CoreLogic Mortgage Fraud & Valuation Consortium November 3, 2016

Freddie Mac Valuation Update

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A Better Freddie Mac …and a better housing finance system

For families, customers and taxpayers by… ...innovating to improve the liquidity, stability and affordability of mortgage markets

...competing to earn their business

...reducing their exposure to mortgage risks

Better Business Together …every day in every way

–  Deliver better solutions to make more loans, lower costs and drive servicing excellence

–  Provide better support to strengthen business opportunities

–  Look for better ways to provide greater certainty

–  Develop a better infrastructure for the future

Freddie Mac’s Twin Goals

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Better Solutions: The Loan Advisor Suite

Helping our customers succeed through an innovative suite of solutions designed to deliver certainty, efficiency, reliability, and usability

Reliability Instill trust that

our tools are available when you

need them

Certainty Give you confidence

that the loans you originate meet the

requirements for delivery and sale

to Freddie Mac

Efficiency Lower the cost to originate via automated data validation and focus your attention where it needs to be

Usability Provide intuitive and easy-to-use tools, with clear and actionable feedback

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Loan Advisor Suite Component Overview

Application Processing / Underwriting Pre-Closing Closing Loan Delivery Servicing

Loan

Adv

isor

Sui

te

The Loan Advisor Suite builds the manufacturing quality story required for greater purchase certainty.

Loan Product Advisor

Loan Collateral Advisor

Loan Quality Advisor

Loan Closing Advisor

Selling System

Loan Coverage Advisor

Business Intelligence

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Loan Advisor Suite Interacting with Our Products

Application Processing / Underwriting Closing Loan Delivery

Processor / UW CloserLoan Officer Post Close / Delivery

LP / LPA CR DATA

LP / LPA INCOME DATA ASSET DATA

UAD

UCD ULDD ULDD

Loan Product Advisor

Loan Collateral Advisor

Loan Quality Advisor

Loan Closing Advisor

LOS

LAS

DATA

Feedback

Purchase Eligibility Risk Assessment

Underwriting Guidance Income Verification Asset Verification

Appraisal Quality Risk Valuation Risk

Representation

& Warranty Relief Eligibility Spanning

“3 Cs”

LPA Data Compare Risk Analysis

Purchase Eligibility

UCD Completeness & Integrity

Closing Accuracy

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What is Loan Collateral Advisor SM ?

§  Analyzes appraisal reports submitted to the UCDP §  Provides Freddie Mac’s view of appraisal quality and valuation risk

§  Provides real-time feedback

Customer Benefits: §  Increases operational efficiencies and improved workflow §  Reduces risk of repurchases related to significantly deficient or inaccurate

appraisals

Loan Collateral Advisor

Loan Collateral Advisor results can be flexibly implemented within Sellers current processes!

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Loan Collateral Advisor

Freddie Mac has worked with development partners to ensure ease of use and efficiency gains.

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What do the risk scores indicate?

§  Valuation Score »  Assesses the accuracy of the appraised value relative to Home Value Explorer®, our automated valuation

model

» Considers both overvaluation and undervaluation risk

§  Appraisal Quality Score »  Assesses multiple components of the appraisal – quality, data accuracy and completeness »  Validates that appraisals adhere to our standards and guidelines

Loan Collateral Advisor

Sellers can use the Valuation Score and the Appraisal Quality Score as factors in determining risk tolerances.

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LCA Findings

Messages assist with analysis of the appraisal report and in managing workflow processes.

§  High risk valuation score indicates a significant misalignment between the appraisal value and the HVE value. The review required may be different if the message indicates over valuation as compared to when the message indicated under valuation.

§  High risk Appraisal Quality Score may indicate possible data inaccuracies. The review required may be directed to these elements to determine whether an update or correction to the appraisal is needed.

§  Sales information may help determine the relevance of the comparable sales selected to the pool of available sales.

Loan Collateral Advisor

Sellers are leveraging scores and messages to direct workflow and allocate resources!

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The Current Appraisal Process

A Case Study

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Rethinking Valuation Practices

From: Rethinking Appraisals in a Modernized Housing Finance System, Prepared for Delivery to the Collateral Risk Network’s Third Quarter Meeting, Baltimore, MD, June 29, 2015 by Edward J. DeMarco, Senior Fellow in Residence Milken Institute – Center for Financial Markets.

§ “Our challenge then is to reconsider the appraisal – the process of developing the opinion of value – and the content of the appraisal report to serve the future underwriting, pricing, and management of mortgage credit risk.”

§ “If credit investors can obtain the same or equivalent

information from other sources – we live in a big data world today, after all – then what is the value proposition of an appraisal?”

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Investors: Price or Value?

