Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach
Alexandra Krystalogianni,
George Matysiak () and
Sotiris Tsolacos
Applied Economics, 2004, vol. 36, issue 20, 2347-2356
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.
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)
Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0003684042000280544 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:36:y:2004:i:20:p:2347-2356
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20
DOI: 10.1080/0003684042000280544
Access Statistics for this article
Applied Economics is currently edited by Anita Phillips
More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().