iBuyers use data to buy houses and turn a profit. Or at least that's the hope
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iBuying - it seemed like a slam dunk for online real estate companies like Zillow because they have all this data on home buying and they could make calculations on how much they should pay to buy a house from a seller and turn a profit. At least that was the hope. Stacey Vanek Smith and Adrian Ma from our Indicator podcast look at what happens when the algorithm meets the housing market.
ADRIAN MA, BYLINE: iBuyers - basically, they buy homes online, throw on a coat of paint and turn around and sell them. But Gregor Matvos of Northwestern University - he says they only concentrated in certain places like Phoenix, Atlanta and Las Vegas.
GREGOR MATVOS: They are very present in markets in which you have, let's say, standardized housing in a very fairly narrow price range. It's really houses that you can price pretty darn well using a computer algorithm.
STACEY VANEK SMITH, BYLINE: Like, Gregor says, in areas with a lot of newer kind of cookie-cutter style houses, iBuyers were using all this data that they were collecting on home values and customer interest to just scoop up dozens or, in some cases, hundreds of properties. And to some people, you know, this wasn't landing very well. People started to worry that, you know, these iBuyers had an unfair market advantage. They could actually artificially inflate home prices. Zillow and Redfin, by the way, have issued statements denying this.
MA: And anyway, Gregor does not think this is happening. Despite their growth, iBuyers' share of recent home purchases is only about 1%.
MATVOS: So if they are trying to do this manipulation, they're not doing it very well.
VANEK SMITH: Not only does market manipulation not seem to be happening, but also, profits don't seem to be happening. Zillow, for instance, announced last week it was quitting the iBuying business and laying off a quarter of its staff. Zillow says its iBuying operation lost more than $420 million in the third quarter of this year alone.
MA: Gregor and his colleagues have been studying iBuyers for a few years now, and he says even though these companies have troves of data they can slice and dice, they just can't get around this particular metaphorical fruit.
MATVOS: iBuyers fundamentally are exposed to what in economics we call the lemons problem.
VANEK SMITH: The lemons problem, as in like a car. Like, you buy a used car, and it turns out to have some unexpected defect. It's a lemon.
MA: Right. And the lemons problem is a problem of asymmetric information. That's when a seller has way more knowledge of a thing than the buyer. And this asymmetric information problem has reared its head with iBuyers.
MATVOS: If they want to transact quickly, they can't take the time to very, very precisely value the house. They already need a pretty darn good computer algorithm to price things. But they're still not as good as someone who can take the time and do a very thorough inspection and then take two months to close down the house sale.
VANEK SMITH: So algorithms - for all of the power and insight they have, they do have some weaknesses. You know, an algorithm is not going to know if, like, the neighbors are really noisy or if the basement smells weird. There are just things the data can't capture. And ultimately, Zillow could not figure out a solution for these algorithmic blind spots.
MA: But, you know, Gregor doesn't think that iBuying is doomed.
MATVOS: I think there is room for iBuyers. I think they provide a service that we need. I just don't know. The fundamental economics aren't that great.
VANEK SMITH: The fundamental economics - you know, the pricing problem, the lemons problem, the costs. It all adds up to AI buyers losing money.
MA: Which, to be fair, is a time-honored way of doing things in the tech industry, right?
VANEK SMITH: (Laughter).
MA: Adrian Ma.
VANEK SMITH: Stacey Vanek Smith, NPR News.
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