Sawtimber Stumpage Research Articles

Estimating timber supply elasticity of private forest landowners in the US South

In the US South, private forest landowners own the majority of timberland; therefore, understanding the timber supply behavior of these landowners is a fundamental forest economic question. This study estimates the pine stumpage supply equations and elasticities of private landowners in TimberMart-South (TMS) regions. It uses individual landowner pine stumpage (sawtimber, pulpwood, and Chip-N-Saw [CNS]) sales data from 2010 to 2019 and employs the seemingly unrelated regression (SUR) to estimate supply elasticities. Over the study period, nominal sawtimber stumpage price has decreased, while CNS stumpage and pulpwood stumpage prices have not effectively changed. The SUR estimate results revealed that pine sawtimber (2.452) and CNS (1.675) stumpage supply were price elastic, while pine pulpwood stumpage supply was price inelastic (0.352). Furthermore, there was a significant and negative effect of sawtimber price on pulpwood and CNS volume supplied and vice-versa, however, the effect of the former was larger in magnitude. This study provides not only a nuanced understanding of landowners' supply elasticities of three principal stumpage products but also a valuable empirical basis to better understand the market outcomes and welfare changes that market participants might experience due to new policies and market shocks.

Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network

We conducted a comparative analysis of the predictive ability of classical econometric models and artificial neural networks (ANNs) for pine sawtimber stumpage prices across 22 TimberMart-South regions in the US using quarterly prices from 1976 to 2022. We evaluated model accuracy via root mean square error and mean absolute percentage error metrics and employed the modified Diebold-Mariano test to determine if there was a significant difference in forecast accuracy between the two models. Our findings demonstrate that ANNs outperform classical models in predicting turning points, whereas classical models tend to smooth price trends and produce forecasts that are biased toward the average value. This study provides a basis for predicting timber prices in the southern timber market using ANN models and contributes to ongoing discussions on the effectiveness of machine learning algorithms in generating precise forecasts within the forest industry. The findings can help timberland investors to make informed business decisions in the timber market.

Spatial Spillover Effects of Pine Sawtimber Stumpage Prices in the US South

Abstract Timberland returns have three main drivers: biological growth, timber price, and land value. Among three of these drivers, timber price is the most volatile. Therefore, modeling and predicting timber prices has been of great concern for timberland investors and other stakeholders. In this study, the spatial dependency and spillover effect of sawtimber prices were investigated across 11 southern states with three different spatial panel models. The findings revealed a strong positive spatial dependency in sawtimber prices across these states, and the observed spillover effect for significant independent variables was over 35% of the total effect. Similarly, pulpwood prices not only have positive impact on the sawtimber prices of a given state but also on its contiguous states. In addition, the impact of COVID-19 pandemic on sawtimber prices was positive and significant. These results would facilitate the valuation of timberland investments as well as predict future timber prices. Study Implications: The sawtimber prices of one state positively affect the future sawtimber prices of neighboring states in addition to its own. Similarly, a shock in a given state also affects the sawtimber prices of its neighbors. Given the volatile nature of timber prices, gaining a better understanding of spatial integration of timber markets improves prediction of sawtimber price fluctuations and trends that ultimately help timberland investors to make effective decisions.

Synchronized movement between US lumber futures and southern pine sawtimber prices and COVID-19 impacts

We analyzed the synchronized movements of lumber futures and southern pine sawtimber stumpage prices in the United States since 2011 and their response to COVID-19 events using wavelet analysis and event study. We found that the sawtimber and lumber prices have followed complex comovement patterns in the time–frequency domain and both reacted to COVID-19 events with a higher response intensity of the lumber price. Although they reacted differently to the early COVID-19 episodes and vaccine news, the sawtimber and lumber prices responded similarly to the COVID-19 pandemic declarations by the World Health Organization and US president, the US Food and Drug Administration panel’s recommendation of the first COVID-19 vaccine, and economic stimulus legislation. The patterns of synchronized movements between the sawtimber and lumber prices varied with time and frequency, but their comovement at low frequencies (>64 weeks) has strengthened since 2014 and been led by the lumber futures price; COVID-19 episodes have not changed this trend. The different magnitude of response of the two prices to the COVID-19-related events, as well as the long-term dominance of the lumber price in the comovement, reveals asymmetric price negotiation power and benefit distributions among the agents of the lumber value chain.

