[BEST PRACTICES]: How Machine Learning Can Easily Improve Realtors Efficiency

Successful startups in the real estate space will be the ones targeting vertical applications with a clear need for technology. The residential & commercial real estate space are good examples.

Machine learning techniques can be applied to accurately predict inventory to better manage real estate supply chain, cut on vacancy costs and eliminate back and forth with the customers. According to an Accenture study, machine learning can lead to a 2.6x improvement in supply chain efficiency.

The real estate space is a human industry with repetitive manual processes. Significant manual intervention implies, there are opportunities to optimize with prediction algorithms. In the same residential or commercial real estate supply chain example, inventory needs to be based on historical data but also a lot of intuition.

By leveraging data like how fast in a given neighborhood a place or office is rented, learning models could more accurately predict future transactions.

As startups, we’ll need access to significant amounts of data to train machine learning models effectively. Frontdoor has a head start because we partnered with established corporations to leverage their data to learn about workflow.

We’re reinventing how work gets done and our commitment is to help agents and brokers perform their best work every single day. Our passion is to help real estate professionals save time. We create relentlessly helpful and habit-forming products that stop agents from becoming overwhelmed.

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