Forecasting retail sales just got smarter, thanks to Google searches

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

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.

Nurturing what is known as “promotion focus” can help managers spot fresh ideas.
Photo by Diego PH on Unsplash

Houston researchers: Here's what it takes to spot a great new idea

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Having a “promotion focus” really does create a mental lens through which new ideas are more visible.

Key findings:

  • New ideas can be crucially important to businesses, driving innovation and preventing stagnation.
  • Recognizing those ideas, though, isn’t always easy.
  • Nurturing what is known as “promotion focus” can help managers spot fresh ideas.

Whenever the late surgeon Michael DeBakey opened a human chest, he drew on a lifetime of resources: the conviction that heart surgery could and should be vastly improved, the skill to venture beyond medicine’s known horizons and the vision to recognize new ideas in everyone around him, no matter how little formal training they had.

Appreciating new ideas is the heartbeat of business as well as medicine. But innovation is surprisingly hard to recognize. In a pioneering 2017 article, Rice Business Professor Jing Zhou and her colleagues published their findings on the first-ever study of the traits and environments that allow leaders to recognize new ideas.

Recent decades have produced a surge of research looking at how and when employees generate fresh ideas. But almost nothing has been written on another crucial part of workplace creativity: a leader’s ability to appreciate new thinking when she sees it.

Novelty, after all, is what drives company differentiation and competitiveness. Work that springs from new concepts sparks more investigation than work based on worn, already established thought. Companies invest millions to recruit and pay star creatives.

Yet not every leader can spot a fresh idea, and not every workplace brings out that kind of discernment. In four separate studies, Zhou and her coauthors examined exactly what it takes to see a glittering new idea wherever it appears. Their work sets the stage for an entirely new field of future research.

First, though, the team had to define their key terms. “Novelty recognition” is the ability to spot a new idea when someone else presents it. “Promotion focus,” previous research has shown, is a comfort level with new experiences that evokes feelings of adventure and excitement. “Prevention focus” is the opposite trait: the tendency to associate new ideas with danger, and respond to them with caution.

But does having “promotion focus” as opposed to “prevention focus” color the ability to see novelty? To find out, Zhou’s team came up with an ingenious test, artificially inducing these two perspectives through a series of exercises. First, they told 92 undergraduate participants that they would be asked to perform a set of unrelated tasks. Then the subjects guided a fictional mouse through two pencil and paper maze exercises.

While one exercise showed a piece of cheese awaiting the mouse at the end of the maze (the promise of a reward), the other maze depicted a menacing owl nearby (motivation to flee).

Once the participants had traced their way through the mazes with pencils, they were asked to rate the novelty of 33 pictures — nine drawings of space aliens and 24 unrelated images. The students who were prepped to feel an adventurous promotion focus by seeking a reward were much better at spotting the new or different details among these images than the students who’d been cued to have a prevention focus by fleeing a threat.

The conclusion: a promotion focus really does create a mental lens through which new ideas are more visible.

Zhou’s team followed this study with three additional studies, including one that surveyed 44 human resource managers from a variety of companies. For this study, independent coders rated the mission statements of each firm, assessing their cultures as “innovative” or “not innovative.” The HR managers then evaluated a set of written practices — three that had been in use for years, and three new ones that relied on recent technology. The managers from the innovative companies were much better at rating the new HR practices for novelty and creativity. To recognize novelty, in other words, both interior and external environments make a difference.

The implications of the research are groundbreaking. The first ever done on this subject, it opens up a completely new research field with profound questions. Can promotion focus be created? How much of this trait is genetic, and how much based on natural temperament, culture, environment and life experience? Should promotion focus be cultivated in education? If so, what would be the impact? After all, there are important uses for prevention focus, such as corporate security and compliance. Meanwhile, how can workplaces be organized to bring out the best in both kinds of focus?

Leaders eager to put Zhou’s findings to use right away, meanwhile, might look to the real-world model of Michael DeBakey. Practice viewing new ideas as adventures, seek workplaces that actively push innovation and, above all, cultivate the view that every coworker, high or low, is a potential source of glittering new ideas.

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This article originally appeared on Rice Business Wisdom.

