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BigData in Real
estate
RAJESH CHILAMANTHULA
Agenda
 Traditional real estate operation
 How BigData is changing Real estate
 Challenges
 Proposed Analysis
 Algorithm & Example
Traditional Real estate operation
 Contacting Real estate Agents.
 Classifieds/other sources.
 Pricing comparisons
 Final deal.
How BigData Changing Real estate.
"In God we trust, all others bring data.” - William Edwards Deming
 BigData
 Real estate is next!
 Better Understanding of communities
 Bankers/investors – mortgaging and foreclosures
How BigData Changing Real estate.
 Impact on sales
 Ease of finding rentals - Example – Housing.com
 Estimates for properties.
 Targeted Consumers
 Finding agents for advice.
Challenges
 Collection of Data.
 Expiration of listings/Data.
 Prediction is not easy!
 Other factors like unawareness of neighborhood.
Proposed Analysis
 New York city Home valuations
 New projects in the city
 Effect of new developments on the real estate of City
Algorithm
Linear Regression
 Widely used and well-understood.
 Runs very fast.
 Easy to use and minimal tuning.
 Basis for other machine learning techniques.
Algorithms/technologies used in domain
 Linear Regression
 Classification
 Clustering
 Neuro lab, Matplotlib, Weka.
Linear Regression - Example
 Predicting house values.
Linear Regression - Example
 f(x) = a + bx
f(x)
x
References
 http://dataconomy.com/linear-regression-implementation-in-python/
 Quora
 https://data.cityofnewyork.us/Housing-Development/Projects-in-
construction/8586-3zfm
 Zillow, Trulia
 Housing.com
 http://realtormag.realtor.org/daily-news/2015/08/07/3-ways-big-data-
changing-real-estate
 http://mashable.com/2014/07/09/big-data-real-estate/#G0aeozZZS8qE
Queries?

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Big data in real estate

  • 2. Agenda  Traditional real estate operation  How BigData is changing Real estate  Challenges  Proposed Analysis  Algorithm & Example
  • 3. Traditional Real estate operation  Contacting Real estate Agents.  Classifieds/other sources.  Pricing comparisons  Final deal.
  • 4. How BigData Changing Real estate. "In God we trust, all others bring data.” - William Edwards Deming  BigData  Real estate is next!  Better Understanding of communities  Bankers/investors – mortgaging and foreclosures
  • 5. How BigData Changing Real estate.  Impact on sales  Ease of finding rentals - Example – Housing.com  Estimates for properties.  Targeted Consumers  Finding agents for advice.
  • 6. Challenges  Collection of Data.  Expiration of listings/Data.  Prediction is not easy!  Other factors like unawareness of neighborhood.
  • 7. Proposed Analysis  New York city Home valuations  New projects in the city  Effect of new developments on the real estate of City
  • 8. Algorithm Linear Regression  Widely used and well-understood.  Runs very fast.  Easy to use and minimal tuning.  Basis for other machine learning techniques. Algorithms/technologies used in domain  Linear Regression  Classification  Clustering  Neuro lab, Matplotlib, Weka.
  • 9. Linear Regression - Example  Predicting house values.
  • 10. Linear Regression - Example  f(x) = a + bx f(x) x
  • 11. References  http://dataconomy.com/linear-regression-implementation-in-python/  Quora  https://data.cityofnewyork.us/Housing-Development/Projects-in- construction/8586-3zfm  Zillow, Trulia  Housing.com  http://realtormag.realtor.org/daily-news/2015/08/07/3-ways-big-data- changing-real-estate  http://mashable.com/2014/07/09/big-data-real-estate/#G0aeozZZS8qE