Choosing value-for-money wines

How to find good quality wine at a reasonable price is a popular topic for wine bloggers. Unfortunately, most of the advice is rather vague, usually doing nothing more than telling us why increasing price must follow from increasing quality. The advice is thus often of little practical value, no matter how well-intentioned or informative it is. Perhaps we can do a bit better here by looking at some specific data.

In my previous blog post I presented a specific dataset, which we can use here. It is representative of many other data sets that I have looked at, and therefore it can act as a typical example.

The data set came from Richard Jennings at the RJ on Wine blog, and involves 230 US chardonnays representative of what can be found on grocery store shelves in late 2015. That is, they represent the wines that average people buy, as opposed to those bought by people with a special interest in wine.

The data are shown in the first graph here. Each point in this graph represents a wine, located according to its quality score (horizontally) and price (vertically). However, quite a few wines are super-imposed in the graph, because they have the same score and price. Note, also, that the quality assessment assesses only the sensory qualities of the wine; other definitions of "quality" may not apply.

For this data set we can clearly see the relationship between quality and price, which is what is known as an exponential relationship. The line represents the "expected" value for money — at any given quality score we can expect to pay the amount of money shown by the line, on average. To put it another way, for wines near the line we are paying for what we get, and we are getting what we pay for. It is also sometimes interpreted as being the price that we would be expected to pay if both the producer and the consumer had perfect knowledge about wine quality.

The red dot at the bottom of the graph is the best-value wine in this data set. That is, it is the point that is furthest directly below the line. Since the line represents the "expected" price for a wine of any given quality, the further below the line then the better "bang for the buck" is the wine. This particular wine happens to be the 2013 ForestVille Chardonnay; but it is only the best value if you can get it US$ 6 or less.

The orange points represent some of the other wines that are good value for money, as these are also a long way below the line. That is, they are the cheapest wines for each quality level, or the highest-quality wines at each price point, depending on which was you want to look at it. We could do a lot worse than buy these wines, if we happen to be looking for value-for-money U.S. chardonnay.

The second graph highlights those wines that are are very poor value for money. The further above the line, then the worse is the value for money. The red dots at the top of the graph are all further from the line than is the red dot at the bottom. So, there are 25 wines that are worse value than the best wine is good value! This is, unfortunately, to be expected in economics — there are more factors that tend to increase prices than there are factors that decrease them.

Given some knowledge about the quantitative relationship between wine quality and price (as represented by the graph), there are two basic strategies for optimizing the buying of wine. In economics, the optimal decision is usually taken to mean maximizing the quality : price ratio (QPR) — that is, getting the most for your money. [Note that the novice wine buyer usually does not have any knowledge about the relationship between wine quality and price. Acquiring some of this knowledge is often the first step away from being a novice, because better informed buyers are more likely to identify bargains.]

First, we could set a maximum price and then choose the highest quality wines below that price. Second, we could set a minimum quality level and then choose the cheapest wines above that level. Note that we cannot optimize both price and quality simultaneously — we need to choose one of these two features and then optimize the other one.

These are not the only possible ways to buy wine, of course, but all of others involve subjective decisions for each individual person.

Let's look at these two strategies in relation to the data set presented here. To the graph we can add a few more lines of our own, indicating possible quality or price categories. For example, the third graph adds two lines representing particular price points ($12 and $18).

These are not arbitrarily chosen points. In my experience you will rarely find good wines below the pink price line, and you usually need to buy wines above the green line to get any really good stuff. In other currencies, these price points are currently:

US$ 12 = € 11 = £  8 = SEK 100
US$ 18 = € 16 = £12 = SEK 150

For our example data, you will note that below the pink line no wines exceed 87 quality points. Furthermore, no wines below the green price line exceed 91 quality points. So, these can be taken as a reasonable quality expectations for these price points. That is, if I choose my maximum price then I am also (indirectly) setting my maximum expected wine quality. My optimal buying strategy must be to get as close to that maximum as I can.

If you want to try the second strategy, instead, then the final graph adds a line representing a particular quality point (87.5). This is also not an arbitrary choice — I have discovered over the years that my preferred wines tend to score 88 points or more.

