AI-Powered Real Estate Price Prediction Platform for Data-Driven Investment Decisions

As the solution was developed from scratch, the initial idea was to aggregate and verify open-source data from many data streams and refine it into a uniform database. Then, it evolved. That’s why we trained an applied AI model to drive insights from the dataset and make valuable predictions on future price volatilities based on the following metrics.

Such data analysis criteria included employment rate, population growth, population density, household income, per capita income, type of structure, and ownership of occupied units in each state and metropolitan area. Moreover, the clients should have been able to see dashboards with house price index, rates per square meter, etc.

As soon as the project specifics included a big share of data analysis work, the senior data science engineers were a perfect fit. An additional choice of full-stack developers and one UI designer precisely catered to the needs and challenges of the project. The overall team size reached up to 5 people.

Developing this platform, we faced complexities typical for AI-powered data platforms in real estate. The project involved dealing with fragmented data, building predictive models from scratch, and delivering results quickly. Among the main challenges were:

• The amount of raw data was impressive — vast geography and the clustering criteria KPI promised a pile of work.
• The multi-sourced data were inaccurate and outdated, so we needed to add extra criteria to reveal, sort, and test it before use.
• No prior pre-configured AI-powered products existed for investment attraction analysis and forecasting. We had to learn and test our model from day one.
• To keep the project within time and budget, we needed to implement additional mechanisms for resource optimization.