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Sticky House Price?
Vorada Limjaroenrat∗
Abstract
The assumption of fully flexible house price is widespread in several general equilib-
rium monetary models. In this paper, I provide selective survey of existing evidence,
arguing that rigidities do exist in house price movements, along with empirical and
theoretical contributions. In the 18 OECD countries evidence-based VAR analysis of
monetary transmission mechanism, a rent puzzle arises as real rent increases in re-
sponse to exogenous increase in interest rate, opposite with what the theory suggests.
In the final part of the paper, 18 OECD countries are devided into two subgroups
of low and high credit market flexibility. The results present interesting linkages be-
tween sticky price, bubbles, monetary policy, and credit market condition that should
be high on future research agenda.
1 Introduction
During the past decade, housing bubble has played an important role in triggering finan-
cial crises in several countries. Questions arise whether monetary policy is an efficient
tools in controlling housing bubbles, whether “leaning against the wind” monetary policy
is consistent with housing market, and whether monetary policy should react to house
price movements. The answers to these questions differ significantly depending on the
assumption regarding rigidities the model imposed. However, despite the importance of
housing to the whole economy, the role of house price rigidities is unclear in the workhorse
monetary model- referred to as New Keynesian model(NK model).
Existing conclusions reached are, i.g. it is optimal for monetary policy to stabilize
price in the one-sector NK model(See e.g. Woodford(2003)), monetary policy should
stabilize price in the sticky price sector if there are both sticky price and flexible price
sector in the NK model(See e.g. Aoki(2001); Benigno(2004); Huang and Liu(2005) ), or
monetary policy should not stabilize house price as its fluctuation is an efficient response
movement(See e.g. Bernanke and Gertler(1999) ).
∗This paper is my master thesis at Barcelona Graduate School of Economics, Barcelona, SpainEmail: [email protected]
I am highly indebted to my advisor, Jordi Galı, for his continued guidance and support. I owespecial thanks to Luca Gambetti for technical suggestions. I wish to thank all BGSE faculty membersfor helpful conversations and encouragements. Needless to say, all remaining errors are my own.
1
The NK model, however, up to my knowledge, has not yet explicitly incorporate
rigidities in house price despite the evidence that exists. Thorough understanding of this
interaction and precise modeling, thus, should be considered for future research.
The organization of the paper is as follows: Section (2) first presents the preliminary
analysis of the cyclical behavior of housing market variables. Section (3) lays out the em-
pirical model used and provides related literature regarding rigidities in housing market.
Section (4) focus the analysis on housing bubbles: do bubbles exist in housing market,
how does housing bubbles response to monetary policy shock, and how do the responses
differ under different credit market flexibility. Section (5) concludes.
2 Cyclical Behavior of Housing Market Variables
In this section, I present the cyclical properties of the U.S. housing market variables with
NBER recession shading: real house price and real rent, quaterly data. The sample period
is 1971Q1-2011Q21.
Figure(1) and figure(2) shows the movement of real house price and real rent price,
respectively, at the business cycle frequencies. The vertical axis is the percentage deviation
from trend while the horizontal axis is year in quarters. Both data, (log) real house price
and (log) real rent, are detrended by Hodrick-Prescott filter(λ = 1600).
From figure (1), we can see that real house price(hp-filtered) adjusts slowly to its trend.
Percentage deviation from trend of real house price always remain higher than that of
real GDP or real rent. It is evident that real house price is procyclical(cross correlation
with the cyclical component of real GDP 0.6188) while real rent is countercyclical(cross
correlation with the cyclical component of real GDP -0.2152). Moreover, we can observe
that house price is a very highly volatile variable that its standard deviation is 1.3962
times of real GDP and it .
For the evidence of international housing market, Table 1 shows that house price is
also procyclical in all 18 sampled OECD countries and the standard deviation is high
compared to real GDP or real rent. However, it is unclear for real rent whether it is
pro-cyclical or countercyclical as the results differ in sampled countries.
Figure 1: U.S. real house price at business cycles frequencies
1See Appendix A. for detail of data used.
2
Figure 2: U.S. real rent at business cycles frequencies
x = real house price y = real GDP x = real rent y = real GDP
Country Correlation Relative Std. Dev Correlation Relative Std. Dev
ρ(x, y) σxσy
ρ(x, y) σxσy
U.S. 0.6188 1.3962 -0.2152 0.6977
Japan 0.4273 2.5022 0.4117 0.6767
Germany 0.3417 0.9817 -0.0489 0.6411
France 0.5720 2.7766 0.1160 0.7584
Italy 0.0111 4.3626 0.0976 1.6676
Canada 0.6136 4.0394 -0.0516 1.3144
UK 0.3941 2.9416 -0.2739 1.0738
Spain 0.4864 4.1282 -0.0898 1.0717
Finland 0.6443 2.8487 0.3417 1.0911
Ireland 0.6335 2.4520 0.4186 4.2641
Netherland 0.3159 3.9204 0.0028 0.8131
New Zealand 0.6429 3.0754 0.2398 2.1006
Switzerland 0.5717 2.4374 -0.6028 0.7550
Denmark 0.6679 3.7547 0.3246 0.7414
Norway 0.5263 3.6096 -0.1626 2.1751
Sweden 0.4890 2.5706 -0.3922 1.2558
Australia 0.4184 3.3230 0.0202 1.2484
Belgium 0.3704 2.5283 0.2088 0.6808
Table 1: Cyclical components of real house price and real rent in international countries
3 Rigidities in Housing Markets
3.1 The Empirical Model
The model used for empirical analysis is vector autoregression(VAR). Define xt ≡ [yt, prt , pt, p
ct , it, p
ht ]
where yt, prt , pt, p
ct , it, p
ht denote (log)output, (log)real rent , (log)price level, (log)commodity
price index, short term interest rate, (log) real house price index respectively. Augmented
Dickey Fuller test reveals that all log variables are I(1), therefore, we first consider first
difference VAR in the next subsection. Cointegration test will be performed later in the
following section. The model takes the form of an autoregressive(AR) model. Details of
3
the data used are reported in appendix A.
