Discussion Paper No. 76
Condominium Prices and Inflation:
The Role of Financial Inflows and Transaction
Volumes in Japan
Jun Nagayasu

March , 2018

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANCondominium Prices and Inflation:
The Role of Financial Inflows and Transaction
Volumes in Japan
Jun Nagayasu‡

Abstract

We investigate the dynamics of condominium prices by using a new dataset containing national and regional data on prices and disaggregate transaction volumes forJapan. In particular, we are interested in the role of capital inflows and transaction
volumes, which have recently been discussed worldwide as important determinantsof property prices. First, by using the multivariate cointegration method, we showthat the condominium market has not experienced real estate bubbles since 2008.
We document several economic fundamentals; notably, real income, mortgage rates,
and capital flows, have influenced the long-term trend in condominium prices. Second, on some occasions, we find that condominium price inflation can be explainedby transaction volumes, which are positively linked to information inflows, consistent with the market microstructure model. Transaction volumes influence priceinflation in Tokyo, in particular at times of high market activities.
Keywords: Real estate market, condominium price, transaction volume, unit root,
cointegration, JapanJEL classification: R2, C5‡

Tohoku University, Graduate School of Economics and Management, Address: 27-1 Kawauchi, Aobaku, Sendai, Miyagi 980-8576 JAPAN. Email: jun.nagayasu.d8@tohoku.ac.jp, Tel: +81 22 795 6265, Fax:
+81 22 795 6270. The earlier version of this paper was presented at the Housing Research & Advanced
Foundation of Japan as well as the biannual meetings of the Japanese Economic Association and theJapan Society of Monetary Economics. I made substantial changes to the previous version circulatedas ”The bubbles and inflation in the Japanese condominium market”, and the first two sections of thispaper are generally the same as the ones in the early version. I would like to thank Kei Harano, MikiSeko, Kazuto Sumita, Masayuki Susai and Norifumi Yukutake for useful comments and discussion. I also
thank Gabriel Cordoba for research assistance.

1

Introduction

The analysis of residential property prices is important for a number of reasons. First,
a place to live is a necessity like food. Second, residential property is often the mostexpensive item that people purchase in their entire lives. Because it forms a significant
part of their wealth, the bursting of real estate market bubbles has catastrophic effects on
the quality of life. Thus, a number of studies have been conducted in order to investigatechanges in the prices of residential properties, especially houses, and the recent macroeconomic analyses consider the real estate market as essential factor in explaining businesscycles and financial crises (Leung 2004, Jorda et al. 2016).
Traditionally, housing prices have been analyzed based on economic fundamentals.
Macroeconomists consider such economic fundamentals as mortgage rates, household income, and housing stock while analyzing housing prices (Ashworth and Parker 1997, Meeseand Wallace 2003, Abelson et al. 2005, Stevenson 2008). More micro-oriented analyses
consider population and the location of residence (Cameron et al. 2006). However, these
economic fundamentals are not always sufficient to explain housing price movements.
During bubble periods, non-economic factors (e.g., consumer expectations) are believed
to have more influence over housing prices than economic fundamentals. Because bubbles
are unobservable, previous studies often have regarded sizable deviations of market prices
from economic fundamentals as evidence of bubbles (Black et al. 2006, Muellbauer andMurphy 2008, McMillan and Speight 2010).
More recent theoretical research has utilized information about the volume of housingtransactions (e.g. Stein 1995, Ortalo-Magne and Rady 2006). Transaction volume isbelieved to explain transitory movements or volatility in housing prices, and is expected
to be positively correlated to housing prices. Following such theoretical developments,
recent research has examined the usefulness of transaction volume in explaining housing
prices in advanced countries such as Finland (Oikarinen 2012), the United States (US)(Akkoyun et al. 2013), the United Kingdom (UK) (Andrew and Meen 2003, Tsai 2014),
and the Netherlands (de Wit et al. 2013). Similarly, capital inflows are discussed asimportant determinants of housing prices in advanced countries (Sa et al. 2014, Sa andWieladek 2015).
Against this background, we investigate the recent price movements of condominiums
2

in Japan, which have not been examined as a main research topic so far, probably because of lack of statistical data available to researchers.1 Although there are several typesof residences, like (detached) houses and multi-family residences (e.g., condominiums orapartments), we focus only on condominiums, which are called “apartments” or “mansions” in Japan, for the following two reasons. First, with a relatively high population
density, living in a condominium is a popular choice, even for couples with children. Indeed, according to the 2011 Population Census (Kokusei-chosa) of Japan, approximately
41.6% of households live in condominiums. This proportion is higher in urban areas suchas Tokyo (67.7%), Kanagawa (54.9%), and Osaka (54.1%).2 The trend of living in condominiums has increased in recent years, while other types of residences have become lesspopular. Second, in line with the popularity of condominiums, their prices have behaved
differently from those of other types of real estate during the past decade. Notably, after
the collapse of the Lehman Brothers (2008), the sharp increase in condominium prices
outpaced other real estate prices. Condominium prices reached a record high (46 million
yen on average) in 2015, and today the condominium market, which seems to be exhibiting early warning signs of a potential bubble, is of great interest to market participants
and analysts.
This paper departs from previous studies since we introduce capital inflows and transaction volumes into the standard pricing model. This modification is motivated by increases in real estate acquisition by non-Japanese people as well as recent research on
changes in the value of other financial assets, such as stocks and exchange rates (e.g.,
Campbell et al. 1993, Lyons 1995, Barron and Karpoff 2004). Previous studies useda market microstructure model, i.e., a Bayesian mechanism through which prices were
updated through an inflow of new private information to traders (O’Hara 1997), andtransaction volume was incorporated in order to link changes in asset prices to traders’information and expectations. Increases in the volume of transactions will raise the proportion of informed traders in the market and thus help them make appropriate financial
decisions, leading to the theoretically predicted price changes. We analyze the relation1

Studies of the overall Japanese residential property market, which includes real estate types otherthan condominiums, are rather limited due, in part, to the lack of data. For example, Adams and Fass(2010) who studied 15 international housing markets did not cover the Japanese market.
2
The proportion of people living in condominiums in urban cities in Japan is higher than the European
average of 41.1% (as of 2013) and the New York average of 51%.

