Assessing the existence of a bubble in the housing market using an Error Correction Model

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Assessing the existence of a bubble in the housing market using an ECM Laurent Cyrus

Assessing the existence of a bubble in the housing market using an Error Correction

Model1

Cyrus Laurent

Master of Science internship thesis

Master of Science in Economics

University of Lausanne, Unil

Supervisor: Philippe Bacchetta

Traineeship

European Commission, JRC,

Euro Area Macroeconomics modeling unit

Supervisor: Marco Ratto

Expert: Ivan Jaccard

European Central Bank

Monetary Policy Research Division

January 27, 2010

I would like to thank Paolo Paruolo for his useful comments and suggestions

1 The work is of the responsibility of the author, in no way does it engage the responsibility of the university, the European Commission nor of the supervisors.

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Abstract

Detecting the existence of the housing bubble in the early 2000’s would have permitted policy makers to act in

order to deflate it slowly. Arguments in favor of taking action to deflate housing prices have to be strongly

justified given the potential negative impact of a non-required intervention. This implies that the methodology

used to assess the existence of a bubble must produce reliable results. We examine what conclusions can be

made from an Error Correction (EC) model through a study of the recent bubble in the US housing market. We

compare the results from two models anterior to the burst of the bubble which have different conclusions, one

from McCarthy & Peach (2004) concluding that there was no bubble and one from Soerensen(2006)

concluding that there was a bubble. We try to identify the key differences which could have led to this

conclusion. Then, we try to construct a model similar to the one from McCarthy and Peach (2004), in order to

have another comparison point and we identify some key issues in the construction of such model. Finally, we

assess the validity of such models by comparing them with a model without fundamentals. The results

obtained lead us to conclude that this methodology is not able to give clear conclusion about the existence of a

bubble. However, we try to point out the key issues that one would face when using this methodology.

Résumé

Détecter l’existence d’une bulle immobilière au début des années 2000 aurait permit aux dirigeants politiques

d’intervenir de manière à la déflater lentement. Les arguments en faveur d’une action pour déflater les prix

immobiliers doivent ètre solidement justifiés étant donné l’impact potentiel qu’aurait une intervention inutile.

Ceci implique qu’une méthodologie utilisée pour évaluer l’existence d’une bulle doit produire des résultats

fiables. Nous examinons les conclusions pouvant ètre formulées à partir d’un modèle à Erreures Corrigées à

travers l’étude de la bulle récente sur le marché immobilier américain. Nous examinons les résultats de deux

modèles antérieurs à l’éclatement de la bulle ayant différentes conclusions, l’un de McCarthy et Peach (2004)

concluant qu’il n’y avait pas de bulle et l’autre de Soerensen (2006) concluant à l’existence d’une bulle. Nous

essayons d’identifier les différences clés qui pourraient avoir mener à cette conclusion. Puis, nous essayons de

construire un modèle similaire à celui de McCarthy et Peach (2004), de façon à avoir another point de

comparaisons et d’identifier les problèmes relatifs à la construction de tel modèles. Enfin, nous examinons la

valilité de tels modèles en les comparant avec un modèle sans fondamentaux. Les résultats obtenus nous

pousse à conclure que cette méthodologie ne peut pas donner de conclusions claires quant à l’existence d’une

bulle. Toutefois, nous essayons de souligner les points important à considérer lors de l’utilisation de cette

méthodologie.

Keywords: ECM; Housing market; Bubble

JEL classification: C51; C52; R31; E22

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CONTENTS

1. INTRODUCTION ........................................................................................................................... 4

2. LITERATURE REVIEW................................................................................................................. 7

2.1 The economic foundations ....................................................................................................... 7

2.2 Econometric Issues in the methodology .................................................................................... 8

2.3 Soerensen (2006)’s and McCarthy & Peach (2004)’s models ................................................... 10

3. THE EMPIRICAL APPROACH .................................................................................................... 11

3.1 Motivations ............................................................................................................................ 11

3.2 Economic theory .................................................................................................................... 12

3.3 Empirical analysis .................................................................................................................. 15

3.3.1 The model ...................................................................................................................... 15

3.3.2 Long run identification ................................................................................................... 20

3.3.3 EC representation ........................................................................................................... 23

3.3.4 Interpretation of the results ............................................................................................. 25

4. CONCLUSION ............................................................................................................................. 30

REFERENCES...................................................................................................................................... 32

ECONOMETRIC SOFTWARE ......................................................................................................... 35

APPENDICES ...................................................................................................................................... 36

A- Data ....................................................................................................................................... 36

B- STATISTICAL RESULTS..................................................................................................... 39

C- NORMALITY GRAPH ......................................................................................................... 40

D- CONSTANCY GRAPH ......................................................................................................... 42

E- IDENTIFICATION OF THE LONG RUN RELATIONS ........................................................... 44

F- COINTEGRATION RELATIONS ............................................................................................. 46

G- VECM IDENTIFICATION RESULTS .................................................................................. 46

H- AR IDENTIFICATION RESULTS ....................................................................................... 48

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1. INTRODUCTION

Detecting the existence of the housing bubble in the early 2000’s would have permitted policy

makers to act in order to deflate it slowly. Arguments in favor of taking action to deflate housing

prices have to be strongly justified given the potential negative impact of a non-required

intervention. Policy makers have to balance a trade off between two types of error, intervening

on the market where it was not necessary or letting a bubble inflates without making any

intervention. In order to reduce the possibility of policy decision mistakes in cases such as these,

it is necessary to have reliable tools to assess the existence of a bubble. In this paper, we examine

what conclusions can be made from an Error Correction (EC) model through a study of the

recent bubble in the US housing market. We compare our model and its results with the results

from two previously conducted studies. One from McCarthy and Peach which reached the

conclusion that there was no bubble at the end of 2003 and the other from Soerensen which

concluded that there was some evidences of a bubble during the same period. These two papers

offer an example of the work that could be obtained from a real-time analysis.

One method used to assess whether an asset is overvalued is to consider deviations from their

long-run value of some ratios of the housing prices. The information given by these measures is

often sufficient to provide a fairly good idea of the evolution of the value of the asset in the long-

run. These ratios are expected to have fairly stationary values in the long run, so if an

observation of a significant deviation from the long-run value is seen then it is probable that a

correction will occur in the near future. In the housing market, the conventional metrics used for

this purpose are the prices-to-income ratio, the prices-to-rents ratio and the growth rate of real

housing prices. They are all intended to be relatively stationary, however, in 2000, this was not

the case. At this time, these ratios were indicating some important and persisting deviations in

housing prices from the value implied by the long-run value of the ratios while at the same time,

the economic situation was quite unusual due to the recent burst of the Dotcom bubble. Given

these economic conditions, it was difficult to assess whether the rapid increase in housing prices

was part of a cycle and could be justified by the value of the fundamentals or if the increase was

primarily due to speculation. The burst of the Dotcom bubble changed the way that investors

were attributing risk to the different assets in which they can invest, and the housing sector

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appeared to be relatively less risky than the stock market which had just crashed. This led to an

increase in housing demand and an increase in housing prices. Home owners saw the value of

their houses rapidly increasing, creating a wealth effect on the economy. This change in the

saving behavior of the household softened the crisis as mentioned by Shiller (2004). At the same

time, the GDP growth rate increased due to an increase in productivity. In such a context,

distinguishing the housing prices changes driven by the fundamentals from the speculation part

was quite challenging.

Using the prices-to-rent ratio and the prices-to-income ratio, a number of papers failed to predict

the existence of a bubble, for example Case and Shiller (2003), or Himmelberg, Mayer and Sinai

(2005). They justified the deviations from the long term value of the ratios mainly by the low-

interest rate policy implemented at this time. By comparing housing prices index and rents,

Learner (2002), and Hatzius (2004) pointed to some evidences of overvaluation of the housing

prices. And Krainer and Weil (2005), using the same methodology found similar conclusions.

Due to the apparent lack of link between these ratios and the other variables driving the housing

market, economic forecasters face considerable difficulties in making reliable conclusions

regarding the over-valuation of housing prices. A misunderstanding of the relation between

housing prices and their fundamentals is partly responsible of the wrong assessment of the

situation. A model which relates housing prices to the values for all the fundamentals would give

a better analysis of such situation by including all the relevant information in a joint analysis.

When the housing prices are above the equilibrium value determined by the model, there is a

positive deviation and if this deviation remains positive for a given period of time, then it is a

bubble. This method of defining a bubble is compatible with the commonly used definition from

Stiglitz : "the basic intuition is straightforward: if the reason that the price is high today is

because investors believe that the selling price will be high tomorrow-when "fundamental"

factors do not seem to justify such a price-then a bubble exists." (Stiglitz 1990, p 13).

This paper investigates the quality a a methodology based on the Error Correction Model (ECM).

The model is used to decompose the movements in housing prices into short and long-run

determinants. The long relation is assumed to define the fundamental value of housing prices

toward which they will converge in the long run. The use of an ECM allows avoiding spurious

regression problems and estimating more precisely the fundamental relation(s) of housing prices

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than what would be obtained using classical regressions. McCarthy and Peach used their model

to analyse the impact of monetary policy on the housing market in a first paper published in 2002

and then used it as a tool to assess the valuation of housing prices in M&P2 (2004). The model

from M&P failed to predict an overvaluation of the housing prices. It is difficult to determine

whether this is due to their methodology of assessing the existence of a bubble or to its

implementation. This paper tries to identify some clues of the reasons which could explain it.

