High Income Rentals are Low Income Rentals

Have you heard about the abundance movement? It basically says that we should enact a mix of regulatory and supply side reforms in order to produce more for everyone, especially the least economically advantaged. The reforms extend to the housing market and ensuring adequate housing.

There’s an argument that building any housing, even at the high end, can reduce the cost of shelter for everyone – even people who would never live in the newly built housing. The idea is that high income people switch to the newly built housing and leave less attractive housing. Someone else in that high income bracket snatches up the older place, leaving their prior housing vacant. The vacancy shuffles around high priced rentals until, ultimately, the vacant rental price must fall in order to attract a renter, such as someone further down the income distribution. Then the entire process continues, with the game of vacancy musical chairs working its way down the renter income distribution.

The more overlap that there is between housing consumption choices the quicker there is an impact on lower priced housing.   If you think that high income people consume higher priced housing, then you might think that there is a substantial difference between housing consumption choices and that it will take a long time for this ‘trickle down’ to get to the people who need it most. If income groups compete more for the same housing, then the effects on price will occur sooner for the lower income people.

How much Rental Overlap is there?

Miami, Florida has some of the highest housing costs in the US. Below is a histogram of annual rental costs in Miami by household income quartile (ACS 2024).   I restricted the data to positive incomes and rents and the highest rents are censored down to $98.4k annually. First, we can definitely see that the highest incomes (quartile 4) have the most censored annual rents and that the 1st income quartile (lowest) has the most annual rent payments nearer to zero. So, the histograms make sense in that way. But I was surprised by how much overlap there is. Different income quartiles are consuming many units in the same price range!

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Bubble Worries Mean Diversify…. But How?

Mike is concerned that US stocks could be a bubble, as are many others:

if we take parallels to the past seriously, there arises the question of whether a 40 year old with a 100% index fund portfolio should consider hedging/reallocating their portfolio a decade sooner than they planned on becuase, well, just look at that [CAPE] chart.

But what to do about it?

So, seriously. Are we hedging? And what are we hedging into? Asking for a friend. Who is an economist. And also me.

My answer: I’m diversifying into global stocks, which tend to be much lower-priced: 

Source: The Idea Farm, Global Valuations Update July 2026

But how do you practically do this?

A simple and cheap way to get a highly diversified basket of global stocks is VEU (Vanguard ex-US). Over 3000 stocks from around the world, P/E of 19, rock-bottom fees. One disadvantage for diversification purposes is that its biggest 5 holdings make up 12% of the index and they are all AI-related.

If you’re willing to make more concentrated and specific bets in order to get even cheaper, here are some other foreign indices I like:

  • FRDM (Freedom 100 Emerging Markets ETF): Invests in emerging markets with the highest economic freedom ratings. I love the theory & it’s performed very well for me & its overall PE is a reasonable 17, though it’s largest holdings are in Taiwan and South Korea, so not a good way to get away from AI.
  • ILF (iShares Latin America 40): Invests in large Latin American companies. P/E 11 with low AI exposure.
  • FYLD (Cambria Foreign Shareholder Yield ETF): Invests in high-yielding companies in developed countries outside the US. Measures yield using stock buybacks in addition to dividends. This serves as a value screen that keeps PE low (currently 13) and avoids expensive sectors like AI.
  • EYLD (Cambria Emerging Shareholder Yield ETF): Same idea as FYLD but for emerging markets. Current PE 12

I also hold single-country indices for Japan (FLJP, PE 20), Poland (EPOL, PE 13), and India (EPI, PE 17). These are probably riskier than the broad indices above, but still fairly diversified across companies.

While foreign stocks are my preferred hedge, there are many others. For US stocks there are small-cap or value indices, or just buying individual stocks you like (not necessarily crazy). The 20- and 30-year US treasury bonds are intriguingly yielding over 5%, though between my age and my inflation concerns I don’t own any. TIPS are much more attractive, yielding 2-3% over inflation, I don’t own any yet but I’m considering it. Gold indices like IAUM are another good hedge.

Of course this isn’t necessarily a bubble that will pop any time soon, or at all- AI is a real technological advance- so between that & general efficient market principles I don’t think the answer is to sell everything & go to cash, much less short the market. But Mike is right to ask how to hedge, and I think anyone holding ~100% US stocks should consider diversifying.

Disclaimers: Not investment advice, I hold some of the indices named.

Everything At the Grocery Store is On Sale Relative to 1980

Back in May 2024, I wrote about grocery prices in 2024 compared to 1980. Relative to average wage increases, almost everything was more affordable — the one exception was bacon.

