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

houston voices

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.”

---

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.

From Your Site Articles
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

houston voices

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.”

---

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

houston voices

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.”

------

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.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Property Showcase

Intuitive Machines lands $600M satellite deal, NASA ‘spacecraft bus’ contract

space deals

Houston-based space infrastructure company Intuitive Machines has scored two astronomical deals.

The deals add to the company’s soaring success. As of June 30, Intuitive Machines had a record-high $1.8 billion backlog of orders, a $1.5 billion increase from the end of last year. The current backlog includes orders for more than 80 spacecraft.

The company, which went public in 2023, expects this year’s revenue to total $900 million to $1 billion. In the first half of 2026, Intuitive Machines generated nearly $393 million in revenue.

Intuitive Machines estimates its total available market is valued at more than $150 billion.

$600 million-plus deal represents ‘important milestone’

On Monday, Intuitive Machines said it picked up a $600 million-plus deal to develop three commercial satellites for an undisclosed customer over the course of about two years.

Intuitive Machines says it will design, manufacture, set up and support several spacecraft “for a critical communications infrastructure mission.”

Steve Altemus, the company’s CEO, says the deal represents “an important milestone for Intuitive Machines and reflects the confidence our customers place in our ability to deliver high-performance spacecraft for a broad range of mission needs.”

Company nails down NASA deal for ‘spacecraft bus’

A day after announcing the $600 million-plus deal, Intuitive Machines said it secured a new contract with NASA.

Intuitive Machines says NASA’s Jet Propulsion Laboratory in Southern California will use the company’s IM 300 “spacecraft bus” for an EAGLE-VSWIR Earth observation mission. The mission is scheduled to launch in 2028.

Aside from supplying the IM 300 bus, Intuitive Machines will carry out mission support services.

The low-Earth-orbit mission will be equipped with Intuitive Machines’ hyperspectral visible to shortwave infrared (VSWIR) instrument. This technology sees colors and details that aren’t visible to the human eye.

The instrument is “designed to perform surface biology and geology observations from Earth orbit while demonstrating technologies that could support future lunar and Mars exploration missions,” Intuitive Machines says.

Intuitive Machines builds mission-critical spacecraft, systems, and infrastructure for business and government customers. To date, the company has produced more than 300 spacecraft, delivered over 575 pounds of payload to the moon and launched about 100 satellites.

9 Houston-based companies make Fortune Global 500 list in 2026

Worldwide Rankings

Nine Houston companies landed on the 2026 Fortune Global 500 list, which ranks the world's largest corporations by revenue for the 2025 fiscal year.

Houston's showing on the list was led by energy companies, with Spring’s ExxonMobil claiming the top local spot. Here’s what Houston-area companies made the list, and where they ranked:

  • No. 15 ExxonMobil
  • No. 36 Chevron
  • No. 61 Phillip 66
  • No. 159 Sysco
  • No. 243 ConocoPhillips
  • No. 292 Enterprise Products Partners
  • No. 343 Plains GP Holdings
  • No. 460 SLB
  • No. 481 Hewlett Packard Enterprise

After 12 years as No.1, Arkansas-based Walmart was replaced this year by Seattle-based Amazon in the top spot for 2026. Amazon achieved this by bringing in $700 billion in revenue in 2025, representing a 12 percent increase from the previous year.

"Across global business, we see again and again that the leaders who are winning are those who embrace change,” Alyson Shontell, Fortune's editor in chief and chief content officer, said in a news release. "Amazon has topped the Fortune Global 500, knocking Walmart off its pedestal. The company has continually reinvented itself across new businesses and bold bets—including a $200 billion capital commitment, largely to building its capacity for AI and cloud computing, in this year alone."

The U.S. has 141 companies on the 2026 Fortune Global 500 list, which is the most of any country. Companies in America generated $15.5 trillion in aggregate revenues, a 6 percent increase from the previous year.

The number of women CEOs at Fortune Global 500 companies reached a record of 34 top leaders, who represented 6.8 percent of CEOs of companies on the list.

Technology was the standout growth industry on this year’s list, with 38 companies earning revenues that grew 20 percent to about $4 trillion in 2025, with profits climbing 36 percent to $835 billion. The financial sector accounted for the largest share of companies on the list again, with 123 companies in that sector. The energy sector claimed the No. 2 industry spot with 77 companies making the list.

In June, the Fortune 500 list was released, and Texas led the United States with 57 Fortune 500 companies headquartered in the state, generating $2.8 trillion in combined revenue.

Fast-growing Houston real estate startup surges to No. 7 on Inc. 5000

growth report

Houston-based Epique Realty has ridden the AI wave to rank among the Inc. 5000’s 10 fastest-growing private companies.

With three-year revenue growth of 23,210 percent, the AI-powered real estate brokerage appears at No. 7 on this year’s Inc. 5000 list. The 2026 list ranks private companies based on percentage revenue growth from 2022 to 2025.

