The phantom of the recession: how real timber prices and harvest timing define forestland value, 2021–2025 and beyond. Understanding inflation-adjusted timber economics in a cycle of hidden decline
The phantom of the recession: how real timber prices and harvest timing define forestland value, 2021–2025 and beyond. Understanding inflation-adjusted timber economics in a cycle of hidden decline
William E. Schlosser, a,*
a: D&D Larix LLC, Pullman, WA, USA.
*Corresponding author: E-mail: Schlosser@Resource-Analysis.com
Citation: Schlosser WE. 2025. The phantom of the recession: how real timber prices and harvest timing define forestland value, 2021–2025 and beyond. Understanding inflation-adjusted timber economics in a cycle of hidden decline. J.For.Bus.Res. 4(1): 73-95. https://doi.org/10.62320/jfbr.v4i1.68
Received: 26 April 2025 / Accepted: 5 June 2025 / Published: 23 June 2025
Copyright: © 2025 by the author
ABSTRACT
This paper introduces the “Phantom of the Recession,” a forest sector recession that was obscured by inflationary distortions and masked by nominal price gains from 2021 through 2025. Unlike prior downturns, this economic phase eluded conventional indicators while eroding timberland value in real terms. Through the lens of the Real Price Appreciation (RPA) Forecast Tool — embedded within the Forest Resource Analysis System Software (FRASS) — I reveal how timber markets entered a hidden cycle of value decline, despite appearances of growth. The RPA Forecast Tool models inflation-adjusted log prices by species, sort, and grade, projecting future valuation paths and optimizing harvest timing across three timber rotations. I present econometric evidence, case studies, and forecasting insights that demonstrate how integrating biometric and economic signals enables landowners and public agencies to navigate recessionary cycles. Findings indicate that failing to recognize real price devaluation risks can lead to suboptimal harvests, undervaluation of public trust assets, and misaligned resource allocation. My approach provides cycle-aware, landowner-specific valuation tools that restore strategic clarity. While the Phantom of the Recession may escape GDP headlines, its imprint is etched into forestland valuation — with lasting implications for landowners, investors, and policymakers alike.
Keywords: forest econometrics, forestland valuation, harvest timing optimization, inflation-adjusted asset value, phantom recession, public trust assets, real timber prices, recession cycles, RPA Forecast Tool, timber market forecasting
INTRODUCTION
The past four years (2021-2024) have ushered in an economic phenomenon that has gone largely unrecognized by mainstream indicators — a recession not of production, but of purchasing power; not of volume, but of value. This paper introduces the concept of the “Phantom of the Recession,” a term used to describe the prolonged decline in real (inflation-adjusted) timber prices masked by nominal gains and monetary stimulus. Unlike traditional recessions characterized by widespread job loss or GDP contraction, this downturn has emerged in the subtleties of inflationary masking (Visco, 1984) — eroding asset value while appearing to increase it.
In the forestry sector, the consequences of this misread economic climate are substantial. Forestland valuation, harvest timing, and revenue expectations are all sensitive to inflationary pressure. Yet, few tools exist within forest economics that can distinguish between real and nominal trends with precision. This paper outlines the theory, measurement, and strategic implications of the phantom recession, presenting a path forward for forestland owners and managers to respond. It utilizes the Forest Resource Analysis System Software (FRASS, software henceforth) (2025) and its embedded Real Price Appreciation (RPA) Forecast Tool (tool, henceforth) (Schlosser 2020).
Positioning the analytical framework
This manuscript introduces a structured valuation framework designed to reconcile biometric growth dynamics, competitive market pricing, and multi-rotation financial planning within a unified economic model. While the core structure of this approach has been operational for over a decade within professional forestland appraisal environments, its logic has not been previously formalized in peer-reviewed literature. The methods presented here reflect an internal valuation system—developed iteratively through practical application—that addresses longstanding challenges in rotation timing, market volatility, and forward-looking valuation. Rather than promoting a particular software, this manuscript aims to articulate the economic reasoning underlying that system, to advance replicable standards for rotation-based valuation that align with national appraisal guidelines and real market behavior.
BACKGROUND AND MOTIVATION
Timberland valuation has long relied on biometric data (Arney 2015) — including tree growth rates, site index, and yield tables (FVS 2024, ESSA 2021) — paired with general economic assumptions. Early econometric models of harvest supply emphasized policy-driven constraints and market sensitivity, but lacked cycle-aware forecasting (Adams et al. 1982). But valuation models have not kept pace with the volatility and distortion of modern markets. Inflation, interest rate manipulation, and speculation have detached timber prices from their biological underpinnings (Phillips et al. 2013). This phenomenon is not limited to forestry; expansionary monetary policy has historically inflated asset prices beyond real economic fundamentals (Bordo and Landon-Lane 2013).
