Housing is something that we all have experience with, but many don’t understand the complexities of the market. This week Travis Jungroth talks about how HouseCanary uses data to make the business of real estate more transparent.
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- Your host as usual is Tobias Macey and today I’m interviewing Travis Jungrot about HouseCanary, a company that is using Python and machine learning to help you make real estate decisions.
Interview with Travis Jungroth
- How did you get introduced to Python?
- What is HouseCanary and what problem is it trying to solve?
- Who are your customers?
- Is it possible to get data and predictions at the neighborhood level for individual homebuyers to use in their purchasing decisions?
- What do you use for your data sources and how do you validate their accuracy?
- What are some of the sources of bias that are present in your data and what strategies are you using to account for them?
- Can you describe where Python is leveraged in your environment?
- What are some of the biggest software design and architecture challenges that you are facing while you continue to grow?
- What are the areas where Python isn’t the right choice and which languages are used in its place?
- What are the biggest predictors of future value for residential real estate?
- Can your system be used to identify risks associated with the housing market, similar to those seen in the bubble that triggered the 2008 economic failure?
- What are some of the most interesting details that you have discovered about real estate and housing markets while working with HouseCanary?
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