Is Private Equity "Good" for the Housing Market?

Even many who support allowing market forces to work might question whether private equity involvement in the U.S. housing market “has been a good thing.”

The impact on housing supply, rental unit supply or prices for those products is contestable.

And while we might agree that other elements of the housing supply market arguably also are very important (zoning and other regulatory “red tape” issues), we might also agree that, so far, private equity involvement in U.S. housing has not been clearly positive, in terms of increasing supply or producing affordability gains. 

PE firms are estimated to own at least 239,000 single-family rentals, over one million apartment units, and 275,000 manufactured home lots as of mid-2022, representing about 1.6 percent of all rental homes nationwide but up to 12 percent to 20 percent in some markets such as Atlanta or Phoenix. 

To be sure, this activity arguably has some benefits for renters, though that argument remains contestable. 

PE firms focused on rental operations can expand rental options and inject capital into the market, also reduces for-sale inventory, elevates prices, and imposes some higher costs on renters, some argue. 

In some ways, the criticisms are similar to those made of extensive conversion of residential housing to short-term lodging such as provided by AirBnB operations, shifting housing supply from “available for full-time dwelling” to “commercial short-term rentals.”

Arguably, PE investments influence both for-sale and rental supply, often shifting homes from ownership to rental markets. Supporters of PE involvement say the impact is minimal. 

Critics contend it exacerbates scarcity for buyers and does not necessarily create lower prices for renters.

The argument is that PE firms have bulk-purchased distressed or entry-level homes, converting them into rentals. This removes inventory from the for-sale market, particularly affordable starter homes, worsening a national shortage estimated at three million to five million units. Again, the impact might be “at the margin” in many cases. 

Investors bought 26 percent of affordable homes in 2023, outbidding families and pushing first-time buyers out, critics allege. Studies show institutional buyers reduced for-sale stock by one percent to two percent nationally but up to 10 percent to 15 percent in Sun Belt metros post-2020.

On the positive side, PE arguably has expanded rental options by rehabilitating foreclosed properties and investing in build-to-rent developments. 

Since 2012, firms have spent over $25 billion on single-family rentals, adding supply in underserved suburbs and reducing vacancy rates. 

A Federal Reserve analysis notes that PE helped stabilize markets post-2008 by shortening foreclosure timelines and boosting local construction employment. In multifamily, PE owns about 10% of U.S. apartments (over 2.2 million units), including new builds that close supply gaps. Some research indicates this diversifies neighborhoods, lowers segregation by attracting lower-income, diverse tenants, and even nudges rents down through added competition.

While PE claims to boost economic growth by investments such as $280 billion in life sciences-related real estate (with spillover to housing), critics highlight opportunistic tactics, such as deferring maintenance or constraining new builds to keep occupancy high. 

In affordable housing, PE's short-term focus can lead to unsustainable practices, reducing long-term supply quality. Empirical data shows no broad increase in total supply; instead, PE often repositions existing stock for higher-margin rentals, displacing potential owners.

PE's target 20-percent financial returns typically raise costs through higher charges, fees, and market consolidation. However, in some cases, increased rental supply can moderate rents, though evidence leans toward net upward pressure on both ownership and rental prices.

Some argue PE acquisitions have driven up home prices by 40 percent to 50 percent since 2020, partly due to reduced for-sale supply and cash-heavy bidding. Others make the opposite argument.

But PE defenders argue PE is a symptom, not cause, of high prices from underbuilding.

Rents have risen 30 percent nationally since 2020, with PE-linked properties showing aggressive hikes. Firms add fees boosting revenue 12 percent to 16 percent, and studies link PE ownership to 17 percent to 26 percent higher evictions in Minneapolis-St. Paul. 

PE advocates, like the Private Equity Stakeholder Project, emphasize benefits such as $18.4 billion raised for affordable multifamily housing (2019-2024) and professional management improving quality. 

