Your data center is either closer than you think or much farther away

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A map of U.S. data centers. Courtesy of Rice Businesses Wisdom

A new study shows why some facilities cluster in cities for speed and access, while others move to rural regions in search of scale and lower costs. Based on research by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard).

Key findings:

  • Third-party colocation centers are physical facilities in close proximity to firms that use them, while cloud providers operate large data centers from a distance and sell access to virtualized computing resources as on‑demand services over the internet.
  • Hospitals and financial firms often require urban third-party centers for low latency and regulatory compliance, while batch processing and many AI workloads can operate more efficiently from lower-cost cloud hubs.
  • For policymakers trying to attract data centers, access to reliable power, water and high-capacity internet matter more than tax incentives.

Recent outages and the surge in AI-driven computing have made data center siting decisions more consequential than ever, especially as energy and water constraints tighten. Communities invest public dollars on the promise of jobs and growth, while firms weigh long-term commitments to land, power and connectivity.

Against that backdrop, a critical question comes into focus: Where do data centers get built — and what actually drives those decisions?

A new study by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard Business School) provides the first large-scale statistical analysis of data center location strategies across the United States. It offers policymakers and firms a clearer starting point for understanding how different types of data centers respond to economic and strategic incentives.

Forthcoming in the journal Strategy Science, the study examines two major types of infrastructure: third-party colocation centers that lease server space to multiple firms, and hyperscale cloud centers owned by providers like Amazon, Google and Microsoft.

Two Models, Two Location Strategies

The study draws on pre-pandemic data from 2018 and 2019, a period of relative geographic stability in supply and demand. This window gives researchers a clean baseline before remote work, AI demand and new infrastructure pressures began reshaping internet traffic patterns.

The findings show that data centers follow a bifurcated geography. Third-party centers cluster in dense urban markets, where buyers prioritize proximity to customers despite higher land and operating costs. Cloud providers, by contrast, concentrate massive sites in a small number of lower-density regions, where electricity, land and construction are cheaper and economies of scale are easier to achieve.

Third-party data centers, in other words, follow demand. They locate in urban markets where firms in finance, healthcare and IT value low latency, secure storage, and compliance with regulatory standards.

Using county-level data, the researchers modeled how population density, industry mix and operating costs predict where new centers enter. Every U.S. metro with more than 700,000 residents had at least one third-party provider, while many mid-sized cities had none.

ImageThis pattern challenges common assumptions. Third-party facilities are more distributed across urban America than prevailing narratives suggest.

Customer proximity matters because some sectors cannot absorb delay. In critical operations, even slight pauses can have real consequences. For hospital systems, lag can affect performance and risk exposure. And in high-frequency trading, milliseconds can determine whether value is captured or lost in a transaction.

“For industries where speed is everything, being too far from the physical infrastructure can meaningfully affect performance and risk,” Pan Fang says. “Proximity isn’t optional for sectors that can’t absorb delay.”

The Economics of Distance

For cloud providers, the picture looks very different. Their decisions follow a logic shaped primarily by cost and scale. Because cloud services can be delivered from afar, firms tend to build enormous sites in low-density regions where power is cheap and land is abundant.

These facilities can draw hundreds of megawatts of electricity and operate with far fewer employees than urban centers. “The cloud can serve almost anywhere,” Pan Fang says, “so location is a question of cost before geography.”

The study finds that cloud infrastructure clusters around network backbones and energy economics, not talent pools. Well-known hubs like Ashburn, Virginia — often called “Data Center Alley” — reflect this logic, having benefited from early network infrastructure that made them natural convergence points for digital traffic.

Local governments often try to lure data centers with tax incentives, betting they will create high-tech jobs. But the study suggests other factors matter more to cloud providers, including construction costs, network connectivity and access to reliable, affordable electricity.

When cloud centers need a local presence, distance can sometimes become a constraint. Providers often address this by working alongside third-party operators. “Third-party centers can complement cloud firms when they need a foothold closer to customers,” Pan Fang says.

That hybrid pattern — massive regional hubs complementing strategic colocation — may define the next phase of data center growth.

Looking ahead, shifts in remote work, climate resilience, energy prices and AI-driven computing may reshape where new facilities go. Some workloads may move closer to users, while others may consolidate into large rural hubs. Emerging data-sovereignty rules could also redirect investment beyond the United States.

“The cloud feels weightless,” Pan Fang says, “but it rests on real choices about land, power and proximity.”

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This article originally appeared on Rice Business Wisdom. Written by Scott Pett.

Pan Fang and Greenstein (2025). “Where the Cloud Rests: The Economic Geography of Data Centers,” forthcoming in Strategy Science.