Jing Zhou is the Mary Gibbs Jones Professor of Management and Psychology in Organizational Behavior at the Jones Graduate School of Business of Rice University. Zhou, J., Wang, X., Song, J., & Wu, J. (2017). "Is it new? Personal and contextual influences on perceptions of novelty and creativity." Journal of Applied Psychology, 102(2): 180-202.

ChatGPT enhances creativity and problem-solving in ways that traditional search tools can’t match. Photo courtesy of Rice Business Wisdom

Houston researchers find AI provides fresh perspectives to everyday problems

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We all know ChatGPT has forever changed how we do business. It’s modified how we access information, compose content and analyze data. It’s revolutionized the future of work and education. And it has transformed the way we interact with technology.

Now, thanks to a recent paper by Jaeyeon (Jae) Chung (Rice Business), we also know it’s making us better problem solvers.

Key findings:

  • A recent study finds ChatGPT-generated ideas are deemed an average of 15% more creative than traditional methods.
  • ChatGPT enhances “incremental,” but not “radical,” innovation.
  • ChatGPT boosts creativity in tasks normally associated with human traits, like empathy-based challenges.

According to the study published in Nature Human Behavior by Chung and Byung Cheol Lee (University of Houston), ChatGPT enhances our problem-solving abilities, especially with everyday challenges. Whether coming up with gifts for your teenage niece or pondering what to do with an old tennis racquet, ChatGPT has a unique ability to generate creative ideas.

“Creative problem-solving often requires connecting different concepts in a cohesive way,” Chung says. “ChatGPT excels at this because it pulls from a vast range of data, enabling it to generate new combinations of ideas.”

Can ChatGPT Really Make Us More Creative?

Chung and Lee sought to answer a central question: Can ChatGPT help people think more creatively than traditional search engines? To answer this, they conducted five experiments.

Each experiment asked participants to generate ideas for solving challenges, such as how to repurpose household items. Depending on the experiment, participants were divided into one of two or three groups: one that used ChatGPT; one that used conventional web search tools (e.g., Google); and one that used no external tool at all. The resulting ideas were evaluated by both laypeople and business experts based on two critical aspects of creativity: originality and appropriateness (i.e., practicality).

In one standout experiment, participants were asked to come up with an idea for a dining table that doesn’t exist on the market. The ChatGPT group came up with suggestions like a “rotating table,” a “floating table” and even “a table that adjusts its height based on the dining experience.” According to both judges and experts, the ChatGPT group consistently delivered the most creative solutions.

On average, across all experiments, ideas generated with ChatGPT were rated 15% more creative than those produced by traditional methods. This was true even when tasks were specifically designed to require empathy or involved multiple constraints — tasks we typically assume humans might be better at performing.

However, Chung and Lee also found a caveat: While ChatGPT excels at generating ideas that are “incrementally” new — i.e., building on existing concepts — it struggles to produce “radically” new ideas that break from established patterns. “ChatGPT is an incredible tool for tweaking and improving existing ideas, but when it comes to disruptive innovation, humans still hold the upper hand,” Chung notes.

Charting the Next Steps in AI and Creativity

Chung and Lee’s paper opens the door to many exciting avenues for future study. For example, researchers could explore whether ChatGPT’s creative abilities extend to more complex, high-stakes problem-solving environments. Could AI be harnessed to develop groundbreaking solutions in fields like medicine, engineering or social policy? Understanding the nuances of the collaboration between humans and AI could shape the future of education, work and even (as many people fear) art.

For professionals in creative fields like product design or marketing, the study holds especially significant implications. The ability to rapidly generate fresh ideas can be a game-changer in industries where staying ahead of trends is vital. For now, take a second before you throw out that old tennis racquet. Ask ChatGPT for inspiration — you’ll be surprised at how many ideas it comes up with, and how quickly.

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This article originally appeared on Rice Business Wisdom. Based on research by Jaeyeon (Jae) Chung and Byung Cheol Lee (University of Houston). Lee and Chung, “An empirical investigation of the impact of ChatGPT on creativity.” Nature Human Behavior (2024): https://doi.org/10.1038/s41562-024-01953-1.


