Case Study

House Price Prediction With AI (POC)

Solution: AI & ML Enabled Price Prediction Software

Industry: Fintech &Real-estate Industry

About House Price Prediction Software

Real-estate house pricing in current scenarios is a major headache when it comes to new or even old buyers. Based on several items like – the area where the house is, decoration, construction etc. the price varies significantly. Therefore when a customer’s budget and expectation matches with the price then a deal takes place quickly. And often times it is really hard to predict the price considering all these ever-changing features.

AI & ML-enabled house price prediction model thus help to tackle this problem efficiently. The main goal of this model is to improve its performance based on the available phenomenons of a given data-set like: crime rate, zones, industrial area, nitric oxides concentration, the average number of rooms per dwelling, age, distance to major 5 working ground, distance to highway, TAX, pupil-teacher ratio, lower status of population etc.


Its tough to predict the pricing of  a house considering multiple criteria or phenomenons. 


Multi-linear regression was used to solve this issue. And Azure Services were used to deploy the software.

Business Impact

House price prediction software helps fin-tech (for loan applicants) and real-estate industry to gain more customer faith. Since this model considers real facts of data-sets to predict the price, buyers can have the most approximate price for any chosen property. In addition to that, the model can predict the future price range of any property for a given timeline which helps buyers to make concrete decisions before buying any property according to their convenience. So, it’s evident real-estate companies would be able to get the most customers by showing more appropriate & accurate pricing for a certain property. Also, fin-tech industry will gain more traction in home loan services. 

Technology Used

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