xt = A0 +A1xt−1 +A2xt−2 + ...+Apxt−p + ut (1)
where ut is the vector of reduced form innovation, white noise Gaussian process with zero
mean and covariance matrix Σt. ut is assumed to follow a linear transformation of the
structural shocks, εt where ut ≡ Stεt, E{εtε′t} = I, E{εtε′t−k = 0} for all t and k ≥ 1,
StS′t = Σt
The identification of monetary policy shock is the one of Christiano, Eichenbaum,
and Evans(2005): monetary policy shock does not affect GDP, real rents, or inflation
contemporaneously. Moreover, it is assumed that central bank do not respond contem-
poraneously to house price innovations.
Letting the monetary policy shock be the fifth element of εt and to satisfy the above
identification, let St be the Cholesky factor of Σt.
3.1.1 VAR in difference
In this section2, xt ≡ [4yt,4prt ,4pt,4pct , it,4pht ] The rest of the model and shock iden-
tification follow from what describe above.
Here, I present the result from the above empirical model. Figure (3) represents
the impulse response function to monetary policy shock. The solid line is the estimated
response to the shock while the two dotted lines are the 84% confidence interval.
Tightening monetary policy will increase both real and nominal interest rate, lower
(log) real GDP, and eventually lower (log) GDP deflator. Figure (3.c) shows that (log)
real rent will increase, as it is shown to be countercyclical conditional on monetary policy
shocks being the only source of fluctuations for the U.S. housing markets. In figure (3.f),
(log) real house price fall in response to monetary policy tightening, consistent with the
fact that it is procyclical (estimated correlation between real GDP and real house price
conditional on monetary policy shocks is 0.9787, estimated correlation between real GDP
and real rent conditional on monetary policy shocks is -0.9450)3; however, it is worth
noticing that (log) real house price does not fall immediately in response to monetary
policy tightening, instead, it is sticky and slowly responds to the shock.
3.1.2 VAR in levels
As all variables in xt : (log)output, (log)real rent , (log)price level, (log)commodity price
index, (log)real house price index, are I(1). I then check whether there exist cointegrating
relationship among variables. Since Johansen cointegration test reveals that there are at
least two cointegrating vectors, estimate VAR in levels seem to be justified.4 The result
from empirical VAR in levels is shown in figure (4). However, with VAR in levels, the
2Specific detail of identification can be found in Christiano, et al.(1999), Galı and Gambetti(2013)3Method of calculation can be found in Appendix B.4Details in Table 2. Appendix C.
4
0 5 10 15 20−1
−0.5
0
0.5a.) GDP
0 5 10 15 20−1
−0.5
0
0.5b.) GDP Deflator
0 5 10 15 20−0.2
0
0.2
0.4
0.6c.) Real rent
0 5 10 15 20
0
0.5
1d.) Real Interest Rate
0 5 10 15 20
0
0.5
1e.) Federal funds rate
0 5 10 15 20−2
−1
0
1f.) Real house price
0 5 10 15 20−4
−2
0
2g.) Irf of price − Irf of fundamental
0 5 10 15 20−2
−1
0
1h.) Fundamental Component
0 5 10 15 20−2
−1
0
1i.) Price and Fundamental
FundamentalHouse Price
Figure 3: Monetary Policy shock(U.S), VAR in difference
0 5 10 15 20−0.1
−0.05
0
0.05
0.1a.) GDP
0 5 10 15 20−0.02
0
0.02
0.04
0.06b.) GDP Deflator
0 5 10 15 200
0.02
0.04
0.06c.) Real rent
0 5 10 15 20−0.2
0
0.2
0.4
0.6
0.8
d.) Real Interest Rate
0 5 10 15 20−0.2
0
0.2
0.4
0.6
0.8
e.) Federal funds rate
0 5 10 15 20−0.1
−0.05
0
0.05
0.1f.) Real house price
0 5 10 15 20−1
0
1
2g.) Irf of price − Irf of fundamental
0 5 10 15 20−2
−1
0
1h.) Fundamental Component
0 5 10 15 20−1.5
−1
−0.5
0
0.5i.) Price and Fundamental
FundamentalHouse Price
Figure 4: Monetary Policy shock(U.S), VAR in level
5
analysis does not change much from VAR in difference. Moreover, as Johansen coin-
tegration test is highly sensitive to the number of lag chosen, I therefore choose to work
with VAR in difference in later analysis for consistency.
3.2 Related Literature regarding Rigidities in Housing Market
Some literatures assumed, with insufficient evidence that house price is fully flexible.
