The 2019-2024 Outlook for Millwork in the United States

This study covers the latent demand outlook for millwork across the states and cities of the United States. Latent demand (in millions of U.S. dollars), or potential industry earnings (P.I.E.) estimates are given across some 12,600 cities in the United States.

For each city in question, the percent share the city is of its state and of the United States as a whole is reported. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others.

This statistical approach can prove very useful to distribution and/or sales force strategies. Using econometric models which project fundamental economic dynamics within each state and city, latent demand estimates are created for millwork. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved.

This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all cities in the United States). This study gives, however, my estimates for the latent demand, or potential industry earnings (P.I.E.), for millwork in the United States. It also shows how the P.I.E. is divided and concentrated across the cities and regional markets of the United States. For each state, I also show my estimates of how the P.I.E. grows over time. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on strategic planning at graduate schools of business.

Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures assume the long-run efficiency of currency markets.

Figures, therefore, equate values based on purchasing power parities across geographies. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates were collected from official sources, and extrapolated using standard econometric models. The report uses the dollar as the currency of comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln.

1.3 THE METHODOLOGY
In order to estimate the latent demand for millwork across the states and cities of the United States, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created.

In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions.

Latent demand functions relate the income of a state, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized.

For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem.

In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function.

He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume.

This type of consumption function is shown as "B" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. For a general overview of this subject area, see Principles of Macroeconomics by N. Gregory Mankiw, South-Western College Publishing; ISBN: 0030340594; 2nd edition (February 2002).

Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles.

In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households with no income eventually have no consumption (wealth is depleted).

While the debate surrounding beliefs about how income and consumption are related is interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for millwork across the states and cities of the United States.

The smallest cities have few inhabitants. I assume that all of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these states having wealth; current income dominates the latent demand for millwork. So, latent demand in the long-run has a zero intercept. However, I allow different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for millwork. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories and geographic locations, not just millwork in the United States.

1.3.1 STEP 1. PRODUCT DEFINITION AND DATA COLLECTION
Any study of latent demand requires that some standard be established to define "efficiently served". Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key indicators are more likely to reflect efficiency than others.

These indicators are given greater weight than others in the estimation of latent demand compared to others for which no known data are available. Of the many alternatives, I have found the assumption that the highest aggregate income and highest income-per-capita markets reflect the best standards for "efficiency".

High aggregate income alone is not sufficient (i.e. some cities have high aggregate income, but low income per capita and cannot be assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income).

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of millwork is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential products and/or services that might be incorporated within millwork fall under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the "whole". Rather, it starts with the "whole", and estimates the whole for all states and cities in the United States (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, this study covers millwork as defined by the North American Industrial Classification system or NAICS (pronounced "nakes").

The NAICS code for millwork is 32191. It is for this definition that aggregate latent demand estimates are derived.

Millwork is specifically defined as follows:
32191 This industry comprises establishments primarily engaged in manufacturing hardwood and softwood cut stock and dimension stock (i.e., shapes); wood windows and wood doors; and other millwork including wood flooring. Dimension stock or cut stock is defined as lumber and worked wood products cut or shaped to specialized sizes. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

321911 This U.S. industry comprises establishments primarily engaged in manufacturing window and door units, sash, window and door frames, and doors from wood or wood clad with metal or plastics.

3219111 Wood window units

32191111 Double hung wood window units

3219111111 Double hung wood window units, cladded

3219111121 Other double hung wood window units

32191112 Casement wood window units

3219111231 Casement wood window units, cladded

3219111241 Other casement wood window units

32191113 All other wood window units, including horizontal sliding, awning and single hung

3219111351 Horizontal sliding wood window units, cladded

3219111361 Other horizontal sliding wood window units

3219111391 All other wood window units, including awning and single hung

3219113 Wood sash, excluding sash shipped in window units

32191131 Wood sash, excluding sash shipped in window units

3219113111 Knockdown and open wood sash, excluding sash shipped in window units

3219113121 Glazed wood sash, excluding sash shipped in window units

3219115 Wood window and door frames

32191151 Wood window and door frames, including door frames shipped in door units, excluding window frames shipped in window units

