House Prices and Stock Prices: Evidence from a
Dynamic Heterogeneous Panel in China
Nannan Yuan
Shigeyuki Hamori
Wang Chen

October 2014
Discussion Paper No.1428

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANHouse Prices and Stock Prices: Evidence from a Dynamic Heterogeneous Panel in
China

Nannan Yuan
Graduate School of Economics, Kobe University
Address: 2-1, Rokkodai, Nada-ku, Kobe 657-8501, Japan.
E-mail: wsyuanan@hotmail.com (N. Yuan)

Shigeyuki Hamori (Corresponding author)
Faculty of Economics, Kobe University
Address: 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan.
E-mail: hamori@econ.kobe-u.ac.jp (S. Hamori)

Wang Chen
Graduate School of Economics, Kobe University
Address: 2-1, Rokkodai, Nada-ku, Kobe 657-8501, Japan.
E-mail: tinou3776@yahoo.co.jp (W. Chen)

Abstract: Several studies have focused on the relationship between stock and house
prices, but they reached contradictory conclusions. This paper contributes to theliterature by analyzing the effects of stock prices on house prices with panel data of 28
regions in China for the 2003:Q1 to 2012:Q4 period. Compared with the previous
studies, we explain these contrasting effects by distinguishing between the long- andshort-run effects of stock prices. Our main results show that stock prices have a negative
effect on house prices in the long run but positive effects in the short run. Furthermore,
we determine that high market openness and low composition risk account for the
opposite short-run effects of stock prices.
Keywords: house prices, stock prices, dynamic relationship

JEL Classification: G32, E31, E44

The research of the second author is in part supported by the Grant-in-Aid of the
Masaka Sawamura research fund.

1. Introduction
Houses can be regarded as both investment goods and consumption goods, and thisdifferentiates them from financial assets such as stocks (Algieri, 2013). The potential dynamic
relationship between house and stock prices has been the subject of substantial debate in boththe academic and practitioner literatures (Quan & Titman, 1999; Piazzesi, Schneider, & Tuzel,
2007). Pinpointing the relationship between stock and housing markets is essential to explain
the housing price dynamics of an economy, since it is one of the leading indicators of real
economic activity, inflation, or both, and hence serves as indicator to where the real economy
is heading to (Stock & Watson, 2003; Ibrahim, Padli, & Baharom, 2009). Besides, similar toother real estate markets, such as Singapore 1 , the housing market in China, consisting of
construction, renovation, and trade services, is estimated to be over 6% of the country’s GDP2.
In view of the importance of housing in an economy, it is crucial for us to analyze the dynamic
effects of stock prices on house prices.
However, the stock price effects on house prices are controversial. Owing to the special
characteristic of housing, Algieri (2013) pointed out that the effect of stocks on house prices
can be considered as substitution and wealth effect. Real estate and stocks are both speculative
assets, and so houses and stocks can be considered as investment alternatives (Shiller, 2014).
In this case, the relationship between the two assets (stocks and houses) can be interpreted as a
substitution effect. This means that high returns in the stock market can cause investors to leave
the housing market and thereby decrease housing demand and prices. Therefore, stock prices
have a negative effect on house prices. Worzala and Vandell (1993) find a negative correlationof -0.0971 between housing and stock returns in the United Kingdom. The literature has
examined the contemporaneous correlation between housing and stock returns. Considering thecorrelation endogenous, Eichholtz and Hartzell (1996) show that property and stock indexes
have a negative correlated in the United Kingdom, Canada, and the United States. More recently,
Ayuso, Blanco, and Restoy (2006) applied an Error Correction Model (ECM) with annual dataof Spain from 1978 to 2002 to analyze the dynamics of house prices and found that the elasticity

1

In Singapore, according to Liow (2006), real estate investment and development firms account for about 15% of
the Republic’s stock market capitalization.
2
According to Hilbers, Hoffmaister, Banerji, and Shi (2008), for many countries, house activities consist of 5% to
10% of GDP.
1

