Seeing Through Currency Noise: Interpreting $PEP Sales Trends
In many of my sales charts for companies with significant international exposure, I include a “USD index”—like the orange line on the chart below. In this case, the chart is for $PEP (PepsiCo). The annotations and text box on the chart highlight periods of substantial change in the USD’s value versus a currency basket (I use the DXY, which includes the EUR, JPY, GBP, CAD, SEK, and CHF). The accompanying table below the chart details the specific dates and quantifies the total and annual appreciation or declines during each cycle.
The index is “inverted,” so it moves higher as foreign currencies strengthen against the USD. It means that when the orange line rises, companies like Pepsi—which report sales in USD—get a boost from currency translation on their international sales. Conversely, when the line declines, it acts as a headwind for reported international sales.
I don’t use this chart to make predictions. Instead, it’s a tool for context. If international sales, reported in USD, look strong, it’s worth checking whether this is due to real underlying growth or simply a weaker dollar. That was certainly the case in the early 2000s. But since mid-2008, the USD has strengthened considerably against other major currencies, so international sales growth, in USD terms, has slowed or even reversed.
Last, there’s been plenty of commentary in 2025 about the “unprecedented” weakness of the USD. While there’s some truth to that, the DXY index is not far from its late-2016 peak—and, in fact, the USD only reached a higher high (represented by a lower point for the orange line) in 2022.
The takeaway? It’s essential to maintain a long-term perspective on FX rates. As the table below demonstrates, cycles of appreciation and depreciation can persist for many years (see the years and months for each cycle listed in the table).
$MLKN and the “AI Arms Race”: Why Tech Spend and Furniture Sales No Longer Move Together
While updating my analysis for $MLKN (MillerKnoll; the largest office furniture company in the Western hemisphere), I was struck by the sharp rise in Information Processing Equipment and Software spending (dark blue line)—take a look at the first chart below. The most recent datapoint, highlighted by the yellow arrow, shows annualized sales reaching $250 billion.
The second chart, which tracks year-over-year changes, illustrates why I’ve included not only MillerKnoll’s sales but also two broader industry data series (including Office Furniture Manufacturing). Notice how the volatility in the blue lines closely mirrors the swings in MillerKnoll sales (in red), and even lines up with significant industry downturns reported by BIFMA (the Business and Institutional Furniture Manufacturers Association) in both 2001 and 2009.
Perhaps someday, Information Processing Equipment and Software spending will once again move in step with office furniture sales. For now, though, it seems primarily driven by today’s “AI arms race.” The latest year-over-year increase surpassed 41%—the highest on record for this data series. It’s also striking that if you adjust for inflation, this series remained at a similar level from the post-Internet 1.0 bust in 2002 up until early 2020. The dramatic surge started only after that point. It will be interesting to see in the coming years how sustainable (or not) this pace really is.
Why “Simple” Numbers Lie: Lessons from $FDX and the Art of Financial Analysis
Why do I devote so much effort to detailed financial analysis of the companies in RIM’s CofC(*)? It’s because companies are constantly evolving, and off-the-shelf calculations like “sales growth” or “margin trends” frequently become meaningless in the real world.
Take $FDX (FedEx) as an example. In the picture below, you’ll see how FedEx has reported ADV (Average Daily Volume) and ADP (Average Daily Packages) for their Express segment over the last 20 years, along with “base case scenario” forecasts for the coming decade.
First, notice the significant jump in ADP in 2017. That spike came right after the acquisition of TNT Express—a major European operator. The timing, however, was unfortunate: only months later, FedEx was hit by the NotPetya ransomware attack, which severely impacted TNT’s IT systems. Nearly every hub, facility, and depot had to have its systems rebuilt from scratch. The recovery was extensive, and FedEx estimated immediate losses of at least $300 million due to reduced shipping volumes, lost revenue, and higher remediation costs.
Fast-forward to recent years, and the numbers became complicated again. FedEx has merged its Ground and Express segments, moving closer to the model used by UPS (which already operates a unified network) and adjusting to changing parcel volumes after the ecommerce surge. Although no new company was acquired this time, the way FedEx reports its numbers has changed—once again making direct year-over-year comparisons challenging.
