REAL ESTATE BD Uses Dale Ross - RPR Sept. 10, 2014
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2 Defense Base Realignment and Closure Commission (BRAC) U.S
Congress-November 2005 Ten Year Plan Transfer and Disposal of
Military Institutions (Reduce pork-barrel politics)
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3 State of Maryland-Map 1
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4 State of Maryland Bethesda (Suburb of Washington D.C.)
National Institutes of Health National Naval Medical Center-Now
Walter Reed National Medical Center (November 2011) 20,000 Jobs
(est.) Frederick (50 miles north of Washington D.C.) Ft. Detrick
Army Medical Research-Institute of Infectious Diseases 5,000 Jobs
(est.) Aberdeen (26 miles north of Baltimore) U.S. Army
Communication Electronic Command 22,000 Jobs (est.) 2
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5 State of Maryland Lexington Park (65 miles southeast of
Washington D.C.) Naval Air station Patuxent River (Pax River) Naval
Air Warfare Center-Aircraft Division Air Test Wing Atlantic 17,000
Jobs (est.) Ft. Mead (Between Washington D.C. and Baltimore)
National Security Agency (NSA) Largest Consumer of Electricity in
Maryland Largest Employer in the State of Maryland Largest
Concentration of Mathematicians in the World 54,000 Jobs (est.)
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6 The 3 keys to Big Data Preparation Analysis Context Growth of
Structured vs. Unstructured Data Source: Oracle Unstructured
Structured 4
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270 Terabytes of Property Data and Images 1 Terabyte =
1,099,511,627,776 bytes Processed on ultra-fast drives providing 1
million input/output operations per second Support for more than
1000 fields of MLS data Property characteristics covering 99% of
the US population RPR Platform Assets 7 5
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8 Big Data Starts with a Big Shovel Its an absolute myth that
you can send an algorithm over raw data and have insights pop up. -
Jeffrey Heer, University of Washington Source: New York Times, Aug.
17, 2014 RPR Data Preparation High-level Workflow 6
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9 Property Data Changes Constantly With many thousands of
sources updating 24-7, millions of U.S. properties change status
every day On any given day, more than 5% of all MLS feeds are
broken or changing at the source Countless data entry errors,
typos, and duplicates must be detected and removed from MLS and
public data 7
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10 Gaining Real Competitive Advantage Whats the current return
on a bathroom remodel in Cheeseville, Wisconsin? 8
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11 Preventing Fall-thru Show only short sales within
servicer-approved price guidance 9
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12 Commercial Consumer Trends Neighborhoods with high household
growth potential in areas with concentration of financial
professionals 10
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13 Commercial Consumer Trends Market Leakage graphs show where
consumers are spending, both inside and outside of their
communities 11
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14 Big Data-Real Estate Open New Offices Franchise Affiliation
Profile for Future Agents Profile Future Sellers (Homeowners)
Profile for Future Purchasers (Apartment Dwellers) Job Growth
Trends (Future Customers) 12
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Pent-Up Demand 15 Lawrence Yun, Ph.D. Chief Economist NATIONAL
ASSOCIATION OF REALTORS Presentation at NAR Leadership Summit
Chicago, IL August 18, 2014 20002013 Existing Home Sales5.2 m5.1 m
New Home Sales880 K430 K Mortgage Rates8.0%4.0% Payroll Jobs132.0
m136.4 m Population282 m316 m 13
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National Housing Forecast 16 20132014 forecast 2015 forecast
Housing Starts925,0001.1 million1.4 million New Home
Sales430,000Near 500,000Near 700,000 Existing Home Sales5.1
million4.9 million5.3 million Median Price Growth+ 11.5%+ 5% to 6%+
3% to 5% 30-year Rate4.0%4.4%5.4% Lawrence Yun, Ph.D. Chief
Economist NATIONAL ASSOCIATION OF REALTORS Presentation at NAR
Leadership Summit Chicago, IL August 18, 2014 14
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17 Benchmarking and Forecasting Predicting Inventory and
Pricing at Market and Company Level 15
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19 Mistaking Analysis for Insight With Big Data, it is easy to
find correlations that are neither meaningful nor actionable. The
U.S. murder rate corresponds highly to changes in the usage of
Microsofts browser Source: Gizmodo 17
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20 You Get Out What You Put In IBM's Watson fails third-grade
geography test World's smartest computer thinks Toronto is a U.S.
city - NetworkWorld 18