Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical investor create models of how a stock price might change in the future.

Real estate investors do not have access to the same data and tooling. Most people who invest in apartment buildings are using a combination of experience, news, and basic reports.

Real estate data is very different from stock data. Real estate assets are not fungible–each one is arguably unique from all others, whereas one share of Google stock is the same as another share. But there are commonalities between real estate assets.

Just like collaborative filtering can be applied to find a new movie that is similar to the ones you have watched on Netflix, comparable analysis can be used to find an apartment building that is very similar to another apartment building which recently appreciated in asset value.

Skyline.ai is a company that is building tools and machine learning models for real estate investors. Or Hiltch is the CTO at Skyline.ai and he joins the show to explain how to apply machine learning to real estate investing. He also describes the mostly serverless architecture of the company. This is one of the first companies we have talked to that is so heavily on managed services and functions-as-a-service.

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