The 2009-2014 Outlook for Commercial Screen Printing in the United States

WHAT IS LATENT DEMAND AND THE P.I.E.? The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if the United States is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability). The latent demand for commercial screen printing in the United States is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a market. For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends). If inflation rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level of latent demand. As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. In fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be latent demand for commercial screen printing at the aggregate level. Product and service offerings, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand. THE METHODOLOGY In order to estimate the latent demand for commercial screen printing across the states and cites 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 labeled "A" 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 shown as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. 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 commercial screen printing 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 commercial screen printing. 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 commercial screen printing in the United States. 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 commercial screen printing in the United States. 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 can not 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. Official Chinese Agencies, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, and the World Bank). Depending on original data sources used, the definition of “commercial screen printing” 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 product or service that might be incorporated within commercial screen printing falls 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 “commercial screen printing” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of commercial screen printing, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for commercial screen printing is 323113. It is for this definition of commercial screen printing that the aggregate latent demand estimates are derived for the states and cities of the United States. “Commercial screen printing” is specifically defined as follows: 323113 This U.S. industry comprises establishments primarily engaged in screen printing without publishing (except books, grey goods, and manifold business forms). This industry includes establishments engaged in screen printing on purchased stock materials, such as stationery, invitations, labels, and similar items, on a job order basis. Establishments primarily engaged in printing on apparel and textile products, such as T-shirts, caps, jackets, towels, and napkins, are included in this industry.  3231131 Screen printing, except on textiles  32311311 Screen printed labels  3231131111 Screen printed paper labels, custom and stock, including bordered, pressure~ sensitive, flat  3231131112 Screen printed labels  3231131116 Screen printed paper labels, custom and stock, including bordered, pressure~ sensitive, rolls  3231131121 Other screen printed paper labels, custom and stock, including bordered  3231131126 Screen printed labels made of materials other than paper or cloth, custom and stock, including bordered  3231131181 Screen printed greeting cards, printed for publication by others  32311312 Screen printed advertising materials  3231131211 Screen printed advertising materials  3231131231 Screen printed advertising display posters (including outdoor advertising, car cards, window, etc.)  3231131236 Screen printed advertising display material (including counter, floor display, point_of_purchase, and other printed advertising display material), except display posters  3231131241 Other screen printed advertising material (including book jackets, brochures, pamphlets, etc.), except display  32311313 Screen printed decalcomanias and pressure_sensitives (self_adhesive) (including bumper stickers, etc.), except labels  3231131346 Screen printed decalcomanias and pressure sensitives (self-adhesive)  32311314 Screen printing, nec, except on textiles  3231131411 Screen printing, nec, except on textiles  3231131451 Screen printing on metal  3231131456 Screen printing on glass or plastics containers for others  3231131491 All other general commercial screen