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Forecasting house prices in Italy

Author

Listed:
  • Simone Emiliozzi

    (Bank of Italy)

  • Elisa Guglielminetti

    (Bank of Italy)

  • Michele Loberto

    (Bank of Italy)

Abstract

Forecasting house prices is a difficult task given the strong relationship between real estate markets, economic activity and financial stability, but it is an important one. This paper evaluates the out-of-sample forecasting performance of various models of house prices in a quasi-real time setting. Focusing on Italy, we consider two structural models (using simultaneous equations) and a Bayesian VAR and compute both conditional and unconditional forecasts. We find that the models perform better than a simple autoregressive benchmark; however, the relative forecast accuracy depends on the forecast horizon and also changes over time. For the full sample period the simultaneous equation model, which takes into account credit supply restrictions and real estate taxation, shows the best performance measured in terms of root mean squared forecasting error (RMSFE). In the first part of the sample (2005-2010), medium-term forecasts of house prices greatly benefit from conditioning on the evolution of households� disposable income, whereas from 2010 onwards the path of the stock of mortgages becomes important.

Suggested Citation

  • Simone Emiliozzi & Elisa Guglielminetti & Michele Loberto, 2018. "Forecasting house prices in Italy," Questioni di Economia e Finanza (Occasional Papers) 463, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:opques:qef_463_18
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2018-0463/QEF_463_18.pdf
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    References listed on IDEAS

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    3. Kai Carstensen & Oliver Hülsewig & Timo Wollmershäuser, 2009. "Monetary Policy Transmission and House Prices: European Cross Country Evidence," Working Paper / FINESS 7.4, DIW Berlin, German Institute for Economic Research.
    4. Angelini, Paolo & Cetorelli, Nicola, 2003. "The Effects of Regulatory Reform on Competition in the Banking Industry," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(5), pages 663-684, October.
    5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    6. Allen, Franklin & Gale, Douglas, 2000. "Bubbles and Crises," Economic Journal, Royal Economic Society, vol. 110(460), pages 236-255, January.
    7. Ryan Niladri Banerjee & Kristian S Blickle, 2016. "Housing collateral and small firm activity in Europe," BIS Working Papers 575, Bank for International Settlements.
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    Cited by:

    1. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2023. "Taste of home: Birth town bias in Geographical Indications," Economics & Statistics Discussion Papers esdp23089, University of Molise, Department of Economics.
    2. Michele Loberto & Andrea Luciani & Marco Pangallo, 2022. "What Do Online Listings Tell Us about the Housing Market?," International Journal of Central Banking, International Journal of Central Banking, vol. 18(4), pages 1-52, October.

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    More about this item

    Keywords

    house prices; forecasting; structural model; BVAR;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

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