Basically, "use algorithms and machine learning to snatch up every single house that could possibly be rented by normal humans."
I've long had a suspicion based on my reconnaissance of a couple local markets that the REITs were snatching up everything vaguely normal thereby driving up prices. Gives you an advantage when you're looking at weird houses and a severe disadvantage when all you want is a place you aren't paying rent on.
The rentiers are winning.
Sure you could use an algorithm to do this, but realistically how many houses actually get listed in any of the top 20 real estate markets on any given day in a price range/size that is suitable for renters. You could manually scan through all of them with a peon that gets paid $15-20 an hour. So really what they are selling is a promise that an algorithm can do it better so they can sell themselves for 100-300x of what they are actually worth
You're looking at GRM. How much does it cost you to buy vs. how much money can you make off of it. And if you're crawling the data, you aren't limited to the top 20 real estate markets; you're limited to anywhere you have sales and rental data. And if you're a REIT... well, look at the numbers. If you're sitting on seven billion dollars and are tasked with the mission of turning a profit by renting single family homes, you're well beyond manually scanning. And if you aren't Blackstone, you start looking at doing your search third-party.The initial public offering of Invitation Homes on Jan. 31 raised more than $1.5 billion. The single-family REIT founded by private equity firm Blackstone in 2012 immediately became the largest listed company in the sector with an equity market cap approaching $7 billion. It boasts a portfolio of nearly 50,000 rental homes spread from coast to coast in 13 markets ranging from Atlanta to Los Angeles to Minneapolis.
While my gut wants to be all, "eeww! Technology is fucking everything up!", I actually think this is a good thing overall, for a couple of reasons. Responsibility. A company looking at the numbers is going to be FAR more responsible of a landlord than Joe Landlord. Because you - the renter - are just a number. If the dishwasher isn't working, that lowers the value of the apartment, and makes the entire building marginally less valuable (due to bad word of mouth), the longer it is left unrepaired. Making repairs quickly and professionally costs the company a little money, but the goodwill and reputation is FAR more valuable than putting off the repair until next month, or half-assing it, like Joe Landlord would do. Market Stability. If the price of apartments is algorithmically defined, then it isn't going to be susceptible to price gouging. If all the 2-bedroom 1-bath 500 sq ft apartments in an specific area are all $1500/month, then it becomes simply availability and personal preference. The rapacious landlord is removed from the process, and his properties sit unfilled, because people want STABILITY in their home. They don't want notices saying rent is going up $500 next month... or MOVE. I've had so many friends screwed by idiotic, short-sighted, petty landlords. Landlords that make bad decisions that result in their buildings getting black-listed by renters. "Don't rent from Mike Lee. He's an asshole." It's a way better business decision to have a single tenant for 10 years, with a 10% increase every 2 years, than wildly fluctuating prices, or pricing at the top end, and having constant renter turnover. Algorithms get that. Landlords don't. Interesting article...
I disagree with your every assertion. Let's start with the fact that this approach allows REITs sitting on top of tens or hundreds of millions of dollars to pay cash prices against the FHA-first-home-buyer-mortgage-insurance-burning-40%-of-their-income-on-rent humans they're competing with and the rest of it is fuckin' moot. Go venture forth into the housing market with all your arrayed financing and rub up against "overseas cash offer" and tell me how good you think things are; I watched in LA as foreign real estate trusts snapped up every.single.available.property within three miles of the coast with cash offers over bid. If the dishwasher isn't working you as a tenant can deduct the price of repair from your rent if you have put your complaint in writing. If the dishwasher isn't working you as the landlord are better off waiting to fix it until you're putting new tenants in. If the dishwasher isn't working you as the landlord can make a human decision based on how you feel about these tenants. If the dishwasher isn't working you as the administrator of a REIT have a fiduciary duty to maximize the return of your investors which is going to be "minimal investment in depreciating assets." If I own a noteworthy percentage of all the available rental properties with ready access to the park'n'ride and the good school, I can suddenly decide that rent just went up $500 in that neighborhood. Sure - a couple punters can undercut me at $400 but they've only got one house. I've got seven. My revenue went up $3500 a month, my neighbors thank me for increasing the rent, and every tenant that wants to live in that school district has to find a way to make an extra six grand a year. Say there are fifteen houses in a specific area. I own five of them. I decide $2000 a month is the going rate. I raised the rent 25% but the overall rent increase is only 10%. And maybe those other ten landlords say "the rent is too damn high!" and stick to their guns and keep their rent at $1500. And then you woke up. What they actually do is look on craigslist, see what rents are at, then start their own ads two weeks early at $2200 and let the market come to them. And just last month the rent was only $1500. LOLLLLLLLLLLLLLLLerskates because the tenant has no fucking power here. If you sign a lease, and it includes a provision for month-to-month, I am probably forbidden by some municipal statute from jacking your rent through the goddamn roof (this is what rent control is about). I probably have a 6% cost-of-living or whatever. But if housing is going up, it's in my best interest to jack you through the roof and kick you the fuck out because if you're a new tenant I can charge whatever the market will bear. And lookitthat. Because I can buy every available dwelling with cash and convert it to a rental, the market will bear whatever I want it to bear. See above. Yet you somehow think a REIT will do a better job. As to landlords v. tenants, don't get me started. I'll say this: I've never seen a corporation show more empathy than a human. This is simply not the case. It's a way better business decision to amortize the shit out of every item in the house and keep the rent as high as humanly possible. Sure - you've got a house and you want a little passive income from it and the best way to do that is to get in nice people who aren't any fuss who sit there placidly paying you every month for a place to take a shit with their clothes off or whatever but you aren't a business. You're a lazy sonofabitch with a spare house. There's a reason all the biggest apartment complexes have consistently negative reviews on every social media site you've ever seen: it's good business to be ruthless with tenants.I actually think this is a good thing overall, for a couple of reasons.