That’s why I continuously adapt my valuation models to account for these reporting changes. If I don’t understand exactly what changed (and when), the risk of producing misleading forecasts rises dramatically. Back to the model…
(*) CofC = Circle of Competence
The Manufacturing Construction Surge: Signal or Noise for $FAST?
As I work on my analysis of $FAST (Fastenal)—a major industrial and construction supply distributor specializing in fasteners and related maintenance, repair, and operations (MRO) products—I’ve decided to update my analysis tracking construction spending in the USA. After all, the more activity there is in this sector, the more demand Fastenal might see for its products.
The data, from the US Census Bureau, begins in the early 2000s. The first chart below shows subcategories of a broader category: private non-residential construction. I’m highlighting this first because it reveals the abnormal increase in manufacturing-related construction. Manufacturing spending, measured in millions of dollars per month, doubled from around $10 billion to almost $20 billion per month in just a couple of years[*]. The increase starts in 2022 for a clear reason: the CHIPS and Science Act, the Inflation Reduction Act (IRA), and the Infrastructure Investment and Jobs Act (IIJA) provided substantial federal subsidies, tax credits, and direct funding specifically aimed at boosting domestic manufacturing, particularly in sectors such as semiconductors and clean energy technologies.
However, it’s essential to put this increase in a broader context, hence the second chart. The green line represents the sum of all private non-residential investments, totaling more than $60 billion per month—a level it has maintained since recovering from the significant decline during the GFC[**]. Notice, however, that private residential construction (in red) is the most prominent subcategory in this chart, with almost $80 billion in monthly spending. When you combine both private-residential and private-nonresidential, you get more than $140 billion in monthly construction spending. The other lines refer to public construction, which is dominated by nonresidential projects (such as highways, sewage, and water treatment). However, this represents a relatively minor component of construction spending, totaling approximately $40 billion per month.
So, is a $10 billion increase in manufacturing-related construction significant? When you combine both private and public spending (totaling around $185 billion), it represents slightly more than 5% of the total. It’s not nothing, but it shouldn’t be the reason for someone to expect a massive increase in construction activity nationwide. The government might intervene even further with additional subsidies. However, the country’s level of debt and current twin deficit should make it more challenging to do so. But we live in times where fiscal restraint appears irrelevant to governments, so time will tell how much more abnormal activity we’ll see in this subsegment of the construction space.
[*] All figures in this chart are adjusted for inflation and population growth [**] Global Financial Crisis
The Cass Transportation Index: Interpreting the New Wave of Red
A few months ago, I introduced you to the Cass Transportation Index. If you’d like a refresher on the various time series Cass releases, you can revisit my earlier post here.
Today, I’m turning the spotlight to the shipments series—take a look at the chart below. The line in blue tracks shipments and has now logged its 29th consecutive negative year-over-year print. For this update, I opted to display the series as-is (in my previous chart, I had multiplied it by three) to emphasize the significant inflation we’ve seen in U.S. transportation services. Simply put, shipment volumes have increased far less than costs (as illustrated by the red line). The primary factor here is the sharp rise in the “inferred rate,” which reflects the actual price that shippers are paying to move goods.
You’ll also notice on the blue series: whenever the data runs above the trendline, the gap turns green. When it dips below, the area is filled with red. Historically, when the red area became substantial, it coincided with recession—think early 1990s, late 2000s, and the coronavirus pandemic period. Conversely, a dominant green area marked periods of above-trend economic activity, most notably in the early/mid-2000s during the first housing bubble.
So, what should we make of the fact that the red area is now so pronounced? Is this recession territory? If so, why don’t GDP figures show a slowdown? At first, you could argue it’s the economy “working off” the excesses of the pandemic. But this red patch is already much larger than the green area seen when government stimulus was being poured out. Even if we start to see a recovery in transportation volumes, the size of the red area will continue to grow for some time yet.
As earnings season gets underway, I’ll be watching closely for commentary from companies regarding overall activity levels. I sense that few will sound especially optimistic about the landscape in their sector.
Consumer Weakness and Tariff Pressures: Inside $HELE’s Latest Results
Yesterday brought the first quarterly earnings release from $HELE (Helen of Troy), and the impact of tariffs was unmistakable. The company reported its 1QFY26 results[*], and the numbers were sobering. Excluding the effect of its recent acquisition, sales declined 17.3% year-over-year (see picture below).
