As real estate forms a major part of the mixed asset balanced portfolio, it is critical that analysts and institutions employ a wide-range of techniques for forecasting the performance of real estate assets. Thus, property market models have one overriding aim which is to predict reasonable estimates of key variables based on the known information in hand. However, broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in a possibility of regression from known back to unknown conditions. Past underlying economic drivers are as the key determinants of the property market models whilst the extreme events are considered as statistical outliers in modelling and forecasting.

Due to the significant impact of these downside risks in commercial property, structural changes and Black Swan events in the economic environment should not be dismissed as statistical outliers. Therefore, this study shall seek to answer the primary research question: How to model extreme downside risk drivers in forecasting the performance of the commercial property market? This can be constructed through the identification of pitfalls in common forecasting models for predicting the outliers in real estate performance and then determine solutions to integrate them in modelling.

The current research study follows pragmatism knowledge claim using pluralistic approaches to derive the knowledge about the problem. Hence, the study involves a sequential approach which begins with a quantitative method to be followed by a qualitative method. Secondary real estate forecast data is collected to determine the accuracy of econometric forecasts at the presence of extreme downside risks. The research will then move forward to obtain primary data from a semi structured interviews of leading property researchers to develop a conceptual model of integrating extreme downside risk drivers to forecast the future performance of the commercial property market.

Currently, the PhD research is in the second year of candidature with significant progress made on literature review and the secondary data collection. The secondary economic and property data were obtained through the Australian Financial Review – Quarterly Survey of Economists, Property Council of Australia, Australian Bureau of Statistics and from Reserve Bank of Australia.  The researcher proceeded with analysing these longitudinal time series data with the aid of IBM SPSS Statistics software.