3

ship between condominium price inflation and transaction volume by trader type at bothnational and regional levels in Japan. To our knowledge, this is the first attempt to utilize such disaggregated data. Furthermore, given that price inflation is likely to containextreme values, this relationship will be examined using the quantile regression methodthat estimates the effects of transaction volumes on price inflation at different quantilesof data.

2

Theoretical Determinants of Condominium Prices

The majority of previous studies define financial bubbles as temporary phenomena whereasset prices deviate significantly from economic fundamentals, using the present value
models.3 Based on finance literature on stock prices, the relationship between prices (P )and economic fundamentals (F ) can be expressed in terms of returns (r ) on investmentas:rt+1 = (Pt+1 + Ft+1 )/Pt − 1(1)where subscript t represents time (t = 1, . . . , T ). Eq. (1) is popular in equity priceresearch, and dividends are universally used as economic fundamentals. In terms of prices,
this return equation can be shown with an expectation notation, E, as:Pt = Et

Pt+1 + Ft+1
1 + rt+1

(2)

Eq. (2) shows that future economic values will determine the present prices. Solving thisidentity forwardly to infinity, we can obtain the following dynamic relationship between
prices and economic fundamentals.

Pt = Et

"∞
h 
X Y
h=0

k=0

1
1 + rt+k!
Ft+h + lim

h−→∞

h 
Y
k=0

1
1 + rt+k

#


Pt+h

(3)

In other words, the present prices are determined by the expected values of P and F. Whenthe product of future prices and discount factors does not diminish to an insignificant
level, the market is often considered to have experienced rational bubbles. In statistical
3

Theoretical explanations of movements in condominium prices can be considered as identical to thoseof detached houses since they are economic goods with similar characteristics.

4terminology, this is a case where P and F are not cointegrated. On the other hand, themarket is said to be tranquil when this product becomes insignificant, and cointegration
exists between P and F. To put this concept into practice, it is important to understandwhat composes economic fundamentals.
The simplest form of fundamental determinants of housing prices consists only of rental
costs (Meese and Wallace 1994, Phillips and Yu 2011), mortgage rates (McGibany andNourzad 2004), residential land (Ooi and Lee 2006), or household income (Gallin 2006).
Renting is another alternative to purchasing a house, i.e., an opportunity cost. A mortgage
rate is considered as closely associated with the user cost of residential capital based on
the neoclassical investment model (Kearl and Mishkin 1977). Thus, for a mortgage rate
to have an expected influence on property markets, a country should possess developed
capital and financial markets. While a mortgage rate can be considered to influence
the supply-side as it is closely linked with a variety of interest rates, McGibany andNourzad (2004) argue that its effect is predominantly demand-oriented. Land availability
may be increasingly important in areas with limited landmass and/or heavy government
regulation. Finally, the price-income relationship of houses is closely linked to housing
affordability, which represents the difficulty of purchasing a house.
A pricing model may comprise both demand- and supply-side factors such as real
income and the user cost of capital (e.g., Oikarinen 2012). Other possible variables considered are private sector housing starts (Drake 1993, Ashworth and Parker 1997), realcredit to the private sector (Hofmann 2004), construction costs (Adams and Fass 2010),
employment (Meese and Wallace 2003), and capital inflows (Sa and Wieladek 2015). In
short, there are several variables that can possibly explain housing price movements intheory, but there is little consensus among researchers regarding the exact definition of
fundamental determinants of housing prices.
Moreover, there is no clear theoretical consensus on whether these explanatory variables determine permanent or transitory movements in housing prices. Probably, transaction volume, which is considered a determinant of housing price inflation, is a very rare
exception. The distinction between permanent and transitory factors becomes key in thisstudy since we base our analysis on a concept of integration where a permanent relationship is statistically equivalent to the presence of cointegration, and transitory price5

movements should be captured by stationary variables.
The theoretical relationship between prices (inflation) and transaction volumes has
been developed in the market microstructure model using Bayesian learning. The information prevalence indicates the increased number of informed traders, which results in
expected price changes. Therefore, in the field of finance, the motivation for considering transaction volume is to capture investors’ information that is relevant to short-term
movements in asset prices. Considering transaction volume is also consistent with technical analysts view that trading volume influences asset prices (Karpoff 1987).
While using transaction volumes to explain movements in property markets is relatively rare in the analysis of real estate markets worldwide and has certainly never been
done for Japan, they have been used to explain movements in stock prices and exchange
rates. Most previous studies reported a positive correlation between trading volume andcontemporaneous asset returns. For example, a positive relationship between stock returns and trading volume was reported by Karpoff (1987) and Gervais et al. (2001).
Karpoff stated two stylized facts from previous literature: a positive relationship between
volume and the absolute value of changes in stock prices, and a positive relationship between trading volume and changes in stock prices. Gervais et al. argued that this highvolume premium is created by stock’s visibility, which results in increased demand. Furthermore, trading volume is often discussed in finance literature as having a very highpositive correlation with information; in other words, more information is conveyed toequity investors when trading volumes are high. In exchange rate studies, order flows,which are transaction volumes with signs, are used to explain intraday changes in exchange rates that happen within very short time periods (Lyons 1995, Evans and Lyons
2002).4
Stein (1995) underscored the importance of down-payments among property markets
using transaction volumes, and went on to demonstrate numerically that the positive
relationship between prices and volume is more robust for volatility implications. Furthermore, this relationship is shown to be stronger for repeat buyers who put existinghomes on sale prior to purchasing new ones than for first-time buyers. Follain and Velz
4

In financial research, high frequency (intraday) data are used to capture short-term movements.In
contrast, in this study monthly data are used because that is the frequency of data available. Monthly
data are appropriate for analyzing the real estate market, since trades in the real estate market are muchless frequent compared to equity and foreign exchange markets.

6

(1995) documented the negative relationship between home prices and home sales whendown-payments are significant.