Time series central subspace with covariates and its application to forecasting pine sawtimber stumpage prices in the Southern United States

To model and forecast a monthly pine sawtimber (PST) stumpage price, $$y_t$$ , data collected across 11 southern states in the U.S., we adopt a new semiparametric approach where the first phase adopts a nonparametric method called “Time Series Central Subspace with Covariates” (TSCS-C) to extract sufficient information about $$y_t$$ through a univariate time series $$\{d_t\}$$ , which is a linear combination of a set of past values of $$y_t$$ and a high dimensional covariate vector $${{\mathbf {x}}}_t$$ of sale characteristics. Then, $$\{d_t\}$$ alone is used as the predictor series to build a parametric nonlinear time series model for $$y_t$$ . This yields a new semiparametric nonlinear time series model for $$y_t$$ . Assessment in terms of out-of-sample forecasts of monthly PST stumpage prices show that our semiparametric model with the covariate $${{\mathbf {x}}}_t$$ has the smallest average forecasting error compared to another semiparametric nonlinear time series model without $${{\mathbf {x}}}_t$$ and two other parametric counterparts based on multiplicative seasonal autoregressive integrated moving average models with and without $${{\mathbf {x}}}_t$$ . This data underscores the ability of our semiparametric approach to first reduce the dimensionality of $${{\mathbf {x}}}_t$$ and a set of past values of $$y_t$$ significantly using the TSCS-C nonparametric methodology and then to produce a superior nonlinear time series model.

The 30-year dynamic of the softwood sawtimber stumpage market in Louisiana: insights from quarterly data from 1988 to 2017

Based on a 30-year quarterly data set, this paper examines the dynamics of the softwood sawtimber stumpage market in Louisiana, United States, from 1988 to 2017. Cointegration analysis and the vector error correction model were used to examine the demand and supply of softwood sawtimber stumpage products in Louisiana. Competition among different stumpage products, seasonality patterns in the market, and the impact of some exogenous events were also investigated. The results showed that the demand for softwood sawtimber products is inelastic to its own price, whereas the supply is elastic. Strong seasonality patterns were detected for both stumpage price and harvest quantity. Significant substitution effects were also found among different types of stumpage products. In addition, the estimation results of the dynamic model suggested that, when facing an exogenous impact, the harvest quantity adjusts far quicker than the stumpage price in the Louisiana softwood sawtimber stumpage market.

An Econometric Study of the Hardwood Sawtimber Stumpage Market in Louisiana

Abstract Although the hardwood timber market is an important segment of the forest industry in the United States, little attention has been paid to modeling hardwood stumpage and lumber markets. Based on the annual data series from 1955 to 2014, we estimate simultaneous demand and supply models of the hardwood sawtimber stumpage market in Louisiana. A permanent structural break in 1993 is detected in the hardwood sawtimber stumpage market, and the modified cointegration test and structural vector error correction approach are used to estimate the demand for and supply of hardwood sawtimber stumpage. The results show that own-price elasticity values in both the demand and the supply equations are inelastic in the long run. Hardwood and softwood sawtimber are found to be substitutes. Moreover, the softwood timber market always leads in the feedback adjustment process and can help predict the hardwood timber price in Louisiana.

Modeling Prices for Sawtimber Stumpage in the South-Central United States

The South-Central United States, which includes the states of Louisiana, Mississippi, Texas, and Arkansas, represents an important segment of the softwood sawtimber market. By using the Seemingly Unrelated Regression (SUR) method to account for the linkage among the four contiguous timber markets, this study examines the dynamics of softwood sawtimber stumpage markets within the region. Based on quarterly data from 1981 to 2014, the findings reveal that both pulpwood and chip-and-saw (CNS) prices have a positive influence on the Texas and Arkansas sawtimber markets. Moreover, Granger-causality tests suggest that unidirectional causality runs from pulpwood and CNS markets to the respective sawtimber market. Compared to the pre-financial crisis period, sawtimber prices in these four states are 9%–17% lower in the recent years.

