visual decision making - fstvisual decision making matt lythe head of geospatial solutions august...
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Visual Decision Making
Matt Lythe
Head of Geospatial Solutions
August 2018
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Topics
• CoreLogic and the property ecosystem
• What is Visual Analytics
• Visual Decision Making Examples
1. Market Value – from geographic region to street
2. Buyer classification – over time and space
3. Relationship mapping
4. Performance monitoring
• Out-takes
2
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CoreLogic
at a Glance
3
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Insurance
Carriers
CoreLogic connects the property ecosystem
Public Sector
Utilities
Buyers
Lenders
Sellers
Contractors
Investors
Valuers
Real Estate Agents
Mortgage Brokers
Homeowners
Home Improvement
Internet of Things
OEM
Inspectors
Insurance Companies
4
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Residential property underpins NZ’s wealth
5
As at the end of March 2018
Residential Real Estate
$1.08 Trillion($249 Billion in home loans)
NZ Listed Stocks
$127 Billion
NZ Super and KiwiSaver
$79 Billion
Commercial/ Industrial Real Estate
$201 Billion
1.6 millionNumber of
dwellings
$247 billionOutstanding
mortgage debt
55.8%Household wealth
held in housing
90,482Total sales p.a.
$56.3 billionGross value of
sales p.a.
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What is Visual Analytics?
6
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is the science of
analytical reasoning
facilitated by interactive
visual interfaces
Visual Analytics…
7
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Case Study 1 –
Understanding Market Value
8
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Mapping the Market New Zealand
Space and time discovery of market performance in the main centres
Mapping the Market NZ
2013 2018
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Housing Affordability – a space-time analysis
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• The Mapping the Market tool shows changes in market value across NZ’s main centres
over two time periods – May 2013 and May 2018
• Its interactive format provides valuable insight into how property values change across cities and the country, as well as how values have shifted over time.
• The decline in affordable housing over Auckland is clear on the video showing the
disappearance of properties under $650,000 over the past decade
• Using grids shows the importance of scale on property value, with variations at city, town,
neighbourhood and street level
Questions these visual analytics answer
11
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Case Study 2 –
First Home Buyers
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First Home Buyer Behaviour
13
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• Where are the First Home Buyers?
• How has their behavior changed over time?
• How is each main centre performing?
• Where are the affordable homes?
• Where should the government focus efforts around Kiwibuild?
Questions this visual analytic answers
14
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Case Study 3 -
Relationship Mapping
15
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Change in Property
Value between 2012
and 2017
Auckland
< 25%
25% - 50%
50% - 75%
75% - 100%
> 100%
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Crown owned
Residential
Housing
Density
Auckland
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Crown owned Residential
Housing Density Map
Christchurch
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Crown owned Residential Housing
Density Map
Hamilton
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Relationship between property
value change and Crown
residential property
Wellington
< 25%
25% - 50%
50% - 75%
75% - 100%
> 100%
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Relationship between property value
change and Crown residential
property
Lower Hutt
< 25%
25% - 50%
50% - 75%
75% - 100%
> 100%
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Relationship between property value
change and Crown residential
property
Auckland
< 25%
25% - 50%
50% - 75%
75% - 100%
> 100%
1 - 10
10 - 50
50 - 100
100 - 200
200+
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Value Change in Auckland by Residential Crown Property Density
0 1 - 25 25 - 50 50 - 100 100 - 200 200+
90%
86%
91%93%
95%
102%
Mean
Crown Property Density
Valu
e C
hange (
5 y
ears
)
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Property Value in relation to Crown parks
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Relationship
Mapping
Capital Value
v
Land Value
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• There is no significant correlation between the rate of change in property value and the
density of Crown residential property across the country
• Other factors such as affordability, transport routes, school zoning and property condition
are more influential on property value
• The bivariate choropleth map shows where capital value is appropriate to land value
Questions these visual analytics answer
26
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Case Study 4 –
Performance Monitor
27
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Interactive dashboardsAn interactive dashboard illustrates how visual analytics is possible using a combination of
maps, dials, charts and query filters to present business indicators
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• What is my market share?
• How are my channels performing?
• How does this relate to housing stock age and market value?
• What are different buyer classes doing?
• How many sales have there been in this period?
Questions this visual analytic answers
29
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• The world is increasingly being modeled digitally, blending scientific techniques in the
process.
• Actionable intelligence is achieved through the human interpretation of geospatially
presented data
• Geospatial platforms allows decision makers to vet their assumptions and make changes
to their visualisations on the fly
• A deeper analysis provides a more refined view of the locations and density at which
customer segments change
Summary
30
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Matt Lythe
Head of Geospatial Solutions
Thank You
31
Future of Financial Services, Auckland