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: vorada.limjaroenrat@barcelonagse.eu

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

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