Big Data Revolution: Increasing Transparency to Risk and Valuation

Big Data Revolution Creating Transparency to Risk and Valuation

January 28, 2015

Copyright © 2015 by Canary Analy5cs, Inc. All rights reserved.

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Introductions

Jeremy Sicklick Co-Founder & CEO

Chris Stroud Co-Founder & Chief of Research

Partner & Managing Director, The Boston

Consulting Group

• Led Real Estate – advised top builders,

developers & PE investors

• Prior - Arthur Andersen - CPA

Education

• MBA, The Wharton School, University of

Pennsylvania

• BS, University of Southern California –

Summa Cum Laude

PhD Candidate, Applied Statistics,

University of Texas at San Antonio

• Dissertation Topic – Dynamic Models of

Financial Risk

• Other Research Interests – Bayesian

Hidden Markov Models, Bayesian Decision

Theory, Dynamic Time Series

Education

• MA & BA, Economics, UC Santa Barbara

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We build products that combine

proprietary data and predictive analytics

to help people make better real estate decisions.

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HouseCanary Overview

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The most objective, consistent, and

efficient way to appraise homes

The most advanced tool for

real estate professionals

NATIONAL HOME CONSTRUCTION DATABASE

Powered by

The data source for valuing and

appraising new construction

Launching Q4 2015

Patent pending

Big Data Is Growing Exponentially

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Where We Are Going Where We Have Been

“If you recorded all human

communication from the dawn

of time to 2003, it takes up

about five billion gigabytes

(5,000 petabytes) of storage

space. Now we’re creating that

much data every 7 hours”

Every 7 hours

Source: IDC

6.12 10.87 21.61 40.03 2014 2016 2018 2020

7x…every hour

Data in millions of petabytes

‘Big Data’ will revolutionize real estate like it has other industries

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Local and Macro Fundamentals

Housing Data

Capital & Credit Markets

Local Market &

Consumer Data

Household-Level

Appraiser Data

120M parcel level details geo-coded

Property details and valuation

•  900+ MLS

•  3,000+ County Assessors

Land supply available

Permits

Flood

Real Estate Is Big Data

Local jobs / employment

Construction jobs

Consumer flows in-out areas

Consumer equity vs. debt

Affordability components

Household net worth

Debt to income

Local economy (GMP)

Recession probability forecasts

Inflation measures

Schools

Crime

Housing Makeup

Livability / amenities

Career & Income

Commute times

Migration patterns

Potential demand

Family makeup

Education level

Rent vs owner

Comparable choices

Value adjustments

Key value drivers

C & Q ratings

Mortgage volume & mix

Mortgage health & delinquency

Homebuilder capital growth

Residential & Mortgage REIT indices

Construction materials futures

Mortgage yields & spreads

Mortgage Debt, ARM, RMBS growth

20,000 home price indices

Sales volume

New starts

Foreclosures

Months supply

Market clusters

Market risk scores

Single vs. multi-family mix

Rent versus own economics

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Huge volumes of data

may be compelling at first glance,

but without an interpretive

structure they are meaningless.

Great Applications of Big Data are Simple & Powerful

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Millions of Data Points

+

Predictive Analytics

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Industry Expertise

+

Simple User Interfaces Transformed Government Intelligence

Common Elements

Understand Weather Data & Forecasts

Transformed Global Search

Big Data Can Augment Human Intellect, Not Replace It

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Computers Are Less Equipped to Understand Subjective Things

Computers Can Run Large Quantities of Known Calculations

Google Image recognition output as of 11/2014

Stress simulation, 3D CAD model

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Go to PollEverywhere.com/HCCRN

How well do current data and tools empower you to make better decisions, consistently as an appraiser? o  Extremely well

o  Ok, but opportunities to improve

o  Far below expectations

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10

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Source: CRN conference attendee survey, 1/28/2015; N=50

How well do current data and tools empower you to make better

decisions, consistently as an appraiser?

