real estate's big data revolution: the new way to create value

Real Estate’s Big Data Revolution:

The New Way to Create Value

January 21, 2015

Introductions

2

Partner & Managing Director, The Boston Consulting Group

•  Led Homebuilding & Real Estate

•  Deep experience advising Fortune 500 &

PE firms

Prior

•  Marakon Associates

•  Arthur Andersen (CPA)

Education

•  MBA, Wharton, University of Penn

•  BS, USC– Summa Cum Laude

Jeremy Sicklick CO-FOUNDER & CEO

PhD Candidate, Applied Statistics, University of Texas at San Antonio

•  Dissertation Topic – Dynamic Models of

Financial Risk

•  Other Research– Bayesian Hidden Markov

Models, Bayesian Decision Theory,

Dynamic Time Series

Prior

•  California Economic Forecast

Education

•  MA, Economics, UC Santa Barbara

•  BA, Economics, UC Santa Barbara

Chris Stroud CO-FOUNDER &

CHIEF OF RESEARCH

Regional Vice President, Strategic Marketing, PulteGroup

•  Led strategic planning and acquisition

underwriting

•  Emphasis on decisions through data-

driven decision-making

Prior

•  Hewlett-Packard

Education

•  MMC, UGA – Magna Cum Laude

•  BSBA, UTulsa – Cum Laude

JP Ackerman PRESIDENT, STRATEGIC REAL

ESTATE PRODUCTS

Big Data Is Growing Around Us Exponentially

3

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 many other industries

AGENDA

4

About HouseCanary and Big Data

Case Examples Using Big Data

Where We Are Headed

5

We build products that combine

proprietary data and predictive analytics

to help people make better real estate decisions.

5

5

Residential Real Estate Data 1.0 Lacks Sophistication

There is no sophisticated, comprehensive, localized information source for professionals to use to make investment decisions about residential real estate.

6

Problem

Real estate is a volatile industry with huge investments at stake. Consistently winning requires sophisticated information and insights.

•  Data aggregators charge exorbitant rates for raw data, that lacks intelligence

•  Rear-view looking data is used, that lacks forward-looking market realities

•  Most information is too high-level (county / MSA level) and lacks the local relevance to decisions at hand (zip code, block, property level)

Real Estate Market Data 2.0 is Analytics & Applications

Real Estate Market Data 1.0

7

Real Estate Smart Data 2.0

= Data Aggregation

= Data Aggregation

+ Predictive Analytics

+ Applications to Business Decisions

+ Data Feedback Loops

Our Products

8

NATIONAL HOME CONSTRUCTION DATABASE

Powered by

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

HouseCanary will create the “New Home MLS”

Launching Q4 2015

The most advanced tools and advisory for real estate professionals

What We Do

9

Accurate big data analysis

Create an information advantage

Predict future monthly prices… All 381 US MSA’s 20,000 Major Zip Codes Using big data…. 1000’s of variables analyzed 40+ years of monthly data …and state of the art analytics Leading finance methods + Leading statistical methods

Reports & infographics that explain how price will move & why Communicate risk / opportunity clearly and simply Translate forecasts into impact Enable a culture of disciplined decision-making

Dispersion of returns is increasing

at a local level

Volatility is increasing and will

continue with rising rates

Demographic shifts creating

profound change in supply /

demand

The Need for Good Data & Insight Is Increasing

10 -‐8% -‐6% -‐4% -‐2% 0% 2% 4% 6% 8%

Raleigh, NC Houston, TX

Austin, TX Dallas-Fort Worth, TX

Seattle, WA Baltimore, MD Charlotte, NC

San Francisco Bay Area, CA Phoenix, AZ

Tampa, FL Las Vegas, NV

Riverside-San Bernardino, CA Orlando, FL Atlanta, GA

Los Angeles-Orange Cty CA Chicago, IL

Washington DC Metro Miami, FL Detroit, MI

Sacramento, CA 7.0% 6.8% 5.7% 5.6% 5.6% 5.5% 5.4% 5.4% 5.3% 5.2%

5.0% 4.9% 4.9% 4.1% 3.7% 3.5% 3.5% 3.1% 2.7% 2.7%

Difference (Max – Min)

Dispersion of Returns (10 year CAGR – unlevered)

