Defining the three Rs of commercial property market performance. Issue 6 (7th September 2015)

• Record Type: Journal Article
• Authors: Higgins, David
• Is Part Of: Journal of property investment & finance. Volume 33:Issue 6(2015)
• Page Start: 481
• Page End: 493
• Publication Date: 2015-09-07
• Subjects: Real estate investment -- Great Britain -- Periodicals; Real property -- Valuation -- Great Britain -- Periodicals; Real estate development -- Great Britain -- Finance -- Periodicals; 332.6324094105
• DOI: 10.1108/JPIF-08-2014-0054
• Languages: English
• ISSNs: 1463-578X

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Practical implications</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – 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.</p> </sec> </abstract>