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The predictability of real office rents

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  • Russell Chaplin

Abstract

The prediction and forecasting of office rents is undertaken routinely and formally by major surveying practices and consultants in the UK. These predictions and forecasts (which may be adjusted in-house) are used as tools in the investment decisions of the major institutions to inform on the relative performance of property market sectors/regions and the property market as a whole. This paper examines the predictability of the national Hillier Parker real office rent index by using a recursive modelling strategy and maximized selection criteria to choose a predictive model from a set of 15 in each year from 1985 to 1994. Predictions one year ahead are made using the chosen models. The results show that it is very difficult to choose the best predicting model in any one year. The selected models are often beaten by naive competitors such as 'no change' from, or 'same change' as, previous period and the ranking of a model in terms of its historic fit usually bears no relationship to its ranking in terms of how well it can predict relative to the other models in the set. Whilst the results are generally disappointing, in that the best predicting model is often not chosen, they indicate that there is some degree of predictability in the series.

Suggested Citation

  • Russell Chaplin, 1999. "The predictability of real office rents," Journal of Property Research, Taylor & Francis Journals, vol. 16(1), pages 21-49, January.
    • Handle: RePEc:taf:jpropr:v:16:y:1999:i:1:p:21-49
    DOI: 10.1080/095999199368247
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    References listed on IDEAS

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    1. Patric H. Hendershott & Colin M. Lizieri & George A. Matysiak, 1999. "The Workings of the London Office Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(2), pages 365-387, June.
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    Cited by:

    1. Allison M. Orr & Colin Jones, 2003. "The Analysis and Prediction of Urban Office Rents," Urban Studies, Urban Studies Journal Limited, vol. 40(11), pages 2255-2284, October.
    2. Patrick Mcallister & Graeme Newell & George Matysiak, 2008. "Agreement and Accuracy in Consensus Forecasts of the UK Commercial Property Market," Journal of Property Research, Taylor & Francis Journals, vol. 25(1), pages 1-22, June.
    3. Pat McAllister & Graeme Newell & George Matysiak, 2005. "Analysing Uk Real Estate Market Forecast Disagreement," Real Estate & Planning Working Papers rep-wp2005-13, Henley Business School, University of Reading.
    4. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
    5. Fotis Mouzakis & David Richards, 2007. "Panel Data Modelling of Prime Office Rents: A Study of 12 Major European Markets," Journal of Property Research, Taylor & Francis Journals, vol. 24(1), pages 31-53, February.
    6. 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.
    7. Pat McAllister & Graeme Newell & George Matysiak, 2005. "An Evaluation Of The Performance Of UK Real Estate Forecasters," Real Estate & Planning Working Papers rep-wp2005-23, Henley Business School, University of Reading.
    8. Paul Gallimore & Patrick McAllister, 2004. "Expert judgement in the Processes of Commercial Property Market Forecasting," Real Estate & Planning Working Papers rep-wp2004-11, Henley Business School, University of Reading.
    9. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2012. "Examination of property forecasting models - accuracy and its improvement through combination forecasting," ERES eres2012_082, European Real Estate Society (ERES).

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