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.

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., MarketWatch.com: “‘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.

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.

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. 

Zombie Trees

Halloween seems an appropriate time to highlight a national horror story.  Although this post looks at it through the prism of Idaho, sadly the tale is unfolding in many places throughout the U.S. West.

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We start the tale innocently enough: comparing the number of standing trees per acre on forest land across Idaho.  But first, a bit of background.  In USFS nomenclature “forest land” is the broadest classification of land with trees growing on it, including lands that have at least 10 percent forest cover; timberland refers to forest land that surpasses a productivity threshold and is not legislatively reserved from being actively managed. 

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According to the USFS's Forest Inventory and Analysis ("FIA") unit, there are nearly 21.5 million acres of forest land across the state.  Eighty percent of the forest land in the state is managed by the federal government, with the vast majority (76 percent of the state’s forest land) managed by the United States Forest Service (“USFS” or “NFS”).  The State of Idaho manages six percent of the forest land in Idaho with the remaining 14 percent managed by private land owners.  These lands are interspersed with one another across the state.

With that is background, let’s plunge ahead with our tale.  For the three primary forest managers/owners in Idaho, the USFS, the State of Idaho (“IDL”, for Idaho Department of Lands), and private land owners, the average forest land acre carries roughly the same number of live trees (122 per acre for USFS, 119 per acre for IDL, and 108 per acre for private).  However, the number of standing dead trees per acre on the USFS (43) is roughly double the number of standing dead trees per acre on State (20) and Private lands (14).  Why would this be?  Remember, the USFS doesn’t represent an isolated pocket of property but rather is by far the most significant land manager in terms of geography within the state.

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IDL and private land is generally more actively managed, including higher harvest activity, than forest land managed by the USFS.  For instance, as can be seen in the graph below, although USFS managed lands represent 76 percent of all forest land, it currently accounts for less than 15 percent of the annual timber harvest in the state.   On the other hand, the state, comprising six percent of the forest land acreage, accounts for nearly 30 percent of the harvest.  In the case of private land owners, 60 percent of the annual harvest is sourced from their acreage, representing 14 percent of the forest land in the state.

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To further examine the link between active management and tree mortality, the next chart focuses on USFS land only.  As can be seen there are 10 columns in all, comprising five sets of two columns each.  Each set of two columns shows the number of living trees and the number of standing dead trees per acre.

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The far right set of two columns on the chart above corresponds to the USFS graph shown in the prior trees per acre by ownership chart.  The other four sets of two columns segregate USFS forest land into four different categories.  The first two sets look at more productive forest land  while the second two sets of columns look at less productive forest lands.  The more productive forest land area is further sub-divided between acres that are not reserved, i.e. that are open to active management, and acres that are reserved, or legislatively withdrawn from any active management occurring on them.  Designated wilderness areas are an example, the most common in fact, of acreage that is reserved from active management.  Similarly, the less productive acres are sub-divided between not reserved and reserved.

Several patterns emerge when the data is analyzed in this way.  First, the more productive lands carry more lives trees per acre than less productive lands.  Second, the ratio of the number of dead trees per acre compared to the number of live trees per acres is higher on less productive lands.  But, third, and most important here, is regardless of productivity, there are more dead trees per acre on lands where no active management is possible.  Fourth, on the most productive areas that are open to be actively managed, the number of standing dead trees per acre is still nearly double that seen on IDL and private ownership.  Thus, on the USFS even acres where ostensibly active management can be practiced, not enough is being done and trees are dying.

Beyond, the charts and numbers, the reality is all too obvious to anyone who looks at Idaho’s national forests.  Suppression of fire and significant reduction harvesting, both for regeneration and thinning, have allowed forest managed by the USFS to become overcrowded and old.  In the relatively dry climates of the Inland West, the old, overcrowded conditions weaken trees, making them more susceptible to insects, disease, and death.

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Trees per acre are one thing and seem bad enough but when the number of dead trees per acre are multiplied across the size of the USFS holdings in the state the magnitude of the losses becomes even more sobering.  The next chart compares the volume represented in standing dead trees on the USFS compared to volume in live trees on other ownerships in Idaho.  As can be seen, the volume represented in the standing dead trees on USFS timberland (not reserved and productive lands) exceeds the live volume on any other single ownership class in the state.  Total standing dead volume on all USFS forest land is nearly equal to the total live volume on all other ownerships combined in the state.

