Even adjusting for gas prices, consumers went on a (wealth effect- generated?) spending spree in May

 

 - by New Deal democrat

Consumer spending is about 70% of the economy, and retail sales is our first wide measure of that spending. And since consumption leads employment, it is an important real world measure. In May, after two months of being dominated by gas prices, it was even more decisively driven by a likely splurge tied to the wealth effect from Booming stock prices.

Nominally, total retail sales rose 0.9% in May, but after taking the monthly 0.5% increase in consumer prices into account, real sales rose 0.4% (blue):

Since gas prices have been a major driver of inflation in the past few months, here’s a look at the monthly % changes in nominal retail sales excluding gas stations (orange) vs. total retail sales (blue). Retail sales excluding-gas increased 0.7%:

Since CPI increased 0.5%, and excluding energy rose only 0.2% in May, both of these are positive in “real” terms as well.

On a YoY basis, nominal total retail sales were up 6.9%, but in real terms were only up 2.6%. Excluding gasoline, nominal sales YoY were up 5.4%, and deflating by using CPI excluding energy were up 2.5%:

In both the March and April personal income and spending reports, we saw that consumes handled gas prices increases by essentially just putting the extra spending on their credit cards. Last month I suggested that consumers might have switched to a “wait and see” mode, but this month’s report indicates that consumers have been spending with wild abandon - as has been indicated for weeks by the Redbook sales report, the four week average of which as of this week is up 9.1% YoY nominally!:

This isn’t because wages have been increasing sharply. Quite the contrary, as we saw with the employment report. Rather, it is *very* likely that this is “wealth effect” spending by upper income consumers triggered by the near 20% rise in the stock market since the end of March.

Finally, since consumption leads employment, here is the update of YoY real retail sales (/2 for scale) together with employment (red):

This suggests that on a YoY basis the rebound we have seen in the last three jobs reports is likely to continue in the next several months. Which is all good news, provided the stock market wealth that is likely driving consumption reflects a Boom rather than a bubble.

Housing continues in the doldrums, but its decline failed to give rise to a post-pandemic recession

 

 - by New Deal democrat

When it comes to housing, the post-pandemic economy has been odd. That’s because, as of last year by all accounts housing had deteriorated  sufficiently that a recession should already have begun. Not only did that not (quite) happen, but the current situation would be more congruent with such a recession ending than ongoing. In general, that’s because even though job creation stagnated, real incomes did not, and real consumer spending was sufficient to keep the economy in expansion.

In the past few months, the situation has reversed, with real incomes declining, but manufacturing and job creation rebounding. And housing is playing a role in that conundrum as well. Let’s take a look.

In May, almost all of the headline housing data was negative. Starts (blue), which are noisier and slightly lag permits (gold), declined sharply, down -215,000 to 1.177 million units annualized. Permits declined -10,000 to 1.413 million. The one slightly bright spot was that the metric which is the least noisy as well as being most leading, single family permits (red, right scale), rose  5,000 to 886,000: 

Permits, including single family permits, were near five year lows, but close to average readings for the past year. But housing starts had their worst month in over 5 years! But because they are so noisy, I am discounting that. Still an “average” down in the doledrums month is definitely not positive.

Perhaps more interesting is that the recent uptick in mortgage rates (blue in the graph below) due to the Iran war has not really had an effect on the sideways trend:

Still, on a YOY% basis, starts are down -8.7%, while permits are down -0.2% and single family permits are down -7.1%:

This is a sector that, to repeat, is down in the doldrums, and shows no sign of improvement.

On the other hand, as I have noted for the past several months, the YoY downtrend has not been worsening for many months. Typically permits and starts have been down 20% or more at the onset of recessions in the past, although in the 1991 and 2001 recessions, they were only down about -10%; and there have been a number of times, for example 1966, 1987, and 1995, where construction has been down -10% or more YoY without a recession occurring:

On the contrary, the negative but relatively minor and stable negative YoY changes beginning last year have been just as consistent with mid-expansion slowdowns as with recessions, and stable if negative YoY changes have sometimes occurred during recessions a few months before recoveries.