With the characteristics of house price that are notoriously volatile, prices are posted in
advance but can be negotiated between sellers and buyers, there are reasons to believe that
house price is flexible. Moreover, according to Bils and Klenow(2004), one of the most
comprehensive work on sticky price that has investigated frequencies of price adjustment
from over 350 categories of goods and services, they show that durable goods have higher
frequency of price adjustment compared to other goods. However, it is arguable that
durable goods in Bils and Klenow(2004) does not include “long-lived” durables such as
housing. Further evidence and careful analysis are thus needed. In this section, I will
provide a survey on existing study and evidence in arguing for the stickiness of house
price movements.
One provocative work regarding the importance of the price stickiness of durable goods
done by Barsky, House, and Kimball (2003, 2007). They employ the sticky-price general
equilibrium model to argue that in order to understand the transmission of monetary
policy shock, pricing behavior of the durable goods sector(whether it is sticky or not) is
more crucial than the pricing behavior of the non-durable goods sector. In particular, if
durable goods(housing) prices are flexible while the price of non-durable goods are sticky,
tightening monetary policy will increase durable goods production(housing) and exactly
decrease non-durable goods production; leaving neutral effect on aggregate output and
production under perfect financial market assumption. The intuition given is a result
of constant shadow value of durable goods. Monetary contraction will have non-neutral
effect on sticky price(non durable) sector: markup price can deviate from the desired
markup, lower employment and lower production. In the flexible price sector, markup
price is constant. In the durable goods sector, shadow value will be nearly constant. As
markup is the ratio between marginal disutility of labor and shadow value of durable
goods, marginal disutility of labor must be constant. Constant total employment can be
achieved only through increase employment (and thus increase production) in flexible price
durable goods sector as it is decrease in the sticky price non durable goods sector. This
is referred to in a wide range of literatures as co-movement puzzle: lack of co-movement
between durable goods and non-durable goods production.5
There has been several attempts in modeling monetary general equilibrium model to
reconcile this puzzle while assuming house price to be fully flexible. Barsky et at(2003)
has suggested two possible solutions for this co-movement puzzle: nominal wage sticki-
ness(supply side) and credit constraint(demand side).
5The co-movement puzzle from the model is, however, contrary to the stylized evidence provided byErceg and Levins (2005) VAR model which shows that durable goods production decline in response toincrease in interest rate.
6
In the vein of incorporating nominal wage stickiness, monetary policy tightening will
increase real wage, reduced the desired output from durable goods firms. Studies in this
direction can be found in, i.e. Erceg, et al. (2000), Carlstrom and Fuerst (2006) . In the
vein of incorporating credit constraints, these lines of works include ,i.e. Monacelli(2005),
Carlstrom and Fuerst(2010). The intuition is that by incorporating credit frictions to
the NK model with durables as a collateral constraint, borrowing constraint will act as a
substitute for nominal rigidities in the price of durable goods.
Recent researches, however, recognize that house price is sticky downward: whenever
excess supply or demand occurs in housing market, house price does not immediately
moves to clear the market, instead, sellers tend not to sell houses as their expected price
is higher than the buyer during the recession.
DiPasquale and Wheaton (1994) was among the first to argue strongly that price
stickiness is crucial to understand the behavior of house prices. Specifically, they develop
a structural model of single family market employing U.S. annual data from 1960s to
1990s to explicitly test how rapid house price adjusts to equilibrium. They found that it
takes several years for U.S. house price to clear the market, returning to long-run steady
state, even though it is possible for housing market to be in equilibrium in every period.
The paper thus support that it is more important to study gradual dynamic adjustment of
house price and construction level instead of focusing only on equilibrium level. The model
is later applied to Chinese housing market in Chow et al.(2008) and similar conclusion of
gradual price adjustments are reached.
Supporting DiPasquale et al.(1994), Riddel(2002) apply the same set of U.S. data to
the two-sided disequilibrium model, supply and demand side disturbances. The results
show that U.S. housing market is characterised by periods of disequilibrium which results
from slow price adjustment in clearing the market.
Case(1994) present the result supporting the fact that if nominal price does not move
to clear the market, then it is expected that the market will be “quantity clearing”. He
presents statistics of the U.S. housing market in different cities, showing that housing
boom made sales dropped dramatically, unsold inventories reached the highest point,
but house price fell only slightly. Strong evidence of house price stickiness can be found
in the statistics of inventory(unsold house) which rise invariably at the onset of every
recession. Supporting the view of quantity clearing in housing market, Leamer(2007) also
claim that as housing has the volume cycle not the price cycle; thus, housing is crucial
in explaining business cycle/recession. Questionnaire survey for nearly two decades from
Case and Shiller(1988, 2003) during the slow market reported that very few fraction of the
respondents are willing to lower the house price to get them sold in the sluggish economy
period, most of them has lower reservation price that they are willing to wait.
Moreover, as price rigidity is a kind of market inefficiency and downward price stick-
iness means that house price adjusts asymmetrically. Empirical evidence of asymmetric
house price adjustment is provided in Tsai and Chen(2009). They use GJR-GARCH
model to demonstrated that the volatility in U.K. house price series are asymmetric,
7
when bad news occur, the variance decreases, price is sticky downward. Gao et al.(2009)
apply autoregressive mean reversion(ARMR) model to U.S dataset and found that house
price is likely to overshoot the equilibrium, its serial correlation is higher, in the appre-
ciation period than the declining period; in other words, house price tends to grow fast
but reduce slowly.