So some might note that PE involvement in rental housing has modestly increased rental supply and significantly reduced for-sale inventory. 

Harder to determine is the specific PE impact on higher home prices (up 40 percent to 50 percent) and rents (up 30 percent), which arguably have climbed for all sorts fo reasons. 

Fear Versus Greed: Electricity Transformed into Value (Bitcoin) and Insights (Inferences)

“Fear and greed” notoriously are drivers of equity market sentiment and that is clear in the yo-yo behavior surrounding artificial intelligence equities recently. The fear is that AI investment levels are a bubble, overinvestment that will ultimately not pay off. 

The greed flows from the belief that AI is a transformative new technology that will create new winners and losers in the broader economy. 

A likely third position is that AI is not a bubble on the order of the do-com mania, but will produce excess investment that has to be rationalized, eventually, as all great new technology waves have done so. 

Optimists might agree with Mara Holdings CEO Frederick Thiel that “ electrons are the new oil,” referring to the idea that computational resources underlie the ability to wring value from AI, while the data centers that provide the computation now are dependent on access to large and affordable amounts of electricity. 

Mara believes future winners will be high-performance compute providers who have the lowest costs to produce insight per token; insight per kilowatt of power consumed, especially for enterprise private compute operations. 

As Thiel puts it, his firm, which originally ran bitcoin mining operations, now provides a high-performance computing infrastructure  that converts energy into both value (bitcoins) and intelligence (AI computing).

The broader vision for the company, as is true for many other former bitcoin miners, is "transforming energy into intelligence.” In other words, consuming electricity to power AI models and the inferences to be drawn from using those models. 

The analogy is not unlike that sometimes made to the export of alfalfa from the U.S. great plains to the Middle East. The production of the alfalfa consumes water, which becomes livestock food, which essentially also represents the value of the water consumed to grow the produce. So exporting alfalfa also is akin to exporting the water used to grow it. 

“We believe energy, not compute, really becomes the primary constraint on AI growth,” says Thiel. 

Pursuant to that belief, Mara has a venture with MPLX, formed by Marathon Petroleum Corporation, the largest petroleum refinery operator in the United States, to develop and operate multiple integrated power generation facilities and state-of-the-art data center campuses in West Texas. 

MPLX will provide long-term access to lower-cost natural gas at scale, while Mara will develop and operate on-site power generation and compute infrastructure. 

The initial capacity is expected to reach 400 megawatts with the option to expand to up to 1.5 gigawatts across three plant sites.

But Mara also is basing its business on “inference” rather than model training, as that allows it to use application specific integrated circuits (ASICs) rather than graphics processor units (GPUs), thus lowering its capital investment. 

That approach also enables use of smaller data centers and air cooling rather than the more-expensive liquid cooling. The strategy is not especially new, as others in the data center and connectivity spaces have chosen to become specialists in smaller markets (either in terms of geography or types of customers). 

But all that happens within the context of a market that is volatile. 

A positive development such as a new chip announcement, a major partnership like the AWS/OpenAI compute services deal, or strong earnings from an AI leader pushes the market into "extreme greed" territory, driving up prices quickly.

But then reports of high AI capital expenditure, delayed profitability for end-users, or a general sentiment survey warning of a "bubble" causes profit-taking and selling, plunging the market into "fear" sentiment, leading to sharp, temporary pullbacks.

Month	Major Event	Sentiment	Notable Impact
2025-01	DeepSeek Launch	Fear	Sharp drop, infrastructure risk flagged
2025-04	Trump Tariffs Threat	Fear	Market volatility spiked, quick rebound after walkback
2025-09	NVIDIA-OpenAI Chip Deal, Fed Rate Cut	Greed	Strong surge, positive sentiment returned
2025-10	Bubble Talk Surge	Fear	Renewed caution, market exhaustion warnings

The cycle resets because the fundamental belief in AI's future remains generally strong. Investors who sold out of fear often rush back in for fear of missing the next leg up (greed), making the dips short-lived and creating the current high-volatility, upward-trending cycle. 

