Do buyers really determine the sales price?
Debra Hitchcock-Gale is a leading and trusted agent when it comes to buying and selling homes in the Northwoods of Wisconsin. From waterfront cabins, vacant lots & land and residential homes; her unparalleled industry knowledge, experience, and local expertise will help guide you in the right direction as you navigate the world of real estate. Read below to learn how buyers are affecting sales price and how today’s AI-powered pricing models will play an increasingly important role in property pricing, and they could solve two of agents’ thorniest problems: contingent sales and price reductions (BY BERNICE ROSS).
On the heels of our first-ever Agent Appreciation month, Inman is leaping into February with our Residential Finance theme month. Join us as we investigate how buying and selling a home is changing, from companies backing consumers in new ways to integrated services that handle the entire transaction.
For many years, I’ve made the analogy that the real estate market is like the stock market: It’s the buyers — not the sellers or the agents — who determine the price. Whether you believed this in the past, this is certainly no longer the case today.
At Inman Connect New York, I had a chance to chat with Allan Dalton, the CEO of Real Living, senior VP of Berkshire Hathaway Homes Services, and past CEO of realtor.com.
Although we talked about a wide variety of topics, what really jumped out at me was our discussion about who determines the price. Dalton directly challenged my belief about the buyers determining the price.
AI-powered algorithms have nosed their way into the pricing equation
There are a number of factors that determine the price over and beyond the market and the buyer’s offering price. Not only do the sellers have to agree to the buyer’s price, but also other events such as appraisals, inspections, natural disasters, etc., often result in price adjustments once the property is under contract.
There’s another element that is rapidly becoming a major influence on the pricing equation — artificial intelligence-powered algorithms that are being used to price portfolio purchases, as a cross-check in the standard residential appraisal process, as well as a critical component in establishing values for those using the iBuyer model.
Is Zillow your new BFF when it comes to price reductions?
At CEO Connect, Zillow‘s Chief Industry Development Officer Errol Samuelson alluded to something that was an undercurrent throughout ICNY, namely that AI-powered algorithms are playing an increasingly larger role in determining the ultimate closing price of properties.
“We have an algorithm which figures out when it’s time to drop the price and that algorithm sends a message to the agent,” he said.
Many consumers trust Zillow’s prices more than they trust those from real estate professionals.
Can you imagine turning the tables on a seller who told you, “Zillow says my house is worth more” and then advising them at a later date that “Zillow says it’s time for a price reduction”?
Having a third party such as Zillow initiate this conversation could be pure gold. Moreover, if the seller’s price reduction doesn’t increase Zillow’s traffic on the property, the algorithm sends out another notification that the price needs to be reduced even further.
Are algorithms being used to set iBuyer prices?
Many industry experts view iBuyer offers as setting the floor on prices. In fact, the public seems to have come to this conclusion as well. The issue is how are these prices determined?
According to Opendoor, it relies on three things to determine how it values your home:
- Your inputs about your home’s condition, features and updates
- Its robust data model
- Its team of local pricing experts
Based on Opendoor’s approach, local pricing experts still pay a key role in setting the correct price even when an algorithm is used.
Josh Team, the president of Keller Williams, noted that while 12 percent of KW buyers inquire about using an iBuyer model, 96 percent of these sellers end up using a traditional model to sell their property.
In this case, the agent plays a key role in providing an alternative to the iBuyer model, an approach sellers overwhelmingly choose when they’re presented with an informed choice between the iBuyer and traditional sales model.
Will ‘hy-buyers’ ultimately dwarf iBuyers?
Enter what Mike DelPrete has called, the “hy-Buyer” (hybrid iBuyer) model. At ICNY DelPrete described the hy-Buyer model as including: “Companies that are doing things adjacent to iBuying — purchasing and selling homes, helping people move more quickly, etc. — but that don’t fit the more conventionally narrow definition of an actual iBuyer.”
Hy-buyer models that provide the greatest promise are those that solve the contingent sale problem. Examples include Flyhomes, HomeLight and Orchard.
Although these models vary somewhat, they allow a buyer to purchase their next home all-cash without having to first sell their existing home. Instead, the hy-Buyer purchases the home, qualifies the buyers for a mortgage on their new purchase and sells their previous home.
In other words, these models eliminate the contingent sale problem.
In this sponsored post on Inman, HomeLight explains how its hy-buyer model works once it acquires the property (note that hy-buyer is not a term used by the company):
“[HomeLight] uses the traditional listing process with a top agent in HomeLight’s network to sell the home and share 100% of the profits with the original owner. Unlike other iBuying and trade-in solutions, this enables the homeowner to fully maximize their home’s value.”
On its site, HomeLight also emphasizes the importance of the seller’s input in this process:
“Most home value algorithms don’t know the little things that make your home different. That’s where you come in. Pair your answers to a few questions with housing market data from multiple trusted sources and we can predict your home’s current value with far greater accuracy.”
Algorithms may play an increasing role in pricing, but agent feedback is still needed
Because today’s AI-powered pricing models draw from thousands of data points, they will play an increasingly important role in property pricing, especially for lenders and for Wall Street where portfolio purchases are involved.
For agents and brokers, these models also provide an avenue to solve two of the thorniest problems the industry faces, contingent sales and obtaining price reductions.
No matter how powerful these models are, however, they can never substitute for the boots-on-the-ground that a local real estate professional can provide when it comes to determining the ultimate sales price of the property.
Bernice Ross, President and CEO of BrokerageUP and RealEstateCoach.com, is a national speaker, author and trainer with over 1,000 published articles. Learn about her broker/manager training programs designed for women, by women, at BrokerageUp.com and her new agent sales training at RealEstateCoach.com/newagent.
Have questions about buying or selling? Contact me today!
Thanks for reading!