More stylized evidence on (asymmetric) stickiness of real house price can be found
in the relationship between inflation and real house price adjustment. Girouard, N., et
al.(2006), employing data from 18 OECD countries, report the scattered plot between
average annual inflation rate and duration of real house price falls(in quarters). The
plot exhibits a negative trend of their correlation; put differently, it takes longer time for
house price to adjust in low inflation(recession) period, while it takes less time for house
price to adjust in high inflation period. Also, they present the scattered plot showing
the negative relationship between average annual inflation rate and average percentage
change in real house price: real house price adjust less, in percentage, during the low
inflation period(recession) compared to high inflation period.
Another avenue of explanation in house price being very sticky downward comes
from behavioral economics: loss aversion, sellers are averse to loss and expected price
at least what they paid for in the past. According to Genesove and Mayer (2001), En-
gelhardt(2003), there exist an evidence of nominal loss aversion in housing market. Do-
brynskaya(2008) presents the result from his behavioral model that as loss aversion exist
among the behavior of real estate traders, house price downward rigidities should also
exist.
Supporting the assumption that house price is sticky, result on the evidence-based
VAR analysis of the effect or monetary policy shock on real house price in Figure(7),
Appendix E present the impulse response of empirical model in section(3) for 18 OECD
countries. The results imply that house price does not fall immediately in response to
monetary policy tightening, instead, it is sticky and slowly response to the shock.
4 Housing Bubbles
4.1 Theoretical Issues of Rational Asset Price Bubbles
In this section, I consider the theoretical issue of rational asset pricing. Following Galı
and Gambetti(2013) partial equilibrium model, agents are assumed to be risk neutral.
Asset price, Qt is interpreted to be the sum of “fundamental component(QFt )” and
“bubble component(QBt )”,
Qt = QFt +QBt (2)
where the fundamental component is defined as the present discounted value of fu-
ture dividends: QFt ≡{∑∞
k=1
(∏k−1j=0(1/Rt+j)
)Dt+k
}where the log linearized equation
becomes
qFt = const+
∞∑k=1
Λk[(1− Λ)Et{dt+k+1} − Et{rt+k}] (3)
8
where Λ ≡ Γ/R < 1 with Γ and R are denoting, respectively, the (gross) rates of dividend
growth and interest along a balanced growth path.
How does these variables affected by monetary policy shock. Let εt be monetary policy
shock, we have∂qt+k∂εmt
= (1− γt−1)∂qFt+k∂εmt
+ γt−1
∂qBt+k∂εmt
(4)
where γt = QBt /Qt denotes the bubble share in the asset price and from the definition of
the fundamental component, we get
∂qFt+k∂εmt
=
∞∑j=0
Λj(
(1− Λ)∂dt+k+j+1
∂εmt−∂rt+k+j
∂εmt
)(5)
Both theory and evidence agree on the fact that in response to monetary contraction,
interest rate will increase while dividend will decrease(∂rt+k∂εmt
> 0 and∂dt+k∂εmt
≤ 0)
for k =
0,1,2,.. Therefore, following equation (4), asset price will decline in response to monetary
contraction(∂qFt+k∂εmt
< 0)
for k = 0, 1, 2, ..
The response of rational bubble component to monetary policy shock, however, is
unclear. As we know that QtRt = Et{Dt+1 + Qt+1}, it follows that the definition of
fundamental component satisfies
QFt Rt = Et{Dt+1 +QFt+1} (6)
where it could be checked that the bubble component will satisfy:
QBt Rt = Et{QBt+1} (7)
log-linearizing yields:
Et{∆qBt+1} = rt (8)
I will refer to this later as the first channel that interest rate can affect the bubble
component: increase in interest rate increase the expected growth of the bubble, bubble
expected return, where under risk neutrality assumption it must be equal to the interest
rate.
The second channel, through which it is possible for the interest rate to affect the
bubble component: a possible systemic comovement between (indeterminate) innovation
in the bubble with the surprise component of the interest rate. To see this, reevaluate
equation(8) and eliminate the expectation to obtain:
∆qBt = rt−1 + ξt (9)
where ξt ≡ qBt − Et−1{qBt } is an arbitrary process satisfying Et−1{ξt} = 0 for all t. Note
that the innovation in the size of the bubble, ξt may or may not related to innovation in
the interest, εt. Thus,
ξt = ψ(rt − Et−1{rt}) + ξ∗t (10)
9
ψ is a (possibly random) parameter of which both its sign and size cannot pin down
by theory, {ξ∗t } is a zero mean martingale difference process. Therefore, the dynamic
response of the bubble component to interest shock is given by
∂qBt+k∂εmt
=
ψt ∂rt∂εmt, k = 0
ψt∂rt∂εmt
+∑k−1
j=0∂rt+j∂εmt
, k = 1, 2, ...(11)
As we can see, the theory of rational bubble open doors for different predictions: the
initial response of the bubble to interest rate is capture by ψ which is indeterminate
both sign and size. The long run impact of monetary policy shock on the bubble size,
limlimk→∞
∂qBt+k/∂εmt , will be positive or negative depending on whether the persistence of
real interest rate response is sufficient to offset the negative impact.
