Key performance indicators to predict the future performance of office nodes

Abstract

The property sector is globally regarded as one of the best asset classes to invest in. There is substantial data available in respect of the historical performance of the property sectors and geographical locations. The challenge today however, is to be able to predict which office nodes will, in this fast changing environment, be the best performing nodes in the future. This research project endeavours to answer this burning research question. Interviews were conducted with 18 commercial property experts specialising in the different nodes of the main metropolitan regions of South Africa, namely Pretoria, Johannesburg, Cape Town and Durban. Through the interviews, it became evident which key performance indicators (KPIs) are regarded by the property specialist as the most important KPIs to consider when investigating office nodes’ performance. In the model formulated, total return was used as the measure of the performance of the different nodes. The most relevant KPIs mentioned by the specialists were used in a multiple regression model as the independent variables and total return as the dependent variable. Twenty years of data from MSCI was examined in the multiple regression model. The regression models were used to further determine which of the KPIs contributed the most towards explaining total return as the measurement of performance. The purpose of the different regression models were to determine a model with the highest adjusted R-square, F-value, as well as the highest significance of all the KPIs used in the model, to enable the researcher to use the Beta values to determine the total return of the different nodes in the future. The model formulated enables the investor to identify the best performing office nodes in the future.