THERE are many things that complex algorithms and artificial intelligence (AI) do very well. It turns out that home flipping — probably the most common form of speculative investment for mom-and-pop investors around the world — just is not one of them.
For most families, from Asia to the Americas and Australia, home is their biggest lifetime investment. There are a lot of tried-and-tested methods to buy and sell homes. A seller might get his property evaluated, hire an estate agent and list it for sale. Buyers could hire an agent who might show them the sort of homes they are looking for in desirable neighbourhoods. The process can take a few days, weeks or, indeed, even months, as many frustrated sellers and buyers of property will tell you.
Traditional full price realtors in the US can make between 5% and 6% on the sale of every home: split between a listing broker, selling broker, seller’s agent and buyer’s agent. Online real estate firms cut this down to 4% — or even lower, in many cases. There is a lot of money in buying and selling homes because there is a lot of friction in the entire process. If you have ever sold a home and have had dozens of strangers come in and inspect every nook and corner over several weeks only to return for a second or third look at your home, you probably know what a hassle selling through a human property agent can be.
What if a proprietary computer algorithm helped you buy a nice home at a bargain and then helped you sell it at the highest possible price when you are ready to move on? What if the whole process was completely automated using state-of-the-art semiconductors and great software running on super-fast computers leveraging huge data about local property markets that had been accumulated for decades?
Data is the new oil and the streets are paved with gold for whoever can monetise it in as many ways as possible. There are already driverless cars and robotaxis on trial in San Francisco and Phoenix suburbs. So, how difficult can it be to make money buying and selling homes using sophisticated algorithms, AI and machine learning?
Online property portals are almost as old as the internet itself. Initially, they just replaced the classified property sections of newspapers. Over time, they morphed into online real estate agents as well. So, aside from listing fees or old classified advertising, they began earning commissions on sales as well. Unlike classified pages of newspapers, online listings are media-rich. There are tons of beautiful photos of homes and neighbourhoods, 3D scans of the interiors as well as water and skyline views for house hunters to browse. Americans call the phenomenon of gawking at real estate photos “property porn” that families spend hours on.
At the core of online real estate is a treasure trove of data. One company, Seattle-based Zillow Group Inc, has become the icon for house hunters in the US. Earlier this year, Zillow was even featured in a Saturday Night Live comedy skit.
Having collected data for years, online real estate firms such as Redfin Corp and Opendoor Technologies Inc suddenly decided they could make some extra dough on the side by flipping homes. Why not just go in, buy homes by quickly giving sellers a quote, take it off their hands, then turn around, splash on a coat of paint, fix a few things and sell it by re-listing it as a newly refurbished home? There is a nice profit to be made on the flipping.
The phenomenon is known as instant buying, or iBuying. For home sellers, iBuying promises a selling experience that is quick, easy and hassle-free. Over the past six years, American iBuyers have flipped tens of thousands of homes for profit.
Opendoor, Redfin and OfferPad Solutions Inc have for years used algorithms to make sellers of homes an instant cash offer for their properties in a matter of days. You list your home on an online platform, algorithms discover it, the iBuyer slices and dices the data you have put up there about your home and you get an offer for your property. It is fast, painless and non-discriminatory. The algorithm does not know the colour of your skin, your ethnic group or the language you speak at home.
I have heard stories of people who listed their home on a Monday, sold it on Wednesday and were told that the sale proceeds would be credited to their account the following week. Sure, that is not as fast as trading stocks for free on Robinhood but, for something that took a lot of hassle and time, iBuying feels almost as quick.
iBuyers have all the data needed to sell, including what sort of homes people are searching for, which neighbourhoods are suddenly hot and what homes people are clicking on and liking. They have all the city, regional and national data that goes back years, which even the best property agent in the world with an encyclopaedic memory could not match.
Redfin, Zillow and their ilk know what sort of photos buyers click on or what families in certain neighbourhoods look for. When you list your property for sale, you probably just post a few random photos of your home and hope the buyer will somehow fall in love with the property. iBuyers, or their algorithms, know exactly what photos to post, what sort of lighting to use when the home for sale is being photographed or what angle the photographer should use when highlighting the kitchen island, living room and front lawn.
