Housing Markets in Europe (eBook)

Olivier de Bandt holds a PhD in economics from the University of Chicago. He is currently Director of Business Conditions and Macroeconomic Forecasting at the Bank of France. He is also associate professor of economics at the University of Paris Ouest and co-editor with Heinz Herrmann and Giuseppe Parigi of "Convergence or Divergence in Europe?" published by Springer Verlag. Thomas Knetsch holds a PhD in economics from the Freie Universität Berlin.He currently heads the structural issues unit of the Macroeconomic Analysis and Projections Division in the Economics Department of the Deutsche Bundesbank. He has published a number of research papers in empirical macroeconomics, applied time-series analysis and economic forecasting. Juan Peñalosa has developed his career as an economist in the Economics, Statistics and Research Directorate General of the Bank of Spain, having been member of different European Committees both in Brussels and in Frankfurt linked to monetary, financial and macroeconomic issues. At present he is head of the Forecasting and Conjunctural Analysis Division in the Bank of Spain. Francesco Zollino received a PhD in economics from the University of Southampton (UK) and was visiting fellow at Princeton University (USA). He is currently Deputy Head of the Economic Outlook Division in the Research and International Relations Area of the Bank of Italy.

Housing Markets in Europe 3
Acknowledgements 5
Preface 7
1 Housing markets in Europe as a relevant topic of economic research 8
2 Is housing a leading indicator of the business cycle? 10
3 What drives housing cycles? 12
4 Wealth effects from housing 13
5 Implications for economic policy and financial stability 15
Contents 17
List of Contributors 19
Part I Introductory Lecture 21
Housing in DSGE Models: Findings and New Directions 22
1 Introduction 22
2 Seven facts about housing and the macroeconomy 23
3 A DSGE model of housing 29
4 New directions 31
4.1 The role of financial intermediation 31
4.2 The determinants of housing prices 31
4.3 The time-series properties of housing price inflation 32
4.4 How to stabilize house prices 33
5 Conclusions 34
References 34
Part II Housing and the Business Cycles 36
Housing and the Macroeconomy: The Italian Case 37
1 Introduction 37
2 Cyclical analysis of the Italian housing market 39
2.1 The “business cycle” approach 39
2.2 The “growth cycle” approach 42
3 A SVAR analysis of monetary policy and the housing market 44
3.1 Housing in a monetary VAR: the recursive approach 45
3.2 Housing in a monetary VAR: a sign restriction approach 47
4 Conclusions 51
References 52
Appendix A - Data description 54
Appendix B - VAR identification 55
Cyclical Relationships Between GDP and Housing Market in France: Facts and Factors at Play 57
1 Introduction 57
2 Comparison of cycles 59
2.1 Correlation analysis 60
2.2 Turning point analysis 63
2.2.2 Concordance analysis of turning points 65
3 Structural factors affecting long-term cycles in the housing market 66
4 Conclusions 72
References 72
Appendix 74
Does Housing Really Lead the Business Cycle in Spain? 79
1 Introduction 79
2 The leading nature of housing 80
2.1 Housing and other expenditure side GDP components 81
2.2 Additional real and nominal construction variables 84
3 Brevity and violence of expansions and contractions 86
4 Concluding remarks 89
References 90
Appendix 1: Database description 92
Appendix 2: Seasonally adjusted (or original) series (1980:1 2008:4) 93
Appendix 3: Estimating cycles: methodological considerations 94
1 The ideal band-pass filter 94
1.2 Kernel regressions 96
1.3 Comparisons with other filters 97
Appendix 4: Cyclical components (1980:1 2008:4) 100
Appendix 5: Cross correlation of variables with GDP. Butterworth filter 101
Appendix 6: Cross correlation of variables with GDP. Epanechnikov filter 102
Housing Cycles in the Major Euro Area Countries 103
1 Introduction 104
2 Methodology 105
3 Results 107
