Commercial property : a required rate of return investigation

Abstract

When faced with an investment opportunity in commercial real estate, the investor requires knowledge of the discount rate since it can be used to convert expected future cash flows from the property in today's terms and in doing so, place a value on the property. The so-called required rate of return would be the appropriate conversion rate since it compensates the investor for risk and, if attainable, will induce the investor to invest. An inaccurate assessment of the discount rate could, depending on the direction of the error, lead to a potential over or under estimation of the property value. A number of single or multiple variable frameworks for required return have been derived by other researchers for the US, UK and EU property markets. Each of the variables encountered in these frameworks acts as a proxy for some aspect of systematic risk associated with the investment. However, locally, such models are either not extensively published or well described and are limited to single explanatory variables. Some professionals prefer to avoid frameworks and simply divert to qualitative, gut-feel and experienced based considerations in order to derive at required return rate. This dissertation addressed the possible local need for an explanatory framework of required return on commercial property. The scope of work entailed: (i) a review of the literature to establish the theoretical determinants of return and (ii) an empirical study to test a short-list of parameters for Retail, Offices and Industrial sites in Cape Town, Pretoria, Bloemfontein and Durban, respectively. Three categories of explanatory variables were identified: (i) Capital market variables and alternative investment opportunities in the form of stocks on the JSE, (ii) economic activity indicators and (iii) property market fundamental parameters. The empirical study entailed a three-phase methodology, which included the following steps: (i) data sampling and processing, (ii) screening variables through the simple regression and correlation coefficients and (iii) multiple regression complemented by statistical significance testing. Between 69% and 98.2 % (alpha=O.1) of the variation in returns could be explained in terms of the variation by the explanatory variables that passed the rigorous screening process. The relative good results are likely to be related to the higher explanatory power of the multi-factor approach. The remaining unexplained portion of return can potentially be decreased by using larger samples and pursuing some of the other recommendations for additional research.