2006
DOI: 10.1017/s1931436100000171
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What Determines the Future Value of an Icon Wine? New Evidence from Australia

Abstract: To what extent can the future price of icon wines be anticipated from information available at the time of their initial sale by wineries? Using a seemingly unrelated regression model we show that weather variables and changes in production techniques, along with the age of the wine, have significant power in explaining the secondary market price variation across different vintages of each of three icon Australian red wines. The results have implications for winemakers in determining the prices they pay for gr… Show more

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Cited by 44 publications
(37 citation statements)
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“…2 This is to be distinguished from consumer perceptions over time of the changing quality of ultrapremium wines as they mature in bottle following the initial sale by the winery, as captured by time series of prices in the secondary auction markets. According to Ashenfelter (2000), Ashenfelter et al (1995), Byron and Ashenfelter (1995) and Wood and Anderson (2002), key determinants of the vintage-to-vintage variation in the quality of maturing wines are a few straightforward weather variables in the growing season -information that consumers appear to have ignored in the past. Schamel (2000) estimates a hedonic pricing model based on United States data for sensory quality ratings, individual wine quality and regional reputation indicators for two premium wine varieties: a white (Chardonnay) and a red (Cabernet Sauvignon).…”
Section: Previous Studiesmentioning
confidence: 99%
“…2 This is to be distinguished from consumer perceptions over time of the changing quality of ultrapremium wines as they mature in bottle following the initial sale by the winery, as captured by time series of prices in the secondary auction markets. According to Ashenfelter (2000), Ashenfelter et al (1995), Byron and Ashenfelter (1995) and Wood and Anderson (2002), key determinants of the vintage-to-vintage variation in the quality of maturing wines are a few straightforward weather variables in the growing season -information that consumers appear to have ignored in the past. Schamel (2000) estimates a hedonic pricing model based on United States data for sensory quality ratings, individual wine quality and regional reputation indicators for two premium wine varieties: a white (Chardonnay) and a red (Cabernet Sauvignon).…”
Section: Previous Studiesmentioning
confidence: 99%
“…The sample design for estimating the relation between weather and wines prices varies across studies. Some studies focus on a particular individual wine over its numerous vintages (Byron and Ashenfelter, 1995; Wood and Anderson, 2006); others on a particular region, such as Bordeaux or Napa Valley, over numerous vintages (Ashenfelter, 2008; Ramirez, 2008); and others are predominately cross section in their focus over a region such as Médoc (Ginsburgh et al, 2013) or variety such as Pinot Noir (Haeger and Storchmann, 2006).…”
Section: Theoretical Considerations and Previous Literaturementioning
confidence: 99%
“…Two Australian studies (Byron and Ashenfelter, 1995; Wood and Anderson, 2006) have examined the weather effects on auction prices for single iconic wines over numerous vintages. The key employed weather variables are average growing-season temperature, the average temperature differential over the season, rain leading up to harvest, and wind.…”
Section: Theoretical Considerations and Previous Literaturementioning
confidence: 99%
“…(inter alia, see Oczkowski, 1994Oczkowski, , 2001Nerlove, 1995, Coombris et al, 1997Smith, 1997, 1998;Blair and Burley, 1998;Wade, 1999;Angulo et al, 2000;Corsi and Ashenfelter, 2000;Schamel, 2000;Schamel and Anderson, 2001;Ling and Lockshin, 2003;Bombrun and Sumner, 2003;Wood and Anderson, 2003;and Cardebat and Figuet, 2004). The underlying intent of the majority of these studies appears to fly in the face of what wine econometrics should be doingwhich is attempting to interpret and address the customer need for value, and to model price-based regressions accordingly.…”
Section: Theoretical Framework and Literature Reviewmentioning
confidence: 99%