3.1 Correlation analysis 109
3.1.2 Construction cycles 110
3.1.3 House price cycles 111
3.2 Concordance analysis 112
3.2.1 Aggregate activity cycles 113
3.2.2 Construction cycles 114
3.2.3 House price cycles 116
3.3 Increasing comovements in the Monetary Union 116
4 Conclusions 117
References 119
Appendix 1: Description of the 10 variables includes in the dataset 121
Common Business and Housing Market Cyles in the Euro Area from a Multivariate Decomposition 122
1 Introduction 123
2 Unobserved components time series models 125
2.2 Multivariate unobserved component models 127
3 Data 129
4 Empirical findings 130
4.1 Preliminary analysis 130
4.2 Empirical model specification 132
4.3 Parameter estimation and signal extraction 132
4.4 Univariate analysis for each series 133
4.5 Bivariate analysis for each country 136
4.6 Four-variate analysis for GDP and house prices 138
4.7 Multivariate analysis for all eight variables 141
5 Conclusions 143
References 144
The International Transmission of House Price Shocks 146
1 Introduction 147
2 Empirical methods 148
2.1 FAVAR models for the analysis of the international transmission of house prices 148
2.1.1 FAVARModel in the lines of Stock andWatson (2005) 150
2.1.2 The FAVAR model by Bernanke et al. (2004) 151
2.2 Non linear single equations : the Smooth Transition Autoregressive (STAR) and the LSTAR specifications 153
2.2.1 The STAR model 153
2.2.2 The LSTAR specification 153
3 Data 155
4 Modelling approach and empirical results 156
4.1 Univariate linear models 157
4.1.2 Robustness to crisis events 158
4.1.3 Transmission through other macroeconomic variables 160
4.2 LSTAR models 163
4.3 FAVAR models and causality analysis 164
5 Conclusions 166
References 168
Appendix A: Single equation approach 169
Appendix B: CAUSALITY in FAVAR models of reduced orders 171
Appendix C: CAUSALITY tested from systems of equations 173
Appendix D: CAUSALITY tested from FAVAR models of order 12 174
Part III Macroeconomic Models of Housing 176
The ’Housing Bubble’ and Financial Factors: Insights from a Structural Model of the French and Spanish Residential Markets 177
1 Introduction 178
2 Estimation strategy 179
2.1 Estimation methodology 179
2.2 The basic model 180
3 Construction and data sources 183
3.2 Spain 184
3.3 Unit root and causality tests 185
4 Equilibrium values from long term equations 186
4.2 Demand side: is the overvaluation a ’pure’ bubble phenomenon or does it reflect changes in financial factors? 189
4.3 Supply 193
5 Short-term equations: which adjustment path to equilibrium? 194
5.1 Demand 194
5.2 Supply 195
6 Conclusion 196
References 197
Appendix 199
Trend and Cycle Features in German Residential Investment Before and After Reunification 203
1 Introduction 203
2 Long-run determinants of residential investment 205
2.2 Cointegration analysis 209
3 Cycle features of residential investment 213
3.1 Adjustment to the housing market equilibrium 214
3.2 A trend-cycle decomposition of residential investment 217
3.3 Pitfalls for univariate statistical filtering 219
4 Conclusion 223
References 224
Appendix 225
User Costs of Housing when Households Face a Credit Constraint: Evidence for Germany 228
1 Introduction 228
2 Theoretical model 230
2.1 The set up of the model 230
2.2 The maximization problem of the household 231
2.3 Defining basic and extended versions of the user costs 233
3 Empirical analysis 234
3.1 The relevance of the inflation gap in the user costs of housing 234
3.2 A long-run equilibrium relationship for housing demand 235
3.2.1 Econometric setup 236
3.2.2 Determining the lag order 237
3.2.3 Determining the cointegration rank 238
3.2.4 Hypothesis tests 240
3.2.5 Estimating the parameters of the cointegrating space 240
3.2.6 Insights from estimated cointegrating vector b 241
3.2.7 Diagnostic checks - break tests and residual checks 243
3.3 An extended measure of the user costs of housing 244
4 Conclusions 246
References 247
Appendix 1: Time series used for variables f a, f l, di and hi 249