Grocery purchase data can accurately predict credit risk for individuals without traditional credit scores, potentially broadening the pool of qualified loan applicants. Photo via Unsplash

Houston researchers find alternate data for loan qualification

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Millions of consumers who apply for a loan to buy a house or car or start a business can’t qualify — even if they’re likely to pay it back. That’s because many lack a key piece of financial information: a credit score.

The problem isn’t just isolated to emerging economies. Exclusion from the financial system is a major issue in the United States, too, where some 45 million adults may be denied access to loans because they don’t have a credit history and are “credit invisible.”

To improve access to loans and peoples’ economic mobility, lenders have started looking into alternative data sources to assess a loan applicant’s risk of defaulting. These include bank account transactions and on-time rental, utility and mobile phone payments.

A new article by Rice Business assistant professor of marketing Jung Youn Lee and colleagues from Notre Dame and Northwestern identifies an even more widespread data source that could broaden the pool of qualified applicants: grocery store receipts.

As metrics for predicting credit risk, the researchers found that the types of food, drinks and other products consumers buy, and how they buy them, are just as good as a traditional credit score.

“There could be privacy concerns when you think about it in practice,” Lee says, “so the consumer should really have the option and be empowered to do it.” One approach could be to let consumers opt in to a lender looking at their grocery data as a second chance at approval rather than automatically enrolling them and offering an opt-out.

To arrive at their findings, the researchers analyzed grocery transaction data from a multinational conglomerate headquartered in a Middle Eastern country that owns a credit card issuer and a large-scale supermarket chain. Many people in the country are unbanked. They merged the supermarket’s loyalty card data and issuer’s credit card spending and payment history numbers, resulting in data on 30,089 consumers from January 2017 to June 2019. About half had a credit score, 81% always paid their credit card bills on time, 12% missed payments periodically, and 7% defaulted.

The researchers first created a model to establish a connection between grocery purchasing behavior and credit risk. They found that people who bought healthy foods like fresh milk, yogurt and fruits and vegetables were more likely to pay their bills on time, while shoppers who purchased cigarettes, energy drinks and canned meat tended to miss payments. This held true for “observationally equivalent” individuals — those with similar income, occupation, employment status and number of dependents. In other words, when two people look demographically identical, the study still finds that they have different credit risks.

People’s grocery-buying behaviors play a factor in their likelihood to pay their bills on time, too. For example, cardholders who consistently paid their credit card bill on time were more likely to shop on the same day of the week, spend similar amounts across months and buy the same brands and product categories.

The researchers then built two credit-scoring predictive algorithms to simulate a lender’s decision of whether or not to approve a credit card applicant. One excludes grocery data inputs, and the other includes them (in addition to standard data). Incorporating grocery data into their decision-making process improved risk assessment of an applicant by a factor of 3.11% to 7.66%.

Furthermore, the lender in the simulation experienced a 1.46% profit increase when the researchers implemented a two-stage decision-making process — first, screening applicants using only standard data, then adding grocery data as an additional layer.

One caveat to these findings, Lee and her colleagues warn, is that the benefit of grocery data falls sharply as traditional credit scores or relationship-specific credit histories become available. This suggests the data could be most helpful for consumers new to credit.

Overall, however, this could be a win-win scenario for both consumers and lenders. “People excluded from the traditional credit system gain access to loans,” Lee says, “and lenders become more profitable by approving more creditworthy people.”

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This article originally ran on Rice Business Wisdom based on research by Rice University's Jung Youn Lee, Joonhyuk Yang (Notre Dame) and Eric Anderson (Northwestern). “Using Grocery Data for Credit Decisions.” Forthcoming in Management Science. 2024: https://doi.org/10.1287/mnsc.2022.02364.


Using biased statistics in hiring makes it more difficult to predict job performance. Photo via Getty Images

Houston research finds race, gender ineffective predictors of employee productivity

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The Latin phrase scientia potentia est translates to “knowledge is power.”

In the world of business, there’s a school of thought that takes “knowledge is power” to an extreme. It’s called statistical discrimination theory. This framework suggests that companies should use all available information to make decisions and maximize profits, including the group characteristics of potential hires — such as race and gender — that correlate with (but do not cause) productivity.