The model from Soerensen (2006) is based on the same methodology and is presented as an

illustration that one could obtain correct conclusion using an EC framework, and that the results

from McCarthy and Peach are not representative of the methodology. We discuss some possible

reasons for the good results obtained by this model. We show that the theorical foundations of

the model used by M&P are commonly used in the literature and that despite some issues related

to their implementation, they seem not to be the cause of their failure. The construction of an EC

model using the same theorical foundations is used following this statement. Despite some strong

evidences of overvaluation, the construction of our model is subject to some issues and doesn’t

allow us to obtain satisfying results. We are not able to estimate stable long-run relations which

would prevent these results to be reliable in a real time analysis. Moreover, we show that the

quality of the fundamentals choice may be difficult to justify through the comparison of the

ECM predicted prices and the one from a model without fundamentals.

Our review of the methodology doesn’t identify clearly the reason that could explained its

failure. The estimation of a non-observed relation is difficult and subject to many statistical

issues related to the construction of the model. This leads us to recommend a transparent and

detailed presentation of the model in order to increase their reliability and to consider with

caution the conclusion issue from this methodology.

2 We denote McCarthy and Peach by M&P in the remaining part of the paper.

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2. LITERATURE REVIEW

2.1 The economic foundations

The model developed in this paper is based on M&P (2002) which is itself based on one of the

references of the empirical housing literature from Poterba (1984). This model from Poterba has

emerged as being an important reference of the housing sector, it is chosen here to give more

strength to the argument that the choice of fundamentals is complete. A condition which must be

fulfilled in order to produce reliable conclusions. This simple model is composed of two main

equations, a demand equation (Eq (1)) and a supply equation (Eq (5)). Using a small asset-

pricing model of the housing rents market, the model integrates the interest rate through the

notion of user cost first introduced by Jorgenson (1963). The user cost represents the opportunity

cost of living in a house instead of renting it. It has as advantage that it relates the rent-to-price

ratio to the interest rate and therefore, links monetary policy to the housing market3. This was

perfectly suited to the analysis of the situation at the beginning of the 2000’s, when the impact of

the policy of low interest rate was often given as the reason for the fast growth of the housing

prices. It also integrates the ratio of consumption to the housing stock which allows

consideration of an important factor of the dynamic of the housing market, the wealth effect

generated by changes in housing prices as recently documented in Guo (2009). These two terms

are related to the ratio of the housing prices to consumption prices in the demand equation. One

can mention as an important omission on this side of the market a liquidity variable, for

measuring the quantity of credits available for investment. This would be important to model the

liquidity constraint effect and the consumption dynamics. As mentioned by Soerensen (2006),

credits availability plays an important role in the short run dynamics of the housing market. This

variable would have been really useful to explain the dynamic of the housing market after the

crash of the Dotcom bubble. However, it is difficult to find a proper measure to use in an

empirical analysis of the housing sector. And this variable is left out of many of the empirical

studies of the housing market.

The main features of the supply side of the housing market are explained in Topel and Rosen

(1988) or Poterba (1984). They find that the supply curve is inelastic, interest rate is a demand

3 see Mishkin (2007) for a complete explanation of the importance of the monetary policy in the housing market

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shifter and it is hard to estimate the link between prices and houses supplied due to the

sluggishness of adjustment in the supply. These features led to a consensus that the housing

supply was accurately modeled using the Tobin’s Q theory (1954) as exemplified by Summers

(1981), Poterba (1991), Abraham and Hendershoot (1996), Rosen and Topel (1986), and Barrot

(2001). M&P (2002) assumed in their model that the fundamental prices are defined only by the

demand side of the market following the consensus about the inelasticity of the supply side.

Some additional elements can be added to the model specification depending on the scope of the

study. For example, Mankiw and Weil (1989) and Madsen (2008) introduce demographic factor.

Barrot (2001) introduces the debt to income ratio in a similar model for the Swedish market,

Berger-Thomson and Ellis (2004) estimate a model for different countries to analyze the effect of

institutional factor, Kenny (2004) identifies the impact of monetary policy for Ireland, and

Poterba (1984) analyzes the impact of taxes changes. The variables used in the model here are

often presented as being sufficient for the determination of the fundamentals but many other

elements may be added as suggested by Soerensen (2006). This model from Poterba (1984) is

also mentioned or used in Besson and Boissinot (2005) and Soerensen (2006) and prove to be

trustable for people using this methodology.

2.2 Econometric Issues in the methodology

The quality of the statistical properties of the model should be of main concerned as they may

change the conclusion from the model. A model proving to have good statistical properties

would be more reliable and of higher value for policy-making.

The implementation of our theory is subject to many problems. One of the main problems is the

lack of proper data. Demers (2005) for example found that using a perpetual inventory equation

(Eq (4)) to compute the housing stock may lead to find a root of order two in the system,

invaliding the properties of model. In his study of the Canadian housing market, Demers found

that if the depreciation rate is small enough4, the housing stock will be by construction integrated

of order two. To solve this problem, he mentioned that one could extend his work by using a

4 The relation can be rewritten as ∆

. If the average value of ⁄ is not too different from on

the sample considered, one may not observed a too strong I(2) behavior I the model, and ignore it.

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polynomial cointegration for stock-flow model as suggested by Haldrup (1994) and Tom

Engsted and Niels Haldrup (1997). Using our dataset, we find some evidences of I(2)5 roots

under many different assumptions and sample size using a rank test. Some I(2) roots in the

dataset would prevent one from imposing the theorical constraints used in M&P’s papers as the

residuals left after the estimation of the “long-run” relations would be non-stationary. This

wouldn’t invalidate their interpretation as long-run relations6 and some properties of the model.

No attempt has been done to build an I(2) model of the housing market to my knowledge. The

I(2) model is more difficult to interpret than an I(1) model, and also much more difficult to build.

This problem could be solved in a few cases when a reduction of the I(2) model to an I(1) can be

implemented. This is what is called the nominal-to-real transformation (Kongsted (2005)). After

a few trials, it seems that this is not suited in our case; the possible transformations were rejected

by the data on the sample where some signs of I(2) roots are present. After many trials to deal

with this problem, we chose a sample such that the I(2) properties of the model were minimized.

In many studies, this problem is not mentioned, and it is difficult to know whether the possibility

that the dataset contains some I(2) roots has been taken into consideration. As mentioned in

Juselius (1999), the prices index are often found to have an I(2) root. The number of possible

sources of I(2) is important in such dataset7, not checking whether it is effectively driven by

some I(2) roots may be misleading. It wouldn’t invalidate the prediction properties of the model

and would still allow the model to be used in its complete form. However, it would impact the

reliability of the results found as the model would features a low-mean reversion in the

cointegration relations and loose its interpretation into economics terms as it would be possible

to qualify the cointegrations relations of long-run relations. The model developed in Soerensen

(2006) is less subject to I(2) problems. The use of annual data changes the statistical properties

due to the reduction in the frequency. The long swings in the value of the variables under

quarterly frequency look rather like simple fluctuations of an I(1) stochastic variable at an annual

frequency. This choice of frequency gives to the model better statistical properties, and leads to

the estimation of more stable fundamental relations than what can be obtained from a quarterly

analysis.

5 I(2) is used as qualitative expression for “Integrated of order two” 6 The interpretation of the “long-run” relations has to be modified as explained in Juselius K. (2004). 7 Prices index are often found to be I(2) as mentioned in Juselius K. (1999)

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The studies of the housing market are often conducted with a careless consideration for the

statistical properties of the model. The number of cointegration relations is sometimes chosen

with few attentions for the statistical results due to their fragility as in Bessone and Boissinot

(2005). This kind of choice is done following an a priori about the number of relations to be

found in the data but it may create a bias in the whole model and its results.

2.3 Soerensen (2006)’s and McCarthy & Peach (2004)’ s models

As pointed out in Dirk J. (2009), a review of the literature forecasting the crisis, the findings of

Soerensen (2006) were noteworthy as they were among the few ones predicting a crisis. In his

empirical analysis of the housing market in different countries, Soerensen defined a bubble in a

similar way as M&P, suggesting that this kind of approach use to assess the existence of a bubble

may have some potential. The fundamental relation is defined as being the long-run relation

estimated in the two steps procedure of the construction of an ECM. A positive difference

between the observed value and the equilibrium prices defined by the model is a deviation from

the prices implied by the fundamentals. Therefore, if the deviation persists, it is a bubble8. One

drawback of this methodology is that the fundamental relation must be properly defined. The

original aim of Soerensen was to use a dataset similar to the one of M&P, but due to the

difficulty of acquiring the data on his sample, he renounced and used a subset of the original one.

Our two benchmark models have been constructed using a dataset ranging until the end of 2003,

Soerensen’s model using annual data and M&P using quarterly data. To compensate his choice

of a lower frequency, Soerensen use a dataset starting in 1913. One drawback of using a long

sample as Soerensen is that the fundamental relation may have been subject to structural

changes, and that the estimated relation may not be accurate to assess the valuation of housing

prices in 2004. On the other hand, using a shorter sample makes the final relations more sensitive

to the choice of its beginning and to the influence of outliers and structural break.

One problem in M&P’s model is that some choices are assumed based on the theory, but these

choices may not be verified in the data. A more objective analysis of the data would analyse all

the possible fundamentals available, as done in Soerensen (2006). The choice to use different

8 A precise definition should specify the duration and the size of the deviations required in order to claim about the existence of a bubble. We left this element for future analysis.

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variables to describe the short run and the long run or the choice of using only the estimate of the

demand as fundamental prices is choices which can and should be tested. The discussion of the

construction of their model is rather short, and it is difficult to assess the point which may have

lead to the failure of their model. Finally, a last important methodological difference which could

explain the different conclusions is that M&P used a multivariate procedure for the estimation,

and then assumed that they can consider only one of their two equations to assess housing prices

valuation. Such problems illustrate an important point raised by the Dahlem report9 (2009). In

examining why the economists failed to warn about the impending crisis, it mentioned an

obvious problem of communication about the limitations of their models. The model from M&P

is rather employed as an illustration of an idea rather than an objective analysis of the US

housing market. The limitations of their model are not discussed. On this aspect the paper from

Soerensen, even if some other points could be criticized, gives a much more transparent

description of the construction of his model, and can be considered in this respect as a better

approach of modeling.