Search trends predict future retail revenue up to three quarters in advance. Photo via Pexels

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

There's no crystal ball, but this researcher from Rice University is trying to see if some metrics work for economic forecasting. Photo via Getty Images

Houston researcher tries to crack the code on the Fed's data to determine economic outlook

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Research by Rice Business Professor K. Ramesh shows that the Fed appears to harvest qualitative information from the accounting disclosures that all public companies must file with the Securities and Exchange Commission.

These SEC filings are typically used by creditors, investors and others to make firm-level investing and financing decisions; and while they include business leaders’ sense of economic trends, they are never intended to guide macro-level policy decisions. But in a recent paper (“Externalities of Accounting Disclosures: Evidence from the Federal Reserve”), Ramesh and his colleagues provide persuasive evidence that the Fed nonetheless uses the qualitative information in SEC filings to help forecast the growth of macroeconomic variables like GDP and unemployment.

According to Ramesh, the study was made possible thanks to a decision the SEC made several years ago. The commission stores the reports submitted by public companies in an online database called EDGAR and records the IP address of any party that accesses them. More than a decade ago, the SEC began making partially anonymized forms of those IP addresses available to the public. But researchers eventually figured out how to deanonymize the addresses, which is precisely what Ramesh and his colleagues did in this study.

"We were able to reverse engineer and identify those IP addresses that belonged to Federal Reserve staff," Ramesh says.

The team ultimately assembled a data set containing more than 169,000 filings accessed by Fed staff between 2005 and 2015. They quickly realized that the Fed was interested only in filings submitted by a select group of industry leaders and financial institutions.

But if Ramesh and his colleagues now had a better idea of precisely which bellwether firms the Fed focused on, they still had no way of knowing exactly what Fed staffers had gleaned from the material they accessed. So the team decided to employ a measure called "tone" that captures the overall sentiment of a piece of text – whether positive, negative, or neutral.

Building on previous research that had identified a set of words with negatively toned financial reports, Ramesh and his colleagues examined the tone of all the SEC filings accessed by Fed staff between one meeting of the Federal Open Markets Committee (FOMC) and the next. The FOMC sets interest rates and guides monetary policy, and its meetings provide an opportunity for Fed officials to discuss growth forecasts and announce policy decisions.

The researchers then examined the Fed's growth forecasts to see if there was a relationship between the tone of the documents that Fed staff examined in the period between FOMC meetings and the forecasts they produced in advance of those meetings.

The team found close correlations between the tone of the reports accessed by the Fed and the agency’s forecasts of GDP, unemployment, housing starts and industrial production. The more negative the filings accessed prior to an FOMC meeting, for example, the gloomier the GDP forecast; the more positive the filings, the brighter the unemployment forecast.

Ramesh and his colleagues also compared the Fed's forecasts with those of the Society of Professional Forecasters (SPF), whose members span academia and industry. Intriguingly, the researchers found that while the errors in the SPF's forecasts could be attributed to the absence of the tonal information culled from the SEC filings, the errors in the Fed’s forecasts could not. This suggests both that the Fed was collecting qualitative information that the SPF was not—and that the agency was making remarkably efficient use of it.

"They weren’t leaving anything on the table," Ramesh says.

Having solved one mystery, Ramesh would like to focus on another; namely, how does the Fed identify bellwether firms in the first place?

Unfortunately, the SEC no longer makes IP address data publicly available, which means that Ramesh and his colleagues can no longer study which companies the Fed is most interested in. Nonetheless, Ramesh hopes to use the data they have already collected to build a model that can accurately predict which firms the Fed is most likely to follow. That would allow the team to continue studying the same companies that the Fed does, and, he says, “maybe come up with a way to track those firms in order to understand how the economy is going to move.”

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This article originally ran on Rice Business Wisdom and was based on research from K. Ramesh is Herbert S. Autrey Professor of Accounting at Jones Graduate School of Business at Rice University.

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Texas solar power poised to surpass coal for the first time in 2026

Powering Texas

Solar power promises to shine even brighter in Texas this year.

A new forecast from the U.S. Energy Information Administration (EIA) indicates that for the first time, annual power generation from utility-scale solar will surpass annual power generation from coal across the territory covered by the Electric Reliability Council of Texas (ERCOT).

Solar generation is expected to reach 78 billion kilowatt-hours in 2026 in the ERCOT grid, compared with 60 billion kilowatt-hours for coal, the EIA forecast says. The ERCOT grid supplies power to about 90 percent of Texas, including the Houston area.

“Utility-scale solar generation has been increasing steadily in ERCOT as solar capacity additions help meet rapid electricity demand growth,” the forecast says.