Grocery prices have continued to climb since 2024, but so have wages. What does the comparison look like now? Well, I have good news for bacon lovers:

Figure 1

The chart shows the change in relative affordability, as measured by how many minutes of work at the average wage it would take to purchase the item (using consistent product sizes and weights). As I wrote in that 2024 post, these are not items that I cherry picked. These are all 24 grocery items where BLS has price data in both 1980 and 2026 (out of about 150 items total). Perhaps there is some survivorship or selection bias as to which items are available in both years, but looking at the list this seems like a pretty reasonable shopping cart for a typical consumer (well, maybe there aren’t buying all the meats every week, but probably every month). The prices are updated through July 2026, with the CPI data just released this morning.

While I have used average wages here, that isn’t a trick. We don’t have a median wage for 2026 yet, but using a measure of median earnings you can see that average wages and median weekly earnings increased at exactly the same rate since 1980.

But consumers probably aren’t thinking about prices relative to 1980. Their time horizon is likely shorter. What if we made the same comparison to 2026 using the 2019 prices, which is right before the pandemic and within most shopper’s recent memory:

Figure 2

Relative to 2019, things do look quite as rosy. Some items are “on sale” in terms of affordability, but a lot of items aren’t, especially a lot of proteins. And no doubt many consumers will focus on the items that are less affordable, rather than those that are more affordable (and even for these items, the nominal price is higher, so consumers might still be frustrated).

It is important to note that this basket of 24 items isn’t a perfect representation of all the items consumers purchase. Compared to the CPI “food at home” index, average wages have actually increased more since the beginning of 2019. But consumers are right to feel that beef and few other items are much less affordable than 2019, even if over the long run they are more affordable.

Figure 3

Boy Wonder Leopold Aschenbrenner Blows Up His $45 Billion Situational Awareness Hedge Fund

Leopold Aschenbrenner is a very bright guy. Born in Germany to physician parents, he skipped enough grades to graduate from high school at age 15, allowing him to enroll at Columbia University in 2017 at that same age. He went on to graduate from Columbia at age 19 as valedictorian with a degree in economics and mathematics-statistics. An econ prof at the time said his “record of scholarship exceeds that of any student in the department in the previous 20 years.” The Mercatus Center, a think tank at George Mason University, recognized his potential, awarding him an Emergent Ventures grant.

Leopold Aschenbrenner, Columbia Class of 2021 Valedictorian

After graduation, Aschenbrenner moved into the effective-altruism research and grantmaking world, making notable contributions in various ways. In 2023, he joined the newly created “Superalignment” team at OpenAI, that was charged with making conceptual and engineering progress on aligning systems “much smarter than humans” before such systems were built. The next year he was discharged by OpenAI; the company said it was because of a security leak, but his version (which I find more credible) is that he was ousted as retaliation for circulating an internal memo arguing that the company’s protections against model-weight theft and algorithmic exfiltration were inadequate for AGI-relevant work.

Two months after his departure from OpenAI, he self-published Situational Awareness: The Decade Ahead, which you can download here.    This monograph synthesized scaling-law extrapolations, geopolitical analysis, and AI-lab security commentary into a single forecast: that the largest AI labs, on current trends, will plausibly reach AGI around 2027 and that an intelligence explosion to superintelligence could occur in the subsequent few years. This work went viral on Wall Street, and before you can say “monetization”, he was leading a hedge fund named, appropriately enough, Situational Awareness. There he put into practice his convictions that the demand for compute would be voracious, and would be limited by physical constraints such as electricity and chip fabs.

Thus, in his fund he went long companies like SanDisk (memory fab), Bloom Energy (makes solid oxide fuel cells), and Nebius (builds whole data centers). Very long, in fact, with leverage reportedly as high as 400%. He tried to hedge this long book by shorting software companies which are viewed as vulnerable to disruption by AI. This strategy worked fabulously for a while. His assets under management (AUM) climbed to $45 billion, with returns in the first 6-7 months of 2026 approaching 400%. Not bad for a 25-year-old.

But then, genius failed (yet again)- -in a stunning reversal, the market rebelled in late July against big AI capex spends, dumping memory fabs and infrastructure, and bought into the maligned software (SaaS) sector. So, BOTH his long and short legs went against him, followed in due course by the dreaded margin calls. Aschenbrenner’s $45 billion shrank to a measly $10 billion in a matter of days, as he was forced to sell off his public equities at a discount to those friendly capitalist sharks at Citadel. Wise old heads wagged, saying, yup, this sort of Black Swan event always happens sooner or later, and you then get carried out on a stretcher if you run a highly leveraged bet that is not truly hedged.

Down, but not out – – The word on the Street is that folks with money to invest are already lining up to entrust more bazillions to our altruistic wizard. We have not heard the last of Leopold Aschenbrenner.