Helen of Troy markets a range of consumer goods through brands you likely recognize. In their Home & Outdoor segment, they own OXO, Osprey, and Hydro Flask. In Beauty & Wellness, they own or have the rights to brands such as Revlon, Honeywell, Vicks, Braun, Olive & June, and others. If you’re curious, you can browse their products here.
What stands out from the report is the company’s ability to pinpoint sales lost directly to tariffs. Some clients deliberately held off on orders, hoping to ride out the current tariff environment and replenish inventory later—ideally at a lower tariff rate, but without sacrificing sales in the meantime.
Not all of the sales decline could be tied to specific customer actions. The company also classified portions as “business volume” losses and “retail inventory” adjustments. Regardless of the breakdown, this marks the fourth consecutive year of declining sales at $HELE. The first two years could be chalked up to post-pandemic normalization. Still, the last two years reinforce a trend I’ve highlighted here before: American consumers are simply not consuming at pre-pandemic levels.
Even if, over time, imported products are replaced by domestic alternatives, the initial effect is predictably negative. For $HELE, it’s highly unlikely their contract manufacturers will relocate production to the US—labor costs are simply too high for US-based factories to compete with Asian manufacturers, even if tariffs reach triple digits.
It remains to be seen how the shifting US tariff landscape will ultimately shape the broader economy.
[*] Their fiscal quarters end in February, May, August, and November.
The Surprising Upside for $CHH (Choice Hotels) in a Soft Market
I’ve just finished updating my analysis on $CHH (Choice Hotels). The company franchises a wide range of hotel brands—including Comfort (Inn, Suites), Quality Inn, Econo Lodge, Rodeway Inn, Sleep Inn, Country Inn & Suites, Ascend Hotel Collection, Clarion (including Clarion Pointe), WoodSpring Suites, and MainStay Suites. These brands span the spectrum from economy to upscale. Altogether, CHH has nearly 8,000 properties, representing over 650,000 rooms.
On the most recent conference call, management was asked about the softness in leisure and lower-end chain scales—an outcome that runs counter to expectations of “trade-down” in a weaker economy. The CEO’s response was telling: in uncertain economic periods, Choice’s established brands with strong name recognition tend to attract more independent hotels looking to join a larger system.
To illustrate just how much “economic times”—something no company can control—can affect business (sometimes positively), take a look at the first chart below. The blue line shows the actual royalty rate that CHH charges hotel owners to be part of its system. While Choice Hotels was already working to increase fees, it was able to raise them by about 25% (from ~4% to ~5%) almost instantly during the pandemic.
What stands out is that this increase in fees happened during a period when both average occupancy and ADR (Average Daily Rate) were lower than pre-pandemic levels (all figures in USD are adjusted for inflation). These two ratios combine to produce RevPAR (Revenue Per Available Room*), which remains below pre-pandemic years—nearly 20% lower in 2024 compared to 2017 (the peak year over the past two decades).
In other words, it wasn’t a boom in business that drove thousands of independent hotels to join the Choice system. Rather, Choice Hotels’ brands are strong enough that, even with significantly higher royalty fees than before the pandemic, the net return for hotel owners is still attractive.
[*] RevPAR = Occupancy x ADR
How Government Incentives Are Shaping $WM’s Capital Decisions
Today, I’m deep into my analysis of $WM (Waste Management). On any given day, the company’s teams collect waste and recyclables from 21 million homes and businesses, operate fleets along set routes, and move materials to processing or disposal facilities.
As I incorporate insights from the latest Investor Day, I’m reminded how influential—and sometimes questionable—regulations can be from an economic perspective. Back in 2016, nearly a decade ago, I added a note on the CapEx (capital expenditure) section of the WM’s model: “Since 2005, organic growth has been mostly negative—therefore, I will assume no Growth CapEx; this means that recent CapEx is an excellent estimate of necessary Maintenance CapEx.”
That forecast primarily held. For years, most of Waste Management’s CapEx focused on maintaining existing operations. If you look at the first chart below, you’ll see that CapEx was, until 2022, dominated by red (Maintenance CapEx), with only a modest amount in blue (Growth CapEx). But in 2022, there’s a clear uptick in growth investment. What changed?
