Clearing the Mist

Clearing the Mist is real-time commentary by Delphi Advisors on developments, clues, patterns, and events we believe could affect the U.S. economy, and particularly the Forest Products sector...

...or sometimes it's just a way to let off some steam.

Monday, July 23, 2018

Behind the Eight Ball: Housing and Population Growth

News articles about U.S. housing frequently posit a shortfall in the supply of new housing relative to demand. E.g., the lead paragraph of a March 18 Wall Street Journal (WSJ) article reads, “America is facing a new housing crisis. A decade after an epic construction binge, fewer homes are being built per household than at almost any time in U.S. history.” But is that true; is the population-adjusted rate of new home construction really near an historical low? After all, if new supply is keeping pace with population growth, is there really a crisis?

To begin exploring this topic, we derived a metric -- called “starts per person added” (SPPA) -- in which monthly not-seasonally adjusted single- and multi-family starts are divided by the monthly change in the U.S. population. As Figures 1 and 2 below indicate, the cyclicality of population changes and construction activity within and among calendar years creates considerable month-over-month and year-over-year variability in both the single-family and multi-family metrics (Figure 3). Comparing long-term averages (Table 1) against recent results – and especially 12-month moving averages (Figure 4), which reduce seasonality effects and make broader trends more apparent -- reveals interesting patterns, however.

Figure 1. U.S. population and population growth, by month, since 1960. Source: U.S. Dept. of Commerce, Bureau of Economic Analysis (BEA). Click image for larger view.

Figure 2. Not-seasonally adjusted monthly single- and multi-family housing starts since 1960. Source: U.S. Census Bureau (CB). Click image for larger view.

Figure 3. Monthly single- and multi-family housing starts per person added to the population, since 1960. Sources: BEA, CB and Delphi Advisors. Click image for larger view.

For the entire period of January 1959 to May 2018 the average single-family SPPA = 0.405 (Table 1); the pre-2000 average = 0.420 (i.e., 0.420 single-family start per person added to the population). Put another way, prior to 2000, one single-family home was started for every 2.38 people added to the population. We selected 2000:01 as a breakpoint because it is a convenient middle ground among opinions regarding when the housing “bubble” began. During 2000:01-2007:07, the period encompassing the housing “bubble” and early part of the subsequent crash, the single-family SPPA averaged 0.520 -- reflecting the overwhelming market preference for single-family units.

Table 1. Average monthly housing starts per person added to the population during selected time intervals, by type of housing. Sources: BEA, CB and Delphi Advisors. Click image for larger view. 

The 1959:01-2018:05 average multi-family SPPA = 0.174; the pre-2000 average = 0.198. I.e., pre-2000, one multi-family unit was started for every 5.05 people added to the population. During 2000:01-2007:07 the multi-family SPPA averaged 0.123, again confirming the preference for single-family units.

As mentioned above, we calculated 12-month moving averages of the monthly data to reduce seasonality effects and to make broader trends easier to see (Figure 4). In the following discussion, we refer to the 12-month moving averages of single-family SPPA as “SPPA1F” and “SPPAMF” for multi-family.

Figure 4. Twelve-month moving average, or “MA(12),” of monthly single- (SPPA1F) and multi-family (SPPAMF) housing starts per person added to the population, since 1960. Sources: BEA, CB and Delphi Advisors. Click image for larger view.

Between 2000:01 and 2007:07, SPPA1F exceeded its pre-2000 average during 84 of those 91 months, peaked at 0.628 in November 2005, and averaged 0.520. Although SPPA1F has been consistently rising from its October 2009 minimum of 0.162 (2009:10-present average = 0.277), it has yet to regain the pre-2000 average. When adjusted for population growth, the single-family housing bust (which helped spawn the Great Recession) and its aftermath was deeper and, to date, has been over 50% longer than the preceding bubble.

The multi-family segment exhibits a different pattern. SPPAMF rebounded off its October 2009 low of 0.021 (2009:10-present average = 0.127), but has been gradually subsiding on trend since February 2017’s peak of 0.174. Like its single-family counterpart, SPPAMF has yet to regain its pre-2000 average.

These two metrics would suggest, then, that housing supply has indeed failed to keep pace with population growth. Moreover, unless multi-family starts again trend higher, the gap could continue widening. However, our findings do not confirm the WSJ’s contention that starts are presently near an all-time low.

The above paragraph led us to wonder how many more/fewer units have been built over time than might be justified by population growth. Attempting to answer that question requires a review of Figure 1. Notice that prior to 1990, monthly population growth was bouncing around in a range between roughly 150,000 and 225,000. Beginning in 2Q1990, however, population growth rocketed higher such that 225,000 became a “floor” rather than a “ceiling;” moreover, that higher trend growth rate was sustained through the subsequent decade. The Census Bureau observes “the population growth of 32.7 million people between 1990 and 2000 represents the largest census-to-census increase in American history,” but cautions “this increase may be caused by changes in census coverage, as well as births, deaths, and net immigration.” I.e., the jump may have been as much an artifact of statistical methodology as genuine, organic growth.

The reason for making the above observation is that SPPA1F and SPPAMF both tumbled below their respective long-term averages during the 1990s at least in part because estimates of population growth “reset” to a much higher pace. To account for the population-growth “step function” in 1990 when estimating what housing starts might be justified by population growth, we regrouped the SPPA data into two new intervals: pre-1990 and 1990:01-present. The bottom two rows of Table 1 present the relevant SPPA averages. If, for example, between 1959:01 and 1989:12 SPPA1F fell below 0.445 during a particular month, fewer single-family houses were built than justified by population growth. The “shortfall” in starts can be estimated by multiplying that month’s deviation from 0.445 by the corresponding population growth for that month. For comparisons starting in 1990, the average single-family SPPA was reset to 0.359 as a reflection of the population-growth step function. December 2017 was chosen as the ending point for estimating the SPPA averages since that date is now outside the range of data subject to monthly Census Bureau revisions.