Epique, founded in 2021, also ranks as the No. 1 fastest-growing company in Houston, No. 1 fastest-growing real estate company in the U.S., and No. 2 fastest-growing company in Texas.

Epique’s annual revenue surpasses $91 million

Between 2022 and 2025, the company’s annual revenue skyrocketed from $391,654 to more than $91.2 million. In 2025, the brokerage closed more than 23,000 deals and surpassed $7 billion in total sales, elevating Epique to the country’s 14th-largest real estate brokerage as measured by volume.

Epique’s network has more than 4,000 agents.

“To debut in the top 10 of the Inc. 5000 is absolute proof that when you relentlessly put agents first, exponential growth takes care of itself,” co-founder and CEO Joshua Miller said in a news release.

“We didn’t achieve this by following the industry playbook; we achieved this by burning it,” Miller added. “By fully funding our agents’ success through free health care, proprietary AI, and world-class leads, we’ve built a company where agents can finally thrive.”

The company’s other co-founders are Chris Miller, chief operating officer and vice president of expansion, and Janice Delci, chief financial officer.

Epique expands business to Canada, Mexico, Australia

The Millers and Delci have guided the company’s rapid expansion.

“Scaling our corporate support team to match [our] hyper-growth while seamlessly expanding across all 50 states and internationally to Canada, Australia, and Mexico takes an incomparable operational infrastructure,” Miller said.

“We have built an enterprise-grade technology ecosystem that allows us to absorb overhead and empower our agents at lightning speed,” he added. “This ranking validates that our disruptive model is working, and it is completely redefining the global industry standard.”

Epique launched its platform in 2023, touting itself as the industry’s first AI-powered brokerage. The startup’s platform provides AI tools for real estate agents to improve their marketing, streamline content creation, and boost engagement with clients and prospects.

Among Epique’s AI tools are:

  • ChatGPT for generation of property descriptions
  • AI-assisted creation of blog posts and agents’ bios
  • Production of Instagram quotes for social media marketing

“When we started Epique, we wanted to build a company that genuinely cared for its agents’ financial and physical well-being,” Delci says. “To see that vision translate into this level of historic record-breaking growth is a beautiful testament to the true power of radical generosity.”

Epique and fellow honorees will be recognized Oct. 14-16 at the 2026 Inc. 5000 Conference & Gala in Dallas.

Six other Houston-area companies land in top 250

Here are the six other Houston-area companies that claimed spots in the top 250 on the Inc. 5000 list. Each company name is followed by its ranking, headquarters city, and three-year growth rate.

  • No. 27 Empact Technologies, 8,275 percent
  • No. 60 Action1, 4,512 percent
  • No 75 Signs By G, 3,684 percent
  • No. 79 The ’Pause Life, 3,469 percent (Galveston)
  • No. 110 Turtlebox Audio, 2,576 percent
  • No. 178 Dahnani Private Equity Group, 1,904 percent (Stafford)

How did companies in Texas’ other major metros fare?

Here’s a breakdown of companies in the Austin, Dallas-Fort Worth, and San Antonio areas that made the top 250 on the Inc. 5000. Again, each company name is followed by its ranking, headquarters city, and three-year growth rate.

Austin (10 companies)

  • No. 9 Investment Watches, 15,741 percent
  • No. 72 Razor Metrics, 3,856 percent
  • No. 91 Autonomize AI, 2,921 percent
  • No. 102 Choose Your Horizon, 2,719 percent
  • No. 132 Wander Staffing, 2,296 percent
  • No. 144 Everyday Dose, 2,179 percent
  • No. 148 NetRise, 2,118 percent
  • No. 163 Nutrabound Labs, 1,999 percent (Bastrop)
  • No. 179 Steadily, 1,890 percent
  • No. 208 Tiny Health, 1,624 percent

Dallas-Fort Worth (11 companies)

  • No. 3 Yantran, 258,740 percent (Allen)
  • No. 12 Paek Management Group, 12,520 percent (Irving)
  • No. 82 Elite Robotics and Automation, 3,334 percent (Fort Worth)
  • No. 133 Outamation, 2,291 percent (Southlake)
  • No. 147 Red Creek Solutions, 2,119 percent (Frisco)
  • No. 155 JobTread Software, 2,071 percent (Dallas)
  • No. 164 DAX Eyewear, 1,977 percent (Nevada)
  • No. 186 Optimized Waste Removal, 1,830 percent (Fort Worth)
  • No. 204 Freight Flex, 1,642 percent (Denton)
  • No. 212 Maverick Power, 1,591 percent (McKinney)
  • No. 229 Innovative Life Sciences, 1,494 percent (McKinney)

San Antonio (one company)

  • No. 118 Hire With Near, 2,421 percent
View All Listings