3

Data Summary and Preliminary Analyses

We use a unique dataset that allows us to investigate Japanese real estate markets. Indeed,
until recently, the dataset for Japan’s real estate markets has not been well organized,
which is one of the reasons that Japanese real estate markets were not been fully analyzed
in the past. Only recently, as part of an IMF initiative, did Japan’s Ministry of Land,
Infrastructure, Transport and Tourism (MLIT) start compiling Japanese real estate data
and disseminating them on the MLIT website. These data are available at the nationaland regional levels; however, due to limited data availability on that website and theneed for consistency with other statistical data used in this study, our regional analysis
focuses on four prefectures that are generally large, in terms of landmass and population:
Hokkaido, Tokyo, Nagoya, and Osaka. Of these four prefectures, Hokkaido is the only
one that is not a metropolitan area and where the population is declining (Table 1).
Monthly condominium price data are based on the settlement prices and are available
from April 2008 (see Table 1).5 Further, condominium price indexes (2010=100, Figure1) cover mainly secondary condominiums traded in the private sector and are constructedfrom Hedonic regression considering different characteristics of condominiums such as
the area, size, location, and age of the building. Other considerations are whether thecondominium is renovated and/or south-facing. This figure, with 2010 as a base year
for the price index, shows that condominium prices have been increasing most rapidly
in Hokkaido, a non-metropolitan area where economic recovery from the Lehman Shock(2008) was initiated much later than the metropolitan areas and which now faces a strong
demand for condominiums due to an inflow of people from rural areas.
Figure 2 shows the peculiarity of condominium prices (Condominium) compared toprices of other types of real estate such as residential land (Land), detached houses (House)
and overall residential property (Overall). In particular, inflation in the condominium
market is noticeably higher than inflation in other real estate markets, while prices of5

While empirical results may be sensitive to data frequency (e.g., Berkovec and Goodman 1996,
Akkoyun et al. 2013), we do not focus on the low frequency due to the limited number of observations.

7other types of real estate appear to be more highly correlated among themselves. Increases in condominium prices are notable since those of residential land have been in a
declining trend. This phenomenon is also demonstrated in the correlation matrix (Table
2). Condominium price inflation is plotted by fractional points of data (Figure 3). A
steep slope at low and high fractions of data in this figure presents extremely high andlow inflation that cannot be observed frequently.
Data on transaction volumes for the nation and for the four regions are obtained from
the MLIT. The national level data show that about 13,000 transactions are recorded eachmonth and a third of the total transactions take place in Tokyo (Table 1). Furthermore,unlike the data used by previous studies that analyzed real estate markets of other countries, these data are also available by the type of trader. To be more specific, four types oftransaction volumes are recorded for condominiums: from individuals (Ind) to Ind, fromInd to companies (Com), from Com to Ind, and from Com to Com. Among these fourtypes of transactions, Ind to Com-sale accounts for most of the transactions. Here, companies cover a broad range of general corporations in the private sector. In this study, we
use this information to identify who has contributed most to condominium price inflation.
The aggregate transaction volume is plotted in Figure 4. We see a typical transaction
phenomenon in Japan; there are significant increases in transaction during spring, whichcoincides with business people’s movement at the end of fiscal year (March). However,
we do not observe any notable seasonality in condominium prices in Figure 1.
In addition to real estate data, economic determinants of condominium prices basedon previous literature are gathered. Net monthly migration data for each region of Japanare available from the Japan’s Ministry of Internal Affairs and Communications (MIAC).6
We would expect an increased inflow of people to increase the demand for condominiums
resulting in a rise in condominium prices, all else being constant. For the national level
analysis, we use monthly data on Japanese population obtained from the MIAC.
Data on other variables, such as employee compensation, mortgage rates, and new
housing (condominium) construction-starts are not available at a regional level; in thosecases, we use national data. The number of new condominium construction-starts is
introduced in order to capture supply-side changes in the real estate market. For the
6

The MIAC’s migration data are based on information from local registers (Jumin-hyo).

8mortgage rate, we use the floating rates for city housing loans. The real mortgage rateis then calculated, based on Fisher equation, by subtracting expected inflation from thenominal rate (it ); expected inflation is assumed to be equal to observed inflation (∆pt ,in which p is the consumer price index (CPI) in logarithmic form).7 Monthly CPI at thenational level were obtained from Datastream, and monthly CPI at the regional level wereobtained from Japan’s MIAC.
International capital inflows are considered to capture additional demand originatingfrom foreign countries and are thought to contribute to creation of bubbles in large cities
such as London, New York, and Sydney. As a proxy for cross-border positions of acountry via-a-vis other countries, the Bank for International Settlements (BIS) compiles
claims and liabilities using gross information from banks balance sheets in 43 countries,
mainly advanced and emerging countries. Following Bruno and Shin (2015) who show
an acceleration of global financial flows since 2000, we use data on liabilities as a proxyfor financial inflows from the BIS Locational Statistics. Since our study focuses mainly
on domestic issues, capital flow data are expressed in terms of the Japanese yen and are
re-expressed in real terms using the CPI. All data are available on a monthly basis except
employee compensation and capital flows; monthly employee compensation and financial
flows are converted from quarterly data.8
As a preliminary investigation, we check time-series properties of our data since thepresence of bubbles can be detected by analyzing a linear combination of nonstationary
variables. Table 3 reports the results of the conventional unit root test (i.e., the Augmented Dickey-Fuller (ADF) test) applied to real condominium prices, mortgage rates,
income, and capital flows.9 Determining the appropriate lag length by the Akaike information criterion (AIC), the ADF tests are conducted for both the level and the firstdifference of the data. With the null hypothesis of the unit root against the alternative
hypothesis of the stationarity, we generally find that our key variables are nonstationary
and are indeed integrated of order one, I(1). We fail to reject the null hypothesis for data
in level, but can reject it for the differenced data. Therefore, the standard multivariate
cointegration test (Johansen 1991) can be used to analyze the presence of bubbles in the
7

Changes in inflation formation (i.e., ∆pt+1 ) would not alter the overall conclusion.
A function (cubic-match last) of Eviews 8 is used for frequency conversion.
9
While not reported here due to limited space, other explanatory variables used in the paper are foundto be I(0).
89condominium market. A failure to reject the null for real condominium prices implies
that nominal condominium prices have not moved in tandem with prices of general consumption goods, i.e., the CPI, again pointing to peculiar movements in condominium
prices.
Finally, Table 4 reports a causal relationship between condominium price inflationand transaction volumes. There is evidence of unidirectional causality from transaction
volumes to condominium price inflation. This is determined by Granger non-causality
tests, which reject the null hypothesis that transaction volumes do not affect price inflation
but fail to reject the null hypothesis that condominium price inflation does not influencetransaction volumes. However, this unidirectional causality can be found only in aggregate
data, and disaggregate data do not show clear causal relationships.