Monthly deviations were accumulated over time; both the monthly and cumulative deviations for single-family starts are presented in Figure 5. It is worth noting that, although monthly deviations have been both positive and negative over time, the cumulative deviation remained negative (bottoming at -2.142 million units in October 1971) until August 2003. Consistent positive monthly deviations after February 1998 ultimately drove the cumulative deviation to a peak 2.348 million units in December 2007. Negative monthly deviations between 2008:01 and 2017:07 subsequently erased the “excesses” of the housing bubble. Although the cumulative deviation line has been turning upward since August 2017, it remains negative; as of May 2018, the cumulative deviation stood at -384,000 units. 

Figure 5. Monthly and cumulative deviations of single-family housing starts relative to interval averages. Sources: BEA, CB and Delphi Advisors. Click image for larger view.

Monthly deviations in the multi-family component have also oscillated both positive and negative over time (Figure 6). Interestingly, the cumulative deviation made it into positive territory during one 23-month period (1974:01 to 1975:10) -- the result of a flood of multi-family construction after 1967:01, perhaps related to Great Society programs. Since then, however, the cumulative deviation has remained negative; the near-term low was -1.042 million units in 4Q2012. The rebound in multi-family starts since 2009:10 has helped to trim the negative cumulative deviation, but as of 2018:05 it stood at -447,000 units.

Figure 6. Monthly and cumulative deviations of multi-family housing starts relative to interval averages. Sources: BEA, CB and Delphi Advisors. Click image for larger view.

To summarize, our analysis -- whether in terms of starts per person added to the population, or cumulative deviations in the number of units started relative to the number that might be justified by population growth -- shows that residential construction has not kept up with population changes. Perhaps this is a piece of the puzzle explaining why purchase- and rental-price affordability continue to be issues in many parts of the country. Particularly for the multi-family market, our findings also highlight potential upside for mass-timber market applications.

This analysis also buttresses our perception that the Millennial demographic wave has yet to truly enter the “shelter” market as its predecessors did at comparable stages of life. High shelter costs and other financialhurdles being experienced by the Millennial cohort likely mean more people per housing unit (both single- and multi-family) going forward. Finally, it implies that any Millennial demographic-led housing-start peak may not only be delayed but also more muted than otherwise expected as a function of the cohort’s size.

Saturday, May 13, 2017

On Tariffs and Taxes – The Tariff on Canadian Softwood Lumber

The April 25, 2017 headline on The Wall Street Journal’s commentary (behind a paywall) said it all:
Trump’s New Housing Tax
A tariff on foreign lumber will raise the cost of U.S. homes.
Not might, may, or could raise the cost of U.S. homes. It “will.” Drop the mic.

The lead sentences of the WSJ commentary are unequivocal, and in our opinion, a bit snarky:

“Commerce Secretary Wilbur Ross announced Monday that the Trump Administration will raise the cost of new single family homes in the U.S. as part of its promise to ‘make America great again.
“Mr. Ross didn’t put it quite that way. He said the Administration will impose a 20% tariff on softwood lumber imports from Canada, which total about $5 billion a year. But that’s a lot of lumber and the tariff will add an additional $1 billion in new costs for U.S. construction. Most of those costs will be added to the price of new American housing, not counting the higher costs that will come as U.S. producers raise their prices to match the competition and pad their bottom lines.”
But then reality struck. The tariff announcement was made on April 24, and one might have expected the lumber futures market to shoot still higher in response to such news. This, per Dow Jones Newswire reporting on the tariff:
“Builders say lumber costs are already at the highest in a decade, even before the prospect of increased tariffs.… The prospect of U.S. duties on Canadian lumber imports has driven up prices this year, with lumber futures up more than 25% in the early months of 2017 and peaking at their highest point in over 12 years.”
Zerohedge’s comments on April 25 succinctly stated the case, and apprehensions, of many:
“And with lumber prices already at 13-year highs, one can only imagine what this will do to the price of houses in America.”
But somewhere amongst the rhetoric and theory, markets intervened (see last six months of lumber futures prices in the chart below). By 4/28, Friday of the week of the announcement, futures contract prices were down 1-2% compared to the average price for the prior week (4/17-4/21) rather than increasing as had been expected. By 5/12 the May 2017 contract was down by 4%; the decline by 5/12 for the more distant contracts ranged from 7 to 9% compared to the average price for the week 4/17-4/21. This response definitely ran counter to the expectation of many.

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A close-up (see chart below) of the last month focuses on market responses in the aftermath of the announcement. Further, the convergence among the various contract terms is striking. In late January 2017 the gap between the May 2017 and March 2018 averaged over $46; the average between May 2017 and March 2018 contracts over the last two weeks (10 days) is just under $2. While it could be argued tariff impacts may not be seen in lumber prices for the next six months (e.g., the Nov 17 contract), the convergence of more distant contracts with current pricing belies, at least for the moment, an expected 20% explosion in lumber pricing as a result of tariffs.

Click image for larger view

The question is why would prices drop in the aftermath of the announcement rather than increase? We can think of four possible reasons; undoubtedly there are others but, to our way of thinking, these seem at the top of the list. A caution: the first and fourth reasons will take a bit of time to unpack.

1. There will be a downturn in U.S. housing starts due to higher lumber prices as a result of tariffs which futures traders are betting will take prices lower as demand retreats.

For this to be the case higher lumber prices would have to hike new home prices sufficiently to discourage marginal buyers. Some media reporting could certainly lead to that conclusion. For example, USA Today states, citing NAHB, “…[a price] increase in newly built homes is likely. The NAHB, which opposes the tariffs, says a 19.9% tariff would result in a 6.4% increase in prices paid by U.S. consumers. The price of a single-family home would increase by $1,236, it says. ‘This is going to increase the cost of construction of residential houses," said Jerry Howard, CEO of NAHB. "Producers will try to pass on the cost to consumers if they can.’“

Before proceeding, a bit more context is in order. The average 2016 home price was $372,500. So, combining this fact from the U.S. Census Bureau with USA Today reporting on the impact of the tariff, either the average home price is $1,236 divided by 6.4%, or $19,313 (which is only 5% of the U.S. Census Bureau’s reported average home price), or the expected increase is $372,500 multiplied by 6.4%, or $23,840. If the latter interpretation is correct then one can understand why housing starts might fall, driving demand lower and taking lumber prices with it. Regardless, it seems something was lost in translation from NAHB’s assessment to how the media reported it.

