Predicting Customer Affluence through Property Prices
Proprator Tool for a Bank
CLIENT & PROBLEM STATEMENT
- The Client is a leading Private sector bank in India.
- It has strong customer base in retail, corporate and NR space.
- The Client wanted to use Real Estate portals to extract the required Properties information for assessing the real estate prices and thus the predicted income levels of people living in the properties.
APPROACH
- Shortlisted various portals & extracted data for gathering Property prices from various property portals.
- We extracted the various data points such as outright prices, real estate details, are/sub area etc. form square yards across cities.
- We performed preprocessing and cleaning of the unstructured data.
- The unsupervised ML model to cluster the respective area and residents classified in upto 7 clusters.
SOLUTION & OUTPUT
- The output is provided in G-Square’s Proprator tool.
- The output provides the detailed analytics of each portal wrt city, area, prices and property features etc.
- Arrive at the affluence value of each customer.
- By clustering properties and residents into distinct affluence categories, the bank could optimize its marketing, provide personalized financial products, and improve overall customer targeting.

