Upswings of Hong Kong Real Estate Prices in Post-Crisis Surge:
Quantitative Easing and REIT Markets Developments
By
Tatsuyoshi Miyakoshi a, Kui-Wai Li b, Junji Shimada c, Yoshihiko Tsukuda dAbstract
This paper examine whether the quantitative easing policy conducted in Japan, EU and the US raised
the real estate prices in Hong Kong through the development in the Hong Kong REIT market. The
empirical results show that the quantitative easing policy affected the price of real estate, and the
H-REIT prices led to a rise in office price after a lag of two periods. This finding appears in a carrytrade senior, which seems to be a plausible senior. Moreover, this finding is sharper in the high grade
office price than in the low grade office price. These findings imply that the quantitative easing
policy spillover did not restrict only to the money market, but also markets in the real sectors
immediately through the development in the REIT market. Also, the quantitative easing served as
inflow of hot money that had a greater spillover effect on higher office grade than the lower officegrade.

Keywords: Quantitative Easing; REIT market developments; real estate prices; carry trade.
JEL Classification Numbers: G01; G12; O16aCorresponding author: Tatsuyoshi Miyakoshi, Department of Industrial and System Engineering, Hosei

University,

3-7-2,

Kajino-cho,

Koganei,Tokyo,

184-8584,

Japan.

E-mail: miyakoshi@hosei.ac.jpbDepartment of Economics and Finance, City University of Hong Kong, Hong Kong.

c

School of Business, Aoyama-Gakuin University, Japan.

dGraduate School of Economics and Management, Tohoku University

1

Tel:

042-387-6352,

Abstract
This paper examine whether the quantitative easing policy conducted in Japan, EU and the US raised
the real estate prices in Hong Kong through the development in the Hong Kong REIT market. The
empirical results show that the quantitative easing policy affected the price of real estate, and the
H-REIT prices led to a rise in office price after a lag of two periods. This finding appears in a carrytrade senior, which seems to be a plausible senior. Moreover, this finding is sharper in the high grade
office price than in the low grade office price. These findings imply that the quantitative easing
policy spillover did not restrict only to the money market, but also markets in the real sectors
immediately through the development in the REIT market. Also, the quantitative easing served as
inflow of hot money that had a greater spillover effect on higher office grade than the lower officegrade.

1.

Introduction
Simply defined, the quantitative easing (QE) policy was aimed to increase the monetarybase through the buying of the government bonds or assets so as to induce lower interest rates.
Recent studies have focused on the impact of the QE policy on the stock markets in advanced
industrialized countries. Since the 2008 crisis, however, studies in Japan, European Union (EU) andthe US have examined the international spillovers of the QE policy on the stock and bond markets.
For example, Lim and Mohapatra (2016) and Duca et al. (2016) examined the bond prices,
Tillmann (2016) found significant effects on the stock prices of developing and developed country.
Brana et al. (2012) also found that excess liquidity at the global level has spillover to the output andprice in Asian and other emerging countries, while the impact on the real estate price in emerging
countries is less clear. Engsted et al. (2016) used the quarterly data of 18 OECD countries from 1970
to 2013 to study the housing market bubbles, while Vansteenkiste and Hiebert (2011) studied the2housing price increase in the Euro area. However, these studies did not touch on the relationshipbetween housing price bubbles and the QE policy,
According to the International Monetary Fund report (IMF, 2014, p. 35) the housing prices
in Hong Kong, Malaysia, Singapore and Taiwan had increased rapidly since the 2008 crisis. Suchmovement in real estate price corresponded closely to the development of the Real Estate Investment
Trust (REIT) markets in Asia, as shown in Figure 1. Indeed, the REIT outstanding in Singapore andHong Kong has, respectively, ranked the 7th and 8th world largest, as seen in Table 1. The REIT
provides to its beneficiaries a stream of income derived from rents with long contracts that work
similar to real estate ownerships. Since the REIT can diversify its risk in various trust funds traded in
the market, the REIT effectively incorporates the merits of stock investment into real estate
investment. As such, the developments in the Asian REIT could have enabled the rise in housing
price by the finance shock of capital inflow through the REIT markets in the post-2008 crisis period.

Figure 1 Market Capitalization in the Asian REIT Market (US$ million)
60000
50000
40000
30000
20000
10000
0
01/04

01/06

Singapore

01/08

01/10

Hong Kong

TaiwanSource: Compiled by authors based on S&P data.

