The drivers of housing cycles in Spain

Abstract

The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.

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The Drivers of Housing Cycles in Spain

Oriol Aspachs-Bracons and Pau Rabanal

WP/09/203

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© 2009 International Monetary Fund





WP/09/203
IMF Working Paper

Research Department
The Drivers of Housing Cycles in Spain1

Prepared by Oriol Aspachs-Bracons and Pau Rabanal

Authorized for distribution by Gian Maria Milesi-Ferretti

September 2009

Abstract

This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent
those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are
published to elicit comments and to further debate.

Since Spain joined the EMU, two main important factors behind the housing boom appear to
be the decrease of nominal interest rates and demographic factors. In this paper we estimate a
New Keynesian model of a currency area, using data for Spain and the rest of the EMU to
study the importance of those factors. We also examine the role of different rigidities and
find that labor market frictions are crucial to explain main features of the data. On the other
hand, financial frictions that impose a collateral constraint on borrowing do not appear to be
relevant.

JEL Classification Numbers: E44, E52, F41
Keywords: Housing, Monetary Policy, Financial Constraints
Author’s E-Mail Address: oaspachs@lacaixa.es; prabanal@imf.org

1 Paper prepared for the Banco de España Conference on "Estimation and Empirical Validation of Structural
Dynamic Stochastic Models for the Spanish Economy," Madrid, March 13, 2009. We are thankful to Michel
Juillard for help with DYNARE programming. Oriol Aspachs-Bracons is an economist in the Research Department
at Caixa d'Estalvis i Pensions de Barcelona, Avinguda Diagonal 621-629, 08028 Barcelona. Pau Rabanal is an
economist in the IMF Research Department. This paper should not be reported as reflecting the views of Caixa
d'Estalvis i Pensions de Barcelona ("la Caixa") or the International Monetary Fund or its Executive Board. Any
errors and omissions are our own.

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References................................................................................................................................37

Figures
1. Residential Investment (y-o-y real growth rate)............................................................7
2. House Price Indices (y-o-y percent growth rate)...........................................................7
3. 3-Month T-Bill Rates in Spain and in the EMU............................................................8
4. Number of Households and Population. Annual Growth Rates....................................8
5. Priors (black solid line) and Posteriors (red dashed line)............................................22
6. Priors (black solid line) and Posteriors (red dashed line)............................................24
7. Model Simulation with Smoothed Shocks. Percent Contribution of Each
Shock to Overall Volatility.................................................................................28
8. Posterior Impulse Responses (mean and 95% C.I.) to a Technology Shock
in the Housing Sector..........................................................................................29
9. Posterior Impulse Responses (mean and 95% C.I.) to a Preference Shock in
the Housing Sector..............................................................................................30

Contents Page
Introduction....................................................................................................................5
The Model....................................................................................................................10
A. Households..........................................................................................................10
B. Producers.............................................................................................................13
B.1 Final Goods Producers............................................................................14
B.2 Intermediate Goods Producers................................................................15
C. Closing the Model...............................................................................................16
C.1 Market Clearing Conditions....................................................................16
C.2 Monetary Policy Rule.............................................................................17
Bayesian Estimation.....................................................................................................18
A. Data.....................................................................................................................18
B. Priors and Posteriors...........................................................................................19
C. Implications of the Model: Posterior Second Moments and Impulse
Responses................................................................................................25
C.1 Second Moments.....................................................................................25
C.2 Model Simulation....................................................................................26
C.3 Impulse Responses..................................................................................28
Robustness: The Role of Financial Frictions and Labor Market Rigidities.................32
Conclusions..................................................................................................................34

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Tables
1.
2a.
2b.
3a.
3b.
4.
5.

Posterior Impulse Responses (mean and 95% C.I.) to a Monetary Policy
Shock in the Euro Area.......................................................................................31
Calibrated parameters..................................................................................................20
Prior and Posterior Disributions...................................................................................20
Prior and Posterior Disributions...................................................................................23
Second Moments in Spain ...........................................................................................25
Second Moments in the rest of EMU...........................................................................26
Variance Decomposition (in percent)..........................................................................26
Model Comparison.......................................................................................................34

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I. Introduction
The origins of the current global crisis are multiple and complex but, undoubtedly, the
housing sector has played a central role in its amplification and propagation. Indeed, a
recent paper by Leamer (2007) puts the housing sector at the center stage of most U.S.
economic downturns. Reinhart and Rogoff (2009) have studied historical banking crises
episodes and have showed their strong link, among other variables, to housing prices.
Aspachs-Bracons (2009) has also studied the correlation of housing prices with main
macroeconomic variables in an international context, and finds a significant relationship
between economic downturns and housing price declines in a sample of 18 industrialized
countries. Following a cumulative decline of housing prices of 23 percent in a five-year
period, real GDP declines 1.5 percent in the following year, and the unemployment rate
roughly doubles in 3 years. The importance of the housing sector comes from the central
role it plays in the households’ process to accumulate nonfinancial assets. Also, housing
wealth can be used as a way to finance nondurable consumption. The housing spillovers to
the rest of the economy have been studied by estimating the marginal propensity to
consume out of housing wealth, either using reduced form regressions or structural
models.1Therefore, understanding the causes and consequences of the housing cycle, and
its implications for the broader economy and the appropiate policy response have become
key in recent years, and the focus of attention of central banks and international
institutions (IMF, 2008).
Given the widely recognized importance of this sector, it is not suprising that the most
recent generation of dynamic stochastic general equilibrium (DSGE) models have been
extended to incorporate a housing sector and all the economic effects associated to it. In a
highly influential paper, Iacoviello (2005) extends an otherwise standard DSGE model to
allow for durable (housing) consumption. In such a model housing plays a double role.
First, it allows households to use their housing stock as a saving vehicle, in addition to
providing instantaneous utility. Second, if a fraction of individuals face borrowing
constraints, but financial markets allow them to borrow against their housing wealth, then
monetary policy can have a stronger propagation mechanism. An expansionary monetary
policy shock will lead to higher nondurable and durable consumption because of lower
interest rates. Increased housing prices will increase the wealth of those who are long on
housing, typically most households in the economy, boosting nondurable consumption
even further.2Monacelli (2008) has stressed the role of this type of credit frictions to help
explain several features of the data, most importantly the comovement between residential
investment and private consumption to a monetary policy shock. Despite the strong
1An excellent survey that summarizes studies for the United States can be found in CBO (2007). For the
spanish case, see Bover (2007).
2Kiyotaki and Moore (1997) and Bernanke, Gertler and Gilchrist (1999) initiated the large literature em-
phasizing the role credit constraints play in the transmission mechanism of shocks. Aoki et al. (2004) formal-
ize these ideas by building a model where housing plays a critical financial accelerator role for consumers in
the UK.