Forecasting Performance of Commercial Property Investments in Lagos Metropolis
DOI:
https://doi.org/10.15641/jarer.v5i2.915Keywords:
Real Estate Investment, Forecasting, Investment Performance, Lagos Metropolis, NigeriaAbstract
Investors rely on statistical forecasts to guide their investment decisions. Given the relative opportunity cost associated with these decisions, and the huge financial implication of commercial property investments, such insights are invaluable; because investors can choose investments from an informed position. Despite this recognised benefit of forecasting, there has been little focus on forecasting the performance (total returns) of commercial property investments in Lagos Metropolis. This paper, therefore, aims to forecast the total returns of two commercial property investment types (shops and offices) in five sub-markets (Yaba, Ikeja, Ikoyi, Victoria Island and Lagos Island) within the Lagos property market. In doing so, the study uses longitudinal data for the capital and rental values of commercial property investments in Lagos between 2006 to 2018 alongside a simple regression model for 2019-2021 predicted total returns. Autocorrelation was used in testing the predictive validity of this data set. Furthermore, multiple-forecasts were evaluated simultaneously for accuracy and, together, they illustrate the difficulty of compiling a robust dataset in the absence of a central database. This paper suggests that the sampled total returns for the five sub-markets fluctuate and tend to decline as seen in the Ordinary Least Square Regression technique for 2019 to 2021. The results also suggest a low autocorrelation in most of the sub-markets, which indicates that the observed pattern of returns may not continue. This paper recommends that investors be wary of commercial property investment in Lagos Metropolis, due to the observed poor performance (low and fluctuating total returns). It is also recommended that a property database be constructed to improve property data reliability and allow for the application of complex quantitative forecasting techniques.