An assessment of property performance forecasts: consensus versus econometric
Purpose ‐ The paper seeks to evaluate accuracy and efficiency of consensus forecasts for all property rents and total returns in the UK. The aim of the paper is to investigate whether consensus forecasts, containing a high degree of judgement, are better than forecasts produced by uncomplicated time-series and econometric models that practitioners can easily estimate and use for forecasting. Design/methodology/approach ‐ This study estimates simple models of all property rents and returns and generates forecasts for one- and two-year horizons on a rolling basis over the period 1999 to 2004. These forecasts are real time forecasts. That is they are made using information available to the analyst at the time of the forecast each year and no future knowledge is assumed. The forecasts made by these models are compared with the corresponding consensus forecasts. The comparative assessment is based on conventional tests for bias, variability and efficiency of forecasts. Findings ‐ If attention is focused on rents, the consensus forecast is ranked best for the one-year horizon but it is outperformed by the regression model and a combination of the statistical models for the two-year horizon. For the one-year and two-year forecasts of total returns a simple regression model with interest rates clearly improves upon the consensus forecasts. There is clear evidence that the consensus forecasts fail to incorporate the information contained in recent interest rate movements. Therefore subject to the sample period for this analysis the survey forecasts of total returns cannot be considered impartial. Analysts should include base rate information into their predictions. Originality/value ‐ This is one of the few attempts to formally evaluate consensus forecasts in the real estate field and perform a direct comparison with quantitative forecasts. It produces initial evidence suggesting that highly judgemental consensus forecasts do not necessarily outperform quantitative forecasts based on fundamentals. It prompts property forecasters and investors to engage in forecast evaluation and include missing information and past errors in their forecasts.
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