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An evaluation of the use of combination techniques in improving forecasting accuracy for commercial property cycles in the UK

Jadevicius, Arvydas


Arvydas Jadevicius


In light of the financial and property crisis of 2007-2013 it is difficult to ignore the existence of cycles in the general business sector, as well as in building and property. Moreover, this issue has grown to have significant importance in the UK, as the UK property market has been characterized by boom and bust cycles with a negative impact on the overall UK economy. Hence, an understanding of property cycles can be a determinant of success for anyone working in the property industry.
This thesis reviews chronological research on the subject, which stretches over a century, characterises the major publications and commentary on the subject, and discusses their major implications. Subsequently, this thesis investigates property forecasting accuracy and its improvement. As the research suggests, commercial property market modelling and forecasting has been the subject of a number of studies. As a result, it led to the development of various forecasting models ranging from simple Single Exponential Smoothing specifications to more complex Econometric with stationary data techniques.
However, as the findings suggest, despite these advancements in commercial property cycle modelling and forecasting, there still remains a degree of inaccuracy between model outputs and actual property market performance. The research therefore presents the principle of Combination Forecasting as a technique helping to achieve greater predictive outcomes. The research subsequently assesses whether combination forecasts from different forecasting techniques are better than single model outputs. It examines which of them - combination or single forecast - fits the UK commercial property market better, and which of these options forecasts best. As the results of the study suggest, Combination Forecasting, and Regression (OLS) based Combination Forecasting in particular, is useful for improving forecasting accuracy of commercial property cycles in the UK.

Thesis Type Thesis
Deposit Date Feb 13, 2015
Peer Reviewed Not Peer Reviewed
Keywords UK property market; property market forecasting; commercial property; Combination Forecasting;
Public URL
Contract Date Feb 13, 2015
Award Date 2014-09


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