I just did some math, and I'm wondering if I have any kind of case here.
My FICO score as of 6-15-07 was 668.
On 6-21, my score dropped 13 points down to 665, due to a hard pull inquiry - I applied for a Best Buy store card.
On 6-27, my score dropped 33 points down to 622, due to the fact that my Target card account reported a history of past dues/late pays, a collection account reposted to the account, and a paid in full installment account changed some basic information (this last thing is a positive item and actually didn't alter my score).
On 6-29, my score dropped 37 points down to 585, due to two hard pull inquiries for the COAF and RoadLoans auto loans that I applied for on 6-16.
On 7-3, my score went up 10 points to 595, because a new positive account reported to my file.
On 7-6, my score went up 6 points to 601, because a couple of information changes and a positive account was added, but the inquiries from 6-21 and 6-29 were deleted from my account (believe it or not, CSC just deleted them - AND the rest of my inquiry history - while they were investigating some other inquiries which were outdated). So, at this point I have NO hard inquiries at all on my EQ report!
Now, here's my question - and it has to do with reverse engineering of scores. If they eliminated the inquiries and other negative aspects of my file that dropped my score 50 points (13 on 6-21 plus 37 on 6-29), why wouldn't my score be UP 50 points at the present time?
What I mean is, if those inquiries had never dropped my score, but the Target and collection account still posted as they did on 6-27, then shouldn't the following be the way it should look?
6-15: 668 6-21: 668 (No Best Buy inquiry - therefore, no 13 pt. drop) 6-27: 635 (33 pt. drop - Target and collection account) 6-29: 635 (No car loan inquiries - therefore, no 37 pt. drop) 7-3: 645 (10 pt. increase) 7-6: 651 (6 pt. increase)
the problem is that it is an algorithm for which we have no way of knowing all of the factors that are taken into account. sure, we know some generalities, like the percentages of our scores that some very broad categories make up; however, we start getting into problems in reverse engineering the algorithm when taking into account factors like scorecards, and especially the interrelationships of those broad categories. for instance, it is quite possible (and likely, imo) that the payment history factor is affected by certain criteria in the age of accounts which is affected by other criteria in utilization, etc. certainly these are factors that cannot be reverse engineered because first we would have to identify the relationships and then we would need access to the vast amount of data that is used in the algorithm, i.e. individual credit reports. as we all know, that data is impossible to obtain. the good thing is that there are a lot of folks who have collaborated and gained enough knowledge to provide general info on factors that help and hurt, but i believe it is impossible to ever reverse engineer the process to any reasonable degree of certainty. let's not even talk about the fact that the algorithm is likely ever-changing.
I have been working on an extensive Excel spreadsheet that simulates current and future FICO scoring based on various factors that are relevevant to me, and I would be glad to share it for comments with anyone who would like to take a look at it.