printing, nec (excluding printing on apparel or fabrics)  32311316 Other commercial and general job screen printing  3231131651 Screen printing on metal  3231131656 Screen printing on glass or plastics containers for others  3231131691 All other commercial and general job screen printing, except on textiles, including customized stationary  3231132 SCREEN PRINTED LABELS  32311321 Screen printed labels  3231132111 Screen printed labels, made of paper, custom and stock, including bordered, pressure_sensitive, flat  3231132116 Screen printed labels, made of paper, custom and stock, including bordered, pressure_sensitive, rolls  3231132121 Screen printed labels, made of paper, custom and stock, including bordered, except pressure_sensitive  3231132126 Screen printed labels, made of materials other than paper (including cloth), custom and stock, including bordered  3231133 Screen printing on garments, apparel, and other fabric articles  32311331 Screen printing on garments, apparel accessories, and other fabric articles  3231133111 Screen printing on apparel and apparel accessories, made of any material  3231133116 Screen printing on fabric articles other than apparel or apparel accessories  3231133121 Stamped art goods for embroidering, punching, and needlework  3231134 SCREEN PRINTING ON GARMENTS, APPAREL ACCESSORIES, AND OTHER FABRIC ARTICLES, EXCEPT LABELS  32311341 Screen printing on garments, apparel accessories, and other fabric articles, except labels  3231134111 Screen printing on apparel and apparel accessories, made of any material  3231134116 Screen printing on fabric articles other than apparel and apparel accessories, except labels  3231134121 Stamped art goods for embroidering, punching, and needlework  32311362 screen-printed advertising materials  3231136231 screen-printed advertising display posters  3231136236 screen-printed advertising display material  3231136651 screen printing on metal  3231136656 screen printing on glass or plastics containers owed by others  323113M Miscellaneous receipts  323113P Primary products  323113S Secondary products  323113SM Secondary products and miscellaneous receipts   Furthermore, the definition of NAICS code 323113 includes the following: Address lists screen printing without publishing Agricultural magazines and periodicals screen printing without publishing Art prints screen printing without publishing Atlases screen printing without publishing Business directories screen printing without publishing Business forms (except manifold) screen printing without publishing Calendars screen printing without publishing Cards (e.g., business, greeting, playing, postcards, trading) screen printing wit Catalogs of collections screen printing without publishing Catalogs screen printing without publishing Comic books screen printing without publishing Commercial screen printing Databases screen printing without publishing Directories screen printing without publishing Discount coupon books screen printing without publishing Financial magazines and periodicals screen printing without publishing Globe covers and maps screen printing without publishing Greeting cards (e.g., birthday, holiday, sympathy) screen printing without publis Guides, street map, screen printing without publishing Job printing, screen Juvenile magazines and periodicals screen printing without publishing Magazines and periodicals screen printing without publishing Maps screen printing without publishing Music, sheet, screen printing without publishing Newsletters screen printing without publishing Newspapers screen printing without publishing Patterns and plans (e.g., clothing patterns) screen printing without publishing Periodicals screen printing without publishing Postcards screen printing without publishing Posters screen printing without publishing Print shops, screen Printing, screen (except books, manifold business forms, grey goods) Professional magazines and periodicals screen printing without publishing Racetrack programs screen printing without publishing Racing forms screen printing without publishing Radio guides screen printing without publishing Radio schedules screen printing without publishing Religious magazines and periodicals screen printing without publishing Scholarly journals screen printing without publishing Scholastic magazines and periodicals screen printing without publishing Screen printing (except books, manifold business forms, grey goods) Screen printing apparel and textile products (e.g. caps, napkins, placemats, T-sh Sheet music screen printing without publishing Shipping registers screen printing without publishing Stationery, screen printing, on a job-order basis Technical magazines and periodicals screen printing without publishing Telephone directories screen printing without publishing Television guides screen printing without publishing Trade journals screen printing without publishing Trade magazines and periodicals screen printing without publishing Yearbooks screen printing without publishing. Step 2. Filtering and Smoothing Based on the aggregate view of commercial screen printing 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 an 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. 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. 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 cross-sectionally pooled time series model”. 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 commercial screen printing 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. Step 5. Fixed-Parameter Linear Estimation Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because 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 commercial screen printing 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 commercial screen printing). 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. 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.

1 INTRODUCTION 9 1.1 Overview 9 1.2 What is Latent Demand and the P.I.E.? 9 1.3 The Methodology 10 1.3.1 Step 1. Product Definition and Data Collection 11 1.3.2 Step 2. Filtering and Smoothing 16 1.3.3 Step 3. Filling in Missing Values 16 1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16 1.3.5 Step 5. Fixed-Parameter Linear Estimation 17 1.3.6 Step 6. Aggregation and Benchmarking 17 2 SUMMARY OF FINDINGS 18 2.1 Latent Demand in The US 19 3 FAR WEST 20 3.1 Executive Summary 20 3.2 Latent Demand by Year - Alaska 22 3.3 Cities Sorted by Rank - Alaska 23 3.4 Cities Sorted by Zipcode - Alaska 24 3.5 Latent Demand by Year - California 26 3.6 Cities Sorted by Rank - California 27 3.7 Cities Sorted by Zipcode - California 48 3.8 Latent Demand by Year - Hawaii 69 3.9 Cities Sorted by Rank - Hawaii 70 3.10 Cities Sorted by Zipcode - Hawaii 72 3.11 Latent Demand by Year - Nevada 75 3.12 Cities Sorted by Rank - Nevada 76 3.13 Cities Sorted by Zipcode - Nevada 77 3.14 Latent Demand by Year - Oregon 79 3.15 Cities Sorted by Rank - Oregon 80 3.16 Cities Sorted by Zipcode - Oregon 84 3.17 Latent Demand by Year - Washington 88 3.18 Cities Sorted by Rank - Washington 89 3.19 Cities Sorted by Zipcode - Washington 96 4 GREAT LAKES 105 4.1 Executive Summary 105 4.2 Latent Demand by Year - Illinois 107 4.3 Cities Sorted by Rank - Illinois 108 4.4 Cities Sorted by Zipcode - Illinois 122 4.5 Latent Demand by Year - Indiana 137 4.6 Cities Sorted by Rank - Indiana 138 4.7 Cities Sorted by Zipcode - Indiana 144 4.8 Latent Demand by Year - Michigan 152 4.9 Cities Sorted by Rank - Michigan 153 4.10 Cities Sorted by Zipcode - Michigan 162 4.11 Latent Demand by Year - Ohio 171 4.12 Cities Sorted by Rank - Ohio 172 4.13 Cities Sorted by Zipcode - Ohio 185 4.14 Latent Demand by Year - Wisconsin 199 4.15 Cities Sorted by Rank - Wisconsin 200 4.16 Cities Sorted by Zipcode - Wisconsin 211 5 MID-ATLANTIC 222 5.1 Executive Summary 222 5.2 Latent Demand by Year - Delaware 224 5.3 Cities Sorted by Rank - Delaware 225 5.4 Cities Sorted by Zipcode - Delaware 226 5.5 Latent Demand by Year - District of Columbia 227 5.6 Cities Sorted by Rank - District of Columbia 228 5.7 Cities Sorted by Zipcode - District of Columbia 228 5.8 Latent Demand by Year - Maryland 229 5.9 Cities Sorted by Rank - Maryland 230 5.10 Cities Sorted by Zipcode - Maryland 236 5.11 Latent Demand by Year - New Jersey 243 5.12 Cities Sorted by Rank - New Jersey 244 5.13 Cities Sorted by Zipcode - New Jersey 254 5.14 Latent Demand by Year - New York 264 5.15 Cities Sorted by Rank - New York 265 5.16 Cities Sorted by Zipcode - New York 293 5.17 Latent Demand by Year - Pennsylvania 321 5.18 Cities Sorted by Rank - Pennsylvania 322 5.19 Cities Sorted by Zipcode - Pennsylvania 339 6 NEW ENGLAND 356 6.1 Executive Summary 356 6.2 Latent Demand by Year - Connecticut 358 6.3 Cities Sorted by Rank - Connecticut 359 6.4 Cities Sorted by Zipcode - Connecticut 364 6.5 Latent Demand by Year - Maine 369 6.6 Cities Sorted by Rank - Maine 370 6.7 Cities Sorted by Zipcode - Maine 375 6.8 Latent Demand by Year - Massachusetts 382 6.9 Cities