Top quartile return for sub-markets Bottom quartile return for sub-markets

11

Local and Macro Fundamentals

Housing Data

Capital & Credit Markets

Consumer Data

Household-Level

Local Market Data

Mortgage volume & mix

Mortgage health & delinquency

Homebuilder capital growth

Residential & Mortgage REIT indices

Construction materials futures

Mortgage yields & spreads

Mortgage Debt, ARM, RMBS growth

120M parcel level details geo-coded

Property details and valuation

•  900 MLS

•  3,000 County Assessors

Land supply available

Permits

Local jobs / employment

Construction jobs

Consumer flows in-out areas

Consumer equity vs. debt

Affordability components

Income driven

20,000 home price indices

Sales volume

Foreclosures

Starts / permits

Months supply

REO

Single vs. Multi-family

Rent versus own economics

Career & Income

Commute times

Migration patterns

Potential demand

Family makeup

Education level

Ethnography

Rental vs owner makeup

Predictive Analytics: Integrating Disparate Data into Insight Thousands of data elements tracked monthly that align with the following areas

Household balance sheet

Sub-market Ratings

Market risk scores

Local economy (GMP)

Recession probability forecasts

Inflation measures

Schools

Crime

Housing Makeup

Livability / amenities

Our Approach to Forecasting

12

How we achieve strong results

•  Use Bayesian model averaging to scan the space of 4 Billion potential models for each area, each month

•  Average across the best ~1,000 models

•  Requires mass computing power

Percent Change in Home Prices - Los Angeles

Time

perce

nt ch

ange

2004 2005 2006 2007 2008 2009

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

n Potential Models

Forecast Average

Actual

How We Forecast & Backtest

How We Apply HouseCanary to Builders/Developers

13

Market location: Where to invest

Market timing: When to invest

Consumer focus: Who to focus on

Product design: What to build

Pricing: How to price

How should we value new communities? How should we change price monthly to get margin & absorptions?

Which sub-markets should we focus our projects and land buying? How should we deploy capital across our portfolio?

Can we call the top of the market? Can we define the recovery? When should we deploy / pull-back capital?

Supply: Where is the land

Where is there land available? How much supply is coming on line? How are new homes absorbing locally?

Which consumer segments live there / are moving there? Who should we focus on to maximize growth? What supply of product exists in the market today? What lot size and product design is the highest and best use?

Where to Invest Case Study 1: Market Location

14

•  Company often outbid by the herd of competitors in A submarkets.

•  Focus was on defining a strategy to maximize returns by using a forward-looking perspective.

Forecast Models Used to Define Potential Growth

15

Focus is then to de-average markets down to the zip code level

Phoenix−Mesa−Scottsdale, AZ

Time

Hom

e Pr

ice

Inde

x

2000 2005 2010 2015

100

120

140

160

180

200

r2 of average model = 0.95

Begin by Derive Sub-market Clusters and Inherent Risk

16

A–F Submarket Clustering for Riverside Area

Across 20,000 zip codes, we clustered zip codes Several factors together help us cluster zip codes

•  Median Home price •  Income & wealth •  Education level •  School scores •  Crime level •  Demographic / International mix •  Owner / rental mix •  Commutability

These zip code clusters have very different behavior and price volatility within a market

F D C B A

0

50

100

150

200

250

300

350

400

Submarkets Within an MSA Behave Very Differently

17

A

B

C

There is value from differentially investing in A, B, C… markets based on where we are in the cycle We can derive A, B, C…. markets and forecast them accurately in all MSA areas

