figure A2: Actual and predicted prices of housing in other 28 provinces from 2002 to 2009