With that phone in your pocket, that social media site you post to and the calls and messages you send, did you know you are constantly broadcasting where you are, what you are doing and who you interact with? And to top it off, the algorithms used by big web companies such as Google and Facebook are both listening and turning your constant stream of activity data into predictions about what you like, what you are doing and, most importantly, what you will buy.
So what happens in the near future when algorithms are used to find out who wants to sell their house, and who is looking to buy? Could the mere act of searching for real estate on a phone, or simply asking for quotes from painters, be enough to flag someone as likely to be listing their property? Perhaps these occurrences will trigger a cascade of connections from the large web companies tracking you.
The seller might be connected directly to buyers in the area, with the web company happily charging a connection fee but otherwise not involved. Or a group of buyers might be able to bid online for the property after arranging to see if first with the seller. In the future, this may result in an agent becoming redundant and being pushed out of the process.
Does that all sound like science fiction? Well, maybe. But for a moment think about the taxi industry and the evolution of Uber. Uber arrived with little fanfare; a simple app allowing the passenger to order a taxi or limo conveniently on their phone. Great features in the app link passenger to driver, helping identify and geo-locate each other to allow for easy connection in both time and location. Uber worked well for the professional driver and the time-poor, service-poor cab passenger. Better service, predictable timing and flexible billing worked for all.
UberX and UberPool then arrived and allowed almost all types of journeys to be shared or sold, both from private drivers and where passengers ride-share with each other and split the costs. The algorithms behind Uber allowed for high levels of service and linked each passenger or group of passengers to a driver simply and effectively. The middleman – the taxi industry and in some cases the professional taxi driver – lost out because the buyer was linked directly to supplier.
Airbnb similarly shares accommodation of various types for rental.
In my previous articles I have brought up various ways in which CoreLogic RP Data has built metrics to predict properties likely to list, and to help agents acquire those listings. I have also separately talked about how we create valuation models that are used to provide an indicative value of a property. We, too, are in the algorithm business – or, more formally, we create “analytics” – that help predict values of properties, behaviours of sellers and the like.
The difference, though, is that we recognise that selling a property is different to hiring a taxi. The buyer and the seller need professionals they can rely on to guide them through the process. We recognise that the professional real estate agent is integral to the property market.
But that doesn’t mean we don’t recognise the power of algorithms, or analytics. Targeting and predicting who will sell and who will buy (for example, renters) makes the market and our customers more efficient, plus it helps sellers and buyers achieve their goals quickly.
As an agent it is important to use the algorithms companies such as CoreLogic RP Data provide to keep ahead in the algorithm game and to be more efficient at getting the listings. Big data is here, but I think it will help, not hinder, the professional.