Dow Jones Newswires (DJN) reporting provides additional details that help to clear some of the fog. NAHB reports that based on 2016 analysis (“last year”) a builder spends an average of $15,413 for softwood lumber in a single-family home which represents about 7% of the total construction cost (i.e., not price) of a home. Those values translate into $15,413 divided by 7% for $220,186 construction cost of a new home. This interpretation of the NAHB data is consistent with an analysis by Moody’s Investment Service regarding the impact of the tariff on home builder margins
However, we’re not quite square yet. The $1,236 increase on the $15,413 of softwood lumber cost in a new home is an 8%, not 6.4%, increase. Why the difference?

The aforementioned DJN article notes that year-to-date (YTD) 2017 lumber futures prices are already up 25%; other reporting places YTD gains at 22% to 29%. The DJN article speculates the prospect of U.S. duties on Canadian lumber imports is responsible for lifting lumber prices YTD, and others (here, here, here, and here) concur with that assessment. This leads to estimates on the increased lumber costs already seen this year in a new home ranging from “an estimated $3,000” to “nearly $3,600.” Applying a YTD 22% softwood lumber increase to the $15,413 softwood lumber cost in new home construction yields an increase of $3,391 per home; applying a 25% increase yields an increase of $3,853 per home, and applying a 29% increase yields an increase of $4,470 per home. Averaging these three estimates provides a “middle-ground” value for the average softwood cost increase thus far this year of $19,318 ($15,413 + $3,905, a 25.3% increase) in a home being built using elevated YTD2017 lumber prices. Taking NAHB’s estimated increase of $1,236 per home due to Canadian tariffs divided by this new estimate of $19,318 yields an estimated increase of 6.4%.

Now that we understand what the reported “data” relate to we can use them more intelligently to infer market responses. But before doing that, let’s summarize

  • The NAHB estimate of an additional $1,236 per home due to the tariffs is on top of a higher estimate of softwood lumber cost in a home incorporating a 22-29% YTD lumber increase.
  • Further, most analysts believe a key driver for the YTD increase was in anticipation of tariffs being enacted.
  • Thus, the $1,236 per home increase on the revised base of $19,318 in softwood lumber cost in a home rationalizes with NAHB’s estimated increase of 6.4%.
Our first observation is that the cited 6.4% increase applies to the cost of softwood lumber in the home, not the home price. That increase, using NAHB’s assumptions, is $1,236 per home -- or 0.3% based on the 2016’s average price of a new home; note this percentage would be lower still if compared to YTD2017 home prices since home prices have continued to increase. Our conclusion is that it seems unlikely a 0.3% increase in new home prices would cause a dramatic decline in housing starts.

Further, if YTD lumber prices already “priced in” the anticipated tariff, as many suspect, then the NAHB estimated increase would constitute double-counting by adding an additional impact on top of the increase in home prices due to higher lumber costs in 2017. Moody’s Investor Service and Fitch rating service both make their calculations of the tariffs impacts at 20% on top of the unadjusted softwood lumber cost in a home ($15,413), avoiding double-counting the effect (this is always a plus when doing analysis of any kind). Thus, it is arguable in light of the current run-up in lumber prices YTD the impact of the tariff on the number of homes sold may be negligible as that impact is already being priced into the market and houses continue to be built.

The conclusion from all of this is that it seems unlikely futures prices fell after the tariff announcement because traders are expecting a significant reduction in demand due to higher lumber prices.

2. Lumber markets had priced in a higher tariff than actually was announced and so the subsequent lumber price reduction reflects that new information.

Earlier we had quoted from a DJN piece:

“Builders say lumber costs are already at the highest in a decade, even before the prospect of increased tariffs.… The prospect of U.S. duties on Canadian lumber imports has driven up prices this year, with lumber futures up more than 25% in the early months of 2017 and peaking at their highest point in over 12 years.”
So which is it? Are lumber prices up and tariffs will add to them? Or are lumber prices up in anticipation of tariffs being enacted? You can’t have it both ways.

As already noted, the preponderance of opinion comes down on the side the lumber market was pricing in expectations of tariffs being enacted. The question was how high would they be? A Bloomberg article reports the countervailing duties (the more accurate term for the tariff being imposed) came in below some analysts’ expectations. For example, the article quotes Kevin Mason, managing director of ERA Forest Products Research: “I think a lot of people were bracing for a higher duty.” How much higher? The same Bloomberg piece cites CIBC’s Hamir Patel’s forecast that the combined countervailing (already announced) and anti-dumping (yet to be announced; decision expected in June) duties could reach 45 to 55%.

We’ve already cited several YTD increases ranging from 22 to 29% for a simple average of 25.3%. Between 4/24 and 5/12 prices have dropped by 4 to 9%, depending on the contract, for an average of a 7% drop across the May 2017 to Mar 2018 contracts. Subtracting the 7% declines from the average 25.3% reported increase yields 18.3%, roughly in line with the announced 20% average tariffs. Perhaps it’s coincidental, but this result might lend credence that even higher tariffs were already priced into the market, and once the tariff was announced the market recalibrated to the news by lumber prices declining.

3. Markets expect Canadian suppliers to ramp up production prior to actual enactment of the tariffs, expanding lumber supply and so dropping market prices.

This prospect seems unlikely for several reasons. First, except for Canfor, J.D. Irving, Resolute FP Canada, Tolko, and West Fraser, tariffs will be retroactive for 90 days from date the preliminary determination is published in the Federal Register (which was April 28, 2017). Thus, as of the date of this writing the preliminary duties are already in force for the five firms listed, with 90-day retroactive duties from April 28, 2017 being enforced for all others. Consequently, there is no “production window” incentive. Second, even if there had been such a window, it is unlikely Canadian production could have increased significantly given Canadian sawmills are already operating at high capacity utilization rates.