Sorted by Rank - Massachusetts 383 6.10 Cities Sorted by Zipcode - Massachusetts 392 6.11 Latent Demand by Year - New Hampshire 401 6.12 Cities Sorted by Rank - New Hampshire 402 6.13 Cities Sorted by Zipcode - New Hampshire 406 6.14 Latent Demand by Year - Rhode Island 411 6.15 Cities Sorted by Rank - Rhode Island 412 6.16 Cities Sorted by Zipcode - Rhode Island 413 6.17 Latent Demand by Year - Vermont 415 6.18 Cities Sorted by Rank - Vermont 416 6.19 Cities Sorted by Zipcode - Vermont 419 7 PLAINS 423 7.1 Executive Summary 423 7.2 Latent Demand by Year - Iowa 425 7.3 Cities Sorted by Rank - Iowa 426 7.4 Cities Sorted by Zipcode - Iowa 431 7.5 Latent Demand by Year - Kansas 436 7.6 Cities Sorted by Rank - Kansas 437 7.7 Cities Sorted by Zipcode - Kansas 440 7.8 Latent Demand by Year - Minnesota 444 7.9 Cities Sorted by Rank - Minnesota 445 7.10 Cities Sorted by Zipcode - Minnesota 452 7.11 Latent Demand by Year - Missouri 459 7.12 Cities Sorted by Rank - Missouri 460 7.13 Cities Sorted by Zipcode - Missouri 466 7.14 Latent Demand by Year - Nebraska 473 7.15 Cities Sorted by Rank - Nebraska 474 7.16 Cities Sorted by Zipcode - Nebraska 476 7.17 Latent Demand by Year - North Dakota 478 7.18 Cities Sorted by Rank - North Dakota 479 7.19 Cities Sorted by Zipcode - North Dakota 480 7.20 Latent Demand by Year - South Dakota 481 7.21 Cities Sorted by Rank - South Dakota 482 7.22 Cities Sorted by Zipcode - South Dakota 483 8 ROCKIES 484 8.1 Executive Summary 484 8.2 Latent Demand by Year - Colorado 486 8.3 Cities Sorted by Rank - Colorado 487 8.4 Cities Sorted by Zipcode - Colorado 491 8.5 Latent Demand by Year - Idaho 496 8.6 Cities Sorted by Rank - Idaho 497 8.7 Cities Sorted by Zipcode - Idaho 498 8.8 Latent Demand by Year - Montana 501 8.9 Cities Sorted by Rank - Montana 502 8.10 Cities Sorted by Zipcode - Montana 503 8.11 Latent Demand by Year - Utah 506 8.12 Cities Sorted by Rank - Utah 507 8.13 Cities Sorted by Zipcode - Utah 510 8.14 Latent Demand by Year - Wyoming 514 8.15 Cities Sorted by Rank - Wyoming 515 8.16 Cities Sorted by Zipcode - Wyoming 516 9 SOUTHEAST 517 9.1 Executive Summary 517 9.2 Latent Demand by Year - Alabama 519 9.3 Cities Sorted by Rank - Alabama 520 9.4 Cities Sorted by Zipcode - Alabama 525 9.5 Latent Demand by Year - Arkansas 531 9.6 Cities Sorted by Rank - Arkansas 532 9.7 Cities Sorted by Zipcode - Arkansas 535 9.8 Latent Demand by Year - Florida 539 9.9 Cities Sorted by Rank - Florida 540 9.10 Cities Sorted by Zipcode - Florida 556 9.11 Latent Demand by Year - Georgia 573 9.12 Cities Sorted by Rank - Georgia 574 9.13 Cities Sorted by Zipcode - Georgia 581 9.14 Latent Demand by Year - Kentucky 588 9.15 Cities Sorted by Rank - Kentucky 589 9.16 Cities Sorted by Zipcode - Kentucky 593 9.17 Latent Demand by Year - Louisiana 598 9.18 Cities Sorted by Rank - Louisiana 599 9.19 Cities Sorted by Zipcode - Louisiana 604 9.20 Latent Demand by Year - Mississippi 609 9.21 Cities Sorted by Rank - Mississippi 610 9.22 Cities Sorted by Zipcode - Mississippi 613 9.23 Latent Demand by Year - North Carolina 616 9.24 Cities Sorted by Rank - North Carolina 617 9.25 Cities Sorted by Zipcode - North Carolina 625 9.26 Latent Demand by Year - South Carolina 633 9.27 Cities Sorted by Rank - South Carolina 634 9.28 Cities Sorted by Zipcode - South Carolina 639 9.29 Latent Demand by Year - Tennessee 644 9.30 Cities Sorted by Rank - Tennessee 645 9.31 Cities Sorted by Zipcode - Tennessee 650 9.32 Latent Demand by Year - Virginia 656 9.33 Cities Sorted by Rank - Virginia 657 9.34 Cities Sorted by Zipcode - Virginia 662 9.35 Latent Demand by Year - West Virginia 667 9.36 Cities Sorted by Rank - West Virginia 668 9.37 Cities Sorted by Zipcode - West Virginia 670 10 SOUTHWEST 672 10.1 Executive Summary 672 10.2 Latent Demand by Year - Arizona 673 10.3 Cities Sorted by Rank - Arizona 674 10.4 Cities Sorted by Zipcode - Arizona 677 10.5 Latent Demand by Year - New Mexico 682 10.6 Cities Sorted by Rank - New Mexico 683 10.7 Cities Sorted by Zipcode - New Mexico 685 10.8 Latent Demand by Year - Oklahoma 687 10.9 Cities Sorted by Rank - Oklahoma 688 10.10 Cities Sorted by Zipcode - Oklahoma 692 10.11 Latent Demand by Year - Texas 696 10.12 Cities Sorted by Rank - Texas 697 10.13 Cities Sorted by Zipcode - Texas 715 11 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 734 11.1 Disclaimers & Safe Harbor 734 11.2 ICON Group International, Inc. User Agreement Provisions 735

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