Home Price Index

D

F

Market Examples Beta

A 92210 – Indian Wells 1.3

B 92590 - Temecula 1.6

C 92882 - Corona 2.0

D 92503 - Riverside 2.1

F 92543 - Hemet 2.7

Where to Invest? Leverage Zip Forecasts to Counter Market Norms: 2013-15

18

Strong Sales Volume

•  Established, extreme competitive

set

•  Easy commute

Rapid Appreciation

•  Prices accelerating at unsustainable

levels

Extreme Competition

•  Finished lot values nearly doubled

Conventional Wisdom Corona-Eastvale: Zip 92880


19

Limited Volume

•  Virtually no competition

•  Available lot supply

•  Development required

Rapid Appreciation Ahead

•  Record low affordability

•  Similar price increases ahead

Early Cycle Opportunity

•  2011-13 appreciation < Corona

•  3-Year forecast outpacing Corona

HouseCanary Riverside: Zip 92505


20

Limited Volume

•  Virtually no competition

•  Available lot supply

•  Development required

Rapid Appreciation Ahead

•  Record low affordability

•  Similar price increases ahead

Early Cycle Opportunity

•  2011-13 appreciation < Corona

•  3-Year forecast outpacing Corona

HouseCanary Riverside: Zip 92505

21

Strong Sales Volume

•  Wide availability of land

•  Strong, established competition

•  Draw from master plans

Rapid Appreciation

•  15%+ in recent years

•  Outpacing growth in nearer-in

markets

•  “best of what’s left” perception

Perceived Affordability

•  Lower price points than more job

proximate locations

Conventional Wisdom Menifee: Zip 92584



22

Mid-to-Late Cycle Play

•  Stable, well-rated market

•  Most competition going deeper to

Inland Empire for perceived

affordability

Stable Future Pricing Growth

•  Local affordability well below

historical peaks

•  Job proximate

Controlled Land Supply

•  Available via master plans

•  Infill opportunities

HouseCanary Ontario: Zip 91764


23

Mid-to-Late Cycle Play

•  Stable, well-rated market

•  Most competition going deeper to

Inland Empire for perceived

affordability

Stable Future Pricing Growth

•  Local affordability well below

historical peaks

•  Job proximate

Controlled Land Supply

•  Available via master plans

•  Infill opportunities

HouseCanary Ontario: Zip 91764

24

Insight

Created a historical & projected perspective on the flow

of appreciation through the markets in the Inland Empire

Client was able to develop a differentiated geographic

strategy for near- and long-term deals

Identified the vast disparity in returns

31% returns ‘14 – ‘17 from contrarian investments

•  Fontana Montclair, Riverside, Hemet, Perris

versus

22% returns ‘14 – ’17 from traditional A & B submarkets

•  Rancho Cucamonga, Corona, Eastvale, Temecula

Impact

Able to underwrite acquisitions at higher returns and

purchase land at discounted rates

+2 years In Additional Lots with No Additional Capital

+50% Increase in Exposure to Market Appreciation

Who to Serve Case Study 2: Consumer Focus

25

•  Company evaluated opportunity to increase focus on 55+ segment.

•  Once 55+ strategy was defined, needed a way to operationalize 55+ at a local level.

Longer-term Household Growth Driven by the Boomers

Growth in Household

Requirements (by segment)

(5,000,000)

-‐

5,000,000

10,000,000

15,000,000

20,000,000

1970 1980 1990 2000 2010 2020 2030 2040 2050

65+

45-‐64

20-‐44

Mix of Growth

in Household

Requirements

(by segment)

Entry level Move-up Senior Age segment

Ages 1960-1970 1970-1980 1980-1990 1990-2000 2000-2010 2010-2020 2020-2030 2030-2040 2040-2050

20 - 44 37% 64% 56% 20% -11% 32% 25% 28% 40%

45 - 64 34% 12% 15% 66% 84% 10% -6% 28% 23%

65+ 29% 23% 29% 14% 27% 58% 81% 43% 38%

Total 100% 100% 100% 100% 100% 100% 100% 100% 100%

Source: HouseCanary, Census

Across time, following the Boomers has been a successful strategy

$65k

Households

Current Households (Million)

Future 2030 Households (Million)

Avg. Wealth of segments ($k)

Homeownership rate of segments

22

Boomers: Growing, Wealthier, and Own Homes

$802k

42%

62%

77%

20

21

38

$573k 24

25 - 34

35 - 44

45 - 54

55 - 75

$217k

Source: Census Bureau; HouseCanary. Note: Bubble Size is shown to scale

12 $678k n/a 75+

72%

23

26

61

10

Decline in Homeownership Except Among Oldest Cohorts

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1980 1990 2000 2010

65+ years

15 – 24 years

25 – 34 years

35 – 44 years

45 – 54 years

55 – 64 years

Home ownership rate by age of householder

Source: HouseCanary, Federal Reserve Bank of New York, Equifax, Census

The Wealth Factor Separates the Age Cohorts

High net worth of older cohorts offsets the impact of rate hikes by enabling

larger down payments

Younger cohorts already hitting limits

of DTI given lower wages and increasing

debt load

Younger cohorts will be further

devalued in an increasing interest rate

environment

3.5%

10%

20%

35%

50% 50%

0%

10%

20%

30%

40%

50%

60%

0

50

100

150

200

250

Ages <35 Ages 35-44 Ages 45-54 Ages 55-64 Ages 65-74 Ages 75+

Sources: HouseCanary, Federal Reserve, Census Bureau 2013

Typical Down Payment % Median Annual HH Income & Net Worth (K)