As a result, a growing awareness within both public and private sectors points to the need for dynamic, predictive valuation systems — systems that capture not just static value, but the temporal leverage embedded in harvest timing. This challenge is especially acute in commodity-dependent economies where monetary policy influences both investment timing and resource pricing (Shapoval 2022). This is the core tension that sets the stage for the Phantom of the Recession.
In the 2009 Great Recession, real timber prices dropped sharply but gradually recovered as housing markets stabilized (Harrison 2010). Economists observed that the decline and recovery followed cyclical trends (Hansen 1957) — driven not only by market psychology, but also by structural forces such as housing starts (U.S. Census Bureau and U.S. Department of Housing and Urban Development 2025, Bouchaud et al. 2008), mortgage availability, and commodity speculation. Traditional forecasting approaches often assumed timber prices followed a random walk or adhered to Markovian independence — where future values depend solely on current conditions, without memory of prior cycles (Hamilton 1989, 1990). While these models advanced the analysis of regime shifts in macroeconomics, they proved inadequate for capturing the persistent, multi-decade cycles seen in forest product markets. A cycle-aware forecasting tool emerged in this context (Schlosser 2020), designed to project timber prices by aligning historical price trajectories with embedded cycle length, amplitude, and inflation-adjusted trends — offering an alternative to static valuation frameworks.
Following the 2009 trough of the Great Recession, nominal prices for Douglas-fir 2-Sawmill logs began a steady climb — rising from just $350/MBF in April 2009 to a peak of $963/MBF in July 2022 (Figure 1). At face value, this thirteen-year surge appeared to signal a strong bull market. In reality, it was largely driven by high inflationary forces, not fundamental increases in demand. The illusion of appreciation misled many into believing that timber values would expand, when in reality, purchasing power and returns on forest assets had already begun to erode.
By December 2023, nominal prices had fallen to $663/MBF, then rebounded to $750, only to decline again. This erratic pattern of nominal peaks and troughs — fueled more by monetary expansion, speculative volatility, and policy uncertainty than true demand-side dynamics — signals the onset of a new devaluation cycle (Haim et al. 2014). Figure 1 illustrates this divergence between nominal and real prices, laying the groundwork for understanding how the Phantom Recession took hold in plain sight.
Figure 1. Nominal price path of Douglas-fir 2-sawmill logs: Illusion of recovery, 2009–2025 (U.S. Bureau of Labor Statistics 2025, WaDNR 2025).
The Phantom of the Recession diverged. Beginning in 2021, real timber prices began to fall even as nominal prices continued to climb (WaDNR 2025). Stimulus checks, artificially low interest rates, and inflationary spikes created an illusion of growth (Vighi 2024, Ray and Pal 2022, Nakamae 2014). But behind the scenes, the real value of logs — particularly sawlogs — was falling (Figure 2).
While nominal log prices surged from 2021 to 2023, real timber values — adjusted for inflation — declined steadily. This divergence, shown in Figure 2, captures the defining tension of the Phantom Recession: a perceived boom masking a real bust (Chevalier et al. 2003).
Figure 2. RPA forecast and real prices for Douglas-fir 2-Sawmill logs (202504), 2006–2025
(WaDNR 2025, U.S. Bureau of Labor Statistics 2025).
ECONOMETRIC FOUNDATIONS OF THE RPA FORECAST TOOL
RPA: Real Price Appreciation as a metric
Real Price Appreciation (RPA) is a core metric in software that measures inflation-adjusted changes in timber prices over time. It isolates purchasing power changes in log values by comparing historical nominal prices with corresponding Producer Price Index (PPI) adjustments (U.S. Bureau of Labor Statistics 2025). This allows users to observe whether timber prices are truly appreciating or merely reflecting broader inflation.
The RPA metric forms the basis of the forecast algorithm: by analyzing historical RPA patterns, including the timing and amplitude of price declines and recoveries, the tool estimates future RPA trajectories. These forecasts then serve as inputs for stand-level valuation models and harvest timing optimization.
Forecasting delivered log prices
Without inflation-aware pricing models, timing decisions are based on illusions — not reality. Each growth projection is evaluated through the lens of the tool, which assigns inflation-adjusted prices to every timber sort and grade within a defined marketing region. Rather than assume equilibrium pricing or constant growth, the tool identifies inflection points in past market cycles and applies those insights to current price paths.