One additional comment: If reports by the Madison Lumber Reporter are correct, prices may increase -- not because of tariffs, but rather because of logistics breakdowns at Canadian/U.S. border crossings for Canadian lumber being imported into the U.S. (emphasis in the original):

“Operators are suffering total bewilderment as U.S. Customs and Border officials cannot provide clarity on vital details of application of the new softwood lumber duties to entry waybills and pro-forma invoices…"
“Wood destined for the U.S. from Canadian sawmills two weeks ago is literally trapped at the border right now because operators and agents do not know how to fill out the Customs forms.”
If this situation is true, widespread, and persistent the U.S. lumber market will be affected. Canadian imports represented 30% of U.S. softwood lumber consumption in 2016 per Western Wood Products Association (WWPA). However, we expect this is a momentary hiccup and there won’t be any extended lumber market impacts as a result of this supply chain breakdown at the border.

4. With a tariff on Canadian softwood lumber imports in place the competitiveness of Canadian softwood lumber imports
 is reduced, prompting U.S. sawmills to ramp up production, thereby expanding supply and taking prices lower.

An implicit assumption of much analysis is the U.S. market needs Canadian lumber production to meet U.S. consumption. Under this assumption, there is a lack of U.S. softwood lumber capacity and Canadian imports offer the lowest-cost means to fill this gap. This logic is reinforced by relatively high capacity utilization rates (per WWPA 2016’s rate was 86%, only 4% less than WWPA’s 90% capacity utilization estimate for Canadian manufacturers).

The corollary to this reasoning is the lead time to bring on more U.S. capacity is a minimum of two years. Paul Jannke of Forest Economic Advisors succinctly states this view:

“‘We won’t be able to meet new demand with the existing capacity base.’…
“According to Jannke, it takes two years from the time when a company decides to invest in a new mill until the time that mill is operational. ‘That means it would be 2019 before we would get any significant new capacity coming on line,’ he says. A few new mills have been announced, but their combined capacity won’t be enough to meet demand if we were to see 1.5 million to 1.7 million starts—and Metrostudy is predicting 1.52 million starts just next year.”
First, as an aside, we don’t see housing starts approaching 1.52 million starts next year so we discount the prospect of a significant near-term ramp-up in demand.

Unquestionably U.S. softwood sawmilling capacity declined sharply as a result of the Great Recession and has yet to recover to pre-Great Recession levels. But, beyond that, we believe there is another dynamic in play that is generally overlooked by most analysts. The U.S. Census Bureau, in conjunction with the Department of Defense, conducts periodic capacity utilization surveys of various industry sectors -- sawmills being one of them. The survey reports two estimates of capacity utilization: actual and emergency production levels.

What the Census Bureau reports as “actual capacity utilization” appears closely aligned with more conventional reports of capacity utilization, understanding that capacity utilization is a slippery term. For example, when WWPA reports on capacity utilization it inserts the adjective “practical” in front of capacity.

The second capacity utilization that is reported is production as a percentage of what manufacturers produced compared to what they could ramp up to produce rapidly for the period of one or more years. This is the “emergency” capacity utilization, and presumably it is why the Department of Defense is a co-funder of the quarterly survey.

It’s interesting to compare the differences between these two levels of capacity utilization (see chart below). To highlight the differences between the capacity metrics, two Census Bureau manufacturing categories are reported: (1) sawmills (includes hardwood and softwood) & wood preservation plants and (2) pulp mills. In addition, WWPA’s utilization of practical capacity for softwood sawmills is included for comparison.

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While the capacity utilization rates are slightly different between WWPA and the Census Bureau’s actual rate for Sawmills and Wood Preservation the difference is easily rationalized; the WWPA utilization rate is for softwood sawmills only while the Census Bureau includes hardwood and wood preservation plants as well.

The key point is comparing the actual to emergency rates. Note that the difference between the Census Bureau’s sawmill actual and emergency capacity utilization is 18.9% while there is no difference between actual and emergency capacity for pulp mills. For pulp mills this is not surprising; the high fixed costs of pulp mills require mills to be run nearly continuously for any hope of profitability. For sawmills, however, fixed costs are relatively low and many are running only single shifts. As a result, if conditions warrant, such as a national emergency, production can ramp up rapidly as mills add a shift and/or extend shifts.

That latent capacity is available to increase lumber production not only under national emergency conditions but also if economic conditions, such as high lumber prices, provide opportunity for profitable domestic lumber manufacturing. The strong U.S. dollar against the Canadian dollar and no softwood lumber agreement between the U.S. and Canada have made U.S. mills cautious about ramping up production even while pricing has been strong. Most recently the strong U.S. dollar has placed U.S. mills at a competitive disadvantage of about 25%. However, with tariffs being applied to Canadian lumber imports the likelihood of U.S. production ramping up is higher as this competitive disadvantage due to exchange rates is either eliminated or at least reduced.

We believe this largely ignored “latent” capacity in the sawmill industry accounts for historical lumber price spikes but no ability to sustain peak pricing for an extended period of time. If the only means to bring on additional production was through new capacity investment peak pricing would be sustained for longer periods of time. However, as prices climb, in addition to new capacity coming online through investment, manufacturers produce more by increasing/extending shifts at mills that already exist. The production response due to increased and extended shifts is able to occur quite rapidly, thereby expanding lumber supply and so causing prices to crash.

While the mechanism described may not be well understood, the market reality of lumber price volatility is recognized. This is why we suspect that, in addition to futures prices retreating since the tariffs were announced, more distant contract prices have been converging (see table below).

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On 1/17/2017 the March 2018 contract was $43.10 higher than the May 2017 contract. By 3/1/2017 the difference had shrunk to $29.00. Clearly this is due in part because lumber futures only rarely break above $400 (see chart below) and as the near-term contract climbed toward $400 traders recognized it was highly unlikely prices would be near that level roughly one year later. As we have explained, the reason for that occurring is because of the latent capacity that can be tapped into quickly when lumber prices are at high levels.

