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Value Dynamics in the Secondary Market: Advancing a Model for Product Line Valuation of Used Goods Presented to the American Marketing Association Winter Educators Conference, San Antonio, February 14, 2015 Kashef Majid University of Mary Washington kmajid@umw.edu Cristel A. Russell American University russell@american.edu

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Used Goods? Defined as the market where goods that have previously been owned by another consumer are sold (Shulman and Coughlan 2007) This includes refurbished products, i.e. previously owned products repaired for resale The global market secondary market is estimated to be in excess of $50 billion (Czaga and Fliess 2005) 3

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Model of Valuation in the Secondary Market 4

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Hypotheses We examine factors associated with value loss when a product moves to the secondary market. Value loss/ retention is a function of the product line, a products ranking in the line, and the range of prices in the line The higher a products ranking the more value it loses when it enters the secondary market The lowest priced product has a negative impact on the amount of value it retains The size of the range in prices within the product line negatively impacts the value that all products in the line retain 5

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Hypotheses Two Moderating Relationships 6

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Data and Empirical Context The United States auto industry in 2012 Sample consists of sales data on 1,027 different vehicles sold in the secondary market across 106 different product lines manufactured by 27 different automakers sold in April and May 2013 Pricing data was obtained from the NADA Official Used Car Guide, which lists average retail prices based on actual sales of used cars by dealers in the Eastern region of the United States As espoused by Sullivan (1998) the main advantage of NADA used-car prices is that they are based on market transactions and therefore reflect car buyers actual valuations of the products 7

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Model Development We developed a hierarchical linear model (HLM) to model the value retention of cars in the secondary market The HLM model was most appropriate because the individual vehicles are contained within a product line, the product line may have disproportionate impact on the value that certain vehicles retain The intra-class correlation was 99.58 which signals a close clustering of Residual Values within each product line and suggests that an OLS regression would yield misleading results (Singer 1998) 8

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Results 9 Fixed Estimates of Residual Value, (N = 1,027), Dependent Variable is the Residual Value (Price in the Secondary Market/ MSRP) VariableFixed Estimate Ranking-1.185*** (.209) LogLPrice-.442*** (.107) LogRange-.110*** (.028) LogRange*Ranking-.118*** (.035) LogLprice*Ranking.120* (.054) *p