3. THE EMPIRICAL APPROACH

3.1 Motivations

The methodology presented here is frequently used to assess the value of an asset, see for

example Bessone and Boissiniot (2005), Antipa and Lecat (2009), Francke, Vujic and Vos

(2009), Wong (2001) and Shen, Chi-Man Hui Liu (2005) for the housing market or Miller and

Ratti (2009), Gautier (2001) for the stock market or Adland (2006) for the shipping industry.

The benchmark paper for the empirical approach is the paper from M&P (2004) which describes

an Error Correction (EC) model for the housing market. This approach gives a good description

of the variables and is based on economical foundations. Approaches similar to the one of M&P

are commonly used to study the housing market or to assess the value of assets. It defines an

9 The Dahlem report makes reference to the paper issued from the discussions in the Dahlem workshop : Colander, D. & Föllmer, H. &Haas, A. & Goldberg, M. & Juselius, K. & Kirman, E. & Lux, T. & Sloth, B. (2009) “The Financial Crisis and the Systemic Failure of Academic Economics,” Kiel Working papers 1489, Kiel Institute for the World Economy

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equilibrium in which some deviations can occur. The long-run equilibrium is defined in the

model by the cointegration relations. They are identified so as to be in agreement with the same

economic theory. The short-run dynamic is defined by the lags of the variables which are

considered as relevant for the definition of the dynamic of the variable.

We use the Johansen maximum likelihood estimation to estimate the long-run relations in a

Cointegrated Vector Autoregressive (CVAR) model. This method is used to deal with the

problem of spurious regression between I(1) variables, and allow to interpret the system in terms

of long run and short run dynamics. The CVAR model can be rewritten in an EC form. This

representation incorporated the influence of the deviations from the long-run relations in the

variables called EC terms. They stand for the difference between the observed values and the

values predicted by the long-run relations and impact the variables of the system and therefore

the system is driven back to the dynamic equilibrium10. M&P use the CVAR model to deal with

the problems of estimation of the long-run relations only. The short run coefficients are

estimated through an univariate procedure. In order to have a more objective description of the

data, we choose to estimate a CVAR model in its Vector Error Correction representation rather

than using an univariate approach; this choices takes into account the correlation between the

different variables and the different steps of the construction of the model can be tested.

With this model, we illustrate that an approach based on a similar theory and framework as M&P

could indicate some evidences of a bubble. We use this estimation to compare these results with

the one of an AutoRegressive (AR) process of order one. We show that the reliability of our EC

model is quite low and that a model without fundamentals like an AR(1) could find results which

are qualitatively similar to a model with fundamentals. From these results, we conclude that

when using this methodology,one has to be really carefull in the formulation of his conclusions.

3.2 Economic theory

Using simple ratios to evaluate the possible overvaluation of housing prices is confusing as the

link between the information of these different elements is not explicitly defined. A model of

10 A complete description of the CVAR model in terms of pushing and pulling forces can be fond in Johansen (1996)

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demand-supply can be used to link the different fundamentals in a unique framework. The real

housing prices, the ratio of consumption to housing stock, the real rents, the ratio of housing

prices to construction and the growth rate of the housing stock are variables which are supposed

to be stationary to some extend. These variables are related in these foundations through to

equations, deviations in one of them can persist as long as it would be compensated by a

deviation in one or several other variables. This formulation of the metrics in this model is less

restrictive than using them independently, and should be less likely to indicate some evidences of

a bubble. We present briefly here the theorical foundations used by M&P and our model. They

include a supply and a demand equation in addition to the notion of user cost.

On the demand side (Eq. 1), the ratio of housing prices ( ) to consumption prices (

) is a

function of the ratio of consumption () to housing wealth (), and of the user cost (). The

first term integrates that housing consumption should depend on its affordability.

!"#$% &, (1)

The permanent-income theory from Friedman (1957) states that the proportion of the permanent

income of households11 used for consumption should be stationary in the long run, because

consumption decisions are based on expected total lifetime income. Permanent deviations from

the ratio should be due to change in relative prices. We expect a positive coefficient as a

permanent increase in the ratio of consumption to the housing stock should be enhanced by a

change in the relative prices.

The user cost relates interest rate to the rent-to-price ratio through an arbitrage condition. It links

the housing market to the interest rate variable, and is used to estimate the impact of the

monetary policy on the housing market. The coefficient of this variable represents the degree of

substitution between renting and buying a house. Different approximations of the user cost are

employed in the literature. The one used in M&P (2004) is defined by equation (2):

(

) *1 ,

-.*/ ,. 012

3 (2)

11 Permanent income is usually proxy by consumption of non-durable and services as in M&P. We use the general definition of consumption which as been found to give more stability.

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Where 012)3 01 2

243 is the expected inflation of real housing prices, 2

is the housing

prices inflation and 24 is the consumption prices inflation. 5 is the real12 rental price, 6 is the

real housing price, ,-is the income tax rate, is the real depreciation rate including other cost

(reparation and maintenance cost). We expect a positive sign related to the user cost13. The last

term in the equation (1), stands for the deviations from the estimated demand equation and is

supposed to be stationary.

The housing stock is defined in our data according to the perpetual inventory equation (Eq. 4).

7 11 3 (4)

This equation is often used in the housing market empirical literature (e.g. Barot (2001),

Kenny(1999)). It defines the value of the housing stock which is also used as a proxy for the

quantity of housing unit provided14.

The equation (5) is the supply equation, and is based on the Tobin’s Q theory:

8

9

8

:;

8<!"#$% &, (5) .

The Q-theory represents the value of one extra unit of housing relative to its replacement cost. If

the Tobin’s Q is above one, the marginal value of one extra unit of housing is above its marginal

cost, and investment increase. It is often assumed that the shadow cost of construction is equal to

the construction cost (Summers (1981), Poterba (1991), Abraham and Hendershott (1996)). In

this analysis, due to the lack of data on the land prices, we allow for 8 to be different from -1 as

the construction cost is not properly defined. The coefficient of the growth rate of the housing

stock ⁄ is expected to be positive, and the trend is corresponding to the quarterly

depreciation rate so we expect 8<to be negative and equal to a few percentage points.

12 Real means here that the prices index is normalized using the consumer index prices 13 Our version of the user cost is a simplified one. Due to some data problem, we compute it using the approximation presented in the appendices. 14 The formulation of the model implies that there is no vacancy

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3.3 Empirical analysis

3.3.1 The model

This quarterly analysis is based on the following data set: is the real consumption per capita,

is the consumer prices index,

=> is the construction price index, is the housing

price index, is the real housing investment per capita, is the user cost of housing and ,

is the housing stock per capita. The dataset15 used has been transformed using log.

The initial representation of the data is the following:

∆X@ Γ∆X@ Γ∆X@ ΠXB@ ΦD E t = 1,…,T (6)

where the variables aredefinedasfollow: Dt is a vector of deterministic components. Γi, Π and Φ

are matrix of coefficients. The residuals εt are independent and identically distributed, with zero

mean and positive definite covariance matrix.

FG HF, I"#$%J′

F K , , ,,

=> , ,

L′ is the vector of endogenous variables,

D HMN$OI. J′

Under the assumption that our variables are integrated of order 1, the matrix Π is statistically not

full rank and a linear combination of the rows or columns can be found to approximate another

one such that this matrix can be decomposed as: Π 8R (7)

where 8 and are p×r full rank matrix, p ≤ r . are the cointegration vectors. They determine

the linear combination of the variables which are stationary and interpreted as being the long run

relations. 8 are the vector of parameters measuring the speed of adjustment of the system toward

its long-run equilibrium defined by the long-run relations. Γ∆X@ Γ∆X@ are the lags of the

variables in differences, they define the short run effect of the variables.

We proceed in an iterative way to determine the optimal number of lags, the rank, the

deterministic components, and the exogeneity assumption imposed on our model. Because the 15 Variables definition and sources are given in the Appendix A

Page 16 of 48


choice of each assumption will influence the results of the tests for the other assumptions, they

have to be determined jointly.

We use an unrestricted constant in our model. Based on the inspection of the data, it seems well

suited to have a trend restricted to the cointegration relations. This allows linear, but no quadratic

trends in the variables. This means that we can have trend-stationary variables or trend-stationary

cointegrating relations. From a visual inspection of the graph of the data in appendices, it seems

that a break in the trend will be required to take into account the change in the growth rate of

most of the variable. We identify it as being the results from the oil prices shock and the crash in

the housing market which occurred at this time. These events impacted the US economy as a

whole and are found to be statistically significant using a t-test.

Another structural change occurred at the beginning of the 2000’s due to the burst of the Dotcom

bubble. Using the data after 2000 may be misleading, as the period is subject to important

instability and the number of observations which could be gained from using M&P sample is

very low. The instability of the cointegration relations leads us to avoid using the exact same

sample as M&P. As a consequence, we restrict ourselves to a sample ranging until 2000 in the

estimation of the model in order to have good statistical properties. The estimation of the

unrestricted model features long swings in the cointegration relations. Using some seasonal

dummies doesn’t help to solve this problem. M&P use four lags in order to capture the unstable

dynamic of the system but this has been found not to be helpful in solving the problem in our

case. The low mean reversion of the cointegration relations at this step suggests that the

cointegration properties of the model are poor. In order to estimate cointegration relations that

would be robust to the choice of the sample, one has to find an important number of mean

reversions, indicating that the system is pushed or pulled toward the equilibrium defined by the

model.