3219115111 Wood window frames, excluding window frames shipped in window units

3219115121 Wood door frames, including door frames shipped in door units

3219117 Wood panel, flush, and molded face doors, interior and exterior

32191171 Wood panel, flush, and molded face doors, interior and exterior, including doors with glazed sections

3219117111 Panel douglas fir doors, interior and exterior, including doors with glazed sections

3219117115 Panel western pine doors, interior and exterior, including doors with glazed sections

3219117121 Other panel wood doors, interior and exterior, including doors with glazed sections

3219117131 Flush, hollow core, softwood faced doors, interior and exterior, including doors with glazed sections

3219117135 Flush, hollow core, hardwood faced doors (including lauan, birch, oak, etc.), interior and exterior, including doors with glazed sections

3219117141 Flush, hollow core, hardboard faced doors, interior and exterior, including doors with glazed sections

3219117145 Flush, hollow core, other faced doors, interior and exterior, including doors with glazed sections

3219117151 Flush, solid wood stave core, hardwood faced doors (including lauan, birch, oak, etc.), interior and exterior, including doors with glazed sections

3219117155 Flush, solid composition core, hardwood faced doors (including lauan, birch, oak, etc.), interior and exterior, including doors with glazed sections

3219117161 Flush, solid core, other faced doors, interior and exterior, including doors with glazed sections

3219117171 Molded face doors, interior and exterior, including doors with glazed sections

3219119 Other wood doors, incl. garage, bifold, patio, cabinet, screen, storm & louver

32191191 Other wood doors, including garage, patio, bifold, cabinet, screen, storm, and louver

3219119111 Wood garage doors

3219119121 Wood bifold doors

3219119131 Wood patio doors, sliding

3219119141 Wood patio doors, swinging

3219119151 Wood cabinet doors

3219119191 Other wood doors, including screen, storm, and louver

321911M Miscellaneous receipts

321911P Primary products

321911S Secondary products

321911SM Secondary products and miscellaneous receipts

321912 This U.S. industry comprises establishments primarily engaged in one or more of the following: (1) manufacturing dimension lumber from purchased lumber; (2) manufacturing dimension stock (i.e., shapes) or cut stock; (3) resawing the output of sawmills; and (4) planing purchased lumber. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.

3219121 Hardwood lumber, not edge worked, made from purchased lumber and edge worked

32191211 Hardwood lumber, made from purchased lumber

3219121111 Beech rough lumber, not edge worked, made from purchased lumber

3219121121 Oak rough lumber, not edge worked, made from purchased lumber

3219121131 Other hardwood rough lumber, not edge worked, made from purchased lumber

3219121141 Hardwood dressed lumber, not edge worked, made from purchased lumber

3219121151 Hardwood lumber, edge worked (tongued, grooved, rabbeted, etc.), made from purchased lumber

3219123 Softwood lumber, not edge worked, made from purchased lumber and edge worked

32191231 Softwood lumber, made from purchased lumber

3219123111 Softwood rough lumber, less than 2 inches in nominal thickness, not edge worked, made from purchased lumber

3219123121 Softwood rough 2-inch lumber, 2 inches in nominal thickness only, not edge worked, made from purchased lumber

3219123131 Softwood rough lumber and timbers, more than 2 inches in nominal thickness, not edge worked, made from purchased lumber

3219123141 Softwood dressed lumber, less than 2 inches in nominal thickness, not edge worked, made from purchased lumber

3219123151 Softwood dressed 2-inch lumber, 2 inches in nominal thickness only, not edge worked, made from purchased lumber

3219123161 Softwood dressed lumber and timbers, more than 2 inches in nominal thickness, not edge worked, made from purchased lumber