Statistical discrimination theory suggests that if there's a choice between equally qualified candidates — let's say, a man and a woman — the hiring manager should use gender-based statistics to the company's benefit. If there's data showing that male employees typically have larger networks and more access to professional development opportunities, the hiring manager should select the male candidate, believing such information points to a more productive employee.

Recent research suggests otherwise.

A peer-reviewed study out of Rice Business and Michigan Ross undercuts the premise of statistical discrimination theory. According to researchers Diana Jue-Rajasingh (Rice Business), Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan), hiring outcomes actually improve when decision-makers ignore statistics that correlate employee productivity with characteristics like race and gender.

Here's Why “Less is More”

Statistical discrimination theory assumes a correlation between individual productivity and group characteristics (e.g., race and gender). But Jue-Rajasingh and her colleagues highlight three factors that undercut that assumption:

  • Environmental uncertainty
  • Biased interpretations of productivity
  • Decision-maker inconsistency

This third factor plays the biggest role in the researchers' model. “For statistical discrimination theory to work,” Jue-Rajasingh says, “it must assume that managers are infallible and decision-making conditions are optimal.”

Indeed, when accounting for uncertainty, inconsistency and interpretive bias, the researchers found that using information about group characteristics actually reduces the accuracy of job performance predictions.

That’s because the more information you include in the decision-making process, the more complex that process becomes. Complex processes make it more difficult to navigate uncertain environments and create more space for managers to make mistakes. It seems counterintuitive, but when firms use less information and keep their processes simple, they are more accurate in predicting the productivity of their hires.

The less-is-more strategy is known as a “heuristic.” Heuristics are simple, efficient rules or mental shortcuts that help decision-makers navigate complex environments and make judgments more quickly and with less information. In the context of this study, published by Organization Science, the heuristic approach suggests that by focusing on fewer, more relevant cues, managers can make better hiring decisions.

Two Types of Information "Cues"

The “less is more” heuristic works better than statistical discrimination theory largely because decision makers are inconsistent in how they weight the available information. To factor for inconsistency, Jue-Rajasingh and her colleagues created a model that reflects the “noise” of external factors, such as a decision maker’s mood or the ambiguity of certain information.

The model breaks the decision-making process into two main components: the environment and the decision maker.

In the environment component, there are two types of information, or “cues,” about job candidates. First, there’s the unobservable, causal cue (e.g., programming ability), which directly relates to job performance. Second, there's the observable, discriminatory cue (e.g., race or gender), which doesn't affect how well someone can do the job but, because of how society has historically worked, might statistically seem connected to job skills.

Even if the decision maker knows they shouldn't rely too much on information like race or gender, they might still use it to predict productivity. But job descriptions change, contexts are unstable, and people don’t consistently consider all variables. Between the inconsistency of decision-makers and the environmental noise created by discriminatory cues, it’s ultimately counterproductive to consider this information.

The Bottom Line

Jue-Rajasingh and her colleagues find that avoiding gender- and race-based statistics improves the accuracy of job performance predictions. The fewer discriminatory cues decision-makers rely on, the less likely their process will lead to errors.

That said: With the advent of AI, it could become easier to justify statistical discrimination theory. The element of human inconsistency would be removed from the equation. But because AI is often rooted in biased data, its use in hiring must be carefully examined to prevent worsening inequity.

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This article originally ran on Rice Business Wisdom based on research by Rice University's Diana Jue-Rajasingh, Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan). For more, see Csaszar, et al. “When Less is More: How Statistical Discrimination Can Decrease Predictive Accuracy.”

As corporate debt markets continue to grow in importance, it will become crucial for investors and regulators to understand the nuanced factors influencing their liquidity. Photo via Getty Images

Rice research on bond and stock market differences, earnings variations

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At the end of every quarter, publicly traded companies announce their profits and losses in an earnings report. These updates provide insight into a company’s performance and, in theory, give investors and shareholders clarity on whether to buy, sell or hold. If earnings are good, the stock price may soar. If they’re down, the price might plunge.

However, the implications for the stock price may not be immediately clear to all investors. In the face of this uncertainty, sellers will ask for high prices, and buyers will offer low ones, creating a significant “bid-ask spread.” When this happens, it becomes more costly to trade, and the stock becomes less liquid.