Zillow dived into iBuying because co-founder and CEO Rich Barton, a serial entrepreneur and former Microsoft executive, saw competitors such as Redfin and Opendoor making a lot of money flipping homes. If Redfin could do it, Zillow could do it far better. Get great photos, and buyers will take that home off your hands quickly.
Barton helped found online travel giant Expedia Group Inc within the Seattle-based software powerhouse after convincing the then top honchos Bill Gates and Steve Ballmer that, eventually, home buyers would buy or sell houses and apartments online. After Expedia was spun off as a standalone firm, Barton founded travel photography site Trover, which was later acquired by Expedia.
Last week, Zillow announced that it was shutting down its iBuying operations, having lost US$381 million (RM1.6 billion) home flipping in the last quarter.
Zillow, which fired a quarter of its 8,000 staffers, has since received offers from institutional buyers such as American Homes 4 Rent and Invitation Homes Inc, real estate investment trust or REIT-like investors that buy homes in bulk then rent them to pay their yield-hungry shareholders.
“With the price-forecasting volatility we have observed and now must expect in the future, we have determined that the scale would require too much capital, create too much volatility in our earnings and balance sheet, and ultimately result in a far lower return on equity than we imagined,” CEO Barton was quoted as saying last week.
Mother-of-all home booms
The irony is that one of the largest home flippers in the US was quitting smack in the middle of the mother-of-all booms. Over the past year or so, the country has been experiencing the biggest house price boom in history. The Case-Shiller National Home Price Index, a key barometer, was up nearly 20% in the 12 months to August 2021, the biggest one-year increase in the country’s history.
The National Association of Realtors says home prices recently marked 116 consecutive months of year-on-year price increases. Goldman Sachs expects US home prices to increase another 16% over the next 12 months. The best time for flipping homes is when prices are rising as they have been in the US for the past decade. Covid-19 only helped accelerate the trend as interest rates fell to record lows. There has never been a better time to sell a home (or, for that matter, a worse time to buy one) than during the recent pandemic.
Yet, algorithms failed to work for Zillow. While there was plenty of data that online real estate firms could slice and dice, Zillow just was not getting the competitive advantage it needed to flip the homes it was buying for a quick buck. When humans estimate the price of the home you have put up for sale, they tend to walk around the house because they know what to look for — termites, plumbing, roofing, noise pollution, smelly basements or stuff that algorithms cannot spot in photos, Matterport 3D scans or text.
Technology aside, Zillow had trouble finding workers who could do basic repair work and deal with soaring raw material costs due to supply chain issues. Contractors were charging an arm and a leg for a paint job or to fix up little things.
Any home flipper will tell you that if you pay more to fix things up, you need to charge a lot more to break even. Even in a hot market with prices up 20%, if you are paying 40% more to fix it up before flipping, it will eat into your margins. Indeed, as Zillow found, in some cases it was difficult to even sell at cost. If you are an institutional home flipper, you just cannot afford to lose US$30,000 on every other home you sell.
Algorithms also failed to anticipate the recent slowdown in house price increases. Even as the pace of home price appreciation slowed, Zillow kept buying new homes, which left it with huge unsold inventory.
An iBuyer, which anticipates on average a 20% annual increase in prices, can afford to buy a property for US$400,000 and spend US$20,000 to do it up because, eventually, it can sell it for between US$450,000 and US$480,000. If the cost to repair and repaint increases to US$40,000 and the price increase slows to 10% or 8%, the flipper has no margin for error.
Ivy Zelman, CEO of consultancy Zelman & Associates, who called the top of the housing market in 2007, believes overbuilding in the single-and-multifamily home sector, clouded by dual ownership and institutional capital, could lead to a correction next year as interest rates rise.
No longer the prima donna of online real estate, Zillow now needs to pivot before it becomes the next Yahoo! or Myspace, online portals that were powerhouses before Google and Facebook became the dominant search engine and social media. Still, real estate is one big sector that remains ripe for disruption. Clearly, any tech company that takes some friction out of the arduous process of buying and selling homes stands to make a ton of money. The future of home buying is neither just traditional real estate agents nor sophisticated computers and software but a combination of human verification and algorithms.
Assif Shameen is a technology and business writer based in North America