CircleID

Caveat emptor. Since we are still in the begininng stages of, or as some people say, living the equivalent of the Wild West - Gold Rush with respect to domain name buying, you cannot value or appraise any domain name. Yes, there are ways to approximate value by analyzing traffic and ranking yet the true value is in what the domain name "owner" places on it and what the perspective buyer is willing to pay. Its all about vision and feelings, not data! Using mathematical equations or any type of formula to assess the value of a domain name is purely a waste of time. Don't kid yourself or others, an appraisal of a domain name is simply an opinion with very little factual real life data. As an Attorney who missed an opportunity to obtain www.BocaRatonLawoffice.com years ago for $500.00 because I had it appraised at 150.00, I also used an online service to value or appraise www.acefla.com, a private investigation company that is for sale, and was told its worth 175,000? Go figure? JM, West palm Beach, FL

Jaysen,

Thanks for pointing out the “newness” of domain names, another erroneous argument, which I missed in my post.

Below are three plausible interpretations of newness and the associated errors:
1.  Not enough data to conduct meaningful statistical tests. Not true, as noted in the post.

2.  Enough data, but it is pure noise, i.e., no statistically significant patterns in the data. Not true, as noted in the post, statistical data mining techniques suggest otherwise.

3.  A domain name’s best use is not yet established. To a certain degree this is true, as in a large number of inventions including the birth of the PC and fax, to name just a few.  For domain names, the public discourse includes development, e-commerce, and leasing as use options. Nevertheless, if an entrepreneur finds a new value-adding use, it does not imply that a previously valid appraisal was meaningless at the time it was conducted.

Just because certain arguments didn't work for one example doesn't mean they do not work for another.  Domain valuation, is predicting it really the same as supreme court decisions?

I would LOVE to see a strong model able to predict values well.  Even with the samples I have worked with getting an R^2 of 50% is very hard (i have only got to ~48).  There is almost 2 markets to be considered, reseller and end user.  Just the way buyers value domains is done at two different levels alone which throws off accuracy of most models.

Since we are all aware you provide appraisals and your models, care to share how strong a model you've developed?  I would be interested in R^2, and N and how many variables (don't need to reveal them if you don't want).

You raise interesting issues. However,

1.  The objectives of the post are two fold:

(a) To provide arguments for the viability of statistical methodology for estimating the transactional (ie, not for IRS, trademark, or antitrust considerations) value of a domain name, and
(b) To outline some erroneous arguments regarding the use of scientific methods to value domain names.

Thus, the post is not about the use of a specific statistical technique or a specific appraiser.

2.  R^2

(a) Given any dataset, one can construct a regression model with an R^2 of 100%. Thus, it is not necessarily a good measure of goodness-of-fit.
(b) In some statistical techniques, such as regression-trees, R^ is not used as a criterion for selecting viable independent variables and thus, it is not used as a measure of goodness-of-fit.
(c) As the post is related to the use of statistical techniques to predict value rather than to estimate some parameters, a better goodness-of-fit criterion is the estimated model’s predictive power.  This can be achieved, for example, by dividing the total sample of observations into two mutually exclusive groups: one for estimation and the other for determining its predictive power.

3.  Increasing the number of explanatory variables does not necessarily improve the goodness-of-fit, even if one were to use R^2 as a measure.

Thanks for responding, but I think you have dodged the questions.

You believe it is viable, I would like to see what sort of approach you would use or do use and how accurate a model you can actually create.  I have tried personally and I don't think you can create an accurate statistical model, at least not a strong one.  You say R^2 may not be a good measure of fit because you can make any r^2 100 given a data set.  Even if you did this, when you use the model to predict with an R^2 of 100 it should be extremely accurate, which would be a good test of whether you just manipulated the data to suit your needs or actually created a good model, no?  lastly, I am aware increasing the number of variables doesn't always improve goodness of fit, there were a lot of variables I tried that made the model worse.  However, it is interesting to see how many factors really play a role and can be measured in a statistical model.  Would you care to answer any of my original questions now?

Thanks for responding, but I think you have dodged the questions.

Glad that you are interested in the topic. I am not trying to dodge, but I would like to keep the discussion focused on the post and the issues you raised in your reply, which I address below.

You believe it is viable, I would like to see what sort of approach you would use or do use and how accurate a model you can actually create.

It would be constructive if we start with what you mean by accurate.

I have tried personally and I don't think you can create an accurate statistical model, at least not a strong one.

What constitutes strong vs. weak?

You say R^2 may not be a good measure of fit because you can make any r^2 100 given a data set.  Even if you did this, when you use the model to predict with an R^2 of 100 it should be extremely accurate, which would be a good test of whether you just manipulated the data to suit your needs or actually created a good model, no?

The answer is no! In practice, you will never get 100% R^2, because there is always “measurement error.” An R^2 of 100% can be achieved, for example, by running the estimated line through every observation. However, the resulting model would very likely be practically useless in prediction and thus, such a perfect-fit model defeats the purpose of statistical estimation. Another example of useless perfect fit is running a regression of “right shoe” on “left shoe.” Both examples do not involve manipulating or massaging the data.

lastly, I am aware increasing the number of variables doesn't always improve goodness of fit, there were a lot of variables I tried that made the model worse.  However, it is interesting to see how many factors really play a role and can be measured in a statistical model.  Would you care to answer any of my original questions now?

We agree that increasing the number of variables does not necessarily improve the fit. Thus, in general, the number of significant variables is irrelevant. Moreover, in practice, we find that certain functional forms of the product of “click volume” and CPC have better predictive power than using them separately. Furthermore, due to the nature of regression trees, the number of variables for each cluster of similar domain names is not necessarily the same. Hence, the number of variables used in the estimated model does not provide any useful information.