3219123171 Softwood lumber, edge worked (tongued, grooved, rabbeted, etc.), made from purchased lumber

3219125 Hardwood cut stock and dimension

32191251 Hardwood furniture cut stock, rough or surfaced, cut to size

3219125111 Hardwood furniture cut stock, rough or surfaced, cut to size, for cabinets

3219125115 Hardwood furniture cut stock, rough or surfaced, cut to size, not for cabinets

32191252 Hardwood furniture dimension, semimachined, including edge and face glued parts

3219125221 Hardwood furniture dimension, semimachined, including edge and face glued parts, for cabinets

3219125225 Hardwood furniture dimension, semimachined, including edge and face glued parts, not for cabinets

32191253 Hardwood furniture dimension, fully machined, ready for assembly

3219125331 Hardwood furniture dimension, fully machined, ready for assembly, for cabinets

3219125335 Hardwood furniture dimension, fully machined, ready for assembly, not for cabinets

32191254 Hardwood industrial cut stock and dimension, and compression~modified or densified wood

3219125441 Hardwood industrial cut stock, rough or surfaced, cut to size

3219125444 Hardwood industrial dimension, semimachined, including edge and face glued parts

3219125447 Hardwood industrial dimension, fully machined, ready for assembly

3219125451 Compression~modified or densified wood (whether or not impregnated with synthetic resin)

3219126 HARDWOOD CUT STOCK AND DIMENSION

32191261 Hardwood furniture cut stock

3219126111 Hardwood furniture cut stock, rough or surfaced, cut to size, for cabinets

3219126115 Hardwood furniture cut stock, rough or surfaced, cut to size, not for cabinets

32191262 Hardwood furniture dimension, semimachined, including edge and face glued parts

3219126221 Hardwood furniture dimension, semimachined, including edge and face glued parts, for cabinets

3219126225 Hardwood furniture dimension, semimachined, including edge and face glued parts, not for cabinets

32191264 Hardwood industrial cut stock, rough or surfaced, cut to size and semi and fully machined, ready for assembly

3219126441 Hardwood industrial cut stock, rough or surfaced, cut to size

3219126449 Hardwood industrial cut stock, semi and fully machined, ready for assembly

3219127 Softwood cut stock and dimension

32191271 Softwood cut stock and dimension

3219127111 Softwood furniture cut stock

3219127121 Softwood industrial cut stock

3219127131 Softwood semimachined and fully machined furniture and industrial dimension

3219128 SOFTWOOD CUT STOCK AND DIMENSION

32191281 Softwood cut stock and dimension

3219128111 Softwood furniture cut stock

3219128121 Softwood industrial cut stock

3219128132 Softwood semimachined and fully machined furniture and industrial dimension

3219129 Sawn wood fence stock, wood lath, and contract resawing and planing

32191291 Sawn wood fence stock, wood lath, and contract resawing and planing

3219129111 Sawn wood fence pickets, posts, and rails not assembled into fence sections

3219129121 Wood lath

3219129131 Receipts for contract resawing and planing

321912M Miscellaneous receipts

321912P Primary products

321912S Secondary products

321912SM Secondary products and miscellaneous receipts

321918 This U.S. industry comprises establishments primarily engaged in manufacturing millwork (except wood windows, wood doors, and cut stock).

3219181 Wood moldings, except prefinished moldings made from purchased moldings

32191811 Wood moldings, except prefinished moldings made from purchased moldings, including moldings covered with metal, plastics, etc.

3219181111 Pine wood moldings, except prefinished moldings made from purchased moldings, including moldings covered with metal, plastics, etc.

3219181121 Other softwood moldings, except prefinished moldings made from purchased moldings, including moldings covered with metal, plastics, etc.

3219181131 Hardwood moldings, except prefinished moldings made from purchased moldings, including lauan and hardwood covered with metal, plastics, etc.

3219183 Prefinished wood moldings made from purchased moldings

32191831 Prefinished wood moldings made from purchased moldings, including wood moldings covered with metal and plastics

3219183100 Prefinished wood moldings made from purchased moldings, including wood moldings covered with metal and plastics

3219183111 Prefinished softwood moldings made from purchased moldings, including softwood covered with metal, plastics, etc.

