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AlternativesFebruary 3, 2025

Institutional Investors See Resilience in Commercial Real Estate

While the industry is forever changed following the pandemic, portfolio managers expect a rebound ahead.

Art by Melinda Beck

Five years after the start of the COVID-19 pandemic, the commercial real estate market is forever changed. Office space has been a drag on investors’ portfolios for the past several years; the move toward remote work has been devastating to the prices of office buildings.

But many institutional investors think the downturn is nearing an end, and they see buying opportunities as the market bottoms. Leasing is up, and demand for office space appears to be coming back. For institutional asset allocators, the best time to buy commercial real estate could be right around the corner.

The End of WFH?

The era of working from home appears to be coming to a close, with more and more companies, especially New York-based financial institutions, mandating a return to the office five days per week. JPMorganChase and Goldman Sachs are among these large employers calling for a return to the office.

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“You’ve … seen some return-to-work announcements from some really big employers,” says Greg Kuhl, a portfolio manager on the global property equities team at Janus Henderson, who also points to large tech firms, like Amazon, mandating that employees be in the office five days per week.

KPMG’s 2024 CEO outlook found 83% of CEOs surveyed said they expected a full return to the office within the next three years.

Chad Tredway, head of real estate in the Americas at J.P. Morgan Asset Management, says the stars are aligning for office space demand.

“With strong economic growth, more workers returning to the office, and fundamentals improving, especially for top-quality assets, the sector appears to be very well positioned to see gains in the coming quarters,” Tredway says.

State of the Market

Yet the number of new office construction starts continues to plummet, which could significantly affect supply-and-demand dynamics. According to the CBRE Group’s 2025 outlook, new supply is expected to fall to 15 million square feet this year, well below the 10-year average of 44 million.

“The development pipeline is at the lowest level since the global financial crisis, representing less than 1% of stock across the country, while conversions of former office buildings are accelerating,” Tredway says. “Combined, these improvements are making for the most favorable supply-demand dynamic the industry has seen since the beginning of the pandemic.”

Glenn Brill, managing director in FTI Consulting’s real estate solutions practice, says the trends appear favorable for office space investments.

“Given the considerable amount of distress in the office market, it appears a consensus may have formed that the office market has bottomed out,” Brill says. “Buyers are seeing pricing below replacement cost and opportunities for redevelopment, with the hope for future office space demand as large employers continue to commit to ‘in-the-office’ culture and seek a quality work experience for their employees.”

Janus Henderson’s Kuhl says fundamentals are pointing to a market bottom. Net office absorption—how much space is leased net of how much is delivered—recently turned positive for the first time in years, Kuhl says.

“From a new-supply perspective, that was positive in the fourth quarter [of 2024] for the second time post pandemic, and it [had been] been negative for seven or eight quarters in a row,” Kuhl says.

Deloitte, in its 2025 commercial real estate outlook noted that a rebound for commercial real estate depends on the future of global interest rates.

“For some in the commercial real estate industry, the shift to prospective rate cuts has boosted sentiment for the remainder of 2024 and 2025,” the report stated. “That said, a single rate cut alone is not expected to immediately alleviate lingering concerns around refinancing risk for maturing loans or make capital and debt for acquisitions suddenly cheaper or easier to attain.”

Best Buying Opportunities

Kuhl says New York has been the best market for offices from a fundamental perspective; the city does not have enough Class A—the most expensive—office space, to meet demand. He also pointed to markets like Boston and some Sun Belt states as improving. West Coast markets, including in California and Seattle, are not seeing signs of improvement, but they also are not getting worse, he adds, backed up by J.P. Morgan’s Tredway.

“The tip of the spear for the recovery continues to be top-quality buildings,” Tredway says. “In Manhattan, premier assets are essentially back to pre-pandemic utilization rates, while Class A properties are at 85% and rising. Clearly, there are challenged pockets of the market, but with 60% of the market’s vacancy concentrated in the bottom 10% of buildings, it is a smaller subset than many think.”

Leasing activity is also picking up. In the third quarter of 2024, office leasing in Manhattan grew by 5.6%; while modest, it was the market’s strongest quarterly volume in two years. In the first three quarters of 2024, leases were signed covering 23.14 million square feet. While still below 2019’s leasing volume of 42.97 million square feet, the availability rate fell to 17.3%, the lowest in Manhattan in 18 months.

Kuhl also makes the case for investors adding exposure through real estate investment trusts.

“It’s not uncommon to see office buildings that could be valued at a billion dollars or more,” Kuhl says. “For an investor, that could be a lot of risk concentrated in one asset. I think this is where buying the shares of a REIT are attractive from a diversification perspective, because you can get that exposure without deploying so much capital.”

Who’s Buying?

With trends indicating a potential turnaround on the horizon for commercial real estate, some managers are already getting in early.

“Recently, [Norges Bank Investment Management] purchased office assets in Boston, San Francisco and Washington, DC,” Brill says. “No doubt they were attracted by the high quality of the assets in relation to price today and anticipated market conditions into the future,” referring to the fund’s December 2024 purchase of a $976.8 million stake in an office portfolio across those markets. The $1.573 trillion fund has a 1.7% allocation to real estate.