Appendix 2: Time series used for calculating user cost series 250
Appendix 3: Unit root tests 253
Causes and Welfare Consequences of Real Estate Price Appreciation 255
1 Introduction 255
2 Facts 256
3 The Model 258
3.1 Environment 258
3.2 Demographics 258
3.3 Technology 259
3.3.1 The Firm’s Problem 259
3.3.2 The Financial Institution’s Problem 259
3.4 Preferences and Endowments 260
3.5 Timing and Information 261
3.6 Consumer’s Problem 261
4 Calibration 263
4.2 Discount factor and interest rate 264
4.3 Income process 264
4.4 Preferences and Technology 264
4.5 Market Arrangements 265
5 Results 266
5.1 Aggregates and Real Housing Prices 267
5.2 Welfare 268
6 Conclusion 271
References 272
Part IV Wealth Effects 274
Wealth Effects on Private Consumption: the French Case 275
1 Introduction 276
2 Theoretical background 276
2.2 Models based on the consumption function 277
3 Wealth effect approach debate and empirical estimations for France 279
3.1 Elasticities versus marginal propensity to consume 279
3.2 Empirical results for France 280
4 Econometric results 281
4.1 Empirical MPC model investigation 282
4.2 Logarithm or elasticity approach 284
4.3 Main conclusions of both approaches 286
5 Conclusions 288
Appendix 289
References 293
An Assessment of Housing and Financial Wealth Effects in Spain: Aggregate Evidence on Durable and Non-durable Consumption 295
1 Introduction 295
2 Stylised facts of wealth effects in Spain 296
3 Theoretical background 301
4 Data and econometric methodology 302
5 Econometric results 304
5.1 The basic empirical model 304
5.2 Extensions of the basic model 312
6 Conclusions 315
References 316
Appendix 317
The Effects of Housing and FinancialWealth onPersonal Consumption: Aggregate Evidence for Italian Households 318
1 Introduction 319
2 Searching for macroeconomic facts for consumption in Italy 320
3 The theory of wealth effects 323
4 Empirical evidence: an overview for Italy 326
5 The modelling framework 328
6 Data and preliminary analysis 329
6.1 The dataset 330
6.2 Preliminary analysis 331
7 Econometric results 333
7.2 Long run relationships 334
7.3 Equilibrium correction 337
8 Permanent - transitory decomposition 337
9 Conclusions 341
References 342
Appendix I - Unit root test results 344
Appendix II - Recursive tests of parameter constancy 345
Housing and Portfolio Choices in France 348
1 Introduction 348
2 Households’ portfolios in France 350
2.2 Portfolio composition 351
2.3 Determinants of housing portfolio and stockholding 352
2.3.1 Housing portfolio 353
2.3.2 Stockholding 357
3 The impact of housing risk on stockholding 357
3.2 Empirical analysis 358
4 Conclusion 360
References 364
Appendix 365
Part V Housing, Economic Policy and Financial Stability 368
House price Boom/Bust Cycles: Identification Issues and Macro-prudential Implications 369
1 Introduction 369
2 The methodologies used to detect house price booms and busts 371
2.1 Extended Hodrick-Prescott (EHP) method 372
2.3 Band-pass filter (BP) method 373
2.4 Moving Average (MA) method 373
3 Identification of house price booms 374
4 The determinants of house price booms: a non-parametric approach 378
4.2 A non-parametric analysis 380
5 The determinants of house price booms: a discrete-choice (logit) model 383
6 Conclusion 387
References 388
Appendix 390
Impact of Fiscal Policy on Residential Investment in France 394
1 Introduction 394
2 Overview of fiscal intervention on residential investment in France 396
2.1 Subsidies on residential investment 396
2.2 Taxes on residential investment 399
3 The VECM methodology 399
3.2 Testing procedure 400
3.2.2 Determining the cointegration rank 401
3.3 Impulse response functions and variance decomposition 402
4 Empirical results 403
4.2 Impulse response analysis and variance decomposition 405
4.3 Specification tests 406
4.4.2 Financial factors 407
4.5 A disaggregated approach 410
4.6 Changes in fiscal activism over time 411
5 Concluding remarks 412
References 413
Appendix 414