Estimating Aggregate Data Center Investment with Project-level Data

Eirik E. Brandsaas, Daniel Garcia, Robert Kurtzman, Joseph B. Nichols and Adelia Zytek
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Eirik E. Brandsaas: https://www.federalreserve.gov/econres/eirik-e-brandsaas.htm
Joseph B. Nichols: https://www.federalreserve.gov/econres/joseph-b-nichols.htm

No 2025-109, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Data center investment in the U.S. has increased rapidly in the post-pandemic era, and plans for future investment have surged further. Forecasting investment at such a turning point is an important but potentially fraught exercise, especially given lags in aggregate data availability. We develop a straightforward method to forecast aggregate investment using project-level microdata and a small number of parameters: specifically, abandonment rates, time from planning to start, and time from start to completion. As a key validation of our approach, we generate estimates that match the recent history of aggregate data center investment in the NIPAs. We then use our method to generate nowcasts of aggregate data center investment in the short run, with the mean forecast indicating that investment will increase to $370 billion annualized by 2026:Q2. We can extend our methodology further out, but our forecasts then become conditional on the assumed flow of new data center plans. Assuming future plans range from one-fourth to twice the average pace of plans from 2024-2025 implies a range of investment forecasts of $360 billion to $930 billion in 2027, demonstrating the substantial upside and downside risks to future levels of investment.

JEL-codes: E22 E32 L74 R33 (search for similar items in EconPapers)
Pages: 27 p.
Date: 2025-12-22
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:102368

DOI: 10.17016/FEDS.2025.109

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