Korea’s National Pension Service recently announced an $800 million venture with Almanac Realty Investors, the private real estate investing arm of Neuberger Berman, to invest in real estate platforms and . The pension fund established a real estate platform investing team in 2024.

“As the real estate investment landscape evolves, platform investing has emerged as a transformative trend reshaping the industry,” said Insub Park, senior portfolio manager of NPS’ real estate platform investment team, in a statement with the Neuberger partnership announcement. “This approach not only optimizes value creation, but also aligns with long-term growth strategies, making it a cornerstone for forward-thinking investors.”

In 2023, sovereign wealth funds increased their investments in real estate by 50% from the year before, according to data from the International Forum of Sovereign Wealth Funds. The increase represents $14.7 billion in investments, a level of commitment not seen since 2018.

Still, some institutional investors are staying on the sidelines for now. The largest public pension funds in Canada are significant investors in real estate, and in the past few years, their losses from the asset class are well publicized. In fiscal 2024, PSP Investments took a 15.9% hit in its real estate portfolio, while CPP Investments sold a 29% stake in a Manhattan office building at 360 Park Avenue to Boston Properties for the token sum of $1.

“With a [negative 15.9%] return, Real Estate was hit particularly hard by the structural changes in the office sector and supply-demand dynamics in certain regions as well as higher rates, which have pushed prices down and cost of financing up,” stated PSP Investments’ 2024 annual report, published last March.

CPP Investments had an 11.3% allocation to real estate in fiscal 2020. By fiscal 2024, it had shrunk to 8%. In its 2024 annual report, the fund attributed low returns—including 0.7% in the real estate portfolio—to the “transition towards e-commerce and the impact of evolving hybrid workplace trends.”

China’s DeepSeek Puts a Question Mark on AI Spending

The startup’s innovative AI model is raising questions about the future of AI stocks and capex.

By Matt Toledo

This month saw the release of DeepSeek’s R1, an artificial intelligence model from a Chinese company and one that reportedly goes toe-to-toe with offerings from U.S. tech leaders OpenAI, Meta, Anthropic, Google and others. The company’s open source R1 model is touted to be magnitudes cheaper to train than other leading models.

In a paper, DeepSeek stated that costs were less than $6 million, far cheaper than the $100 million it cost to train OpenAI’s GPT-4, for example, raising questions about the need for massive capital expenditures on AI infrastructure.

In fact, DeepSeek’s breakthrough may have come out of necessity. The administration of former President Joe Biden restricted China from Nvidia Corp.’s most advanced chips, including the H-100, used by many companies to train their AI models. Officials in the administration of President Donald Trump have, in recent days, also called for tightening restrictions on selling high-powered chips to companies in China.

DeepSeek used the Nvidia H800 GPU, a chip designed to comply with U.S. export restrictions, to train its models. The company used Huawei 910C chips to run inference—when a model uses the data it has been trained on to answer a query.

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“The success of DeepSeek threatens the business models of companies like OpenAI and Anthropic, which rely heavily on revenue from proprietary AI model sales,” says Drayton D’Silva, CEO and CIO of multi-asset investment firm Tower Hills Capital. “However, companies like Microsoft and Meta, with their massive distribution advantages, will benefit because cost-effective open-source models like DeepSeek massively reduce down inference costs. Inference is the critical phase whe[n] AI models apply their training to real-world data.”

Tech companies known as the “Magnificent Seven”—Alphabet, Amazon.com, Apple, Meta, Microsoft, Nvidia and Tesla—account for nearly one-third of U.S. equity growth over the past two years. These companies trade at high multiples and are spending billions in capital on AI and its underlying infrastructure. DeepSeek’s release of R1 spooked investors, sending technology stocks falling and exposing a risk at the biggest driver of equity strength.

Stocks Fall

DeepSeek’s R1 model was released on January 20, but it took about a week for the market to digest its significance. Tech stocks fell on January 27, with Nvidia falling nearly 20%. Other AI and semiconductor stocks fell by double digits. Most tech stocks recovered during the week, as retail investors poured billions into Mag Seven names.

“Big Tech companies [that] have planned significant investments in AI infrastructure now face scrutiny over whether such spending is justified, and this will mean short-term volatility and a softer tailwind for chip and semiconductors companies,” D’Silva says.

While most institutional asset allocators are not too worried about short-term price movements, the implications of the R1 model could indicate challenges ahead, both for technology companies and an economy that depends on them. The S&P 500 has been held up for the last two years by the Magnificent Seven, which at the start of 2025 made up about 33% of the index’s weighting.

Capex Into Question

AI hyperscalers and investors are collectively spending hundreds of billions of dollars on AI infrastructure, particularly data centers, due to the computational and storage needs required. In 2024, $282 billion was spent on data centers, according to Synergy Research Group. One day after Trump’s inauguration—and the release of R1—OpenAI and investors SoftBank Group Corp., Oracle Corp. and MGX Fund Management Ltd. announced the Stargate Project, which intends to invest $500 billion in AI infrastructure in the U.S. over the next four years.