of house prices with respect to stock market returns is -0.3 in the long run. Ibrahim et al. (2009)
apply an autoregressive distributed lags (ARDL) approach with quarterly data from 1995:Q1
to 2006:Q2 to analyze the long-run relationship between house and stock prices. Although notstatistically significant, the estimated results from ARDL show that stock prices have a negative
effect on house prices (for semi-detached houses, -0.613; for town houses, -0.051).
When houses serve as consumption goods, the well-known wealth effect stresses on atransmission channel from stock to housing (Sim & Chang, 2006). All income and wealth,including financial, housing, and human wealth, as well as any expected disposable income,have a positive impact on total consumption spending. Since stocks provide households with
unexpected gains, the wealth effect from these unexpected gains increases the spending on
houses as consumption good. Put differently, the wealth effect leads to a positive relationshipbetween stock and housing prices because high returns in the stock market tend to increase thetotal wealth of households and their capability to invest in other assets (Koivu, 2012). For
example, Gyourko and Keim (1992), Hoesli and Hamelink (1997), and Fu and Ng (2001) show
a contemporaneous positive relationship between stock and housing returns in the United States,
Switzerland, and Hong Kong, respectively. Using the annual data of 17 countries over a periodof 14 years, Quan and Titman (1999) find a significant positive relationship between real estate
price changes and stock returns when they pooled the countries’ data and considered longer
measurement intervals. When Sutton (2002) applies a vector autoregression (VAR) model toexplain changes in housing prices with quarterly data covering six industrialized countries3, theresults show that changes in stock prices have a positive effect on changes in housing prices,
the magnitude ranging from 1.0 to 5.0. Further, Algieri (2013) finds a positive stock priceelasticity for house prices for seven countries4 in the long run, with the magnitude varying from0.01(in Italy) to 0.23 (in the Netherlands). Ibrahim et al. (2009) analyze the long-run
relationship between house and stock prices in Thailand by applying the dynamic ordinary leastsquares (DOLS) and maximum likelihood (ML) estimation methods. Their DOLS and ML
results show that stock prices have a positive impact on house prices. Lean and Smyth (2014)
apply cointegration and Granger causality tests to examine the dynamic relationship between

3

The six countries are the United States, the United Kingdom, Canada, Ireland, the Netherlands, and Austria (seeSutton, 2002).
4
The seven countries studied by Algieri (2013) are Germany, France, Italy, Spain, the Netherlands, the United
Kingdom, and the United States.
2

house price indices, stock prices, and interest rates in Malaysia. They too find that stock prices
have a positive effect on house prices.
In China, Huang and Ge (2009) apply a linear standard regression model with monthly datafrom 2006:04 to 2008:07 to analyze stock market impacts on house market and find that thecorrelations between housing and stock prices are relatively weak. However, Yang and Ye (2010)
find no correlation between real estate and stock market returns in China in a study using
monthly data from 2005 to 2010.
Several studies have analyzed the long-run relationship between stock and house prices(Ayuso et al., 2006; Ibrahim et al., 2009; Oikarinen, 2010; Lean & Smyth, 2014), but as far as
we know, very few illustrate the problem explicitly by distinguishing and analyzing both thelong- and short-run coefficients. Whether positive or negative, the correlations between stockand house prices have been found sufficiently low, implying significant diversification
opportunities (Oikarinen, 2010). Hence, it is crucial for us to explicitly distinguish between thelong- and short-run effects of China’s stock prices on its house prices and to check whether
there exists any difference between them. This paper applies a pooled mean-group (PMG)
estimator of dynamic heterogeneous panel data to distinguish between the long- and short-run
effects of stock prices. Another improvement of this study is that we further analyze the possible
economic reasons for cross-sectional heterogeneity in short-run effects, after first estimating
the cross-sectional pattern of variations in short-run coefficients.
The rest of this paper is organized as follows. Section 2 describes the data and econometricmethodology used in this study. Section 3 presents our estimation results. Section 4 analyzes
the short-run heterogeneous effects. Section 5 provides our concluding remarks.

2. Data and Econometric Methodology
The primary focus of this study is on the effects of stock prices on house prices. However,
employing bivariate analysis is not an appropriate method because the relationship between thevariables might be specious and reflect common factors (Quan & Titman, 1999; Ibrahim, 2010),
requiring us to add certain other control variables. A study by Jud and Winkler (2002) showsthat the housing price appreciation observed in 130 metropolitan areas across the United States
was strongly influenced by stock market appreciation as well as interest rates and income. In
3China, land prices have the greatest impacts on house prices (Hua, Sun, & Borgia, 2012). Thus,
the control variables used in this paper include individual lending rates for housing, land prices,and per capita disposable income.
In view of the heterogeneity between its different provinces and cities, we use panel data of
28 regions of China for the 2003:Q1 to 2012:Q4 period5. The quarterly data of house prices,
stock prices, and individual lending rate are from the CEIC database6. Land price is the landtransaction fee divided by land transaction area, and the related data are from the China
Economic Information Network (CEINET) database. The data of house prices, stock prices,land price, and per capita disposable income are taken from natural logarithms. For the control
variables, land price and per capita disposable income are expected to positively impact house
prices (Hua et al., 2012; Algieri, 2013; Pan & Wang, 2013), while the individual lending rates
are expected to negatively affect house prices (Madsen, 2012; Zhang, Hua, & Zhao, 2012).
Sluggish and autocorrelated adjustments of house prices to shocks in fundamentals are likely
to create a lead–lag relationship between stock and housing price movements (Lean & Smyth,2014). In addition, housing prices might react slowly to shocks in fundamentals, althougheconomic fundamentals are important factors for the movement of housing prices (Himmelberg,
Mayer, & Sinai, 2005). Therefore, a dynamic panel data model that includes lagged values ofthe dependent as well as contemporaneous and lagged independent variables is a proper method
to analyze the dynamic relationships between house and stock prices.
As a dynamic panel data model, the autoregressive distributed lag ( ARDL(p, q, q,, q) )

model can be shown as an equation:pq
hpit   ij hpi,t  j    it X i,t  j  i   it
j 1
j 0