The answer lies in the Inflation Reduction Act (IRA), which introduced a range of incentives for renewable energy. RNIs (Renewable Identification Numbers) and LCFS (Low Carbon Fuel Standard) credits now make producing RNG (Renewable Natural Gas) from landfills “economically” attractive.
Why the quotation marks around “economically”? If you dig into the actual costs of producing RNG from landfills, estimates range from $ 7.50 to $ 21.50 per MMBtu. For context, take a look at the second figure below, which shows the price of natural gas in the U.S. over the last 20 years. The green line marks the lowest cost to produce RNG—notice how it compares to market prices. In other words, without incentives, RNG production is not profitable.
The key risk for Waste Management is that if government incentives are withdrawn, these new assets could quickly become uneconomical, even if we treat the initial CapEx as a sunk cost. Time will tell whether this was a prudent long-term investment.
The Magnificent-7 and the Rest: What History Suggests for Investors
As some broad indexes (like the Nasdaq and S&P 500) approach all-time highs, I wanted to share a few thoughts on what’s driving these moves. We’re living through a highly unusual period, where a small group of companies have achieved extraordinary scale, profitability, and market valuations. I’m referring to the Magnificent-7 ($AAPL, $MSFT, $GOOGL, $AMZN, $NVDA, $META, and $TSLA), whose performance has had an outsized impact on the indexes mentioned above.
The first chart below highlights just how significant this has been: it shows the cumulative performance of the Magnificent-7 compared to the other 493 companies currently in the S&P 500(*). The gap is remarkable—since 2005, the Magnificent-7 have outperformed the rest by a factor of 13.7. It’s easy to forget how difficult it would have been to predict, twenty years ago, just how dominant these businesses would become. But what about more recently? Take a look at the second chart, which starts the clock in January 2020. Even over this shorter period, the Magnificent-7 outpaced the rest of the S&P 500 by 3.7 times—surpassing their outperformance during the entire prior 15 years (which was 3.6 times).
I’m not here to tell you whether these companies are overvalued—that’s outside my “Circle of Competence,” and I have no stake in whether their valuations are justified. However, I do worry about the potential consequences if their valuations were to come under pressure. The best parallel I can draw is with what happened after the Internet Bubble burst in 2000.
The third chart below compares two groups. In blue, the “Internet Darlings”: Amazon, eBay, Microsoft, Cisco Systems, Intel, Oracle, Apple, Qualcomm, Adobe, and Priceline (now Booking Holdings). These are all survivors—I’ve excluded any high-flyers that later imploded, which actually improves the group’s performance. The grey line represents 50 companies that could be considered “value names”—think Coca-Cola, Pepsi, Home Depot, Walmart, Pfizer, and so on. I’ve even included some companies in this group that were expensive at the time (as I discussed in a previous post here), so the comparison is not stacked in their favor.
The results are striking. Over the next two and a half years, the Internet Darlings declined by 70%. Meanwhile, holders of the more “mundane” companies saw their investments appreciate by 11%—not spectacular, but certainly preferable to being left with only 30% of your capital. Will history repeat itself? There’s no way to know for sure. Still, I find some comfort in knowing that my investments today are much more closely aligned with the kinds of companies represented by the gray line in the early 2000s.
(*) All calculations are performed using Python—let me know if you’d like a copy of the code. All figures include dividends.
$FLS and the New Oil Order: Why Middle East Turmoil Might Have a Smaller Impact Than Before
As part of my ongoing analysis of $FLS (Flowserve Corporation), a global leader in the design, manufacture, and service of flow control systems—including pumps, valves, seals, automation, and related services for the oil and gas, chemical, power, and water industries—it’s essential to understand the broader energy market context in which the company operates.
The first chart compares the price of oil in both nominal terms (blue line) and inflation-adjusted terms (red line). A striking feature of the chart is the dramatic spike in oil prices during mid-2008, when the inflation-adjusted price of oil exceeded $220 per barrel. In contrast, current oil prices are significantly lower, both in nominal and real terms, highlighting how much less expensive oil is today compared to that historic peak.