4

Empirical Results

4.1

Condominium Prices and Economic Fundamentals

We use time-series methods; in particular, the relationship between condominium prices
and their determinants is investigated by use of cointegration (Engle and Granger 1987).
The cointegration method has been applied in the analysis of housing markets by a number of researchers (Hendry 1984, Meese and Wallace 2003, McGibany and Nourzad 2004,
Gallin 2006, Adams and Fass 2010, Oikarinen 2012, De Wit et al. 2013). Given that
housing prices have often been characterized as following a nonstationary process, cointegration is attractive because it allows us to test for the presence of bubbles, as well as tomodel a long-run path and the dynamics of prices to return to this path. More specifically,
we use a vector error correction model (VECM), which is a popular statistical model used
to analyze nonstationary (especially I(1)) time-series data. The I(1) assumption is in linewith statistical characteristics of many economic and financial data (Nelson and Plosser
1982).
The VECM can be expressed in the context of the Johansen approach (1991) that has
a number of attractive features. First, the VECM extends the standard vector autoregression (VAR) model and incorporates error correction models (ECMs) and nonstationary
variables. The VAR suffers from a misspecification bias when data are nonstationary
10and contain cointegration. Second, unlike the Engle-Granger method based on a single
equation specification, the Johansen approach accommodates more than one cointegrating relationships. When modelling even a partial economy, economic theories can predict
multiple cointegration (steady-states). Third, because all key nonstationary variables (y)are endogenous, there is no need to consider a causal relationship among them. The
VECM can be derived from the standard VAR(p):yt = Ayt−1 + A2 yt−2 + · · · + Ap yt−p + et(4)

where t = 1, . . . , T and et ∼ N (0, σe2 ). A matrix y consists of K nonstatioinary (I(1)) timeseries variables. This VAR can be transformed to the VECM(p) that can be expressedwith extra variables (z ) as:∆yt = a +pXbi ∆yt−i + cECMt−1 + f zt−1 + ut

(5)

i=1

where c = −(I −Pp

i=1 Ai )and bi = −Pp

j=i+1 Ai

The residual follows the standard

normal, ut ∼ N (0, σu2 ) and ∆ is the difference operator. In our study, y is a vectorof real condominium prices, income, mortgage rates, and capital inflows; and z consistsof stationary variables such as transaction volumes, housing starts, net migration (orpopulation changes), and centered seasonal dummies.10 Eq. (5) deals with an endogeneity
issue in y, but not between y and z; therefore, the lagged z is used to ensure exogeneityof this matrix. The choice of economic variables is determined by the availability andthe stationarity of data, and this equation will be estimated by the maximum likelihood
method (Johansen 1991).
In order to specify Eq. (5), we need to identify the number of cointegrating vectors(r ). This identification can be investigated by decomposing parameter, c, into adjustment
and cointegrating parameters (α and β respectively):
H1 (r) : c = αβ0(6)α measures the speed of adjustment to return to the long-run path. In the presence of10

Our model specification is similar to that of Adam and Fass (2010).

11

a long-run relationship, cointegrating parameter β is super-consistent and the ECM is
stationary I(0). Then, according to the Granger Representation Theorem (Engle andGranger 1987), α should be −1 < α < 0. A value for parameter α that is close to -1
indicates fast adjustment to return to the long-run path, and a parameter α value that isclose to 0 indicates slow adjustment to the long-run path. In contrast, when there is nolong-run relationship among y, α will not lie within this theoretical range, which implies
that there are significant deviations of prices from economic fundamentals and this istraditionally considered evidence of bubbles.
The number of cointegrating vectors is equal to the rank of c (rank(c)), which can beevaluated by the significance of characteristic roots (λi ) of c. Using λi , we can construct
trace statistics to find the presence and the number (r ) of cointegrating vectors.

λ(r) = −TnXln(1 − λi )

(7)

i=r+1

This is known as a trace statistic and evaluates the null of rank (c) = r against thealternative of rank (c) > r, where 0 < r < K.11 When λi is insignificant, the trace
statistic becomes zero and yields evidence of no cointegration. The trace statistic does
not follow the conventional distribution, and thus we use the critical values provided by
Osterwald-Lenum (1992) in order to evaluate the null hypothesis.
The results from the Johansen multivariate cointegration approach are summarizedin Table 5, where the AIC is used for the selection of lag lengths in order to minimize
size distortions (Lutkepohl and Saikkonen 1999). We find evidence of one cointegration(i.e., one equilibrium condition) in all regions from the full sample analysis (2008M12017M4). The null hypothesis of no cointegration can be rejected, while we fail to reject
the hypothesis of r > 1. Finding cointegration in our specification implies the absenceof bubbles in the condominium market.12 Furthermore, while our research is confined to
condominium markets, our conclusion of no bubbles in this market implies the absenceof bubbles in the Japanese property market in general. This is in line with Figure 2
where prices of other property markets have shown a rather slow recovery after Lehman’s
11

We use trace statistics to identify the number of cointegration following the recommendation of
Lutkepohl et al. (2001), but maximum eigenvalue tests also yield similar results.
12
The conclusion of no bubbles is consistent with that of Nagayasu (2016) who used the explosive(right-hand side) unit root tests.