X%

Net worth (K)

Annual HH Income (K)

Avg down payment %

Major Risk: Forecasted Interest Rate Increase - 4.3% to 6% Other Risks

Age Group Personal Balance 4.3% Rate 6% Rate Impact to Demand Rationale

Millennials (15-34)

Income $59k Worth $10k Debt Equity

30% 35% -36% •  No/low savings •  Anemic income/career growth •  No / low gain from stock & housing

rebound •  Heavy debt load (school, personal) •  Hard to underwrite given QM

Young Gen X (35-44)


21% 25% -24% •  Rocked by housing downturn •  Limited savings •  Heavy debt

Old Gen X (45-54)


18% 21% --% •  Home equity •  Stock market rebound •  High income/career trajectory

Boomers (55+)


17% 19% --% •  Large savings & net worth •  Limited housing / personal debt •  Large down payments •  Large equity investments

% Income Used for Housing

Source: HouseCanary, BLS, Census Bureau, Nov 2014

Young Households Greatly Impacted by Future Rate Hikes

Pinpointing Migration of 55+ is Critical for Investment

Understanding Who & How Much Consumers Can Afford

0"

2,000"

4,000"

6,000"

8,000"

10,000"

12,000"

14,000"

16,000"

18,000"

0)49k" 50)99k" 100)149k" 150)199k" 200)249k" 250)299k" 300)399k" 400)499k" 500)749k" 750)999k" >1mil"

HH"Age:""65+" HH"Age:"45)64"

HH"Age:"15")"44" Renters"

Supply"

0"

2,000"

4,000"

6,000"

8,000"

10,000"

12,000"

14,000"

16,000"

18,000"

0)49k" 50)99k" 100)149k" 150)199k" 200)249k" 250)299k" 300)399k" 400)499k" 500)749k" 750)999k" >1mil"

HH"Age:""65+" HH"Age:"45)64"

HH"Age:"15")"44" Renters"

Supply"

Demand 4.3% Interest Rate

Demand 6.0% Interest Rate

# of Households

House Price

# of Households

House Price

33

Insight

Huge opportunity with 55+ over next several decades

55+ opportunity remains robust as interest rates increase

Operationalizing the opportunity requires deep insight

•  Where target consumers live

•  Where target consumers move within area

•  Where target consumers move from outside area

•  What target consumers can afford

Impact

•  Leveraged data to identify priority submarkets for

overall growth strategy

•  Capitalized on underserved consumer segment

•  Drove underwriting with targeted age segment needs

$500M Identified Growth Opportunity at a Local Level

What to Build Case Study 3: Product Optimization

34

•  Company trying to establish competitive differentiation within master plan segmentation.

•  Focus was on maximizing pricing power to serve established families among heavy competition.

Product Optimization Target Product Sizes with Imbalanced Supply & Attribute Premiums

-20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30%

1500-1999sf

2000-2499sf

2500-2999sf

3000-3499sf

3500-3999sf

4000-4499sf

4500-4999sf

5000sf+

Submarket

1-‐Story Prem

3-‐Car Prem

0.0 5.0 10.0 15.0 20.0

1500-1999sf

2000-2499sf

2500-2999sf

3000-3499sf

3500-3999sf

4000-4499sf

4500-4999sf

5000sf+

Zip Code MOS: 6.2

MSA MOS: 7.2 % Price Premium Paid for 1-story homes & 3-car garages by Square Footage Range

35

Healthy Months of Supply in Targeted Size Ranges 3-Car Garages Matter; Single Stories Don’t

Product Optimization Maximum Functionality Drives Price Based on Consumer Profile

$97

$100

$112

$98 $97

$100

$105

$129

$80

$90

$100

$110

$120

$130

3 4 5 6 2 3 4 5

Price per Foot

Bedroom Count Bathroom Count

+4%

+11% -12%

+5%

+23%

+4%

+29%

Source: MLS, December 2014, SFD only, 2000-4000sf 36

Product Components Premiums Leverage Location, Product Footprint and Configuration to Maximize Revenue

