Real estate pricing is extremely unpredictable and complex today, as they no longer follow typical seasonal patterns. Real estate professionals are finding it more difficult to make the right investment decisions and now need to pay close attention to market prices with higher precision and frequency.
Current methods involve manually screening data from multiple listing sites, a practice that is considered outdated and inaccurate.
Such inaccuracies are due to the self-reporting nature of datapoints, e.g. a real estate agent may list a home with inflated sizes to attract buyers. As such, many real estate professionals still rely on manual methods to find the right sale or rental comparables in order to underwrite deals with higher degrees of confidence and precision.
This technology automatically extracts large amounts of data points from both traditional and non-traditional sources to provide accurate and high-quality data. It incorporates Machine Learning, Natural Language and Image Processing techniques to help real estate professionals understand pricing impacts and gain actionable insights.
This technology uses advanced automation tools to monitor real estate pricing trends in granular detail.
Machine Learning techniques, like Natural Language Processing and Image Recognition, are applied to collected data to provide actionable insights for real estate developers and investors.
Real Estate Developers
Track and underwrite deals with high accuracy and confidence, backed by high-quality, high-frequency data
Adjust pricing based on market changes to maximise revenue
Real Estate Investors
Enter new markets and fact-check assumptions 10x faster than existing manual methods
Make informed decisions for investment properties and fact check agent/broker supplied information
The technology owner is looking to collaborate with companies in the real estate industry that can provide in-house data, specifically, accurate transacted prices for home rentals and sales. Additional collaboration opportunities are of interest to the technology owner; specifically, organisations with access to datasets that may have a direct impact on real estate pricing, e.g. images of apartment views, sound pollution maps, as well as property managers, building owners, brokers, and banks with commercial rentals and sales data, which will enable further optimisation of the AI algorithm.