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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Report: Houston reclaims top 10 ranking among America's best cities

Houston has made a triumphant return to America's 10 best cities for 2026, certifying the city is a cornerstone of the country's growth and economic prosperity.

Houston ranks No. 9 nationwide in the annual "America's Best Cities" report from Canada-based real estate and tourism marketing firm Resonance Consultancy. Each year, the report ranks the relative qualities of livability, cultural "lovability," and economic prosperity in 393 American cities with metropolitan populations of 500,000 or more.

Dallas surpassed H-Town as the No. 8 best city in America, and the Lone Star State boasts a strong presence among the top 25. Austin and San Antonio, respectively, were named the 11th and 24th best American cities this year.

Previously, Houston was dubbed the 13th best American city in 2025, down from its No. 10 ranking in the 2024 report.

Rather than profiling each individual city like in past reports, the 2026 edition focuses on regional and state prosperity. Texas' economic dominance is second only to Florida's, and the state's growth is solidified by the Dallas-Houston-Austin "triangle," where each metro has its own distinct economic identity, but when combined "form one of the most formidable regional economies in the world."

"In our 2026 survey, Dallas ranks third nationally as the place Americans believe offers the best job opportunities, Austin fifth, and Houston seventh," the report's author wrote. "That concentration of perceived economic opportunity in a single state is unmatched, and the GDP data confirms it isn’t just perception."

After being named one of the best places to start a business or a career earlier in 2026, Houston has continued to punch above its weight with its success in tourism, education, and housing growth.

Overall, the report found a correlation between a city's population growth and its latest ranking, with bigger cities appearing higher up on the list. The top three best American cities — New York, Los Angeles, and Chicago — are coincidentally the three largest metros, while Dallas and Houston are the fourth and fifth largest but appear eighth and ninth on the list.

"Scale compounds at the large city level — more people generate more economic activity, more cultural infrastructure, more employer presence, which attracts more people," the report said.

The top 10 best cities in America for 2026 are:

  • No. 1 – New York
  • No. 2 – Los Angeles
  • No. 3 – Chicago
  • No. 4 – Miami
  • No. 5 – San Francisco
  • No. 6 – Seattle
  • No. 7 – Las Vegas
  • No. 8 – Dallas
  • No. 9 – Houston
  • No. 10 – Boston

New probe into Tesla after vehicle slams into Houston-area home at high speed

Tesla Talk

The top U.S. auto regulator opened an investigation Monday, June 22, after a Tesla using an automated driving feature slammed into a Texas home at high speed and killed a 76-year-old woman standing inside.

The National Highway Traffic Safety Administration said it's opening a special investigation into the Tesla Model 3 crash on Friday near Houston, a significant probe because the car was using technology that Elon Musk considers key to the company's future.

The Tesla CEO is rolling out robotaxis using automated software in several U.S. cities this year and plans to invite Tesla owners to put their cars into the fleet using the same system across the country.

The driver told the Harris County Sheriff's Office that he was using the technology, according to a police report on the crash, but it's not clear what role, if any, it played in the incident.

Tesla did not respond to a request for comment but the head of the company's artificial intelligence efforts suggested on social media later Monday that the self-driving feature was not to blame.

“In this case, the driver manually overrode self-driving by pressing the accelerator all the way to 100% of the accel pedal in this residential area,” wrote Ashok Elluswamy on X, the platform that is now part of Musk's rocket company, SpaceX. “They reached a speed of 73 mph during the crash, and had the accelerator pressed even after the crash.”

The police report noted that the driver was not drunk and is cooperating. It identified the woman killed as Martha Avila.

Video obtained by KHOU-TV shows the car traveling at top speed over the front lawn of a brick home in Katy, then ramming into a front room. The next shot shows the car encased in the home amid piles of crumbling plaster, split beams and bits of furniture.

The auto safety regulator, known as NHTSA, has launched several investigations into Tesla, including one late last year into 58 incidents in which Teslas reportedly violated traffic safety laws while using self-driving technology, leading to more than a dozen crashes and fires and nearly two dozen injuries.

A few months earlier, the NHTSA opened an investigation into why Tesla apparently had not been reporting crashes promptly as required.

As for special crash investigations, the NHTSA has opened 46 involving Teslas using self-driving or driver-assistance technology over the past decade, according to the agency's records. In more than a dozen of those crashes, at least one person — a driver, passenger or pedestrian — was killed.

Tesla stock fell sharply early last year as car sales plunged amid a boycott of Musk after he waded into politics, leading President Donald Trump's budget-cutting Department of Government Efficiency initiative and embracing European extremist candidates.

Musk has since shifted the Tesla story to one less about car sales and more about AI and robotaxis, and done so successfully. The stock is up 16% in the past year.

Intuitive Machines lands $1M grant to expand robotics operations

Expansion mode

Houston-based Intuitive Machines is expanding its operations around the country.

The space tech company—which has offices and labs in Texas, California, Arizona, Colorado and Maryland—announced that it has received a $1 million grant from Maryland Gov. Wes Moore through the state's Build Our Future Grant. The funding will go toward expanding Intuitive Machines’ Super Cislunar Robotics Assembly Building (Supa-CRAB) Mechanisms and Robotics Center of Excellence in Anne Arundel County.

The company will move into a 69,000-square-foot facility and build out additional lab and office space. It will also procure equipment that will allow for in-house Assembly, Integration and Test (AI&T) activities, according to a news release. Intuitive Machines says the expansion will take place this fall.

“This collaboration shows how industry, state programs, and education can reinforce one another,” Steve Altemus, CEO of Intuitive Machines, said in the release. “Maryland invests in innovation, companies grow and hire, students gain experience, and communities benefit from new opportunities and long-term career pathways. Together with Governor Moore, the state of Maryland, and Anne Arundel County leaders, we are building a permanent path to long-term lunar operations, an advanced robotics and mechanisms center of excellence, and a technology edge for our nation.”

Intuitive Machines first launched operations in Maryland in 2021 and has since expanded five times in the state. The company officially opened its robotics and mechanisms facility in 2024.

The Maryland team has built robotics and mechanisms for the Nova-C landers and IM-1 and IM-2 missions. In the future, Intuitive Machines expects the Maryland team to work on its IM-3 Rover Deployment Mechanism (RDM), a 360 pan-tilt camera for panoramic views, the Main Engine Gimbal (MEG), and the company's first data relay satellite, known as Altus-1.

Intuitive Machines moved into a new $40 million headquarters at the Houston Spaceport in 2023. The company announced an expansion of its lease last year.

The company announced a $175 million equity investment to fuel growth in March. It's since landed a $180 million NASA CLPS award to deliver seven payloads to the moon's Mons Malapert on the IM-5 mission.

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