Inflation is not treated as an external assumption but is integrated into the forecast structure itself. This dual-model approach — projecting both biometric yield and real price change — produces valuation outputs that are sensitive to both growth and macroeconomic trends. Forest owners are increasingly influenced by objectives beyond net present value maximization, including cultural, ecological, and intergenerational concerns (Silver et al. 2015).
In the Delivered Log Market Model (DLMM), the landowner retains full control over the forest operation, including harvest planning, contractor selection, road layout, and direct negotiation with mills for log sales (Schlosser et al. 1996). The logger is hired to serve the landowner's interests, and the proceeds from the sale are paid directly to the landowner, who compensates the operator accordingly. In contrast, the Stumpage Market Model (SMM) transfers most decision-making authority to the logging operator, who typically controls harvest timing, road layout, log marketing, and mill selection. This model often results in reduced transparency and limited landowner engagement in value realization — a concern long documented in forestry tax guidance (Schlosser et al. 1998). Once timber is sold as stumpage, the operator controls the harvest schedule, selects the market, and retains the revenue from log sales, effectively removing the landowner from subsequent decisions.
The software was explicitly designed to support the DLMM structure, where strategic decision-making, valuation precision, and landowner equity are paramount. All assumptions, forecasts, and financial simulations within this manuscript reflect DLMM conventions.
The tool was integrated into the software in 2009 and remains pivotal to its function. It incorporates each marketing region’s actual delivered log history and tailors forecasts for each sort and grade. It even adjusts for counter performance trends in pulpwood relative to sawlogs, enabling realistic valuation under changing market dynamics.
Timber stand valuation across rotations
Financial optimality in the software approach is revealed through iterative simulations of harvest values across three full timber rotations. The first rotation is modeled for its convergence of biometric growth and economic timing, producing an optimal harvest year based on discounted net value (Schlosser 2023). The second rotation begins immediately and is evaluated at 5-year intervals, extending up to 500 years into the future to explore a wide range of timing possibilities.
The third rotation is projected into perpetuity using the Faustmann framework (1849) — a classical land expectation value model that has guided forest economics for over 170 years (Willassen 1998, Klemperer 1996).
Economic theory becomes actionable only when applied to real landowners facing real decisions. The following cases demonstrate how RPA-informed software outputs reshape forestland planning. Software does not assume fixed harvest timing — it finds it by evaluating every rotation year across all three timber cycles. The resulting solution reveals how each rotation can be shortened or extended to align biological maturity with long-run financial optimality. The outcome is a harvest schedule tailored to each stand — optimized for value, not assumptions.
Integrated strategic management through economic cycle awareness
Traditional models often assume that long-run timber prices appreciate at or above the rate of inflation. This assumption distorts value expectations, particularly as equilibrium conditions are approached — as was evident in the post-pandemic pricing plateau of 2021–2024. This software’s approach challenges this logic by embedding cyclical structure into its valuation framework, reflecting that timber markets operate not in smooth escalation, but in dynamic, thread-like cycles of appreciation, stagnation, and correction. As demonstrated by Schlosser (2023), harvest preference is not static — it is shaped by inflation expectations, impatience, and policy volatility, all of which the software incorporates in its decision loop.
What emerges is a paradigm of strategic alignment between forest management decisions and market valuation phases.
When real prices are declining or stagnant — the software identifies as the “phantom peak” period — this is not a signal to pause all activity. Rather, it is the time to redirect attention toward low-value or underperforming stands, where premature liquidation may offer long-run gains through regeneration, replanting, or selective thinning. These interventions not only reallocate biomass but often improve habitat conditions, biodiversity, and silvicultural outcomes.
Conversely, when econometric strategy signals a long-run market upcycle, management should shift toward high-quality, high-value timber stands — those with ecological and cultural co-benefits — to leverage both economic and ecosystem service returns. Investing in these sites during high-value windows optimizes net revenue while supporting multifaceted landowner objectives.
Put plainly: informed cycle tracking enables smart forest management. Adhering to the true rhythm of market cycles — not their nominal illusions — empowers landowners, managers, and agencies to allocate time, capital, and effort where it matters most (Prestemon and Abt 2002).
RESULTS AND DISCUSSION
RPA cycles and the phantom of the recession
Just because a sale occurs at a peak does not mean it was a wise decision — only time and context can prove that. Over the past two decades, timber’s real price trajectory has been volatile — sometimes increasing modestly, but more often stagnant or declining when adjusted for inflation (Figure 2). The tool predicted and accurately described the phases of each market cycle: from initiation to recovery (Schlosser 2020). During the Great Recession, the devaluation phase showed the econometric decline in real value as it reached its zenith of value loss. After this trough in the recession, the consistent trend followed a real price appreciation as it approached equilibrium. That price balance was estimated to level out by 2028, but the Phantom of the Recession cycle broke formation (Figure 2).