3

01/12

01/14
Malaysia

The purpose of this paper is to ask whether the QE policy in Japan, EU and the US has
increased the real estate prices in Hong Kong through the development in the Hong Kong REIT
(H-REIT) market. We use the H-REIT monthly price data from January 2005 to December 2014
mainly because Hong Kong is the only Asian economy that has a complete set of housing, shopping
mall and the various grades of office price data. Through the use of the H-REIT development, thisstudy shall clarify whether the QE policy would spillover to influence the real estate market,
including the impact on different grades of offices.
Section 2 briefly reviews the data used and the development of the H-REIT markets since2005. Section 3 describes the econometric methodology. Section 4 shows the preliminary analyses,
Section 5 reports the empirical results and examines how the real estate price and the stock price areaffecting the H-REIT prices from the two events in 2008 and 2011.

Section 6 presents theconcluding remarks.

2.

Data and Overview
The H-REIT was first listed in the Hong Kong Stock Exchange in November 2005, and by

November 2014, a total of 12 H-REITs are listed. As of November, 2014, the H-REIT market has
become the third biggest in Asia following Japan and Singapore, and is the world’s 8th largest in theamount of outstanding (see Table 1). The share of the US-REIT in the world reached 60.2% at theend of November 2014, which surpassed the second largest of Australia (6.3%) and the third of Japan
(6.1%). The world’s largest eight REITs occupied 93% of the world total, as shown in Table 1.

4

Table 1 REITs Outstanding in the World Market (End of November, 2014)
Order
1
2
3
4
5
6
7
8

Country

Number of REITs

USA
Australia
Japan
FranceUK
CanadaSingapore
Hong Kong
Others
Total

Market Capitalization(US$ million)
825,493
86,16984,10075,041
66,06955,54952,04926,812
100,493
1,371,775

231
52
46
33
22
49
37
12
183
665

World Ratio (%)
60.2
6.3
6.1
5.5
4.8
4.0
3.8
2.0
7.3
100.0

Source: MSCI ACWI REIT Index Fact Sheet.

As seen in Figure 2, the total return (TR) of H-REIT that includes the H-REIT price anddividend has increased rapidly from 100 to 500 index points in six years between September 2008
and end of 2014. Corresponding to the increase of the H-REIT(TR) price, the office price in Hong
Kong also has a three-fold increase. Similar to other REITs, the H-REIT has various types of realestates in its portfolio, including offices, houses, commercial facilities and hotels, and the unified orcomposite type that includes several kinds of real estates based on assets holding. Thus, besides theprice of office, the prices of houses, commercial facilities and hotels also increased.
The US and EU have implemented QE policy by increasing the supply of money basebeginning from September 2008. In the case of Japan, after conducted the QE policy between 2000
and 2006, the QE policy was redeployed in 2013. Studies, such as Tillmann (2016), have shown thespillover effects of the QE policy to both developed and developing countries. In the case of Hong
Kong, the spillover effect from QE to the stock price can visually be seen in Figure 2. However, theworld REIT markets shown in Table 1 and Figure 1 are relatively young markets when compared tothe more matured stock markets. Although the H-REIT has been established in 2005 and there isstrong evidence based on the visual presentation, the spillover effect has to be confirmed using
strong analytical methodology.

5

Figure 2 Office, REIT(TR), STOCK Price Index in Hong Kong and Base Money(a) REIT(TR) Price

600

500.0
400.0
300.0
200.0
100.0
0.0

400
200

Trillion Yen

5000
4000
3000
2000
1000
0

300
250
200
150
100
50
0
1/31/05
10/31/05
7/31/06
4/30/07
1/31/08
10/31/08
7/31/09
4/30/10
1/31/11
10/31/11
7/31/12
4/30/13
1/31/1410/31/14(c) Stock Price (TR)Billion US$, Euro (d) Base_Money

1/31/05
10/31/05
7/31/06
4/30/07
1/31/08
10/31/08
7/31/09
4/30/10
1/31/11
10/31/11
7/31/12
4/30/13
1/31/1410/31/14

100000
80000
60000
40000
20000
0

1/31/05
10/31/05
7/31/06
4/30/07
1/31/08
10/31/08
7/31/09
4/30/10
1/31/11
10/31/11
7/31/12
4/30/13
1/31/1410/31/14