You can see from the graph that I will therefore have to pay at least $12 for these wines (or SEK 100, actually, since I live in Sweden), and that I can expect to pay $19 (SEK 160) on average. So, any time I find an 88-scoring wine below $19 then I am getting a bargain; and my optimal buying strategy must be to reduce my costs as much as I can. In this example, I would therefore be interested in those wines with a score of at least 88 and a price less than (say) $18 — there are 11 wines that fit into this group, from which to choose.

My experience seems to agree with a number of other commentators on the internet. As just one example, the sommelier Jörn Kleinhans, interviewed at Business Insider (How to get the best quality wine at any price point), suggests:

$10 — the vast majority of wine is awful at this level
$20 — this is where quality wine begins, but there is great variation
$30 — wines become reliably high-quality around this price.

Similar suggestion have also been made at Food & Wine (Taste vs. price: how to find wine value). You will note that in the graphs above there are two wines at the $20 price that do not have good quality scores, which emphasizes the erratic nature of wines at this price point.

I rarely enter into discussions about wine beyond these price ranges, because that is out of my financial league. I almost never go beyond SEK 350 (US$ 45), and even then the wine would need to be more than 20 years old to tempt me.

That leaves us with the question as to where higher-priced wines fit into the quality : price relationship, which is a topic that I will discuss in a later post.

The relationship of wine quality to price

Quite a few researchers have looked at the relationship between the quality of wine (measured in any way you choose) and its sale price (measured in any currency you like), as shown in the literature list at the bottom of this post. In particular, see the review paper by Oczkowski & Doucouliagos in the list, as well as the one by Estrella Orrego, Defrancesco & Gennari.

However, few of these researchers seem to have looked at the relationship between price and quality in any simple way that might be of practical use to people buying wine. This is my objective here.

Note, first, that I am not talking about personal preferences. The fact that any of us like something is a separate issue from whether it is of high quality. The old adage that something is "good" if you like it, is nonsense. We can like high-quality things as well as things of lower quality. However, if you find a wine repulsive, then you are unlikely to buy it no matter how high its quality — such is the nature of personal preference.

The relationship between quality and price is complex for any product, let alone wine. For any given wine, the perceived quality is the product of many different characteristics, including producer, region, grape type, vintage, maturation, bottle age, serving temperature, and so on. Moreover, the price that we are charged for the wine is determined by, among other things, the producer, the distributor, the vendor, the media, and the government.

Nevertheless, these features are all combined when we are actually looking at a bottle of wine, either in a bottle shop or online, or in a restaurant listing, and wondering whether to buy it. So, for practical purposes, we need to investigate the relationship between quality and price in some general sense. Other people can talk about how complex the world is (eg. see the book by Mike Veseth listed below), but here I wish to see whether there are any simple patterns within the complexity.

In the world of economics, this search for simplicity is an attempt to find an uncomplicated model that approximates the real-world complexity. The better is the mathematical model then the better it will fit reality (ie. some observed data).

In the finance world, there are three simple models that have been shown to be useful most of the time. What we need to do is find out which one is the best general model for wine prices. These three models are illustrated in the first graph, followed by a brief description. (There is also a good picture in Wikipedia.)

Linear, or additive, model

Each unit of increase in quality leads to the same increase in price, so that that we simply add to the price each time we go for a better quality wine. For example, if 1 unit of quality costs 1 unit of money, then 2 units of quality costs 2 units of money, 3 units costs 3 units, 4 units costs 4 units, etc. Experience suggests that this is a very unlikely situation, in general — in practice, it becomes rapidly more difficult to increase quality, and this is therefore more expensive due to the increased effort on the part of the producer (and time is money).

Power, or multiplicative, model

Each unit of increase in quality leads to a proportional increase in price, so that we must multiply the price for each better quality wine. For example, cost might increase by a factor of 2 for each unit of increase in quality. Thus, if 1 units of quality costs 1 units of money, then 2 units of quality will cost 2 units of money, 3 units will cost 4 units of money, 4 units will cost 8 units of money, 5 units will cost 16 units, etc. Price thus increases more rapidly than in the linear model.

