Sticky House Price? (Paper)

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Barcelona GSE Master Project by Vorada Limjaroenrat

Master Program: Macroeconomic Policy and Financial Markets

About Barcelona GSE master programs: http://j.mp/MastersBarcelonaGSE

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Sticky House Price? (Paper)

  1. 1. 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, Spain Email: vorada.limjaroenrat@barcelonagse.eu I am highly indebted to my advisor, Jordi Gal´ı, for his continued guidance and support. I owe special thanks to Luca Gambetti for technical suggestions. I wish to thank all BGSE faculty members for helpful conversations and encouragements. Needless to say, all remaining errors are my own. 1
  2. 2. 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 1 See Appendix A. for detail of data used. 2
  3. 3. 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) σx σy ρ(x, y) σx σy 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, pr t , pt, pc t, it, ph t ] where yt, pr t , pt, pc t, it, ph t 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
  4. 4. 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, t where ut ≡ St t, E{ t t} = I, E{ t t−k = 0} for all t and k ≥ 1, StSt = 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 t and to satisfy the above identification, let St be the Cholesky factor of Σt. 3.1.1 VAR in difference In this section2, xt ≡ [ yt, pr t , pt, pc t, it, ph t ] 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 2 Specific detail of identification can be found in Christiano, et al.(1999), Gal´ı and Gambetti(2013) 3 Method of calculation can be found in Appendix B. 4 Details in Table 2. Appendix C. 4
  5. 5. 0 5 10 15 20 −1 −0.5 0 0.5 a.) GDP 0 5 10 15 20 −1 −0.5 0 0.5 b.) GDP Deflator 0 5 10 15 20 −0.2 0 0.2 0.4 0.6 c.) Real rent 0 5 10 15 20 0 0.5 1 d.) Real Interest Rate 0 5 10 15 20 0 0.5 1 e.) Federal funds rate 0 5 10 15 20 −2 −1 0 1 f.) Real house price 0 5 10 15 20 −4 −2 0 2 g.) Irf of price − Irf of fundamental 0 5 10 15 20 −2 −1 0 1 h.) Fundamental Component 0 5 10 15 20 −2 −1 0 1 i.) Price and Fundamental Fundamental House Price Figure 3: Monetary Policy shock(U.S), VAR in difference 0 5 10 15 20 −0.1 −0.05 0 0.05 0.1 a.) GDP 0 5 10 15 20 −0.02 0 0.02 0.04 0.06 b.) GDP Deflator 0 5 10 15 20 0 0.02 0.04 0.06 c.) 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 0.1 f.) Real house price 0 5 10 15 20 −1 0 1 2 g.) Irf of price − Irf of fundamental 0 5 10 15 20 −2 −1 0 1 h.) Fundamental Component 0 5 10 15 20 −1.5 −1 −0.5 0 0.5 i.) Price and Fundamental Fundamental House Price Figure 4: Monetary Policy shock(U.S), VAR in level 5
  6. 6. 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). 5 The co-movement puzzle from the model is, however, contrary to the stylized evidence provided by Erceg and Levins (2005) VAR model which shows that durable goods production decline in response to increase in interest rate. 6
  7. 7. 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
  8. 8. 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 Gal´ı 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(QF t )” and “bubble component(QB t )”, Qt = QF t + QB t (2) where the fundamental component is defined as the present discounted value of fu- ture dividends: QF t ≡ ∞ k=1 k−1 j=0 (1/Rt+j) Dt+k where the log linearized equation becomes qF t = const + ∞ k=1 Λk [(1 − Λ)Et{dt+k+1} − Et{rt+k}] (3) 8
  9. 9. 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 t be monetary policy shock, we have ∂qt+k ∂ m t = (1 − γt−1) ∂qF t+k ∂ m t + γt−1 ∂qB t+k ∂ m t (4) where γt = QB t /Qt denotes the bubble share in the asset price and from the definition of the fundamental component, we get ∂qF t+k ∂ m t = ∞ j=0 Λj (1 − Λ) ∂dt+k+j+1 ∂ m t − ∂rt+k+j ∂ m t (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 ∂ m t > 0 and ∂dt+k ∂ m t ≤ 0 for k = 0,1,2,.. Therefore, following equation (4), asset price will decline in response to monetary contraction ∂qF t+k ∂ m t < 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 QF t Rt = Et{Dt+1 + QF t+1} (6) where it could be checked that the bubble component will satisfy: QB t Rt = Et{QB t+1} (7) log-linearizing yields: Et{∆qB t+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: ∆qB t = rt−1 + ξt (9) where ξt ≡ qB t − Et−1{qB t } 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, t. Thus, ξt = ψ(rt − Et−1{rt}) + ξ∗ t (10) 9
