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ata science is becoming a crucial factor in commercial real estate (CRE) investing, with firms seeking advanced analytics to gain a competitive edge and optimize portfolios. Despite widespread recognition of data's potential, there remains uncertainty about its implementation, sophistication, and impact.
Altus Group's global research report, "The state of data science in commercial real estate investing," provides insights into how CRE firms are adopting data science, the challenges they face, and opportunities for differentiation. The report is based on a survey of over 400 investment decision-makers from North America, Europe, and Asia-Pacific.
A paradox exists in the industry: while half of investors use data science tools, many believe they are ahead of their peers despite lacking clear benchmarks. This confusion leads to complacency or misplaced anxiety among investors. Furthermore, there's a distinction between data analytics and data science capabilities, with few firms leveraging advanced applications like predictive modeling and machine learning.
Larger firms, particularly those with over $1 billion in assets under management, are leading the charge in internal data science development. They have dedicated teams, Chief Data Officers, and proprietary models. Regionally, Asia-Pacific firms lead in hiring and training internal teams, while North American firms focus on external solutions.
Firms must decide whether to develop data science capabilities internally or leverage external providers. The report reveals a mixed view, with 29% of firms mainly developing internal capabilities and 46% using a mix of internal and external resources. Internal development comes with challenges like securing high-quality data and hiring qualified talent.
External providers offer access to specialized tools, expertise, and datasets, but also present challenges like software usability and integration. CRE firms view data science as a means of differentiation, with the top reason being to gain a competitive advantage. However, with data science becoming an expectation, firms must find innovative ways to leverage analytics for a true edge.
Implementation challenges persist, including data quality and availability, organizational buy-in, talent acquisition and retention, and ROI uncertainty. Firms in North America and Europe underestimate resources required for data science initiatives, while APAC firms struggle with setting realistic expectations.
Looking ahead, firms prioritize investments in statistical tools, predictive modeling, automation systems, and enhanced benchmarking. External providers will continue to support CRE firms, but there's room for improvement in areas like software usability and flexibility. Data science is no longer optional; it's a fundamental component of CRE investment success.