Exponential, or log-linear, model

This model is also multiplicative, but in this case the price increases even more rapidly than in the power model, as the proportional increase in price itself increases with quality. That is, the rate of price increase is proportional to the quality, rather than being constant, as in the power model. For example, at low quality the increase in price might be a factor of 2 for each unit increase in quality, but it might be a factor of 3 at higher quality levels.

I have looked at a number of suitable data sets, most of which I will show you in future blog posts. In all cases, the exponential model is as good as or better than the power model in terms of fit to the data, and both of these models are much better than the linear model. The review papers by Oczkowski & Doucouliagos and by Estrella Orrego, Defrancesco & Gennari (listed below) indicate that most research studies also find a log-linear relationship. So, from now on, I will use the exponential model whenever I look at wine data.

In the rest of this post I will use a single set of data, in order to illustrate the relationship between quality and price.

Some data

Wine is what is known as "experience goods", since we cannot observe the quality prior to consumption. The simplest way to assess wine quality is therefore to consult experts, who test the wine for us. This sort of quality assessment assesses only the sensory qualities of the wine. There are other ways to define "quality", of course, but they are rather more complex than simply consulting an expert.

There are innumerable experts lurking on the internet, although those working at the Wine Spectator, the Wine Advocate, the Wine Enthusiast, Vinous and so on, are the best known. In general, there is usually a consensus among critics about wine quality, although they may disagree for any given wine. For consistency, I will pick one assessor for my example. In subsequent posts I will look at what happens when we combine quality scores from several critics. (It turns out to be exactly the same thing.)

Similarly, wine prices vary dramatically throughout the world, although in general the "pecking order" of wines tends to be the same everywhere. However, I will pick a single location for my example. By choosing a single quality assessor and a single retail market, I will eliminate a lot of the complexity that might obscure any simple relationship between quality and price. In exchange I may lose some generality, since the chosen assessor and market may not be globally representative. In particular, how wines are assessed often varies between regions — for example, a Burgundy pinot noir is not necessarily evaluated in the same manner as a California pinot noir.

I also need to pick a particular grape variety for my example. It has been shown a number of times that the relationship between price and quality varies between varietals far more than between geographical regions (eg. see the papers by Taylor & Barber and by Snipes & Taylor).

Richard Jennings at the RJ on Wine blog has recently produced a quite remarkable data set for his Grocery Store Chardonnay Project. This involved assessing 230 US chardonnays representative of what can be found on grocery store shelves in northern California (he sampled the Lucky Store in Sunnyvale, Safeway in Cupertino, and the Mountain View Costco). He bought most of the wine himself (at a cost of close to US$ 4,000!), and "proceeded to taste through them several nights a week over a three month period" (you can read an interview with him on The Gray Report). Each wine was recorded for its retail price and its quality on the standard 100-point scale.

This data set is remarkable not just because Jennings paid for the wine himself, rather than relying on freebies, but because this resulted in a comprehensive survey of wines rather than a restricted sampling based on what trade representatives happen to have supplied. Clearly, trade samples cannot represent what you and I are likely to buy retail, as they will be biased towards those sectors where money can be spent on advertising.

It seems to me that Jennings should be a suitable source of wine quality ratings, although there is no necessary reason for his ratings to mean that any given person will like his high-scoring wines. He presents his credentials on his About Me blog page. Choosing a single rater working over a relatively short period of time ensures that the wines have consistent ratings; and the patterns in Jennings' data certainly seem to be comparable to the other data sets that I have looked at. Also, the choice of a single grape type, sampled at a single time point and intended for a single retail market, also standardizes the comparison. (By the way, Jennings is accepting PayPal donations to help him with the rest of his Grocery Store Wine ratings project, which is moving on to other grape varieties. More power to him.)

The complete chardonnay data are shown in the next graph. Each wine is represented by a single point in the scatterplot, located according to its quality score (horizontally) and price (vertically).

The data points are very scattered, indicating a lot of variation in wine price for any given quality score. This presumably reflects the myriad of different influences on wine quality and wine price, which can act independently of each other. Wine price is not determined solely by perceived quality! Indeed, mathematically only 38% of the variation in price is related to quality, in this particular example.