1/31/05
11/30/05
9/29/06
7/31/07
5/30/08
3/31/09
1/29/10
11/30/10
9/30/11
7/31/12
5/31/13
3/31/140(b)Hong Kong Office Price

US_BaseM

Billions Euros

JP_BaseMoney

The discussion on how the QE has affected the stock prices of Hong Kong is far from
adequate. The investigation in this paper includes the use of a carry trade scenario proposed by
McKinnon (2011, 2012). This scenario appears when large interest rate differentials have given riseto investment in assets with high interest rate returns or dividends while the investment funds wereborrowed at low interest rates. For comparison, the lending rates of Hong Kong, Japan, US and EU
are shown in Figure 3.

6

Figure 3 Lending Rates
9
8
7
6
5
4
3
2
1
0

2005M1
2005M5
2005M9
2006M1
2006M5
2006M9
2007M1
2007M5
2007M9
2008M1
2008M5
2008M9
2009M1
2009M5
2009M9
2010M1
2010M5
2010M9
2011M1
2011M5
2011M9
2012M1
2012M5
2012M9
2013M1
2013M5
2013M9
2014M1
2014M5
2014M9%China,P.R.:Hong Kong

3.

Japan

United States

Germany

Econometric Methodology
To examine quantitatively the dependency (co-movement) of the H-REIT price, the stock priceand the real estate price expressed in logarithm, we assume that the data used in Yt = ( y1,t , ,y3,t ) 'are possibly generated from a nonstationary VAR(p) model with constant terms of A0 , Γ, Φ , theexogenous variables ∆X t −1 =( ∆x1,t −1 , ,∆x 3,t −1 ) ' and the Gaussian errors ε t :1

Y=
A0 +
t

p

∑ A Y + Φ∆X + ε , =t 1,,T .
i

t -1

t-i

t

i =1

(1)

Equivalently expressed in an error correction form, the model becomes:∆Yt = A0 +p -1

∑ Γ ∆Y + Π Y + Φ∆X
i

t-i

t-i

t -1 +

εt ,

(2)

i =1

1

The H-REIT price will be given by the discounted sum of future cash flows of real estate. The future cash

flows are generally affected by the past housing price index and stock price index, which are assumed to be a
linear function of those variables. Other factors affecting the H-REIT price are included in stochastic term. Most
empirical studies in time series analysis for stock markets use VAR model.
7

p

p

i =1

j =i +1

where Π = I − ∑ Ai and Γi = − ∑ A j (i = 1, ..., p − 1) . Johansen (1991) showed that if Yt is
cointegrated, the coefficient matrix Π has a reduced rank of r and can be represented as Π = αβ ' ,where α αnd β are n × r matrices. We test the rank of Π in order to determine the precise data
generating process.
On the basis of preliminary analysis for cointegration (‘non-cointegration’ shown inAppendix 1, we apply the following data generating process that consists of the H-REIT price return,the stock price return and the real estate price return: 2∆Y=
A0 + Γ1 ∆Yt-1 +Φ∆X t -1 + ε t . t = 1, … , T.
t

(3)

The error term ε t follows a multivariate EGARCH model with constant conditional correlation(CC) proposed by Bollerslev (1990) as ε t Ω t −1 ~ N (0, H t ) , where Ω t −1 denotes the information set
up to time t – 1. The variance–covariance matrix (H t ) is factorized into the product of variance andcorrelation matrices:RH=
t = D t RD t ,1 ρ12 ρ13 = ρ12 1 ρ 23  , Dtρ ρ 13 23 1  h11,t

 0

 0


0
h22,t
0

0 

0 ,

h33,t (4)

where Dt = diag ( h111,t2 ,, h133,t2 ) is a diagonal matrix of variances, and R is an 3 × 3 constantcorrelation matrix.3 The conditional variance of the ith element follows a univariate EGARCH model
as explored in Nelson (1991):

2

The Augmented Dickey–Fuller (ADF) tests for a unit root show that all data are I(1) processes. The
Schwartz Information Criterion (SIC) indicates a lag length of p = 2 in (1). There are no cointegration
relationships, which is resulted from the trace test. Moreover, for any lag length p=2, 3, 4, and 5, there are nocointegration relationships. Based on these observations, we apply the model in equation (3). See Appendix 1.
We found that the estimated ρ12 , ρ13 and ρ 23 yield the positive definite of conditional covariance matrix,
Ht .
8