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Since March that convergence has continued. On the date of the tariff announcement the spread between the highest contract and front contract dropped below $8.00. On May 12, the last date of trading for the May 2017 contract, the May contract was the highest of the six contract dates and the spread showed the lowest contract to be $19.10 below the May 2017 contract. This reaction runs totally contrary to the many pronouncements made shortly after the tariff announcement that prices were about explode higher still.

Instead we believe the combination of tariffs on Canadian lumber and high prices will stimulate more U.S. lumber production, causing lumber prices to decrease rather than climb higher. The latent capacity indicated by the difference between actual and emergency capacity utilization is indicative of that potential.

We realize some might argue one reason for sawmill capacity not growing rapidly back to pre-Great Recession levels in the U.S. is due to lack of adequate timber supply. An analysis of that type is beyond the scope of this blog post but suffice it to say -- based on other analyses the author has done -- there is adequate timber across most of the U.S. South and in pockets across the U.S. Pacific Northwest (PNW). The chart below provides a simplistic picture of current timber supply conditions.

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The data are softwood sawlog inventories on private lands only. The South consists of the states of Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. The PNW consists of the states California, Idaho, Montana, Oregon, and Washington. Only inventory in stands 25 years and older was included in the analysis as well as only inventory on slopes less than 30%. The condition of only stands over 25 years for the PNW is too low for rotation-aged inventory but it does at least knock out sawlog inventory “trapped” within younger-aged stands from the analysis. The 30% slope condition focuses this analysis only the more economically viable stands. As can be seen, inventories have been expanding both in the U.S. South as well as the U.S. West. This indicates additional softwood sawlog harvest could occur without jeopardizing the current level of timber inventory, and so timber supply does not currently present an impediment to U.S. sawmill capacity expansion.

In summary, we think the recent lumber price declines in the aftermath of the announced softwood lumber tariffs on Canadian lumber can be explained by near-term lumber prices having overshot the size of tariffs announced on April 24. For more distant contracts the recognition the lumber market has not been able to sustain high pricing over an extended of time is contributing to convergence of those contracts at or below the levels seen on the May 2017 contract price. We suggest the reason lumber prices don’t sustain peak prices for extended periods can be explained by the latent capacity in the sawmill industry. A clue to the magnitude of this latent capacity can be detected by comparing the difference between actual and emergency capacity utilization rates. As a result, while tariffs will most certainly impact portions of the supply chain we don’t expect there to be significant increases in lumber prices or home prices, or significant reductions in housing starts.

Closing Thoughts
In closing, we want to underscore we are not arguing for or against the Canadian tariff on softwood lumber imports; rather, our goal is to accurately gauge the likely market response to such tariffs. There is a certain irony, however, that we can’t resist pointing out.

We started this post quoting the WSJ article proclaiming the Trump administration had just levied a tax on “new houses.” While we disagree the tariff will result in a meaningful “tax” on U.S. consumers in this case, we would be remiss to ignore the reverberations of the environmental-regulation “tax” levied on U.S. consumers. In fact, one could advance the argument this environmental regulation “tax” has paved the way for further market interventions such as the recently announced tariff.

Although perhaps well-intentioned when enacted, the implementation of environmental restrictions regarding the active management, including timber harvest, of Federal forest lands has precipitated profound changes in the U.S. forest products industry beginning in the 1990’s, with continuing impacts to this day. USFS harvest swooned (see chart below), causing raw material prices to initially skyrocket.

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Then economic adaptation happened: mill closures, capacity shifts to other regions of the country (the impact of the environmental restrictions was greatest in the western U.S., see chart below), and increased imports. A new, but higher, equilibrium cost for timber emerged which, for the sawmill industry in North America, represents 55 to 75% of a mill’s manufacturing cost. Further, the regulations played a significant role in rendering the western U.S. forest products industry less competitive from a global perspective.

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Rural communities across the U.S. West have paid -- and continue to pay -- the steepest price, but the U.S. consumer continues to be unwittingly “taxed” too as a result of these policy decisions and their implementation through increased timber costs. These increased costs, in turn, contribute to higher lumber prices to cover at least a portion of the cost of production.

Recall, too, the logic supporting the tariff against the Canadian industry is that the Canadian government subsidizes its industries by providing it with cheap timber. A cynic might ask, cheap compared to what? High-cost timber in the U.S. due in part to U.S. environmental policies? We know it’s more complicated than that, but we feel it should be pointed out the indirect impact U.S. environmental regulations might be playing in the cross-border tariff tiff.

In addition to these market affects, federal coffers are deprived the revenue from USFS timber sales as a result of these regulatory impacts. In our opinion perhaps even more impactful is the lack of forest management on federal land which has resulted in forests becoming too old and overcrowded. We believe that this is resulting in increased tree disease, insect infestation, tree mortality, and catastrophic wildfire.

We realize this topic should start a whole new post as these observed outcomes (increased disease, insect infestation, tree mortality, and catastrophic wildfire) are being attributed to other factors -- such as global climate change -- rather than lack of management, so we’ll leave addressing that topic for another day. We will leave it, however, by posing this question: If global climate change is the major factor driving this pestilence in western forests, why is the hammer falling demonstrably harder on federal forest lands rather than intermixed forest lands owned by other owners and under different management regimes?

While death and taxes are inevitable, it seems to us neither is specifically in play -- despite the rhetoric -- for the U.S. consumer in the case of the Canadian softwood lumber tariff; rather, we see only modest (at most) impacts. Instead, we see policy interventions such as the tariff as simply a Band-Aid on more impactful decisions made decades ago that continue to send ripples through both our economy and society today.

Monday, October 3, 2016

Wood Products Quarterly Financial Review 2016Q2

Each quarter the Census Bureau surveys a broad array of U.S. corporations to create a snapshot of business sector health and activity. The program, known as the Quarterly Financial Report (QFR) survey, has been collected and published for over 60 years by various federal agencies – the Census Bureau being the most recent. These data provide a standardized and comprehensive look at the financial condition of the industry.