Nevertheless, there is also a clear increasing trend in the data, and this is summarized by the line. This line represents the best-fitting exponential model. This model indicates two things: the wine price is multiplied as quality increases, and the multiplier itself also increases with quality. High-quality wine costs a lot more than low-quality wine, and the higher the quality then the worse the price difference gets.

It is worth noting here that this analysis differs somewhat from most of those listed in the Literature below. Most of the literature studies have looked at the relationship between price and a whole swag of variables (that is, they have used multiple regression), rather than showing a non-linear relationship with a single variable. My approach is intended to be more practical. In particular, the research studies almost never show a graph of the actual relationship between quality and price, but instead confine themselves to providing a detailed theoretical analysis. Of what practical use is that?

As you can see from the graph, there is an upper limit to quality in this data set — the maximum score is 92, but the scores nominally go to 100. Thus, the very top echelon of wines are not in the sample. This reflects the fact that the wine was sold in supermarkets rather than specialist boutiques — you can't expect the best wines to be sold next to groceries, in the USA or anywhere else. In future posts I will present some data sets with much higher-scoring wines, and which therefore cost outrageous amounts of money.

The most practical thing to note, however, is the huge price variation within any given quality score. It is possible to pay twice as much money for wines at any given quality level. More to the point, anything below the line is good value for money (the wines are under-priced), while anything above it is not good value (the wines are over-priced). A more expensive wine is not necessarily an indication of higher quality.

In particular, you will note that there are wines extending much further above the line than below it, indicating that it is easier to find wines that are very poor value for money than very good value for money. The excess of wines above the line is due to both the "marketing premium" and the "brand prestige". Indeed, at 78 points there are two wines that are clearly atrocious value. (If you are interested, you can look them up in Jennings' blog post.)

In my next post I will consider how we might use this information about the quality : price relationship to make rational choices about buying wine (Choosing value-for-money wines).

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Research Literature

Héla Hadj Ali, Sébastien Lecocq, Michael Visser (2008) The impact of gurus: Parker grades and en primeur wine prices. Economic Journal 118:F158-F173.

Ana María Angulo, José María Gil, Azucena Gracia, Mercedes Sánchez (2000) Hedonic prices for Spanish red quality wine. British Food Journal 102:481-493.

Beth A. Benjamin, Joel M. Podolny (1999) Status, quality, and social order in the California wine industry. Administrative Science Quarterly 44:563-589.

Jean-Marie Cardebat, Jean-Marc Figuet (2009) Estimation of a hedonic price equation for Alsace, Beaujolais and Provence wines. Applied Economics Letters 16:921-927.

Pierre Combris, Sébastien Lecocq, Michael Visser (1997) Estimation of a hedonic price equation for Bordeaux wine: does quality matter? Economic Journal 107:390-402.

Pierre Combris, Sébastien Lecocq, Michael Visser (2000) Estimation of a hedonic price equation for Burgundy wine. Applied Economics 32:961-967.

David Cox (2009) Predicting consumption, wine involvement and perceived quality of Australian red wine. Journal of Wine Research 20:209-229.

María Jimena Estrella Orrego, Edi Defrancesco, Alejandro Gennari (2012) The wine hedonic price models in the “Old and New World”: state of the art. Journal of the Faculty of Agricultural Sciences, UNCUYO 44:205-220.

James Fogarty (2006) The Economics of Wine: Pricing, Quality and Rate of Return. PhD thesis, University of Western Australia, Perth.

Omer Gokcekus, Dennis Nottebaum (2011) The buyer’s dilemma — whose rating should a wine drinker pay attention to? American Association of Wine Economists Working Paper No. 91.

John W. Haeger, Karl Storchmann (2006) Prices of American pinot noir wines: climate, craftsmanship, critics. Agricultural Economics 35:67-78.

Ira Horowitz, Larry Lockshin (2002) What price quality? An investigation into the prediction of wine-quality ratings. Journal of Wine Research 13:7-22.

Gregory V. Jones, Karl-Heinz Storchmann (2001) Wine market prices and investment under uncertainty: an econometric model for Bordeaux crus classés. Agricultural Economics 26:115-133.

