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.

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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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Houston scientist wins prestigious Pew Scholar award for brain cancer research

standout scholar

Christina Tringides, an assistant professor of materials science and nanoengineering at Rice University, is one of 21 scientists to win a prestigious Pew Biomedical Scholar award.

She is the first faculty member from Rice to win the distinction, which provides $300,000 over four years for advances in biomedicine, according to the university. The awards are granted to researchers who are in the first few years at the assistant professor level.

In Tringides’ case, the funding will support her innovative new method of modeling glioblastoma, a common and extremely aggressive form of brain cancer. Thanks to producing its own blood supply, glioblastoma spreads quickly, weaving tendrils of blighted tissue throughout the brain. Because of this, surgery is difficult and conventional therapies ineffective.

Understanding the way glioblastoma spreads is crucial to the search for a cure. Tringides is using hydrogels that mimic the brain’s extracellular matrix. Using cultures and a microscopic labyrinth, her team can see how the cancer spreads, bonds with neurons and changes cell wall activity. Essentially, Tringides has devised an intelligence test for tumors in hopes of learning how to outsmart them.

“As cancer crawls through the maze, we can look at how it is interacting with the neurons more and more, and measure how electrical activity is changing as a result,” she said in a news release from Rice.

Examining how cancer cells grow can reveal which conditional changes slow them down. Finding ways to alter the structure of brain matter in a way that makes it inhospitable to the cancer could lead to therapies that would impede growth or even reverse it. Using her custom-made ersatz brain maze makes it easier to observe changes than it would be in a patient’s brain.

“Imaging synapses is time-intensive ⎯ it can involve large data files that are hard to visualize, but if we know that the only place where we might have a synapse is this tiny 1-by-4-by-10 micron channel, it makes it much faster and reliable to image them,” Tringides said.

Born in Ames, Iowa, Tringides received her doctorate in biophysics from Harvard before joining Rice in 2024 through a Cancer Prevention and Research Institute of Texas (CPRIT) recruitment award.

Her research was also one of the first four projects to receive research awards through the Rice Brain Institute and TMC Neuro Collaboration Seed Grant Program.

Texas residents earn 11th highest income in U.S., says 2026 study

Money Matters

A new WalletHub study comparing income disparities across America has ranked Texas residents No. 11 on the list of states with the highest earning residents in the nation.

The report, "States Where People Have the Highest Income (2026)," analyzed U.S. Census Bureau income data in all 50 states and the District of Columbia. The report evaluated the average annual income of the top five percent, the median annual household income, and the average annual income of the bottom 20 percent of residents in every state, all adjusted for the cost of living.

The report's data revealed the top five percent of Texans, the highest earners, make $520,378 on average yearly after adjusting for the cost of living. That's the seventh-highest income among the top five percent of earners nationwide.

Meanwhile, the median annual income of a Texas household is just under $76,000. The bottom 20 percent of Texas residents make $17,651 a year, the report found.

For additional context, the latest data from the Federal Reserve shows an American household's median yearly income is about $83,700. WalletHub analyst Chip Lupo also found that the highest earning 10 percent of individuals in the U.S. earn over 12 times more than those in the lowest-earning 10 percent, based on the latest Census data.

"By measuring the income of various percentiles against a state's median income, we can better identify where income disparities are more prevalent, which could help us better understand why residents of certain states struggle more to make ends meet," said Lupo.

Virginia is the state where residents earn the highest income in the U.S., WalletHub said. Based on the report's findings, the top five percent of Virginians make $545,097 on average per year after adjusting for the cost of living. The median annual income of a Virginia household comes out to $95,339, and the bottom 20 percent of residents make $19,671 annually on average.

Conversely, West Virginia is the state where people have the lowest income in the U.S. A West Virginia household makes a median annual income of $56,610, the third-lowest nationally, and the bottom 20 percent of residents make $13,260 on average per year, which is the fifth-lowest in the nation. The top five percent of West Virginians make $372,218 on average per year.

The top 10 states where residents have the highest income are:

  • No. 1 – Virginia
  • No. 2 – New York
  • No. 3 – New Jersey
  • No. 4 – Washington
  • No. 5 – Connecticut
  • No. 6 – Utah
  • No. 7 – Colorado
  • No. 8 – Minnesota
  • No. 9 – Illinois
  • No. 10 – Massachusetts

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This article originally appeared on CultureMap.com.

23 Houston companies rank among America’s most future-ready businesses

future focused

By one measure, Spring-based tech giant Hewlett Packard Enterprises reigns as the most future-ready Houston-area company on the S&P 500 stock index.

HPE sits at No. 72 in a first-time ranking of the best S&P 500 companies for the future. Including HPE, 23 Houston-area companies appear on the list.

Published by The Wall Street Journal, the ranking was created by Bendable Labs for the WSJ Leadership Institute. It evaluates how S&P 500 companies stack up in six areas: AI readiness, innovation, talent readiness, financial fitness, resilience and agility. To be ranked, a company had to be part of the S&P 500 as of Dec. 31.

Among the six categories, HPE ranked highest for innovation (No. 30) among local companies. The WSJ didn’t say why HPE scored so well for innovation. However, the company stands out in this category thanks to:

  • Creation of the El Capitan and Frontier supercomputing systems
  • Research into photonic computing and quantum networking
  • Last year’s $14 billion acquisition of Juniper Networks, giving HPE an edge in AI-native networking
  • Establishment of the everything-as-a-service GreenLake hybrid cloud platform for data centers, colocation facilities and edge computing environments

In an interview with the Six Five podcast at HPE Discover 2025 in Las Vegas, CEO Antonio Neri said the company’s strategy is “basically founded on innovation, and that innovation drives shareholder value over the long term.”

While HPE fared well in the innovation category, it ranked toward the bottom for financial fitness. What’s behind the No. 430 ranking in the financial category? HPE’s low score likely reflects a debt-heavy acquisition strategy coupled with a historically low-margin hardware business.

Here’s the full list of the 23 Houston-area companies included in the ranking of the best companies for the future:

  • No. 72 Hewlett Packard Enterprise
  • No. 105 SLB
  • No. 120 Baker Hughes
  • No. 125 ConocoPhillips
  • No. 158 NRG Energy
  • No. 176 Targa Resources
  • No. 185 Chevron
  • No. 195 Halliburton
  • No. 223 Coterra Energy
  • No. 229 Waste Management
  • No. 235 Exxon Mobil
  • No. 250 Kinder Morgan
  • No. 257 Quanta Services
  • No. 276 CenterPoint Energy
  • No. 285 Sysco
  • No. 313 Occidental Petroleum
  • No. 318 Camden Property Trust
  • No. 333 EOG Resources
  • No. 365 LyondellBasell Industries
  • No. 373 Comfort Systems USA
  • No. 401 Crown Castle
  • No. 408 Phillips 66
  • No. 500 APA
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