Turning to the second chart, we see the evolution of land and offshore rig counts in North America. Historically, geopolitical tensions in the Middle East have had a pronounced impact on the US economy, mainly due to the country’s reliance on imported oil. However, the shale revolution has fundamentally altered this dynamic. The 2000s saw a substantial increase in the number of rigs operating in the US, coinciding with significant advancements in hydraulic fracturing (fracking) technology. This surge in domestic production has reduced the US economy’s vulnerability to external oil shocks and has been a key driver of energy independence.
Interestingly, the most recent spike in oil prices did not result in a proportional increase in rig count, as seen in previous cycles. This could suggest several things:
- Higher Break-Even Prices: Many fracking wells now require higher oil prices to be economically viable, as the most accessible reserves were tapped during the initial fracking boom.
- Productive Well Inventory: A substantial inventory of productive wells may still exist, reducing the immediate need for new drilling activity.
- Industry Caution: Operators may be exercising greater capital discipline, focusing on maximizing returns from existing assets rather than aggressively expanding capacity.
For Flowserve, these dynamics are highly relevant. The company’s growth prospects are impacted by capital spending cycles in the oil and gas sector, which are influenced by both oil prices and geopolitical stability. While current Middle East tensions have injected fresh volatility into the market, the structural resilience provided by U.S. shale production and a more cautious approach to new drilling may temper the impact on equipment demand in the near term.
Consumer Confidence and RV Sales: Insights from Thor Industries ($THO)
I just finished updating my analysis of $THO (Thor Industries), the largest manufacturer of RVs (Recreational Vehicles) in the US. The U.S. RV market is dominated by a few major players, often called the “Big 3”: Thor, Forest River (a Berkshire Hathaway company), and Winnebago, an iconic name in the RV world.
Thor’s management shared in their latest earnings release that retail demand has generally aligned with expectations, despite some challenges in the first half of FY25 (August 2024 to January 2025). While there was improvement in the second half, it was less than initially anticipated, prompting a revision of prior guidance. The slight increase in consumer confidence in May 2025 is a positive sign for retail demand through the end of FY25 (July 2025). However, aggressive tariff policies could weigh on demand in the latter half of the calendar year if their impact on Average Sales Prices (ASPs) is not effectively managed industry-wide. They also expect the first quarter of fiscal 2026 (June to August 2025) to be challenging.
The main reason for this cautious outlook? Tariff uncertainties. Now, consider that Thor has already been navigating significant fluctuations in demand—see the picture below. The blue line illustrates quarterly deliveries in their “towables” segment, the company’s largest. Imagine running a production line that must handle between 20,000 and 70,000 units every three months, without knowing in advance when demand will be strong or weak.
Thor sells highly discretionary products—you don’t really need an RV! Because of this, their sales levels serve as a strong indicator of real consumer confidence. I’ll be watching their numbers closely as we move through what remains an unnecessarily volatile economic and operational environment.
$PCAR Update: Where Are We in the Heavy Truck Market Cycle?
I’ve just finished updating my analysis on $PCAR (Paccar), a leading heavy truck manufacturer in the US. Much like my earlier post on Volvo trucks (here), $PCAR’s performance tends to mirror broader economic cycles.
Take a look at the chart below:
The red line tracks monthly retail sales of heavy-weight trucks (in thousands of units, left axis). Because monthly figures can be quite volatile, I also include a rolling 12-month (LTM) sales figure—shown as the blue dashed line on the right axis (annualized, in thousands)—to help clarify the underlying cycle.
The green line represents PACCAR’s own industry data (also right axis). This closely follows the LTM sales trend, though there’s some divergence since PACCAR uses a slightly narrower market definition. Still, the correlation between the LTM data and PACCAR’s data is an impressive 97%, underscoring how similarly they reflect industry cycles.
Where does that leave us in the current cycle? The purple dashed line shows the units per capita index (left axis), which adjusts for population growth and offers a normalized view of demand over time. I’ve also marked the median value of this index on the chart, providing a useful benchmark to gauge whether current sales are running above or below historical norms.
At present, we’re in the second year of a downturn that began in late 2023. The pandemic era saw trucking companies enjoy strong profits, which led to a mini-boom in truck sales. Now, as we wait for 2Q 2025 earnings (due out in July), it will be interesting to see how the latest round of tariffs impacts the industry. Stay tuned.