3

log(h ii,t ) =α i0 + α i1 (| ui,t-1 | - E (| ui,t-1 |)) + βi1log(h ii,t-1 ),where u i,t-1 =e i,t-1/ h ii,t-1 for i = 1,2,3(5)

where α i2 < 0 in α i2ui,t-1 showing asymmetrical effects are excluded for a parameter saving. In fact, wecannot find asymmetrical effects in two of the equations. The CC-EGARCH approach can besummarized as follows. The conditional variance is formulated in Equation (5), and the constantcorrelation coefficients are formulated in Equations (4). 4 Using the conditional variance and theconstant correlation coefficients yields the conditional variance covariance matrix (H t ). The maximum
likelihood method is applied to estimate the correlation coefficients ρ , the conditional variance
α i0 ,α i1 ,α i2 , βi1 for i = 1, 2,3 , and additional parameters in the mean equations in Equation (3). The fullset of parameters for both the mean equations in Equation (3) and the multivariate CC-EGARCH
specification in Equation (4) and Equation (5) is denoted by θ . Based on a sample of size T, thelog-likelihood function L is

∑{

}

1 T
−1
L (θ ; ∆Y1 , ..., ∆Yt ) =−log(2π ) + log det( H t ) + ee
t Ht
t ' .
2 t =1(6)

We calculate several coefficients using the CC-EGARCH model. The sequence of verificationis shown as follows:1.

In Section 4, we describe the statistics of the data and their sources. Tables 2 and 3 report theresults.

2.

In Section 5.1, we use the maximum likelihood method and estimate the parameters of θ for
the CC-EGARCH model in Equations (3), (4) and (5). The results are reported in Table 4 forthe causality between the H-REIT, stock and real estate (office prices), together with Japan,
the US and the EU QE policy’s spillover effects. The estimated results show the determinants

4

The dynamic conditional correlation (DCC) proposed by Engle (2002) was applied, but we found that thecorrelation was not dynamic but constant. These results are not shown in the text.
9

of the office price. The spillover effects of QE policy appears in the H-REIT price, whichcauses the office price with some time lags. If so, the development in the new H-REIT market
created a new channel for the QE policy to spillover to the real estate price. Thus, thefinancial shock could have an immediate impact on the real economy.
3.

In Section 5.2, we show that a carry trade scenario proposed by McKinnon (2011, 2012)
appears. Each lending rate is shown in Figure 4. The results are reported in Table 5.

4.

In Section 5.3, we use the data for the office price and housing price differently depending on
the grade. These results will show which grade is affected by the QE policy with higher orlower grade prices. The results are reported in Table 6.

4.

Data Description and Preliminary Analysis
The monthly data comprise of the H-REIT(TR) price index, the STOCK(TR) price index and

Office Prices index for the period between January 2005 and December 2014, compiled by using the
S&P Capital IQ dataset. We also consider the monthly monetary base of Japan (line:14...ZK.; trillionyen.), US (line:19MA.ZF; billion US$), and the EU (line: 14..UZK; billion Euro) obtained from theInternational Financial Statistics. We use the following notation.
∆y1,t :H-REIT(TR) price index return at time t;∆y2,t :STOCK(TR) price return at time t;∆y3,t :

Office price index return at time t;∆x u ,t :US monetary base growth at time t;∆x j ,t :Japan monetary base growth at time t; and∆X e,t :Euro monetary base growth at time t.

10All variables are logarithmic differenced. Figures 2(a), 2(b) and 2(c) show the data for the
H-REIT(TR) price index, office price index, stock(TR) price index, respectively. The exogenous
variables of the monthly monetary base of the US, Japan and the EU are shown in Figure 2(d). We
observe the following characteristics: (i) all variables have changed along with each other over time;and (ii) all variables increased rapidly after the global financial crisis in 2008–2009. Table 2 reports
the descriptive statistics for the price index returns, shown as: ∆Yt =( ∆y1,t , ∆y2,t , ∆y2,t ) ' , and confirms
the stylized facts on asset return in the form of highly significant skewness, high kurtosis, andsignificant autocorrelations in squared yields. Table 3 shows the contemporaneous unconditionalcorrelations between the different returns. The order of the three market returns are shown according to
the degree of correlation with the H-REIT(TR) price index returns. The STOCK(TR) price index
return has the highest correlations, while the office price index return has lower correlations.
Table 2 Descriptive Statistics for the Price Index Returns
Mean Std. dev Skew Ex-Kurt Q(4)Q(4)-2
H-REIT(TR) Price
1.17
5.51
-1.31
4.53
1.00
3.38
STOCK(TR) Price
0.81
6.24
-0.79
2.87
3.52
13.22
Office Price
1.12
2.89
-0.30
4.30
40.60
13.02
Notes: Q(4) denotes the Ljung–Box statistic with four lags for the log-difference variable, andQ(4)-2 denotes the corresponding statistics for the squares of those variables. The 10% criticalvalue of the Q(4)-statistic is 7.78.

