I was thinking of one graph for each cohort (a1 to f5) but if that is too time consuming, then one overall graph with just A to F as a start
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You'd probably want to run it as a line graph, with a separate line for grade (A, B... F) [imagine plotting by cohort would overlap too much]. Alternatively if using a bar graph, may need to run a separate graph for each cohort.
Thanks Myles :) Must be interesting being able to work all that data - just under 50% of the loans out there!
Haven't had much time to play :(
But here is the latest:
Unique Loans: 21249
Total Loan Value: $432,596,325.00
Attachment 10047
Number of loans that defaulted for each bar has been added, which is a good addition :t_up:
Just adding this one as it is significantly different from what was shown in the past:
Attachment 10048
Calculated by determining the minimum date where a loan has a PP and only included loans on and after that date. More like what most expect it to be.
I wonder if the PP are taken up more by the borrowers in the risker grades and thus have more defaults. Could you do one showing % of PP taken by the various grades? If the percentages are about even, then we can safely conclude to stay away from PP loans. But if it shows that more borrowers in the risker grades take PP then that conclusion may not be valid.
Probably need to plot this differently but the details are shown:
Attachment 10050
The ratios are pretty even across all grades, but more PP taken up in mid risk grades.
Great work Myles, adding the sample size is very helpful. Some interesting patterns there. Have you got graphs by 6m cohort too?
Great work Myles. Thank you.
I think we can safely conclude that PP loans are a greater risk than non-PP loans (although not to the extent that the earlier aggregate chart shows). With partial PP performing a bit worse than full PP.
With us having to pay HM for the sales commission even for fail PP loans, that makes the loss to us investors even higher. Another factor to take into account when investing manually.
Thanks again.
The hard part is determining if the increased income out-ways the increased risk from PP's. I've never looked at the numbers but 'trusted' Harmoney's suggested 1% gain?
Need to correct what I said earlier too, now looking again at that last PP graph:
There is more PP taken up in higher risk grades in proportion to loans in that grade (not what I said before about mid grades - that was volume of PP not proportion)...needed to be plotted differently to highlight that...but the detail is there.
I agree with you that that must be whats happening, but its interesting if you look at the figures for full payment protect in particular this group of people must be pre disposed to events or problems that cause defaults that are NOT Death, Terminal Illness, Disability or Involuntary Redundancy, because if the cause was any of these it would be a payment protect claim rather then a default
Had wanted to work out how to create a 'Fence' chart (finicky to get right) so thought I'd give it a go for this. Result below:
Attachment 10054
Not sure if there is enough detail to be meaningful? Would running it for individual grades (i.e. A1, A2) be useful? Let me know what you think.
Perhaps there is a better way to show what you want - or it might be best to use a Pivot Table once you have the data and slice and dice as you want?
Argh, lost two posts :( stupid mobile interface has far to big of a Delete button...
Anyway, 3rd time lucky.
I realised I got my wires crossed with what you guys are asking for - I thought you wanted 'Enquiries last 6 months' vs Grades rather than 6 monthly period borrower cohort vs Grades.
Unfortunately, I don't think we are going to be able to generate this. When Harmoney started selling off debt they used the 'Last Payment Date' field for some unknown reason. By doing this they overwrote the date when the loan actually defaulted (i.e. when the last payment was made).
Will have to have a look and see...but that last time-lapse showed a very large chunk of loans being sold off and effectively having their default date wiped into the future.
Myles,
No, I meant the date the loan originates. So the cohorts are loans originating in 1h2015, 2h2015, 1h2016, 2h2016 etc.
So that, for example, we can see out of all the loans originating in 1h2015, how many A, B,...F have been sold/charged off etc..
X axis 1h2015A, 1h2015B...etc
Y axis percentage of loans that are charged off.
No time lapse, just bar charts.
This is so that we can see the percentage charge off rates for older cohorts compared to younger cohorts.