But skepticism and hope continue to coexist and oscillate. 

Beyond the volatility, we might argue that “high-performance computing capability” has become a strategic commodity.

High-performance compute capacity arguably has become the single most critical, scarce, and expensive strategic resource in the AI industry. 

If so, long-term, multi-billion-dollar compute contracts are now a competitive necessity, resembling procurement models for essential commodities like energy or raw materials. But volatility will persist until some future time when there is much more predictability about AI investments and revenue gains. 

So nobody knows yet whether the investment boom in artificial intelligence we now see is a bubble, or not. Much conventional wisdom seems to suggest AI is a bubble, but there is disagreement. 

And if some argue it is a bubble, there remains an argument that there is a significant difference between a dot-com style bubble and an “ordinary” investment bubble associated with introduction of any major new technology. 

To be sure, for some of us, there are hints to parallels of excesses akin to the excessive dot-com investment at the turn of the century. As I was writing one startup business plan, I was told “there’s plenty of money, make it bigger.” 

As it turned out, “this time is different” and admonitions that some of us “did not get it” were wrong. Economics was not different and normal business logic was not suspended. 

But some might note that there are important differences between AI investment and dot-com startup investment. Back then, many bets were placed on small firms with no actual revenue. 

Today, it is the cash flow rich, profitable hyperscalers that dominate much of the activity. Investment burdens are real, but so are immense cash flows and profits to support that investment. 

And by some financial metrics, valuations do not seem as stretched as they were in the dot-com era, though everyone agrees equity market valuations are high, at the moment. 

We also can’t tell yet what impact artificial intelligence might have on productivity and economic growth, much less future revenues for industries and firms. 

And that might be crucial to the argument that there actually is not an investment bubble; that there are real financial and economic upsides to be reaped; new products and industries to be created. 

There is some thinking by economists that AI impact could be greater than electricity and at least as important and positive as information technology in general. 

General-Purpose Technology	Primary Timeframe of Peak Impact	Estimated Annual Productivity Boost (Peak Rate)	Macro-Level Impact Metric
Steam Engine	Mid-19th Century (Decades after invention)	0.2% - 0.3%	Contribution to annual TFP* or Labor Productivity Growth
Electrification	1920s - 1940s (30+ years after initial adoption)	~0.4% - 0.5%	Contribution to annual TFP or Labor Productivity Growth
Information Technology (IT) / Computers	Mid-1990s - Early 2000s	~1.0% - 1.5%	Acceleration in annual Labor Productivity Growth (U.S.)
Artificial Intelligence (AI) (Current Forecasts)	Early 2030s (7–15 years after GenAI breakthrough)	1.0% - 1.5%	Projected increase in annual Labor Productivity Growth over 10 years

Study/Source	Projection Focus	Estimated Gain (Over Baseline)	Caveats
Goldman Sachs (2023)	Macroeconomic Forecast (Global/U.S.)	7% increase in Global GDP over 10 years; 1.5 ppt annual U.S. labor productivity growth	Highly optimistic, assuming rapid adoption and task automation.
McKinsey Global Institute (2023)	Economic Potential of Generative AI	$2.6 to $4.4 Trillion added annually to the global economy.	Based on value from 63 specific use cases across business functions.
Acemoglu (MIT)	Conservative Macroeconomic Model	0.7% increase in TFP  over 10 years (U.S. economy).	More modest, based on historical adoption rates and cost-benefit analysis of task automation.
Brynjolfsson et al. (Micro Studies)	Firm/Task-Level Productivity	10% - 40% increase in productivity for tasks like coding, customer service, and professional writing.	These are early, firm-level gains, which historically take time to translate into aggregate macro statistics.

Each of us has to make a call: bubble or not; big bubble or only “normal” overinvestment?