But the statistic in this release that most closely aligns with the actual economic activity represented by the sector is the number of housing units under construction. In May they also declined, by -11,000 to 1.266 million annualized, only 4,000 above their post-pandemic low in March, down -7.1% YoY, and down -26.1% from their peak:

The historical version of this metric shows that more often than not in the past by the time a decline in units under construction had declined by this much, a recession had already begun:

Indeed, typically this metric does not turn upward until after the recession has been over - part of the current conundrum.

Finally, let’s update the graph that additionally shows the typical last shoes to drop before recessions, including houses for sale (gold) and residential construction employment (red, right scale), all normed to 100 as of their respective post-pandemic peaks:

Residential construction employment has been close to flat for the past nine months, and the number of housing units for sale actually rebounded slightly as of its last report for April.

It’s fair to say that the housing market did not follow its typical predictive pattern following the pandemic, and the big increase in interest rates that began in 2022. Although all of the relevant housing metrics fell by percentages from peaks that normally signaled recessions in the past, that did not happen this time. In large part that was because of the huge backlog in building that meant that units under construction did not significantly decline until the mortgage rate and manufacturing situations in the economy stabilized. The latter in particular began to improve substantially beginning in the latter part of 2024, despite the subsequent headwinds of new tariff costs imposed last year. And the continued increases in real incomes and payrolls through 2025 meant that the 75% or so of the economy that is the service sector continued to be fueled by consumer spending. 

But housing is also not contributing positively to the economy, either. That means that if the recent downturn in real incomes continues, the rest of the economy may yet go down.

Goods production sector of the economy remained in expansion in May

 

 - by New Deal democrat

Late last year I noticed that the regional Fed manufacturing indicators were improving - despite the “Liberation Day” tariffs and the general chaos coming out of Washington. It appeared that manufacturers had found a modus vivendi and had adapted to the new environment. That subsequently showed up in a number of manufacturing related statistics, like durable and capital goods orders, and even in manufacturing employment.

It also showed up - as did the Boom in AI data center construction - in industrial production, which was updated this morning for May. I used to call Industrial Production “the King of Coincident Indicators,” but with the shrinking of manufacturing as a share of the US economy to about 25%, that is no longer the case. Nevertheless, it is an important coincident indicator, with the emphasis on *coincident.* It doesn’t tell us where we are going, but is an important signpost about where we *are.*

In April manufacturing (red in the graph below) broke out of that range to the upside, increasing 0.6% for the month, while the more noisy utility production (gold, right scale) increased 1.9%, driving total production (blue) to a 0.7% increase and a new post-pandemic high. May total prodcution and manufacturing continued to be positive, although much more subdued. Total production increased 0.1%, manufacturing production less than 0.1% rounded to 0. Meanwhile utilities declined -0.4% [Note: all values are normed to 100 as of September 2018, the peak month for production in the last expansion]:

On a YoY basis, manufacturing production was up 1.7%, manufacturing up 1.4%, and utilities up 3.1%:

There is no sign yet of any break in the recent trend in any of these indicators. Simply put, the goods producing sector of the economy remained in expansion in May.

Weekly Indicators for June 8 - 12 at Seeking Alpha

 

 - by New Deal democrat

My “Weekly Indicators” post is up at Seeking Alpha.

There was little change this week, but restaurant reservations - the first expense I would expect stretched consumers to cut back on - did slacken a little bit. And withholding tax payments over the past four weeks barely kept pace with YoY inflation.

As usual, clicking over and reading will bring you up to the virtual moment as to the state of the economy, and reward me with a penny or two for my efforts collecting and organizing the data for you.

The entire US economy is downstream of the AI buildout dam, and that dam is almost certain to fail

 

 - by New Deal democrat

I’ve been ambivalent about whether the mania for AI is a Boom or a Bubble. That’s because whether its use is transformative or, like Steve Jobs’ claims that the Segway would revolutionize transportation, not so much. 

