<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>opportune</author><text>I don’t think it’s just that they had a poor model, but the combination of that and adverse selection.&lt;p&gt;If you pledge to purchase at the Zestimate then people who reasonably think they can get more than the Zestimate on the open market don’t have an incentive to sell their house to Zillow (besides convenience). But people who think the Zestimate is an over estimate will of course sell to Zillow. So instead of a normal distribution of actual value:estimated value you end up with a skew towards the end where the estimate is over the actual value.&lt;p&gt;Trading housing is very different from normal market making because houses are not fungible commodities like most securities are. For most entities trading securities at low frequency it does not really matter whether a market maker skims off a few pennies on their trade; it’s worth it for the liquidity. Houses are less liquid (because they are non fungible) so the liquidity is more valuable, but the price improvement routing around a MM can also be many percentage points of a trade because there are not only so many factors affecting their valuation, but also just chance and random noise (bidding war, a particular buyer falling in love with the property, not-price-conscious buyers).</text><parent_chain><item><author>skohan</author><text>&amp;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.&lt;p&gt;I totally agree.  It&amp;#x27;s not impossible to imagine their model working: why couldn&amp;#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.&lt;p&gt;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.&lt;p&gt;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&amp;#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&amp;#x27;s a non-trivial problem to solve for even the best ML and it&amp;#x27;s not clear how you would automate this.&lt;p&gt;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.</text></item></parent_chain></comment><story><title>Zillow lost money because they weren&apos;t willing to lose money</title><url>https://www.stevenbuccini.com/zillow-offers</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>martincmartin</author><text>&lt;i&gt;[W]hy couldn&amp;#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.&lt;/i&gt;&lt;p&gt;Indeed, I believe this is what OpenDoor does.  From The Economist article [1],&lt;p&gt;&amp;quot;They [OpenDoor] charge a fee for the services they provide: buying and selling homes immediately, with zero fuss. The quick in-and-out makes them more like marketmakers than property investors, who buy to hold.&lt;p&gt;...&lt;p&gt;&amp;quot;A former Zillow employee told Business Insider that management had been hellbent on catching up with Opendoor, the front-runner. In order to compete, the employee alleged, the company pushed to offer generous deals to potential clients. It called this “Project Ketchup”. Now it has its own fake blood on its hands.&amp;quot;&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.economist.com&amp;#x2F;finance-and-economics&amp;#x2F;2021&amp;#x2F;11&amp;#x2F;13&amp;#x2F;a-whodunnit-on-zillow&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.economist.com&amp;#x2F;finance-and-economics&amp;#x2F;2021&amp;#x2F;11&amp;#x2F;13&amp;#x2F;a...&lt;/a&gt;</text><parent_chain><item><author>skohan</author><text>&amp;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.&lt;p&gt;I totally agree.  It&amp;#x27;s not impossible to imagine their model working: why couldn&amp;#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.&lt;p&gt;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.&lt;p&gt;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&amp;#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&amp;#x27;s a non-trivial problem to solve for even the best ML and it&amp;#x27;s not clear how you would automate this.&lt;p&gt;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.</text></item></parent_chain></comment><story><title>Zillow lost money because they weren&apos;t willing to lose money</title><url>https://www.stevenbuccini.com/zillow-offers</url></story>