(1)

where hpit represents the natural logarithm of house prices; X it is the vector of explanatory
variables, including the logarithms of stock prices and other control variables for group i ; irepresents fixed effects; ij and  it are coefficients;  it is a time-varying disturbance; andthe subscripts i and t represent region and time, respectively.
5

The following are the 28 regions: Anhui, Beijing, Chongqing, Fujian, Guangdong, Gansu, Guangxi, Guizhou,
Hainan, Hebei, Henan, Heilongjiang, Hubei, Hunan, Inner Mongolia, Jiangsu, Jiangxi, Liaoning, Ningxia, Qinghai,
Sichuan, Shandong, Shanghai, Shaanxi, Shanxi, Tianjin, Xinjiang, Zhejiang.
6
Because the data (house prices) provided are on monthly basis, we consider the data of March, June, September,and December as the quarterly data. For consistency of data, we use the same method for the data of stock prices
and individual lending rate.
Individual lending rate is the average of lending rates up to 5 years and over 5 years.
4

To clearly illustrate the dynamic relationship, we examine both long- and short-run effectsof stock prices on house prices. For this, we use an empirical model encompassing the longand short-run coefficients on stock prices, because this would help us formulate and empiricallyexplain the apparently contradictory effects of stock prices on house prices. Thus, we use anerror-correction model where both long- and short-run effects can be estimated jointly by using
the ARDL model. This can be shown as
p 1

q 1

j 1

j 0

hpit  i (hpi ,t 1  iX it )   it*hpi ,t  j    it*X i ,t  j  i   it(2)

where  i gives the coefficients on the error correction term,  i is the long-run coefficient,and it and  it are short-run coefficients.
Two estimators of Eq. (2) are widely used, the mean group (MG) and pooled mean-group(PMG) estimators, introduced by Pesaran and Smith (1995) and Pesaran, Shin, and Smith(1999), respectively. The MG estimator provides the averaged coefficients of individual timeseries regressions for each group of the panel; this indicates that all the individual regressors
are heterogeneous. However, the PMG estimator imposes a homogeneity restriction on thelong-run relationship between variables. Thus, the PMG estimator relies on a combination ofpooled coefficient for a long-run relationship and it averages the individual coefficients for
short-run dynamics. Therefore, in Eq. (2), the PMG estimator restricts the long-run coefficients i ; this is common across all regions. The error-correction speed of adjustment parameter  iand the short-run adjustment coefficients it and  it vary across all regions. The speed ofadjustment parameter  i should be negative; this would indicate the tendency of house prices
to return to the long-run equilibrium level. Once the estimates of short-run coefficients forindividual countries are obtained, we can consistently estimate the cross-region mean of theseparameters asN *j   ij* / Ni 1

j  1 , 2 , . p. .,1(3)

j  0 , 1 , .q. . ,

1

(4)

N

 *j    ij* / Ni 15where N represents the number of regions.
One important merit of these two estimators is that they can be used to estimate nonstationary
dynamic panels in which the parameters are heterogeneous across groups. Compared to MG
estimators, as pointed out by Pesaran et al. (1999), PMG estimators are consistent andasymptotically normal regardless of whether the regressors are I (0) or I (1) . Another meritof PMG estimators over MG estimators is that they rely on economic theory that links changes
in housing prices to stock prices and other control variables and generate more efficientestimates when the homogeneity restrictions imposed by theory are valid 7 (Koetter &Poghosyan, 2010).
Depending on the characteristics of PMG estimators, a comparison of the estimated regionspecific short-run coefficients allows us to examine the region-specific patterns of variations inestimated short-run coefficients. Our second estimation strategy is to illustrate the reasons forregional variations in short-run coefficients. This is closely linked to the strategy of Loayza andRancière (2006), who use a PMG estimator to examine the long- and short-run relationships
between financial development and economic growth.