In order to increase the quality of the cointegration relations of the model, we assume that some

of the variables are exogenous. Such an assumption is strong. The variables with this property

generate a stochastic trend which, in the long-run, drives the dynamic of the system but these

variables are not impacted by the system itself. Consumption and the user cost are found to be

statistically weakly exogenous. We imposed them as being strictly exogenous on the system.

Page 17 of 48


=> is also found statistically as being weakly exogenous. However, we decide not to impose

it as exogenous in the system as it doesn’t help to improve the model.

It can be seen in the graphs of the differences of the variables (appendices A: figures 5 to 11) that

some of the variables could be integrated of order two. In order to construct an I(1) CVAR

model, one has to carefully check the order of integration of the variables. The long swings

which can be found in the cointegration relations are often a sign of I(2) roots in the model. We

start the rank determination by checking that the model is not I(2) using a rank test.

Table 3.1 Models corresponding to the statistics presented in table 3.216

p-r r Models

5 0 10,03 T 10,13 T 10,23 T 10,33 T 10,43 T 10,53 = 103

Y

4 1 11,03 T 11,13 T 11,23 T 11,33 T 11,43 113

Y

3 2 12,03 T 12,13 T 12,23 T 12,33 = 123

Y

2 3 13,03 T 13,13 T 13,23 = 133

Y

1 4 14,03 T 14,13 = 143

S2 5 4 3 2 1 0

The table is constructed in the following way: a statistic is computed for each model under all the

possible numbers of I(1) and I(2) roots for each rank choice. p-r is the number of common

trends, s2 the number of I(2) roots and r the rank. These models are partially nested in the way

presented in the table 3.1 implying that the numbers of I(1) and I(2) roots/trends have to be

determined jointly. The models on the same row have all the same rank and are all nested, the

models with the higher number of I(2) roots are the most restricted. The models with no I(2)

roots are also all nested implying that it is possible to compare the models between different

rows. A model with a smaller rank number r is more restricted. This allows us to determine the

correct model starting from the top-left corner and proceeding row-wise until the first acceptance 16 Interpretation of the I(2) rank test can be found in Nielsen and Rahbek (2007), and a formal description of the I(2) model in Johansen (1995).

Page 18 of 48


level. All the previous models are rejected, and all the models with a significant probability

cannot be rejected. The model which should be chosen is the more global one. The number of

I(1) trend in our model is determined using the relation p-r=s1+ s2 where s1 is the number of I(1)

roots. Despite the swings which can be observed in the difference of the variables in appendix,

the I(2) rank test in table 3.2 suggests that an I(1) model with two I(1) roots is correct.17

Table 3.2 Test of the 2 rank indices in the unconstrained VAR p-r r 5 4 3 2 1 0

5 0 610.716 416.541 322.537 273.757 242.187 227.397

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

4 1 298.392 216.302 175.654 146.192 142.307

(0.000) (0.000) (0.000) (0.000) (0.000)

3 2 160.173 116.042 90.812 88.260

(0.000) (0.000) (0.000) (0.000)

2 3 76.377 53.323 47.493

(0.000) (0.000) (0.002)

1 4 26.817 18.753

(0.014) (0.081)

The results from the I(2) rank test are not very robust. The power of the test is generally very low

for I(2) or near I(2) data. This test is use to investigate the presence of I(2) but it shouldn’t be

used as a substitute for the I(1) rank test. Due to the low power of the rank tests, the choice of the

rank should be done using a much informaton as possible. Table 3.3 presents the results of the

I(1) rank test (Nielsen&Rahbek (2004)). This table gives the statistics for different rank number

of nested model. The models are nested in the following way: H1" 03 T H1" 13 T [ T

H1" 3, where p is the number of variable in the system. The choice of the rank is determined

by a top-to-bottom inspection of the table. We stop at the first significant probability which

indicates that the hypothesis of having a rank "\ is not rejected. Then the rank "\ is accepted.

The I(1) rank test suggests four cointegration relations in accordance with the results given by

the I(2) rank test.

17 For the purpose of assessing the value of housing prices, some I(2)ness in the model wouldn’t have any significant impact on our conclusion as it doesn’t impact the prediction properties of the model.

Page 19 of 48


The unrestricted cointegration relations found are approximatively stationary. This confirms the

number of cointegration relations determined by the rank tests.

The overall statistical properties of the residuals are good according to all the criteria18 and the

properties required to make correct inference are satisfied19. The skewness is small and the

kurtosis close to three. There is no significant autoregressive conditional heteroskedasticity effect

or important autocorrelation which implies that the quality of the estimates should be good. The

size and the number of large residuals are reasonably small enough to have good statistical

properties. There are no outliers that we should correct for, the kurtosis of the consumption

prices index is slightly above three, but still remains reasonable.

Table 3.3 Simulated rank test p-r r Eig.Value Trace Frac95 P-Value

5 0 0.659 227.397 119.146 0.000 4 1 0.495 142.307 90.184 0.000 3 2 0.403 88.260 63.123 0.000 2 3 0.305 47.493 40.280 0.018 1 4 0.211 18.753 20.861 0.129

The table 3.4 indicates that housing prices have the biggest standard error. This is due for a large

part to the asset properties of the housing stock which is subject to speculations of investors. The

correlation of the residual is important between investment and housing stock. Using a

multivariate estimation of the model allows us to take into consideration the correlation in the

residuals. The other correlation coefficients are significant and will make the identification of the

short-run structure less precise but won’t affect the results of the estimation of the long-run

equations.

Table 3.5 Lags length selection test Model Lags T Regr Log-Lik SC H-Q

VAR(5) 5 76 41 2458.126 -53.006 -56.780 VAR(4) 4 76 34 2395.497 -53.352 -56.482 VAR(3) 3 76 27 2346.937 -54.069 -56.554 VAR(2) 2 76 20 2317.447 -55.287 -57.128 VAR(1) 1 76 13 2244.530 -55.363 -56.559

18 The complete results of this test can be found in the appendix 19 In our particular case, the choice of this assumption is very important as the failure in some particular assumptions (see Nielsen and Rahbek 2000 for the relative importance of the failure of these assumptions) would have important consequences on the appropriate choice of rank

Page 20 of 48


The table 3.5 presents the results from the lag length selection tests. The number of lag suggested

by the Hannan Queen test is 2 and by the Schwartz criterion is 1. The choice of only one lag is

likely to fail to catch the dynamic of the system of variables, and choosing a higher number of

lags would increase too much the number of parameters to estimate without improving

significantly the fit.

3.3.2 Long run identification

The model has been estimated on the sample 1981:1 to 1999:4 as it has been found to have better

statistical properties. Our identification of the economic foundations is accepted only when we

restricted ourselves to this sample. Consumption and the user cost are imposed as exogenous. A

structural break has been modeled through a permanent dummy on the period ranging from

1990:01 to 1999:4.Using longer periods would probably lead to find a model subject to a bias

due to the instability after 2000, and lead to find less obvious evidences of bubbling growth. By

choosing such sample, we assume that the fundamentals are well defined on this period after

modeling the 90’s periods with a structural break, and that the fundamental equations defined by

our model can still be applied on the periods after the Dotcom bubble.

Our final statistical representation of the data is the following:

∆X@ Γ∆X@ Γ∆X@ αβ′XB@ ΦD E t = 1,…,T (8)

where the variables are defined as previously. %_`9001 and b"#cd9001 are the restricted

deterministic component corresponding to the inclusion of the break in the trend.

FG HF, , , b"#cd9001, I"#$%J′

F H ,

, , ,

=>J′,

D H%_`9001, MN$OI. J′

The β structure is identified with a relatively low acceptance probability of the overidentifying

restriction of 0.079. The estimated Tobin’s Q equation for the supply side is:

1.009 9

0.327

:;

0.006 !"#$% &, (9)

Page 21 of 48


The coefficient of the real construction price index is lower than what we would expect but it has

the correct sign. The trend can be interpreted as being the depreciation of the housing stock. It

implies an annual depreciation rate of 2.4% in agreement with what we would expect. The

estimates found are of the expected sign and they are all significant20.

The estimated housing demand is:

0.756

0.312 0.009 !"#$% &, (10)21

We assumed a unitary elasticity between housing wealth and consumption as in M&P in order to

find estimates of the correct value. All signs are of the correct sign and are significant.

In addition to these two equations predicted by the economic theory, we have two other

equations describing the dynamic equilibrium of the system. The first relation (Eq. 11)

approximates the housing stock as a broken stochastic trend.

0.015 !"#$% 0.01b"#cd90 &<, (11)

This restriction is accepted due to the fact that the variation of the housing stock is very small

compared to its absolute value. The graph of the first differences of the housing stock shows

clearly a change in its growth rate at the beginning of the 90’s.

And the second relation is an empirical relation approximating the long-run relation between

investments and housing stock.

3.2 0.002 !"#$% & , (12)

The graphs of constancy22 presented in appendix are used to detect some possible instability in

the model. They are the result of the computation of some elements on different sample. The low

fluctuations of these elements in the R-form suggest that the long-run estimates are more robust

20 The complete results of the estimation can be found in the appendix. 21 Equation (10) has been restricted following M&P so as to have ratio of consumption to housing wealth equal to the unity. Consumption has been found weakly exogenous and the exogeneity has been imposed on the system. It seems that our system doesn’t integrate a long-run impact of the collateral constraint. The weakly exogeneity results indicate that consumption is not affected by the other variables in the long run. Due to the large number of omitted factors influencing the evolution of consumption, it was difficult to relax this assumption without creating an important instability of the system. 22 For an explanation about the way to interpret these graphs, see “the cointegrated VAR model” from K. Juselius

Page 22 of 48


than the short run, but their stability seem not very good. The important differences between the

R and the X form indicate that the short-run estimates are more sensitive to the sample used for

their estimation. The stability of the coefficients of the long run relation(s) as well as the

acceptance probability of the identifying restrictions are important indicators of the stability of

the fundamental relation(s) defined.