While nominal price tribulations surged through 2021’s housing boom, real prices continued to fall. The illusion of value appreciation, fed by an inflated cash economy, distorted decision-making across the forestry sector. The tool revealed the dissonance — quantifying the gap between perceived market strength and actual purchasing power.
These insights were not theoretical. They directly informed harvest timing shifts. Landowners who relied on nominal signals risked early liquidation. Those guided by RPA-adjusted forecasts preserved asset value by aligning harvest with actual recovery potential.
Accuracy of the tool
Most appraisers measure what has already happened. The tool anticipates what is likely to unfold.
The tool was first published in Forest Policy and Economics (Schlosser 2020) and has remained a foundational element of the software. While its predictive power has grown with refinement and application across multiple marketing areas, its core logic has remained unchanged: The tool assumes that delivered log prices are cyclical and respond to inflationary and recessionary pressures in a patterned, non-random manner.
Unlike linear extrapolations or moving averages, the model identifies devaluation and appreciation phases using a cumulative RPA differential tied to the Producer Price Index (PPI) and real delivered log prices. Each timber sort and grade is assigned a historical market trajectory and evaluated under inflation-adjusted pricing regimes (Barro 1986). This logic parallels advances in stochastic revealed preference theory, where economic agents are modeled not as static optimizers but as actors navigating uncertain value signals (Bandyopadhyay et al. 1999). The heterogeneity in discount rates reflects owner-specific utility and opportunity costs, consistent with models of private forest behavior under uncertainty (Kuuluvainen et al. 1996). The software tool models a similar behavioral implication: rather than assume clear preferences, it models harvest decisions as stochastic responses to shifting inflationary cues.
The result is a predictive model that does not simply describe past cycles — it anticipates new ones. From the depths of the 2009 Great Recession trough, the model accurately forecast a recovery through the 2010s, aligning with subsequent price movements — matching observed trajectories in both price rebound and stand-level harvest responses as documented in parcel-level valuation runs (Figure 2). However, in 2021, the Phantom of the Recession cycle broke formation. While nominal prices rose rapidly amid inflationary distortions and speculative behavior, the tool identified a new downturn phase in real terms. This divergence became the hallmark of the Phantom.
The interaction of the landowner’s impatience factor with projected inflation determines whether the current stand should be harvested sooner or held. As shown in Growth and Decay (Schlosser 2023), these impatience factors are not arbitrary — they reflect owner-specific opportunity costs and risk profiles. When paired with inflation forecasts, they produce customized harvest schedules grounded in the income capitalization approach, a foundational principle in timberland valuation outlined by The Appraisal Foundation (2024) and detailed in forest economics literature (Klemperer 1996).
Inflation, currency devaluation, and the illusion of rising prices
From 2020 through 2023, nominal log prices surged in many U.S. markets, buoyed by stimulus-driven housing demand and speculative capital (Reimer 2021). But inflation-adjusted prices told a different story. By removing the inflation factor, the real price of timber logs showed steady declines — especially among key structural grades (Figure 2).
Policymakers misread the inflationary surges of 2021–2023 as signals of genuine economic recovery. Stimulus spending, aggressive monetary easing, and suppressed interest rates led to nominal price increases across many commodities — including timber. These gains were interpreted as demand-driven, when they were just symptoms of high inflation and supply chain volatility. Agencies accelerated harvests, scheduled timber sales, and approved land exchanges based on inflated nominal benchmarks. The result was a systemic misvaluation of forestland assets. This misinterpretation was not merely a failure of foresight — it was rooted in outdated valuation models unable to distinguish nominal price increases from real economic performance.
The Phantom of the Recession is not a conventional downturn. It is a distortion — a recession in real value hidden by inflation. When adjusted by the PPI, delivered log prices have declined consistently since mid-2021, undermining the profitability of long-term forestland investments (Figure 2).
This analysis revealed misalignment as early as Q2 2021. Real prices began falling even as market headlines claimed record highs (Conrad and Blinn 2024). For forestland owners, the divergence proved costly. Holding timber under assumptions of increasing value backfired; real NPV eroded.