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For those concerned about global climate change and green house gases, there is an additional element of this carnage that is often overlooked.  As is well known, trees grow by utilizing atmospheric elements, including carbon dioxide, other nutrients from the soil, sunlight energy, and water.  One result of this growth process is a reduction in carbon dioxide, fixing carbon within the tree's cellular structure and producing oxygen as a by-product  When a tree dies and begins a slow process of decay, the fixed carbon within the tree is released.  However, this process will take a long time in the dry Inland West so a significant amount of carbon remains ‘stored’ in the dead trunk for an extended period of time.  However, no new tree can take its place while it stands, and so that portion of the forest is no longer in effect exchanging carbon dioxide for oxygen as it once did while carbon that had been stored within the tree during decades of growth slowly leaks away.

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But standing dead trees and standing dead volume speaks to what has been --- not what is happening.  The chart below begins to look at what is happening now, not what has been.  It compares the volume in trees that die annually to the volume of trees harvested annually by ownership.

 

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The first two stacked columns on the chart compare mortality on non-reserved forest land acres – acres that are open to active management – to 2012’s harvest.  Each column is sub-divided by ownership to depict the contribution of each ownership category to the state total.  The comparison is made on non-reserved acres because those are the only acres on which active management, including timber harvest, can occur.  The last column on the chart depicts the annual mortality occurring on forest land acreage reserved from active management.

 

Total mortality, on both non-reserved and reserved forest land, is nearly 4 times the amount of volume being harvested annually state-wide.  The annual mortality on the USFS acres that are open to active management is over 18 times the volume of the annual harvest from those same acres.  Finally, the annual mortality on the 3.8 million acres of reserved – Wilderness – acres in the state exceeds the annual harvest on the 16.4 million acres of forest land in the state open to active management across all ownerships.  In a word, the Idaho Wilderness is being slaughtered.

The chart below shows net annual growth and annual harvest on Idaho forest lands.  The ratio of net annual growth to annual harvest ("growth/drain ratio") is a common, sometimes overused, metric used to test harvest intensity.  Net annual growth is the amount of growth occurring in a year minus the amount of annual mortality.    As can be readily seen, not only does state-wide net annual growth exceed state-wide harvest, the net annual growth on each ownership exceeds the annual harvest for each individual ownership class in the state.  Notably, the "growth/drain" on private ownerships (1.33) exceeds the same ratios on USFS (1.17) and IDL (1.12).

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Looking at those results on the USFS one might be tempted to conclude harvest levels could be increased only slightly before harvest exceeds net annual growth, prompting forest inventory to fall.  However, this is one example where the growth-drain ratio alone doesn't provide an adequate picture of forest conditions; in this case, the high level of mortality distorting what is occurring on USFS lands.

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The chart above separates the components of net annual growth apart and further separates them by non-reserved (i.e. open to active management) and reserved forest lands for each ownership class.  As can be seen on this chart, on most ownerships annual mortality is a fairly small relative to annual growth (16 percent on IDL and private forest land in Idaho).  Unfortunately, this is not the case for the USFS.  On USFS non-reserved forest lands annual mortality is a whopping 76 percent of annual growth.  However, on reserved forest land the picture is even more dire; annual mortality is 206 percent of annual growth.  Idaho’s spectacular wilderness and back-country areas are slowly dying.  Dying by starvation, dying by thirst, dying by strangulation.  Combining the non-reserved and reserved acreage results in a slightly positive net annual growth where annual growth exceeds annual mortality by four percent.

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The next chart (see above) now completes the picture, pitting harvest against net annual growth on the acres open to active management.  On USFS it’s clear the net annual growth on acres open to active management exceeds harvest by over 500 percent.  This is a radically different picture than when comparing the annual USFS harvest to the USFS property total where net annual growth is much lower due to the annual mortality occurring in Idaho’s wilderness areas.  However, the extremely positive net annual growth to harvest on USFS lands needs to be understood in the context of other clear trends on USFS lands: rampaging mortality on reserved forest lands and mortality that is rapidly increasing on non-reserved forest lands.

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Will Zombie Trees overrun the national forests in Idaho?  And remember, Idaho is simply a snapshot of the dynamic underway in many public forest lands across the U.S. West.  As bad as it is, unfortunately, the story does not end with a moonscape of standing dead trees.  Ultimately a lightning strike will cremate the standing and tilting remains of zombie trees, polluting expansive air sheds, further ravaging wildlife habitat, exposing streams and rivers to significant sediment loads and reduced shade, and sometimes sterilizing soils for a generation.