If the dishwasher isn't working, that lowers the value of the apartment, and makes the entire building marginally less valuable (due to bad word of mouth), the longer it is left unrepaired.
If the price of apartments is algorithmically defined, then it isn't going to be susceptible to price gouging.
If all the 2-bedroom 1-bath 500 sq ft apartments in an specific area are all $1500/month, then it becomes simply availability and personal preference.
The rapacious landlord is removed from the process, and his properties sit unfilled, because people want STABILITY in their home. They don't want notices saying rent is going up $500 next month... or MOVE.
I've had so many friends screwed by idiotic, short-sighted, petty landlords.
t's a way better business decision to have a single tenant for 10 years, with a 10% increase every 2 years, than wildly fluctuating prices, or pricing at the top end, and having constant renter turnover.
Comes down to corporate incentives, doesn't it? A corporation's only incentive is money. Short term, long term, whatever. You form a corporation in order to enjoy its profit structure. You then proceed to externalize everything not related to profit on anyone and everyone available. Saint Milton himself said it this way: Corporations have to do everything to maximize profit within "the rules" and if those rules don't protect people from corporations, it's proof that people didn't really want to be protected. A community, on the other hand, has many incentives: Community well-being. Traffic management. A clean environment. Low crime. A stable population. Community ties. All sorts of things that cut into the profit margins of large corporations. So really, socialism.
Full text follows To help Wall Street buy tens of thousands of houses, Martin Kay and his colleagues taught a computer to spot a sunny kitchen. Ever since last decade’s foreclosure crisis, institutional investors have been gobbling up single-family houses and becoming landlords. They have criteria just like individual buyers: three or more bedrooms, two baths, a garage, good schools, low crime, high rental yields—and bright, sunlit kitchens. Unlike them, investors buy in volume and don’t have time to go to thousands of showings. Enter Mr. Kay’s computers. On any given day, there are tens of thousands of properties available for sale in each of the booming markets where these investors are active, including Atlanta, Charlotte and Nashville. They have many places to look: the multiple listing services that Realtors compile, online sellers, lists of nonperforming bank loans, foreclosure auctions. Data scientist Martin Kay founded Entera Technology after using machine learning to comb through home listings to identify plum properties. “Going from 40,000 houses to 12 is a machine problem,” he said. “Going from 12 to one is a human problem.” Data scientist Martin Kay founded Entera Technology after using machine learning to comb through home listings to identify plum properties. “Going from 40,000 houses to 12 is a machine problem,” he said. “Going from 12 to one is a human problem.” Mr. Kay, who had built data platforms for the U.S. Energy Department and ConocoPhillips, started buying rental properties in Texas in 2010 at the depths of the housing crash. He used machine learning to mine mountains of home listings for those that might attract the type of tenants he wanted. For Mr. Kay and like-minded investors, that typically meant families seeking suburban lifestyles. “Going from 40,000 houses to 12 is a machine problem,” he said. “Going from 12 to one is a human problem.” Rivals noticed Mr. Kay’s knack for snapping up plum rental properties and some asked for help. The company he and his partners created to work with them, Entera Technology LLC, is now one of several racing to apply sophisticated technology to Wall Street’s house hunt. Progress Residential, which has built the third-largest pool of rental homes in the U.S., says its proprietary technology can find properties fitting its investment criteria within minutes of their listing. Following the housing crash, Amherst Residential, which has purchased and manages about 20,000 rental houses, adapted its existing system for valuing mortgage-backed securities to churn out acquisition leads, estimate renovation costs and predict rental yields. A.J. Steigman, a former child chess champion and investment banker, won funding and a prominent business-school competition this spring for a plan to use pattern-recognition software to identify mispriced homes. “The financial crisis created a catalyst for a lot of institutional capital and minds to tackle the opportunity, but technology is what really transformed this into a business,” said Drew Flahive, Amherst Residential’s president. In Amherst’s Manhattan office, employees search screens showing available homes in each ZIP Code. At the click of a mouse, the projected rental yields pops up above each property on a map. The estimates arise from a multitude of inputs, including renovation costs, which machine-learning tools constantly adjust to account for the outcomes of completed jobs on similar properties. Amherst has invested more than $100 million in the system, which has helped the firm pin renovation estimates to within about 5% of actual costs, down from the 20% overruns that were routine a few years ago, executives said. For Entera, the technology became the business. Mr. Kay and his partners have been selling the Texas homes they bought after the crash to fund Entera’s transition to a software company, reasoning that their specialty was big data, not collecting rent. Plus, their rivals had much more to spend, and there is a potentially huge market of smaller investors for the company’s services. Early customers included American Residential Properties Inc. and Colony