§  Investors want to know whether the collateral will cover the loan amount at some point in time in the future should the borrower default.

§ Even in a competitive market, I might not want to underwrite the loan using the current price, unless the market is also in equilibrium.

§ Current appraisal practices do not provide Investors with all the information needed to make informed decisions.

New Analytics: Fitch’s model and Market Condition Indicators.

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§ The current appraisal process performs four critical functions:

Researching the Current Appraisal Process

2nd base

3rd base

1st base

Home Loan!

Estimate Value

Inspect the Property

Protect Against Fraud

Mitigate Repurchase Risk

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“Thinking, Fast and Slow”

A Case Study

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Group A: “Chesterfield MO is a nice suburb of St. Louis.”

Group B: “Chesterfield MO is a nice suburb of St. Louis. Do you think the median sale price in Chesterfield is more or less than $900,000?”

Group B’s estimate was ~ 40 to 52% higher!

Appraisers Suffer from Anchoring Bias

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Source: CoreLogic LoanApp data, purchase only, originated in or after 2008, origination LTV between 10 and 100, and origination credit score between 620 and 850. Notes: 1. Percentage_diff=0 if abs(appraised value-contract price)<= $100. Only -0.15<=percentage_diff<=0.15 are shown. 2. Other filters are applied to ensure data robustness

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Weird Density of Estimates of Value – Loan Application Data

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Failed Metrics – Calibrator Period: Jan 1, 2015 – April 15, 2016. Total Number of Appraisers: 56,653

§ HVE Calibrator model output ranges from 300 – 900. If score <= 600, appraisal is at risk of being overvalued by at least 10% and is referred to as appraisal values that “failed” HVE Calibrator.

# of properties appraised by a given appraiser

% o

f Loa

ns th

at “f

aile

d” A

VM

che

ck

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How Good are Appraisers at Making their Adjustments?

Study using Appraisals for Relocation Loans http://www.corelogic.com/blog/authors/michael-g.-bradley/default.aspx#.VyeIk03VzX4

What features get adjusted? http://www.corelogic.com/blog/authors/jon-wierks/2015/10/ what-adjustments-have-the-most-influence-on-appraisal-reports.aspx#.VyeJOE3VzX4

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Thought Experiment

103 Main StreetSale Date: YesterdaySale Price: $200,000Attributes:

2,000 Sq. Ft. ¼ acre lot 3 bedrooms

•  Using adjustments ‘recommended by a model’, the adjustments come to $25,000.

•  What to do now?

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Thought Experiment (continued)

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Market

§  Text mining can help with both valuation and condition. http://www.corelogic.com/blog/authors/matt-cannon/default.aspx#.VyeC1k3VzX4

§  Data mining tools can help protect against fraud.

How Can Big Data and Advanced Analytics Help?

Neighborhood

Property

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Data on the Uniform Collateral Data Portal (UCDP)

Source: Freddie Mac C-Ratings: describe the property condition, maintenance and/or improvements

§  C1 = new or no physical depreciation §  C6 = substantial damage / neglect

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§ Knowledge Graph is a knowledge base that connects millions of pieces information to enable “smarter” search capabilities.

§ Google Knowledge Graph was introduced in 2012 using many information sources such as Search Clicks, Linked Pages, etc. to extrapolate the edges (relations) between the nodes (entities).

§ Facebook has the Social Graph whose nodes and edges are constructed from the social network’s millions of users and their data.

Knowledge Graphs

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§  We can use Graph clustering algorithms to map out neighborhoods programmatically and determine the right neighborhood boundaries.

Application of Knowledge Graphs to Mortgages!

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§  Imagine estimating the value of a subject home (yellow outline) with three comparable homes. We can use the Home Knowledge Graph to find homes that are similar to the three comps and increase the number of properties used to model the subject home’s value, decreasing the noise in the system.

Home Knowledge Graph

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§  Map out neighborhoods and determine the right neighborhood boundaries

§  Increase the number of properties used to estimate the subject home’s value

Application of Knowledge Graphs to Appraisals

Similar Neighborhood

Estimation

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A Cautionary Tale

* From: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil, Crown Publishing, New York, N.Y., (2016).

§ “The math-powered applications powering the data economy were based on choices made by fallible human beings….many of these models encoded human prejudices, misunderstanding, and bias into the software systems that increasingly managed our lives”*

§ Examples:

- Models used to evaluate teachers

- Model that ranks universities and departments

- For-profit universities

- Parole models

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The Road Ahead

FCRA Regulatory Guidance

Fair Lending

§  Recent paper by Google on Lattice Regression http://arxiv.org/pdf/1505.06378.pdf

How to Leverage in the Context of a Regulated Financial Institution?

Emulation Model

Machine Learning

Deep Learning

Entity Resolution