4.1.1 Empirical Result: Real Rent
In studying the monetary transmission mechanism, VAR model has been widely used to
study the effect of monetary policy shock on core macroeconomic variables. However,
this methodology is subjected to the criticism that the variables in the VAR are related
by by some simple recursive causal structure, give rise to some well-known puzzles.
Here, I focus the analysis on the response of real rent price on interest rate innovation.
Figure (7) in Appendix E. shows that, contrary to both conventional wisdom and rational
asset pricing theory which predicts a negative response of dividend to exogenous monetary
innovations, real rent(housing dividend) increase in response to monetary contraction in
most of the countries.
The result is also in contrast with dividend from other type of asset, i.e. stock. Below,
I report the results from Galı and Gambetti(2013) in Figure(5) , which perform the same
VAR analysis on stock, for explicit comparison. Stock dividend declines in response to
exogenous monetary policy tightening; hence, consistent with conventional analysis. I will
refer to this unexpected movement of real rent price in response to interest rate shock as
rent puzzle. Further investigation to solve the puzzle is still needed.
4.1.2 Empirical Result: Rational Housing Bubbles
In this section, I focus the analysis on the effect of monetary policy on the fundamental
component and try to recover its effect on the bubble component.
From figure(3.h), the fundamental component fall immediatedly in response to mon-
etary contraction even though real rent increases, consistent with both the theory and
related evidence. Notice that, equation (4) can be viewed as
∂(qt+k − qFt+k)∂εmt
= γt−1
(∂qBt+k∂εmt
−∂qFt+k∂εmt
)(12)
Figure (3.g) plot the gap, left hand side of equation (12). The initial response of the
gap between asset price and fundamental component to interest rate shock is positive,
10
coefficient γt is positive, meaning that bubble component does exist in housing market
(recall that γt ≡ QBt /Qt represent the share of bubble in the observed price). At first
glance, it seems reasonable to conclude that “leaning against the wind” monetary policy
might be true in housing market: tightening monetary policy reduce the gap between
bubble component and fundamental component in the long run. However, as the response
of fundamental component to monetary policy shock relies on the response of real rent
to monetary policy shock(equation 5), which is still unclear from the real rent puzzle,
the response of the gap between bubble and fundamental component of house price is
subjected to rent puzzle as well.
Figure (5) below is the result from Galı and Gambetti(2013) showing the impulse
response function of monetary policy shock on U.S. stock market. Comparing figure (3)
to figure (5), we can see that the two types of assets(housing and stock) and their bubbles
behave differently in response to monetary policy shock. Even though both house price
and stock price are highly volatile variables, rigidities exist only in housing markets and
,also, puzzle exists only in housing markets.
0 5 10 15 20−1.5
−1
−0.5
0
0.5a.) Dividend
0 5 10 15 20−1
0
1
2
3b.) Irf of price − Irf of fundamental
0 5 10 15 20−2
−1.5
−1
−0.5
0
0.5c.) Fundamental Component
0 5 10 15 20−2
−1
0
1
2Price and Fundamental
d.) FundamentalStock Price
Figure 5: Monetary Policy shock(U.S) on stock market. Source: Galı and Gambetti(2013)
4.2 Monetary Policy, Bubbles, and Credit Constraint
With the powerful interaction between credit market and asset price bubbles, this section
will focus the analysis on the effect of monetary policy shock on asset price bubbles under
different credit conditions. The method used is VAR-based analysis, same with the one
proposed in section 3. However, in this section, I will split the sample into two groups6:
the first group constitutes of countries with high credit market flexibility (their credit
market flexibility indicator is higher than the median of the indicator) , the second group
6detail in Table 3, Appendix G
11
constitutes of countries with low credit market flexibility (their credit market flexibility
indicator is lower than the median of the indicator ). The credit market flexibility indicator
here are mortgage debt to GDP ratio, loan to value ratio, whether the interest rate is
fixed or variable. High(low) credit market flexibility means high(low) mortgage debt to
GDP ratio, high(low) loan to value ratio, and variable(fixed) interest rate.
The impulse response function is calculated country by country, then pooled together
by giving the weight to each country equal to inverse of standard deviation of the data.
Figure 9-11 in Appendix H,I,J shows that in responding to monetary policy shock,
real house price will be less sticky downward under the high market flexibility condition.
The result is robust whether the indicator of credit market flexibility is mortgage debt
ratio, loan to value ratio, or interest rate adjustment.
5 Conclusion and Discussion
The main purpose of this paper is to raise macroeconomists’ awareness regarding the
existence of stickiness in house price which is worth considering for certain reasons:
First, despite the presence of inefficiency in housing market and the evidence of real
house price rigidities, most monetary general equilibrium model simply ignore the role
of housing market and the role of house price stickiness altogether. The New Keynesian
model, referred to as the major framework in studying the effect of monetary policy,
inflation, and business cycle, up to my knowledge, has not yet explicitly study these
linkages. As housing plays an important part in the economy and in the crisis, housing
market has a close relationship with the business cycle, monetary policy can be used
as an important tool in affecting housing market. In designing a proper policy, a clear
understanding of rigidities in housing sector and attempts in modeling it is needed.
