Title: Zillow Prize [Archived Project]
July 2017 - Jan 2018
Zillow’s Zestimate home valuation has shaken up the U.S. real estate industry since first released 11 years ago.
A home is often the largest and most expensive purchase a person makes in his or her lifetime. Ensuring homeowners have a trusted way to monitor this asset is incredibly important. The Zestimate was created to give consumers as much information as possible about homes and the housing market, marking the first time consumers had access to this type of home value information at no cost.
“Zestimates” are estimated home values based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. And, by continually improving the median margin of error (from 14% at the onset to 5% today), Zillow has since become established as one of the largest, most trusted marketplaces for real estate information in the U.S. and a leading example of impactful machine learning.
Zillow Prize, a competition with a one million dollar grand prize, is challenging the data science community to help push the accuracy of the Zestimate even further. Winning algorithms stand to impact the home values of 110M homes across the U.S.
Leader(s): Lakshay Chauhan - (slack:@lakshay, email: email@example.com)
MDST Participants: Yongwen Zhuang (@yvonnez), Tapan Dangerwala (@tapandan), Josh Bochu (@jbochu)
Link to the Code: gitlab.eecs.umich.edu/mdst/zillow
Link to the Dataset: umich.box.com/s/e195u3m5oozhfpcdgel20o3w72m0qdeu