The Analysis of Pine Stumpage Prices Based on Timber Sale Characteristics of the Southern United States

Abstract The stumpage price changes were calculated and analyzed from the data collected by Timber Mart-South from 1998 to 2007. We analyzed the relationship between pine sawtimber stumpage prices and timber sale characteristics using hedonic pricing method. Quadratic transformation was employed for sale size and contract length. Stumpage prices increased with sale size, contract length, bid sales, and the number of bidders. The presence of above average or excellent grade, market conditions, and logging conditions also are positively related to stumpage prices.Key Words: stumpage prices, hedonic pricing method, timber-marth-south, timber sale characteristics Received: February 26, 2014. Revised: September 25, 2014. Accep ted: September 29, 2014.Corresponding author: Hojung KimThe Forest Center of Climate Change, Korea Forest Research Inst itute, Seoul 130-712, Republic of KoreaTel: 82-2-961-2888, Fax: 82-2-961-2879, E-mail: hojung.kim.17@gmail.com Introduction Forest landowners often need access to current timber market information because they need to know what they have and how to sell it. Since timber markets basically de-pend on the supply and demand, timber buyers and sellers need up-to-date timber market information such as market trends and trade news. However, it is not an easy task to predict timber price (Mei et al. 2010). Timber market is a function of the relationship between timber and a variety of factors, such as wood consumption, wood supply, pro-duction technology, finished product demand, and stum-page prices, and also the change in timberland ownership may have had a significant influence on timber markets (TMS 2009). Delivered prices include harvesting, trans-portation, and other markups above the stumpage price, and fuel costs and distances to mills will have effect on transportation costs, where wood quality and tract size are the main factors concerning harvest costs (TMS 2009). The hedonic price method is an approach which most commonly uses regression analysis to estimate the implicit values of characteristics from a value of commodity price (Rosen 1974). In the process of manufacturing, some pro-duction inputs could be diverse and have significantly dif-ferent characteristics. In such cases, a hedonic pricing ap-proach is suitably employed for estimating the implicit pri-ces of the various characteristics of an input and the de-mand for the input subsequently (Ladd and Martini 1976). Thus, a hedonic model can be used to explain production factors or the prices of differentiated products. This he-donic price approach has been adapted to timber markets with heterogeneous inputs such as species composition, tree size, volume, and quality based on the assumption that such characteristics affect the lumber production (Puttock et al.

Modeling and forecasting pine sawtimber stumpage prices in the US South by various time series models

Among the three timberland return drivers (biological growth, timber price, and land price), timber price remains the most unpredictable. It affects not only periodic dividends from timber sales but also timber production strategies embedded in timberland management. Using various time series techniques, this study aimed to model and forecast real pine sawtimber stumpage prices in 12 southern timber regions in the United States. Under the discrete-time framework, the univariate autoregressive integrated moving average model was established as a benchmark, whereas other multivariate time series methods were applied in comparison. Under the continuous-time framework, both the geometric Brownian motion and the Ornstein–Uhlenbeck process were fitted. The results revealed that (i) the vector autoregressive model forecasted more accurately in the 1-year period by the mean absolute percentage error criterion, (ii) seven out of the 12 southern timber regions played dominant roles in the long-run equilibrium, and (iii) conditional variances and covariances from the bivariate generalized autoregressive conditional heteroscedasticity model well captured market risks.

Transaction evidence analysis: stumpage prices and risk in central Georgia

This paper uses hedonic regression techniques to analyze timber bid transactions in central Georgia. Softwood stumpage prices from pay-as-cut transactions are regressed against timber sale and stand characteristics. We identify observable factors that are statistically associated with the volatility of pine sawtimber stumpage prices in the market. The remaining price volatility, defined as market risk, characterizes undiversifiable price volatility in the market. Isolating market risk in this way has implications for relative price risk across predefined timber markets. Applications of this these techniques suggest that analyzing market price variability with total measures alone, such as standard deviation, may provide false senses of timber price risk.