Comparable Price Subject Property Price Prem / (Dscnt) %

Bath Count 4 v.5 Bathrooms

Bed Count 4 v. 5 Bedrooms

New v. Resale Built Before 2010 v. Built Since

Optimized Product 2-car, 4 bed, 4-bath v. 3-car, 5 bed, 5 bath new

Location MSA v. Zip

$102/ft $100/ft (2%) $96/ft $125/ft 30% $96/ft $107/ft 11% $99/ft $119/ft 21% $99/ft $107/ft 8% $86/ft $169/ft 96%

Source: MLS, December 2014, SFD only, 2000-4000sf, 5,000-14,999sf lots only

Garage Count 2 v. 3-Car Garage

37

Drive Pricing Strategy through Product Attributes

38

Clo

sin

g P

rice

(s

quar

e fe

et)

House size (square feet)

70’ Lots Positioning Strike Zone

60’ Lots

$400,000%

$450,000%

$500,000%

$550,000%

$600,000%

$650,000%

$700,000%

$750,000%

$800,000%

$850,000%

2000% 2500% 3000% 3500% 4000% 4500%

Single%Stories% 36Car%Garages% 26Car%Garages%

Submarket%(ALL)% Built%Since%2010% Shea%BackCountry%6%Shadow%Walk%(40'%Wide%Product)%

Shea%BackCountry%6Water%Dance%(45'%Wide%Product)% Log.%(Single%Stories)% Log.%(36Car%Garages)%

Log.%(26Car%Garages)% Log.%(Submarket%(ALL))% Log.%(Built%Since%2010)%

Competitor 1

Competitor 2

39

Insight

Target consumers placed less value on bedroom counts

and higher value on additional garage space.

Combination of the two yielded increased value, allowing

the client to set themselves apart from the competition.

Client created a unique vantage point allowing them to:

•  better underwrite the land for its optimal value, and

•  design more consumer-refined product for the market

Impact

•  Maximize pricing power

•  Increase revenue disproportionately v. cost

$50K Per Home in Additional Price

$8M+ Total Impact

Where We Are Going With Big Data

About HouseCanary

40

41

Overview

The most advanced tools for real estate professionals

Answers five key questions for builders, developers, banks, and funds:

Market timing: When to invest?

•  Analyze the housing cycle

•  Identify downturn risk

•  Pinpoint market changes

Location: Where to invest? •  Target sub-markets

•  Understand local market changes

•  Define where and why the market is changing

Product design: What to build?

Pricing: What to charge?

Marketing: Who to sell to?

42

All in one from an iPad •  Communication •  Data integration: Market data, property data, maps •  Photos, Property measurement •  Comp analysis, adjustments, valuation

Flips the focus onto value •  From 90% time on form filling to 90% focusing on appraising

value & risk

Scientific analytics at your fingertips…no PhD degrees needed •  Valuation and quantified comps analysis •  Analytically derived value adjustments •  Value drivers and hierarchy

Leave your work at the job site •  Enter the site, do what you need to, submit in just 30 minutes

Seamless, paperless integration •  Links to UADP forms in MISMO format •  All mobile, all paperless, all stored in the cloud •  Platform is 'form' agnostic, streamlining the production of

alternative valuation products

Overview

43

NATIONAL HOME CONSTRUCTION DATABASE

Powered by

What did HouseCanary win?

•  Exclusive arrangement to build the National Home Construction

Database (NHCD) awarded 11/2014

•  We will build a service where builders can upload new home

sales data, in return receiving aggregated / anonymized data

•  Competitive bid against top data companies

Why is this valuable to the new home market?

•  First look at proprietary new home sales data

•  First residential real estate dataset compiled nationally through

single entity

•  Opens many other business opportunities over time

Overview

Creating and owning the “New Home MLS”

Jeremy Sicklick CO-FOUNDER & CEO

office: +1-866-729-7770

mobile: +1-213-422-2577

email: [email protected]

For Questions

300 Brannan St. #501

San Francisco, CA 94107

1617 Pacific Coast Highway, #H

Redondo Beach, CA 90277

JP Ackerman PRESIDENT, STRATEGIC REAL ESTATE PRODUCTS

office: +1-866-729-7770

mobile: +1949-444-3600

email: [email protected]

real estate's big data revolution: the new way to create value

Data & Analytics