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.

Purpose Whilst existing literature on real estate risk management focusses almost exclusively on holistic risk management techniques, documented increases in frequency and magnitude of unforeseen, rare and extreme events can throw up sudden, unexpected shocks that can challenge recognised real estate decision-making strategies. The paper aims to discuss this issue. Design/methodology/approach To advance real estate decision-making practice in this area, this research paper takes the skilfully conceptualised downside risk framework presented by Diebold et al. (2010), being the known (K), the unknown (u) and the unknowable (U) risk categories, to provide a blueprint for effective real estate decision making in a changing global environment. Findings In recording categories of risk, managing uncertainty can be achieved by an interrelated approach of adaption, robustness and resilience. This is important part of a real estate manager’s decision-making toolkit as risk recognition and knowledge of KuU event categories can augment an effective management strategy. Originality/value The mastery of modern real estate risk management can be better served by understanding and managing extreme downside risk events. Creating a comprehensive risk management framework can enhance comparative real estate performance whereby unprepared competitors fail in a world increasingly affected by large, highly improbable and unpredictable events.

PurposeThis paper critically reviews economic impact assessment methods adopted in construction-related projects, to develop and present a novel bottom-up approach suitable to estimate regional economic impacts of building maintenance projects.Design/methodology/approachA thorough literature review of economic impact assessment in construction projects is carried out to identify the most relevant approach to estimate wider economic impacts of building maintenance projects. Based on these findings, a model based on the bottom-up approach to estimate wider economic impacts is developed. The applicability and face validity of the developed model is demonstrated through a case of cladding replacement program in Australia.FindingsThe literature review revealed that bottom-up models are better suited for estimating regional economic impacts of maintenance projects, given the challenges of obtaining micro-level economic data in the maintenance sector. In relation to the total economic impacts (direct and indirect), the results show that for every $1 of government spending on similar projects the Gross State Product would increase by $1.34. In terms of employment impact, over 70% of the direct economic value addition is driven by the increase in labour, where close to 3 FTE jobs will be required for each $1 million of spending on cladding replacement projects.Originality/valueThis paper presents a model to estimate the wider economic impacts of building maintenance projects, which is typically overlooked in the construction management field. The proposed model is developed to incorporate the variability of different building maintenance projects so that the economic impact resulting from these projects could be estimated more accurately. This model can be used by local government decision-makers to justify and prioritise maintenance projects in a similar manner to new construction projects.

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Property market forecasting is an integral element of decisionmaking. It is critical that property analysts employ a wide -range of models and techniques for property forecasting. These models have one overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption) based on the independent variables of core economic activities. However, a broad-fronted social, economic, technical, political and ecological evolution can throw up sudden, unexpected shocks that result in a possibility of sceptical to unknown risk factors. These structural changes decrease, even eliminate predictability of property market performance. Hence, forecasting beyond econometrics is raised as the research problem in this study. This study follows a qualitative research approach, conducting semi-structured interviews with open-ended questions.