Zillow lost money because they weren't willing to lose money
&gt; 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.<p>I totally agree.  It&#x27;s not impossible to imagine their model working: why couldn&#x27;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.<p>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.<p>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&#x27;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&#x27;s a non-trivial problem to solve for even the best ML and it&#x27;s not clear how you would automate this.<p>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.