Bubbly

At what point does the advice to “invest in an index fund and forget about it until you retire” become insufficient advice?

Stock Market hits 2nd most expensive valuation in history, far surpassing the Crash of 1929 and only slightly behind the Dot Com Bubble 🚨 🚨

Barchart (@barchart.com) 2026-08-09T22:44:44.719929281Z

Because I’m not a financial planner. I’m not even an active investor. I’m an economist who follows the standard economist dictum regarding passive investing and low-fee index funds. But at some point the, if we take parallels to the past seriously, there arises the question of whether a 40 year old with a 100% index fund portfolio should consider hedging/reallocating their porfolio a decade sooner than they planned on becase, well, just look at that chart.

Maybe AI is great. The internet certainly was an is pretty great. But that doesn’t mean there won’t be a massive dotcom bubble-esque correction, and a 20% hedge can be the difference between 4 years getting back to even versus 7 years getting back to even. Even at the risk of misisng out on some growth in the longer term, the calculus with basic risk aversion checks out.

So, seriously. Are we hedging? And what are we hedging into? Asking for a friend. Who is an economist. And also me.

Joy on Severance and Wild Problems           

For EconLog, I wrote a reflective piece on Season 2 of Severance and how it relates to the book Wild Problems by Russ Roberts.

You Cannot Outsource Life: Severance and Wild Problems (EconLog link)

The essay is about pain and a meaningful life.

Rather than accepting that work, grief, and love may transform us, Lumon divides experience from identity.  An adult who is primarily asking, “Who do I want to become?” likely would reject the Lumon…

Read more at the link above. Remember that the first words spoken in Season 1 were “Who are you?” Maybe Russ Roberts should do a whole podcast on this show.

During a rare slower week in the summer (thanks to my sister) I was able to binge Season 2. The genre could be described as Science Fiction. S2 does answer some of the questions raised in S1 but ends with a new cliffhanger to bring you back for Season 3. If you want to enjoy the show, the trick for me was not to take it too seriously. Ben Stiller is a producer and you can see traces of what feels like Zoolander humor to me.

I think the dialog is great. The sibling relationship and marital disputes and office inside jokes feel realistic.

As I said about Season 1, this show could be, among other things, a meditation on AI alignment. When you think enough about AI alignment, I guess you start seeing it in your TV shows. I wrote about that previously in: Artificial Intelligence in the Basement of Lumon Industries

Other previous posts on Severance, based on Season 1:

Lumon Industries and Drudgery (Joy)

Perks in Severance (Joy)

Severance and the Disutility of Work (Mike)

Announcing the Disability Records Project

Did you know that we have access to digital copies of the historical US census rolls? You can also find the digitized data at IPUMS. However, the data for people with disabilities is not great. It depends on the year, but those data have error rates on the order of 20% or higher.  We have the digital census rolls, the data just doesn’t match them.

So, I created a non-install windows computer application that lets people identify disabled people on those digital census rolls. Complemented with machine learning, my goal is to improve the accuracy of historical records about people with disabilities. Historical and quantitative research about disabled populations is relatively thin. We can do better. If you have students who would benefit from this research experience, then do please let me know! I can approve your institution’s email domain and we can get started.

The application is really straightforward with basically two user-facing features.

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CBO Wants Your Research

The Congressional Budget Office released a series of posts explaining questions they have about major federal budget policies that past research does not adequately answer. For any economist looking for paper ideas or for a way to influence policy, CBO’s posts are a great place to look:

A Call for New Research on the No Surprises Act

A Call for New Research in the Area of Permitting Requirements for Investments in Physical Infrastructure

A Call for New Research in the Area of Spending on Medicare Part D

A Call for New Research in the Area of Nutritional Standards in SNAP

A Call for New Research on Energy and the Environment

A Call for New Research in the Area of Finance

A Call for New Research in the Area of Health

A Call for New Research in the Area of Labor

A Call for New Research in the Area of Macroeconomics

A Call for New Research in the Area of National Security

A Call for New Research in the Area of Hepatitis C

A Call for New Research in the Area of New Drug Development

A Call for New Research in the Area of Obesity

A Call for New Research in the Area of Taxes and Transfers

At AEAs this year Heidi Williams emphasized how huge bills like permitting reform are being discussed by Congress without much research to inform key aspects of the bills, so CBO & some Congresspeople would genuinely like to see your work on these questions if it is well done

This also your regular reminded that I maintain a page of economics paper ideas. Until now all the ideas there have been my own, but I will be adding links to pages where others share their own paper ideas, starting with CBO’s.

Seven-Year Grocery Inflation is Running High

A recent poll tells us that 66 percent of Americans think groceries are unaffordable. Is this a reasonable position? Let’s add some context.

