Home sales prediction service

The AI-driven platform achieved a 20% reduction in lead acquisition costs, generating qualified leads at a fraction of the typical market cost.

Client

Real estate technology startup automating the process of home selling for the agents.

Objective

Client was looking to build an AI-driven recommendation service that forecasts homeowner’s intention to sell a property for a given geographical market in order to generate leads for the agents.

Solution

Our data science team reviewed both public and private data sources, analyzed and labelled the data, selected the proper algorithm, and finally built the Machine Learning model that generates home sales predictions. Then our software engineering team wrapped the model into a cloud-based microservice API and integrated the solution with the client’s platform.

The service offers the following features:

Multi source data fusion: location, sales transaction history, property listings, census data on county level, etc.
Integration with 3rd party GIS services to fetch, process and visualize the data
Classification and labeling of the documents based on the predefined rules
Propensity modeling: predicting “intention to sell” probability for any given property
Predicting seasonal market trends for defined area

Result

Our AI-powered recommendation engine serves as a secret sauce of the client’s real estate platform. It gives the platform a major competitive edge and allows to generate qualified leads at a fraction of a typical market cost.

Competitive edge: the implementation of the AI-powered recommendation engine provided the client’s platform with a 23% increase in user engagement, contributing to a significant competitive edge in the real estate market.
Lead generation efficiency: the AI-driven platform achieved a 20% reduction in lead acquisition costs, generating qualified leads at a fraction of the typical market cost, showcasing a substantial improvement in lead generation efficiency.
Market trends insights: the AI-powered recommendation engine accurately predicted seasonal market trends, contributing to a 32% improvement in property sales forecasting accuracy, empowering agents with valuable insights into dynamic sales dynamics.

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