Price transmission between products at different stages of manufacturing in forest industries

The theory of demand and supply implies a positive relationship, or “price transmission” between the prices of products at different stages of manufacturing. This relationship was investigated with quarterly prices of softwood stumpage in the US South, and national prices of forest products, from 1977 to 2002. All prices, net of inflation, were found to be nonstationary and there was no evidence of co-integration between prices. Vector autogressive models, augmented by Granger causality tests and multiplier analysis showed that there was a one-to-one permanent positive response of the southern sawtimber stumpage price to a permanent change in the national lumber price. There was also a one-third permanent positive response of the national paper price to a permanent change in the national pulp price. There was no relation between regional pulpwood prices and national pulp or paper prices. When price transmission was significant, the full adjustment took about 2 years.

Evaluation of U.S. southern pine stumpage market informational efficiency

The literature on informational efficiency of southern timber markets conflicts. Part of this conflict is because of differences in how efficiency was tested. In this paper, price behavior tests are based on deflated ("real") southern pine (Pinus spp.) sawtimber stumpage prices, using some of the same data and tests used in previous research and some new data and tests. Here, different results are found in many cases regarding price behavior, as compared with the existing literature. Using a valid and consistent data-based model selection procedure, augmented Dickey–Fuller tests cannot reject a null of a unit root for most deflated monthly and all quarterly southern pine timber price series evaluated. Regressions of long-term deflated timber price ratios on their own lags lead to results similar to those offered by other authors when not corrected for bias but produce fewer similarities when bias is addressed. The results of those regressions support a contention that most of the monthly series contain nonstationary as well as stationary components and that quarterly prices tested in this framework using data through 2001 are closer to pure nonstationary processes. These results have implications for harvest timing approaches that depend on serial dependence of timber prices, provide support for certain kinds of policy and catastrophic shocks modeling procedures, and address the validity of statistical approaches best suited to evaluating interconnections among timber markets.

Harvest, inventory, and stumpage prices: Consumption outpaces harvest, prices rise slowly

America's appetite for timber will continue to grow, and consumption will exceed domestic harvest over the next 50 years. Imports will supply the bulk of softwood consumption growth in the next 15 years. After 2015, both domestic softwood harvest and imports will expand. Private softwood inventories will rise in all regions and will be higher by 2050 than current levels. Softwood sawtimber stumpage prices will trend upward in all regions, while pulpwood prices will rise only in the final two decades of the projection. Southern hardwood inventories will fall after 2010 with rising stumpage prices. Northern hardwood harvest and inventories will grow, yielding stable pulpwood prices but modestly rising sawtimber prices.

A Technique for Merging Areas in Timber Mart-South Data

Abstract For over 20 yr, Timber Mart-South (TMS) has been distributing prices of various wood products from southern forests. In the beginning of 1988, the reporting frequency changed from monthly to quarterly, a change readily addressed through a variety of established statistical techniques. A more significant statistical challenge is Timber Mart-South's change in 1992 from (typically) three reporting regions per state to two. We developed a conversion technique to address this change in reporting areas, permitting longitudinal analyses of the current two regions per state but extending back to the beginning of Timber Mart-South's reports in 1976. We report conversion factors for every state's regions, verify the statistical nature of all time series created using them, and report tests of seamlessness. We find that our technique enables the creation of new, seamless series for pine sawtimber stumpage and delivered sawlogs, and pine and hardwood pulpwood stumpage and delivered logs. In only 30 out of 126 cases were we able to identify remaining regime shifts in the time-series of quarterly prices that corresponded with the 1992 boundary reconfigurations. However, these statistically significant shifts may not be related to the boundary reconfigurations. South. J. Appl. For. 24(4):219-229.

Optimal Management of Shortleaf Pine Plantations in the Central United States

Abstract A dynamic programming (DP) algorithm was developed for shortleaf pine in the Central United States. All thinning strategies examined (above, below, above and below, and mechanical) improved economic returns with thinning from above returning the highest soil expectation value (SEV). A planting density of 500 trees/ac maximized economic returns under all economic conditions. SEV was found to increase with site, and rotation length decreased on better sites. As sawtimber stumpage prices decreased relative to pulpwood prices, thinning was no longer economically advantageous. Finally, under conditions of uncertainty related to future prices, rotation length decreases slightly. North. J. Appl. For. 14(2):67-71.

