Residential property market performance and extreme risk measures

Author/s: David M. Higgins

Date Published: 2/01/2017

Published in: Volume 23 - 2017 Issue 1 (pages 1 - 13)

Abstract

Residential property is a popular investment option and has historically attracted small Australian individual investors with debt financing lowering the initial equity component, favourable tax structure and past evidence of good returns. A major concern with this approach is uncertainty, where stable assumptions cease to hold and there is concentrated negative price movement. This extreme downside volatility may not be fully reflected in traditional risk calculations. This research studies 40 years of quarterly Melbourne established residential property market performance data for normal distribution features and signs of extreme downside risk. The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent for ungeared residential property data. This is magnified as the gearing is increased to an extent where the outermost data point on 80% debt leverage shows an unrealistic probability of a 1 in 192 year event. Alternatively adopting the Cubic Power Law of returns, the probabilities of the most extreme event occurring drops to a realistic 1 in 38 year event. In highlighting the challenges to measuring the impact of leverage on residential property market performance, the analysis of extreme downside risk should be separated from traditional standard deviation risk calculations.

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Keywords

Extreme Risk - Housing Market Performance - Power Law Distribution - Standard Deviation

References

  • Abelson, P.Joyeux, R.Milunovich, G.Chung, D. (2005) Explaining house prices in Australia: 1970–2003, Economic Record. 81, p:S96-S103. Economic Record.
  • ABS. (2015) Residential dwellings: Values, mean price and number by state and territories. Canberra:Author. Residential dwellings: Values, mean price and number by state and territories.
  • ASIC. (2015) ASIC money smart: Financial guide you can trust. Sydney: ,Borrowing to invest. Author. ASIC money smart: Financial guide you can trust.
  • Bawa, V. (1975) Optimal rules for ordering uncertain prospects, Journal of Financial Economics. 2, –.p:95-121. Journal of Financial Economics.
  • Bodman, P.Crosby, N. (2004) Can macroeconomic factors explain high house prices in Australia, Australian Property Journal. 38, p:175-179. Australian Property Journal.
  • Buchanan, M. (2013) Forecast: What physics, meteorology and the natural sciences can teach us about economics. London:Bloomsbury. Forecast: What physics, meteorology and the natural sciences can teach us about economics.
  • Case, K.Shiller, R. (1989) The efficiency of the market for single-family homes, The American Economic Review. 79, p:125-137. The American Economic Review.
  • Damodaran, A. (2008) Strategic risk taking: A framework for risk managementWharton School Publishing. Strategic risk taking: A framework for risk management.
  • Fishburn, P. (1977) Mean-risk analysis with risk associated with below-target returns, American Economic Review. 67, –.p:116-126. American Economic Review.
  • Gabaix, X. (2008) Power laws in economics and finance, Cambridge, MA:National Bureau of Economic Research.
  • Heaney, R.Higgins, D.Di Ioria, A. (2012) Investment portfolios and the three dimensions of real estate investment, Pacific Rim Property Research Journal. 18, p:445-467. Pacific Rim Property Research Journal.
  • Higgins, D. (2014) The value of debt on property investment market performance, Pacific Rim Property Research Journal. 20, –.p:45-54. Pacific Rim Property Research Journal.
  • Higgins, D. (2015) Defining the three Rs of commercial property market performance: Return, risk and ruin, Journal of Property Investment and Finance. 33, –.p:481-493. Journal of Property Investment and Finance.
  • Kohler, M.van der Merwe, M. (2015, September Quarter) Long-run trends in housing price growth, Reserve Bank of Australia Bulletin. p:21-30. Reserve Bank of Australia Bulletin.
  • Lux, T. (2006) Financial power laws: Empirical evidence, models, and mechanism Working paper, Department of Economics Germany:University of Kiel.
  • Mandelbrot, B.Hudson, R. (2008) The (mis)behaviour of markets: A fractal view of risk. London: Profile Books. The (mis)behaviour of markets: A fractal view of risk.
  • MandelbrotB.TalebN. (2006, March 23) A focus on the exceptions that prove the rule, Financial Times. (2006, March 23) p:40. Financial Times.
  • Markowitz, H. (1952) Portfolio selection, Journal of Finance. 7, –.p:77-91. Journal of Finance.
  • Powell, R. (2008, February/March) Measuring extreme financial risk with power laws, Bank Accounting and Finance. 21, –.p:31-36. Bank Accounting and Finance.
  • RBA. (2014a, April) Submission to the financial system inquiry. Sydney:Reserve Bank of Australia. Submission to the financial system inquiry.
  • RBA. (2014b, September) Households’ investment property exposures: Evidence from tax and survey data. Sydney:Reserve Bank of Australia Bulletin. Households’ investment property exposures: Evidence from tax and survey data.
  • Silver, N. (2012) The signal and the noise: Why so many predictions fail – But some don’tThe Penguin Press. The signal and the noise: Why so many predictions fail – But some don’t.
  • Taleb, N. (2009) The black swan: The impact of the highly improbable. (2nd ed. ed.). London.Penguin Book. The black swan: The impact of the highly improbable.
  • Trahan, F.Krantz, K. (2011) The era of uncertainty: Global investment strategies for inflation, deflation and the middle ground. Wiley. The era of uncertainty: Global investment strategies for inflation, deflation and the middle ground.
  • Weatherall, J. (2013) The physics of finance, predicting the unpredictable: Can science beat the markets. London:Short Books. The physics of finance, predicting the unpredictable: Can science beat the markets.
  • WheelanC. (2013) Naked statistics: Stripping the dread from the data. W.W. Norton and Company. Naked statistics: Stripping the dread from the data.