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Housing market volatility in the OECD area: Evidence from VAR based return decompositions

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  • Tom Engsted

    () (Aarhus University and CREATES)

  • Thomas Q. Pedersen

    () (Aarhus University and CREATES)

Abstract

Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash ?ow (rent) news and discount rate (return) news. Only for two countries - Germany and Ireland - do changing expectations of future rents play a dominating role in explaining housing return volatility. For the majority of countries news about future returns is the main driver, and both real interest rates and risk premia play an important role in accounting for housing market volatility. Bivariate cross-country correlations and principal components analyses indicate that part of the return movements have a common factor among the majority of countries. However, in a minority of countries (Germany, Japan, and the Netherlands) return movements have been basically unrelated to return movements in other countries.

Suggested Citation

  • Tom Engsted & Thomas Q. Pedersen, 2013. "Housing market volatility in the OECD area: Evidence from VAR based return decompositions," CREATES Research Papers 2013-04, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2013-04
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    Cited by:

    1. Weida Kuang & Qilin Wang, 2018. "Cultural similarities and housing market linkage: evidence from OECD countries," Frontiers of Business Research in China, Springer, vol. 12(1), pages 1-25, December.
    2. Xian Zheng, 2015. "Expectation, volatility and liquidity in the housing market," Applied Economics, Taylor & Francis Journals, vol. 47(37), pages 4020-4035, August.
    3. Renhe Liu & Eddie Chi-man Hui & Jiaqi Lv & Yi Chen, 2017. "What Drives Housing Markets: Fundamentals or Bubbles?," The Journal of Real Estate Finance and Economics, Springer, vol. 55(4), pages 395-415, November.
    4. Engsted, Tom & Pedersen, Thomas Q., 2015. "Predicting returns and rent growth in the housing market using the rent-price ratio: Evidence from the OECD countries," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 257-275.
    5. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    6. Engsted, Tom & Hviid, Simon J. & Pedersen, Thomas Q., 2016. "Explosive bubbles in house prices? Evidence from the OECD countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 14-25.
    7. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.

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    More about this item

    Keywords

    Housing return volatility; variance decomposition; dynamic Gordon growth model; innovation and news components; VAR model; principal components; OECD countries;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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