Second, concerning the effect of monetary policy on rational asset price bubble, when
comparing stock price bubble to housing bubble, the responses are clearly different. It is
worth notice here that the characteristics of the two asset markets are at odds: though
stock market are more highly volatile, downward price rigidity is observed only in housing
market. Therefore, for policy design issue of how should monetary response to asset price
bubble, asset price stickiness should also be considered. The linkages between asset price
stickiness, bubbles, and monetary policy, thus, should be further explored.
Finally, this paper presents preliminary results calling into attention the relationship
between monetary policy, bubbles, and credit market condition. Higher credit flexibility
make real house price less downward sticky from interest rate tightening. Even though
the effect is unclear for other variables, the difference is robust for real house price and
the gap between bubbles and the fundamental component that it should not be disregard.
12
A Appendix : Data
The list of 18 countries include : Australia, Belgium, Canada, Denmark, Finland, France, Ger-
many, Ireland,Italy, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, UK.,
and the US. The sample preiod is 1971Q1-2011Q27. The following paragraph provides detailed of
the data used.
Real GDP
GDP. expenditure approach. Millions of national currency, volume estimates, annual level,
quaterly and seasonally adjusted, OECD reference year=2010(Measure: VOBARSA). Downloaded
from: http://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE1#
Real Rent Price
Real rent prices are recovered from price-to-rent ratio, deflated by GDP deflator.
Nominal house price to rent price obtained from OECD. All data is quaterly and seasonally
adjusted(index based in 2005). Download from the Department of Economics of Queen’s Univer-
sity : www.econ.queensu.ca/files/other/HousePriceindices%20(OECD).xls Sources: OECD.
Price Level/ GDP Deflator
GDP Deflator is calculated by nominal GDP/real GDP.
Nominal GDP. expenditure approach. Millions of national currency, current prices, annual
level, quarterly and seasonally adjusted(Measure: CARSA).
Consumer Price Index
CPIs are presented as an index where the year 2010 is the base year. The data is quaterly
and unadjusted. Downloaded from OECD website : http://stats.oecd.org/index.aspx?
querytype=view&queryname=221#
Short term interest rate
Short term rates are usually either the three month interbank offer rate attaching to
loans given and taken amongst banks for any excess or shortage of liquidity over several months
or the rate associated with Treasury bills, Certificates of Deposit or comparable instruments, each
of three month maturity.
For Euro Area countries the 3-month “European Interbank Offered Rate” is used from the
date the country joined the euro.
All data are quarterly and unadjusted. Downloaded from OECD website and UPF data
streaming. Sources: OECD, Main Economic Indicators - complete database.
Real House Price Index
Nominal house price deflated by GDP deflator. Nominal house price data is quaterly and sea-
sonally adjusted(index based in 2005). Download from the Department of Economics of Queen’s
University : www.econ.queensu.ca/files/other/House Price indices%20(OECD).xls. Sources: OECD.
7except the following countries due to data availability of historical short term in-terest rate: Norway(1979Q1-2011Q2), New Zealand(1974Q1-2011Q2), Sweden(1980Q1-2011Q2),Switzerland(1974Q1-2011Q2), Australia(1972Q3-2011Q2), Belgium(1976Q2-2011Q2), Denmark(1979Q2-2011Q2)
13
B Appendix: Conditional Correlation Estimators
In calculate the impulse response function, we can express∆yt
∆prt∆pt
∆pctit
∆pht
=
C11(L) C12(L) C13(L) C14(L) C15(L) C16(L)
C21(L) C22(L) C23(L) C24(L) C25(L) C26(L)
C31(L) C32(L) C33(L) C34(L) C35(L) C36(L)
C41(L) C42(L) C43(L) C44(L) C45(L) C46(L)
C51(L) C52(L) C53(L) C54(L) C55(L) C56(L)
C61(L) C62(L) C63(L) C64(L) C65(L) C66(L)
ε1tε2tε3tε4tε5tε6t
where ε5t is identified to be monetary policy shock(εmt ). Correlation conditional on monetary policy
shock between real GDP and real house price can be obtained by:
ρ(∆yt,∆pht |m) =
∑∞j=0 C
1mj C6m
j√var(∆yt|m)var(∆pht |m)
where var(∆yt|m) =∑∞
j=0(C1mj )2 and var(∆pht |m) =
∑∞j=0(C6m
j )2 are conditional variance of
real GDP and real house price. Correlation conditional on monetary policy shock between real
GDP and real rent can be calculated in the same manner.