IMPLICATIONS FOR FORESTLAND MANAGEMENT AND POLICY
To demonstrate the practical effect of cycle-aware valuation under the Phantom of the Recession, we analyze parcel 16N04E0416324016, a 40-acre tract (SE¼ NW¼, Sec. 27, T16N R04E WM) located in the Cascade foothills of western Washington (Figure 3). This parcel contains four timber stands of varying site indices and species composition (Table 1), and varying levels of maturity (Table 2). offering a realistic scenario for testing valuation differentials under traditional and RPA-adjusted forecasts. The divergence in outcomes is stark — and reveals how overlooked devaluation cycles can significantly distort perceived asset value.
Parcel-level application: demonstrating the financial delta of cycle-aware forecasting
The comparative financial implications of RPA-integrated modeling versus traditional timber appraisal are substantial, as demonstrated in the following valuation analysis.
To ensure a consistent basis for comparison, both valuation scenarios — traditional and RPA-integrated — were modeled under identical economic and operational assumptions. The inflation rate was set to 3.12%, based on the trailing 10-year Producer Price Index (PPI) trend, and the landowner discount rate was held at 2.0%, representing a typical state forestland impatience factor (Schlosser 2023). Road access fees, construction costs, and haul expenses were fixed. Competitive harvest expenses were estimated at $80/MBF for logging, $65/MBF for trucking, and $25/MBF for overhead administration. Pulpwood logging costs were reduced to $5/MBF to reflect low-value market conditions. Reforestation costs were applied at $375/acre, charged one year after harvest. These constants isolate the effect of price forecast differences — clarifying how cycle-aware modeling alone drives the observed divergence in asset value.
Using traditional appraisal assumptions, the parcel’s present value was estimated at $654,821, (Table 3) favoring early harvests at current nominal prices. In contrast, when modeled using the Phantom of the Recession RPA Forecast, the optimal harvest schedule shifted — deferring select stand entries — and raised total valuation to $839,376 (Table 4), a 28% increase. The primary driver? Recognition of real price devaluation and the financial leverage embedded in waiting for recovery.
Interpretation: what the valuation gap reveals
The 28% valuation increase observed under the RPA Forecast scenario is more than a statistical uplift — it is a strategic signal. These results reinforce a central lesson of the Phantom of the Recession: failure to account for real price devaluation leads not only to premature harvests but to systemic undervaluation of the land itself. This misalignment compounds across ownership portfolios, investment decisions, and intergenerational trust obligations.
By isolating forecast method as the only variable between the two scenarios, this case study illustrates the financial leverage embedded in cycle-aware modeling. The software, grounded in inflation-adjusted pricing and multi-rotation harvest simulation, elevates timberland valuation from static appraisal to dynamic strategy. As timber stand-level decisions scale upward to parcel and ownership-level impacts, the use of conventional forecasting methods during recessionary phases exposes landowners — public and private — to significant opportunity costs.
This divergence does not simply reflect academic nuance. It affects mill availability, carbon sequestration strategies, Tribal restoration priorities, and long-term public trust fiduciary compliance. When valuation tools fail to reflect economic reality, resource planning becomes a liability. The Phantom of the Recession reveals that the real risk is not recession itself but misreading it — and mispricing the land because of it.
Table 1. Timber stand statistics on parcel 16N04E0416324020, found at SE¼ NW¼, Sec. 27, T16N R04E WM (FRASS 2025).
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Timber Stand Statistics (current) |
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Table 2. Timber volume characteristics on parcel 16N04E0416324020 (FRASS 2025).
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Table 3. Parcel 16N04E0416324020 Harvest Volumes & Value Summary with No RPA Portfolio (FRASS 2025). |
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Table 4. Parcel 16N04E0416324020 harvest volumes and value summary with Phantom Recession portfolio (FRASS 2025). |
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Figure 3. Aerial view of parcel 16N04E0416324016, with stand boundaries delineated. This visual provides spatial context for the economic dynamics explored in Tables 1 and 2 (FRASS 2025).
Operationalizing strategic timing in forestland portfolios
Financial optimality is determined through an iterative simulation of harvest values across three full timber rotations, where each rotation’s conclusion initiates the next. This begins by simulating rotation 1 across a 500-year period, with potential harvests modeled at every 5-year increment—resulting in 100 distinct rotation 1 endpoints. At each of these, rotation 2 begins and is also simulated for 500 years in 5-year steps, producing 100 additional timing permutations. This dynamic sequence creates 10,000 unique timing combinations across two sequential rotations. At the conclusion of each of these rotation 2 scenarios, a third and final sequence—the Faustmann rotation into perpetuity—is appended, capturing the long-run economic value of continuous timber cycles (Faustmann 1849).