3. Estimation Results
Before our actual estimation, we confirm the lags of all variables according to the Akaike
Information Criterion (AIC) and Schwarz Bayesian Information Criterion (SBIC). Table 1
shows the values of AIC and SBIC for models with different lags. The results indicate that theproper model is ARDL (3, 2, 2, 3, 3), with 2 lags of stock prices, 2 lags of lending rate, and 3
lags of the other variables. We estimate the proper ARDL model with PMG and MG estimators;
the results are shown in Table 2. The Hausman test (1978) reveals that the null hypothesis ofhomogeneous long-run coefficients cannot be rejected for all parameters, just as for individual
parameters. We therefore focus mainly on the results of PMG estimators.
The estimates of long-run coefficients on land price and disposable income are positive andstatistically significant, indicating that higher changes in land prices and disposable income areassociated with increasing changes in house prices in the long run. However, the long-run

7

The latter assumption can be tested empirically with the Hausman test (1978).
6

coefficient of individual lending rates on house prices is negative and statistically significant;this indicates that individual lending rates are negatively associated with increasing changes inhouse prices. In the long run, a statistically significant coefficient of share prices has a negative
effect on house prices. This result is consistent with the previous studies of Eichholtz andHartzell (1996), Ayuso et al. (2006), and Ibrahim et al. (2009). The negative effects of stock
prices on house prices show a long-run substitution effect dominating the relationship between
stock and house prices. This also indicates that houses serve as an investment good in the longrun, in agreement with Englund, Hwang, and Quigley (2002), suggesting that investors should
hold 15%–50% of housing assets for longer-period low-risk portfolios. The reason is consistent
with the viewpoints of Lean and Smyth (2014) that houses are expensive, income is high, andreal estate is used more as an investment vehicle.
The positive estimates of short-run coefficients on stock prices indicate that stock prices areassociated with house prices in the short run. However, the estimates of region-specific shortrun coefficients on stock prices are heterogeneous, as shown in Figure 1. The mean of one-lag
short-run coefficients on stock prices is positive and statistically significant. The different longand short-run effects of stock prices on house prices and the heterogeneous cross-region shortrun effects provide rationale for a deep analysis of PMG estimators.
The estimated coefficient of the error-correction term is negative and statistically highly
significant, indicating the tendency of house prices to return to the long-run equilibrium level.
However, the relatively low magnitude of the coefficient implies that it takes a long time forhouse prices to return to its long-run equilibrium level once it is shocked. The slow speed ofadjustments underlines the importance of distinguishing between long- and short-run effects
and thus provides another rationale for a deep analysis of PMG estimators.
As a robustness check, we use the Shanghai stock prices as alternative stock prices; the longrun coefficient is found negative, whereas the mean of short-run coefficients is found positive.
The estimated results of the Shanghai stock prices are shown in Table 3. One lag of short-run
coefficient on the Shanghai stock prices is also statistically significant.
In summary, the above estimation results indicate that stock prices can have different andeven contradicting effects on house prices, depending on the time horizon. More specifically,
we find that in China, even if stock prices reduce house prices in the long run, they tend toincrease house prices in the short run. In other words, stock prices show a substitution effect on
7

house prices in the long run and a wealth effect in the short run 8.

4. Analysis of short-run heterogeneous effects
In this section, we further discuss and develop the explanation of apparently contradictory
short-run effects of stock prices on house prices. For this, we take the estimates of regionspecific short-run coefficients on stock prices. The development of stock and housing markets
are associated with contradictory effects of stock prices. Taguchi (2011) shows that capital
inflows affect stock markets through a direct channel. Kim and Yang (2009) point out that
capital inflow shocks contribute to increasing stock prices. Particularly in China, net inflow
shocks of Foreign Direct Investment (FDI) affect both the housing and stock markets (Feng,
Lin, & Wang, 2012). Thus, the proportion of FDI to Gross Domestic Product (GDP) plays animportant role in the contradictory short-run effects of stock prices. This proportion serves as
an indicator of market openness, a higher value indicating a higher level of market openness.
The average of this proportion for each region by annual data from 1995 to 2012 can be used
as a measure of market openness.
In addition, a mechanism of transmission effect from stock prices to house prices comprises
a composition risk; this relates changes in asset prices to changes in expenditure share (Lean &Smyth, 2014). This also represents that the correlation between stock prices and house prices isaffected by returns in both markets. Therefore, we utilize the relative returns between houseand stock prices as the proxy of composition risk. A higher relative return indicates a higher
composition risk; this indicates a higher investment risk in the housing market than in the stock
market. With regard to data, we average the relative returns for each region by quarterly data
for the 2002:Q4 to 2013:Q3 period. The averaged values give the proxy of composition risk foreach region.
The correlation coefficients based on estimated short-run coefficients on market openness
and composition risk are shown in Table 4 (the results of the Shanghai stock market represent
a robustness test). The reported standard and rank correlations are positive for market openness
and negative for composition risk. Although the rank correlation between short-run coefficients
8

