At ComplianceTech, we love data analysis, so last Friday’s release of new HMDA data was a truly exciting day.
Unfortunately, we will have to wait until the release of the CFPB “snapshot” data for the full data to be in LendingPatterns(TM). Those who are really anxious to look at their competitors’ data can download the modified HMDA LAR from the CFPB’s website.
Steps to Analyze the Modified HMDA LAR
Open the file in Excel’s Text Import Wizard, making sure to preserve as text the information in the geography fields (County and Census Tract).
If you’re using a new version of Excel you want to follow the instructions here to restore the “legacy” Text Import Wizard.
The screenshots at the bottom of this blog can guide you through the Text Import Wizard.
- Copy the field names from the data structure here to the first row of your file.
- Add a sequence number field and call it “LOANID”.
- Delete the age, DTI, and multifamily units fields. (After inserting the loan ID field, these fields will be in columns AP through AS, BL, and BX.)
- Recode values in the units field that are 5 and above to “5”. (After inserting the loan ID field, this field will be in column BW.)
You can upload this file to the HMDA Prep module in Fair Lending Magic(TM) to obtain a HMDA TXT file that you can then upload to LendingPatterns(TM).
The power of new 2018 HMDA data, in pictures and tables
I performed some quick analysis on one bank’s data, comparing the bank’s set of 2017 closed loans in the Minneapolis MSA to its 2018 consumer-purpose lines of credit, which are newly required reporting and identifiable in the data.
In the top image below are the 8,487 2017 loans across all products. Below are the 2,007 2018 consumer-purpose lines of credit. The overall volume in the 2018 sample is about a quarter (24%) of the 2017 volume, and the share of transactions in majority-minority tracts (MMTs) is also lower (2.72%, compared to 4.76%). The 2018 map clearly shows more MMTs with no activity, more lending gaps that could require an explanation.
LendingPatterns(TM) Population Penetration Profile reports provide another view of the data. In 2017, the share of black loans is 46% of the black share of the MSA population. In 2018, the under-penetration is more stark: the black population share is more than 12 times the black share of originated consumer-purpose HELOCs.
These tables and maps illustrate the power of the 2018 data. Please let us know how we can help you answer your questions on the data as you dig in. Our LendingPatterns(TM) support address is firstname.lastname@example.org.
Text Import Wizard screenshots