Optimality is revealed by computing the net present value (NPV) of each three-rotation sequence using parcel-specific discount rates. Each combination—Rotation 1 → Rotation 2 → Perpetuity—is evaluated for financial culmination and asset timing. Through this dynamic layering of cycles, the valuation system identifies the harvest year that yields the maximum present economic value. This enables the highest and best use appraisal logic to be both discovered and defended, fully in alignment with national standards of the Uniform Standards of Professional Appraisal Practice (USPAP) (The Appraisal Foundation 2024).
Each rotation is modeled for its biometric and economic convergence, producing an optimal year series based on discounted net present values (Chang 1983). This three-rotation structure forms the foundation of long-run optimization (Klemperer 1996, Hartman 1976), with 10,000 rotation ending sequences created. Through these 10,000 combinations the analyst is exposed to present value blends for all possible sequences of rotation length harvest options.
The software does not assume fixed rotation length timing, as shown by Chang (1983), earlier harvest timing may become more financially optimal when future rotations yield higher net returns. It finds it — through millions of calculations across variable growth, price, and inflation conditions. The interaction of the landowner’s impatience factor with projected inflation determines how the current stand will be harvested sooner or held, extending Hartman’s (1976) insight, this reinforces that valuation cannot rely solely on biological yield maximization. The software completes the analytical array of these combinations for the analyst to consider and see the financial tradeoffs between each option’s value.
Biometric and economic convergence becomes the new gold standard. These integrated steps within this software system enable financial optimality to emerge clearly and rigorously, aligning with the USPAP’s Appraisal Foundation standards for highest and best use (Schlosser 2023).
A Federal imperative: stewarding public trust assets with real value metrics
Across the U.S. Forest Service and the Bureau of Land Management, billions of dollars in timberland value are managed on behalf of the public (Vlosky 2000). Yet most internal valuation models still rely on volume-centric metrics or average stumpage assumptions. They miss timing. They miss inflation. And they miss the cycles that determine when value is maximized.
The software introduces a viable alternative. Its appraisal outputs are USPAP-compliant (The Appraisal Foundation 2024), automated, and defensible — ideal for land exchanges, valuation audits, or internal reporting. More importantly, it aligns harvest timing with real market signals. This avoids both premature liquidation and delayed harvest loss.
Agencies already collect biometric data. Many have decades of it. Integrating these data allows for long-horizon modeling and policy alignment without additional fieldwork.
A turning point: recession response, not recession regret
Every recession reveals two types of leaders: those who act on real conditions, and those who wait for consensus. The Phantom of the Recession has created a rare moment of divergence between perception and real performance. While headlines cite record timber prices, adjusted returns tell a more sobering story.
The software empowers agencies, Tribes, and private landowners to act on the real data — not just the noise. By providing transparent, customizable insights tied to owner-specific goals and risk tolerances, it closes the gap between policy and profitability.
The question is not whether the market will recover — it always does. The question is who will be ready when it does. Forestland value is not lost in one bad year. It is lost by making good decisions too late.
CONCLUSION
The Phantom of the Recession has not merely clouded timber prices — it has distorted the entire framework through which forestland value is understood, measured, and acted upon. While nominal price trends offered momentary comfort, they failed to reveal the erosion of real value and the critical importance of timing in resource management. This recession’s initial devaluation phase was not invisible; it was misread.
Through the integration of forest biometrics and advanced econometric forecasting, the econometric software analysis approach, powered by the tool, exposes the deeper economic realities shaping forest asset performance. From tribal lands to federal forests, and across private portfolios, the ability to see through the inflationary haze has become a defining competency.
The lessons of this recession are clear: real value must be tracked, not assumed. Timing must be optimized, not guessed. And decisions about harvesting, planning, and stewardship must be made with financial optics that account for inflation, impatience, and the cyclical nature of commodity markets.
“Managing forestland without reference to value cycles is like navigating without a compass — decisions can still be made, but direction and timing suffer.”
Forestland management is entering a new era — one that demands precision, transparency, and the courage to act before consensus forms. This manuscript stands as both a warning and a roadmap. The tools exist. The insights are here. As Silver et al. (2015) emphasize, valuation blind spots in forestry arise not just from technical limitations, but from institutional inertia — precisely what cycle-aware systems like this software are designed to overcome. The question is whether the forestry profession, and the institutions that support it, will seize the mantle of foresight — or remain captive to illusions cast by nominal shadows.
