iBuyer’s Use of PropTech to Make Large-Scale Cash Offers

Abstract

The expansion of iBuyer’s use of PropTech to major housing markets raises a series of questions for both buyers and sellers when making instant, all-cash offers. This study uses a sequence of experiments to identify the proper implementation of existing behavioral real estate concepts to improve the iBuying process, a burgeoning area of residential real estate. We find strong evidence of anchoring for all-cash offers in that sellers are nearly twice as likely to transact when they are first presented with the net proceeds offer price (market value minus costs) rather than starting with the higher gross market value offer price. After the sale, seller regret aversion becomes strong when the seller’s house is subsequently sold for 10% or more than the all-cash buyer paid, but regret aversion is mitigated with communication of the improvements made to enhance the selling price. We further find that sellers do not know which all-cash buyer’s Automated Valuation Model (AVM) is the most accurate and are therefore much more influenced by brand awareness than model sophistication. Finally, while the extant literature has examined offer price strategies for home sellers, this is the first investigation of buyer offer price strategies. In stark contrast to selling strategies, pricing strategies do not matter when making an offer to buy.