Strictly speaking, causality can run in the reverse direction; so, house prices might affect stock prices. Liow
(2006) points out that house prices have a positive effect on stock prices in the long run. However, there is noplausible explanation for the effect of house prices reducing stock prices in the short run. Hence, we interpret ourestimation results as indicating causality running from stock prices to house prices.
8and market openness is not statistically significant at the 5% level, it is significant whenmeasured by the standard correlation. The magnitudes of correlation values show that they areappropriately large.
The positive correlation between market openness and short-run coefficients indicates thathigher market openness increases short-run coefficients. Taguchi (2011) and Feng et al. (2012)
illustrate that capital inflow shocks have a positive effect on stock prices. Oikarinen (2010)
show that because of large transaction costs, real estate investments should typically be a longterm investment. Englund et al. (2002) also suggest that for short periods, an efficient portfolio9
for investors should not hold assets in housing. With this in mind, we assume that in the shortrun, housing tends to be a consumption good. In this case, the wealth effect dominates therelationship between stock prices and house prices. Therefore, we can conclude that higher
market openness pushes up stock prices. Households gain more unanticipated benefits in stock
prices, increasing the amount of housing demand and therefore house prices (Lean & Smyth,2014). Thus, high market openness promotes the positive short-run effects of stock prices onhouse prices.
Nevertheless, there is a negative correlation between short-run effects and the composition
risk in that a higher composition risk is associated with decreasing the positive short-run effectsof stock prices. A higher composition risk reveals higher relative returns of housing compared
to stock, indicating a higher speculative risk in the housing market compared to the stock market.
Lean and Smyth (2014) point out that owing to the risk aversion motive, investors tend to invest
in low-risk assets, and hence the inter-temporal substitution mechanism could lead to therelationship between stock prices and house prices. Therefore, a high composition risk
dominants the substitution effect between stock and house prices. When stock prices increase,to avoid risk, consumers prefer to improve their share of other consumption and investment
goods, rather than housing goods.
In summary, our results indicate that higher market openness and lower composition risk areassociated with the contradictory short-run effects of stock prices on house prices.
We now examine whether an apparent difference exists in mean of short-run coefficients
between regions with high market openness and low market openness, as well as between

9

In Englund et al. (2002), the household investment portfolio consists of housing, common stocks, bonds, and tbills.
9regions with high composition risk and low composition risk. We split our sample into two
subsamples based on median value of indicators of market openness and composition risk,
respectively. We show the means of short-run coefficients for each subsample and the t-test
results for the significance of their differences in Table 5. As a robustness test, we show the
related values and tests in the Shanghai market in the right-hand side of Table 5. For regionswith different levels of market openness, the mean of short-run coefficients is higher for regionswith high market openness than for regions with low market openness. This indicates thatmarket openness is positively related to short-run coefficients, which is consistent with our
results shown in Table 3. The t-test results show that the differences in mean values of thedifferent subsamples’ short-run coefficients are statistically significant. This indicates thatmarket openness is an important factor affecting opposite short-run coefficients. In the sameway, from Table 4 we find that the mean value of short-run coefficients is higher in regions withlow composition risk than in regions with high composition risk. The negative relation between
composition risk and short-run coefficients is consistent with our results shown in Table 3. The
statistically significant results of the related t-tests indicate that composition risk is anotherimportant factor explaining the opposite short-run coefficients.
Using the figures of frequency distribution, we further explain whether visible differences
exist in short-run coefficients between the two subsamples. Figure 2(a) and (b) shows thefrequency distributions for each subsample respectively divided by market openness andcomposition risk. From the figures, we can visually conclude the same results of Table 4, thatis, regions with high market openness have a higher mean of short-run coefficients than regionswith low market openness. The same goes for the criterion of composition risk.
In general, statistical evidence indicates that high market openness and low composition risk
are associated with contradictory short-run effects of stock prices on house prices. Regional
differences in market openness and composition risk are two main reasons for the different
short-run coefficients.
5. Conclusions
The relationship between stock and house prices has been the subject of substantial debate in
the academic literature. Existing studies have focused on the long-run relationship andcontemporaneous correlation between stock and house prices. However, these studies have10

reached contradictory conclusions. By necessity, we use panel data of 28 regions of China forthe 2003:Q1 to 2012:Q4 period and we explain these contrasting effects by distinguishing
between the long- and short-run effects of stock prices on house prices. Furthermore, we explore
the possible reasons for the contradictory short-run effects of stock prices.
Our results show that stock prices have different and even contradicting effects on house
prices, depending on the time horizon. In the long run, stock prices have a negative effect onhouse prices, indicating that the substitution effect dominates the relationship between stockand house prices. However, in the short run, the positive effects of stock prices on house prices
are statistically significant 10 , revealing the remarkable wealth effect of stock prices. Fromprevious studies, we assume that the contradictory short-run effects of stock prices areassociated with the regional specifics of market openness and composition risk. The short-run
correlation coefficients of market openness and composition risk prove our assumption. We
then test the difference in mean values and show the frequency distributions of short-run
coefficients, which are divided depending on market openness and composition risk, and wefind that these two aspects are the main factors affecting the opposite short-run effects of stock
prices on house prices.
Since stock prices lead house prices in the short run across regions with high market openness,the stock market is important for stability in the real estate market, especially in such regions.
The actual annual growth rate of house prices in these regions is very high11, indicating thenecessity to stabilize the housing market. From our evidence that is consistent with the shortrun stock price wealth effect on house prices, the implication for policymakers is to implement
policies to promote stability in the stock market. As Zhou and Sornette (2004) have pointed out,
the immaturity of China’s stock markets makes it quite different from the Western markets andit has to be developed. Following the Asian financial crisis, similar to Malaysia’s stock market,
China’s stock market also should put in place a series of standards designed to improvetransparency, disclosure, accounting, and corporate governance, even though these standards
might still fall short of international standards (Shimomoto, 1999).