Table 3 Contemporaneous Unconditional Correlations between Price Returns
H-REIT(TR) Price
STOCK(TR) Price
Office Price

5.

Empirical Results

5.1.

Parameter Estimates

H-REIT(TR) Price

STOCK(TR)Office Price

1.00
0.61
0.34

1.00
0.39

1.00

Based on the preliminary analysis shown above, we estimate a VAR model from Equations(4), (5) and (7), shown as:
11

DY=
A0 + Γ1DYt -t
1 + Φ D DX t 1 + ε t : ε t Ω t-1 ~ N ( 0, H t ) , t = 1, … , T
 γ 1R , γ 1S , γ 1O Γ = γ 2 R , γ 2 S , γ 2O  , Φ=Dγ , γ , γ  3 R 3 S 3O  1,
Dt = 
 0,

 φ1U + φ1′U Dt′ φ2U + φ2U Dt
 φ + φ′ D
3U t
 3U

(7)

φ1J + φ1′J Dt , φ1E + φ1′E Dt  ∆x u ,t 

φ2 J + φ2′ J Dt φ2 E + φ2′E Dt  , ∆X t-1 = ∆x j ,t  , ∆x 
φ3 J + φ3′J Dt φ3 E + φ3′E Dt e ,t 


if Sep.2008 ≤ tother

The parameters are γ 1R (H-REIT to H-REIT), γ 1S (STOCK to H -REIT) and γ 1O (Office to H -REIT) . The
results in Table 4 reveal the first finding that all the estimated EGARCH terms, a i1 and βi1 inEquation (5), as well as the constant conditional correlations ρ12 , ρ13 and ρ 23 in Equation (4) aresignificant. 5

Moreover, when compared to the alternative model, namely the Dynamic Conditional

Correlation-EGARCH (DCC-EGARCH), we found that the dynamic conditional correlations forρ12,t , ρ13,t and ρ 23,t are constant over time by using both statistical test and visual inspection (notshown in the paper). This implies that the CC-EGARCH specification is valid for modeling priceindex returns. The results in Table 4 reveal the findings for causality. The significant-causality from
H-REIT to office price,=γ 1R 0.120 > 0 , is shown at 5% significance level. The adverse direction does
not hold, while we can find stylized fact such that the causality from stock to office price as pointed
out in other studies.
One interesting finding is that the increases of base money in Japan and the EU have apositive causality on the H-REIT(TR) price in pre-2008 period: φij =1.222 and φiE =0.155. In the
post-2008 crisis period, however, the effects of monetary base by Japan and the EU show a positive
but smaller price increase on the H-REIT(TR) price: φij +φ’ ij =1.222-1.011=0.211 and φiE +φ’ iE=0.155-0.140=0.015. On the other hand, the increases of the monetary base in Japan and the EU have

Note that the asymmetric effect of volatility α i 2 <0 is not found in the H-REIT, Stock and Office market. We
found that the estimated ρ12 , ρ13 and ρ 23 yield the positive definite of conditional covariance matrix, H t . Tables 4
to 6 show the positive definite for H t .
125

the same effects on the office price increase, which is the direct effect on the office price, but issmaller than the effect on the H-REIT(TR) price. The result implies that the QE policy in Japan andthe EU cause the H-REIT price to increase more than the office price, while the indirect effect on theoffice price through the H-REIT price is relatively large. Thus, the H-REIT price increase contributes
an important role to the increase of office prices in Hong Kong. In Equation (7), we include the lag
three variables 0.026 for ∆y1,t −1 , 0.008 for ∆y1,t − 2 , 0.120 for ∆y1,t −3 in the third equation, ∆y3,t , which
gives an equation on the office price. Since it is only the coefficient of 0.120 in the REIT price∆y1,t −3 is significant, we then denote 0.120** in Table 4. We conclude that impact of the H-REIT

price on the office price could have some time lag when compared to the stock price.