EPILOGUE: THE RECOVERY CURVE AND THE COST OF DELAY
As the Phantom of the Recession continues to obscure value in the timber sector, the timing of recovery holds serious financial consequences for landowners and public managers alike. Forecasts using these analytic tools reveal a non-linear recovery path, where timber prices — in real, inflation-adjusted terms — will not return to full pre-devaluation equilibrium until 2041. At that time, Douglas-fir 2-Sawmill logs are projected to reach $913/MBF in 2025 real dollars.
Yet the nuance of this trajectory matters. Approximately 85% of real value is projected to recover by 2030, just five years from the current writing. That rate climbs to 90% by 2033, signaling that the steepest value returns will be realized in the first half of the recovery arc. Beyond that point, marginal gains become more gradual, tapering toward full equilibrium by 2041.
The lesson is clear: timing is not a footnote to valuation — it is the central variable. Landowners who harvest too early may lock in nominal revenue at the expense of long-term value. Agencies using outdated models may misprice assets, misallocate trust obligations, or miss the chance to leverage inflation-aware harvest planning.
What the Phantom of the Recession teaches us is that clarity, not haste, drives optimization. The timing window for strategic forestland management is narrow — but for those using cycle-aware tools like the RPA Forecast within the software, the signal has never been stronger.
CONFLICTS OF INTEREST
I confirm that I am the sole author of this manuscript and the developer and owner of the FRASS software and the RPA Forecast Tool. A patent application has been filed covering enhancements to the RPA Forecast Tool, including cycle-aware timing calibration and inflation-response valuation logic. I have no financial relationships with any organizations that might benefit from the publication of this work, and no other conflicts of interest to declare. This disclosure is provided to ensure transparency and does not alter the integrity or objectivity of the research presented.
REFERENCES CITED
Adams DM, Haynes RW, Dutrow GF, Barber RL, Vasievich JM. 1982. Private investment in forest management and the long-term supply of timber. J Agric Econ. 64(2):232-241.Arney, J. (2015, December 14). Biometric Methods for Forest Inventory, Forest Growth and Forest Planning. Portland, Oregon, USA. https://doi.org/10.2307/1241127
Bandyopadhyay T, Dasgupta I, Pattanaik P. 1999. Stochastic revealed preference and the theory of demand. J Econ Theory 84(1):95-110. https://doi.org/10.1006/jeth.1998.2499
Barro RJ. 1986. Reputation in a model of monetary policy with incomplete information. J Monet Econ 17(1):3-20. https://doi.org/10.1016/0304-3932(86)90003-6
Bordo MD, Landon-Lane J. 2013. Does expansionary monetary policy cause asset price booms; some historical and empirical evidence. Natl Bur Econ Res. https://doi.org/10.3386/w19585
Bouchaud JP, Farmer J, Fabrizio L. 2008. How markets slowly digest changes in supply and demand. Paris, France: Academic Press. [accessed 2025 Jun 22]. https://doi.org/10.2139/ssrn.1266681
Chang SJ. 1983. Rotation age, management intensity, and the economic factors of timber production. J For Econ 29(2):267-277.
Chevalier J, Kashyap A, Rossi P. 2003. Why don't prices rise during periods of peak demand? Evidence from scanner data. Am Econ Rev 93(1):15-37. https://doi.org/10.1257/000282803321455142
Conrad JL, Blinn CR. 2024. Logging business challenges in the US South and Lake States. J For 122(5-6):493-504. https://doi.org/10.1093/jofore/fvae016
ESSA. 2021. FVS/Prognosis. In: Robinson D, editor. Forest Vegetation Simulator. [accessed 2018 Jun 2]. https://essa.com/explore-essa/tools/fvsprognosis/
Faustmann M. 1849. Berechnung des Werthes, welchen Waldboden, sowie noch nicht haubare Holzbestande für die Waldwirthschaft besitzen. Allg Forst Jagdztg 25:441-455.
FRASS. 2025. Forest Resource Analysis System Software. In: William ES, editor. Forest Econometrics. [accessed 2024 Feb 18]. https://frass.forest-econometrics.com/
FVS. 2024. Forest Vegetation Simulator. US Forest Service. [accessed 2024 Jan 19]. https://www.fs.fed.us/fvs/index.shtml
Haim D, Adams D, White E. 2014. Determinants of demand for wood products in the US construction sector: an econometric analysis of a system of demand equations. Can J For Res 44(10):1217-1226. https://doi.org/10.1139/cjfr-2014-0022
Hamilton J. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2):357-384. https://doi.org/10.2307/1912559
Hamilton J. 1990. Analysis of time series subject to changes in regime. J Econom 45(1):39-70. https://doi.org/10.1016/0304-4076(90)90093-9
Hansen AH. 1957. Trends and cycles in economic activity. Rev Econ Stat 39(2): [accessed 2025 Jun 22]. https://doi.org/10.2307/1928527
Harrison F. 2010. Boom bust: house prices, banking and the depression of 2010. London: Shepheard-Walwyn.