How Policy Shapes Tractor Sales: Insights from $DE (Deere)
As Congress debates a new bill that includes stimulus measures for farmers, I thought it would be timely to share a chart from my recent analysis of $DE (Deere). The chart breaks down tractor sales into four distinct categories. To better understand industry dynamics, I’ve converted all units into a “Compact” equivalent index (referring to 40-100 HP machines).
What stands out in the chart is the significant impact government policies can have on this industry—sometimes even distorting sector activity. Notice the sharp rise in tractor sales in the early 2000s, coinciding with the introduction of the “ethanol mandate.” Back in the late 1990s, less than 5% of the corn crop was used for ethanol production (a very inefficient process, when compared with ethanol produced with sugarcane). By 2015, that figure had climbed to nearly 40%. This shift contributed to a substantial bubble in agricultural commodities during that period.
More recently, you’ll see another notable spike in sales, which is only now beginning to subside. Among the broad pandemic-era stimulus efforts was a direct cash payment to farmers. Ironically, agricultural activity—which doesn’t require close contact—was not significantly disrupted. Yet, the result was another artificial boost in tractor sales, disproportionately benefiting companies like $DE.
Let’s see what new distortions might emerge as the agricultural lobby weighs in on the current bill.
Housing Bubble 2.0: How the Lock-in Effect Is Shaping an Unprecedented Market Divergence
The housing market in the United States presents a fascinating case study of how interest rate policy can create significant distortions in asset prices. The chart below reveals a telling story about our current housing market.
The US Census Bureau/HUD Median Sales Price lines for houses (represented by the red lines - the darker ones for new homes; the light pink for all houses) have recently begun reverting toward the green DPI/capita reference line. This indicates that new home prices are returning to historically normal levels relative to disposable personal income. However, simultaneously, the Case-Shiller and FHFA Purchase-Only indices (blue and orange lines, respectively) remain significantly elevated compared to historical norms.
What explains this divergence? The answer lies in the dramatic difference in transaction volume and pricing dynamics between new homes and existing homes. The primary driver of this market distortion is what economists call the “lock-in effect.” As of early 2025, 82.8% of homeowners with mortgages still have an interest rate below 6%. Many of these homeowners refinanced during 2020-2021 when rates hit historic lows, securing fixed-rate mortgages often below 3%.
With current mortgage rates hovering near 7%, these homeowners face a powerful disincentive to sell. Consider the math: trading a 2.75% mortgage for a new one at 7.00% often means paying significantly more each month for a comparable or even smaller living space. Even if a homeowner could pocket some equity in such a transaction (if downsizing), the prospect of a substantially higher monthly payment creates a psychological and financial barrier few are willing to cross.
This lock-in effect has had profound implications: Existing home sales in 2024 dropped to levels last seen in 1995. New home prices are touching the DPI/capita reference line because builders must price their products at levels the market can bear-they have no choice but to adjust to current affordability metrics. Meanwhile, the relatively few existing homes that do come to market in mature neighborhoods command premium prices due to their scarcity.
Despite the powerful lock-in effect, there are forces that will eventually bring existing home prices back toward the DPI/capita line. These are what I call the “four Ds”:
- Death: Estate settlements often necessitate home sales regardless of interest rate considerations
- Divorce: Marital dissolution frequently requires liquidating shared assets
- Displacement: Job relocations or other major life changes that force moves
- Debt: Financial hardship that makes maintaining mortgage payments untenable
We’re already seeing early evidence that these forces are gradually eroding the lock-in effect. As of early 2025, the percentage of homeowners with mortgage rates at or above 6% has increased to 17.2%, up nearly five percentage points from 12.3% in the third quarter of 2023. The price adjustment process is taking its time to play out. I will later post another chart showing the difference in price deflation now vs. the bubble in the mid-2000s.
Railcar Manufacturing: Why 2025 Forecasts Are Pointing Down
It always amazes me how volatile railcar manufacturing in the US can be. The chart below tracks railcar deliveries (blue line), with additional lines representing moving averages and cumulative deliveries over different periods. This data is from my ongoing analysis of $TRN (Trinity Industries), which manufactures, leases, and manages freight and tank railcars for sectors like agriculture, energy, construction, and consumer products.