Table 4 Parameter Estimates of CC-EGARCH Model
H-REIT
STOCK
OFFICE

γ iR

γ iS-0.275**0.233**(-4.48)(5.10)-0.292**0.262**(-2.98)(2.65)

0.120**

0.065**(8.77)(3.06)

γ iO

φ iu

φ ij

φ iE

1.222**

0.155**(-6.94)(3.16)(2.95)

0.215 -0.600**

1.348**

0.026(-4.56)(3.53)(0.311)(2.69)(-3.21)(-3.14)0.311** -0.086**

0.388**

0.054**

0.019 -0.375**-0.022(3.21)(4.75)(0.78)(-1.00)0.089 -0.625**(0.83)(1.63)(6.45)(3.46)

φ’ iu

φ’ ij

φ’ iE

0.350** -1.011** -0.140**(3.48)(-2.59)(-2.69)0.298** -1.460** -0.236**(-2.88)Notes: T-statistics are shown in parentheses. * and ** are significant at 10% and 5% level, respectively.

5.2.

A Carry Trade Scenario
We construct a VAR model from Equations (4), (5) and (8).

DY=
A0 + Γ1DYt -t
1 + Φ D DX t 1 + ε t : ε t Ω t-1 ~ N ( 0, H t ) , t = 1, … , T
 γ 1R , γ 1S , γ 1O 
 φ1U



Γ1 = γ 2 R , γ 2 S , γ 2O  , Φ =φ2U
φ
γ , γ , γ 
 3U
 3 R 3 S 3O 

φ1J
φ2 J
b3 J

φ1E 

 ∆x u ,t  b2 E  , ∆X t-1 = ∆x j ,t  , ∆x φ3 E 
e ,t 
13(8)

where∆ x ≡ max(0, Hong Kong lending Rate − US lending rate) * ∆x
u ,t

u ,t

,

∆ x j ,t ≡ max(0, Hong Kong lending Rate − Japan lending rate) * ∆x j ,t ,∆ x e,t ≡ max(0, Hong Kong lending Rate − EU lending rate) * ∆x e,tEquation (8) is different from Equation (7) in a sense that there are no dummy variable for the
post-2008 crisis, and inflow of monetary base of the US, Japan and the EU to Hong Kong are relative
to the lending rate spreads. However, the large increase in the lending rate spreads after the 2008
crisis can be used as the dummy variables for Φ D in Equation (7). The lending rate spreads are
positive since the post 2008 crisis.
Table 5 reveals the impacts from the QE policy through the H-REIT market. The H-REIT
price determinant was the Japanese monetary base adjusted by the lending rate spread (Japan carrytrade) between Japan and Hong Kong (significantly positive φ1 j = 0.095 >0) and not the US carrytrade. Japan’s lending rate spread of 4% was much larger than the US’s lending rate spread of 2%, asseen in Figure 4. The extent of increase from the H-REIT price to the office price can be seen fromthe coefficient γ 3 R = 0.123 . In general, the coefficients are similar to those shown in Table 4. As such,the dummy variables in Equation (8) can also be considered by the lending rate spreads. The stylized
fact that the stock price increases the real estate price can be found by examining the coefficientγ 3S = 0.068 .

InEquation(8),

weinclude

the

lag

three

variables

such

that

0.015 for ∆y1,t −1 , 0.048 for ∆y1,t − 2 , 0.061 for ∆y1,t −3 in ∆y3,t that show the equation for the office price.

The coefficients of the REIT price ∆y1,t −1 , ∆y1,t − 2 , ∆y1,t −3 are all significantly positive, giving a sum of0.124.

14

Table 5 Parameter Estimates with a Carry Trade
γ iR

γ iS

γ iO

H-REIT-0.242** 0.183**(-5.60)(3.73)

STOCK-0.265**(-4.04)

OFFICE

0.124** 0.068**(7.73)(7.13)

φ iu

φ ij

0.094 -0.131** 0.095**(0.73)(-4.14)(2.81)

0.146*(1.69)

0.378**(2.48)

φ iE
0.023(0.59)-0.181*(-1.85)0.057(1.58)-0.082(-1.51)0.343** -0.037**(9.15)(-3.67)-0.002(-0.22)-0.006(-0.37)

Notes: T-statistics are shown in parentheses. * and ** are significant at 10% and 5% level, respectively.

