Data-driven decisions: The future of CRE valuation and performance benchmarking

How CRE leaders are transforming valuation and performance benchmarking practices

The commercial real estate market has always been dynamic but, over the last decade, the industry has undeniably evolved in complexity and scale. With the influx of new data sources and the rapid expansion into alternative property types, the ability to benchmark valuations and performance effectively has become both a strategic advantage and a core necessity.

In this landscape, CRE professionals must be adept at measuring, interpreting, and acting on performance metrics, all while maintaining increased transparency with stakeholders demonstrating an increased appetite for real-time insights and tailored benchmarks. The question is no longer whether benchmarking matters; but rather, how it can be optimized to craft a more accurate and nuanced narrative for investors and unlock new value across every dimension of the investment landscape.

To pull back the curtain on the state of CRE benchmarking in 2025 and offer key insights into how industry leaders are redefining best practices, Alex Jaffe, Senior Director, Valuation & Advisory at Altus Group was joined by Matt Gilbert, Head of Analytics at CBRE Investment Management, and Bill Maher, Director, Strategy & Research at RCLCO Fund Advisors, at the recent Altus Innovation Summit in New York City. Their discussion tackled many of the questions pressing the CRE industry today, from the evolving role of widely used indices and emerging benchmarks, to the practical challenges of integrating disparate data streams and providing robust coverage across both core and alternative property types. For those eager to shape the future of institutional real estate, success hinges on harnessing data not just for compliance or reporting but for deeper understanding, smarter decision-making, and powerful storytelling.

Altus Innovation Summit in New York City – Panel discussion on valuation and performance benchmarking in CRE

Evolving strategies in CRE performance metrics

Today, the primary value of CRE benchmarking lies in its ability to create a better feedback loop with investors, letting them know precisely what was done in the past and why it worked – or didn’t. Over the last decade, the industry’s approach to analytics has matured, leading to a growing cultural embrace of quantitative performance assessment. “Investors don’t just want end-of-month reports anymore. They want dashboards updated hourly,” Jaffe observes.

Maher notes that historically, outperforming the ODCE index was attainable by strategically underweighting office assets, which was a poor-performing property type. Today, however, the composition of the ODCE index has shifted. “60% of the benchmark is now industrial and apartments, so it’s more difficult to determine whether to underweight or overweight those assets.” As a result, the process now requires making pretty big bets against the benchmark to try to outperform it. When dealing with strategies outside ODCE, Gilbert added that their approach is evolving. “We are starting to look at public market equivalent (PME) and direct alphas to compare our performance against selected benchmarks or indices,” he adds, noting that “both investor and portfolio manager demand are beginning to embrace new measures and approaches for evaluating performance."

Maher also underscores a key challenge for pension funds: how is the appropriate benchmark determined? While the ODCE index — and broader NCREIF data — have offered a consistent foundation for benchmarking core, low-leverage assets over more than two decades, it is important to recognize that every portfolio is unique. These benchmarks will always be integral to industry practices; however, some investors may integrate these indices with additional data sources to better reflect diverse property type mixes or risk-return profiles.

For CRE professionals hoping to effectively blend custom benchmarks with the ODCE index, Jaffe emphasizes the need to identify core portfolio characteristics, such as property types and geographical distribution, to ensure that benchmarks accurately reflect the underlying assets. From there, he advocates for the development of tailored benchmarks that integrate relevant indices while considering the unique nuances of the portfolio. Finally, it’s imperative to regularly update and refine those custom benchmarks to adapt to changing market conditions and investor expectations, ensuring they remain relevant and effective for performance evaluation.

Bridging historical and forward-looking analytics

While historical benchmarking remains foundational, forward-looking analytics are equally critical. To this effect, Maher reveals that his firm now models five to six niche property types — data centers, seniors housing, manufactured housing, among others — forecasting rent growth, cap rate movements, and capital flows at both the national and geographic level. “We’re internally forecasting those [metrics] and also trying to understand them by geography,” he said, illustrating how predictive modeling helps investors anticipate performance beyond rear-view analysis. “If you don’t look forward, you’re flying blind,” he adds.

A growing appetite for “quantitative measures” is also driving valuation conversations. Rather than relying solely on backward-looking income yields or cap rates, the current landscape requires tools that offer “timely, accurate information” immediately upon benchmark release to equip teams with the data story they need before meeting with investors.

The data-granularity imperative

A central challenge highlighted by both Maher and Gilbert is the granularity of available data — specifically, the need to understand not just overall returns but the discrete drivers beneath them, including market rents, rental growth, and operating expenses. Pinpointing the contribution of these components not only clarifies what drives outperformance but also helps investors forecast future results more precisely.

As alternatives gain institutional traction, data parity must improve. “The percentage of data centers in NCREIF is small at this point, but I expect coverage to grow in step with investment flows” Maher shares. For residential rental comps, Gilbert highlights the importance of transparency amid ongoing litigation against data providers: “I’m hopeful it doesn’t impact the availability of information,” he said, underscoring that robust comp data is imperative to accurate yield and valuation analyses.

Lessons from other asset classes

When comparing real estate to other private assets, Maher pointed to a benchmarking paper recently released by the Real Estate Research Institute (RERI). “Real estate is by far and away the best among real assets, thanks to long-established, relative indices,” he notes. On the contrary, benchmarks for infrastructure and timberland are often proxy measures — “CPI plus five,” for example — rather than true market comparators. “Real estate assets are peers by default. You can line up two warehouses in Dallas and compare apples to apples.”

This comparative strength reinforces real estate’s appeal within diversified institutional portfolios. Maher notes a growing trend among large pension funds to invest directly in benchmark vehicles — such as IDR’s fund‐of‐benchmark vehicles. “There isn’t much upside and the downside is worse than the upside,” he concludes. “If the benchmark delivers stable core returns at low cost, why not capture it rather than chase marginal outperformance?”

The path forward: Balancing science and art

Can CRE professionals anticipate a (very near) future where benchmarking marries rigorous analytics with the art of real estate? Gilbert dreams of platforms capable of ingesting massive, attribute-rich datasets and producing firm-wide visualizations overnight — tools that explain why your portfolio did the way it did versus the benchmark. “Imagine ChatGPT-style queries on your portfolio,” he muses. “Show me all underperformers with cap rate drift >20 bps’ — that’s weeks saved.”

Maher, meanwhile, envisions replacing today’s single-point appraisals with probability-driven valuations. By running thousands of Monte Carlo simulations, he argues, an appraisal could report a value range complete with upside and downside probabilities. “Why couldn’t an appraisal be the same thing?” he asks, better capturing market uncertainty than any lone estimate.

Ultimately, real estate benchmarking must remain a living discipline that advances alongside new data streams, evolving investor expectations, and the emergence of advanced predictive analytics. As the field shifts toward forecasting and scenario modeling, the granularity and precision of underlying data becomes even more critical. The more exact the measurement of market rents, growth trajectories, and operating expenses, the more accurate and actionable performance forecasts become. Solutions that are designed precisely for this evolving landscape empower CRE professionals to seamlessly identify the key drivers of performance and transform raw data into richer, forward-looking narratives for all stakeholders.

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