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<p>[QUOTE="Roerbakmix, post: 3842361, member: 100731"]Thanks all for the reactions. Interesting points have been raised here:</p><p><br /></p><p><br /></p><p>This is exactly <i>not</i> what I'm doing. Basically, there is available data, and there is a relation between several parameters (such as estimate, grade, etc.) and the hammer price. What this (rather crude) modelling methods show, is that a) there is indeed a relation and b) the variance of it can partly be explained. <b>It is probably impossible to 100% predict the outcome (ie hammer price). </b>But this is not what I try to do.</p><p><br /></p><p><br /></p><p>This is the same as doing modelling, except that it is done in an unstandardized fashion. Which is not wrong. But conceptually: you have previous data (your experience) and new data (the new auction) and with your experience, you're trying to predict the ideal outcome. So, for example, suppose you've got a coin which is well centered, high grade, but has a hole in it, you will add a certain weight to each predictor (centering, grade, hole) and thus reach your optimal estimate for the actual value of the coin. A model does exactly this: it determines the <u>relative</u> weight of each parameter to the outcome (hammer price).</p><p><br /></p><p>Regarding the unexplained variance (e.g. the cat jumping on the keyboard resulting in a high bid, or the computer crashing resulting in a low bid). This could be regarded as random noise, and given large enough sample size, should level out each other.</p><p><br /></p><p>Regarding differences in grading, or subjective grading: the concept above is probably applicable to that as well, except that there will probably a tendency to overgrade. However, if we assume this to be consistent (so not a thing of the past, i.e. if we assume dealers do not suddenly change this behaviour), this is not a problem either.</p><p><br /></p><p>Now a final note regarding the parameter 'estimated price'. In a private conversation, we reached the conclusion that a) the other parameters are probably included in this estimate (and that this may partially explain the little added explained variance of these predictors), and b) the estimate is usually the starting point of bidding, or a percentage of the estimate. So, a hammer price lower than the estimate is relatively uncommon, and thus a strong positive relation between estimate and hammer price should be expected.</p><p><br /></p><p>In conclusion: the auction results are probably not completely predictable, or perhaps not even moderately. This does not mean that auction results are something 'magic'. It mereley means that there is not enough explainable data. (But this is more a phylosophical standpoint than a practical).[/QUOTE]</p><p><br /></p>
[QUOTE="Roerbakmix, post: 3842361, member: 100731"]Thanks all for the reactions. Interesting points have been raised here: This is exactly [I]not[/I] what I'm doing. Basically, there is available data, and there is a relation between several parameters (such as estimate, grade, etc.) and the hammer price. What this (rather crude) modelling methods show, is that a) there is indeed a relation and b) the variance of it can partly be explained. [B]It is probably impossible to 100% predict the outcome (ie hammer price). [/B]But this is not what I try to do. This is the same as doing modelling, except that it is done in an unstandardized fashion. Which is not wrong. But conceptually: you have previous data (your experience) and new data (the new auction) and with your experience, you're trying to predict the ideal outcome. So, for example, suppose you've got a coin which is well centered, high grade, but has a hole in it, you will add a certain weight to each predictor (centering, grade, hole) and thus reach your optimal estimate for the actual value of the coin. A model does exactly this: it determines the [U]relative[/U] weight of each parameter to the outcome (hammer price). Regarding the unexplained variance (e.g. the cat jumping on the keyboard resulting in a high bid, or the computer crashing resulting in a low bid). This could be regarded as random noise, and given large enough sample size, should level out each other. Regarding differences in grading, or subjective grading: the concept above is probably applicable to that as well, except that there will probably a tendency to overgrade. However, if we assume this to be consistent (so not a thing of the past, i.e. if we assume dealers do not suddenly change this behaviour), this is not a problem either. Now a final note regarding the parameter 'estimated price'. In a private conversation, we reached the conclusion that a) the other parameters are probably included in this estimate (and that this may partially explain the little added explained variance of these predictors), and b) the estimate is usually the starting point of bidding, or a percentage of the estimate. So, a hammer price lower than the estimate is relatively uncommon, and thus a strong positive relation between estimate and hammer price should be expected. In conclusion: the auction results are probably not completely predictable, or perhaps not even moderately. This does not mean that auction results are something 'magic'. It mereley means that there is not enough explainable data. (But this is more a phylosophical standpoint than a practical).[/QUOTE]
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Predicting hammer price: playing a bit with Sixbid data & looking for inspiration
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