Commercial property : a required rate of return investigation / Gerrit Kotze

When faced with an investment opportunity in commercial real estate, the investorrequires knowledge of the discount rate since it can be used to convert expected futurecash flows from the property in today's terms and in doing so, place a value on theproperty. The so-called required rate of return would be the appropriate conversion ratesince it compensates the investor for risk and, if attainable, will induce the investor toinvest. An inaccurate assessment of the discount rate could, depending on the directionof 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 beenderived by other researchers for the US, UK and EU property markets. Each of thevariables encountered in these frameworks acts as a proxy for some aspect ofsystematic risk associated with the investment. However, locally, such models are eithernot extensively published or well described and are limited to single explanatoryvariables. Some professionals prefer to avoid frameworks and simply divert toqualitative, gut-feel and experienced based considerations in order to derive at requiredreturn rate.This dissertation addressed the possible local need for an explanatory framework ofrequired return on commercial property. The scope of work entailed: (i) a review of theliterature to establish the theoretical determinants of return and (ii) an empirical study totest 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 variablesand alternative investment opportunities in the form of stocks on the JSE, (ii) economicactivity indicators and (iii) property market fundamental parameters. The empirical studyentailed a three-phase methodology, which included the following steps: (i) datasampling and processing, (ii) screening variables through the simple regression andcorrelation coefficients and (iii) multiple regression complemented by statisticalsignificance testing. Between 69% and 98.2 % (alpha=O.1) of the variation in returnscould be explained in terms of the variation by the explanatory variables that passed therigorous screening process. The relative good results are likely to be related to thehigher explanatory power of the multi-factor approach. The remaining unexplainedportion of return can potentially be decreased by using larger samples and pursuingsome of the other recommendations for additional research.