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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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

This 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.

A 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.

The 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.

This 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.

Contractors of different scales, operating in different construction industries of varying institutional and economic settings, have different considerations when making bid or no-bid and mark-up decisions. Focusing on the large and medium-sized contractors in the Jilin province, China, the purpose of this study is to examine important factors affecting their decision to bid (d2b) and mark-up decisions and investigate differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

This study used a survey design for timely data collection from a large population. Contractors’ bidding attitudes was collected using an online survey questionnaire with a list of 40 key factors. Statistical analytical methods were applied for comparing the two groups of contractors.

The results of this study indicate that factors related to client conditions are most critical for both large and medium-sized contractors in their d2b and mark-up decisions. The results also show statistically significant differences between the two groups of contractors on a subset of factors affecting their d2b and mark-up decisions. The large contractors have placed more emphasis on projects' potential financial and strategic benefits. Another notable finding is that both groups of contractors have placed great emphasise on “government legislations” in their d2b and mark-up decisions.

These findings should be interpreted in consideration of several limitations. Firstly, the sample size is relatively small, and the focus was on a single province in the China construction industry. Next, this study only explores differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

Contractors could refer list of critical factors in competing for jobs in Jilin province or other provinces of similar institutional and economic settings. Construction clients, on the other hand, should consider the list of critical factors in the formulation of their competitive tendering procedures, thus enhancing the efficiency in their procurement of construction services.

Research on contractors’ bidding decision-making in the context of Chinese construction industry remains scarce; the research findings have implications for the industry stakeholders.