3219183121 Prefinished hardwood moldings made from purchased moldings, including lauan and hardwood covered with metal, plastics, etc.

3219185 Other wood millwork products, inc stairwork, exterior millwork, and softwood fl

32191851 Other wood millwork products, including stairwork, exterior millwork, and softwood flooring

3219185111 Softwood stairwork, including treads, risers, balusters, brackets, crooks, newels, rails, etc.

3219185121 Hardwood stairwork, including treads, risers, balusters, brackets, crooks, newels, rails, etc.

3219185131 Exterior wood millwork, including porch columns, porch rails, newels, trellises, and entrances

3219185141 Nonstandard or specialty softwood moldings, carvings, and ornaments

3219185151 Nonstandard or specialty hardwood moldings, carvings, and ornaments

3219185161 Softwood flooring

3219185181 Other wood millwork products, including shutters, interior millwork, and softwood flooring

3219185191 Other wood millwork products, n.e.c., including shutters and interior millwork

3219187 Hardwood flooring

32191871 Oak flooring

3219187111 Oak flooring (3/4", 1/2", 3/8" T&G and EM strip; 5/16" square edge strip)

3219187121 Oak parquetry

3219187131 Other oak flooring

32191872 Hardwood flooring, other than oak

3219187222 Other hardwood flooring

3219187241 Maple flooring

3219187251 Glued laminated hardwood truck trailer flooring and railroad car decking

3219187291 Other hardwood flooring

321918M Miscellaneous receipts

321918P Primary products

321918S Secondary products

321918SM Secondary products and miscellaneous receipts

This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.

1.3.2 STEP 2. FILTERING AND SMOOTHING
Based on the aggregate view of millwork as defined above, data were then collected for as many geographic locations as possible for that same definition, at the same level of the value chain. This generates a convenience sample of indicators from which comparable figures are available.

If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using a 2-year moving average weighting scheme (longer weighting schemes do not substantially change the results).

If data are available for a geographic region, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a state or city stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.

1.3.3 STEP 3. FILLING IN MISSING VALUES
In some cases, data are available on a sporadic basis. In other cases, data may be available for only one year.

From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national, state, and city-level income.

Based on the overriding philosophy of a long-run consumption function (defined earlier), states and cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that geographic entity.

1.3.4 STEP 4. VARYING PARAMETER, NON-LINEAR ESTIMATION
Given the data available from the first three steps, the latent demand is estimated using a "varying-parameter crosssectionally pooled time series model". The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30) , by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L. Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). Simply stated, the effect of income on latent demand is assumed to be constant unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily same for all states or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all states or cities in the United States are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for millwork is more likely to be similar across states or cities that have similar characteristics in terms of economic development.

This approach is useful across geographic regions for which some notion of non-linearity exists in the aggregate cross-region consumption function. For some categories, however, the reader must realize that the numbers will reflect a state’s or city’s contribution to latent demand in the United States and may never be realized in the form of local sales.

1.3.5 STEP 5. FIXED-PARAMETER LINEAR ESTIMATION
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the the United States consists of more than 15,000 cities, there will always be those cities, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible.

For these cities, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s flow of income). In the long run, if a state has no current income, the latent demand for millwork is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income will not use their savings, in the long run, to demand millwork). In a graphical sense, for low-income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, a low-income city is assumed to have a latent demand proportional to its income, based on the cities closest to it on the aggregate consumption function.

1.3.6 STEP 6. AGGREGATION AND BENCHMARKING
Based on the models described above, latent demand figures are estimated for all major cities in the United States. These are then aggregated to get state totals.

This report considers a city as a part of the regional and national market. The purpose is to understand the density of demand within a state and the extent to which a city might be used as a point of distribution within its state.

From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas.

This influence varies from one industry to another, but also from one period of time to another. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its state. Not all cities (e.g. the smaller towns) are estimated within each state as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same state, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others is used. Figures are rounded, so minor inconsistencies may exist across tables.