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

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  • Engsted, Tom
  • Pedersen, Thomas Q.

Abstract

Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash flow (rent) news and discount rate (return) news over the period 1970–2011. 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. We explain the results in terms of global changes in credit constraints and transactions costs as well as changes in monetary policy over this period. Among other things, our results shed new light on whether excessively low interest rates by the monetary authorities was a major cause for the housing boom up to 2006.

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  • Engsted, Tom & Pedersen, Thomas Q., 2014. "Housing market volatility in the OECD area: Evidence from VAR based return decompositions," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 91-103.
    • Handle: RePEc:eee:jmacro:v:42:y:2014:i:c:p:91-103
    DOI: 10.1016/j.jmacro.2014.07.005
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Xian Zheng, 2015. "Expectation, volatility and liquidity in the housing market," Applied Economics, Taylor & Francis Journals, vol. 47(37), pages 4020-4035, August.
    5. 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.
    6. 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.
    7. Mustafa Ozan Yıldırım & Mehmet İvrendi, 2021. "Turkish Housing Market Dynamics: An Estimated DSGE Model," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 15(2), pages 238-267, May.
    8. 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.

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    More about this item

    Keywords

    Housing return; OECD countries; Risk-premia; Monetary policy;
    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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