From SA to the USA: Election forecasting

  • Jenny P Holloway CSIR Built Environment
  • Hans W Ittmann University of Johannesburg and HWI Consulting
  • Nontombeko Dudeni-Tlhone CSIR Built Environment
  • Peter MU Schmitz CSIR Built Environment


Elections draw enormous interest worldwide, especially if these involve major countries, and there is much speculation in the media as to possible outcomes from these elections. In many of these recent elections, such as the UK and USA, however, forecasts from market surveys, electoral polls, scientific forecasting models and even exit polls, obtained from voters as they leave the voting stations, failed to predict the correct outcome. Election night forecasts, which endeavour to forecast the ultimate result before the final outcome is known using early results, were also carried out, with some more accurate than others.After successfully predicting most of the metropolitan region results correctly in the South African local 2016 municipal elections, using an election night forecasting model developed for South Africa (SA), the question of adapting the model to work outside of SA on a different electoral system was raised. The focus of this paper is to describe the results obtained for the 2016 USA presidential election, on election night, using an adapted version of the SA model. This paper also addresses the applicability of the model assumptions as well as the data issues involved in forecasting outside of South Africa. It is shown that even with many hurdles experienced in the process the model performed relatively well.


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Author Biographies

Jenny P Holloway, CSIR Built Environment
Fields of Specialisation: Statistics and Mathematical Modelling
Hans W Ittmann, University of Johannesburg and HWI Consulting
Fields of Specialisation: Decision support, supply chain management, operations research for development.
Nontombeko Dudeni-Tlhone, CSIR Built Environment
Field of Specialization: Statistics
Peter MU Schmitz, CSIR Built Environment
Fields of Specialisation: GIS, Mathematical Modelling and Supply Chain Management.
Research Articles