In the last post, we looked at where Napa cab prices are headed. In this post, we’ll look at Sonoma County chardonnay, in preparation for an analysis of how the grape cycle impacts grapes in areas with little room left for additional planting. One interesting note: the variables that effect Sonoma County chardonnay differ from those that effect Napa cab. The biggest difference is that Sonoma County chardonnay prices are more strongly influenced by production from outside areas, whereas Napa cab really does behave as if it’s a completely different product than any other cabernet. That is, winemakers, marketeers and/or consumers seem to be more comfortable substituting non-Sonoma chardonnay for the Sonoma chardonnay than they are substituting non-Napa cab for Napa cab.
I use my own proprietary methods to project prices. I have two methods: one is used for long-term projections and attempts to predict cyclical effects. It is meant for long-term planning and must be adjusted annually, since the data that correlate to cycles is not available until the industry is already feeling the effects of the cycle. The other method predicts only that year’s prices, with a projection typically made in January and updated in April. The latter method is the one I used for this projection.
My projections are showing a 2014 average Sonoma County price of $1998.32. There is an approximately two out of three chance that prices will fall somewhere between $1920 and $2075. The chance that prices will be higher than last year’s price of $1940.41 is about 78%. Notice this is a considerably less certain projection than with Napa cabernet.
The Nitty Gritty
A few caveats here: First, I know there to be a rounding error in my formulae that would adjust the predicted price downward by just over $13. So, you may want to make that adjustment, if you’re going to use these numbers.
Caveat Number 2: This is just an estimate. It’s just a market indicator. This number should be used as a baseline and adjusted for your vineyard and your needs. The standard error is $75.64, if you need that. For stat heads out there, the p-values for the various variables I use are miniscule, with the highest one being .0004, indicating a high level of reliability. The model’s correlation, however, is my weakest one yet, with an r-squared of above .95 and a correlation of 97.5%. These numbers are still above any cutoff I would recommend for statistical significance.
If you are looking for customized projections or strategies to get the most out of this type of information, call VFA to see what we can do for you. Click here to contact Vineyard Financial Associates>>>