Informational Efficiency of Markets for Stumpage: Reply

We appreciate the opportunity to reply to Hultkrantz's comment on our analysis of the informational efficiency of markets for pine sawtimber stumpage in the U.S. South (Washburn and Binkley). The efficiency (or inefficiency) of stumpage markets does indeed have important implications for forest management decisions, and we are pleased by Hultkrantz's interest in the subject. His comment makes many valid points and furthers the debate on stumpage market efficiency. There are, however, some issues raised by Hultkrantz that require discussion, clarification, or correction. Our reply proceeds in five stages. First, we clarify the meaning of stumpage market efficiency and illustrate its consequences for the time-series behavior of stumpage prices and for the profitability of priceresponsive timber harvesting strategies. Second, we argue that the price elasticity of aggregate timber supply has little relevance to the question of stumpage market efficiency. Third, we show that Hultkrantz's analysis of the stationarity of southern pine stumpage prices and our analysis of the serial relationship between departures from equilibrium rates of stumpage price change are simply alternative means of testing the hypothesis that stumpage markets are weak-form efficient. Fourth, we argue that Hultkrantz's results are largely consistent with those presented in Washburn and Binkley. Finally, we interpret the results of tests of stumpage market efficiency to date, and suggest a path forward.

Optimal management of loblolly pine plantations with stochastic price trends

An economic analysis of loblolly pine (Pinustaeda L.) plantation management options with stochastic sawtimber and pulpwood stumpage price trends is conducted using the North Carolina State University Plantation Management Simulator, a widely used model in the southeastern United States. Results for stands with a range of site indices suggest that regimes with high planting densities combined with commercial thinning options have higher expected present values than do regimes without thinning options, especially in plantations with hardwood competition. Such regimes are superior because high planting densities increase the returns from pulpwood thinnings without compromising sawtimber volume at rotation age. Further, high planting densities maintain the option to produce either sawtimber or pulpwood depending on the stumpage prices at midrotation. Optimal regimes are conditional on the sawtimber and pulpwood prices at the time of planting. A comparison of results for plantations with and without hardwood competition suggests that when the hardwood stumpage price is likely to increase over time, removing hardwoods with commercial thinning is superior to removing hardwoods immediately after planting. Finally, planting and thinning regimes that are optimal for deterministic price trends provide near-optimal expected returns when employed in an environment where price trends are stochastic.

Harvesting Loblolly Pine Plantations with Hardwood Competition and Stochastic Prices

Abstract This paper presents numerical results of the effects of stochastic stumpage prices on economic optimal thinnings and rotation ages for loblolly pine (Pinus taeda L.) plantations in the Piedmont region of North Carolina. The numerical results are obtained with a dynamic programming model that provides optimal harvest levels as a function of price, stand density, and age. For numerical tractability the network is relatively coarse with a 5-year decision cycle and 50 trees/ac intervals for the harvest control variable. Sawtimber and pulpwood stumpage price probabilities are assumed to be independent and stationary and are based on the means and variances of past price observations. Optimal full-rotation management strategies for both pure and mixed-species plantations show that sawtimber and pulpwood price variation do not affect the timing and intensity of early commercial thinning. Stumpage price variation does affect the timing of dearcutting. Gains in present value are made by timing the clearcut to a period with high sawtimber stumpage price. Results on the effects of hardwood competition show that the opportunity cost of hardwood competition that persists through one rotation is higher than the cost of eliminating the hardwoods early on. The effects of restrictions in the size of the dynamic programming network and the stationarity assumptions in the stochastic price processes are discussed. For. Sci. 37(5):1266-1282.

Softwood Timber Supply and the Future of the Southern Forest Economy

Abstract Fifty-year projections of the Southern forest economy suggest that in the period prior to 2015, nonindustrial owner-ships will be unable to sustain past growth in softwood harvest. Harvest could expand dramatically in later years, however, if the high rates of plantation establishment observed during the 1980s are maintained into the next century. Industrial harvest will continue to rise, but cannot increase fast enough to offset the nonindustrial shortfall. Sawtimber stumpage prices will rise sharply over the period to 2015 and stabilize or decline thereafter. Higher rates of planting influence only the later portion of this projection. Prospects of higher rates of recycled fiber use in U.S. paper and board production could have dramatic implications for the adequacy of Southern timber supply and sharply reduce stumpage price growth in both the near- and long-term. South. J. Appl. For. 15(1):31-37.