Meta Platforms Inc., in an earnings call Wednesday for the fourth quarter of 2024, announced the company would make $60 billion to $65 billion in capital expenditures this year, driven by AI infrastructure spending. Earlier this month, Microsoft Corp. President Brad Smith said the company is on track to spend $80 billion on data centers this fiscal year.

Blackstone Inc. President Jon Gray, speaking at the company’s Q4 2024 earnings call on Thursday, defended the private equity giant’s $80 billion in investments in data centers. Last year, the firm said it has $100 billion in data center investments in the pipeline.

Yet DeepSeek apparently has, far more cheaply, built a model capable of competing with models from large tech companies.

“It certainly creates doubt that building the best AI models can only be done with massive GPU clusters and huge spending,” says Karthee Madasamy, founder and managing partner in Silicon Valley venture capital firm MFV Partners.

Madasamy, who was a chip designer and spent 11 years at Qualcomm Ventures, notes that Silicon Valley has not had a focus on optimization and resource utilization.

“There has been a significant focus on throwing more and more money at computing and memory … rather than focusing on optimization and higher resource utilization,” Madasamy says. “The argument has been that the ROI was not there in terms of effort and time, and the price of computing and memory are falling anyway, so why bother with optimization and utilization?”

Could the Jevons Paradox Apply?

In response to the release of R1, some backers of technology investment have pointed to the economic concept of the Jevons paradox to support the idea that demand for AI chips and their necessary infrastructure will increase. The economic theory states that as a resource becomes cheaper and more efficient, demand for the resource increases. The paradox is often used to refer to energy and how increases in energy efficiency have led to sometimes-unexpected increased demand for energy.

“DeepSeek-R1 enables faster and more efficient inferencing while significantly reducing dependency on high-powered GPUs,” said Srini Koushik, president of AI, technology and sustainability at Rackspace Technology, in a statement. “This breakthrough marks the beginning of a new race to build models that deliver value without incurring prohibitive infrastructure or energy costs.”

Microsoft CEO Satya Nadella highlighted this phenomenon when he shared a Wikipedia link on social media shortly after the release of DeepSeek.

“Jevons Paradox strikes again!” Nadella wrote. “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.”

Nicole DeBlase, an analyst at Deutsche Bank, wrote this week in a note to clients: “Lowering the development cost of AI [large language models] could theoretically increase adoption to a broader swath of end users than would have been the case at prior levels of required investment costs.”

DeBlase added that cheaper AI development costs will positively impact the global economy, as they create opportunities for faster and broader AI-driven productivity gains, and companies other than hyperscalers could increase their AI investments.

“From a capex perspective, this means we could see more AI investment in different verticals, rather than just via hyperscalers, perhaps shifting spending towards the edge,” DeBlase wrote.

Schroders, in a research note, stated that if increased efficiency results in lower demand for chips and AI infrastructure, it could present headwinds for companies like Nvidia. It added the caveat: “However, this outcome is far from certain, particularly given Jevons’ Paradox.”

The firm also noted that this situation could also lower the cash needs of the technology behemoths.

“If this situation results in reduced spending requirements for these companies, it could lower their capital expenditure needs and drive significant increases in free cash flow generations,” the Schroders report stated.

Skepticism

Due to the opaque nature of DeepSeek’s process, it seems too early to draw long-term conclusions about R1’s impact on AI development.

Alexandr Wang, CEO of AI startup Scale AI, suggested in a CNBC interview that DeepSeek trained R1 using 10,000 new Nvidia A100 chips, an export-controlled design not available in China that it cannot disclose due to the export controls. Elon Musk, CEO of X AI, which recently raised $6 billion at a valuation of around $50 billion, and Tesla, another major purchaser of Nvidia AI chips, wrote on social media that that Wang’s assumption was obvious.

The U.S. recently opened a probe into DeepSeek to investigate if the company illegally imported restricted chips through Singapore, according to Bloomberg. Roughly 20% of Nvidia’s revenue comes from Singapore.

OpenAI had accused DeepSeek of distilling, or training, its model on the data of OpenAI’s models.

“We know that groups in the [People’s Republic of China] are actively working to use methods, including what’s known as distillation, to try to replicate advanced U.S. AI models,” a spokesperson for OpenAI said in an email. “We are aware of and reviewing indications that DeepSeek may have inappropriately distilled our models.”

Ross Seymore, a research analyst at Deutsche Bank, summed up the uncertainty of immediately drawing conclusions from the R1 release in a note to clients.

“On the surface, it appears that [DeepSeek’s] innovations led to a total development cost of $5.57 [million], as low as 1/45th of the cost of current offerings,” Seymore wrote. “While this structural reduction in capital is stunning and would greatly reduce the cost of AI investments, we note the true cost of this project remains unclear, as the cited GPU hours claimed in technical papers may definitionally exclude prior training resources.”