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Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

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  • Alexandra Krystalogianni
  • George Matysiak
  • Sotiris Tsolacos

Abstract

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.

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  • Alexandra Krystalogianni & George Matysiak & Sotiris Tsolacos, 2004. "Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach," Applied Economics, Taylor & Francis Journals, vol. 36(20), pages 2347-2356.
    • Handle: RePEc:taf:applec:v:36:y:2004:i:20:p:2347-2356
    DOI: 10.1080/0003684042000280544
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    Cited by:

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    2. Fuerst, Franz, 2007. "Office Rent Determinants: A Hedonic Panel Analysis," MPRA Paper 11445, University Library of Munich, Germany.
    3. Yun-Ling Wu & Cheng-Huang Tung & Chun-Chang Lee, 2017. "The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(1), pages 81-98, January.
    4. Horváth, Áron & Sápi, Zoltán & Révész, Gábor, 2016. "Irodapiaci ciklusok jellemzése a hozam, a bérleti forgalom, az üresedés, a bérleti díjak és az új átadás alapján [Yields, take-up, vacancy, rents and new supply during office-market cycles]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 113-136.
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    6. Hyejung Moon & Jungick Lee, 2013. "Forecast evaluation of economic sentiment indicator for the Korean economy," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the Sixth IFC Conference on "Statistical issues and activities in a changing environment", Basel, 28-29 August 2012., volume 36, pages 180-190, Bank for International Settlements.
    7. Gelper, S. & Lemmens, A. & Croux, C., 2007. "Consumer sentiment and consumer spending : Decomposing the granger causal relationship in the time domain," Other publications TiSEM 55ac7230-2985-41f1-a42c-7, Tilburg University, School of Economics and Management.
    8. Evren Erdoğan Coşar, 2012. "Analysis of cyclical behaviour of investment expenditures for the Turkish economy," Applied Economics Letters, Taylor & Francis Journals, vol. 19(13), pages 1213-1221, September.
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    10. Dimitrios Papastamos & George Matysiak & Simon Stevenson, 2014. "A Comparative Analysis of the Accuracy and Uncertainty in Real Estate and Macroeconomic Forecasts," Real Estate & Planning Working Papers rep-wp2014-06, Henley Business School, University of Reading.
    11. Kim Hin David Ho & Satyanarain Rengarajan, 2017. "Industrial Real Estate Market Dynamics in Singapore: A VAR Approach," International Real Estate Review, Global Social Science Institute, vol. 20(4), pages 417-450.

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