Zillow lost money because they weren't willing to lose money
> They thought they needed to build a machine learning model when they really needed to build an entirely new organization, one that possessed the technical and cultural mindset necessary to succeed in this space.I totally agree.  It's not impossible to imagine their model working: why couldn't you serve as a market-maker for homes at a large scale, especially with the unique insights Zillow could have based on their datasets.However I think where the hubris lay is in how they thought they could leapfrog all the way to an automated solution before building a competency as a house-flipping company.From what I understand, where they failed was partly in building a rich enough model to properly account for the less easily quantifiable elements which ultimately account for a property's value.  I.e. the price per square foot might make a property look like a steal, while something like a sewer main nearby, or problematic neighbor could radically change the value proposition to anyone standing at the site.  That's a non-trivial problem to solve for even the best ML and it's not clear how you would automate this.If you ask me, instead of focusing on building an automated price discovery system, they should have started by trying to build a quality home-flipping organization, and figuring out how to super-charge manual work using their datasets.  Over time you might find ways to optimize the process and increase the level of automation to scale output relative to head-count.