Forecasting Models of Retail Rents

Brooks, Chris; Tsolacos, Sotiris

doi:10.1068/a3332

Cited by 29 publications

(28 citation statements)

References 23 publications

(26 reference statements)

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“…The approach has also been used in papers that have compared alternative forecasting approaches. Brooks and Tsolacos (2000) compare ARIMA models with a number of OLS and VAR based models in the context of British retail property, while Stevenson and McGrath (2003) compare a number of alternative approaches using London office market data.…”

Section: Introductionmentioning

confidence: 99%

A comparison of the forecasting ability of ARIMA models

Stevenson

2007

Journal of Property Investment &Amp; Finance

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -ARIMA models have been extensively examined in the context of the real estate market. The purpose of this paper is to examine issues relating to their application in a forecasting context. Specifically, the paper seeks to examine whether in-sample measures of best-fit and also past forecasting accuracy bear any relation to future forecasting performance. Design/methodology/approach -The forecasting performance of alternative ARIMA specifications are compared over rolling estimation and forecasting windows. The forecasting accuracy of the alternative specifications is compared with specific attention placed on the accuracy of the respective specification that in-sample provides the best fitting model. Findings -The results highlight the limitations in using the conventional approach to identifying the best-specified ARIMA model in sample, when the purpose of the analysis is to provide forecasts. The results show that while ARIMA models can be useful in anticipating broad market trends, there are substantial differences in the forecasts obtained using alternative specifications. The use of conventional measures of best-fit provide little indication as to future forecasting ability, nor does the forecasting performance of a specification in previous periods. Originality/value -ARIMA modelling has frequently been highlighted as a useful forecasting approach. This paper illustrates that care needs to be paid in their use in a forecasting context and full appreciation of the strengths and limitations of the ARIMA approach.

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Section: Introductionmentioning

confidence: 99%

A comparison of the forecasting ability of ARIMA models

Stevenson

2007

Journal of Property Investment &Amp; Finance

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“…Property market models have the overriding aim of predicting reasonable estimates of key dependent variables: demand, supply, rent, returns, yield, vacancy, and cashflows based on information at hand (Brooks and Tsolacos, 2000, 2001Chaplin, 1998Chaplin, , 1999Chaplin, , 2000Matysiak and Tsolacos, 2003). The changes of these variables can be quantified by the internal and the external determinants within which the decisions are made in the market (Higgins, 2000).…”

Section: Commercial Property Market Forecastingmentioning

confidence: 99%

The evaluation of the Australian office market forecast accuracy

Perera

Higgins

Wong

2018

JPIF

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Purpose Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events. Design/methodology/approach This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR). Findings The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts. Research limitations/implications Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data. Practical implications The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts. Originality/value The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

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“…VARs have been used in a number of studies modelling house price dynamics Zhou, 1997;Guirguis et al, 2005) and have also been found to be extremely useful in a forecasting context in commercial real estate markets (e.g. Brooks & Tsolacos, 2000;Stevenson & McGrath, 2003). The VAR model used in this study is similar to that adopted by Stevenson & McGrath (2003) in their analysis of London office market and follows a Bayesian approach.…”

Section: Vector Autoregression Modelmentioning

confidence: 99%