Although natural gas remains the dominant source of electricity generation in ERCOT, accounting for an average 44 percent of electricity generation from 2021 to 2025, solar’s share of the generation mix rose from four percent to 12 percent. During the same period, coal’s share dropped from 19 percent to 13 percent.

EIA predicts about 40 percent of U.S. solar capacity, or 14 billion kilowatt-hours, added in 2026 will come from Texas.

Although EIA expects annual solar generation to exceed annual coal generation in 2026, solar surpassed coal in ERCOT on a monthly basis for the first time in March 2025, when solar generation totaled 4.33 billion kilowatt-hours and coal’s totaled 4.16 billion kilowatt-hours. Solar generation continued to exceed that of coal until August of that year.

“In 2026, we estimate that solar exceeded coal for the first time in March, and we forecast generation from solar installations in ERCOT will continue to exceed that from coal until December, when coal generation exceeds solar,” says EIA. “We expect solar generation to exceed that of coal for every month in 2027 except January and December.”

For 2027, EIA forecasts annual solar generation of 99 billion kilowatt-hours in the ERCOT grid, compared with 66 billion kilowatt-hours of annual coal generation.

In April, ERCOT projected almost 368 billion kilowatt-hours of demand in ERCOT’s territory by 2032. ERCOT’s all-time peak demand hit 85.5 billion kilowatt-hours in August 2023.

“Texas is experiencing exceptional growth and development, which is reshaping how large load demand is identified, verified, and incorporated into long-term planning,” ERCOT President and CEO Pablo Vegas said. “As a result of a changing landscape, we believe this forecast to be higher than expected … load growth.”

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This article first appeared on EnergyCapitalHTX.com.

Intuitive Machines strikes $49.3M deal to expand lunar communications network

space deal

Houston-based Intuitive Machines is bulking up its space-to-ground data network with the acquisition of United Kingdom-based Goonhilly Earth Station and its U.S. arm, COMSAT.

The $49.3 million cash-and-stock deal would add 44 antennas to Intuitive Machines’ network. The acquisition is expected to close in the third quarter.

Intuitive Machines, a space infrastructure and services company, designs, builds, and operates spacecraft and data networks for lunar and deep-space missions. Goonhilly operates a satellite Earth station in Cornwall, England.

Intuitive Machines says Goonhilly’s and COMSAT’s civil, commercial, and government customers will complement its current customer base and broaden its reach into related sectors.

“Customers have been clear that they want a single, integrated, and resilient solution for their communications and [position, navigation, and timing] needs as they accelerate missions at an unprecedented pace,” Steve Altemus, co‑founder and CEO of Intuitive Machines, said in a news release.

Kenn Herskind, executive chairman of Goonhilly, says the acquisition “will allow us to scale that capability globally and directly support the next era of lunar exploration. Together, we will be creating a commercial lunar communications network that is interoperable, resilient, and ready to support Artemis and international missions.”

Modular nuclear reactor co. NuScale Power moves into Houston market

New to Hou

The nuclear energy renaissance continues in Texas with an announcement by NuScale Power. The Oregon-based provider of proprietary and innovative advanced small modular reactor (SMR) nuclear technology announced in April it would be opening office space in Houston’s CityCentre.

“Opening this space in Houston underscores our commitment to meeting rising energy demand with safe, scalable nuclear technology,” John Hopkins, NuScale president and CEO, said in a news release. “This move expands our presence in a key market for partners, prospective customers, and stakeholders in addition to positioning us for the future as we focus on the near-term deployment of our industry-leading technology. Texas is leading the way in embracing advanced nuclear for grid resilience and industrial decarbonization, and we’re proud to expand our footprint and capabilities in this important region.”

Interest in nuclear power has been growing in recent years thanks to tensions with oil-rich nations, concerns about man-made climate change from fossil fuels, and the rapidly increasing power needs of data centers. Both Dow and Texas A&M University have announced expanded nuclear power projects in the last year, with an eye of changing the face of Texas’s energy industry through smaller, safer fission reactors.

Enter NuScale, founded in 2007 from technology developed at the University of Oregon. Their modular SMR technology generates 77 megawatts and is one of the only small modular reactors (SMR) to receive design approval from the U.S. Nuclear Regulatory Commission (NRC). These advances have led to runaway success for NuScale, whose stock has risen by more than 1,670 percent since the start of 2024.

The new operations campus in CityCentre is expected to facilitate the movement, installation and coordination of NuScale technology into the various energy systems. Typically, SMRs are used for off-grid installations, desalination operations, mining facilities and similar areas that lack infrastructure. However, the modularity means that they can be easily deployed to a variety of areas.

It comes none too soon. ERCOT projects that Texas data centers alone will require 77,965 megawatts by 2030.

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This article first appeared on EnergyCapitalHTX.com.

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