If you look at the chart, you’ll notice that the first forecast year (2025) shows a substantial decline compared to 2024. This drop is based on the midpoint of TRN management’s guidance for 28,000 to 33,000 deliveries in 2025-a figure released on May 1, 2025, after recent tariff changes had already taken effect. For context, just a few weeks earlier-in February-Greenbrier (TRN’s main competitor) projected 38,000 industry-wide deliveries for the year.
So, why the nearly 20% drop in expected units? TRN’s management addressed this on their last conference call:
“Market uncertainty in the first quarter continued to slow conversion of inquiries to orders… Inquiry levels at the beginning of 2025 were the highest they’ve been in several years. But customers are taking longer to make capital decisions… We delivered 3,060 new railcars in the quarter and received orders for 695 railcars, evidence of the delayed investment decisions I have previously acknowledged and the lumpiness of orders quarter-to-quarter.”
This is a familiar pattern: when policymakers make abrupt changes to the rules, companies often pause before making new investments. Most CEOs and decision makers prefer to wait for clarity before committing to capital expenditures. Consumers can behave similarly. I expect to post more evidence here in the future about the noise created by the recent economic shifts we’re seeing in the US. Some of these changes may prove beneficial over time, but the probability of a short-term slowdown has clearly increased.
How Tariffs and U.S. Housing Trends Shape $MHK’s Outlook
I’ve just finished updating my analysis on $MHK (Mohawk Industries), the world’s largest flooring manufacturer. $MHK produces and distributes a broad range of flooring products-including ceramic tile, carpet, laminate, luxury vinyl tile, wood, and stone-serving both residential and commercial markets globally.
Let’s start with the first chart below, which tracks local production, imports, and exports of ceramic tiles in the U.S. For over two decades, imports have accounted for more than 70% of total U.S. ceramic tile consumption. If tariffs remain at current levels, the purple line (imports as a percentage of consumption) should show a pronounced decline, as higher prices would likely reduce demand and volume.
Next, take a look at the two scatter-plot charts. These illustrate the relationship between U.S. tile consumption and two key housing metrics: New Home Starts and Existing Home Sales. As you might expect, tile usage is more closely tied to new construction-hence the higher correlation with New Home Starts (about 83%) compared to Existing Home Sales (around 60%). When new homes are built, more tile is installed; existing home sales, while relevant, have a more modest effect.
Correlations like these are precisely why I analyze not just the operations of companies within RIM’s Circle of Competence (CofC), but also the broader industries they operate in-housing being a prime example. For those interested in a deeper dive, I’ve previously shared posts on New Home Starts and on Existing Home Sales, which provide additional context.
Watching $WAB: Rail Metrics and the Ripple Effects of Tariff Policy
I’m finishing up my update on $WAB (WabTec). The company operates in two main segments: Freight-which provides locomotives, components, and digital solutions for freight railroads-and Transit, which supplies components and services for passenger transit systems. Its core products include diesel-electric locomotives, braking systems, doors, electronics, and aftermarket rail parts. $WAB serves railroads and transit agencies worldwide.
The chart below highlights three key drivers for their long-term sales:
Blue line: Public transit rides per person (unlinked trips per person, monthly). Notice that this metric still hasn’t returned to pre-pandemic levels.
Red line: Freight carloads per 1,000 people (including intermodal). The ongoing decline in coal usage has had a pronounced impact on this trend.
Green line: Intermodal-only ton-miles per 1,000 people. Much of the prior growth here reflected a shift in the type of cars used-intermodal is heavily tied to international trade.
Of these, I’ll be watching the green line most closely. It should provide a clear read on how the evolving tariff landscape is affecting rail volumes. Updating these charts is always a useful exercise, even if the monthly data arrives at a glacial pace compared to the rapid moves we see in the stock market. It’s a good reminder that the real economy moves much more slowly than prices or headlines suggest.
Gauging the Downturn: Volvo, $SAIA, $ODFL, $PCAR, and the State of US Trucking
I’ve just finished updating my analysis for Volvo (the truck manufacturer-not the car company). While Volvo isn’t American (most RIM companies are US-based, with only two exceptions), I follow it closely because it owns Mack, one of the leading truck brands in the US.
