Assessing euro area residential property prices

through the lens of key fundamentals*

L. GattiniEuropean Central Bank

December 2011* This presentation represents the views of the presenter which do not necessarily correspond with those of the ECB.

2

Outline

1. Background

2. Data and modelling approach

3. Results

5. Conclusions

4. Other approaches for the euro area & additional results for countries

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Background

Two approaches to the analysis of house price developments:

• Analysis of the boom and bust cycles – Kennedy and Andersen (1994), Alessi and Detken (2009), Borgy et al. (2009) – costly and not costly cycles

• Understanding the contribution of the fundamental components of housing market to price developments – Tsatsaronis et al. (2009), Goodhart and Hofmann (2008)

Mixed set of potential explanatory variables• Real economic variables• “Credit view”: credit, leverage, overall bank balance sheet

• Interest rate and risk taking channel

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Data and modelling approach“Forecasting and assessing euro area house prices through

the lens of key fundamentals” – Gattini & Hiebert, ECB WP 1249

• An empirical framework for the analysis of house prices in the euro area using a vector error-correction model (VECM)

• Real house prices related to selected housing demand and supply fundamentals

• We employ a structural decomposition – its is more suitable for policy analysis and interpretation of results in light of equilibrium/dis-equilibrium

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Data and modelling approach

• Vector Error Correction model – parsimonious specification

• 4 variables: Real Disposable Income, Real Mixed Interest Rate, Real Private Residential Investment, Real House Prices

• Estimated on quarterly data – 1970/2010

• 1 Cointegrating Relation – Johansen Method – unrestricted cointegration rank tests (trace and maximum eigenvalue)

• Sources: ECB, OECD, Eurostat • Data for some countries are interpolated (e.g. DE)

• 5 lags – Akaike Schawarz and FPE criteria

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Data and modelling approach

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Data and modelling approach – literature using ECM approach

Income Interest Rates

Housing Supply Country Period Freq.

Abelson et al. (2005) 1.7 -5.4 -3.6 Australia 75-03 Q

Hunt and Badia (2005) 1.91 -6.0 - UK 72-04 Q

Meen (2002) 2.7 -1.3 -7.9 US 81-98 QMeen (2002) 2.5 -3.5 -1.9 UK 69-96 Q

Jacombsen and Naug (2005) 1.7 -3.2 -1.7 Norway 90-04 Q

OECD Economic Survey (2004) 1.94 -7.14 -0.52 Netherlands 79-02 Q

OECD Economic Survey (2004) 3.6 -4.5 -7.3 Spain 89-03 Q

Min 1.7 -7.14 -7.9Model 3.07 -6.87 -2.21 Euro Area 70-08 QMax 3.6 -1.3 -0.52

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Modelling approach – Structural decomposition

- Common trend approach – King, Plosser, Stock and Watson (1991), Iacoviello (2002)

- We distinguish between structural shocks with permanent and transitory effects

Permanent shocks - baseline identification imposes zero restrictions on the first and second columns of the D(1) elements

- (S)VECM system more suitable for policy analysis - structural decomposition is useful to analyse the responsiveness of thesystem – k*(k − 1)/2=6 restrictions imposed

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Modelling approach – Structural decomposition

Housing market technology shock

- Technological shocks to the construction industry - rarely observed

- Motivated by changes in the regulatory framework (e.g. building regulations and/or the modification of various zoning laws)

- This could cause changes in housing production virtually indistinguishable from housing building technology - Matsuyama (1999)

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Modelling approach – Structural decomposition

The impact on real interest rates would be ambiguous

• The euro area - relatively closed economy

- A substitution effect between categories of investment should nullify possible discrepancies in terms of returns between different categories of investment in the long-run

- Sectoral specific technology shocks can have an impact in the short-run on interest rates – zero in the long-run given counterbalancing effects

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Modelling approach – Structural decomposition

Economy wide technological shock

- Expected to exert some impact on all the variables in the system

Financing cost shock

The outcome of features that permanently alter interest rate risk premia, such as financial innovation or –specific to the case of euro area– convergence in the run up to European Monetary Union.

