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Housing Prices and Transaction Volume

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  • H. Cagri Akkoyun
  • Yavuz Arslan
  • Birol Kanik

Abstract

We use annual, quarterly and monthly data from the US to show that the correlation between housing prices and transaction volume (number of existing houses sold) di�ers across di�erent frequencies. While the correlation is high at the low frequencies it declines to the levels close to zero at high frequencies. Granger causality tests for di�erent frequencies show the way of causality in housing market goes from transactions to housing prices. Our ?ndings provide a litmus test for the existing theories that are proposed to explain the positive correlation between transaction volume and housing prices.

Suggested Citation

  • H. Cagri Akkoyun & Yavuz Arslan & Birol Kanik, 2012. "Housing Prices and Transaction Volume," Working Papers 1211, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:wpaper:1211
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    Cited by:

    1. Tsai, I-Chun, 2019. "Dynamic price–volume causality in the American housing market: A signal of market conditions," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 385-400.
    2. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    3. Tang, Edward Chi Ho & Leung, Charles Ka Yui, 2024. "Icing on the cake: Can the Top-Floor Units serve as a status good and an investment simultaneously?," MPRA Paper 121937, University Library of Munich, Germany.
    4. Robert Forster & Xiaojin Sun, 2024. "Heterogeneous Effects of Mortgage Rates on Housing Returns: Evidence from an Interacted Panel VAR," The Journal of Real Estate Finance and Economics, Springer, vol. 69(3), pages 477-504, October.
    5. James E. Payne & Xiaojin Sun, 2023. "Time‐varying connectedness of metropolitan housing markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(2), pages 470-502, March.
    6. Kwabena Mintah & Woon-Weng Wong & Peng Yew Wong, 2020. "Cross Border Real Estate Investments and Commercial Office Property Market Performance: Evidence from Australia," International Real Estate Review, Asian Real Estate Society, vol. 23(2), pages 837-860.
    7. Kwabena Mintah & Woon-Weng Wong & Peng Yew Wong, 2020. "Cross Border Real Estate Investments and Commercial Office Property Market Performance: Evidence from Australia," International Real Estate Review, Global Social Science Institute, vol. 23(2), pages 211-234.
    8. Arslan, Yavuz & Kanık, Birol & Köksal, Bülent, 2015. "Anticipated vs. unanticipated house price movements and transaction volume," Journal of Housing Economics, Elsevier, vol. 28(C), pages 121-129.
    9. Li Xiangfei & Han Hongli & Sun Minghan, 2020. "Localized or Regional? Urban Housing Policy Spillover in China’s Urban Agglomerations 2010–2018," Journal of Systems Science and Information, De Gruyter, vol. 8(4), pages 325-345, August.
    10. Li, Yuming, 2015. "The asymmetric house price dynamics: Evidence from the California market," Regional Science and Urban Economics, Elsevier, vol. 52(C), pages 1-12.
    11. Nagayasu, Jun, 2016. "Inflation and Bubbles in the Japanese Condominium Market," MPRA Paper 71192, University Library of Munich, Germany.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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