  10. 10. 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 ∂qB t+k ∂ m t =    ψt ∂rt ∂ m t , k = 0 ψt ∂rt ∂ m t + k−1 j=0 ∂rt+j ∂ m t , 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, lim limk→∞ ∂qB t+k/∂ m t , 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 Gal´ı 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 − qF t+k) ∂ m t = γt−1 ∂qB t+k ∂ m t − ∂qF t+k ∂ m t (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
  11. 11. coefficient γt is positive, meaning that bubble component does exist in housing market (recall that γt ≡ QB t /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 Gal´ı 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 0.5 a.) Dividend 0 5 10 15 20 −1 0 1 2 3 b.) Irf of price − Irf of fundamental 0 5 10 15 20 −2 −1.5 −1 −0.5 0 0.5 c.) Fundamental Component 0 5 10 15 20 −2 −1 0 1 2 Price and Fundamental d.) Fundamental Stock Price Figure 5: Monetary Policy shock(U.S) on stock market. Source: Gal´ı 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 6 detail in Table 3, Appendix G 11
  12. 12. 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
  13. 13. 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. 7 except 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
  14. 14. B Appendix: Conditional Correlation Estimators In calculate the impulse response function, we can express        ∆yt ∆pr t ∆pt ∆pc t it ∆ph t        =        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)               1 t 2 t 3 t 4 t 5 t 6 t        where 5 t is identified to be monetary policy shock( m t ). Correlation conditional on monetary policy shock between real GDP and real house price can be obtained by: ρ(∆yt, ∆ph t |m) = ∞ j=0 C1m j C6m j var(∆yt|m)var(∆ph t |m) where var(∆yt|m) = ∞ j=0(C1m j )2 and var(∆ph t |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
  15. 15. D Appendix: Impulse Response of Real GDP to monetary policy tightening in international country 0 10 20 −1 −0.5 0 USA 0 10 20 −0.5 0 0.5 1 Japan 0 10 20 −1 −0.5 0 0.5 Germany 0 10 20 −0.5 0 0.5 France 0 10 20 −1 −0.5 0 0.5 Italy 0 10 20 −0.5 0 0.5 UK 0 10 20 −1 −0.5 0 Canada 0 10 20 −0.5 0 0.5 Spain 0 10 20 −1 −0.5 0 0.5 Finland 0 10 20 −1 0 1 2 Ireland 0 10 20 −1 −0.5 0 0.5 Netherland 0 10 20 −0.5 0 0.5 Norway 0 10 20 −0.5 0 0.5 NewZealand 0 10 20 −1 −0.5 0 0.5 Sweden 0 10 20 −1 −0.5 0 0.5 Switzerland 0 10 20 −0.5 0 0.5 Australia 0 10 20 −0.5 0 0.5 Belgium 0 10 20 −0.5 0 0.5 Denmark Figure 6: Real GDP response to monetary policy tightening 15
  16. 16. E Appendix: Impulse Response of Real House Price to monetary policy tightening in international country 0 10 20 −3 −2 −1 0 USA 0 10 20 −1 0 1 2 Japan 0 10 20 −1 −0.5 0 0.5 Germany 0 10 20 −4 −2 0 2 France 0 10 20 −4 −2 0 2 Italy 0 10 20 −4 −2 0 2 UK 0 10 20 −4 −2 0 2 Canada 0 10 20 −4 −2 0 2 Spain 0 10 20 −4 −2 0 2 Finland 0 10 20 −2 0 2 4 Ireland 0 10 20 −4 −2 0 Netherland 0 10 20 −4 −2 0 2 Norway 0 10 20 −2 −1 0 1 NewZealand 0 10 20 −4 −2 0 2 Sweden 0 10 20 −4 −2 0 2 Switzerland 0 10 20 −4 −2 0 2 Australia 0 10 20 −4 −2 0 2 Belgium 0 10 20 −2 −1 0 1 Denmark Figure 7: Real house price response to monetary policy tightening 16
  17. 17. F Appendix: Impulse Response of Real Rent to monetary policy tightening in international country 0 10 20 −0.5 0 0.5 USA 0 10 20 −0.5 0 0.5 Japan 0 10 20 −0.5 0 0.5 Germany 0 10 20 −0.5 0 0.5 France 0 10 20 −1 0 1 2 Italy 0 10 20 0 0.5 1 1.5 UK 0 10 20 −0.5 0 0.5 1 Canada 0 10 20 0 0.5 1 1.5 Spain 0 10 20 −1 −0.5 0 0.5 Finland 0 10 20 −4 −2 0 2 Ireland 0 10 20 −0.5 0 0.5 Netherland 0 10 20 −1 0 1 2 Norway 0 10 20 −1 0 1 2 NewZealand 0 10 20 −0.5 0 0.5 1 Sweden 0 10 20 −0.5 0 0.5 1 Switzerland 0 10 20 −0.5 0 0.5 Australia 0 10 20 −0.5 0 0.5 1 Belgium 0 10 20 −0.5 0 0.5 Denmark 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
  18. 18. 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 0.2 a.) GDP 0 5 10 15 20 −1 −0.5 0 0.5 b.) GDP Deflator 0 5 10 15 20 −0.5 0 0.5 1 c.) Real rent 0 5 10 15 20 −4 −2 0 2 4 d.) Irf of price − Irf of fundamental 0 5 10 15 20 −3 −2 −1 0 1 e.) Fundamental Component 0 5 10 15 20 −3 −2 −1 0 1 f.) 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
  19. 19. 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 0.2 a.) GDP 0 5 10 15 20 −1.5 −1 −0.5 0 0.5 1 b.) GDP Deflator 0 5 10 15 20 −0.5 0 0.5 1 c.) Real rent 0 5 10 15 20 −4 −2 0 2 4 d.) Irf of price − Irf of fundamental 0 5 10 15 20 −3 −2 −1 0 1 e.) Fundamental Component 0 5 10 15 20 −3 −2 −1 0 1 f.) 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 0.4 a.) GDP 0 5 10 15 20 −1 −0.5 0 0.5 b.) GDP Deflator 0 5 10 15 20 −0.5 0 0.5 1 c.) Real rent 0 5 10 15 20 −4 −2 0 2 4 d.) Irf of price − Irf of fundamental 0 5 10 15 20 −3 −2 −1 0 1 e.) Fundamental Component 0 5 10 15 20 −3 −2 −1 0 1 f.) 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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