Defining the three Rs of commercial property market performance

Abstract

Purpose – Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues. Design/methodology/approach – This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations. Findings – The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk. Practical implications – In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact. Originality/value – Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.

Forecasting Performance of Commercial Property Investments in Lagos Metropolis

“…Furthermore, it is observed that in developed nations (United States of America, UK, Australia, etc. ), property investment performance indices are constructed monthly or quarterly to produce a robust data set (Higgins, 2015;MSCI, 2020). This allows for the capture of the real estate market cycle or an outlier occurrence within a year; annual averages may not properly capture the real estate market cycle.…”

Urban regeneration and economic sustainability of office market: the case of Manchester, UK

“…The author of this research shares the view of Briguglio and colleagues (2009) on economic vulnerability and resilience; they define economic vulnerability as the exposure of an economy to exogenous shocks; and economic resilience as the policy-induced ability of an economy to withstand or recover from the effects of such shocks. Good governance is essential to provide an economic mechanism to eliminate or ease adverse shocks caused, for example, by business cycles to enhance the level of economic resilience together with strong macroeconomic stability (Hill et al, 2008;Stevenson et al, 2014).…”

“…The next step is to calculate the standard deviation (Lim et al, 2008) for these periods to provide information about the volatility or stability of market movements during each examined timeframe. The assumption of the normal distribution (Jorion, 1996;Higgins, 2015) is employed to analyse the results of the standard deviations derived from the data set.…”

The evaluation of the Australian office market forecast accuracy

“…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). The interlinked commercial property markets set out economic relationships that are most relevant to the forecasting models.…”