Percent of respondents

Answer: Majority See Opportunity to Improve

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64

18

-

10

20

30

40

50

60

70

Extremely well Ok, but opportunities to improve

Poorly

Problem: Issues Appraisers Face Today

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Inefficient Takes 6 hours on average to appraise a property

•  Data is fragmented

•  Re-keying necessary

•  Lack of tools to help with analytics of valuation and risk

Costs $1,000+ through lifecycle of loan

•  To review inputs

•  To review calculations, approaches and adjustments

•  To defend repurchase demands

Focus on re-describing property as opposed to valuing it

•  Inefficient use of professional talent

•  Not focused on measuring and communicating risk

•  Not leveraging data and valuation techniques possible

Inconsistent

Not focused on value & risk

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What are the largest challenges facing the appraisal industry today? o  Inefficient

o  Inconsistent

o  Not sufficiently focused on value & risk

o  All of the above

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What are the largest challenge facing the appraisal industry today?


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26

16

4

22

0

5

10

15

20

25

30

35

Inconsistent Inefficient Insufficient focus on risk

Insufficient focus on future value

All of the above

Answer: Consistency, Efficiency are Largest Pain Points

Solution: Apply a Tested Formula

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Millions of Data Points

+

Predictive Analytics

+

Industry Expertise

+

Simple User Interfaces

Common Elements

Case Examples

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1 Understand the market and market risk

2 Understand property details and risk

3 Better value collateral and risk

Understand the market and market risk

Case Study 1

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A–F Submarket Clustering Grading Submarkets

Case Study 1: Market Cluster and Inherent Risk

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Areas of Focus

Across 20,000 zip codes we cluster markets

Several factors together used to cluster

•  Home price •  Income & wealth •  School scores •  Crime level •  Owner / rental mix •  Commutability

“A’s” move earlier in the cycle with lower overall volatility

How does this help me make better appraisal decisions

Objectively measure risk / volatility of a market

Case Study 1: Buyer vs. Seller Market

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Buyer’s – Seller’s Market Scores Areas of Focus

Insights on market environment to support value

•  Seller’s market – higher probability to reconcile at higher end of range

•  Buyer’s market – higher probability to reconcile at lower end of range


Combine factors to summarize market environment

Long time-series of data (ie, decade+) to understand where we are currently

Transparency to underlying drivers

Case Study 1: Market Risk & Affordability

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Risk Score

Insight on market risk to support value

•  Low market risk– reconcile at higher end of range

•  High market risk – reconcile at lower end of range


Areas of Focus

Quantified risk score – probability of future downturn (eg, FICO score for real estate)

•  Leverage HC predictive analytics for 20,000+ zip codes

Drivers of risk – price, affordability, velocity

Case Study 1: Potential Consumer Demand vs Supply

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Will become critical at a local level

•  As affluent baby boomers downsize

•  Interest rates rise

•  Ensure sufficient depth of demand to support value at price level by local area


Areas of Focus

Potential demand vs supply

Understand depth of potential buyers who can make down payment & DTI in market versus supply at that price point

Potential Demand by Price / Age

Case Study 1: Understand the Market with Aerial Imagery

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Market insights to support value

•  Nearby beneficial / adverse affects to value

•  Holistic view of area at time of appraisal


Areas of Focus

Current imagery (less than 1 month old)

View to new public works, new supply

Nearby drivers of value – positive and negative

Up-to-Date Satellite Imagery

Beta

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What is most critical to you to better understand the market and market risk? o  Market Risk Clusters & Inherent Local Risk

o  Buyer vs. Seller Market summary

o  Future Market Risk Score

o  Potential Consumer Demand v. Supply

o  Up-to-Date Aerial Imagery

o  All of the Above are Equally Critical

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What is most critical to you to better understand the market and

market risk? Percent of respondents

Answer: Broad Interest in Tools to Understand Market Risk

32

20 20

14

0

14

-

5

10

15

20

25

30

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Potential consumer

demand vs. supply

Market clusters with similar risk

Market risk score

Buyer vs. Seller's market rating

Up-to-date aerial

imagery

All of the above

Understand property details and risk

Case Study 2

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Case Study 2: Organize & Normalize Property Details

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Auto-populated & normalized Property Details

Minimal rekeying…. mis-keying issues

Focus appraisers’ attention on completing accurately


Areas of Focus

Fully integrate MLS & Assessor data for completeness

Highlight differences between MLS & Assessor

Integrate all other data sources into one user experience – flood, parcel, g-map, permit, etc.

