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 still ranks as No. 1 in U.S. for inbound moves, but growth dips

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Texas continues to be the country’s No. 1 magnet for newcomers from other states, giving a boost to the state’s economy. However, Texas’ appeal weakened in 2024 compared with the previous year, due in large part to spiking home prices.

An analysis of U.S. Census Bureau data by self-storage platform StorageCafe shows Texas saw net interstate migration of 76,000 people in 2024. Texas’ net interstate migration dropped nearly 50 percent from 2023, according to the analysis. Net migration refers to the number of incoming residents minus the number of outgoing residents.

California remained the top source of newcomers for Texas, sending nearly 77,000 residents to the Lone Star State in 2024, the analysis says. Florida ranked second, followed by New York, Colorado and Illinois.

“These trends reveal Texas’ continued pull from both high-cost coastal markets and other large Sun Belt states, resulting in a mix of affordability-driven and job-driven relocation,” StorageCafe says.

Putting a damper on the influx of new residents: a roughly 124 percent surge in Texas home prices over the past decade, according to StorageCafe.

“While the state remains significantly more affordable than California, its top feeder state, the once-wide pricing gap has narrowed,” says StorageCafe. “For many movers, Texas is still a relative bargain, but no longer an undisputed one.”

Nonetheless, Texas keeps attracting young, highly educated people, which bodes well for the state’s long-term economic outlook, StorageCafe says. More than half of new arrivals to Texas in 2024 held at least a bachelor’s degree, and the age of newcomers averaged 32.

Where are most of these young, highly educated newcomers settling?

Lloyd Potter, former Texas state demographer, tells StorageCafe that population growth in Texas is happening most rapidly in suburban “ring counties” at the expense of slowing growth in urban cores. Ring counties are on the outskirts of major metro areas.

“Many people are moving from urban cores to suburban rings seeking lower costs, newer housing, better schools, and more space,” Potter says. “Typically, a move to a suburban county will be within commuting or hybrid‑commuting distance of major metro economies.”

Artemis II makes historic call to space station with help from Houston Mission Control

History in the making

Still aglow from their triumphant lunar flyby, the Artemis II astronauts made more history Tuesday, April 7: calling their friends aboard the International Space Station hundreds of thousands of miles away as they headed home from the moon.

It was the first moonship-to-spaceship radio linkup ever. NASA's Apollo crews had no off-the-planet company back in the 1960s and 1970s, the last time humanity set sail for deep space.

"We have been waiting for this like you can’t imagine,” Artemis II commander Reid Wiseman called out.

For Christina Koch on Artemis II and Jessica Meir aboard the space station, it marked a joyous space reunion despite being 230,000 miles (370,000 kilometers) apart. The two teamed up for the world's first all-female spacewalk in 2019 outside the orbiting lab.

Koch told her “astro-sister” that she'd hoped to meet up with her again in space “but I never thought it would be like this — it's amazing.”

“I'm so happy that we are back in space together,” Meir replied, “even if we are a few miles apart.”

Houston's Mission Control arranged the cosmic chitchat between the four lunar travelers and the space station's three NASA and one French residents.

Koch described being awe-struck by not just the beauty of Earth, “but how much blackness there was around it.”

“It just made it even more special. It truly emphasized how alike we are, how the same thing keeps every single person on planet Earth alive,” she told the space station crew. “The specialness and preciousness of that really is emphasized” when viewing the home planet from the moon.

By late Tuesday afternoon, the Artemis II astronauts had beamed back more than 50 gigabytes' worth of pictures and other data from the previous day's lunar rendezvous, which set a new distance record for humanity. The highlight: an Earthset photo reminiscent of Apollo 8's Earthrise shot from 1968.

"While they are inspirational and, I think, allow all of us to really feel a little bit of what they were feeling, there's also a lot of science hidden inside of those images," said Mission Control's lead lunar scientist Kelsey Young. “The conversations and the science lessons learned are just beginning."

During a debriefing with Young, the astronauts recounted how they spotted a cascade of pinpricks of light on the lunar surface from impacting cosmic debris. The flashes lasted mere milliseconds and coincided by chance with Monday evening's total solar eclipse.

Young said it was too soon to know whether the crew witnessed an actual meteor shower or more random, run-of-the-mill micrometeoroid hits. Either way, there were “audible screams of delight” in the science operations center, she said.

Koch described being awe-struck by not just the beauty of Earth, “but how much blackness there was around it.”