Stuart Landon, Constance E. Smith (1998) Quality expectations, reputation, and price. Southern Economic Journal 64:628-647.

Sébastien Lecocq, Michael Visser (2006) What determines wine prices: objective versus sensory characteristics. Journal of Wine Economics 1:42-56.

Bith-Hong Ling, Larry Lockshin (2003) Components of wine prices for Australian wine: how winery reputation, wine quality, region, vintage, and winery size contribute to the price of varietal wines. Australasian Marketing Journal 11(3):19-32.

Jon R. Miller, Ismail Genc, Angela Driscoll (2007) Wine price and quality: in search of a signaling equilibrium in 2001 California cabernet sauvignon. Journal of Wine Research 18:35-46.

Marc Nerlove (1995) Hedonic price functions and the measurement of preferences: the case of Swedish wine consumers. European Economic Review 39:1697-1716.

Edward Oczkowski (1994) A hedonic price function for Australian premium table wine. Australian Journal of Agricultural Economics 28:93-110.

Edward Oczkowski, Hristos Doucouliagos (2014) Wine prices and quality ratings: a meta-regression analysis. American Journal of Agricultural Economics 97:103-121.

Peter W. Roberts, Ray Reagans (2007) Critical exposure, attention and price-quality relationships for new world wines in the U.S. market. Journal of Wine Economics 2:84-97.

Günter Schamel, Kym Anderson (2003) Wine quality and varietal, regional and winery reputations: hedonic prices for Australia and New Zealand. Economic Record 79:357-369.

Michael Snipes, D. Christopher Taylor (2014) Model selection and Akaike Information Criteria: an example from wine ratings and prices. Wine Economics and Policy 3: 3-9.

D. Christopher Taylor, Nelson A. Barber (2009) A relationship of wine ratings and wholesale pricing, vintage, variety, and region. Hospitality Review 26(2):10-18.

Mike Veseth (2015) Money, Taste, and Wine: It's Complicated! Rowman & Littlefield Publishers.

Peter M. Visscher (2014) Statistical analysis of the price and subjective quality ratings on Australian wines. https://arxiv.org/abs/1402.3646.

Global wine exports

So, where does all of this wine production go? Obviously, some of it is consumed by the inhabitants of each producing country, but a lot of it is also exported. Indeed, wine is an important source of foreign income for many countries.

The first graph here shows the top 11 countries according to the 2015 data recently provided by the International Organisation of Vine and Wine (OIV), in terms of volume of wine exported.

There are no real surprises here.

However, things become a bit more interesting when we consider the monetary value of the wine. After all, it makes a difference whether one is exporting cheap wine or expensive wine. There are serious differences in the monetary stakes between countries, as shown in the next graph.

Indeed, the order of the first three countries changes completely. Apparently, Spain is exporting much cheaper wine than is either France and Italy.

This becomes more obvious if we combine the two data sets, and thus calculate who makes the most money per volume of exports. This is shown in the third graph.

Clearly, France is exporting much more expensive wine than is anyone else, followed by New Zealand. There is a massive difference in wine production between these two places (New Zealand didn't even make it into the graphs in the previous post), but they are both treating wine as a value added commodity for export. That is, the inhabitants are keeping the cheap stuff for themselves, while exporting some of the good stuff.

Spain, South Africa, Chile and Australia, on the other hand, are the world's main purveyors of cheap wine. These countries are trying to make money out of quantity, not quality.

It is thus interesting to compare neighboring countries, such as Chile with Argentina, and Australia with New Zealand. In both cases, there are obviously considerable differences in approach to wine exports between the neighbors, in spite of very similar quality in terms of the wine produced.

This is especially true in the case of Australia and New Zealand, where Australia produces far more high-quality wine than does New Zealand, due to its much larger vineyard area, and yet apparently it is not exporting very much of it. As far as I can see, there is no good reason why Australia could not be making as much money per litre of exports as New Zealand, or the USA, and yet it is apparently choosing not to do so. This does not make much economic sense.