Case Study 2: ‘Smart’ Property Details

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Highlight changes from prior appraisal

Gauge how others measure C & Q to create a consistent view on a subjective measures


Areas of Focus

Transparency to C & Q ratings across time for noted property

Crowd-sourced understanding of (similar) property’s C & Q rating

Natural language processing to identify and manage data points about subject and comps

Suggested Condition/Quality Ratings

Case Study 2: New Insights on the Property

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Sales History & Permit Data

Integrated insights on property to support value

Understand consistency of property to area overall


Areas of Focus

Full sales history

Full integration of permits

Plat map of full lot details

Consistency of property to local market

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What is most critical to you to better understand property details? o  Normalized Property Details that Auto-populate

o  “Smart” Details such as C & Q Ratings

o  New Insights on Property such as Permits


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•  What is most critical to you to better understand property details? Percent of respondents

Answer: Interest in Tools to Improve Property Details

25

13 8

55

-

10

20

30

40

50

60

Normalized property details

that auto-populate

New insights on property such as

permits

"Smart" details such as C & Q

ratings

All of the above

Better value collateral and risk

Case Study 3

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Case Study 3: Objective Comparability Score

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Comp Similarity Scores

Objectively derived comparable sales

Brings sales price to current, critical in fast changing markets


Areas of Focus

Objectively derived multi-variate similarity score

Leverage NLP, location data to identify additional characteristics eg, granite, view, remodeled, etc.

Sales price recalculated to current value using 20,000 proprietary zip code price indices

Case Study 3: Objective Value Adjustments

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Comp Adjustments

Statistically significant as opposed to “based on experience” approach


Areas of Focus

Statistically derived adjustments based on local data

Supported and defensible using analytics & data

Continuously improves through crowd-sourced feedback loops

Case Study 3: Key Value Drivers

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Value Drivers

Clearly communicate key drivers of value for property and surrounding properties

Identify impact of key drivers to value


Areas of Focus

Identifies key drivers of value

Hierarchy of value drivers

Quantification of value drivers through feedback loops

Case Study 3: Big Data Value Reconciliation

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Value Reconciliation

Supports appraiser with holistic view

•  Valuation

•  Risk


Areas of Focus

Define value range from 100+ comparable properties as opposed to 3

Identify market risk and reconciliation range

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What is most critical to you to improve property valuation? o  Objective Comparability Score

o  Objective Value Adjustments

o  Key Value Driver Identification

o  Big Data Value Reconciliation


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What is most critical to you to improve property valuation?


Answer: Interest in Several Tools to Help with Valuation

23 20

11

0

46

-

5

10

15

20

25

30

35

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45

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Objective value adjustments

Key value driver identification

Regression-based value

reconciliation

Objective similarity score

All of the above

The Power of Big Data Are In the Feedback Loops

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Integrated NHCD Tool Better Appraisal

Data & Analytics

Better Valuation &

Risk Accurate & Informative Appraisals

Industry Credibility •  Consumers •  Lenders •  Investors

Better Appraisals

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Our team is hard at work building products for you.

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More objective •  Objectively values property, from which Appraiser can deviate

•  Comps rated by Similarity Score to help appraiser choose objectively

•  Market Risk Score highlights the factors affecting risk of downturn

More consistent •  Automatic data import eliminates input errors •  Transparency into valuation/adjustment approach across appraisers

•  Reduces need for costly manual review process post-appraisal

More efficient •  2x+ faster than best legacy methods

•  Focuses appraisers’ time on valuation vs. keying data

•  Can use iOS app to start and finish on-site or work across app and

desktop to start on-site and finish in the office

Patent pending

The most objective, consistent, and efficient way to appraise homes

•  Objective - More confidence in your valuations. Emulate Collateral Underwriter to mitigate repurchase risk

•  Consistent – Consistent data and approaches to measure risk and support a better final valuation every time

•  Efficient - More efficient so you can earn more while working less

•  Manage Risk - Better manage lending risk by understanding market risk and expected home price trends

•  Better Support - Reduce repurchase expense by having more defensible appraisals supported by better data

•  Reduce Cost – Reduce costs by using fewer appraisal review staff

Using Big Data to Appraise Creates a Win, Win, Win

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For Appraisers For Lenders

Jeremy Sicklick

Co-Founder & CEO

HouseCanary, Inc.

300 Brannan St. #501

San Francisco, CA 94107

office: +1-866-729-7770

mobile: +1-213-422-2577

email: [email protected]

The Time Is Right For a New Way to Appraise

We Want to Hear Your Comments, Feedback, etc.

Slide Deck With Responses Posted to housecanary.com/news

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Date post:	28-Jul-2015
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