The evaluation of the Australian office market forecast accuracy
| Date | 03 April 2018 |
| Pages | 259-272 |
| Published date | 03 April 2018 |
| DOI | https://doi.org/10.1108/JPIF-04-2017-0029 |
| Author | Treshani Perera,David Higgins,Woon-Weng Wong |
| Subject Matter | Property management & built environment,Real estate & property,Property valuation & finance |
The evaluation of the Australian
office market forecast accuracy
Treshani Perera
School of Property Construction and Project Management, RMIT University,
Melbourne, Australia
David Higgins
School of Engineering and the Built Environment, Birmingham City University,
Birmingham, UK, and
Woon-Weng Wong
School of Property Construction and Project Management, RMIT University,
Melbourne, Australia
Abstract
Purpose –Property market model s have the overriding aim o f predicting reasonabl e estimates of key
dependent variables (demand, supply, rent , yield, vacancy and net absorptio n rate). These can be based on
independent drivers o f core property and eco nomic activities. Acc urate predictions c an only be
conducted when ample qua ntitative data are available with few er uncertainties. However, a broad-fr onted
social, technical an d ecological evolution can throw up sudden, une xpected shocks that re sult in
the econometric output s sceptical to unknown ris k factors. Therefore, t he purpose of this paper is to
evaluate Australian of fice market forecast a ccuracy and to determine whether the forecasts capture
extreme downside risk eve nts.
Design/methodology/approach –This study fo llows a quantita tive research app roach, using
secondary data analysis to test the accuracy of economists’forecasts. The forecast accuracy evaluation
encompasses the measu rement of economic and property fore casts under the following phases: te sting for
the forecast accurac y; analysing outliers of foreca st errors; and testin g of causal relationshi ps.
Forecast accuracy measurement incorporates scale independent metrics that include Theil’sU
values (U1andU2) and mean absolute scaled error. I nter-quartile range rule is use d for the outlier analysis.
To find the causal relati onships among variabl es, the time series regr ession methodology is ut ilised,
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.
Journal of Property Investment &
Finance
Vol. 36 No. 3, 2018
pp. 259-272
© Emerald PublishingLimited
1463-578X
DOI 10.1108/JPIF-04-2017-0029
Received 18 April 2017
Revised 6 July 2017
22 August 2017
Accepted 22 August 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1463-578X.htm
259
Australian
office market
forecast
accuracy
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