Retail Building Cycles: Evidence from Great Britain

This study examines the cyclical pattern of retail property development in Great Britain. It develops and estimates an econometric model of the volume of new development starts for retail buildings. Within the theoretical framework proposed, a dynamic specification based on changes in real retail rents and total consumer spending appears to adequately capture the cyclical variation in retail development. Changes in the values of these variables induce new retail construction within two years and an Almon polynomial lag scheme best describes the dynamic distribution of their lagged effects. Investment market influences on retail building development at the national level are not established in this study. There is also some indication of a changing economic relationship between new retail development and retail rents after mid-1995, but this can only be confirmed by appropriate tests when additional observations become available.

Projections in the Industrial Property Market using a Simultaneous Equation System

A three-equation simultaneous system is used to model the industrial property market in Great Britain. Strong relationships are found between new industrial building supply and both real rents and construction costs, and between rents, industrial floorspace availability and the gross domestic product. The performance of ex post simulations of new building supply, rents and floorspace availability are satisfactory with the exception of rent simulations post 1993. The central forecast of the model indicates a quiet market until 2001 (lower level of new supply, constant levels of real rents and increasing availability of industrial space) but a more active market in 2002 and 2003. This study suggests that simultaneous equation models can prove useful alternative tools in analysing the industrial property market and generating forecasts both at the aggregate and more localised level of analysis.

Rent Adjustments and Forecasts in the Industrial Market

This study estimates models of industrial rents at the national level in Great Britain. James Lan Wooten and CB Hillier Parker rent indices are used to model changes in real rents. These changes are positively related to changes in real GDP and inversely affected by absorption, as measured by King Sturge & Co. Additionally, changes in rents convey information not captured by GDP and absorption. The lagged effects of the variables differ for each index, attributable to the construction methodologies followed. Dynamic forecasts for both models show a small overprediction in 1996 and 1997. Ex ante forecasts suggest that both will show positive real growth throughout 1998 and 1999.

Interactions within the Office Market Cycle in Great Britain

This article adopts an unrestricted vector autoregressive framework methodology to examine the cyclical activity of office property development in Great Britain. The empirical analysis provides supporting evidence for the significant influence of office rents on the rate of new office construction. Service sector output has a small impact on office development, whereas the results do not establish a relationship with employment and interest rates. The significance of rents is attributed to the tenure characteristics of the market and the important role of developers and property investors in initiating office projects in Great Britain. A period of up to three years appears to be the optimum period between the time that rental signals are generated and the time that buildings are put in place, as a response to those signals.

Forecasting UK real estate cycle phases with leading indicators: a probit approach

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. We then estimate probit models 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

Forecasting UK Real Estate Cycle Phases With Leading Indicators: A Probit Approach

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. We then estimate probit models 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.commercial real estate, turning points, leading indicators, probit models

Forecasting models of retail rents

The authors model retail rents in the United Kingdom with use of vector-autoregressive and time-series models. Two retail rent series are used, compiled by LaSalle Investment Management and CB Hillier Parker, and the emphasis is on forecasting. The results suggest that the use of the vector-autoregression and time-series models in this paper can pick up important features of the data that are useful for forecasting purposes. The relative forecasting performance of the models appears to be subject to the length of the forecast time-horizon. The results also show that the variables which were appropriate for inclusion in the vector-autoregression systems differ between the two rent series, suggesting that the structure of optimal models for predicting retail rents could be specific to the rent index used. Ex ante forecasts from our time-series suggest that both LaSalle Investment Management and CB Hillier Parker real retail rents will exhibit an annual growth rate above their long-term mean

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models

Does orthogonalization really purge equity based property valuations of their general stock market influences?

This paper uses a recently developed nonlinear Granger causality test to determine whether linear orthogonalization really does remove general stock market influences on real estate returns to leave pure industry effects in the latter. The results suggest that there is no nonlinear relationship between the US equity-based property index returns and returns on a general stock market index, although there is evidence of nonlinear causality for the corresponding UK series

The cyclical relations between traded property stock prices and aggregate time-series

This paper examines the cyclical regularities of macroeconomic, financial and property market aggregates in relation to the property stock price cycle in the UK. The Hodrick Prescott filter is employed to fit a long-term trend to the raw data, and to derive the short-term cycles of each series. It is found that the cycles of consumer expenditure, total consumption per capita, the dividend yield and the long-term bond yield are moderately correlated, and mainly coincident, with the property price cycle. There is also evidence that the nominal and real Treasury Bill rates and the interest rate spread lead this cycle by one or two quarters, and therefore that these series can be considered leading indicators of property stock prices. This study recommends that macroeconomic and financial variables can provide useful information to explain and potentially to forecast movements of property-backed stock returns in the UK