Community Reinvestment Act Disclosure Files, Table D1-1,1996 through 2009

Multi-year CRA disclosures for small business originations, per county, year and bank.

ffiec.gov-cra_disclosure-1996e2009-d11-2.1.1. Modified 2020-10-18T03:59:23

Resources | Packages | Documentation| Contacts| References| Data Dictionary

Resources

  • sb_loan_orig. Table D1-1, small business disclosure records, for years 1996 through 2009

Documentation

The Community Reinvestment Act was enacted by congress in 1997 to encourage investment in underserved communities. The Act requires lenders record and publish data about loans on a per-community basis, specifically, Census tracts and MSAs. The Disclosure reports record individual lender’s loans for MSAs, broken out by loan value and the Median Family income of tracts.

The original data releases are difficult to use, as each year’s disclosures are packages as fixed-width records with all 6 tables in a single file, requiring substantial effort to extract specific record. This data package collects the Small Business Loan origination disclosure records ( D1-1 ) for the years 2010 to 2015, inclusive.

This data package includes years 1996 through 2009. Another data package includes the years 2010 on.

Contacts

Data Dictionary

cra_disclosure

cra_disclosure

Column NameData TypeDescription
table_idtextValue is D1-1
respondent_idtextAssigned by regulatory agency (same as HMDAID if applicable); Right justified with leading zeros
agencyintegerValues are 1=OCC, 2=FRS, 3=FDIC, or 4=OTS
yearintegerFour digit year (e.g. 2012)
loan_typeintegerValue is 4 (Small Business)
actionintegerValue is 1 (Originations)
stateintegerFIPS code with leading zeros or blank for totals across all states
countyintegerFIPS code with leading zeros or blank for totals across all counties
msaintegerAs defined by OMB; Right justified with leading zeros, NA left justified for areas outside of MSA/MD or blank for totals across all MSA/MDs
assessment_areatextValues are 0001 through 9999; Right justified with leading zeros, NA left justified for areas outside of an Assessment Area (including predominately military areas) OR blank for totals across all Assessment Areas
partial_countytextValues are Y = Yes N = No OR blank for totals
split_countytextValues are Y = Yes N = No OR blank for totals
pop_classtextValues are S= counties with 500,000 in population OR blank for totals
income_totaltextValues are 1= 120% of MFI 14= MFI not known (income percentage = 0) 15= Tract not Known (reported as NA) 101= Low Income ( 120% of MFI) 105= Income Not Known (0) 106= Tract not Known (NA) Right justified with leading zeros or blank for totals
report_leveltextValues are 4= Total Inside & Outside Assessment Area (AA) (across all states) 6= Total Inside AA (across all states) 8= Total Outside AA (across all states) 10= State Total 20= Total Inside AA in State 30= Total Outside AA in State 40= County Total 50= Total Inside AA in County 60= Total Outside AA in County Right justified with leading zeros or blank if not a total
num_orig_bus_lt100kintegerNumber of loans less than $100K
tot_orig_bus_lt100kintegerTotal value of loans, in thousands
num_orig_bus_lt250kintegerNumber of loans less than $250K, more than $100K
tot_orig_bus_gt100k_lt250kintegerTotal value of loans, in thousands
num_orig_bus_gt250k_lt1mintegerNumber of loans, less than $1M, more than $250K
tot_orig_bus_gt250k_lt1mintegerTotal value of loans, in thousands
num_orig_bus_lt1mintegerNumber of loans to businesses with gross revenues of less than $1m
tot_orig_bus_lt1mintegerTotal value of loans, in thousands
num_orig_bus_alintegerNumber of affiliate loans
tot_orig_bus_alintegerTotal value of loans, in thousands

References

Urls used in the creation of this data package.

Packages

Accessing Data in Vanilla Pandas

import pandas as pd


sb_loan_orig_df =  pd.read_csv('http://library.metatab.org/ffiec.gov-cra_disclosure-1996e2009-d11-2.1.1/data/sb_loan_orig.csv')

Accessing Package in Metapack

import metapack as mp
pkg = mp.open_package('http://library.metatab.org/ffiec.gov-cra_disclosure-1996e2009-d11-2.1.1.csv')

# Create Dataframes
sb_loan_orig_df = pkg.resource('sb_loan_orig').dataframe()