American Homes Inc., which are now part of American Homes 4 Rent and Invitation Homes Inc., respectively. Entera continues to be among the technology providers to Invitation, which owns more than 80,000 houses. Like a dating app, Entera starts by asking clients what they want. Besides screening for easily quantifiable characteristics like age, number of rooms, square footage, school district, property taxes and flood-zone status, it also attempts to measure qualitative aspects and uses algorithms to predict future value. The mere act of shopping on Entera’s platform—including saying no to some prospects—informs the artificial intelligence, which refines its hunt to suit each investor. “The machine will notice they keep rejecting houses on a busy street,” Mr. Kay said. To determine whether a house has a sunny kitchen, Entera first taught a computer what a kitchen looks like by feeding it tens thousands of photos of indoor cooking spaces and telling it, “This is a kitchen, this is a kitchen, this is a kitchen,” Mr. Kay said. The same was done for brightness and its sources: windows and light fixtures. Entera first taught a computer what a kitchen looks like by feeding it tens thousands of photos of indoor cooking spaces and telling it, “This is a kitchen, this is a kitchen, this is a kitchen,” Mr. Kay said. Entera first taught a computer what a kitchen looks like by feeding it tens thousands of photos of indoor cooking spaces and telling it, “This is a kitchen, this is a kitchen, this is a kitchen,” Mr. Kay said. Once the computer got the picture, it started scanning listing photos. It also pores over written property descriptions for keywords. When more detailed information is available, like the location of the kitchen within, the software sizes up the house’s orientation and looks for any obvious obstructions to light entering, like a big tree outside or a building next door. Want a chef’s kitchen? The computer will hunt for multiple sinks or a second refrigerator, and possibly compare the square footage to that of the rest of the house, Mr. Kay said. Several factors go into predicting financial returns and future value, including proximity to a Starbucks, yoga studio or tattoo parlor—and whether a tattoo parlor signals a neighborhood on the upswing. It probably does if exercise studios and coffee shops are nearby, Mr. Kay says. Entera handles demographic data delicately to avoid violating the 1968 Fair Housing Act, which prohibits discrimination by lenders, sellers and landlords based on race, religion, sex, family status and disability. It applies also to real estate agents and others who facilitate housing deals. Technology’s rising role in the real estate market has sparked debate about how to uphold the law. The Department of Housing and Urban Development last month brought a complaint against Facebook Inc., alleging it violated the Fair Housing Act by allowing landlords and sellers to steer housing ads away from users who expressed interest in handicap accessibility, parenting, or particular countries or religions. In all, Entera attempts to catalog about 850 characteristics for each property as well as thousands of other data points detailing the neighborhood, the home’s location and financial information. The software suggests an offer price based on nearby sales activity. It’s even plugged into Home Depot ’s website, so it can spit out remodeling budgets based on the finishes and appliances with which each investor outfits its properties. Once an investor chooses a house, humans take over: Entera dispatches a representative to double-check the property’s condition and complete the sale.
...Several factors go into predicting financial returns and future value, including proximity to a Starbucks, yoga studio or tattoo parlor—and whether a tattoo parlor signals a neighborhood on the upswing. It probably does if exercise studios and coffee shops are nearby, Mr. Kay says
Entera handles demographic data delicately to avoid violating the 1968 Fair Housing Act, which prohibits discrimination by lenders, sellers and landlords based on race, religion, sex, family status and disability. It applies also to real estate agents and others who facilitate housing deals.
Commercial leases don't have to abide by any of that shit. Yer damn tootin' I ran all sorts of demographic data in establishing where to drop. ESRI will give you reports on gob-smaking amounts of demographic data in 15-minute isochrones. They'll bloody tell you how much toothpaste any given ZCTA buys.
Well, shit - you can pay for an ESRI report while looking for commercial leases. Or just buy it. It's entirely possible that as a commercial business venture, there's nothing forbidding you from buying in whatever neighborhoods suit your demographic needs. Where the FHA gets up in your grille is where you choose who to rent to, and you farm that out to a million penny-ante property managers who may or may not even know who their ultimate masters are. Some of us are cheap bastards. Some of us measured the drive-time from any given birth center in Los Angeles to the nearest Whole Foods, then measured the distance from any given birth center in Seattle to the nearest Whole Foods, then looked for Whole Foods in Seattle without birth centers near them. And then we look at the ESRI reports. And then we get veen to run python scripts on census data to give us births per year per ZCTA for college-educated white women between the ages of 18 and 35. And when all the data lines up we do a happy-dance. You'll laugh. I back-checked the data (before ESRI, before veen) by running a correlation between birth centers and Whole Foods, then midwives and yoga studios. Then to prove a negative correlation I went with midwives and Five Guys Burgers & Fries and birth centers and audiology centers. I are scientific.