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06-10-2018, 11:48 PM
#3761
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07-10-2018, 10:22 AM
#3762
Member
Thanks Myles!!
Fantastic work Myles. Many thanks for the effort. Thanks too to the investor for sharing.
One quick conclusion is that it is much better to invest in rewrites. None of my almost 400 defaults were rewrites too.
Last edited by Cool Bear; 07-10-2018 at 10:29 AM.
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07-10-2018, 11:59 AM
#3763
Member
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07-10-2018, 01:43 PM
#3764
yeah, nah
Originally Posted by alundracloud
y-axis = number of Home Improvement Defaults
x-axis = Residential status
I think this is what you want? You're on the money - looks like most are mortgagors anyways.
defhome.jpg
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07-10-2018, 01:53 PM
#3765
Member
Exactly what I was after, and looks similar to how I expected. Thank-you!
I have some data which you might be interested in also, how best to get it to you?
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07-10-2018, 02:27 PM
#3766
Member
Originally Posted by myles
I think this is what you want? You're on the money - looks like most are mortgagors anyways.
defhome.jpg
Thanks again.
The single default in the "boarding" resulting in the 6.25% (1 out of 16) could be an anomaly due to the low population size. Similarly too for the "Living with Parents" and "others". However, when you combine them, it is a total of 10 defaults out of 88 or 11.36%. And 88 is a credible population size. Even if you add the 2 "unknowns" and 8 "supplied by Employers", it is still a high 10.1% compared to 0% for fully owned, 0.5ish% for mortgagees and even the 'high-ish' 3+% for renters.
Valuable confirmation, Myles.
Last edited by Cool Bear; 07-10-2018 at 02:31 PM.
Reason: clarity
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08-10-2018, 10:20 AM
#3767
Last edited by beacon; 08-10-2018 at 10:36 AM.
Reason: Additional thoughts
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09-10-2018, 01:44 AM
#3768
yeah, nah
There are a few people interested in pooling data and I've got most of what needs to be done to merge and clean the data. I'll not include any data I currently have so if you have given me some previously can you please generate a new report and upload that.
The good thing is that the data in the orders-report-###.csv file provided, is free of lender info - so it will be safe to upload the csv and not have the data associated with the lender. However, the name of the csv file does include an ID number - for anonymity it will be best to rename the file to just orders.csv.
Please generate a new report, rename the file to orders.csv and submit that as it is (don't zip or anything else).
When asked for a name and email address to upload the file just use a fake one, I typically pick on poor old fred:
fred.png
I'm just using a Drop Box file request, which is secure and safe:
Upload orders.csv
There are some issues with timeliness of data i.e. it becomes 'stale' if not updated with any new details of defaults. So I'll only keep this link live for the rest of this week (late Sunday) and then shut it down.
Then there is the question of what to do with the data. I can clean it up and make it available for those who want to do their own analysis and perhaps come up with one 'tidy' set of charts and summary data for those who don't/can't. Some thought needs to be put in around older loans and the 9 month default gap which effects the 'real' values - I'll think about that a bit more.
The 'mechanics' of doing it aren't a problem I don't think i.e. once I've merged and cleaned the data I'll just provide a link to download the compiled data as a csv in the same format as it currently is.
If anyone has suggestions/thoughts please jump in.
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09-10-2018, 08:41 AM
#3769
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09-10-2018, 08:47 AM
#3770
Originally Posted by myles
If anyone has suggestions/thoughts please jump in.
Neat idea. Thanks for making the time to do this, Myles. I have some pooled data too, which I am happy to upload to help with this initiative.
Data labels on bar charts can be very helpful. Eg., in plotting default by coborrower, you currently include sample sizes on x-axis (1079 co-borrowers and 9909 single borrower). Both bars are between 0 to 5%. Could you also include labels on top of the bars like 3% or better still 32, if including them is no trouble to you.
Protect loans might be better compared against non-protect loans (control) after the date protect loans became available, to be more truly representative. So if I were plotting these, I'd begin counting the control sample from the day protect sample began. This means control sample will be a subset of Harmoney loan population and control population will be less than Harmoney total population. Of course, the result may not not be too different.
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