Strategic Use of Data Analytics and Market Intelligence in Commercial Property Investment Decision-Making: A Qualitative Study from Johannesburg, South Africa

Property investment decisions are becoming increasingly data-driven as investors worldwide seek greater accuracy, speed and strategic insight amid volatile market conditions. This shift is especially relevant in emerging economies, where major cities combine strong investment potential with elevated market risk. This study explored the strategic use of data analytics and market intelligence in commercial property investment decision-making in Johannesburg, South Africa. The research pursued four objectives: to examine current analytics practices, to identify barriers constraining strategic adoption, to identify enablers supporting adoption, and to propose recommendations for strengthening data-driven decision-making. A qualitative, exploratory case study design, underpinned by an interpretivist research philosophy and a hybrid deductive-inductive (abductive) analytical approach, was adopted. Data were collected through semi-structured interviews with a purposively selected sample of fifteen experienced commercial property investment professionals and analysed thematically using NVivo (Version 14). The findings indicate that data analytics is operationally present within investment practice, most notably through yield-based investment modelling and comparable sales benchmarking, supplemented by internally accumulated transaction data and multi-source triangulation; a smaller subset of participants also described more advanced geospatial, scenario-based and system-integrated analytical practice, indicating an uneven maturity gradient rather than uniform digital transformation. However, deeper strategic integration is constrained by data quality and completeness concerns, the cost and complexity of implementation, market volatility, and behavioural subjectivity in price negotiation. Enabling conditions include the accumulation of proprietary transaction data, growing technological infrastructure integration, rising investor demand for transparency, and improving digital literacy among practitioners. The study concludes that commercial property investment decision-making in this market operates within a hybrid analytical model in which quantitative tools enhance, rather than replace, professional judgement. Recommendations include strengthening internal data governance, pursuing phased and scalable technology investment, institutionalising analytical transparency as a professional norm, and investing in continuous digital skills development. These findings, based on participants' own accounts and perceptions rather than independently verified transaction or performance outcomes, contribute to the limited empirical literature on PropTech adoption and data-driven decision-making in emerging African property markets.

Data analytics, Market intelligence, Commercial property investment, PropTech adoption, South Africa

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