Equations (9) and (10) are consistent with the economic theory. All the signs found are correct

and the coefficients are all significant23. Equations (10) and (11) are additional relations more

difficult to relate to the economic theory. We are not able to reproduce perfectly the results from

M&P (2004). Our data don’t contain the same information as they are defined differently, and

we may have interpreted the results of some tests differently.

However, our model reflects perfectly the information contained in our dataset, and offer a more

transparent description of the information contain in the data which ensures a higher degree of

reliability. The p-value of 0.079 found may seem small at first sight for the reader compared to

the one found in M&P. This may be due to some data problems such as the construction of the

housing variable or the user cost but it can be also due to the differences in the hypothesis

imposed on the model.

The cointegration relations presented in appendix F features some important non-stationarity.

They can be due to some I(2) features of our model not detected by the I(2) rank test or to the

difficulty to catch the long swings of the housing market by our model. The non-stationarity

becomes more important in the periods after 2000. The slow mean-revertion of the cointegration

relations makes it difficult to interpret them as being long-run relations. A large number of mean

reversions of the cointegration relation(s) increasing the evidences that some corrections occur in

the market toward the relation found. The number of mean reversion in M&P (2004) is relatively

low, this could explain why the model failed to give the good results.

Another argument that can be given is that the fundamental relation has been affected by non-

modeled structural break and that the true relation may have changed. For example, one could

consider the possibility that the proportion of wealth invested in the housing market increases

relatively to the other asset markets due to a change in the relative risk premium attributed to the

housing market after the burst of the Dotcom bubble. This question would need to be assessed. It 23 The complete table of the results can be found in the appendices.

Page 23 of 48


is left out without any answer as the aim of this analysis is not to assess the existence of a

bubble, but to point the important issues related to the construction of such model. A structural

break in our model, it does not have an impact on the supply and demand equation directly but it

has an impact on the demand equation through the estimation of the short-run parameters and of

the other cointegration relations.

A third arguments can be used to explain their failure. Our four long-run relations describe

jointly the long-run equilibrium of the model. A shock can create a deviation from one or several

of these relations but in the long-run, the system is pushed back to the equilibrium and these

deviations do not persist. Deviations from the cointegration relations are the error correction

(EC) term and are noted as &g,. The EC terms have to be considered jointly. The nonstationarity

in one cointegration relation can be balanced by the nonstationarity in one other relation

implying that they compensate which would implied that the system is still in equilibrium. By

construction, all the cointegration relations are linked as it is indicated by the equation (7). The

assumption that the fundamental prices is driven by the demand side as it is used by M&P

simplify the analysis, but may lead even a correct model, to produce misleading conclusions.

3.3.3 EC representation

M&P assume that some additional variables are required to describe the short-run dynamics to

their Error Correction Model (EC). This choice of using some variables for the short-run only

can be justified by the willingness to impose the intensively used theorical foundations from

Poterba. However, including these variables in the long-run estimation and testing whether they

were significant or not is econometrically more correct. Their choice may not be able to give an

appropriate representation of the local data generating process defined in the general-to-specific

methodology24. This methodology ensures that the resulting ECM would be congruent with the

description of the data given by the original CVAR model. The general-to-specific approach

favors the empirical quality of the model over its economical foundations. The drawback of such

approach is that it is likely to reject the theorical foundations that were expected. However, such

choice can be justified as the loss of the reliability grant by the economical foundations may be

24 a complete description of the methodology can be find in Campos, Ericsson Hendry (2005)

Page 24 of 48


compensated by the gain in reliability due to the improvement in the statistical properties of the

model.

In this approach, the reduction of the model is tested until a reasonably good identification is

reached. We use a likelihood ratio test to assess the validity of the elimination of each non-

significant coefficient. We discriminate between the elimination of the coefficients toward the

“best”25 possible identification using the significance of the coefficients.

M&P assumed that the housing market is driven only by the demand. This assumption is in

accordance with the idea that the supply is inelastic but may not be able to give a correct

description of the housing prices dynamic as it was previously mentioned. They chose to assess

housing prices valuation using only the EC term from the demand side. Following the general-to-

specific methodology, this assumption is initially relaxed, by estimating a more general form in

which all the possible EC terms are included. Then, the significance of each of the EC terms is

tested in order to select which one could be removed from the EC equation without a significant

loss of information.

We identify a Vector Error Correction Model (VECM) from our dataset using the sample

ranging until 1999:4. From the constancy graph introduced previously, we saw that the short-run

estimates were subject to an important instability depending on the sample used. Using data

after 2000 would create even more instability in the estimates. In a “real time” modelization of

the housing sector, one should try to construct some model using different samples, in order to

assess how the short-run estimates26 impacted the conclusion given by the model. Using a

sample until 2003:3 as M&P would create a bias in the estimates due to the important growth of

the housing prices at the beginning of the 2000’s therefore may not reflect the fundamental

relation precisely. The model of Soerensen is less subject to this critique. By using annual data,

the number of observation impacted by the burst of the Dotcom bubble is very low, and so their

influence on the parameters estimates smaller. This way of dealing with the parameters

instability is the more objective one, it avoids that through the choice of the sample, one may

also create a bias in the estimate that is not reflecting the true fundamental relation.

25 The identification given here is not unique. Best is used in term of precision of the short-run parameter estimates. 26We found that our long run estimates are less subject to instability, but this question may have to be addressed. For example, the long-run estimates found by M&P in the paper from 2002 and the one from 2004 are quite different. An additional restriction is required in the paper of 2004 in order to identify the same theorical foundations.

Page 25 of 48


Our model is not able to give clear evidences of the existence of a bubble based on the

observations of the long run relations only. We assess the quality of the fundamentals used by

comparing the bubble produce using the complete ECM with the one produce by a model

without fundamentals. This way of processing doesn’t allow considering the long-run relation

only. Due to the important number of cointegration relations use to describe the long-run

dynamic of the system, using only the demand equation would produce misleading results as it

wouldn’t describe properly the equilibrium of the model. The stability of the whole model may

be comparable to the one found for the demand equation only.

To avoid the over-parameterization of the model, we use only one lag for the short-run dynamic.

The equation identified is the following27 :

Δ 0.1153Δ 0.5956Δ 0.0314690i1 0.04704150i2 0.111011

where 0i1, 0i2 stand respectively for the error correction term of the equations (9)

and (10). The two other correction terms have been found to be statistically non-significant.

Housing prices are found to be driven in the short-run by the demand factors which are

consumption and the user cost. Supply side fundamentals are not found to have a direct impact

on housing prices in the short-run, but it is found to have an indirect impact through its

corresponding EC term, in opposition to what is assumed by M&P.

3.3.4 Interpretation of the results

It is difficult to formulate clear conclusions from the error correction graph presented in the

appendix F. Even if one can observe some persisting positive deviations from the cointegration

relations for demand and supply, it is hard to conclude whether they are significant or not. And

this is even more difficult when one had to the analysis the two remaining cointegration relations

as it should be.

In order to illustrate the issues of determining whether deviations is significant or not, we

construct a simple AR(1) model, as benchmark for the results found by the EC model. The

27 The complete indentified VECM model is given in the appendices.

Page 26 of 48


AR(1) model is not using any fundamentals contrarily to the EC model but it should be able to

catch the speculation process through an autoregressive expectation mechanism.

An AR(1) model doesn’t integrate the impact of the fundamentals on the housing prices. It is

well-suited to describe a process with a constant mean and some persistence in the dynamics

such as inflation. The estimated AR(1) with constant term is the following :

Δ 0.3536Δ

0.007

The graph 3.1 presents the one-step-ahead housing inflation prediction from the EC model and

the AR model. The predicted prices defined by both models are rather similar. This observation

shows that a model with fundamentals, the EC model, and a model without fundamentals, the

AR model, can produce results which are qualitatively comparable. They both suggest that the

housing prices were deviating from the equilibrium prices. And produce graphs giving more

evidences that the housing prices were bubbling.

Graph 3.1 Observed and computed housing inflation

Page 27 of 48


The strong similarities of the deviations in the two models suggest that it is difficult to determine

the amplitude of the deviations needed in order to state that the housing prices are creating a

bubble. In a real-time analysis, in light of these results, one could argue to someone presenting

evidences of a bubble using this methodology that his model is not doing better than being

enable to explain the growth of housing prices. The omission of some fundamentals responsible

for the important growth in housing prices may lead to the detection of a bubble even if it doesn’t

exist. The question about whether the fundamentals used in this kind of model produces a proper

description of the housing prices inflation dynamics is difficult to address. Soerensen also

reaches this conclusion in his analysis. A complete analysis of the housing market would need to

assess clearly the relevance of all fundamentals, to determine whether some have been omitted.

Graph 3.2 Bubble processes

The graph 3.2 presents the observed inflation minus the equilibrium prices defined by the one-

step-ahead prediction for both models. It represents the bubble dynamic according to definition

implied by our framework for the AR model and the ECM. It is difficult to distinguish some

important differences in the two bubbles process. One has to be careful in choosing the

fundamentals. The omission of some important factors or the inclusion of some non-relevant

Page 28 of 48


factors may lead to inaccurate results. This graph shows that without any criteria to assess the

difference between the two curves, it is not possible to conclude with certainty whether the

deviations are due to a failure of the model to catch the change in growth rate or whether it is due

to a growth not explained by the fundamentals.