 

While popular science and belief stretch to accommodate and explain such ravages as part of the “natural order”, the fuel loads fanning today’s wildfires are not natural and the results rarely orderly.  Yes, thankfully there is a kind of rejuvenation in the aftermath of wildfire’s annihilation.  But in light of the destruction it seems the question is could such rejuvenation be achieved in a manner with less apocalyptic fury and lower societal cost.  Earthquakes, hurricanes, and pestilence are all natural as well.  Yet, we try to operate in a manner to mitigate their damaging effects on society, not amplify them.  As a society will we find the will to halt the advancing scourge of zombie trees?

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QFR Paper Manufacturing – 2014 First Quarter

With this post we are initiating coverage of the Census Bureau’s Quarterly Financial Review data for the U.S. paper manufacturing industry. Each quarter the Census Bureau samples a broad array of U.S. corporations to gather a snapshot of business sector health and activity. The program, known as the Quarterly Financial Report, or QFR, survey has been collected and published for over sixty years by various Federal agencies, the Census Bureau being the current administrator of the program. 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. What’s reported in this blog is only a snapshot of the other data reported by the QFR survey.

The paper manufacturing data is categorized by asset size: firms with less than 25 million dollars in assets (“small firms”), firms with more than 25 million in assets (“large firms”), and all firms regardless of size. While the data is somewhat dated, it provides a useful perspective on business direction and momentum with respect to the current cycle as well as providing benchmarks for individuals firm’s performance.

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Viewing the 2014Q1 data, the picture that emerges is a gradually improving industry sector that was hampered by adverse 2014Q1 weather conditions, which have been widely reported by the financial media as the principal cause behind 2014Q1's negative real GDP print. Comparisons to the prior quarter (2013Q4) are further complicated by significant non-operating income and a significant federal tax credit received in 2013Q4. Granting these complications to in-depth interpretation of 2014Q1 industry results, probably the most significant observation is although year-over-year industry net sales increased by 5.5%, operating margins fell, dropping from 5.74% in 2013Q1 to 3.49% in 2014Q1. However, despite loss of operating margin, the industry’s focus on asset rationalization has maintained both ROE and ROA in the solidly within the upper quartile of performance relative to the past 10 years.

Selected financial results of paper manufacturers’ first quarter 2014 is summarized in the table shown below. Notable highlights (see table below) include:

• Net Sales – 2014Q1 net sales ($32.9 billion) retreated by 3.2 percent compared to 2013Q4’s net sales, a reduction approximately three times larger (more negative) than the median reduction seen between Q4 and Q1 over the past ten years. Despite dropping from 2013Q4’s sales, 2014Q1’s net sales increased by 5.5% compared to 2013Q1’s net sales level.
• EBITDA – 2014Q1 operating cash-flow, or Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), posted 10-year lows for both large firms and the industry.
• Operating Income – 2014Q1 operating income was in lower quartile of last 10 years for industry and both large and small firms.
• Pre-tax Income – 2014Q1 pre-tax income posted significant declines to 2013Q4, dropping nearly 52 percent compared to a 10 year average percentage change between Q4 and Q1 of nearly 12%.
• Net Income – 2014Q1 net income posted significant declines on both a quarter-over-quarter basis as well as on a year-over-year basis. As noted earlier, quarter-over-quarter comparisons are complicated by large non-operating income received in 2013Q4. However, year-over-year comparisons don’t suffer from this issue and point to significant cost structure increases in 2014Q1 vs. 2013Q1: year-over-year sales improved by $1.7 billion while net income fell by $0.9 billion, a combined implied cost delta of $2.6 billion, or 8.3% on 2013Q1 sales of $31.2 billion.
• Operating Margins – 2014Q1 operating margins showed significant erosion across the board on both quarter-over-quarter and year-over-year comparisons.
• ROE and ROA – Despite apparently increasing operating costs both Return on Equity (ROE) and Return on Assets (ROA) posted year-over-year increases and were in the upper quartile of performance relative to the past 10 years.

(For Larger Image Click on Table)

The key unknown for the industry sector is how much of the increase in 2014Q1 operating costs was attributable to the adverse weather conditions that were blamed for the negative GDP change. The fact 2014Q1 sales contracted from 2013Q4’s net sales level by more than the median percentage between Q4 and Q1 over the past 10 years is consistent with adverse weather impacts. However, 2014Q1’s net sales increase relative to 2013Q1’s net sales potentially challenges weather as the principle explanation for 2014Q1’s EBITDA, operating income, pre-tax income, and net income declines. The second quarter data, due to be released mid-September, will provide additional insights into the industry’s trajectory this business cycle.