5.3.

The QE Effects on the Different Grades of Offices
We investigate the spillover effects of QE on the price of different office grades (A or C) in

Hong Kong. In Hong Kong, grade A offices are located in prime areas, while grade C offices arelocated in the outskirt of the prime office areas. The data for both grades A and C are shown in Figure
4. The hot money inflow through the QE policy could play a speculative role. If so, the spillover effectwould be that the price of grade A office is higher than both the grade C office and the overall price(used in Table 4). Next, we similarly use the VAR model shown in Equations (4), (5) and (8) andreplace the overall office data by the office grade A and C data as ∆y3,t .

Figure 4 Hong Kong Office Price Indexes: Grades A and C
500.0
400.0
300.0
200.0
100.0
0.0

Grade C

1/31/05
10/31/05
7/31/06
4/30/07
1/31/08
10/31/08
7/31/09
4/30/10
1/31/11
10/31/11
7/31/12
4/30/13
1/31/1410/31/14

Grade A

1/31/05
9/30/05
5/31/06
1/31/07
9/28/07
5/30/08
1/30/09
9/30/09
5/31/10
1/31/11
9/30/11
5/31/12
1/31/13
9/30/13
5/30/14

500.0
400.0
300.0
200.0
100.0
0.0

15

Table 6 reveals the impacts from the QE through H-REIT market on the office price ofgrades A and C. Based on the high grade A office price data, the H-REIT price determinant in Table
6(a) is Japan’s monetary base, with a significantly positive coefficient( φ1 j = 0.111 ). This is larger thanthe coefficient by using a low grade data in Table 6(b) ( φ1 j = 0.083 , insignificant), implying the highgrade A office price has a strong effect from the QE spillover. One can consider the impact of the QE
in that the extent of the H-REIT price increase on the high grade A office price can be seen from thecoefficient of γ 3 R = 0.238 , which is larger than the low grade C office coefficient of γ 3 R = 0.053 . In
addition, the QE policy did not affect the H-REIT price as seen in Table 6(b) as φ1u= -0.082 , φ1j= 0.083 and φ1E= 0.031 are insignificant. Thus, one can conclude that the QE policy would easily

spillover to the high grade A office price, implying that the QE policy led to hot money inflow.

Table 6(a) Parameter Estimates for Hong Kong Office Price Indexes: Grade A
γ iR

γ iS

γ iO

φ iu

φ ij

φ iE

H-REIT-0.244** 0.185**(-6.49)(2.58)-0.046 -0.126** 0.111**(-0.91)(-1.52)(2.63)

0.004(0.09)

STOCK-0.269** 0.183**(-4.46)(4.18)-0.023(-0.32)-0.171(-1.55)0.067(1.42)-0.124**(-2.19)OFFICE

0.238** 0.107**(6.53)(3.22)

0.068(1.32)

0.066**(2.29)

0.012(0.60)

0.014(0.71)

Notes: T-statistics are shown in parentheses. *and** are significant at 10% and 5% level, respectively.

Table 6(b) Parameter Estimates for Hong Kong Office Price Indexes Grade C
γ iR

γ iS

γ iO

φ iu

φ ij

φ iE

H-REIT-0.207* 0.204**(-1.84)(2.55)

0.158(1.18)-0.082(-0.85)0.083(1.53)

0.031(0.45)

STOCK-0.267** 0.200**0.110-0.1330.048-0.099(2.03)(0.77)(-1.20)(0.92)(-1.10)0.053** 0.072**

0.285**

0.021 -0.046**-0.019(0.52)(-0.66)(-2.75)
OFFICE(1.86)(3.07)(4.64)(-1.97)

Notes: T-statistics are in parentheses. *and** are significant at 10% and 5% level, respectively.

16

6.

Concluding Remarks
This paper examined whether the quantitative easing policy conducted in Japan, EU and the

US raised the real estate prices in Hong Kong through the development in the Hong Kong REIT
market. The empirical results show that the quantitative easing policy affected the price of real estate,
and the H-REIT prices led to a rise in office price after a lag of two periods. This finding is supportedby the appearance of a plausible carry trade scenario. Moreover, this finding is sharper in the highgrade office price than in the low grade office price. The empirical study imply that the quantitativeeasing policy spillover did not restrict only to the money market, but also markets in the real sectors
immediately through the development in the REIT market. Also, the quantitative easing served as
inflow of hot money that had a greater spillover effect on higher office grade than the lower officegrade.