This is a well-documented effect on equity stock markets. However, according to research by Stefan Huber (Rice Business), Chongho Kim (Seoul National University) and Edward M. Watts (Yale SOM), the corporate bond market responds differently to earnings news. This is because bond markets differ from stock markets in a significant way.

Stocks v. Bonds: What Happens When Earnings Are Announced?

Equities are usually traded on centralized exchanges (e.g., New York Stock Exchange). The exchange automatically queues up buyers and sellers according to the quote they’ve entered. Trades are executed electronically, and the parties involved are typically anonymous. A prospective buyer might purchase Microsoft shares from someone drawing down their 401(k) — or they could be buying from Bill Gates himself.

Corporate bond markets work differently. They are “over-the-counter” (OTC) markets, meaning a buyer or seller needs to find a counterparty to trade with. This involves getting quotes from and negotiating with potential counterparties. This is an inherent friction in bond trading that results in much higher costs of trading in the form of wider bid-ask spreads.

Here’s what Huber and his colleagues learned from the research: Earnings announcements prompt many investors to trade. And on OTC markets, potential buyers and sellers become easier to find and negotiate with.

A Stronger Bargaining Position for Bonds

According to Huber, “When earnings information comes out, a lot of people want to trade. In bond markets, that makes it much easier to find someone to trade with. The more options you have to trade, the stronger your bargaining position becomes, and the lower your trading costs go.”

He compares the process to shopping in a market with a flexible approach to pricing.

“Let's say you're at a farmers market and you want to buy an apple,” Huber says. “If there is only one seller, you buy the apple from that person. They can ask for whatever price they want. But if there are multiple sellers, you can ask around, and there is potential to get a better price. The price you get depends on the number of options you have in trading partners.”

What’s at Stake?

Although bonds receive less attention than equities, the stakes are high. There is about $10 trillion in outstanding corporate debt in the U.S., and more than $34 billion in average daily trading volume.

A detailed record of bond trades is available from the Financial Industry Regulatory Authority (FINRA), which requires that trades be reported via their Trade Reporting and Compliance Engine (TRACE).

The study from Huber and co-authors uses an enhanced version of TRACE to examine trades executed between 2002 and 2020. The team analyzed the thirty-day periods before and after earnings announcements to gather data about volume, bid-ask spreads and other measures of liquidity.

They find that, like on the stock market, there are more investors and broker-dealers trading bonds around earnings announcements. However, unlike on the stock market, transaction costs for bonds decrease by 6 to 7 percent in the form of bid-ask spreads.

What Sets This Research Apart?

“Taking a purely information asymmetry-based view would predict that what happens to stock liquidity would also happen to bonds,” Huber says. “A piece of information drops, and some people are better able to work with it, so others price protect, and bid-ask spreads and the cost of trading go up.”

“But if you consider the search and bargaining frictions in bond markets, you get a more nuanced picture. While information asymmetry increases, like it does on stock markets, the information prompts more investors into bond trading, which makes it easier to find counterparties and get better transaction prices. Consequently, bid-ask spreads go down. This search and bargaining friction does not really exist on equities exchanges. But we cannot ignore it in OTC markets.”

As corporate debt markets continue to grow in importance, it will become crucial for investors and regulators to understand the nuanced factors influencing their liquidity. This study provides a solid foundation for future research.

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This article originally ran on Rice Business Wisdom. For more, see “Earnings News and Over-the-Counter Markets.” Journal of Accounting Research 62.2 (2024): 701-35.

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Houston robotics co. unveils new robot that can handle extreme temperatures

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Houston- and Boston-based Square Robot Inc.'s newest tank inspection robot is commercially available and certified to operate at extreme temperatures.

The new robot, known as the SR-3HT, can operate from 14°F to 131°F, representing a broader temperature range than previous models in the company's portfolio. According to the company, its previous temperature range reached 32°F to 104°F.

The new robot has received the NEC/CEC Class I Division 2 (C1D2) certification from FM Approvals, allowing it to operate safely in hazardous locations and to perform on-stream inspections of aboveground storage tanks containing products stored at elevated temperatures.