“It just made it even more special. It truly emphasized how alike we are, how the same thing keeps every single person on planet Earth alive,” she told the space station crew. “The specialness and preciousness of that really is emphasized” when viewing the home planet from the moon.

The first lunar explorers since Apollo 17 in 1972, Wiseman and his crew are aiming for a splashdown off the San Diego coast on Friday to wrap up the nearly 10-day test flight. The recovery ship USS John P. Murtha left port Tuesday for the target zone.

It sets the stage for next year's Artemis III, a lunar lander docking demo in orbit around Earth. Artemis IV will follow in 2028 with two astronauts attempting to land near the lunar south pole.

As for the Orion capsule’s pesky potty, Mission Control assured the astronauts that no maintenance was required Tuesday. The toilet has been on-and-off limits to the crew ever since last week’s launch, prompting them to rely on a backup bag-and-funnel system for urinating.

NASA Administrator Jared Isaacman told the crew following the lunar flyby Monday night: “We definitely have to fix some of the plumbing” ahead of the next Artemis mission. Engineers suspect a clogged filter in the overboard flushing system.

Aside from the toilet and other relatively minor matters, the mission has gone well, Isaacman noted at a news conference Tuesday, “but I'll breathe easier when we get through reentry and everybody's under chutes and in the water.”

AI-powered Houston startup helps restaurants boost customer loyalty

order up

It’s no secret that restaurant trends move fast and margins run thin. And with the proliferation of platforms like Uber Eats, DoorDash and Easy Cater, customer loyalty is fleeting.

The solution?

How about an AI-powered restaurant technology platform that helps restaurant brands cut back on third-party platforms in favor of driving direct discovery, conversion and loyalty?

Enter Saivory. Founded in 2025 by Stephen Klein, a software investor, and Fajita Pete’s restaurateur Hugh Guill, the Houston-based startup aims to help eateries better understand and activate guest behavior across digital channels as AI increasingly reshapes how consumers discover and engage with brands.

In less than a year, Saivory has partnered with Shipley Do-Nuts and Fajita Pete’s to bring AI-powered ordering to life.

“With Saivory, we were able to answer the question of, ‘what if the ordering process could be reduced to a single step, where customers simply tell us what they want and AI takes care of the rest?’” Klein tells InnovationMap.

The Houston-based startup made such an immediate impact that it was selected as a semi-finalist during Start-Up Alley at MURTEC, the restaurant industry’s leading technology conference, which took place last month in Las Vegas.

“Houston is a great hub for technology innovation, and we were proud to represent the city at MURTEC this year,” says Klein. “We didn’t win, but we were able to talk about some of the work that we have existing in the market for clients right now and a little bit about what we’re working on in the future.”

In the current restaurant technology ecosystem, the third-party aggregators own the customer attention that brings volume to restaurants, while also taking big commissions and having control over the end relationships with the customer.

That can often make it difficult for restaurants to grow loyalty and repeat business from customers. Saivory aims to level the playing field for restaurants, helping them stay more connected to their customers.

Take Saivory’s recent application with Shipley’s Do-Nuts, for example.

Saivory powered the donut giant’s AI-ordering and launched Shipley's website and mobile app to support its over 300 locations in Texas alone.

Shipley’s new AI-powered assistant helps users create personalized order recommendations based on individual or group preferences. And unlike standard chatbox features, the new assistant makes custom recommendations based on multiple customer factors, including budgetary habits, individual flavor preferences and order size. It can also be used for large catering orders.

“They're seeing more traffic to the site and they're seeing when customers use our AI-enabled flows,” Klein says. “And they're seeing higher basket sizes, bigger tickets, by about 25 percent.”

Klein says Saivory’s technology helps strengthen first-party digital relationships, reduce friction and cart abandonment, improve average order value, and delivers personalized, efficient experiences.

“It’s a win-win: the customer gets the right order quickly, while the restaurant gets a bigger margin,” he adds.

Additionally, the technology makes it easier for restaurants to share rewards, loyalty and discounts, ultimately growing more direct traffic and making restaurants less reliant on third-party delivery apps.

Next up for Saivory is adding new components to its platform to enhance the relationship between restaurant and customer, as well as technology around making it easier for restaurants to get found on Google.

“A lot of people are still searching for the best donuts near me,” Klein says. “Or what’s the best Mexican food near me? Customers will increasingly move to AI, where they’re going to ask where they should eat dinner and expect it to just order them dinner. They will eventually expect the technology to know how to do that. So that’s what we’re driving at.”

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