Results from sampled businesses are aggregated and reported by the North American Industry Classification System (NAICS) and by asset size category. There are separate NAICS codes for wood product manufacturing (“321”) and paper manufacturing (“322”). Based upon returned sample surveys, the QFR presents estimated statements of income and retained earnings, balance sheets, and related financial and operating ratios by industry sector and asset size category. This blog reports only a sample of the data collected in the QFR survey.

The wood product manufacturing data is categorized by asset size: firms with less than $25 million in assets (“small firms”), firms with more than $25 million in assets (“large firms”), and all firms regardless of size. While the data are somewhat dated, they provide not only a useful perspective on direction and momentum with respect to the current business cycle, but also benchmarks for individual firm performance.

The picture that emerges at an industry level when viewing the 2016Q2 data (see graph below) is significant operating performance improvement on modestly improving sales. Interestingly, this improvement is in the aftermath of the recently expired North American Softwood Lumber Agreement (SLA). However, there are some undercurrents that provide a more complex assessment than the 30,000-foot fly-over look.

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Selected financial results of wood product manufacturers during 2016Q2 are summarized in the table below. Notable highlights include:

Net Sales – Aggregate net sales for 2016Q2 were at the highest level in the past 10 years for the industry as well as for the portion of the industry with mill assets $25 million and greater; for the portion of the industry with mill assets less than $25 million, net sales were less than the median level over the past 10 years. Industry sales in 2016Q2 increased by 3.6% over 2016Q1 and 7.0% over 2015Q2. However, differences based on mill asset sizes are notable. For mill assets less than $25 million, 2016Q2 sales increased by 8.6% over 2015Q2 while declining by 9.4% from 2015Q2. Meanwhile, for mill assets $25 million and greater 2016Q2 sales grew by 1.4% relative to 2016Q1 but were 17.0% higher than 2015Q2’s sales level.

One possible cause for the year-over-year (YoY) drop in net sales for companies with less than $25 million in assets could be a disproportionate effect of the end of the SLA in October 2015. Net sales results by quarter for mills with assets less than $25 million can be seen in the last chart on this blog. These companies experienced a major sales downshift in 2015Q3 and have yet to recoup that lost ground despite improving industry sales.

Also notable: During the past 10 years, the median Q1 to Q2 percentage increase in aggregate industry net sales was 11.3%, indicating a strong seasonal element in quarter-to-quarter (QoQ) activity; by contrast, 2016Q2 saw an increase of only 3.6% over 2016Q1. The high level of sales, aggregate QoQ sales growth roughly one-third the median rate of the past 10 years, and the strong QoQ sales growth in the smaller0asset portion of the industry may suggest maturation of the current business cycle.

EBITDA – Aggregate earnings before interest, taxes, depreciation, and amortization (EBITDA) or operating cash-flow performance for the quarter was within the upper quartile of the past 10 years for the industry and both industry sub-components: 79% for mills with assets less than $25 million, 100% for mills with assets $25 million or greater, and 92% for the industry. 2016Q2’s increase from 2016Q1 turned in a solid performance across all industry sectors: 23.9% for mills with assets less than $25 million, 39.2% for mills with assets $25 million and greater, and 34.6% across all sectors. Despite sales falling by 9.4% YoY for mills with assets less than $25 million, EBITDA increased by 78% YoY.

Operating Income – 2016Q2 income was above the 80th percentile of the prior 10 years. YoY percentage change in operating income more than doubled for mills with assets less than $25 million; for the industry, operating income increased by nearly 50% between 2016Q1 and 2016Q2 but still lagged the prior 10-year median Q1-to-Q2 percentage change. Lagging the prior 10-year median Q1-to-Q2 change seems more an indication of business-cycle maturation than a sign of general market weakness; indeed, it is difficult to describe operating income of 48.5% and 65.1% improvement for QoQ and YoY, respectively, as “weak.”

Pre-tax Income – Pre-tax operating income performance generally parallels operating income results.

Net Income – Net income performance generally parallels operating income results.

Operating Margins – Operating margins for all sectors are near the 90th percentile of the prior 10 years and all margins expanded on a QoQ basis. Even more notable, industry-wide margin growth exceeded the prior 10-year median Q1-to-Q2 percentage point change.

ROE and ROA – Despite strong sales and improving operating margin growth, the YoY percentage point change in Return on Equity (ROE) and Return on Assets (ROA) fell for all industry sectors. Despite the drop, ROE and ROA returns remain firmly entrenched above the 10-year 50th percentile for large companies and the industry in total; smaller companies’ returns are roughly at the 50th percentile level of the past 10 years.

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Falling ROE and ROA on a YoY basis despite solid operating margins is consistent with the capacity expansion that has been occurring in the sector. Higher ROA and ROE returns for smaller companies despite lower operating margins than larger companies suggest large firms are responsible for the bulk of industry capacity expansion. Capacity expansion is typically another indicator of business cycle maturation.

The recent uptick in ROE and ROA over the last two quarters (see chart below) could either be the start of a new “up leg” of the business cycle or simply a ”hiccup” in the business cycle playing out. Our current interpretation is that soaring Canadian softwood lumber exports to the United States in the wake of the SLA’s expiration drove out marginal U.S. producers even while profitable companies continued adding capacity – resulting in net capacity expansion across the industry (Note: while the NAICS 321 industry code includes all wood products manufacturing (softwood and hardwood, sawmill, plywood and veneer, OSB, particleboard, secondary wood manufacturers, etc), a significant share of economic activity in the sector is driven softwood sawmills). The combination of curtailment of less inefficient mills during 1H2016 precipitated by increased Canadian imports and ongoing capacity expansion at more efficient mills would explain lower returns compared to a year ago but slight improvement in returns over the prior two quarters.

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Taking a longer view, the divergence between small and large firms is becoming more pronounced (compare next two charts, particularly trend lines). Net sales and operating margins are trending higher for large firms; by contrast, net sales for small firms are trending lower while trends in operating margins are essentially flat. This has resulted in a shift in the sales composition by company size; small companies represented 44% of sales in 2001, but averaged only 31% during the last three quarters (i.e., since the SLA expired).

