Property Recommendation Engine

Recommendation Engine, catering to the real-estate portal that helped users to find best property based on their taste and requirements.
AI ML Python

Background and Scope

Our client is a real estate consulting firm based in the UK. The client has developed an ecosystem where they cater to agents, buyers, renters, sellers, and landlords on a single platform. They act as a one stop solution provider which simplifies real estate needs and helps sellers and landlords find the perfect agent for their home by connecting users to them. And assists buyers and renters to find a suitable property of their choice.

The client engaged SoftmaxAI, machine learning development company India to develop a recommendation machine learning algorithm tool which helps their users to purchase / sell a property with the ease of just one click.

Our approach

The objective was to develop a platform using deep machine learning models and support client’s users in finding a property based on their choices. To analyse users’ choices, we have to develop a recommendation system algorithm.

Recommendation systems have become an essential feature in our evolving digital world, as customers are overwhelmed by choice and need help in finding what they are looking at.

To develop an algorithm, initially we designed a questionnaire which focuses on understanding the users’ choices to buy a property. Several questions are asked in the survey, such as preferred location, type of house, budget, etc. Our recommender tool collects the information of users’ choices and based on the users’ feedback, it predicts the data and provides a matched score i.e., criteria matched as per their details provided.

In addition to this, we have also developed a memory-based collaborative filtering algorithm which smartly analyse the like button. For example, if a person (user 1) liked a flat with a balcony and 5 other people liked flats with balconies then our tool will recommend to user 1 what the other 5 users of similar matches are looking at. With this, our tool empowers users to choose / acquire the best property tailored to their choices.