12collapse, and this trend has not changed significantly in recent times.
The unique cointegrating relationship (ECM parameters) is presented in Table 6,
where following the conventional presentation format, ECM is presented as ln (Pt ) −β1 ln (M ortage ratet ) − β2 ln(Incomet ) − β3 ln(Capital inf lowt ) − β4 ) and thus the parameter of condominium prices is normalized. Theoretically speaking, real condominium
prices are positively correlated with real employee compensation and cross-border capital inflows (β2 , β3 < 0), and negatively correlated with the real mortgage rate (β1 > 0).
The ECMs are plotted in Figure 5, which, consistent with the results from the Johansen
approach, suggests the stationarity of ECMs. A sharp deviation of ECMs in early 2014
may be a reflection of the economic impacts of the consumption tax that was increased
from 5% to 8% in April 2014. It had led to an increase in the demand for condominiums
before the implementation of the hike.
Our results show that the coefficients for real mortgage rates and financial flows havethe expected sign in all cases. In contrast, the sign of the coefficient for real compensation
varies by region. It is positively associated with real condominium prices in relatively
large regions, such as Tokyo, Osaka, and Aichi, but is negatively associated with realcondominium prices in Hokkaido where the recovery of the real economy after Lehman’s
collapse lagged behind that of the metropolitan areas. Our result is consistent with Sa
and Wieladek (2015) who find a positive and persistent effect of capital inflows originatingfrom a saving glut on real prices of houses in the US. Thus, we confirm that purchase ofreal estates by nonresidents is a worldwide phenomenon.

4.2

Condominium Price Inflation and Transaction Volumes

As summarized in Section 2, price volatility is theoretically linked with transaction volumes, which in turn are related to the amount of information in the market. Generally,
it is believed that there is more information in the real estate market at times of bubbles.
Although no evidence of bubbles is found in the Japanese condominium market, a positive relationship has generally existed between price inflation and transaction volumes inrecent periods. That is to say, more transactions took place during times when condominium prices were high (Table 7). This phenomenon remains valid for all combinationsof trades, with one exception: transactions from companies to individuals. Given possible
13

heterogeneous responses of condominium price inflation to changes in transaction volumes
at different quantile points, we use a quantile regression method, which estimates different
effects along quantiles of covariates (Koenker and Bassett 1978).
The quantile regression is a popular statistical approach in economic and financial
analyses (Koenker and Hallock 2001) and is considered more robust than the ordinary
least squares (OLS) when data contain extreme values. Although this advantage of thequantile regression has been widely recognized, it has not been frequently used in analysis
because of the complexity involved in the estimation compared to the OLS. However,IT developments over the past decades make this regression method more accessible toresearchers. The major distinction between these methods can be highlighted by theirminimization problems. That is, while the OLS estimates the unconditional population
mean (E(y)), the quantile regression estimates conditional expectation function, E(y|x),
for the τ th quantile.
We apply the quantile method to the autoregressive distributed lag (ADL) model,
which is dynamic and thus captures more appropriately a data generating process thana static model.13 For a bivariate case where both y and x are stationary, the standard
ADL(p,q) can be expressed as:

yt =

p
X

ai yt−i +

i=1

q
X

bi xt−i + εt

(8)

i=0

where the residual, εt , follows the normal distribution. When y and x are I(1), Eq. (8) is
more appropriately transformed to Eq. (9) with possible cointegration between y and x.

∆yt =δECMt−1 +p−1Xαi ∆yt−i +

i=1

q−1
X

φi ∆xt−i + ψzt−1 + et

(9)

i=0

= θmt + et
Like the VECM, this equation contains a vector of exogenous and stationary variables(z ) and an ECM. The lagged value of z has been considered to ensure exogeneity of thisvector. Based on this standard ADL with an ECM, the quantile ADL evaluated at the13

The ADL is used here because, to our knowledge, developments in the quantile VAR are still premature, and to some extent, our finding of one coinegration justifies the reduced form.

14τ th quantile (τ ∈ [0, 1]) can be expressed as:∆yt = δ(τ )ECMt−1 +p−1X

αi (τ )∆yt−i +q−1X

φi (τ )∆xt−i + ψ(τ )zt−1 + ut

(10)

i=0

i=1

This model differs from models that take into account the recent developments in quantile
regression models and are based on a quantile-dependent cointegration in a context ofa reduced form (Xiao 2009) and the ADL (Cho et al. 2015). A quantile-dependent
cointegration implies heterogeneous cointegrating parameters (β) along quantiles and thepresence of multiple equilibria in the market. But, this statistical implication is not usuallyconsidered by the standard economic theory on housing prices. After all, we maintain here
the finding of one equilibrium in the condominium market from the Johansen test, andthe parameters are estimated by minimizing Eq. (11)

min
θ∈R

T
X

ρτ (yi − θmt )

(11)

i=1

where ρτ (u) = u(τ − I(u < 0)), or ρτ (u) = −(1 − τ )u if u < 0 and ρτ (u) = τ u if
u ≥ 0 (Koenker and Bassett 1978). When τ = 0.5, the model becomes the least absolutedeviation estimation. With 4 nonstationary variables and the maximum lag order equal
to 4, we estimate Eq. (11) for different combinations of lag lengths by the OLS and choose
the best model out of 500 combinations using the AIC. Once the specification of the bestmodel has been decided we use the quantile regression for its estimation.
The marginal effects from the quantile regression are summarized in Table 8, wheretransaction volumes are evaluated at the 95% quantile level. The results in this table
are based on the specification of the quantile regression that is generally consistent with
the VECM. We use the steady-state condition summarized in Table 6, and z contains
transaction volumes, construction-starts, population changes, centered seasonal dummies.
Furthermore, the specification of ADL is ADL(1, 2, 0, 0) for a nation, ADL(4, 0, 1, 0) for
Hokkaido, ADL(2, 0, 1, 2) for Tokyo, ADL(4, 0, 0, 2) for Aichi, and ADL(3, 0, 0, 0) for
Osaka. Since all ADLs contain the lagged endogenous variables, inflation inertia exists
in the condominium market. Price inertia is a common phenomenon not only in the realestate market but also in the goods market.