Indeed, this ridiculous situation has been pointed out by a number of commentators on the Australian wine scene, notably Jeremy Oliver and Philip White.

In the 2016 edition of The Australian Wine Annual, Oliver notes that "According to the 2015 Production Profitability Analysis for the Australian Winegrape industry produced by the Winemakers Federation of Australia, 85% of Australian vineyards operate at a loss ... One of the reasons Australian wine is in its current shape is chronic oversupply — a legacy of the industry's previous approach to doing business that confused volume for profitability".

In a similar vein, White highlights the increasing centralization of the Australian wine industry all along the supply chain, in terms of grape growing, wine making and wine sales — the two biggest supermarket chains (Woolworths and Coles) not only completely dominate wine sales but they now also have significant financial control over wine making (see Pollster finds Woolies' way: stranglehold on liquor retail). This inevitably drives down the average price of wine, to the point where the business is uneconomic — see the essay by Malcolm Knox (Supermarket monsters: Coles, Woolworths and the price we pay for their domination).

It is difficult to see how this can be good for the future of the Australian wine industry.

Global wine production

The immediately obvious questions about wine data are: who has the vineyards? and who makes the wines? The answers sometimes surprise people.

The International Organisation of Vine and Wine (OIV) has recently released the 2015 data for global wine production and trade, and these are thus the latest figures. A number of bloggers have commented on these data, notably Per Karlsson at BK Wine Magazine, from whom I have taken the data shown here.

The top 13 countries are shown in the first graph, in terms of vineyard surface area.

The surprise to most people is the presence in the list of China, Turkey and Iran (whose exact data is currently uncertain, and is thus missing from the graph). However, these data refer to vineyards, not wine, and many (if not most) grapes are used for eating (both sultanas and raisins) rather than wine production. For example, in Turkey only c. 3% of the vineyards are currently used for wine production.

China has only recently over-taken France as the number-two contributor of grapes, However, while the oldest known evidence of wine-making dates back 6,000 years, in what are now Georgia and Armenia (see the map), wine-making in China dates back at least 4,500 years.

The top 11 countries for current wine production are shown in the next graph.

The change in the order of the countries shows you which ones have a large production of eating grapes, and which ones concentrate on wine production.

Indeed, we could combine the two data sets, and thus calculate who produces the most wine per vineyard area. This is shown in the third graph.

Obviously, there are differences between countries in how productive their vines are, but this graph still gives you a rough picture of who uses their grape vines for making wine. This provides a very different picture from what most people seem to expect. Even big wine-producing countries such as Spain do not use most of their vineyard area for making wine. It is the so-called New World areas who concentrate on wine production, such as South Africa, Australia, Chile, Argentina, and the USA.

Introduction

A gourd of red wine and a sheaf of poems —
A bare subsistence, half a loaf, not more —
Supplied us two alone in the free desert:
What Sultan could we envy on his throne?
                                                        —Robert Graves

Calabash Palm Wine Gourd
from Cameroon

The wine world is full of opinions; and the blogsphere is full of opinions. It is therefore not unexpected that there are countless blogs offering opinions about wine. Some of these opinions are interesting, and some are informative.

However, behind these opinions is a world of information, showing how the world actually is, rather than how we perceive it. Sometimes these data are hidden and sometimes they are in full view. Either way, I figure that we could do worse than have a look them.

As a scientist, I am interested in exploring the data, rather than reading opinions about it. Data are often fascinating, sometimes they are controversial, and not infrequently they challenge received opinion.

So, in the interests of doing something different to everyone else, this blog will delve into the world of wine data. My intention is to ferret out some of the interesting stuff, and to bring it out into the light, for everyone to see. In particular, I have noted that many (if not most) research studies about wine do not show us an actual picture of their data. So, I would like to do this, rather than provide some theoretical assessment of those data.

At heart, I guess that I want to compare the opinions that people hold to the underlying data (the "facts", as the media naïvely like to put it). Naturally, there will also be opinions of my own along the way. However, I am hoping that the primary interest will be in the information itself, and the use of exploratory data analysis to look at it.

The source for the name of the blog will be obvious to anyone who knows how to say "vineyard" in Swedish.