Graph 3.3 Observed and fundamental real housing prices

In the graph 3.3, we assume that the housing inflation price index is equal to the fundamental

prices in the first observation. The position of the equilibrium prices curve is often chosen in a

way that the average difference between the observed prices and the fundamental prices is equal

to zero, reflecting the assumption that the deviations from the fundamental relation should be

stationary. The graph 3.3 makes clearer the differences between the AR and the EC model than

the graph 3.2. There is an important difference in the 90’s between the prices implied by the two

models. This is due to the fact that the crash in the housing prices in this period is modeled in

the ECM with a structural break. This illustrates one of the critical point of this methodology,

about the importance of structural break in the definition of the fundamentals relation. One can

think that some events impacts the relation between housing prices and fundamentals. For

example, one could think that the burst of the Dotcom bubble changed the proportion of wealth

Page 29 of 48


that was invested in the housing sector due to a change in the relative risk aversion of investors.

M&P don’t mention any possible structural break, but it is important to consider them in order to

produce a model with good statistical properties and stable cointegration relations. The influence

of the growth due to expectations during the bubbling periods is partly taken out of the value of

the coefficients related to the fundamental through their use.

The graphs 3.1 and 3.3 show clearly that the housing prices were growing faster than what was

implied by the models. By the end of 2003, some evidences of housing overvaluation could have

been observed from our EC model. But to conclude whether this observation was significant

enough or not to conclude to the existence of a bubble is difficult to determine. Such analysis

would probably be not sufficient to conclude to the existence of a bubble in the context where

the two papers presented here have been published. The choice of a methodology that define a

bubble as being the error produce by a fundamentals model seems to be not satisfying.

Page 30 of 48


4. CONCLUSION

The conclusions that could be drawn from this methodology are not robust. Defining a bubble as

being the error of a model requires the assumption that the model constructed is correct and

complete. Such assumption is strong and difficult, if possible, to demonstrate.

The problem in M&P’s model is not only that it gave the wrong conclusion but also and mainly

that the lack of details in the construction of their model which doesn’t allow us to identify

clearly its failures. The critics about Soerensen methodology are more difficult to formulate as

the conclusion reached was the correct one. However, his choice of the fundamentals used seems

to be very poor and it is likely not to represent the true fundamental relation. Moreover, the

questions of the stability of the relation and of the degree of significance of the deviations cannot

be addressed.

The important question for policy-makers is the degree of reliability that can be given to the

conclusions of the model. The quality of the statistical properties of the model is an important

issue. The comparison between the results produced by the ECM and the AR model suggest that

it is difficult to distinguish between a lack of fit of the model and a bubble. This methodology

relies mainly on the assumption that the estimated relation is properly defined. It seems that such

analysis cannot be judged as sufficient by itself to obtain the reliability sufficient in order to

make any important choices. Such model is subject to important instability due to the numerous

variables close to be integrated of order two. The CVAR model and its EC form is a good

representative of the model which can be used in what is called the general-to-specific approach.

This approach offers a framework in which every step of the analysis can be tested rather than

assumed. Its correct implementation would insure more reliability to the model than many of the

studies found in the literature which are imposing restrictions based on theorical a priori only.

In order to produce more acceptable results, some issues have to receive careful attention. A

good analysis should try to assess the impact of as many variables as possible to ensure that the

definition of the fundamentals used is complete. In addition to this, many other issues have to be

considered: the stability of the coefficients, the possible existence of non-modeled event(s)

affecting the fundamental relation(s), and the choice of the sampling period. The modeler should

carry out a sensitivity analysis of the impact of the different choices made upon the conclusions

Page 31 of 48


of the model. Transparency in the construction of the model is necessary in order to give some

reliability to the conclusions from the model. The estimation of a relation relating a non-

observed variables such as the equilibrium prices to its fundamentals is a difficult exercise.

Two of the recommendations from the Dahlem report (2009) are that economists should

communicate the limitations of their models and that the methodology used to construct models

should be more data-driven. This paper illustrates these recommendations.

This methodology is also subject to two important limitations which point toward new directions

of research. It doesn’t model the expectations process which is of main importance to assess the

existence of speculation, some expectations mechanism could be integrated in a Dynamic

Stochastic General Equilibrium model. And this methodology use of macroeconomics

datawhereas the housing market features important heterogeneities due to geographic

differentiation. In order to be able to identify local bubbles which may not be identifiable to the

aggregate level one could consider a dynamic panel data approach.

Page 32 of 48


REFERENCES

Adland, R. & Haiying Jia & Siri Strandenes (2006), “Asset Bubbles in Shipping? An analysis

of Recent History in th Drybulk Market,” Maritime Economics and Logistics, Palgrave

Macmillan Journals, vol. 8(3), pages 223-233, September.

Barot, B. (2001). "An Econometric Demand–Supply Model For Swedish Private Housing,"

European Journal of Housing Policy, Taylor and Francis Journals, vol. 1(3), pages 417-444,

December.

Berger-Thomson, L. & Ellis L., (2004), "Housing Construction Cycles and Interest Rates", RBA

Research Discussion Papers rdp2004-08, Reserve Bank of Australia.

Bessone, Heitz and Boissinot (2005) “Marché immobilier, voit-on une Bulle ?”, INSEE note de

conjoncture.

Case, Karl E & Shiller, Robert J, 1989. "The Efficiency of the Market for Single-Family

Homes," American Economic Review, American Economic Association, vol. 79(1), pages 125-

37, March.

Case, K.,Quigley, J. & Shiller, R. J., (2001), "Comparing Wealth Effects: The Stock Market

versus the Housing Market," Cowles Foundation Discussion Papers 1335, Cowles Foundation,

Yale University.

Case, K. & Shiller, R. J., (2003-2), "Is There a Bubble in the Housing Market?," Brookings

Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34,

pages 299-362.

Colander, D. & Föllmer, H. &Haas, A. & Goldberg, M. & Juselius, K. & Kirman, E. & Lux, T.

& Sloth, B. (2009) “The Financial Crisis and the Systemic Failure of Academic Economimcs,”

Kiel Working papers 1489, Kiel Institute for the World Economy

Demers, F. (2005), "Modelling and Forecasting Housing Investment: The Case of Canada",

Working Papers 05-41, Bank of Canada.

Dennis J. G. (2006), CATS in RATS Cointegration Analysis of Time Series, version 2, Estima

Page 33 of 48


DiPasquale, D. (1999)"Why Don't We Know More About Housing Supply?" Journal of Real

Estate Finance and Economics, 18:1, 9-23

Dirk, J. (2009) “”No One Saw This Coming”: Understanding Financial Crisis Through

Accounting Models”, MPRA Paper No 15892

Finicelli, A., (May 2007), “House Price Developments and Fundamentals in the United States”,

Bank of Italy Occasional Paper No. 7.

Francke, M. & Vujic, S. & Vos, G. (2009) Evaluation of the House Price Models Using an ECM

Approach: The Case of the Netherlands source 16th Annual European Real Estate Society

Conference in Stockholm, Sweden

Gauthier, C. 2000. “The Fair Value of the U.S. Stock Market: A Structural VECM Approach.”

Department of Finance, Canada.

Haldrup, N. (1994), The Asymptotics of Single Equation Cointegration Regression Models with

I(1) and I(2) variables. Journal of Econometrics 63, 153-181.

Hatzius, J. (2004), “Housing and the U.S. Consumer: Mortgaging the Economy’s Future”,

Goldman Sachs Global Economics Paper 83.

Himmelberg, C., Mayer C. and Sinai. T. (2005), "Assessing High House Prices: Bubbles,

Fundamentals and Misperceptions" Journal of Economic Perspectives, v19(4,Fall), 67-92.

Isaac F., Megbolugbe ; Allen P., Marks ; Mary B., Schwartz. (1991) The Economic Theory of

Housing Demand: A Critical Review. Journal of Real Estate Research.

Jorgensen R. G. (1963), ″Capital Theory and Investment Behavior″, American Economic Review,

n° 53, pp. 247-259.

Juselius, K. and Johansen, S., 2005. "Extracting Information from the Data: A Popperian View

on Empirical Macro," Discussion Papers 05-05, University of Copenhagen. Department of

Economics.

Juselius, K (2006), The cointegrated VAR model, Oxford University Press

Page 34 of 48


Juselius, Katarina (1999) “Price convergence in the medium and long run. An I(2) analysis of six

price indices,” Discussion Papers 97-21, University of Copenhagen. Department of Economics,

Revised Sep 1999

Juselius, Katarina. (2007) “The PPP puzzle: What the Data Tell When Allowed to Speak,”

Discussion Papers 07-33, University of Copenhagen. Department of Economics, revised Dec

2007.

Juselius, K. and Massimo F. (2007) “Taking a DSGE Model to the Data Meaningfully.”

Economics–The Open-Access, Open-Assessment E-Journal.

Kenny, G., (1999)."Asymmetric Adjustment Costs and The Dynamics of Housing

Supply,"Research Technical Papers 3/RT/99, Central Bank & Financial Services Authority of

Ireland (CBFSAI).

Krainer J. and Wei C., (2004), "House prices and fundamental value", FRBSF Economic Letter,

Federal Reserve Bank of San Francisco, issue Oct 1.

Learner, E. E. (2002), “Bubble Trouble? Your Home Has a P/E Ratio Too”, UCLA Anderson

Forecast.

Malpezzi, S. and Maclennan, D., (2001). "The Long-Run Price Elasticity of Supply of New

Residential Construction in the United States and the United Kingdom," Journal of Housing

Economics, Elsevier, vol. 10(3), pages 278-306, September.

Malpezzi, S. (1999), “A Simple Error Correction Model of House Prices” Journal of Housing

Economics, Elsevier, vol. 8(1), pages 27-62, March

Mankiw, G. and D.N. Weil, (1989), “The Baby Boom, the Baby Bust and the Housing Market”,

Regional Science and Urban Economics, vol. 19, pp. 235-258

McCarthy, J. and Peach, R. W. (2002),”Monetary Policy Transmission to Residential

Investment.” Economic Policy Review, Vol. 8, No. 1, May.