10

At least, the short-run coefficient of lagged stock prices is statistically significant at the 0.1% level.
For example, as Ren, Xiong, and Yuan (2012) pointed out, from 2003 to 2007, house prices in Beijing reported
an annual increase of 22%.
11
11

References:
Algieri, B. (2013). House prices determinants: Fundamentals and underlying factors.
Comparative Economic Studies, 55(2), 315–341.
Ayuso, J., Blanco, R., & Restoy, F. (2006). House prices and real interest rates in Spain. Bancode Espana Research Paper, No. OP-0608.
Englund, P., Hwang, M., & Quigley, J. M. (2002). Hedging housing risk. Journal of Real Estate
Finance and Economics, 24(1/2), 167–200.
Eichholtz, P., & Hartzell, D. (1996). Property shares, appraisals and the stock market: Aninternational perspective. Journal of Real Estate Finance and Economics, 12(2), 163–178.
Feng, L., Lin, C. Y., & Wang, C. (2012). Do capital flows matter to stock and housing returns?
Evidence

from

China.

Availableathttps://editorialexpress.com/cgi-

bin/conference/download.cgi?db_name=SERC2013&paper_id=57 (last accessed June 25,2014).
Fu, Y., & Ng, L. K. (2001). Market efficiency and return statistics: Evidence from real estateand stock markets using a present-value approach. Real Estate Economics, 29(2), 227–250.
Gyourko, J., & Keim, D. B. (1992). What does the stock market tell us about real estate returns?
Journal of the American Real Estate and Urban Economics Association, 20(3), 457–485.
Eichholtz, P. & D. Hartzell. (1996). Property shares, appraisals and the stock market: an
international perspective. Journal of Real Estate Finance and Economics, 12(2), 1996, 163178.
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1371.
Hilbers, P., Hoffmaister, A. W., Banerji, A., & Shi, H. (2008). House prices developments in
Europe: A comparison. IMF: International Monetary Fund, Working Paper, No. 211,
Washington DC.
Himmelberg, C., Mayer, C., & Sinai, T. (2005). Assessing high house prices: Bubbles,
fundamentals and misperceptions. NBER Working Paper No.11643.
Hoesli, M., & Hamelink, F. (1997). An examination of the role of Geneva and Zurich housing
in Swiss institutional portfolios. Journal of Property Valuation and Investment, 15(4), 354–
371.
Hua, X., Sun, L., & Borgia, D. (2012). The influence of fundamental factors on Chinese
12

residential real estate prices: A unique data panel study. Social Science Research Network,
Working Paper No.47.
Huang, Y., & Ge, X. (2009). House prices and the collapse of the stock market in mainland
China, available at
http://www.prres.net/index.htm?http://www.prres.net/Proceedings/2009proceedings.asp(last accessed: June 25, 2014).
Ibrahim, M. H. (2010). House price-stock price relations in Thailand: An empirical analysis.
International Journal of Housing Markets and Analysis, 3(1), 69–82.
Ibrahim, M. H., Padli, J., & Baharom, A. H. (2009). Long-run relationship and dynamic
interactions between housing and stock prices in Thailand. Asian Academy of Management
Journal of Accounting and Finance, 5(1), 93–105.
Jud, D., & Winkler, D. (2002). The dynamics of metropolitan housing prices. Journal of Real
Estate Research, 23(1/2), 29–45.
Kim, S., & Yang, D. Y. (2009). Do capital inflows matter to asset prices? The case of Korea.
Asian Economic Journal, 23(3), 323–348.
Koetter, M., & Poghosyan, T. (2010). Real estate prices and bank stability. Journal of Banking& Finance, 34(6), 1129–1138.
Koivu, T. (2012). Monetary policy, asset prices and consumption in China. Economic Systems,
36(2), 307–325.
Lean, H. H., & Smyth, R. (2014). Dynamic interaction between house prices and stock prices
in Malaysia. International Journal of Strategic Property Management, 18(2), 163–177.
Liow, K. H. (2006). Dynamic relationship between stock and property markets. Applied
Financial Economics, 16(5), 371–376.
Loayza, N., & Rancière, R. (2006). Financial development, financial fragility, and growth.
Journal of Money Credit and Banking, 38(4), 1051–1076.
Madsen, B. J. (2012). A behavioral model of house prices. Journal of Economic Behavior &Organization, 82(1), 21–38.
Oikarinen, E. (2010). Foreign ownership of stocks and long-run interdependence between
national housing and stock markets – evidence from Finnish data. Journal of Real Estate
Finance and Economics, 41(4), 486–509.
Pan, H., & Wang, C. (2013). House prices, bank instability, and economic growth: Evidence
13