15This table shows that while the marginal effects are often positive, they are insignificant in most cases. Exceptions are Tokyo and the nation, where transaction volumes areshown to influence price inflation positively and significantly at the conventional significance level (see also Figure 6). It follows that, consistent with the market microstructure
model, increases in transaction volumes is closely associated with property price increases
in selected regions and only at high quantiles. The disaggregate analysis suggests thattransaction volumes are often positive but are insignificant in many cases; however, trading among individuals has been found to be an important kind of transaction in Tokyo.
This may suggest that compared to institutional traders, heterogeneity in information ismore sizable among individuals, who do not collect relevant information regularly andcomprehensively.
The fact that the relationship between transaction volumes and property price inflation
is somewhat weaker in Japan compared to analyses of other countries may be related tomarket imperfections. Mortgage loans are expected to be repaid by the ages of 70 to
75, and most private transactions in Japan are carried out by first-time buyers, whohave an average age of 35 and who are more likely to face liquidity constraints (due todown-payments). Hayashi et al. (1988) demonstrated that lack of developments in thesemarkets led consumers to save for down-payments and to acquire residential property
later in life. Further, Japan’s secondary real estate market is shallow compared to thoseof other developed countries: existing houses accounted for 37% of total home sales inJapan in 2012, which is substantially lower than in the US (78%) and France (66%). Thissituation has resulted from the Japanese housing policy following World War II, which
was designed to increase the number of available accommodations regardless of quality.
The insignificant relationship between price inflation and transaction volumes may also
be due to other market imperfections (e.g., lack of transparency about properties) that
amplifies the down-payment effect.
Further analysis is conducted for a nation and Tokyo by calculating marginal effects
at 15, 35, 55, and 75% quantile levels in order to check the robustness of our finding
from the 95% quantile level. The results from these quantiles are consistent with our
expectations (Table 9). A statistically significant and positive relationship can be foundwhen transaction volumes are evaluated at their higher quantiles and transactions take16

place among individual traders. In Tokyo, there is a tendency of a statistical significant
relationship above a 75% quantile level from aggregate data on transaction volumes andabove 55% from disaggregate data on transaction volumes between individual traders.
The data from other combinations of traders seem to increase the significant quantile
level in aggregate data. In short, while the relationship between real estate inflation andtransaction volumes is found to be relatively weak in Japan compared to other advanced
countries, our region- and trader-specific findings of some significant relationships arevaluable results given the lower trade frequency in the condominium market.

5

Conclusion

In this paper, we have studied the recent movement of condominium prices in Japan, focusing on the roles of transaction volumes and international financial flows. This research
topic is important because, for many people, a residence is the most expensive productthey purchase in their lifetime and any price increase will significantly affect their qualityof life. Furthermore, in Japan condominiums are a very common type of housing, andtheir price increases have outpaced those of other types of residential properties.
By using the concept of integration, we have then shown that there is no evidenceof bubbles in the Japanese real estate market; the trend in condominium prices can be
explained through economic fundamentals like employee compensation, mortgage rates,
and cross-border financial positions of a country. Furthermore, consistent with theoreticalpredictions of the market microstructure model, transaction volume contains informationthat is useful for explaining transitory movements in condominium prices during times ofhigh economic activities in Tokyo. That is, increases in volume are associated with priceinflation in the condominium market, but this trend is limited to Tokyo alone. Other
regions did not show any significant relationship between them. A relatively weak roleof transaction volume may reflect the imperfection of Japan’s condominium market. Although geographically limited, the finding on the role of transaction volumes in explaining
real estate prices is noteworthy since condominiums are traded less frequently comparedto other financial assets such as stocks and currencies.

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20

Table 1: Basic summary and sources of the data
Mean
Condominium nominal prices
Nation108.917
Hokkaido 120.785
Tokyo
108.137
Aichi
107.751
Osaka
108.824CPI
Nation97.912
Hokkaido 97.678
Tokyo
98.646
Aichi
97.975Osaka
98.200Transaction volumes (Aggregate)
Nation13171.940
Hokkaido 408.688
Tokyo
3974.642
Aichi
594.661
Osaka
1566.596
Transaction volumes (Individuals to Individuals)
Nation6458.853
Hokkaido 229.826
Tokyo
1626.064
Aichi
330.358
Osaka
756.817
Transaction volumes (Individuals to Companies)
Nation2566.807
Hokkaido 78.578
Tokyo
828.973
Aichi
116.615Osaka
324.239
Transaction volumes (Companies to Individuals)
Nation3725.688
Hokkaido 89.661
Tokyo
1342.817
Aichi
134.248
Osaka
433.211
Transaction volumes (Companies to Companies)
Nation411.312
Hokkaido 10.587
Tokyo
174.551
Aichi
13.431Osaka
51.917
Migration (net)
Hokkaido -706.762
Tokyo
5569.147
Aichi
765.101
Osaka
54.881
National
9642.609
Employee compensation
257768.400
Construction starts (apartments)
9640.101
Mortgage rates
2.508
Cross-border position
121886.5
Exchange rate
97.91509

Std Dev

Unit

Source11.67021.295
12.541
11.57911.060Index, 2010=100

MLIT
MLIT
MLIT
MLIT
MLIT

1.643
1.842
1.313
1.604
1.618

Index, 2010=100 Datastream (JPCONPRCE)
Index, 2010=100 e-State-State-State-Stat2260.124
72.879718.964111.667285.601

Unit

MLIT
MLIT
MLIT
MLIT
MLIT

1212.712 Unit
45.933387.87373.057143.069

MLIT
MLIT
MLIT
MLIT
MLIT

688.062
22.629227.138
30.262109.218

Unit

MLIT
MLIT
MLIT
MLIT
MLIT

786.436
25.089297.54433.86498.038

Unit

MLIT
MLIT
MLIT
MLIT
MLIT

93.0555.756
48.771
6.142
22.376

Unit

MLIT
MLIT
MLIT
MLIT
MLIT

1434.605
9131.710
1872.332
1015.528
2739.391
5939.042
2739.327
0.107
19273.39
13.91131

Peoplee-State-State-State-State-StatDatastream (JPCOMEMPB)
Datastream(JPHOUSAPP)
Datastream (JPFHOUSE)
BIS
Datastream (JAPAYE$)

PeopleBillion yen
Unit%
Millions US$
Yen/Dollar

Notes: The Ministry of Land, Infrastructure, Transport and Tourism (MLIT). The e-Stat is a data set organized by the Ministryof Internal Affairs and Communication. The code numbers are stated in the bracket for variables from Datastream. All data were
downloaded on a monthly basis except employee compensation which is converted from quarterly to monthly using the function(cubic-match last) in Eviews 8. The sample period is from 2008M4 to 2017M4.

