economic & COnsumer credit Analytics Merchandise Line Estimates, Forecasts and ZIP Code Potential Methodology

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1 economic & COnsumer credit Analytics Merchandise Line Estimates, Forecasts and ZIP Code Potential Methodology

2 Merchandise Line Estimates, Forecasts, and ZIP Code Potential Methodology Historical Merchandise Line Estimates ( ) Every five years (1992, 1997, 2002, 2007 ) the U.S. Census Bureau undertakes a census of retail trade that all retailers in the country are legally required to complete. One of the outputs of this census is a matrix of merchandise line sales by store type. In other words, sales by merchandise line are provided for each store type. These data are released about three years after the census is taken. (That is, we received the 2007 data late in 2010, although a preliminary version was available a year earlier.) Between the censuses, the Census Bureau provides survey-based estimates of retail sales by store type (on a monthly basis) but no estimates of merchandise line sales. Note that these monthly data go back only until 1992, the earliest we could begin our estimates. The estimates of annual merchandise line sales are developed by first creating annual matrices of the share of sales by merchandise line for each store type. Data from the census matrices are interpolated for the years between 1997 and 2007 and trended from 2008 to Then the sales by store type data from the Census Bureau are run through the matrices to get merchandise line sales estimates for each year. The shares of sales by merchandise line change significantly from census to census, so capturing this change is important in accurately measuring sales by merchandise line. For example, the share of drugs and food sold discount stores has been growing, at the expense of the share of many other merchandise lines they sell. If this shift were not accounted for, sales growth for food would be understated and that for other merchandise lines would be overstated. Additional Issues There were a number of additional issues that had to be addressed in producing the estimates. Incomplete merchandise line data The Census Bureau has a set of aggregate merchandise line categories that are asked of almost all retailers. (Men s apparel, kitchenware and home furnishings, and groceries are examples.) However, the more detailed merchandise lines are only asked for store types where the line is thought to represent a significant enough part of sales. Usually this is sufficient, but sometimes, as in 1992 when there was no detail for apparel categories for discount department stores, there are significant holes that must be estimated. It is usually straightforward to estimate the missing data. In many situations, including the discounter case noted above, the missing shares can be interpolated using previous and following census data. Given the relative weakness of the 1992 census, 1987 data are utilized frequently if history going back that far is desired. An additional area with missing data for significant merchandise lines (200 men s; 220 women s; 240 children s;, 260 footwear; 280 curtains, drapes, etc.; 340 furniture; 360 floor coverings; 380 kitchenware and home furnishings) was nonstore retailers, including electronic shopping and mail order retailers and other direct selling establishments. After consultation with the client and examination of data from NPD, it was determined to allocate these sales to more detailed merchandise lines using the shares from general merchandise stores. Inconsistent merchandise line detailed categories For some major merchandise lines, the number of detailed lines reported for it varies by store type. This can limit the amount of disaggregation that is feasible or force estimation of the distribution of the sales across the more disaggregated lines. This situation is especially prevalent in the home category, although it can be problematic in other categories as well. In general, more aggregate categories are used to avoid this problem census store type definitions The government switched all of their data collection and reporting from an SIC basis to a NAICS (North American Industrial Classification System) basis. All current reporting is on a NAICS basis including monthly retail sales by store type data. However, 1992 and prior economic censuses were done using SIC-based store types. While many of the store type definitions are nearly or completely unchanged between the two definitions (including apparel specialty stores, national and conventional department stores, and drug stores), there were some significant definitional MOODY S ANALYTICS / Copyright

