Product Price Analysis

Product Markups Analysis

Markups are necessary to convert the manufacturing cost of a product to the price paid by consumers. In order to develop markup information, we first identify all of the significant distribution channels through which a given appliance or type of equipment passes from the manufacturer to the purchaser. For example, a manufacturer may sell equipment to a wholesaler, who in turn sells it directly to the purchaser. A manufacturer also may sell the equipment to a wholesaler, who in turn sells it to a mechanical contractor, who in turn may sell it directly to the purchaser or through a general contractor who may sell it and its installation to the purchaser. Often, distribution channels for new and replacement markets differ. 

The manufacturer selling price estimates are converted to purchaser prices using cost multipliers developed from analyses of distribution channel markups. Markups for each type of actor in the distribution channel are estimated using trade association reports or U.S. Census Bureau data.

In addition to developing markups, we include sales tax data to calculate final equipment prices. These final purchaser prices for equipment at differing efficiency levels then can be used to estimate LCC, PBP, and analyses of various impacts at national, state, and other geographic levels.

A recent example of a markups analysis is described in chapter 6 in the Final Rule Technical Support Document for Portable Air Conditioners.

Product Price Forecasting

Historically, the technical analyses conducted in support of U.S. energy conservation standards for residential appliances and commercial equipment have assumed that manufacturing costs and retail prices remain constant during the projected 30-year analysis period. Yet, there exists considerable evidence that manufacturing costs and consumer prices of residential appliances have decreased in real terms over the last several decades. This phenomenon is generally attributable to manufacturing efficiency gained with cumulative experience producing a certain good and is modeled by an empirical experience curve. Using retail price data and/or price data from the Bureau of Labor Statistics, we develop price trends for numerous products, including home appliances, lighting products, HVAC equipment, etc..

Related Publications

Taylor, Margaret, and K. Sydny Fujita. Accounting for Technological Change in Regulatory Impact Analyses: The Learning Curve Technique. (2013) LBNL-6195E. PDF 

Gerke, Brian F., Allison T. Ngo, and Kibret S. Fisseha. Recent price trends and learning curves for household LED lamps from a regression analysis of Internet retail data. (2015) LBNL-184075. PDF