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Forecasting and assessing Euro area house prices through the lens of key fundamentals

Author

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  • Gattini, Luca
  • Hiebert, Paul

Abstract

This paper presents a parsimonious model for forecasting and analysing euro area house prices and their interrelations with the macroeconomy. A quarterly vector error correction model is estimated over 1970-2009 using supply and demand forces central to the determination of euro area house prices in equilibrium and their dynamics: housing investment, real disposable income per capita and a mixed maturity measure of the real interest rate. In addition to house price forecasts using the resulting reduced form equation, a structural decomposition of the system is obtained employing a common trends framework of King, Plosser, Stock, and Watson (1991), which allows for the identification and economic interpretation of permanent and transitory shocks. The main results are twofold. First, the reduced form model tracks closely turning points in house prices when examining out-of-sample one- and two- step ahead forecasts. Moreover, the model suggests that euro area housing was overvalued in recent years, implying a period of stagnation to bring housing valuation back in line with its modelled fundamentals. Second, housing demand and financing cost shocks appear to have contributed strongly to the dynamism in euro area house prices over the sample period. While much of the increase appears to reflect a permanent component, a transitory component has also contributed from 2005 onwards. Specification tests suggest a robustness of the small model to alternative specifications, along with validity of the long-run restrictions. JEL Classification: R21, R31, C32

Suggested Citation

  • Gattini, Luca & Hiebert, Paul, 2010. "Forecasting and assessing Euro area house prices through the lens of key fundamentals," Working Paper Series 1249, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20101249
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Dieter Gerdesmeier & Andreja Lenarčič & Barbara Roffia, 2015. "An alternative method for identifying booms and busts in the Euro area housing market," Applied Economics, Taylor & Francis Journals, vol. 47(5), pages 499-518, January.
    2. Charles Rahal, 2015. "House Price Forecasts with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    3. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    4. Chan Lily & Ng Heng Tiong & Rishi Ramchand, 2012. "A cluster analysis approach to examining Singapore’s property market," BIS Papers chapters,in: Bank for International Settlements (ed.), Property markets and financial stability, volume 64, pages 43-53 Bank for International Settlements.
    5. repec:zbw:rwirep:0294 is not listed on IDEAS
    6. Nannan Yuan & Shigeyuki Hamori, 2014. "Are government interventions effective in regulating China fs house prices?," Discussion Papers 1427, Graduate School of Economics, Kobe University.
    7. Diego Vílchez, "undated". "Assessing the House Price Dynamics in Lima," IHEID Working Papers 09-2015, Economics Section, The Graduate Institute of International Studies.
    8. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 0294, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    9. Sara Ferreira Filipe, 2018. "Housing prices and mortgage credit in Luxembourg," BCL working papers 117, Central Bank of Luxembourg.
    10. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05r, Department of Economics, University of Birmingham.
    11. Martin Schneider, 2013. "Are Recent Increases of Residential Property Prices in Vienna and Austria Justified by Fundamentals?," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 29-46.
    12. Vílchez, Diego, 2015. "Evaluando las Dinámicas de Precios en el Sector Inmobiliario: Evidencia para Perú," Working Papers 2015-013, Banco Central de Reserva del Perú.
    13. Charles Rahal,, 2016. "Housing markets and unconventional monetary policy," Journal of Housing Economics, Elsevier, vol. 32(C), pages 67-80.
    14. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2014. "Forecasting real estate prices in Germany: the role of consumer confidence," Journal of Property Research, Taylor & Francis Journals, vol. 31(3), pages 244-263, September.
    15. repec:eee:soceps:v:58:y:2017:i:c:p:72-86 is not listed on IDEAS
    16. an de Meulen, Philipp & Micheli, Martin & Schmidt, Torsten, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 294, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. repec:nbb:ecrart:y:2017:m:june:i:i:p:61-77 is not listed on IDEAS

    More about this item

    Keywords

    forecasting; house price; Vector autoregression;

    JEL classification:

    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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