Here's a question for you: If the housing market improves, will online home-listing businesses' stock prices rise as well?
You are correct if you replied "yes." That's exactly what you'd anticipate in most cases, and it's exactly what occurred with Zillow, a prominent US real estate listing service. For the corporation, the epidemic was a gift in disguise. Americans were unable to leave their homes. They were bored at home, so they invented a new pastime: fantasising about their ideal dwellings.
They naturally resorted to websites like Zillow to browse real estate listings and purchase the nicest residences they could find. With low mortgage rates, house sales in the United States are expected to reach new highs in 2020. Meanwhile, Zillow's stock almost quadrupled in 2020.
But Zillow wasn't only there to help individuals sell their homes. It also improved people's perceptions of them.
Investopedia says it this way:
Zestimates, a popular consumer tool for determining the value of a house, is one of Zillow's core features. These projections are based on data from sources such as comparable sales and publicly available statistics. Zestimates, which was launched in 2011 with data on 90 million households, has now grown to include data on more than 100 million homes throughout the United States.
Users may also add images of a house to see how it might change the worth in the most recent version. These individuals had vast amounts of data on property values across the United States, and it was only a matter of time until they chose to monetize it in some way.
And in 2018, that thinking became a great concept. The firm decided to put its algorithms and data to the test. They made the decision to purchase underpriced residences straight from the vendors. It's a good idea to renovate it and sell it for a profit. The aim was that by employing "clever algorithms and data science," they'd be able to find incredible discounts all around the nation.
As the housing market began to recover in 2020, Zillow increased its focus on this side project. The corporation acquired roughly 10,000 residences between July and September of this year. It also has an agreement to purchase another 8,172 properties.
Unfortunately, this is when everything began to go horribly wrong. Only 3,000 of the houses were sold through Zillow. Which implies they're still sitting on thousands of properties after securing large sums of money. Its algorithm was incorrect (or the people that created it did).
So, here's how it went down.
When property prices first began to increase, Zillow's algorithms were unable to foresee how rapidly they would rise. As a result, they were bidding much too low on hot properties and, as a result, were not purchasing enough inventory. As a result, the business decided to change its algorithm and make it more aggressive, which resulted in Zillow purchasing a large number of additional houses. Unfortunately, prices had already started to cool by that time. They were also compelled to sell their houses at a loss.
Insane!!!
In the end, the algorithm failed when prices increased. When prices dropped, the algorithm likewise failed. And the firm that was meant to bring in $20 billion in income is now in shambles. In addition, Zillow is writing down about $500 million in investment and laying off 2,000 employees (many of whom are unlikely to have anything to do with the algorithm business). In the meanwhile, the stock has lost roughly 40% of its value in the previous week. In fewer than seven days, $10 billion in equity was wiped away.
That's all there is to it. Zillow's surrender is a warning storey for anybody who believes they can use analytics to control the real estate market. Because the way individual participants in the system think and behave affects real estate values. It incorporates both the techie hoping to relocate to a pleasant Palo Alto suburb after an IPO and the hedge fund manager forecasting the next big slump. Most algorithms are doomed to a life of uncertainty because man-made formulae can seldom capture the depth of human feeling.
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