21

Table 2: Correlation between real estate markets
Overall
Residential landHouse
Condominium

Overall Residential land Housing prices Condominium
1.000
0.247
1.000
0.598
0.753
1.000
0.829
-0.3100.079
1.000

Notes: Correlation based on nominal prices.

Table 3: ADF unit root tests for key data
Real condominium prices
NationHokkaido
Tokyo
Aichi
Osaka
Real mortgage rate
NationHokkaido
Tokyo
Aichi
Osaka
Real income
NationHokkaido
Tokyo
Aichi
Osaka
Real external flow
NationHokkaido
Tokyo
Aichi
Osaka

Level

Difference

1.392
1.569
1.062
0.745
1.870

-3.409 *-4.568 **-3.252 *-3.211 *-3.379 *-2.732-2.824-2.471-2.112-1.873-3.989-3.841-4.22-4.518-4.577-2.182-2.423-1.158-2.099-1.523-3.266 *-2.692 +-3.614 **-3.431 *-3.750 **-0.849-0.870-0.811-0.816-0.814-2.949-2.912-2.966-2.966-2.998

**
**
**
**
**

*
*
*
*
*

Notes: Statistics significant at 10% (+), 5% (*) and 1%(**) levels.

22

Table 4: Causality tests between condominium prices and transaction volumes
Null Hypothesis:
National level
Transaction volume does not cause price inflation
Price inflation does not cause transaction volume
Hokkaido
Transaction volume does not cause price inflation
Price inflation does not cause transaction volume
Tokyo
Transaction volume does not cause price inflation
Price inflation does not cause transaction volume
Aichi
Transaction volume does not cause price inflation
Price inflation does not cause transaction volume
Osaka
Transaction volume does not cause price inflation
Price inflation does not cause transaction volume

Obs.

F-Statistic

Prob.

96

1.905
0.659

0.048
0.785

97

1.079
1.219

0.390
0.290

96

1.031
0.950

0.431
0.504

96

0.958
0.930

0.496
0.522

96

1.629
1.587

0.103
0.115

Notes: The causality test is based on the heterogeneous panel model.

23

Table 5: Johansen cointegration tests
Rank
Nation0
1
2
3
4
Hokkaido
0
1
2
3
4
Tokyo
0
1
2
3
4
Aichi
0
1
2
3
4
Osaka
0
1
2
3
4

Parms

LL

Eigenvalue

Trace statistic

5% critical value

80 1127.582
88 1143.795
94 1152.732
98 1158.837
100 1160.162. 65.160 *0.272 32.732
0.161 14.858
0.113 2.649
0.026

53.12034.91019.9609.420

80 975.016
88 989.157
94 996.675
98 1001.659
100 1003.830. 57.628
0.242 29.347
0.137 14.311
0.093 4.342
0.042

*

53.12034.91019.9609.420

80 1237.229
88 1248.897
94 1259.413
98 1263.909
100 1265.871. 57.285 *0.205 33.949
0.186 12.915
0.084 3.924
0.038

53.12034.91019.960
9.420

48
56
62
66
68

995.1221015.697
1025.408
1028.267
1029.070. 67.897 *
0.327 26.746
0.170 7.325
0.054 1.607
0.015

53.12034.91019.960
9.420

48
56
62
66
68

1050.226
1071.2201081.881
1087.408
1088.548. 76.643 *0.332 34.655
0.185 13.334
0.101 2.280
0.022

53.12034.91019.9609.420

Notes: The endogenous variables are the condominium price, mortgage rate, income, andcapital inflow from abroad. Statistics significant at a 5% (*) level.

24

Table 6: Cointegrating relationships
Coef
NationMortgage rate
IncomeCapital inflow
Constant
Hokkaido
Mortgage rate
IncomeCapital inflow
ConstantTokyo
Mortgage rate
IncomeCapital inflow
Constant
Aichi
Mortgage rate
IncomeCapital inflow
Constant
Osaka
Mortgage rate
IncomeCapital inflow
Constant

Std. Err t-statistic

p-value 95% Conf Interval

0.408-2.539-0.24021.682

0.049
0.901
0.039
.

8.380
-2.820-6.220.

0.000 0.312
0.005 -4.305
0.000 -0.315
.
.

0.503
-0.773-0.164
.

1.310
2.226
-0.583-14.187

0.241
4.856
0.218
.

5.440
0.460
-2.680.

0.000 0.838
0.647 -7.290
0.007 -1.009
.
.

1.782
11.743-0.156.

0.446-1.265-0.66013.438

0.082
1.254
0.139
.

5.440
-1.010-4.760.

0.000 0.285
0.313 -3.724
0.000 -0.932
.
.

0.606
1.194
-0.388.

0.386-3.024-0.18024.865

0.044
0.657
0.033
.

8.710
-4.610-5.480.

0.000 0.299
0.000 -4.311
0.000 -0.245
.
.

0.473
-1.737-0.116.

0.094-3.236-0.22627.787

0.030
0.418
0.023
.

3.170
-7.750-10.000.

0.002 0.036
0.000 -4.055
0.000 -0.270
.
.

0.153
-2.417-0.182.

Notes: The statistics are based on the Johansen test, and the parameter for the condominium price is normalized.