“Our engineering team developed the SR-3HT in response to significant client demand in both the U.S. and international markets. We frequently encounter higher temperatures due to both elevated process temperatures and high ambient temperatures, especially in the hotter regions of the world, such as the Middle East," David Lamont, CEO of Square Robot, said in a news release. "The SR-3HT employs both active and passive cooling technology, greatly expanding our operating envelope. A great job done (again) by our engineers delivering world-leading technology in record time.”

The company's SR-3 submersible robot and Side Launcher received certifications earlier this year. They became commercially available in 2023, after completing initial milestone testing in partnership with ExxonMobil, according to Square Robot.

The company closed a $13 million series B round in December, which it said it would put toward international expansion in Europe and the Middle East.

Square Robot launched its Houston office in 2019. Its autonomous, submersible robots are used for storage tank inspections and eliminate the need for humans to enter dangerous and toxic environments.

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This article originally appeared on EnergyCapitalHTX.com.

Houston's Ion District to expand with new research and tech space, The Arc

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Houston's Ion District is set to expand with the addition of a nearly 200,000-square-foot research and technology facility, The Arc at the Ion District.

Rice Real Estate Company and Lincoln Property Company are expected to break ground on the state-of-the-art facility in Q2 2026 with a completion target set for Q1 2028, according to a news release.

Rice University, the new facility's lead tenant, will occupy almost 30,000 square feet of office and lab space in The Arc, which will share a plaza with the Ion and is intended to "extend the district’s success as a hub for innovative ideas and collaboration." Rice research at The Arc will focus on energy, artificial intelligence, data science, robotics and computational engineering, according to the release.

“The Arc will offer Rice the opportunity to deepen its commitment to fostering world-changing innovation by bringing our leading minds and breakthrough discoveries into direct engagement with Houston’s thriving entrepreneurial ecosystem,” Rice President Reginald DesRoches said in the release. “Working side by side with industry experts and actual end users at the Ion District uniquely positions our faculty and students to form partnerships and collaborations that might not be possible elsewhere.”

Developers of the project are targeting LEED Gold certification by incorporating smart building automation and energy-saving features into The Arc's design. Tenants will have the opportunity to lease flexible floor plans ranging from 28,000 to 31,000 square feet with 15-foot-high ceilings. The property will also feature a gym, an amenity lounge, conference and meeting spaces, outdoor plazas, underground parking and on-site retail and dining.

Preleasing has begun for organizations interested in joining Rice in the building.

“The Arc at the Ion District will be more than a building—it will be a catalyst for the partnerships, innovations and discoveries that will define Houston’s future in science and technology,” Ken Jett, president of Rice Real Estate Company, added in the release. “By expanding our urban innovation ecosystem, The Arc will attract leading organizations and talent to Houston, further strengthening our city’s position as a hub for scientific and entrepreneurial progress.”

Intel Corp. and Rice University sign research access agreement

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Rice University’s Office of Technology Transfer has signed a subscription agreement with California-based Intel Corp., giving the global company access to Rice’s research portfolio and the opportunity to license select patented innovations.

“By partnering with Intel, we are creating opportunities for our research to make a tangible impact in the technology sector,” Patricia Stepp, assistant vice president for technology transfer, said in a news release.

Intel will pay Rice an annual subscription fee to secure the option to evaluate specified Rice-patented technologies, according to the agreement. If Intel chooses to exercise its option rights, it can obtain a license for each selected technology at a fee.

Rice has been a hub for innovation and technology with initiatives like the Rice Biotech Launch Pad, an accelerator focused on expediting the translation of the university’s health and medical technology; RBL LLC, a biotech venture studio in the Texas Medical Center’s Helix Park dedicated to commercializing lifesaving medical technologies from the Launch Pad; and Rice Nexus, an AI-focused "innovation factory" at the Ion.

The university has also inked partnerships with other tech giants in recent months. Rice's OpenStax, a provider of affordable instructional technologies and one of the world’s largest publishers of open educational resources, partnered with Microsoft this summer. Google Public Sector has also teamed up with Rice to launch the Rice AI Venture Accelerator, or RAVA.

“This agreement exemplifies Rice University’s dedication to fostering innovation and accelerating the commercialization of groundbreaking research,” Stepp added in the news release.

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