Need(s) of the project

Ansts Kpndi
Strasbourg, France
Corporate Finance, Legal, Business Intelligence
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His\Her Talents

Years of experience: 5-10 years
Abilities: At ease with ideas, Imaginative, Analysis and critical spirit
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  • Date attended: from: 2014 to: 2016
    School: Université de Strasbourg
    Degree: Master's Degree


  • Name company: none
    Title: none
    Location: Strasbourg, France
    Experience abroad: No
    Time period: from: 02.2016 to: 02.2016
Searching service for attractive real estate objects sold at judicial auctions with the use of machine learning techniques. Potentially huge market: 1.5 billions € of the objects sold only in France for the last three years. Easily expandable business-model.
Some real estate objects are sold with big discount to the market price. There may be found objects with a discount up to 30% to the market price. Such discounts don’t guarantee profitability of a buy, but increase chances.
A lot of data about court auctions is accessible but isn’t entirely used by market participants since they are mostly small family-held companies, which can be a huge competitive advantage of the start up.
So the solution is to develop a data base and a model for predicting winning bids and searching the most attractive real estate investments. Such a service may be interesting for real estate market participants like real estate agencies, brokers, may be interesting for court auction announces aggregators and publishers to buy or can be used by the start-up for its’ internal purposes like investing in such objects by itself.Already collected a data base of court auction announces which real estates objects are being sold at. They are manually marked by 160 parameters and prepared for training. Few models are trained: one for predicting winning bid, others for predicting if winning bid is more or less than minimal market price. Immobilier, fintech, vente aux enchères

Project progress: 2- Prototypeproject-logo-part
  • Python coder

    Partner's function: Engineer
    Skills: Engineering
    Years of experience: 0-5 years
  • FinPartner

    Partner's function: Investor
    Skills: Investment
    Years of experience: 0-5 years
  • Data-engineer

    Partner's function: Engineer
    Skills: Research and Development
    Years of experience: 5-10 years
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