Figure 1 is one way to look at the problem. It shows the cumulative 7-year inflation rate for groceries, going back almost 100 years. 7 years is an arbitrary time period, but I think it makes sense: it can reasonably be described as “recent memory”; right now it encapsulates the period going back about 6 months before the pandemic; and it has a few time periods of around 100% grocery inflation and a few with close to 0%.

Figure 1

First things first: grocery price deflation over a 7-year time horizon is highly unusual. The only time it happened was the 1930s, a time when you had general price deflation and groceries followed that pattern. It was also a pretty bad time for the economy and society. I’m not saying you can’t have general food price deflation with a major depression, but it doesn’t show up in the historical record going back over 100 years.

Now to the present: the most recent 7 years look pretty bad. In absolute terms, 33 percent grocery inflation is above the long-run average of 25 percent, and definitely above the average of the last 40 years of 21 percent (for most adults, the past 40 years is as far back as their memory goes in terms of being acutely aware of grocery prices). Yes, there have been a few time periods with higher grocery inflation, notably the two World Wars and the 1970s.

But the really important context is the 7 years prior to the pandemic, when grocery inflation was so low (4-5 percent every 7 years) that it probably felt like 0% to most people. That was the recent experience people had become accustomed to before the pandemic. The only other time since the Great Depression it was that low was the late 1950s through the 1960s — though that 15-year window is bookended by two periods of around 100% grocery inflation!

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Warsh’s Low/No Guidance Approach at Fed Makes Market Participants Nervous – – Which May Be a Good Thing

Under Jerome Powell, a typical FOMC meeting had become almost a market event in itself. Traders didn’t just care about the rate decision. They dissected every word of the statement, every sentence of the press conference, and especially the “dot plot,” looking for clues about where rates might be six months or a year from now. The Fed wasn’t simply setting monetary policy—it was guiding expectations. Markets often moved as much on hints about future decisions as on the decision itself.

The first two FOMC meetings under Kevin Warsh have felt very different. The dot plots are gone. Forward guidance has largely disappeared. Instead of trying to signal the likely path of policy, Warsh has repeatedly stressed that the Fed will respond to incoming data when it arrives, not commit itself to forecasts that could prove wrong. At his latest press conference, he described avoiding forward guidance as “prudent” given current uncertainty, while reminding reporters that “There is no soft or alternative inflation target—only 2%.”

This is a huge change in communication style, which is having real world consequences.

Warsh long argued that forward guidance can box policymakers into decisions based on yesterday’s forecasts instead of tomorrow’s realities. That is, once their tentative plans had been put out in public, there was a psychological bias among Fed members to lock in on those projections, which would inhibit their ability to rationally interact with the most recent data and situation. So now, rather than telling markets what the Fed expects to do, he wants investors to make decisions based on fundamental economic conditions, knowing that the central bank will react only after the facts justify it. At the latest FOMC meeting he said, “Market participants are learning to play the ball, not the referee—and market prices will continue to respond in the direction and magnitude they see fit. This is, in my view, a change for the better—and we are just getting started.”

That approach chips away at what investors have come to call the “Fed Put”—the belief, built up since the 2008 financial crisis, that the central bank will fairly quickly and forcefully step in to support markets whenever things get rough.

If that belief fades, financiers may think twice before taking excessive risks. Leverage becomes more dangerous if there is less confidence that easier monetary policy will quickly arrive to cushion losses. Risk premiums may better reflect actual economic uncertainty rather than expectations of future Fed support. That is the possible good side of Warsh’s more hands-off approach. Ideally, business people will exercise more prudence on their own, lessening the odds of financial catastrophes that would require Fed intervention.

On the other hand, markets hate uncertainty, and less guidance means more volatility around Fed meetings. I think Powell tried to use sheer talking (jaw-boning) as a tool to influence market rates, lessening the need for the Fed to actually employ its blunt instruments there. Warsh seems to have taken that tool off the table.

Also, I think some (not all) the causation for the rise in 30-year Treasury bonds to twenty-year highs, and of home mortgage rates to one-year highs accrues to Warsh. First, by eliminating dot plots and forward guidance, he has increased uncertainty about the future path of policy. Investors can no longer confidently assume the Fed will ease at the first sign of economic weakness. That uncertainty can raise the term premium, pushing long-term yields higher.

Second, if markets believe the “Fed Put” is weaker, they may demand higher yields to hold long-term bonds because they perceive less protection from adverse economic or financial shocks. In other words, investors require more compensation for risk.

Whether today’s higher long-term rates are a healthy reflection of economic realities, or an unhealth drag on growth, is a matter of debate.