Key Takeaways
- • Commercial real estate valuations depend fundamentally on data quality, encapsulated by the principle "garbage in, garbage out"—poor data inputs directly compromise investment decision-making outcomes for fund managers, portfolio managers, and asset managers.
- • Data staleness poses a critical valuation risk; market volatility over the past two years means property valuations from one month or quarter prior may no longer reflect current conditions, leaving portfolio managers with outdated asset assessments.
- • Altus Group identifies three primary data hurdles affecting commercial real estate valuations, with the first hurdle centered on establishing methodical confidence in internal data quality and ensuring information remains current and reliable.
- • Fund managers across open-end and closed-end commercial real estate structures face systematic challenges in producing consistent, dependable data required for informed investment decisions and accurate portfolio value assessments.
Summary
Commercial real estate valuations operate under a critical constraint: the quality of underlying data directly determines the reliability of valuation outputs. According to Altus Group, the adage "garbage in, garbage out" appropriately describes how inaccurate or poor-quality data undermines the valuation process. However, the data challenges facing institutional real estate investors extend beyond simple appraisal errors; they reflect structural issues embedded in how fund managers collect, maintain, and analyze information across their portfolios.
Altus Group, drawing on hundreds of client engagements and thousands of client conversations involving some of the largest open-end and closed-end commercial real estate funds, has identified three key data hurdles that prevent portfolio managers and asset managers from obtaining the valuation information necessary to make informed investment decisions. The first hurdle centers on establishing methodical confidence in an organization's own commercial real estate data. This requires comprehensive attention to data quality standards, with particular emphasis on ensuring data remains current. Given heightened market volatility over the preceding two years, valuations that accurately reflected property values a quarter or even one month prior may have become obsolete. This temporal decay in data relevance leaves fund managers and their teams operating with materially outdated views of their asset values and overall portfolio positioning.
The correlation between data quality and business outcomes is widely accepted among institutional investors, yet translating this acknowledgment into comprehensive, systematic remediation proves substantially more difficult. Fund managers must implement "good data" checklists that address multiple dimensions of data governance, including timeliness, accuracy, and consistency. Without methodical processes to refresh and validate valuation inputs on an ongoing basis, even sophisticated portfolio management teams risk making capital allocation and risk management decisions based on stale or incomplete information. For alternative investment professionals managing substantial capital commitments in commercial real estate, the stakes of data quality failures are material—they directly impact return projections, risk assessments, and strategic rebalancing decisions across their portfolios.
Altus Group, drawing on hundreds of client engagements and thousands of client conversations involving some of the largest open-end and closed-end commercial real estate funds, has identified three key data hurdles that prevent portfolio managers and asset managers from obtaining the valuation information necessary to make informed investment decisions. The first hurdle centers on establishing methodical confidence in an organization's own commercial real estate data. This requires comprehensive attention to data quality standards, with particular emphasis on ensuring data remains current. Given heightened market volatility over the preceding two years, valuations that accurately reflected property values a quarter or even one month prior may have become obsolete. This temporal decay in data relevance leaves fund managers and their teams operating with materially outdated views of their asset values and overall portfolio positioning.
The correlation between data quality and business outcomes is widely accepted among institutional investors, yet translating this acknowledgment into comprehensive, systematic remediation proves substantially more difficult. Fund managers must implement "good data" checklists that address multiple dimensions of data governance, including timeliness, accuracy, and consistency. Without methodical processes to refresh and validate valuation inputs on an ongoing basis, even sophisticated portfolio management teams risk making capital allocation and risk management decisions based on stale or incomplete information. For alternative investment professionals managing substantial capital commitments in commercial real estate, the stakes of data quality failures are material—they directly impact return projections, risk assessments, and strategic rebalancing decisions across their portfolios.
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