Cognitive Recommendations Using Real Estate Standard Ontology, Dr. Umesh Harigopal, Antony Satyadas, and Matt Kumar. Challenges in generating Real Estate Insights. Maturity Model from PropMix for Real Estate data standards. Evolving meta data into ontology, Graphs for properties, Hierarchical dimensions for property data. Measure the impact of local real estate and associated news on home prices.

1. September 2016
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC
COGNITIVE RECOMMENDATIONS USING
REAL ESTATE STANDARD ONTOLOGY
Dr. Umesh Harigopal, Antony Satyadas and Matt Kumar

2. We are generating various insights
When to sell a home
Who to work with to buy/sell my home
Owner’s Propensity to Sell
Best home to buy – local market or any target market
Predict a home price
Recommend Best Investments
Challenges that we frequently stumble upon:
Semantically inconsistent data
Partially specified data
Integration of disparate information domains
Natural language with incomplete sentences
2 Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC

3. We introduced a maturity model @ the Spring 2016 RESO conference
One of the dimensions for assessing maturity is
Big Data / Insight Retrieval / Delivery Maturity
Structured
Queries & EDI
Queries tied to the data
structures
Rigid data exchange
protocols
B2B Integration
Web Services
Discoverable Web
Services
Well suited for service
to service integration
Use Case
driven APIs
RESTful APIs with JSON
or XML payload
APIs purpose-built for
various use cases –
search, lookup, detail,
etc.
Ontology-based
APIs
Generic API with
generalized querying
RESTful or SOAP APIs
based on a shared
ontology
Contextual & Intentional
NLP Dialog
Natural language request
and response
Use context to understand
user’s intent and respond
with relevant and actionable
information
Heavy-weight SOAP/XML
with flexibility – strongly
typed w/ document
attachments
Leverages the ontology to
understand and respond
to a request
A standard ontology helps us
to understand information
and the questions to be
answered using the
information
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC3

4. Our Solution Approach – Evolve current Metadata into an Ontology
Use the metadata model as the foundation for the Ontology
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC4
Property
Listing
Assessment
Recorder
Neighborhood
Local / Global
News
Personal
Financial
Data
Integration
Natural
Language
Processing
Machine Learning Models
RE Ontology
Analytics Engine
Query Engine
Derived Knowledge Graph
Ontology forms the foundation for associating various concepts within the collected data

5. Property
Pool
Association
Cooperative
Homeowners
Association
Ownership
Heating
Type
Water Feature
Spa
In ground
Above ground
Gas Heated
Solar Heated
Private
Appliance
Water Heater
Gas
Utilities
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC5
Example of a portion of the Ontology – Self Evolving
Begin Here
has
has
Has attribute
Is a
is
is
is
Is a
Is a
Is a
Uses
Uses

6. Generate knowledge graphs for many properties – here is an example for a single property
Property
Pool
Association
Cooperative
Homeowners
Association
Ownership
Heating
Type
Water Feature
Spa
In ground
Above ground
Gas Heated
Solar Heated
Private
Appliance
Water Heater
Gas
Utilities
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC6
has
has
Has attribute
Is a
is
is
is
Is a
Is a
Is a
Uses
Uses

7. Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC7
Introduce more data subjects & dimensions : knowledge graphs evolve to complex structures
Demographics Tenant
Owner
News
Schools Neighborhood
Shopping
Workplaces
Religious Centers
Master Plan
Hierarchical dimensions such as geography & employment
Temporal relationships such as ownership, renovations,
demographics, etc.
The resulting graph is a Longitudinal Property RecordTM
(LPR) but based on a semantic model that evolves

8. We used this model to measure the impact of local real estate and associated news on
home prices
Assess influence of different categories of news on home prices
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC8
Property
Listing
Assessment
Recorder
Neighborhood
Twitter News
Feeds
Personal
Financial
Data
Integration
Stanford NLP
Alchemy
Machine Learning Models
NN, Random Forest Classifiers
RE Ontology (owl)
Analytics Engine
Multi-Variate
Regression
Query Engine
Derived Knowledge Graph (neo4j)
Learn the degree of influence, time period of influence, and “influence decay” curves from
historic and live transaction data

9. Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC9
Results were promising when we back tested against real data
Raw Price Trend Data for 33142 – Miami, FL Predicted Price Trend for 33142 – Miami, FL
The chart above shows the forecasted price based on
news and other well known influencers of prices
Forecasted model begins in March 2012
But that extent of influence will continue to change with
market conditions and economic forces
Ongoing monthly training of the model with actual data
will allow the model to be current
We trained the model to learn the extent of influence of
each factor

10. Dr. Umesh Harigopal, Antony Satyadas and Matt Kumar
September 2016
Confidential. All Rights Reserved. Copyright (c) 2016 PropMix.io LLC