from the threshold model. Journal of Banking & Finance, 37(5), 1720–1732.
Pesaran, M. H., Shin, Y., & Smith, R. (1999). Polled mean group estimator of dynamic
heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634.
Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic
heterogeneous panels. Journal of Econometrics, 68(1), 79–113.
Piazzesi, M., Schneider, M., & Tuzel, S. (2007). Housing, consumption and asset pricing.
Journal of Financial Economics, 83(3), 531–569.
Quan, D. C., & Titman, S. (1999). Do real estate prices and stock prices move together? Aninternational analysis. Real Estate Economics, 27(2), 183–207.
Ren, Y., Xiong, C., & Yuan, Y. (2012). House prices bubbles in China. China Economic Review,23(4), 786–800.
Shiller, R. J. (2014). Speculative asset prices. American Economic Review, 104(6), 1486–1517.
Shimomoto, Y. (1999). The capital market in Malaysia. Asian Development Bank, Manilla.
Sim, S. H., & Chang, B. K. (2006). Stock and real estate market in Korea: Wealth or creditprice effect. Journal of Economic Research, 11(1), 99–122.
Stock, J. H., & and Watson, M. W. (2003). Forecasting output and inflation: The role of asset
prices. Journal of Economic Literature 41(3), 788–892.
Sutton, C. D. (2002). Explaining changes in house prices. BIS quarterly review, Bank forInternational Settlements: Basel, Switzerland, September, 46–55.
Taguchi, H. (2011). Capital inflows and asset prices: The recent evidence of selected East Asian
economies. PRI Discussion Paper Series, No.11A-05.
Worzala, E., & Vandell, K. (1993). International direct real estate investments as alternative
portfolio assets for institutional investors: An evaluation. Paper presented at the 1993
AREUEA Meetings, Anaheim, CA.
Yang, Y., & Ye, E. (2010). Return correlation of China’s real estate and stock markets. Master
Thesis, Umeå School of Business, September 2010.
Zhang, Y., Hua, X., & Zhao, L. (2012). Exploring determinants of housing prices: A Case studyof Chinese experience in 1999–2010. Economic Modelling, 29(6), 2349–2361.
Zhou, W., & Sornette, D. (2004). Antibubble and prediction of China’s stock market and realestate. Physica A: Statistical and Theoretical Physics, 337(1-2), 243–268.

14

Table 1 AIC and SBIC values of models with different lags
House

Stock

Lending

Land

Disposable

pricesprices

rate

price

incomeAIC

SBIC

Model 1

3

2

2

3

3

-3831.43*-3742.45*

Model 2

2

2

2

3

3

-3800.85-3716.81

Model 3

2

2

2

3

2

-3755.74-3676.65

Model 4

2

2

2

2

3

-3779.16-3700.07

Model 5

3

2

2

2

3

-3812.70-3728.66

Notes:The values given in the first five columns are the lags of different variables. For example, Model 1
represents the ARDL (3, 2, 2, 3, 3). The last two columns give the AIC and SBIC values. * The
proper model selected by AIC and SBIC.

15

Table 2 The long- and short-run effects of stock prices on house prices. The dependent variable
is the log value of house prices.
Pooled Mean Group
Variables

Coef.

Std. Er.

Mean Group
Coef.

Std. Er.

Hausman Tests
h-testp-val

Long-Run Coefficients
Stock prices (log)-0.0401

0.01890.06040.07202.09

0.14

Lending rate

-0.03770.0144-0.0726

0.03331.36

0.24

Land price (log)0.09550.01830.11380.12820.02

0.89

Disposable income (log)1.03010.03970.80550.29290.60

0.44

Joint Hausman Test

5.45

0.24

-0.1758

0.0286-0.5494

0.0750

Δ House prices (log) (-1)-0.1549

0.0571-0.0666

0.0594

Δ House prices (log) (-2)-0.0509

0.0333-0.04480.0429Δ Stock prices (log)0.00040.01610.01130.0149Δ Stock prices (log) (-1)

0.04920.01840.02880.0204

Δ Lending rate

0.01080.0066

0.03440.0104

Δ Lending rate (-1)

0.01620.00980.04270.0097Δ Land price (log)-0.0138

0.00860.00700.0190Δ Land price (log) (-1)-0.02400.0068-0.01240.0138Δ Land price (log) (-2)-0.0095

0.0057-0.00620.0101

Δ Disposable income (log)-0.06450.0728-0.42140.1076

Δ Disposable income (log) (-1)-0.1073

0.0698-0.30930.0942

Δ Disposable income (log) (-2)-0.1272

0.0663-0.1601

0.0656

Intercept-0.2315

0.0466-0.7174

0.35840.04960.02440.04010.0253

Error Correction Coefficients
Short-Run Coefficients

Sum of Coefficients on Stock
Price (∑ΔStock price coefs.)

