Forecasting Turning Points in Real Estate Yields
||Tsolacos, Sotiris; Chris Brooks
||Forecasting Turning Points in Real Estate Yields
||20th Annual European Real Estate Society Conference in Vienna, Austria
||Determining the behaviour of yields remains a significant area of research in the real estate field. The last cycle reminded investors of the impact on values from the sudden and largely unpredictable yield changes. Initially, capital values took a major hit from yield rises but subsequently quick reversals in this path generated investment opportunities in several markets. Early detection of yield movements and existence of advance signals about likely forthcoming adjustments in yields is of significant value to investors and lenders. The main interest in this study is to study the predictive content of leading indicator series for turning points in real estate yields defined, in this study, to be the times when yields compress (begin a downward trend) and rise (start following an upward path). More specifically the objective is to take a forward-looking stance and generate probability signals of imminent movements in yields that will represent actionable information for investors. The majority of the previous analysis of yield movements is focused on regression analysis and traditional time-series models such as ARIMAs and vector autoregressions. Such models provide the basis for point forecasts for yields which to a degree can pick up turning points. The present study employs a dichotomous-variable methodology. A probit model, which is known as the natural model to use for the prediction of turning points, is constructed to interpret signals from leading indicators for possible yield swings. The leading indicators represent economic leading indicators, financial and other spreads and expectations-sentiment data. Apparently this approach differs from the previous work on yield forecasting given the focus on calculating turning point probabilities. However the probit based outcomes complement the analysis and predictions from other modelling and forecasting methodologies.Prime office yield data for Munich, London West End, Paris CBD and Madrid are used. The selected office centres aim to test the probit approach with leading indicators in geographies which have had different experiences during the eurozone sovereign debt crisis. The evaluation of the resulting models takes place with the commonly used criteria applied to binary models. However, the probability forecasts will be assessed explicitly in the context of the realised yield swings in the recent cycle. Finally the study provides forecasts for turning points outside the sample period.
||Office yields, Leading indicators, Turning points, Forecast probabilities
Post discussion ...
||G-3: Market Research, Analysis & Forecasting
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