Background of the Study
Sensitivity analysis is a financial modeling tool that evaluates the impact of changes in key variables on investment outcomes, providing valuable insights for decision-making (Brealey et al., 2023). In the Nigerian real estate sector, characterized by high volatility and significant capital requirements, sensitivity analysis is crucial for managing risks and optimizing returns. Mixta Africa, a leading real estate firm, operates in a dynamic environment where economic factors such as inflation, exchange rates, and interest rates significantly affect investment decisions.
By analyzing "what-if" scenarios, sensitivity analysis enables firms to anticipate potential risks and develop strategies to mitigate them (Fabozzi, 2024). Despite its importance, the adoption of sensitivity analysis in Nigerian real estate firms remains limited due to inadequate financial expertise and technological constraints.
This study examines the role of sensitivity analysis in investment decision-making at Mixta Africa, highlighting its benefits and challenges.
Statement of the Problem
Investment decisions in the real estate sector are inherently risky due to economic uncertainties and fluctuating market conditions. Mixta Africa, like many Nigerian real estate firms, faces challenges in predicting the impact of these variables on project outcomes. The limited use of sensitivity analysis in evaluating investment opportunities often results in suboptimal decisions and financial losses (Ajayi & Balogun, 2025).
The lack of robust financial models and tools further compounds the problem, hindering the ability of firms to make informed decisions. This study addresses these issues by exploring the impact of sensitivity analysis on investment decision-making in Mixta Africa.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
The study focuses on Mixta Africa’s use of sensitivity analysis in investment decision-making. Limitations include restricted access to proprietary investment data and potential biases in respondent feedback.
Definitions of Terms
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