HouseCanary - PCBC Presentation

Real Estate’s Big Data Revolution

June, 2015

Current Residential Real Estate Data Lacks Sophistication

No sophisticated, comprehensive, localized information source for

professionals to use to make investment decisions about residential real estate.

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Problem

•  Data is siloed, and lacks the ability to develop a comprehensive view of the details on the

house, consumer, mortgage, market to address the opportunity

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

•  Data is delayed, and lacks the ability to act quickly on the learning

•  Rear-view looking data is used, that lacks forward-looking market risk and reality

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

Real Estate Is Big Data

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Local and Macro Fundamentals

Housing Neighborhood

Data

Capital & Credit Markets

Local Market &

Consumer Data

Household-Level

Appraiser Data

120+M parcel level details geo-coded

Property details and valuation

•  800+ MLS

•  3,000+ County Assessors, Public Records, Notice of Default

Consumer Transaction Level Detail (eg, who, income, family status, LTV, credit score)

Permits

View, Proximity to Water, Etc.

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

Local industries (oil, tech, etc.)

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

20,000 home price indices

Sales volume

New starts

Foreclosures / Notice of Default

Months supply

Market clusters

Market risk scores

Single vs. multi-family mix

Rent versus own economics

Mortgage volume & mix

Mortgage health & delinquency

Homebuilder capital growth

Residential & Mortgage REIT indices

Mortgage yields & spreads

Mortgage Debt, ARM, RMBS growth

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Huge volumes of data

may be compelling at first glance,

but without an interpretive

structure they are meaningless.

Applying Big Data to Shifts Impacting Real Estate

Transitions: Shifting demographics with disparity of wealth creating major

changes in the market

Dispersion: Increasing difference of returns at market and sub-market

Speed: Faster market cycles with increasing volatility

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2

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Shifting Demographics Case Study 1

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Major Demographic Transformation Underway 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

Buyer mix has followed Boomers through their lifecycle

$65k

Households

Current Households (Million)

Future 2030 Households (Million)

Avg. Wealth of segments ($k)

Homeownership rate of segments

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Wealth and Population Differences Growing

$802k

42%

62%

77%

20

21

38

$573k 24

25 - 34

35 - 44

45 - 54

55 - 75

$217k

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

12 $678k n/a 75+

72%

23

26

61

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Population Composition is Radically Different than Owned Homes Southern Cal Example

55+ is 22% of the population, 37% of households, 49% of the owned homes

9,985,874 50%

5,661,414 28%

4,466,654 22%

295,887 8%

1,491,409 43%

1,718,772 49%

1,298,306 20%

2,833,037 43%

2,456,939 37%

Ages 35-54

Ages <35

Ages 55+

Population by Cohort Households by Cohort Homes Owned by Cohort

Note: Includes Los Angeles, Orange County, Riverside, San Bernardino & San Diego Counties

55+

35-54

<35 55+

35-54

<35 55+

35-54

<35

Understand Where They are Moving to Capture Demand

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Understand What They Buy to Optimize Product

Where do they Buy? Sales Volume by Zip Code

Product Optimization

Legend

$352

$334

$303

3 4 5

$339 $338

$328

2 3 4

$343

$330

2 3

$333

$368

Pre-‐2000 Recently Built Resale

Bedrooms Bathrooms

# Garages Recently Built vs. Older Resale

-4% +11%

-3%

-9%

-5%

Understand How Much They Can Afford Depth of Consumer Demand

Dispersion of Market Returns

Case Study 2

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Dispersion of Returns: Submarkets Vary Significantly

Investment strategy can be refined to win in each market cluster

Site

Market Cluster

Ho

usi

ng

Pri

ce I

nd

ex Y

ear

20

00

= 1

00

90#

110#

130#

150#

170#

190#

210#

230#

250#

270#

290#

310#

330#

350#

2000# 2002# 2004# 2006# 2008# 2010# 2012# 2014#

Indian#Wells#5#92210#(A)# Redlands#5#92373#(B)# Corona#5#92883#(C)# Beaumont#5#92223#(D)# Moreno#Valley#5#92553#(F)#

B#

C#

D

F#

32%#

49%#54%#

58%#

70%#

Indian#Wells#5#92210#(A)#

Redlands#5#92373#(B)#

Corona#5#92883#(C)#

Beaumont#5#92223#(D)#

Moreno#Valley#5#92553#(F)#

Trough'to'Present-

533%#537%#

551%#555%#

566%#Indian#Wells#5#92210#(A)#


Corona#5#92883#(C)#



Peak'to'Trough-

71%#

111%#

137%#152%#

200%#

Indian#Wells#5#92210#(A)#


Corona#5#92883#(C)#



2002'Last-Peak-

A#

Pricing Behaviors by A-F Market Cluster

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Forecasting Housing Prices at a Local Level Future Pricing Growth (1 Year)

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Measure the Risk of Pricing Decline Risk Analytics

Our Products

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