Take a look at the chart below, which shows truck deliveries across Volvo’s major regions. The first thing to note is the industry’s cyclical nature-transportation companies tend to move as a herd when it comes to ordering more (or fewer) trucks. You’ll see that we’ve been in a downturn recently (just before the vertical dotted white line). But here’s what Volvo’s CEO said during the latest conference call, just a few days ago:
“…the increased hesitation among customers in North America to place orders given uncertainty in general. We are, therefore, as we speak, adjusting production levels for group trucks North America to minimize the under-absorption in production going forward.”
In other words, the recent shake-up in economic and market conditions has made Volvo’s customers more cautious. It doesn’t help that today, shares of $SAIA (a major LTL* operator in the US and competitor to $ODFL, which is part of RIM’s CofC**) dropped more than 30% after missing earnings estimates by a wide margin.
I’ll be watching Volvo’s truck deliveries closely-they’ll provide a useful signal for how deep this current downturn might get. This will also help me calibrate my ongoing analysis of $PCAR, another major player in the US and European truck manufacturing market.
(*) LTL = Less Than Truckload (**) CofC = Circle of Competence
Tracking $EXPD Through the Global Tariff Storm
I’m working on Expeditors International of Washington, Inc. ($EXPD) today. $EXPD is a Fortune 500, non-asset-based logistics company providing highly customized global supply chain solutions—including air and ocean freight forwarding, customs brokerage, warehousing, and distribution—through a network of more than 340 offices in over 100 countries. The company employs around 18,000 people.
The “non-asset-based” model in logistics means that $EXPD does not own the trucks, ships, planes, or warehouses used to move and store goods. Instead, it acts as an intermediary, leveraging a broad network of third-party carriers and service partners to coordinate and manage logistics operations for clients. This approach offers flexibility and scalability, allowing $EXPD to tailor solutions, adapt quickly to changing demands, and often reduce costs by avoiding the burden of maintaining its own fleet or facilities. In the first picture below, you’ll see the top 25 players in the global logistics sector, with $EXPD highlighted in red.
One aspect of $EXPD’s business that stands out—see the next two charts—is how little its “Net Revenue” (revenue excluding pass-through transportation costs) and employee compensation have changed in real terms over the past 20 years. Adjusted for inflation, both metrics are essentially at the same level as they were two decades ago. Intuitively, I would have expected costs per employee to decline, given the advances in computing power and process automation during this period.
The lesson: trust your intuition less, and make sure you have enough information to understand what’s actually driving a company’s revenue and costs. I’ll be watching $EXPD and its competitors closely—they’re at the center of the ongoing global tariff disputes, and their financials should eventually reflect the consequences of any shifts in the rules of the game
Why Existing Home Sales Are Stuck at 1995 Levels
I’ve just updated my charts with the latest US housing price data. I’m sharing one here that highlights some key trends—see the first chart below. As usual, there are plenty of lines, but let’s focus on a few important ones. The solid black line represents the National Case-Shiller index. You’ll also notice other indices in color, tracking New York, Las Vegas, Miami, and Los Angeles.
Pay particular attention to the purple line, which shows disposable personal income (DPI) per capita. Since home purchases ultimately depend on what’s left after covering essentials, this is a crucial metric. The gap between the Case-Shiller index and DPI per capita is shown by the dashed black line. This makes the mid-2000s housing bubble and the pandemic peak stand out clearly. Currently, the delta between house prices and DPI/capita sits at 27%—a significant spread.
Now, take a look at the second chart. On the left, you’ll see two key figures: new home starts (in red) and existing home sales (in green). What stands out is how low existing home sales are. In 2024, the number of homes sold in the US dropped to levels last seen in 1995.
What’s driving this? It’s a combination of (i) high home prices—in most, though not all, regions—and (ii) mortgage rates that have returned to more typical levels. Together, these factors make it tough for first-time buyers, such as new couples, to enter the market. At the same time, many current homeowners are “locked in” to their low-rate mortgages. For example, trading a 2.75% mortgage for a new one at 7.00% often means paying more each month for a smaller place. Even if you pocket the difference in home values, the prospect of a higher payment for less space is a tough psychological hurdle. As a result, supply remains tight and market activity is subdued.
If tariff issues persist and inflation picks up—pushing mortgage rates even higher—we could see this slow pace in existing home sales drag on. That would be a headwind for the broader economy.
