Atch J.5 - Past Performance Narrative - 11-17-22.docx

DOCX document 27 KB Posted

Attached to
FHA TOTAL Mortgage Scorecard Federal contract opportunity
Solicitation number
86614623Q00001
Issued by
Department of Housing and Urban Development

About this file

This solicitation seeks quotations for a firm-fixed price contract to provide performance evaluations, updates, and re-estimation of confidential scorecard algorithms using Federal Housing Administration loan data and credit histories. The selected contractor will analyze FHA loans insured from 1997 to present to reassess scorecard performance in predicting loan defaults and claims attributable to borrower credit issues, as well as evaluating fair lending impacts. Quotations are due by November 30th and should be submitted electronically to the Contracting Officer at the Department of Housing and Urban Development Fort Worth Office. This is a 100% small business set-aside for North American Industry Classification System code 541990.

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Other files for this federal contract opportunity

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Atch J.7 - Section B Prices and Contractor Personnel - Revised 11-28-22.xlsx XLSX spreadsheet
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Atch J.8 - Price Proposal Spreadsheet for Multiple Year - 11-25-22.xlsx XLSX spreadsheet
Atch J.6 - Past Performance Survey - 11-17-22.docx DOCX document
Atch J.4 - Past Performance Information Chart Spreadsheet - 11-17-22.xlsx XLSX spreadsheet
86614623Q00001- Amend 0001 - FHA TOTAL Scorecard - 11-25-22.pdf PDF
Atch J.5 - Past Performance Narrative - 11-17-22.docx DOCX document
86614623Q00001 - FHA TOTAL Scorecard Questions--Answers - 11-25-22.xlsx XLSX spreadsheet
Atch J.7 - Section B Prices and Contractor Personnel - 11-25-22.xlsx XLSX spreadsheet
Atch J.2 - Credit Data and Fair, Issac Risk Score Confidentiality Agreement - 11-17-22.pdf PDF
Atch J.3 - Labor Mix and Level of Effort Spreadsheet -Multiple Year -11-17-22.xlsx XLSX spreadsheet
Atch J.1 - Nondisclosure-Agreement-between-HUD-and-Contractor-Employee - 11-17-22.docx DOCX document
Atch J.4 - Past Performance Information Chart Spreadsheet - 11-17-22.xlsx XLSX spreadsheet
Atch J.7 - Section B Prices and Contractor Personnel - 11-17-22.xlsx XLSX spreadsheet
Atch J.8 - Price Proposal Spreadsheet for Multiple Year - 11-17-22.xlsx XLSX spreadsheet
Atch J.3 - Labor Mix and Level of Effort Spreadsheet -Multiple Year -11-17-22.xlsx XLSX spreadsheet
Atch J.6 - Past Performance Survey - 11-17-22.docx DOCX document
Atch J.2 - Credit Data and Fair, Issac Risk Score Confidentiality Agreement - 11-17-22.pdf PDF
Atch J.1 - Nondisclosure-Agreement-between-HUD-and-Contractor-Employee - 11-17-22.docx DOCX document
86614623Q00001 FHA TOTAL Scorecard - 11-17-22 FINAL.pdf PDF
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Text version

Name of Offeror _______________________________ Attachment __J.5__ - Past Performance Narrative Submit with Proposal Submission

Solicitation Number __86614623Q00001_

PAST PERFORMANCE NARRATIVE

1. Contract Number, Customer, and Effort Name:

2. Is the Past Performance Effort Recent? State the start and completion dates of the Past Performance Effort. HUD will consider only references with at least (six (6) months) of performance that was performed within the last (three (3) years) dating from this solicitation’s initial closing date for receipt of proposals.

3. Describe in detail the Past Performance Effort and your role in performing that effort (prime, sub, proportion of work, etc.). Clearly explain the relevancy of the Past Performance Effort to the current requirements. See Solicitation at Section M, Past Performance, Paragraph (b) “Relevance.” For this solicitation, Relevancy is defined for this solicitation as follows: Relevant efforts are those that involve one or more of the following: 1) conducting econometric analysis of consumer credit delinquency and default (including mortgages), 2) strong understanding of econometric and economic theory and research literature related to mortgage loan performance, 3) evaluating automated consumer credit approval algorithms for their accuracy and compliance with fair lending and fair credit objectives, including possible disparate impacts to protected classes, 4) strong understanding of credit score and credit attribute data, 5) understanding and use of machine learning and artificial intelligence to formulate algorithms for consumer credit approval, 6) ability to efficiently and accurately work with large data sets, 7) ability to produce statistical code that conforms to best practices including documentation, quality control and replicability standards.

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