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Modelling approach – Structural decompositionTransitory shocks - A two way short-run interaction between real interest rate and real income has been excluded via imposing two zero restrictions - standard lags in the monetary policy transmission mechanism

Housing demand shock - The temporary shift in preferences toward housing assets

- Rationalized in the context of literature on a time-varying housing risk premia (seeWeeken, 2004)

- A temporary shift from non-residential demand to residential demand

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Results – Forecast as a testing tool

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Results – Impulse Response – housing demand shock

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Results – Impulse Response – housing supply shock

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Results – Historical decompositionReal house price

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

1999Q1

2000Q3

2002Q1

2003Q3

2005Q1

2006Q3

2008Q1

2009Q3

Fin. Cost ShockEconomy Wide Technological ShockHousing Market Technology ShockHousing Demand ShockReal house price

Real housing investment

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

1999Q1

2000Q3

2002Q1

2003Q3

2005Q1

2006Q3

2008Q1

2009Q3

Fin. Cost ShockEconomy Wide Technological ShockHousing Market Technology ShockHousing Demand ShockReal housing investment

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Results – Transitory-permanent component – B-N type

Real house price

-0.4

0.2

0.8

1.4

2.0

2.6

3.2

3.8

4.4

5.0

1973Q11975Q31978Q11980Q31983Q11985Q31988Q11990Q31993Q11995Q31998Q12000Q32003Q12005Q32008Q12010Q3

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5.0

FIN. COST ComponentINCOME ComponentINVEST ComponentHP ComponentReal house price (RH axis)Perm. comp. of real house price (RH axis)

Real House Price

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1973Q1

1975Q31978Q1

1980Q31983Q1

1985Q31988Q1

1990Q31993Q1

1995Q31998Q1

2000Q32003Q1

2005Q32008Q12010Q3

Fin Cost ShockINCOME ShockINVEST ShockHP ShockTransitory Component

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Results – Transitory-permanent component – B-N type

Real House Price

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1990Q1

1992Q2

1994Q3

1996Q4

1999Q1

2001Q2

2003Q3

2005Q4

2008Q1

2010Q2

CREDIT ShockINCOME ShockINVEST ShockHP ShockTransitory Component

Real house price

-0.4

0.2

0.8

1.4

2.0

2.6

3.2

3.8

4.4

5.0

1990Q1

1992Q2

1994Q3

1996Q4

1999Q1

2001Q2

2003Q3

2005Q4

2008Q1

2010Q2

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5.0

FIN. COST ComponentINCOME ComponentINVEST ComponentHP ComponentReal house price (RH axis)Perm. comp. of real house price (RH axis)

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Other approaches for the euro area• Crude affordability in the euro area – measured by the ratio of per capita GDP to the house price index – computed relative to long-term trends

• Residual of a simple error-correction framework with real house prices regressed on real GDP per capita, population and the real interest rate

• House price-to-rent ratio computed relative to its long-run average – a simplified static dividend discount model or asset pricing approach

• The evolution of the house price-to-rent ratio computed relative to the real long term interest rate - return on a housing investment should be equal to the returns on alternative investment opportunities

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Other approaches - euro area - MB

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Other approaches - euro area countries - FSR

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Conclusions

• The structural model suggests that euro area housing has been overvalued in recent years – 2006/2007 by 10% -, implying a period of stagnation, which is already started in 2009, to bring housing valuation back in line with its modelled fundamentals

• During the last house price boom much of the increase appears to reflect a permanent component with an increasing importance of real disposable income per capita

• Proposed a structural methodology capturing fundamental demand and supply factors

• A transitory component has also contributed – particularlysince 2006. In particular, housing preference and income shocks were a key driver in explaining house price dynamics over this period

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