In terms of recent industry impacts, smaller companies represented 36% of sales between 2014Q4 and 2015Q3 (i.e., the four quarters immediately before the SLA expired) compared to 31% since the SLA expired. Thus, since SLA’s expiration, following a 4.1% QoQ drop (-5.2% YoY) in 2015Q4 U.S. industry sales, sales in 1H2016 are up 3.6% from 1H2015 when the SLA was in effect. Even more striking, ror large companies 1H2016 sales were up 13.2% compared to 1H2015; for small companies 1H2016 sales were down 12.7% compared to 1H2015. This reinforces our interpretation that the recent improvement in operating margin for small operators is due more to curtailments of less efficient mills rather than improved business conditions.

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In total, the U.S. industry has improved its competitiveness but a sizeable proportion of that improvement is due to curtailment of less efficient manufacturers; the SLA’s expiration in 2015Q4 hastened their departure, or at least their curtailment. High returns during 2013 and 2014 attracted ongoing capital expansion. The pace of net U.S. sales, while still increasing, has slowed; most recently 2016Q2’s net sales increase was substantially below the median QoQ increase of the past 10 years. The result of increased capacity, and hence capital investment, without a commensurate increase in operating cash flow has resulted in returns falling despite historically strong operating margin results. Such patterns are typical of maturing business cycle characteristics.

Tuesday, January 19, 2016

Will Construction Spending Data Revisions Boost GDP Growth Estimates?

The U.S. Census Bureau’s November 2015 construction spending data release came with the following unexpected surprise: “In the November 2015 press release, monthly and annual estimates for private residential, total private, total residential and total construction spending for January 2005 through October 2015 have been revised to correct a processing error in the tabulation of data on private residential improvement spending.”

A number of analysts were quick to claim the revision will have a sizable impact on GDP. E.g., “‘The upward revision to spending in 2014 is enough to raise growth that year from 2.4% to 2.6%-2.7%,’ wrote IHS Global Insight US economist Patrick Newport in a research note. ‘The revisions are likely to boost growth for 2015 as well.’”

Not everyone agreed, however. SitkaPacific Capital Management’s Mike Shedlock was one analyst who begged to differ with the more optimistic view. “The question is not whether 2015 GDP will rise vs. previous estimates,” Shedlock wrote, “but rather by how much it will sink.”

Given the disparity of opinions, we decided to do a bit of exploring on our own. The following graph shows the magnitude of the revisions to the historical data:

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The revisions prior to 2014 are relatively minor, but the changes to 2014 and 2015 are far more substantial and -- at first blush, at least -- seem to support the idea of stronger GDP growth. It is key to remember, however, that GDP growth is driven by quarter-over-quarter or year-over-year (YoY) rates of change. Converting the data to a YoY percentage change basis provides a more nuanced picture:

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The above graph strongly suggests that GDP growth for 2014 could indeed be revised higher. The outcome for 2015 is much cloudier, however; upward revisions early in 2015 appear to be nearly offset by negative revisions later in the year. The following table supports this observation:

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As one can see, 2014 spending was revised higher by $30.4 billion. When combined with the $11.9 billion reduction for 2013, the change to 2014 GDP growth will almost certainly be noticeable. The $36.6 billion revision shown for January through October 2015, on the other hand, did little more than keep 2015 in the same position relative to 2014 that it had been in prior to the revision.

In summary, then, the answer to the question in the title of this post is: “Yes (for 2014) and probably not (for 2015).”
The foregoing comments represent the general economic views and analysis of Delphi Advisors, and are provided solely for the purpose of information, instruction and discourse. They do not constitute a solicitation or recommendation regarding any investment.

Friday, December 18, 2015

Whatever Happened to the Recovery in Southern Pine Sawtimber Stumpage Prices?

When a year draws to a close, it is customary to look back and take stock of what has transpired. If that is done with U.S. Southwide pine sawtimber (i.e., DBH ≥ 12.0 inches) stumpage prices, someone might legitimately wonder if an economic recovery has ever really arrived. Between August 2011 and November 2015, stumpage prices have increased by 10.5% on a trend-line basis; that corresponds to a compound annual growth rate (CAGR) of 2.5%. Yet, the 2015 year-to-date (through November) average of total U.S. housing starts (1.102 million units SAAR) is over 130% higher than the April 2009 low point (478,000 units). In addition, the trended CAGR of Random Lengths' southern yellow pine lumber composite price is up 7.7% since January 2009. So, why has the post-Great Recession stumpage price increase been so muted?

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One reason is the Canadian dollar (CAD). A weaker CAD encourages Canadian lumber exports to the United States, in the process capturing market share from U.S. solid wood manufacturers and -- in turn -- reducing demand for U.S. softwood sawtimber. U.S. imports of Canadian softwood lumber have risen at a trend CAGR of 8.3% since January 2009. 

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In the chart below, we divided total U.S. housing starts by the CAD/USD exchange rate (HS/CAD) to suggest the degree to which the exchange rate affects perceptions that U.S. solid wood manufacturers have regarding housing starts. For those producers the “HS/CAD” line is a more realistic portrayal of domestic lumber demand derived from housing starts than the reported “TotalHS” line. Thus, while total reported housing starts have increased at a trend CAGR of 14.4% since April 2009, HS/CAD have risen by only 11.0% -- a difference of nearly one-quarter. The increase in Canadian softwood lumber imports has largely kept pace with the CAD-adjusted rise in U.S. housing demand.

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A second reason is that, although total housing starts have been rising, the share of total starts claimed by multi-family structures -- which, on average, use roughly one-third the volume of softwood lumber per start compared to single-family homes -- has been expanding (from an annual average of 19.5% in 2010 to 35.8% YTD through November 2015). That larger share of multi-family units also helps explain why total housing starts were up 81% on an annual basis between 2009 and 2014, but U.S. lumber production rose by only 34% (+37% for Southern production) during the same period.