But what has become increasingly clear is that virtually the entire economy has been downstream of its growth. Here’s just how exponential the growth of spending on AI data centers has been:

The stock market has Boomed:

But the stock market Boom has been based almost exclusively on that AI spending:

And the wealth effect of that stock market Boom has led to accelerating spending by the affluent, as shown in Redbook’s weekly retail sales data:

And, as consumption leads employment, that spending has spread out to an increase in not just goods-producing jobs (orange, right scale), but broad service providing jobs (blue, left scale) as well:

Meanwhile, the spike in inflation has caused consumers, most of whom do not have much if any stock holdings, to dig deeper into their savings to a near record low:

In summary, if it weren’t for the Boom in AI related spending, it’s likely that consumer spending would be flat or even negative (remember that real average hourly wages have gone negative YoY, and real aggregate nonsupervisory payrolls have grown just barely in the past year:

In other words, the US economy would likely be in a recession right now.

That’s an awful lot of weight that is being borne by one small sector of the economy.

But I have come to conclude that, even if AI’s usefulness were to live up to its hype, there is one aspect that strikes me as clearly a bubble.

That’s because there are 10 or 20 companies all rushing to build out full-blown all-encompassing data centers. But almost certainly when it all shakes out, even under the best of circumstances there are likely to be only 2 or 3 left. All of the others - and their huge construction and usage footprint - are likely to vanish.

In other words, whether or not the AI Boom is like the dotcom bubble of 1999-2000, it is almost certainly like the browser wars of the 1990s, when there are a variety of providers like Alta Vista and Ask Jeeves, that all got eclipsed by Google and all but vanished from the scene.

And think of the auto industry. Early on there were dozens of manufacturers. But by the end of the second World War, there was one dominant company (GM), one secondary company (Ford) and one also-ran (Chrysler).  In computer chips, there has been one dominant company (Intel) and one (until recently) also-ran (Micron). As Ron Insana pointed out, “In 1895, there w[ere] … 1,000 companies [that] made bikes as the new model of transport. By 1905, they were going out of business.” 

 

Fortunately, I don’t have to write a more extensive piece, because it turns out someone else named Dan Wertman of Noetica/Thomson Reuters got there a short time ago, so I will quote them at length:

“Most people liken the AI boom to the dot com bubble. But the right comparison is the lesser-known portal wars. …

“Let’s go back to 1998. The internet had just gone mainstream, and a new kind of company was taking over the web: AltaVista, Excite, Lycos and Yahoo were each racing to become your home base online–the ‘interface’ to the internet. They competed on adding vertical workflows: features like news, email, weather and shopping. Venture capital poured in and they grew fast. For a moment, it looked like any one of them, or all of them, could win, each differentiating themselves in domains in which they were marginally better from the other. 

Then a pair of Stanford graduate students created a new model, a search algorithm called PageRank, which used the web’s own link structure as a proprietary data signal. … [W]ithin a few years, PageRank became what we know as Google and every other portal had been rendered irrelevant.

“We are watching the same movie with AI startups today. Thousands of companies are building products on top of the same AI foundation models — OpenAI’s GPT, Google’s Gemini, Anthropic’s Claude – with no added proprietary content or data, only workflows, each aiming to be the “portal” for their vertical. They have different names, different user experiences, different pitches to investors. What most of them share is that the intelligence powering their product is available to every competitor, every established company, and increasingly to ordinary consumers at low or no cost because they’ve added nothing proprietary to enhance their offerings.”

It’s as if people build 20 Hoover Dams, when only 1 was needed. And the rest will ultimately sit idle - and probably fail.

So, dear reader, let me conclude. This aforementioned AI buildout, and that buildout  (vs. whatever software value AI has) is all but certain to crash. When, and over what period of time, we do not know.

Hopefully not while the rest of the economy, as it is now, is downstream.