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Big Data Day LA 2016/ NoSQL track - Big Data and Real Estate, Jon Zifcak, CEO & Anton Polishko, CTO, Zulloo

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The real estate industry is generating terabytes of data, but a very small percentage is being utilized or processed. ZULLOO Inc. is creating a artificial intelligence engine utilizing big data and machine learning. The question is, why aren't more data scientists exploring the real estate industry when it represents 15% of the US GDP, measured in the Trillions?

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Big Data Day LA 2016/ NoSQL track - Big Data and Real Estate, Jon Zifcak, CEO & Anton Polishko, CTO, Zulloo

  1. 1. Big Data & Real Estate
  2. 2. Welcomes Jon Zifcak, MBA, MSIM CEO, ZULLOO Inc. @JonZifcak Anton Polishko, PhD. CTO, ZULLOO Inc. @AntonPolishko
  3. 3. NoSQL Why? Benefits?
  4. 4. What is NoSQL? NoSQL encompasses a wide variety of different database technologies that were developed in response to the demands presented in building modern applications: ● Developers are working with applications that create massive volumes of new, rapidly changing data types — structured, semi- structured, unstructured and polymorphic data. ● Long gone is the twelve-to-eighteen month waterfall development cycle. Now small teams work in agile sprints, iterating quickly and pushing code every week or two, some even multiple times every day. ● Applications that once served a finite audience are now delivered as services that must be always-on, accessible from many different devices and scaled globally to millions of users ● Organizations are now turning to scale-out architectures using open source software, commodity servers and cloud computing instead of large monolithic servers and storage infrastructure.
  5. 5. Business Application ● Personalization ● Profile Management ● Real-Time Big Data ● Content Management ● Catalog ● Customer 360° View ● Mobile Applications ● Internet of Things ● Digital Communications ● Fraud Detection
  6. 6. Technical Application
  7. 7. Real Estate Data ● Multiple Listing Service (MLS) ○ History of sales and current properties on the market ○ Property descriptions ○ Structured data (literally just a big spreadsheet) ○ However, fields change over time ○ Different locations have different MLS provider, thus, different format ● Public Records ○ A lot of missing data ○ A huge variety of data (demographics, crime rates, etc.) ● 3rd party providers ○ School reviews ○ Proximity to POIs ○ Same as public records but cleaned up
  8. 8. Real Estate Data ● Multiple Listing Service (MLS) ○ History of sales and current properties on the market ○ Property descriptions ○ Structured data (literally just a big spreadsheet) ○ However, fields change over time ○ Different locations have different MLS provider, thus, different format ● Public Records ○ A lot of missing data ○ Total havok in what can you get (demographics, crime rates, etc.) ● 3rd party providers ○ School reviews ○ Proximity to POIs ○ Same as public records but cleaned up heterogeneous
  9. 9. AVMs ● Automated valuation model (AVM) is a service that can provide real estate property valuations using mathematical modelling combined with a database ● Typical AVM uses hedonic regression, means property value is decomposes ○ number of bedrooms, bathrooms ○ size of lot ○ distance to the city center, schools, etc. ○ etc.
  10. 10. Zulloo Approach ● Real Estate data: ○ Data is combination of structured and unstructured ○ Missing features ○ Geo-specific ● Our goals ○ Common storage for web-development and data science teams ○ Horizontal scalability ○ Geo queries 3rd party provider s MLS Public records
  11. 11. Things to consider ● As a startup ○ Availability of free support ○ Roadmap ● Speed considerations ○ Comparison PostgreSQL vs MongoDB
  12. 12. Zulloo Approach ● Real Estate data: ○ Data is combination of structured and unstructured ○ Missing features ○ Geo-specific ● Our goals ○ Common storage for web-development and data science teams ○ Horizontal scalability ○ Geo queries 3rd party provider s MLS Public records
  13. 13. Current Setup MongoDBMeteor app Input data stream Machine Learning
  14. 14. Roadmap MongoDB GraphQL Meteor app ApolloStack Input data stream Machine Learning
  15. 15. Next big thing ● Graphical databases ○ RE data is just a huge graph anyway ● GPU databases ○ Speed(!!!) but only SQL so far :( ● ApolloStack/GraphQL/etc ○ Clean API between backends and frontends ○ Less time spent digging API documentation
  16. 16. Q& A Info@ZULLOO.com www.ZULLOO.com

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