Hartman R. 1976. The harvesting decision when a standing forest has value. Econ Inq 14(1):52-58. https://doi.org/10.1111/j.1465-7295.1976.tb00377.x
Klemperer WD. 1996. Forest resource economics and finance. New York: McGraw-Hill.
Kuuluvainen J, Karppinen H, Ovaskainen V. 1996. Landowner objectives and nonindustrial private timber supply. For Sci 42(3):300-309. https://doi.org/10.1093/forestscience/42.3.300
Nakamae T. 2014. The monetary stimulus illusion. Int Econ 28(4):34.
Phillips H, Little D, McDonald T, Phelan J. 2013. A guide to the valuation of commercial forest plantations. Dublin: COFORD. [accessed 2025 Jun 22]. https://scsi.ie/wp-content/uploads/2020/11/AGuideToTheValuationofCommercialForestPlantations.pdf
Prestemon J, Abt R. 2002. Timber products supply and demand. In: Wear D, Greis J, editors. South For Resour Assess. Gen Tech Rep SRS-53. Asheville, NC: USDA For Serv; p. 299-326.
Ray P, Pal P. 2022. Fiscal stimulus and the ghost of Keynes: an evolutionary chronicle. In: Yoshino NP, editor. Studies in international economics and finance. Singapore: Springer. https://doi.org/10.1007/978-981-16-7062-6_4
Reimer JJ. 2021. An investigation of log prices in the US Pacific Northwest. For Policy Econ 126(102437). https://doi.org/10.1016/j.forpol.2021.102437
Schlosser WE. 2020. Real price appreciation forecast tool: two delivered log market price cycles in the Puget Sound markets of western Washington, USA, from 1992 through 2019. For Policy Econ 113(102114):9. https://doi.org/10.1016/j.forpol.2020.102114
Schlosser WE. 2023. Growth and decay: forest landowner impatience factor. J For Bus Res 2(1):38-67. https://doi.org/10.62320/jfbr.v2i1.8
Schlosser W, Baumgartner D, Hanley D. 1998. Forest taxes in Washington. Pullman: Washington State University.
Schlosser W, Baumgartner D, Hanley D, Gibbs S, Corrao V. 1996. Managing your timber sale. Pullman: Washington State University Extension.
Shapoval Y. 2022. Monetary policy of commodity-dependent economies. VUZF Rev 7(3):39-48. https://doi.org/10.38188/2534-9228.22.3.04
Silver EJ, Leahy JE, Weiskittel AR, Noblet CL, Kittredge DB. 2015. An evidence-based review of timber harvesting behavior among private woodland owners. J For 113(5):490-499. https://doi.org/10.5849/jof.14-089
The Appraisal Foundation. 2024. Uniform standards of professional appraisal practice (USPAP) 2024 edition. Washington, DC: The Appraisal Foundation.
U.S. Bureau of Labor Statistics. 2025. Producer price index by commodity: all commodities [PPIACO]. [accessed 2025 Apr 20]. https://fred.stlouisfed.org/series/PPIACO
U.S. Census Bureau and U.S. Department of Housing and Urban Development. 2025. New privately-owned housing units started: total units [HOUST]. St. Louis: Fed Reserve Bank of St. Louis. [accessed 2025 Jun 22]. https://fred.stlouisfed.org/series/HOUST
Vighi F. 2024. COVID-19 and the emergency loop of implosive capitalism. Routledge. https://doi.org/10.4324/9781003390183-9
Visco I. 1984. Price expectations in rising inflation. Vol. 152. North Holland, Netherlands: Elsevier.
Vlosky RP. 2000. USDA Forest Service, Bureau of Land Management, and state forester perspectives on certification. For Prod J 50(3):21.
WaDNR. 2025. Washington State Department of Natural Resources log market reports. In: Richards D, editor. Olympia, WA: WaDNR. [accessed 2025 Mar 18]. https://www.dnr.wa.gov/programs-and-services/product-sales-and-leasing/timber-sales/timber-sale-querylog-prices
Willassen Y. 1998. The stochastic rotation problem: a generalization of Faustmann's formula to stochastic forest growth. J Econ Dyn Control 22(4):573-596. https://doi.org/10.1016/S0165-1889(97)00071-7
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