Abstract:
This research examines how much forecasting accuracy can be achieved by modelling
the relationships between listed real estate and macroeconomic time series variables using the logit
regression model. The example data for this analysis included 10-year (2008–2018) transactions. The
Statistical Package for Social Sciences (SPSS, version 25) and Microsoft Excel 2016 were used for
descriptive and inferential analysis. The data collected on the listed real estate transactions for South
Africa and Nigeria represent the largest listed real estate markets in the continent. The study found
that 22.2% variance in the Nigerian real estate market was explained by the lending rate, treasure
bill rate, and Consumer Price Index, while 9.4% variance in the South African real estate market was
explained by changes in the exchange rate and coincident indicators. The strength and similarity of
the model capacity in both countries showed that each market signal has a predictive accuracy of
75% (Nigeria) and 80% (South Africa).
