How Wall Street Investors Become America's Landlords
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With your Money Briefing, I'm J.R. Whalen at The Wall Street Journal in New York. Computers and algorithms putting Wall Street investors in the business of being landlords. We'll explain in a moment. First, these money and market stories you should know. The good news is shoppers are filing back into stores ahead of the holiday season. The bad news is there may not be enough workers to help them. Staffing up for the year-end crush is an annual challenge for retailers. There were 757,000 retail job openings across the country in July. That's about 100,000 more than the same time a year ago. The number of openings surpassed the number of hires from March through June for the first time in a decade. The Manpower Group staffing company says that sales representatives are among the hardest jobs to fill in the U.S.
What's more, it's also a shortage in route drivers that is so severe in some areas, retailers are asking all employees from entry level to executives to pitch in by delivering inventory to stores. And the Wall Street Journal heard on the street team says that people in the Carolinas are about to rediscover the difference between the damage a storm causes and what is covered by insurance.
What retail and labor trends are affecting holiday shopping and staffing?
While wind damage is well covered by insurers and reinsurers, flood damage is absent from most homeowner policies and is typically an optional cover in commercial policies. The real estate data company CoreLogic estimates the total insured losses for wind and storm surge damage in North and South Carolina could be as little as $3 billion. That's much lower than earlier warnings. that losses could be more in the range of $20 billion. But that number does not include any flood-related claims from commercial policies for property damage or business interruption, or for auto insurance or agricultural claims. But it's an indication that private insurance payouts to homeowners will likely be fairly low, even though thousands of homes will have suffered extensive flood damage.
If real estate is all about location, location, location, then finding the right house could be all about computers, computers, computers. Wall Street Journal reporter Ryan Dezember joins us to discuss how institutional investors are using algorithms to buy up large volumes of home for rental property use. So, Ryan, the idea of Wall Street investors becoming landlords is relatively new. It became much more pronounced since the recession.
Yeah. And really, institutional investors didn't really mess with residential single family detached homes. They stuck to apartments, office towers, you know, commercial properties. But the foreclosure crisis gave them an opportunity to buy a lot of homes very cheaply and in the volume they needed, sort of the density wherever they were buying. And now that foreclosures have returned to normal, we're seeing these investors buy homes off the open market. And of course, when they're buying thousands of homes a year, they don't have time to go to showings. They have to sift through a lot of listings to find what they want. And so they've used some sophisticated technology to do that.
And then you enter Martin Kay, who's a data scientist. You spotlighted and he's profiled in your story. He began machine learning about eight years ago to find ideal tenants for rental properties he had bought.
Yeah, so he and some partners were investing some money they had in foreclosed properties just like a lot of other investors. They, because of his background using sort of big data and sort of sorting that and developing algorithms and systems for the computer to sort of learn on its own and handle a lot of these tasks. He started using that in his own investing, and it caught the attention of other investors. And over time, he realized for his skill set, he's probably better to supply the software and do the work for other investors rather than be a big landlord himself.
And the computers involved, they take on a lot of tasks, like asking clients what they're looking for and then doing a search for a home.
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