QFR Wood Products Manufacturing – 2014 First Quarter

With this post we are initiating coverage of the Census Bureau’s Quarterly Financial Review (QFR) data for the U.S. wood products industry. Each quarter the Census Bureau samples a broad array of U.S. corporations to gather a snapshot of business sector health and activity. The program, known as the Quarterly Financial Report, or QFR, survey has been collected and published for over sixty years by various Federal agencies, the Census Bureau being the current administrator of the program. 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. What’s reported in this blog is only a snapshot of the other data reported by the QFR survey.

The wood products manufacturing data is categorized by asset size: firms with less than 25 million dollars in assets (“small firms”), firms with more than 25 million in assets (“large firms”), and all firms regardless of size. While the data is somewhat dated, it provides a useful perspective on business direction and momentum with respect to the current cycle as well as providing benchmarks for individuals firm’s performance.

(Click on Chart for a Larger Image)

Viewing the 2014Q1 data, the picture that emerges is an industry that may have already peaked for this cycle in terms of financial performance. Yet caution is in order: the very tentative housing recovery following the Great Recession could well offer surprises in terms of shape and trajectory of this business cycle. Further, as has been well publicized in the financial media, weather effects were notable in 2014Q1 and these too could be impacting financial results. However, as the graph below shows, net sales have been declining since 2013Q2. Likewise, on trend over the past 12 quarters EBITDA, operating income, and net income increased through 2013Q2 but since then have traced a downward trend. Note net income, operating income, and EBITDA for the weather-affected 2014Q1 is greater than 2013Q4’s (prior quarter) results and roughly on par with 2013Q1’s (prior year) results.

Selected financial results of wood products manufacturers’ first quarter 2014 is summarized in the table shown below. Notable highlights (see table below) include:

• Net Sales - 2014Q1 net sales ($19.3 billion) fell by 4.2% relative to 2013Q4 net sales. Because the wood products industry is highly cyclical, the median Q4 to Q1 change over the past 10 years is computed and shown in the tenth column of the table for the entire industry to facilitate quarter over quarter comparisons. The 2014Q1 drop in net sales from 2013Q4 is greater than the median Q4 to Q1 drop over the past 10 years (-4.2% vs. -3.6%). On a year-over-year basis, the industry showed a slight increase (+0.5%). The increase in net sales was concentrated on firms with assets less than $25 million (+3.4%) while net sales contracted in aggregate for firms with assets greater than $25 million (-1.2%). 
• EBITDA – 2014Q1 operating cash-flow was in the upper quartile of 10-year quarterly performance for all firms, regardless of size. Despite relatively high financial performance compared to the prior 10 years, EBITDA still fell on a year-over-year basis.
• Operating Income –2014Q1 operating income for small firms more than doubled (+115.8%) from 2014Q4’s level while large firms’ operating income increased by 12.3%. Despite the explosive quarter-over-quarter change for small firms, year-over-year operating income expanded by less than 1%. Large firms’ operating income fell by over 8% on a year-over-year basis. The composite results for the industry showed a decline of nearly 5% on a year-over-year basis.
• Pre-tax Income – 2014Q1 pre-tax income increased by over 140% compared to 2013Q4 for all firms and sizes, nearly three times the median quarter-over-quarter increase between Q4 and Q1 over the past 10 years.
• Net Income – Despite small firms’ 2014Q1 net income more than quadrupling 2013Q4’s level, year over year net income still fell by nearly 9 percent. Large firms’ quarter-over-quarter performance, a more than 50 percent increase, registered solid positive year-over-year results as well, pulling industry-wide net income high enough to post a year-over-year expansion of 4.4% in net income.
• Operating Margins – Improved for all firms on a quarter-over-quarter basis but declined for all firms on a year-over-year basis. Despite year-over-year declines, margins were still in the upper 30 percentile compared to the past 10 years, posting an industry average over 8%.
• ROE and ROA – Despite Return on Equity (ROE) and Return on Assets (ROA) for small firms falling on a quarter-over-quarter their levels remain in upper 15 percentile of the past 10 years of financial performance. Year-over-year performance for the industry as a whole showed improvement, posting 15.42% and 5.76% for ROE and ROA, respectively.

(click on Table for a Larger Image)

Second quarter data, which is released the middle of September, will provide important information on either dispelling concerns the peak for this cycle has already been achieved or that recent performance was simply the market grabbing a breath before launching higher still.