McCarthy, J. and Peach, R. W. (2004), “Are Home Prices the Next Bubble?” Economic Policy

Review, Vol. 10, No. 3, December.

Page 35 of 48


Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and

bubbles," Energy Economics, Elsevier, vol. 31(4), pages 559-568, July.

Mishkin, F. S. (2007). "Housing and the Monetary Transmission Mechanism”, NBER Working

Papers 13518, National Bureau of Economic Research, Inc.

Nielsen, H. B. and Rahbek, A. (2007). The likelihood ratio test for rank in the I(2) model.

Econometric Theory, 23 , pp 615-637

Poterba, J. M, (1984). "Tax Subsidies to Owner-occupied Housing: An Asset-Market Approach,"

The Quarterly Journal of Economics, MIT Press, vol. 99(4), pages 729-52, November.

Poterba, J. M. (1991-2), "House Price Dynamics: The Role of Tax Policy," Brookings Papers on

Economic Activity, Economic Studies Program, The Brookings Institution, vol. 22, pages 143-

204.

Rosen S. and Topel R. H., (1986), "A Time-Series Model of Housing Investment in the U.S",

NBER Working Papers 1818, National Bureau of Economic Research, Inc.

Shen Guo, (2009) “Collaterality and the Housing Wealth Effect,” Working papers 0914, Florida

International University, Department of Economics

Shiller, R. J. (2004). "Household Reaction to Changes in Housing Wealth," Cowles Foundation

Discussion Papers 1459, Cowles Foundation, Yale University.

Sorensen, J. K. (2006): The Dynamics of House Prices - International Evidence. Unpublished.

Wheaton, W 1999. "Real Estate “Cycles”: Some Fundamentals," Real Estate Economics,

American Real Estate and Urban Economics Association, vol. 27(2), pages 209-230.

Yue Shen, Eddie Chi-man Hui, Hongyu Liu (2005) “Housing price bubbles in Beijing and

Shanghai” Management Decision Volume: 43 Issue: 4 Page: 611 – 627

ECONOMETRIC SOFTWARE

Estimation of the Cointegrated VAR model has been realized using WINRATS 6.2/CATS

version 2 (Dennis et al. 2005.)

Estimation of the ECM has been done using PcGive version 12 and Matlab 7.8.

Page 36 of 48


APPENDICES

A- Data

User cost is often found to be negative; this implies that it is not possible to do a log-transformation of the

variables. We avoid this problem by using the 30 years mortgage rate as interest rate. The value of this

interest rate is sensibly higher but its fluctuations are very close to the fluctuations are quite similar, and it

doesn’t impact the results. We construct the user cost as being the the nominal interest rate and set the

quarterly depreciation rate at one percent. The expected house price expectations is approximated using a

12 quarter Moving average of past prices inflation.The user cost has been constructed using the mortgage

rate as the nominal interest rate and set the quarterly depreciation rate to 1% and we approximate house

price expectations with a 12 quarter moving average of past house price inflation rates.

The housing stock has been constructed using the perpetual inventory equation. The quarterly

depreciation rate chosen is of 0.0625%, and the initial value of the housing stock has been chosen such

that the variable constructed was able to reproduce some features of the housing stock observed in the

annual data. The housing prices index is the OFHEO index, this is the most commonly used in the

literature, and is used in M&P too.

The data are quarterly and obtained from the BEA, they are seasonally adjusted. Investment housing stock

and consumption are real and per capita. Prices index are transformed taking log.

Page 37 of 48


Page 38 of 48


Page 39 of 48


B- STATISTICAL RESULTS

RESIDUAL ANALYSIS: Tests for Autocorrelation Ljung-Box(19): j(430) = 969.273 [0.000] LM(1): j (7) = 41.732 [0.019] LM(2): j (7) = 38.263 [0.044] Test for Normality: j (10) = 10.554 [0.393] Test for ARCH: LM(1): j (225) = 255.867 [0.077] LM(2): j (450) = 446.981 [0.531]

Mean Std.Dev Skewness Kurtosis Maximum Minimum ∆ ln 6

-0.000 0.004 0.271 3.045 0.013 -0.008 ∆ ln 6

=> -0.000 0.002 0.145 2.886 0.006 -0.006 ∆ ln -0.000 0.017 0.077 3.130 0.044 -0.038 ∆ ln 6

0.000 0.001 0.547 3.782 0.004 -0.004 ∆ ln -0.000 0.001 0.052 3.046 0.003 -0.003

ARCH(2) Normality R-Squared ∆ ln 6

1.792 [0.408] 1.208 [0.547] 0.601 ∆ ln 6

=> 1.468 [0.480] 0.380 [0.827] 0.862 ∆ ln 0.220 [0.896] 0.819 [0.664] 0.827 ∆ ln 6

0.486 [0.784] 4.706 [0.095] 0.914 ∆ ln 6.672 [0.036] 0.521 [0.771] 0.996 Residual correlation coefficients and standard error

Correlation ∆pHouse ∆ => ∆ ∆ ∆

∆ 1.000

∆ => -0.365 1.000

∆ -0.231 -0.027 1.000

∆ -0.149 0.325 0.081 1.000

∆ -0.347 0.226 0.714 0.448 1.000

Standard error 0.0038690 0.0021339 0.0169321 0.0013858 0.0010940

Page 40 of 48


LARGE RESIDUALS: SRes_ln 6

: 2 entries of 79 (2.5%) have absolute value larger than 1.96 (threshold) SRes_ln 6 =>: 6 entries of 79 (7.6%) have absolute value larger than 1.96 (threshold) SRes_ln : 6 entries of 79 (7.6%) have absolute value larger than 1.96 (threshold) SRes_ln 6 : 6 entries of 79 (7.6%) have absolute value larger than 1.96 (threshold) SRes_ln : 4 entries of 79 (5.1%) have absolute value larger than 1.96 (threshold)

C- NORMALITY GRAPH

DPH

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035Actual and Fitted

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-2.4

-1.6

-0.8

-0.0

0.8

1.6

2.4

3.2Standardized Residuals

Lag

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00Autocorrelations

-2.7 -1.8 -0.9 -0.0 0.9 1.8 2.7 3.60.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45SB-DH: ChiSqr(2) = 0.75 [0.69] K-S = 0.94 [5% C.V. = 0.10] J-B: ChiSqr(2) = 0.48 [0.79]

Histogram

DLHP

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 20000.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08


1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-3.2

-2.4

-1.6

-0.8

-0.0

0.8

1.6

2.4


Lag

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75


-3.6 -2.7 -1.8 -0.9 -0.0 0.9 1.8 2.70.00

0.08

0.16

0.24

0.32

0.40

0.48


Histogram

Page 41 of 48


DLIP

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-0.15

-0.10

-0.05

0.00

0.05


1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-2.4

-1.6

-0.8

-0.0

0.8

1.6

2.4


Lag

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75


-2.7 -1.8 -0.9 -0.0 0.9 1.8 2.70.0

0.1

0.2

0.3

0.4

0.5


Histogram

DPCO

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

0.025


1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000-3.2

-2.4

-1.6

-0.8

-0.0

0.8

1.6


Lag

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75


-3.2 -1.6 0.0 1.6 3.20.0

0.1

0.2

0.3

0.4


Histogram

DPC

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 20000.000

0.005

0.010

0.015

0.020

0.025


1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000-4

-3

-2

-1

0

1

2

3Standardized Residuals

Lag

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75


-3.6 -2.4 -1.2 0.0 1.2 2.4 3.60.00

0.08

0.16

0.24

0.32

0.40

0.48


Histogram

Page 42 of 48


D- CONSTANCY GRAPH

Eigenvalue Fluctuation Test

Tau(Ksi) = C(T)||Ksi(t)-Ksi(T)||

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)

5% C.V. (1.36 = Index)Tau(Ksi(1))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)

5% C.V. (1.36 = Index)Tau(Ksi(2))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)

5% C.V. (1.36 = Index)Tau(Ksi(3))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)

5% C.V. (1.36 = Index)Tau(Ksi(4))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)

5% C.V. (1.36 = Index)Tau(Ksi(1)+...+Ksi(4))



1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(1))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(2))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(3))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(4))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(1)+...+Ksi(4))

Trace Test Statistics

The test statistics are scaled by the 5% critical v alues of the `Basic Model'

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 19920.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25X(t)

H(0)|H(5) H(1)|H(5) H(2)|H(5) H(3)|H(5) H(4)|H(5)

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 19920.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25R1(t)

Page 43 of 48




1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(1))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(2))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(3))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(4))

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.00

0.25

0.50

0.75

1.00X(t)R1(t)5% C.V. (1.36 = Index)

Tau(Ksi(1)+...+Ksi(4))

Test of Beta Constancy

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 19920.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6X(t)

R1(t)

5% C.V. (4.67 = Index)

Q(t)

Test of Beta Constancy

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20000.0

0.2

0.4

0.6

0.8

1.0

1.2X(t)

R1(t)

5% C.V. (4.67 = Index)

Q(t)

Page 44 of 48


E- IDENTIFICATION OF THE LONG RUN RELATIONS TEST OF RESTRICTED MODEL: j1103 16.803 H0.079J ln 6

ln 6 => ln ln 6

ln ln ln T(1990:01) TREND

-288.057 94.323 290.648 193.733 -290.648 0.000 0.000 0.000 1.717 -393.040 0.000 0.000 393.040 -297.184 297.184 -122.516 0.000 3.481 < 0.000 0.000 0.000 0.000 74.706 0.000 0.000 0.721 -1.140 0.000 0.000 270.416 0.000 -84.494 0.000 0.000 0.000 -0.540

