AI IN REAL ESTATE

Background/Problem Statement:

Typically, when it comes to data management in the real estate sector, there are several major challenges. Often the required data are either unavailable, not granular enough or outdated.  Even when available, they might not be fully organized across geographic areas which requires lot of manual effort. This also requires other manual data corrections such as missing values or incorrect master data. In other words, there is a risk of ending up with expensive but worthless or even misleading analysis results such as personal bias by the expert “correcting” the data issues.

The objective of the project is to model geo spatial data of any region in the world to accurately predict real estate demand and prices. Data and Insights were predicted from social demographics, rents, purchase prices and geographic points-of-interest (POIs). Typically, useful data in the Real Estate Industry always comes at a potentially steep price which can overcome by accurate modelling/analytics using Minsky™.

Solution Overview:

After a comprehensive evaluation of the parameters corresponding to the image data, we used Minsky to accurately model the historical data. This comprised of geo spatial data such as Suburb, Address, Rooms, Method, Type, Distance, Region Name, Property Count, Land Size, Longitude, Latitude etc. Once the detailed modelling was performed by Minsky for the selected Algorithms, historical data used by MinskyTM was separately analysed to predict the real estate demand and prices-based on geo-spatial parameters.

Key Benefits (Minsky™):

• User-Friendly, cloud based AI platform
• Scalable across various domains/data.
• Provides you a list of % dependency features that can be used to optimize the outcomes.
• Ability to fine tune or optimize the models by trying different algorithms / prediction attributes
• Easy integration with other third-party solutions such as TABLEAU for data visualization

Project Results:

Based on the data, our analysis shows that Gradient Boost Classifier gave the highest model accuracy (89%) for applied techniques. In terms of the attributes data for Ai modelling techniques,  Distance from Central Business District (CBD) showed the highest dependency cause for the pricing estimation criteria for a specific region followed by Type of House information.

Benefits:

• Improved real estate price predictions based on demographics and Geo-Spatial data.
• Better investment choices.
• Faster and Reliable predictions
• Better land utilization.
• Consumers' Expectations with regard to Future real estate Prices