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Another reason is that tree growth continues to outpace the volume harvested. With more supply "on the stump" -- particularly in pine sawtimber, as shown in the following table -- price pressure is reduced.

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This situation has been further exacerbated by the Great Recession’s impact on installed sawmill manufacturing capacity. As noted above, southern lumber prices have increased at a trend CAGR of nearly 8% since January 2009; this is because -- and despite a muted recovery in lumber demand -- roughly 20% of U.S. solid wood capacity (the losses for lumber were even greater) was shuttered as a result of the industrial downturn experienced during/since the Great Recession. Thus, there was insufficient capacity to meet even the comparatively anemic increase in demand as the recovery occurred, prompting lumber prices to rise and capacity utilization rates to climb. That explains the lumber price response, but what about stumpage prices? 

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The loss of manufacturing capacity hindered the sector’s ability to expand lumber supply in response to rising consumer market demand. It also reduced localized geographic demand for stumpage -- both in terms of the quantity of stumpage demanded and the number of mills competing for that stumpage. Coupling the stumpage market situation of fewer mills and lower localized demand with increasing log supply “on the stump” explains why the stumpage price recovery has been muted despite steady trend improvement in lumber prices since 2009.

Taking all of these influences together, and given our expectations of how present trends are likely to “play out,” we see little reason to believe a dramatic change may be coming over the horizon.
The foregoing comments represent the general economic views and analysis of Delphi Advisors, and are provided solely for the purpose of information, instruction and discourse. They do not constitute a solicitation or recommendation regarding any investment.

Sunday, August 2, 2015

Whither Home Prices?

With June’s median existing home price rising to a record-high $236,400 and the median new-home price at $281,900 (just $20,900 off the November 2014 record of $302,700), what direction are home prices likely to take going forward? To hazard a guess we used a metric -- inspired by an Agora Financial 5 Min. Forecast published on 23 July -- wherein median home price (MP) is divided by median household income (MHI) to derive a price/income multiple (PIM).

Each month the U.S. Census Bureau reports the median price of new homes sold; likewise, the National Association of Realtors (NAR) reports the median price of existing homes sold. The Census Bureau reports median household income on only an annual basis, however, and the latest data is for 2013. We first converted the monthly median new and existing home prices to annual observations by calculating averages for each year (the green and purple lines, respectively, in the first graph below). To estimate median household income for 2014 and 2015, we developed an ordinary least squares regression equation (R2 = 0.91) wherein the Census Bureau’s MHI is a function of NAR’s annualized MHI (reported as part of NAR’s housing affordability index data series). The derived MHI estimates are shown as the red segment of the income line in the first graph below.

Several items are noteworthy: Growth in MHI broke off its long-term trend in the wake of the Great Recession (i.e., since 2007) and -- although having regained its pre-recession level in 2012 -- has yet to fully recover its former trajectory. New-home MPs peaked in 2007, bottomed in 2009, essentially regained their pre-recession level by 2012, and have continued higher since then. Existing-home MPs, by contrast, peaked in 2006, bottomed in 2011, and have yet to regain their pre-recession level.

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The Census Bureau’s median home price data extends back to 1984 whereas NAR’s data begins in 2003. For the available years, we divided annualized median home price by annualized median household income to estimate new and existing home price/income multiples. Prices and PIMs are shown in the second graph below. PIMs for new homes have ranged from 3.57 to 5.34, for an average of 4.31; existing-home PIMs have ranged from 3.29 to 4.71, for an average of 3.86. I.e., on average new home prices have been equivalent to 4.31 times MHI, and existing home prices 3.86 times MHI.

For new homes, the PIM peaked at 5.06 in 2005 -- the height of the housing boom. It slid to its low point of 4.31 by 2009 and has since trended to an all-time high of 5.34 in 2015. For existing homes, the PIM peaked at 4.71 in 2005 (despite the MP peaking in 2006), but two additional years were required to hit the bottom of 3.29 (in 2011); as with its corresponding MP, the existing-home PIM has not yet returned to its pre-recession high.

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Particularly in light of the new-home PIM presently at an estimated all-time high and incomes posting only slow growth compared to home prices (especially on an inflation-adjusted basis), we think there is greater downside than upside risk to home prices. The table below shows the implications for prices if the PIMs return to selected historical levels.

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For new homes, the best scenario shown involves the PIM returning to its 2005 level; that would result in a price reversion to $273,334 -- a decline of $15,590. A more dire scenario would have the current PIM retreating by an amount equivalent to that seen between the 2005 peak and 2009 trough; in that case, the PIM would drop to 4.60 with the new-home MP falling to $248,576 (a decline of $40,358).

Because the existing-home PIM has not returned to its pre-recession peak, there is perhaps more upside potential. For example, were that PIM to once again hit the 2005 peak, the existing-home MP could increase by $38,922 to $254,622. The most pessimistic scenario involves the existing-home PIM retreating from its current level by the amount seen between 2005 and 2011; in that case the multiple would slump to 2.57, resulting in the existing-home MP dropping by $76,888 to $138,812.

For housing starts (new homes) and sales (existing homes) to continue climbing higher, we believe it will be necessary for home prices -- particularly new home prices -- to fall so coherence is maintained with households' ability to pay (i.e., MHI). Until that occurs we believe it will be difficult for housing starts to make more substantial progress toward regaining the long-term average level of 1.5 million units per year.

Unfortunately, as the table above shows, home-price reductions will choke off the ability of some current homeowners to refinance or trade up/down, thereby reducing access to potential home equity that might provide additional economic stimulus. On balance, however, lower home prices would likely provide the greater economic boon because the Echo Boom demographic cohort would be able to flex more economic muscle through greater household formations. Regardless, the path forward in housing’s march back toward “normal” is likely to encounter several twists and turns. There will be a variety of counterbalancing impacts to the general economy in the process that, on net, could prove positive.
The foregoing comments represent the general economic views and analysis of Delphi Advisors, and are provided solely for the purpose of information, instruction and discourse. They do not constitute a solicitation or recommendation regarding any investment.