16

Table 3 The long- and short-run effects of Shanghai stock prices on house prices. The dependent
variable is the log value of house prices.
Pooled Mean Group
Variables

Coef.

Std. Er.

Mean Group
Coef.

Std. Er.

Hausman Tests
h-testp-val

Long-Run Coefficients
Shanghai stock prices (log)-0.0406

0.01800.05680.06442.48

0.12

Lending rate

-0.02760.0140-0.0737

0.03332.33

0.13

Land price (log)0.09850.01870.09990.13080.00

0.99

Disposable income (log)1.02030.04110.83920.2923

0.39

0.53

Joint Hausman Test

4.08

0.40

Error Correction Coefficients(Phi)-0.1795

0.0303-0.53890.0752

Δ House prices (log) (-1)-0.1372

0.0544-0.06420.0592

Δ House prices (log) (-2)-0.0475

0.0319-0.0534

0.0424

Δ Shanghai stock prices (log)0.00920.01040.01550.01631)

0.03500.01210.01560.0164

Δ Lending rate

0.01400.0056

0.03770.0099Δ Lending rate (-1)

0.01530.01080.04460.0116Δ Land price (log)-0.01420.00850.00900.0198Δ Land price (log) (-1)-0.0271

0.0064-0.0126

0.0144

Δ Land price (log) (-2)-0.01020.0057-0.00610.0105

Δ Disposable income (log)-0.0679

0.0672-0.4144

0.1129Δ Disposable income (log) (-1)-0.09880.0702-0.2999

0.1004

Δ Disposable income (log) (-2)-0.13230.0703-0.1579

0.0708Intercept-0.21540.0458-0.7335

0.35760.04420.01600.03110.0231

Short-Run Coefficients

Δ Shanghai stock prices (log)(-

Sum of Coefficients on Stock
Price (∑ΔStock price coefs.)

17

Table 4 Correlation between short-run coefficients on market openness and composition risk(a) National stock market
Market openness

Composition risk

Standard correlation

0.3772 (0.0479)-0.4826 (0.0093)

Rank correlation

0.3514 (0.0667)-0.4182 (0.0268)(b) Shanghai stock market
Market openness

Composition risk

Standard correlation

0.3553 (0.0635)-0.5759 (0.0013)

Rank correlation

0.3388 (0.0778)-0.4182 (0.0056)

Note: p-values are reported in parenthesis.

18

Table 5 Test of difference in means of short-run coefficients between subsamples in both thenational market and Shanghai market(a) Low market openness vs. high market openness
National Market
Mean short-run

Shanghai Market
No.

Mean short-run

No.

coefficients

Std. error

obs.

coefficients

Std. error

obs.

Low market openness

0.01490.072414

0.00730.060714

High market openness

0.08640.0849140.08110.0812

14

Test of difference in means: Ho: Diff = 0 vs. Ha: diff ≠ 0
Methoddiffert-valuep-valuediffert-valuep-valuet-test-0.0285

2.32660.0280-0.07372.72090.0115

Satterthwaite-Welch t-test-0.0285

2.32660.0283-0.07372.72090.0119(b) Low composition risk vs. high composition risk
National Market
Mean short-run

Shanghai Market
No.

Mean short-run

No.

coefficients

Std. error

obs.

coefficients

Std. error

obs.

Low composition risk

0.08490.091013

0.08230.079813

High composition risk

0.01890.068415

0.01120.065515

Test of difference in means: Ho: Diff = 0 vs. Ha: diff ≠ 0
Methoddiffert-valuep-valuediffert-valuep-valuet-test

0.0660-2.1868

0.03800.0711-2.5886

0.0156

Satterthwaite-Welch t-test

0.0660-2.14200.04350.0711-2.55120.017819

8
7
6

Density

5
4
3
2
1
0
-.20

-.15

-.10

-.05

.00

.05

Histogram

.10

.15

.20

.25

KernelFigure 1 Frequency distribution of short-run coefficients

20

.30

7
6

Density

5
4
3
2
1
0
-.20

-.15

-.10

-.05

.00

.05

.10

.15

.20

.25

.30

.35

High market openness (Kernel)
Low market openness (Kernel)(a) Low market openness vs. high market openness

6

5

Density

4

3

2

1

0
-.2

-.1

.0

.1

.2

.3

High composition risk (Kernel)
Low composition risk (Kernel)(b) Low composition risk vs. high composition risk
Figure 2 Frequency distributions for each subsample

21.4