C Appendix: Johansen Cointegration (Trace) Test
rank h stat cValue pValue eigVal
None 1 155.0794 95.7541 0.0010 0.3085
At most 1 1 97.1703 69.8187 0.0010 0.2113
At most 2 1 59.8964 47.8564 0.0032 0.1876
At most 3 0 27.2863 29.7976 0.0948 0.0763
At most 4 0 14.8271 15.4948 0.0629 0.0610
At most 5 1 4.9399 3.8415 0.0263 0.0310
Table 2: Johansen Cointegration Test(matlab), lag=4, U.S. data
14
D Appendix: Impulse Response of Real GDP to monetary
policy tightening in international country
0 10 20−1
−0.5
0USA
0 10 20−0.5
0
0.5
1Japan
0 10 20−1
−0.5
0
0.5Germany
0 10 20−0.5
0
0.5France
0 10 20−1
−0.5
0
0.5Italy
0 10 20−0.5
0
0.5UK
0 10 20−1
−0.5
0Canada
0 10 20−0.5
0
0.5Spain
0 10 20−1
−0.5
0
0.5Finland
0 10 20−1
0
1
2Ireland
0 10 20−1
−0.5
0
0.5Netherland
0 10 20−0.5
0
0.5Norway
0 10 20−0.5
0
0.5NewZealand
0 10 20−1
−0.5
0
0.5Sweden
0 10 20−1
−0.5
0
0.5Switzerland
0 10 20−0.5
0
0.5Australia
0 10 20−0.5
0
0.5Belgium
0 10 20−0.5
0
0.5Denmark
Figure 6: Real GDP response to monetary policy tightening
15
E Appendix: Impulse Response of Real House Price to
monetary policy tightening in international country
0 10 20−3
−2
−1
0USA
0 10 20−1
0
1
2Japan
0 10 20−1
−0.5
0
0.5Germany
0 10 20−4
−2
0
2France
0 10 20−4
−2
0
2Italy
0 10 20−4
−2
0
2UK
0 10 20−4
−2
0
2Canada
0 10 20−4
−2
0
2Spain
0 10 20−4
−2
0
2Finland
0 10 20−2
0
2
4Ireland
0 10 20−4
−2
0Netherland
0 10 20−4
−2
0
2Norway
0 10 20−2
−1
0
1NewZealand
0 10 20−4
−2
0
2Sweden
0 10 20−4
−2
0
2Switzerland
0 10 20−4
−2
0
2Australia
0 10 20−4
−2
0
2Belgium
0 10 20−2
−1
0
1Denmark
Figure 7: Real house price response to monetary policy tightening
16
F Appendix: Impulse Response of Real Rent to monetary
policy tightening in international country
0 10 20−0.5
0
0.5USA
0 10 20−0.5
0
0.5Japan
0 10 20−0.5
0
0.5Germany
0 10 20−0.5
0
0.5France
0 10 20−1
0
1
2Italy
0 10 200
0.5
1
1.5UK
0 10 20−0.5
0
0.5
1Canada
0 10 200
0.5
1
1.5Spain
0 10 20−1
−0.5
0
0.5Finland
0 10 20−4
−2
0
2Ireland
0 10 20−0.5
0
0.5Netherland
0 10 20−1
0
1
2Norway
0 10 20−1
0
1
2NewZealand
0 10 20−0.5
0
0.5
1Sweden
0 10 20−0.5
0
0.5
1Switzerland
0 10 20−0.5
0
0.5Australia
0 10 20−0.5
0
0.5
1Belgium
0 10 20−0.5
0
0.5Denmark
Figure 8: Real rent price response to monetary policy tightening
G Appendix : Institutional characteristics of national mort-
gage systems
The following table follows the work of Calza, Monacelli, and Stracca(2013)
17
Country Mortgage to GDP ratio Loan to value ratio Interest rate adjustment
Fixed or Variable
Australia high high variable
Belgium low low fixed
Canada low low fixed
Denmark high high fixed
Finland low low variable
France low low fixed
Germany low low fixed
Ireland high low variable
Italy low low variable
Japan low high variable
Netherlands high high fixed
New Zealand high low fixed
Norway low high variable
Spain low low variable
Sweden low high fixed
Switzerland high high variable
United Kingdom high high variable
United States high high fixed
Table 3: Classification of countries according to mortgage market development indicators
H Appendix: Impulse Response to monetary policy tight-
ening. Indicator: Mortgage Debt to GDP ratio
0 5 10 15 20−0.6
−0.4
−0.2
0
0.2a.) GDP
0 5 10 15 20−1
−0.5
0
0.5b.) GDP Deflator
0 5 10 15 20−0.5
0
0.5
1c.) Real rent
0 5 10 15 20−4
−2
0
2
4d.) Irf of price − Irf of fundamental
0 5 10 15 20−3
−2
−1
0
1e.) Fundamental Component
0 5 10 15 20−3
−2
−1
0
1f.) Real house price
Figure 9: High mortgage debt to GDP ratio: blue line(with red dash error band), Low
mortgage debt ratio: black line(with pink dashed eror band)
18
I Appendix: Impulse Response to monetary policy tight-
ening. Indicator: Loan to value ratio
0 5 10 15 20−0.6
−0.4
−0.2
0
0.2a.) GDP
0 5 10 15 20−1.5
−1
−0.5
0
0.5
1b.) GDP Deflator
0 5 10 15 20−0.5
0
0.5
1c.) Real rent
0 5 10 15 20−4
−2
0
2
4d.) Irf of price − Irf of fundamental
0 5 10 15 20−3
−2
−1
0
1e.) Fundamental Component
0 5 10 15 20−3
−2
−1
0
1f.) Real house price
Figure 10: High LTV ratio: blue line(with red dash error band), Low LTV ratio: black
line(with pink dashed eror band)
J Appendix: Impulse Response to monetary policy tight-
ening. Indicator: Interest Rate Adjustment(Fixed or
Variable rate)
0 5 10 15 20−0.6
−0.4
−0.2
0
0.2
0.4a.) GDP
0 5 10 15 20−1
−0.5
0
0.5b.) GDP Deflator
0 5 10 15 20−0.5
0
0.5
1c.) Real rent
0 5 10 15 20−4
−2
0
2
4d.) Irf of price − Irf of fundamental
0 5 10 15 20−3
−2
−1
0
1e.) Fundamental Component
0 5 10 15 20−3
−2
−1
0
1f.) Real house price
Figure 11: Variable: blue line(with red dash error band), Fixed: black line(with pink
dashed eror band)
19
References[1] Aoki, Kosuke. ‘’Optimal monetary policy re-
sponses to relative-price changes.” Journal of
Monetary Economics 48, no. 1 (2001): 55-80.
