The investment and valuation nexus: exploring the perceptions of Nigerian real estate valuation professionals

Purpose: Despite the importance of investment fundamentals in determining commercial property value, there is a knowledge gap in terms of how investment factors are integrated into the valuation process in developing markets, particularly in Africa. This study investigates how property valuers in Nigeria perceive the investment-valuation nexus, and how this influences their valuation processes and output.

Design/Methodology/Approach: Semi-structured interviews were deployed to 14 professional property valuers across Nigeria. The discussion from the interview transcripts was subjected to thematic and content analysis using the K-mean clustering learning algorithm and Latent Dirichlet Allocation topic modelling.

Findings: Key findings indicate that valuers consider a range of investment factors, including market conditions, location, and property features. However, the study highlights a potential gap in the consideration of cash flow analysis and tenant-related factors. The findings suggest that a more comprehensive approach to valuation is necessary to enhance the accuracy and reliability of property valuations in Nigeria.

Practical Implications: The findings have significant implications for Nigeria and other emerging African markets considering the high volume of property investment capital received by the countries. With key investment fundamentals not being sufficiently captured in the valuation process in line with best practices, current valuation output may be omitting important factors thereby undermining their accuracy and reliability.

Originality: This study provides alternative perspectives on the investment-valuation relationship through the unique lenses of key stakeholders (valuers) in the context of developing countries. This context is important, given that these economies are usually perceived to be less sophisticated and often present significant challenges around standardisation and bias. Secondly, the study provides some insights into the heterogeneity associated with the valuation of assets in highly heterogeneous markets such as Nigeria. Thirdly, the study adopts the K-mean clustering learning algorithm and Latent Dirichlet Allocation topic modelling approaches which have previously not been applied to property valuation.