Drive more value for real estate stakeholders with increased personalization, data-driven decision making and process automation.
Real estate is a complex and multifaceted industry that involves numerous stakeholders, from property owners and managers to renters and buyers. Despite its importance, the real estate sector is also known for its pain points, including inaccurate property valuations, time-consuming property searches, and high maintenance costs. To address these challenges, many real estate companies are turning to AI and machine learning solutions.
According to a report by MarketsandMarkets, the real estate AI market is expected to grow from $387 million in 2019 to $4.263 billion by 2024, representing a CAGR of 62.1%. This growth is fueled by the increasing demand for automation, improved decision-making, and enhanced customer experiences in the real estate industry.
Commercial real estate executives acknowledge that AI can produce operational efficiencies and massive budget savings
Property companies report that implementation of an AI solution resulted in a revenue increase
Reduction in price estimate errors for off-market homes in nearly 30 regions across the US, after an algorithm was implemented by Zillow
Reduce manual repetitive tasks and reduce administrative costs
Manage risk and cybersecurity with industry-leading best practices
Leverage architecture optimized for model building and deployment
Gain customer trust with explainable AI processes
Access all your data on one platform, ready for machine learning
Build custom AI models to scale operations across different regions and markets
I am extremely satisfied with the fraud detection capabilities of RapidCanvas. The platform has significantly reduced the number of fraudulent transactions and given me peace of mind when using their services. I highly recommend RapidCanvas to anyone looking to use AI in the financial services space.
Get ahead of the competition with accurate forecasts of real estate price movements using our AI solution for time-series forecasting.
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