[2] Barsky, Robert B., Christopher L. House, and
Miles S. Kimball. “Sticky-price models and
durable goods.” American Economic Review 97,
no. 3 (2007): 984.
[3] Benigno, Pierpaolo. “Optimal monetary policy
in a currency area.” Journal of International
Economics 63, no. 2 (2004): 293-320.
[4] Bernanke, Ben, and Mark Gertler. “Monetary
policy and asset price volatility.” No. w7559. Na-
tional bureau of economic research, 2000.
[5] Bils, Mark, and Peter J., Klenow. “Some evi-
dence on the importance of sticky prices.” Jour-
nal of political economy 112, no. 5 (2004): 947-
985.
[6] Calza, Alessandro, Tommaso Monacelli, and
Livio Stracca. “Housing Finance And Monetary
Policy.” Journal of the European Economic As-
sociation, European Economic Association 11,
no. s1 (2013): 101-122.
[7] Carlstrom, Charles T., and Timothy S. Fuerst.
“Nominal rigidities, residential investment, and
adjustment costs.” Macroeconomic Dynamics
14, no. 01 (2010): 136-148.
[8] Case, Karl E. “Land prices and house prices in
the United States.” In Housing Markets in the
US and Japan, pp. 29-48. University of Chicago
Press, 1994.
[9] Case, Karl E., and Robert J. Shiller. “Is there a
bubble in the housing market?.” Brookings Pa-
pers on Economic Activity 2003, no. 2 (2003):
299-362.
[10] Case, Karl E., and Robert J. Shiller.“ The be-
havior of home buyers in boom and post-boom
markets” (1989).
[11] Chow, Kenneth, Matthew Yiu, Charles Ka
Yui Leung, and Dickson Tam. “Does the
DiPasquale-Wheaton Model Explain the House
Price Dynamics in China Cities?.” HKIMR
Working Paper 21 (2008).
[12] DiPasquale, Denise, and William C. Wheaton.
“Housing market dynamics and the future of
housing prices.” Journal of urban economics 35,
no. 1 (1994): 1-27.
[13] Dobrynskaya, V. V. “Reference-dependent
preferences, loss aversion and asymmetric price
rigidity.” Center for Advanced Studies Working
Paper 13 (2008): 13.
[14] Engelhardt, Gary V. “Nominal loss aversion,
housing equity constraints, and household mo-
bility: evidence from the United States.” Jour-
nal of Urban Economics 53, no. 1 (2003): 171-
195.
[15] Erceg, Christopher J., Dale W. Henderson, and
Andrew T. Levin. “Optimal monetary policy
with staggered wage and price contracts.” Jour-
nal of monetary Economics 46, no. 2 (2000):
281-313.
[16] Erceg, Christopher, and Andrew Levin. “Opti-
mal monetary policy with durable consumption
goods.” Journal of Monetary Economics 53, no.
7 (2006): 1341-1359.
[17] Galı, Jordi, and Luca Gambetti. “The effects
of monetary policy on asset prices bubbles:
Some evidence.” American Economic Journal:
Macroeconomics, forthcoming
[18] Gao, Andre, Zhenguo Lin, and Carrie
Fangzhou Na. “Housing market dynamics: Evi-
dence of mean reversion and downward rigidity.”
Journal of Housing Economics 18, no. 3 (2009):
256-266.
[19] Genesove, David, and Christopher Mayer.
“Loss aversion and seller behavior: Evidence
from the housing market.” The Quarterly Jour-
nal of Economics 116, no. 4 (2001): 1233-1260.
[20] Girouard, Nathalie, Mike Kennedy, Paul Van
Den Noord, and Christophe Andr. “Recent
house price developments: the role of fundamen-
tals.” OECD Publishing, no.475 (2006).
[21] Huang, Kevin XD, and Zheng Liu. “Inflation
targeting: What inflation rate to target?.” Jour-
nal of Monetary Economics 52, no. 8 (2005):
1435-1462.
[22] Leamer, Edward E. “Housing is the business
cycle.” No. w13428. National Bureau of Eco-
nomic Research, 2007.
[23] Riddel, Mary. “Housing-market disequilib-
rium: an examination of housing-market price
and stock dynamics 19671998.” Journal of
Housing Economics 13, no. 2 (2004): 120-135.
[24] Tsai, I-Chun, and Ming-Chi Chen. “The asym-
metric volatility of house prices in the UK.”
Property Management 27, no. 2 (2009): 80-90.
[25] Woodford, Michael, and Carl E. Walsh. “In-
terest and prices: Foundations of a theory of
monetary policy.” (2005): 462-468.
20