25

Table 7: Transaction volumes by quantiles of condominium prices
Aggregate

Disaggregate
Com to Com

Com to Ind

Ind to Com

Ind to Ind

Quantile (%)
Mean
25 12723.000
50 11994.000
75 13426.630
100 14560.740
25
50
75
100
25
50
75
100
25
50
75
100
25
50
75
100

415.714379.556385.815464.000
4305.071
3479.481
3439.259
3657.481
1867.214
2216.296
2720.000
3489.630
6122.964
5916.407
6863.556
6944.926

Std. Dev.
Min
Max
1919.257 9803 18188
1563.750 8974 17517
2314.210 11016 21088
2421.598 10638 21333
92.18287.59171.35098.773744.433
601.659767.980
726.161252.857
228.869307.759435.5121043.652
847.119
1362.864
1249.128

288
703
234
539
293
580
296
733
3195 6532
2399 5217
2477 5556
2583 5670
1410 2479
1880 2747
2277 3618
2567 4667
4498 9360
4403 9026
5355 11343
5185 10271

Notes: Condominium prices are classified according to the quantile; and thehigher the quantile, the more expensive the condominiums.

26

Table 8: Marginal effects from the quantile regressions

NationHokkaido
Tokyo
Aichi
Osaka
Hokkaido
Com to Com
Com to Ind
Ind to Com
Ind to IndTokyo
Com to Com
Com to Ind
Ind to Com
Ind to Ind
Aichi
Com to Com
Com to Ind
Ind to Com
Ind to Ind
Osaka
Com to Com
Com to Ind
Ind to Com
Ind to Ind

Delta-method
Std. Err.

t-statistic

0.003
0.008
0.003
0.010
0.005

2.080
-0.020
2.550
1.770
0.910

0.040 0.000
0.983 -0.017
0.012 0.002
0.081 -0.002
0.364 -0.005

0.011
0.017
0.014
0.039
0.014

0.005
0.016
0.002
-0.007

0.008
0.009
0.009
0.008

0.630
1.790
0.200
-0.8200.528 -0.011
0.077 -0.002
0.839 -0.017
0.414 -0.023

0.022
0.033
0.020
0.010

0.005
0.000
-0.003
0.018

0.005
0.006
0.006
0.007

1.060
0.050
-0.4402.460

0.291 -0.005
0.958 -0.012
0.658 -0.015
0.016 0.004

0.015
0.013
0.010
0.033

0.001
0.009
0.011
0.005

0.005
0.008
0.007
0.011

0.280
1.040
1.630
0.470

0.778 -0.009
0.303 -0.008
0.106 -0.002
0.643 -0.017

0.012
0.025
0.025
0.027

0.001
0.007
0.005
-0.004

0.005
0.006
0.005
0.009

0.150
1.210
0.960
-0.4606

0.884 -0.009
0.228 -0.005
0.338 -0.005
0.650 -0.023

0.010
0.019
0.016
0.014

MarginAggregate
0.005
0.000
0.008
0.018
0.004
Disaggregate

Notes: Statistics are obtained at the 95% quantile.

27

p-value

95% Conf. Interval

Table 9: Marginal effects from the quantile regressions
Quantile(%)
Nation
75
55
35
15
Tokyo
75
55
35
15
75
Com to Com
Com to Ind
Ind to Com
Ind to Ind
55
Com to Com
Com to Ind
Ind to Com
Ind to Ind
35
Com to Com
Com to Ind
Ind to Com
Ind to Ind
15
Com to Com
Com to Ind
Ind to Com
Ind to Ind

MarginAggregate
0.003
0.003
0.002
0.002
Aggregate
0.004
0.002
0.001
0.000
Dissagregate

Delta-method
Std. Err. t-statistic

p-value

95% Conf. Interval

0.001
0.001
0.001
0.001

2.810
2.640
1.980
1.040

0.006
0.010
0.051
0.301

0.001
0.001
0.000
-0.001

0.006
0.005
0.004
0.004

0.002
0.002
0.002
0.002

2.470
1.620
0.720
-0.2000.015 0.001
0.109 -0.001
0.474 -0.002
0.842 -0.005

0.008
0.006
0.005
0.004

0.004
0.002
0.000
0.009

0.002
0.002
0.003
0.003

1.510
0.920
-0.0102.680

0.135 -0.001
0.361 -0.002
0.993 -0.007
0.009 0.002

0.008
0.006
0.007
0.016

0.003
0.003
0.003
0.003

0.002
0.002
0.002
0.002

1.680
1.700
1.630
2.070

0.097 0.000
0.092 0.000
0.106 -0.001
0.041 0.000

0.006
0.006
0.006
0.007

0.002
0.003
0.004
-0.002

0.002
0.002
0.002
0.003

1.040
1.470
1.790
-0.7300.302 -0.002
0.145 -0.001
0.077 0.000
0.467 -0.007

0.006
0.008
0.009
0.003

0.001
0.004
0.006
-0.008

0.003
0.003
0.004
0.005

0.370
1.150
1.440
-1.5500.714 -0.005
0.255 -0.003
0.153 -0.002
0.125 -0.018

0.007
0.010
0.015
0.002

Notes: Statistics are obtained at the 75, 55, 35, and 15% quantiles.

28

Figure 1: Regional condominium price indexes (nominal values, 2008M4-2017M4)

Figure 2: Real estate price indexes (national average, nominal values, 2008M4-2017M4)

29

Figure 3: A fraction of condominium price inflation (National average)

Figure 4: Transaction volumes (National aggregates)

30

Figure 5: Disequilibrium conditions inferred from cointegration analyses
NationHokkaido

Tokyo

Aichi

Osaka

Note: The error correction models based on Table 6.

31

Figure 6: Quantile regression results from aggregate data
Nation, ADL(1,2,0,0)

Hokkaido, ADL(4,0,1,0)Tokyo, ADL(2,0,1,2)

Aichi, ADL(4,0,0,2)

Osaka, ADL(3,0,0,0)

Note: OLS estimates and confidence intervals are also shown with dot lines.

32