17

Appendix 1

Unit Root and Cointegration Analysis

Before estimating the data generating process of VAR model in (1) equivalently of ECM
model in (2), we must check the stationarity of our time-series data, and further test whether there arecointegrating relationships among those variables if they are not stationary. Table A1 reports theAugmented Dickey–Fuller (ADF) tests for a unit root. All price index are integrated of order one, i.e.,
I(1) except for the test with τ µ for J-Housing, which concludes all variables I(1).
Table A1. ADF Tests for Unit RootADF Test statistic δ

Lag lengthy 1t(H-REIT)

y 2t(STOCK)

y 3t(H-Housing)Level

1st Difference

5%

CriticalLevel

1st Difference

τ

1

1

2.212 -6.883**-1.943τµ1

10.406 -7.276**-2.882ττ

1

1-1.944 -7.395**-3.442τ

1

1

1.218 -6.263**-1.943τµ1

1-1.498 -6.356**-2.882ττ

1

1-2.571 -6.331**-3.442τ

1

12.654 -4.927**-1.943τµ1

1-0.241 -5.424**-2.882ττ

2

1-2.352 -5.399**-3.442

Value

Note: For each time series, we specify the model as∆yt = μ + t ⋅ t +p −1∑ γ ∆y + δ y + ε ,
i

t −i

t −1

t

(

ε t ~ N 0, σ 2

i =1

)

The ADF statistic tests the hypothesis H 0 : δ = 0 vs H 1 : δ < 0 . Similarly, we carry out ADF
tests for the log-difference variables. MacKinnon (1996)’s 5% critical values are shown. The Schwartz
Information Criterion (SIC) is used to choose the lag length. * and ** indicate the level of significanceat 10% and 5%, respectively.
We analyze the price indexes by using the model described in Section 3, in which the threevariables for the J-REIT index, TOPIX and J-Housing Prices index (with the difference of logarithm)
are denoted by Yt = ( y1,t , ,y3,t ) ' and Gaussian errors ε t . It is equivalently in an error correction
form is as in (3). We specify the model as:

∆Y=
A0 +
t

p -1

∑ Γ ∆Y + αβ ' Y + Φ∆X
i

t-i

t-i

t -1 +

i =1

18

εt ,

(A1)

The lag lengths are determined by the Schwartz Information Criterion (SIC). In Table A2,
the minimum value of SIC attains at an optimal lag length of p = 2 out of two to five. We may safely
conclude that the data generating process follows the model of (A2) with p = 2.
Based upon this finding, we test the hypothesis; H 0 : rank ( β ) = r against H1 : rank ( β ) = 3 byusing Johansen’s max-eigen value test. 6 The third column of p = 2 in Table A2 reveals that there isno cointegrating relationship for any countries at the 5% level. For checking the robustness of thisresult, we carry out the trace test for the models with lag length of two through five. As shown in
Table A2, there is no cointegrating relationship for any lag length at the 5% level. The result of
cointegration test is quite robust against the lag length for the model of (3). Preliminary analysis of
this study justifies the model of equation (3) in Section 3 for subsequent analysis.
Table A2. Cointegration Tests
Lag
SIC
H 0 : r = 0 vs H 1 : r = 1(5% Critical Value = 35.19)
Trace Statistics

P-Value

Resulting rank

p-1 = 1p-1 = 2-19.06***-18.92p-1 = 3-18.62p-1 = 4-18.47

29.11

25.14

23.68

24.78

(0.066)(0.161)(0.221)(0.175)

0

0

0

0

Note: The symbol “***” in SIC denotes the minimum SIC. * and ** indicates the level of significance at 10%
and 5%, respectively. Figures in parentheses are p-values (see Mackinnon et al., 1999, p.572) .

6

Strictly speaking, neither the ADF test nor the Johansen’s test is applicable. This is because the
CC-EGARCH-ECM (Constant Correlation EGARCH Error Correction Model) does not satisfy theassumption of independent identically distributed normal errors. However, for simplicity, we ignore this issue(see Seo (2007) for details).
19

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