BETA(transposed) ln 6

ln 6 => ln ln 6

ln ln ln T(1990:01) TREND

1.000 -0.327 -1.009 -0.673 1.009 0.000 0.000 0.000 -0.006 (.NA) (-2.854) (-20.729) (-5.862) (20.729) (.NA) (.NA) (.NA) (-8.527)

1.000 0.000 0.000 -1.000 0.756 -0.756 0.312 0.000 -0.009 (.NA) (.NA) (.NA) (.NA) (20.723) (-20.723) (5.305) (.NA) (-10.245)

< 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.010 -0.015 (.NA) (.NA) (.NA) (.NA) (.NA) (.NA) (.NA) (11.125) (-13.672)

0.000 0.000 -3.200 0.000 1.000 0.000 0.000 0.000 0.006 (.NA) (.NA) (-23.267) (.NA) (.NA) (.NA) (.NA) (.NA) (1.625)

α 8 8 8< 8

∆ ln 6 -0.001 0.000 0.089 -0.039

(-0.013) (0.001) (2.538) (-0.314)

∆ ln 6 => -0.160 0.276 -0.005 0.229

(-3.205) (2.736) (-0.281) (3.298)

∆ ln -1.625 3.374 0.459 2.046

(-4.301) (4.417) (3.149) (3.890)

∆ ln 6 -0.011 -0.029 -0.078 0.039

(-0.342) (-0.448) (-6.377) (0.884)

∆ ln -0.130 0.271 0.009 0.193

(-5.187) (5.372) (0.882) (5.536) Π matrix ln 6

ln 6 => ln ln 6

ln LCP UC T(1990:01) TREND

∆ ln 6 0.002 -0.001 -0.041 -0.001 0.103 0.000 -0.000 0.001 -0.001

(0.033) (-0.013) (-1.841) (-0.012) (2.834) (0.001) (-0.001) (2.538) (-2.976)

∆ ln 6 => -0.038 -0.078 -0.011 0.116 -0.046 0.209 -0.086 -0.000 0.001

(-1.351) (-3.205) (-0.877) (2.252) (-2.288) (2.736) (-2.736) (-0.281) (2.739)

∆ ln -0.957 -0.791 -0.392 1.749 -0.293 2.551 -1.052 0.004 0.004

(-4.473) (-4.301) (-4.184) (4.480) (-1.935) (4.417) (-4.417) (3.149) (2.429)

∆ ln 6 0.045 -0.005 0.023 -0.039 -0.052 -0.022 0.009 -0.001 0.001

(2.498) (-0.342) (2.893) (-1.209) (-4.114) (-0.448) (0.448) (-6.377) (5.047)

∆ ln -0.079 -0.063 -0.002 0.142 -0.062 0.205 -0.085 0.000 0.001

(-5.558) (-5.187) (-0.298) (5.492) (-6.235) (5.372) (-5.372) (0.882) (6.210) Log-Likelihood = 2346.855

Page 45 of 48


The short run matrix of to the identified model : st ∆ ln 6

∆ ln 6 => ∆ ln ∆ ln 6

∆ ln ∆ ln 6

-0.040 -0.091 0.023 -0.061 1.133 (-0.313) (-0.480) (0.980) (-0.206) (2.657) ∆ ln 6

=> -0.142 0.449 0.006 -0.094 -0.301 (-1.991) (4.287) (0.429) (-0.575) (-1.280) ∆ ln 0.423 0.610 -0.181 -0.996 -3.008

(0.778) (0.766) (-1.825) (-0.802) (-1.679) ∆ ln 6

-0.108 0.142 -0.017 -0.034 -0.256 (-2.390) (2.152) (-2.063) (-0.334) (-1.723) ∆ ln 0.078 0.115 0.000 0.049 0.140 (2.175) (2.180) (0.017) (0.600) (1.188) WEAKLY EXOGENOUS/FIXED VARIABLES: ∆ ln ∆ ln ∆ ln ∆ ln DT(1990:01) Const ∆ ln 6

0.067 -0.693 -0.280 0.034 -0.011 -0.645 (0.725) (-4.175) (-1.862) (0.167) (-4.057) (-1.223) ∆ ln 6

=> -0.020 -0.013 0.012 -0.014 0.001 -0.669 (-0.395) (-0.138) (0.143) (-0.126) (-0.464) (-2.296) ∆ ln 4.331 1.124 1.830 1.661 0.009 -9.592 (11.103) (1.612) (2.896) (1.936) (0.815) (-4.332) ∆ ln 6

0.025 0.121 0.125 -0.122 0.002 0.476 (0.763) (2.092) (2.392) (-1.707) (2.696) (2.592) ∆ ln 0.244 0.030 0.072 0.129 (2.696) (2.592) (9.499) (0.661) (1.726) (2.281) (3.560) (-3.498)

Page 46 of 48


F- COINTEGRATION RELATIONS

G- VECM IDENTIFICATION RESULTS

Equation for: ∆ ln 6

Coefficient Std.Error HACSE t-HACSE t-prob F -0.0314690 0.006881 0.006915 -4.55 0.0000 F 0.0470415 0.01151 0.01203 3.91 0.0002 ∆ ln -0.109213 0.08544 0.04982 -2.19 0.0319 ∆ ln -0.594475 0.1472 0.1293 -4.60 0.0000 Constant 0.111011 0.02062 0.01923 5.77 0.0000 u = 0.00497885

Equation for: ∆ ln 6 =>

Coefficient Std.Error HACSE t-HACSE t-prob F 0.117203 0.03129 0.02668 4.39 0.0000 F -0.102818 0.04079 0.03561 -2.89 0.0053 <F -0.0297333 0.01054 0.01206 -2.46 0.0164 F 0.118279 0.02689 0.02385 4.96 0.0000 ∆ ln 6

-0.162290 0.05897 0.06422 -2.53 0.0139 ∆ ln 6

=> 0.362732 0.08366 0.09450 3.84 0.0003 ∆ ln -0.172747 0.08098 0.08382 -2.06 0.0433 Constant -0.176143 0.1307 0.1246 -1.41 0.1622 u = 0.0024349

Page 47 of 48


Equation for: ∆ ln Coefficient Std.Error HACSE t-HACSE t-prob F 3.91530 0.4721 0.4186 9.35 0.0000 F -5.43824 0.5957 0.5029 -10.8 0.0000 <F 0.229649 0.05649 0.04984 4.61 0.0000 F 3.65522 0.4453 0.3782 9.67 0.0000 ∆ ln 6

1.84882 0.7119 0.7362 2.51 0.0145 ∆ ln 6

=> -2.25300 1.029 0.9202 -2.45 0.0171 ∆ ln -11.3330 1.210 1.279 -8.86 0.0000 ∆ ln 1.33238 0.3762 0.3720 3.58 0.0007 ∆ ln 2.26016 0.9827 0.7106 3.18 0.0023 Constant -11.5558 1.439 1.335 -8.66 0.0000 u = 0.0289397

Equation for: ∆ ln 6

Coefficient Std.Error HACSE t-HACSE t-prob F 0.0530529 0.005503 0.004624 11.5 0.0000 <F -0.0480318 0.004734 0.004518 -10.6 0.0000 ∆ ln 6

-0.129237 0.03379 0.03609 -3.58 0.0007 ∆ ln -0.0144980 0.007708 0.006374 -2.27 0.0262 ∆ ln 0.262263 0.03905 0.03358 7.81 0.0000 ∆ ln 0.0979165 0.04411 0.03409 2.87 0.0055 Constant 0.264860 0.02813 0.02754 9.62 0.0000 u = 0.0015582

Equation for: ∆ ln Coefficient Std.Error HACSE t-HACSE t-prob F 0.267007 0.02152 0.03267 8.17 0.0000 F -0.361939 0.02460 0.03736 -9.69 0.0000 F 0.261633 0.01917 0.02789 9.38 0.0000 ∆ ln 6

0.0874043 0.04271 0.04356 2.01 0.0489 ∆ ln 6

=> -0.153848 0.05737 0.05719 -2.69 0.0091 ∆ ln 0.00958565 0.003554 0.002548 3.76 0.0004 ∆ ln 0.103614 0.05611 0.03982 2.60 0.0115 Constant -0.673789 0.06556 0.1006 -6.70 0.0000 u = 0.00172355

Log-likelihood 1680.57466 -T/2log|Ω | 2226.866 no. of observations 77 no. of parameters 38 LR test of over-identifying restrictions: j (4) = 13.042 [0.9320]

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Correlation of structural residuals (standard deviations on diagonal) ∆ ln 6

∆ ln 6 => ∆ ln ∆ ln 6

∆ ln ∆ ln 6

0.0049789 -0.27609 -0.092548 -0.072600 -0.10425 ∆ ln 6

=> -0.27609 0.0024349 -0.011115 0.13400 0.10854 ∆ ln -0.092548 -0.011115 0.028940 0.083222 0.89773 ∆ ln 6

-0.072600 0.13400 0.083222 0.0015582 0.24749 ∆ ln -0.10425 0.10854 0.89773 0.24749 0.0017235

H- AR IDENTIFICATION RESULTS

Identification on the sample 1981:01-2000:01 Coefficient Std.Error t-SE HACSE t-HACSE Part.R2 DPH_1 0.425912 0.1054 4.0418 0.08378 5.08 0.2563 Constant 0.00591894 0.001223 4.8416 0.001009 5.86 0.3144 Sigma 0.00554929 RSS 0.00230959392 R2 0.178858 F(1,75) = 16.34 [0.000]** Log-likelihood 291.7 mean(DPH) 0.0101476 var(DPH) 3.65281e-005

Technology

Assessing the existence of a bubble in the housing market using an Error Correction Model