3 Merchandise Line Estimates, Forecasts, and ZIP Code Potential Methodology changes. For example, supercenters were part of discount department stores on an SIC basis but are grouped with warehouse clubs using NAICS. This changed the merchandise mix reported for discount department stores in a way that is very difficult to adjust for. Other examples include significant redefining of store types within the food store category. This makes estimates prior to 1997 less reliable if provided. The analysis was done on a NAICS basis because that is how the store type data are released. For estimates prior to 1997, it would be assumed that the corresponding SIC store type allocation was representative of the NAICS allocation. Changing merchandise line definitions While not a major problem, there have been a few cases where merchandise line items were added, dropped or moved within major categories. For example, in the 2007 census, new lines were created for some forms of electronic media. They had been counted within other lines previously. Definitional drift There are a few instances where share movements seem extreme. The increase in the share of children s footwear sold at warehouse clubs and supercenters in 1997 compared with both 1992 and 2002 is an example. Issues with women s undergarments may be another. While census did not change the merchandise line definitions, it is possible that the way retailers are reporting has changed. While small judgmental adjustments have been considered, in general, we recommend using reported data. Trend vs. interpolation To avoid extreme results, large shifts in merchandise line sales between censuses are muted somewhat as the shares are extended beyond While there is the benefit of avoiding extremes, this can result in somewhat different growth rates in the 2007 forward period compared with earlier years. Fortunately, this does not appear nearly as significant as changes in growth rates prior to National Merchandise Line Forecasts (Beginning 2011) The forecasts are basically econometrically estimated projections of the history based on the economic outlook and the Moody s Analytics forecasts of corresponding personal consumption expenditure categories. More general drivers of consumer spending including income, employment, interest rates and inflation, among others are also utilized since the PCE data are not as detailed as the merchandise lines forecast in the (WITHELD) project. Additional Issues As noted above, historic estimates are more reliable beginning in If our examination of the data (or statistical tests) suggested a structural change, we only utilized data beginning in 1997 in our estimation process even if earlier data are provided to the client. Forecasts are adjusted to take into account information the model may not be able to incorporate. This would include recent monthly trends in store types important to the merchandise line being forecast, retailer or trade information not incorporated into the model, and feedback from the client analysts. Zip Code Allocation of Potential The consumer expenditure survey (CEX) is a survey of consumer (actually two separate surveys, a diary survey and an interview survey) designed to measure consumer spending by demographic group. While the surveys are conducted on an ongoing basis, data are released annually, with a lag of about a year. Data for 2009 were released in late 2010, for example. The interview survey is used for most the client merchandise lines, as it is more comprehensive. The diary survey focuses on merchandise lines purchased at very high frequency (such as weekly), which includes only a few of the client merchandise lines. From the survey, spending propensities (average spending per household) are computed for households across seven age groups (determined by the age of the reference person in the household), seven income groups, and the four census geographic regions. The exception to this is that national propensities were collected for high-income groups for very young and old age groups because of the sparseness of the data in these cells. Since propensities were needed for years after the latest CEX, the propensities were forecast using time series techniques that smooth out extreme values from the historic data to produce propensities for the needed years. The spending propensities by age and income groups and by region were merged with ZIP code-level counts of households by age and income groups. The latter data are provided by the client. The merging produced an initial estimate of merchandise potential at the ZIP code level for both years. However, because of concerns about inconsistent underreporting of spending across lines in the CEX, the resulting propensities were adjusted for each merchandise line. The adjustment was done to ensure that the resulting propensities summed to the national merchandise line estimates calculated in the previous steps. This produced the final propensities delivered to the client. ** Note: Separate documentation provided to the client provides detailed specifications including exact census of retail trade merchandise lines used, consumer expenditure survey UCC codes used, and age and income groups used. MOODY S ANALYTICS / Copyright

4 About Moody s Analytics Economic & Consumer Credit Analytics Moody s Analytics helps capital markets and credit risk management professionals worldwide respond to an evolving marketplace with confidence. Through its team of economists, Moody s Analytics is a leading independent provider of data, analysis, modeling and forecasts on national and regional economies, financial markets, and credit risk. Moody s Analytics tracks and analyzes trends in consumer credit and spending, output and income, mortgage activity, population, central bank behavior, and prices. Our customized models, concise and timely reports, and one of the largest assembled financial, economic and demographic databases support firms and policymakers in strategic planning, product and sales forecasting, credit risk and sensitivity management, and investment research. Our customers include multinational corporations, governments at all levels, central banks and financial regulators, retailers, mutual funds, financial institutions, utilities, residential and commercial real estate firms, insurance companies, and professional investors. Our web and print periodicals and special publications cover every U.S. state and metropolitan area; countries throughout Europe, Asia and the Americas; and the world s major cities, plus the U.S. housing market and other industries. From our offices in the U.S., the United Kingdom, and Australia, we provide up-to-the-minute reporting and analysis on the world s major economies. Moody s Analytics added Economy.com to its portfolio in Its economics and consumer credit analytics arm is based in West Chester PA, a suburb of Philadelphia, with offices in London and Sydney. More information is available at , Moody s Analytics, Inc. and/or its licensors and affiliates (together, Moody s ). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY COPYRIGHT LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY S PRIOR WRITTEN CONSENT. All information contained herein is obtained by Moody s from sources believed by it to be accurate and reliable. Because of the possibility of human and mechanical error as well as other factors, however, all information contained herein is provided AS IS without warranty of any kind. Under no circumstances shall Moody s have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of Moody s or any of its directors, officers, employees or agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if Moody s is advised in advance of the possibility of such damages, resulting from the use of or inability to use, any such information. The financial reporting, analysis, projections, observations, and other information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell, or hold any securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY S IN ANY FORM OR MANNER WHATSOEVER. Each opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must accordingly make its own study and evaluation prior to investing. MOODY S ANALYTICS / Copyright

5 CONTACT US For further information contact us at a location below: U.S./CANADA EMEA ASIA/PACIFIC OTHER LOCATIONS us: Or visit us: Copyright 2011, Moody s Analytics, Inc. All Rights Reserved.

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