With home prices continuing to soar in many places around the country, the real estate industry as a whole has been upended over the past two years in ways many of us would scarcely have imagined only two short years ago. While that much is obvious, the biggest changes in today’s markets are reflections of what we as practitioners have learned along the way.
Whether it’s large-scale investor presence in the build-to-rent residential market, still-scorching demand in many of the vacation rental destinations or simply continuing shifts in the work-from-home world, the technological process of buying, selling or renting a home is under pressure like never before. That pressure is in turn driving market disruption at an astonishing rate, and one area seeing frenetic change is the aggregative technology behind pricing a home for any given buyer at any given time.
For context, for many years the pricing of a home for either purchase or rent was driven by the traditional demand and supply interaction; that is, a home was priced at a comparison level as compared to other similar homes (we’ll call this a supply-side pricing model). Technology was mainly used here to aggregate comparisons relatively quickly and then to use increasing data sets to highlight subtle value differences between particular homes in a given market. In short, it’s worked and worked well for a long time.
With the crush of Covid-19-related home demand coinciding with relatively large-scale development of machine learning technology, however, there are important, if quiet, developing assaults on the traditional supply-and-demand model ongoing, and those tests are driving the future of real-time pricing.
Said differently, let’s go back to the model of supply-side pricing — in short, the home price is based on the home first as compared to other similar homes. Now let’s compare that model with the newer emerging model — we’ll call this one personalized pricing based not necessarily on the home first but on the potential buyer first, the time frame involved and the specific attributes of a home that are appealing relative to the person and the time frame. Let’s call that personalized pricing as compared to supply-side pricing.
Now, it likely comes as no surprise that different homes are worth different prices to different people for different reasons at different times. That much is fairly obvious, but the challenge, for so long, has been taking that clear idea and finding with any real probability that one person at the right time with the right attribute in a scalable way. That’s where machine learning is breaking new ground, particularly at scale across large volumes of transactions and data sets.
Through the right kinds of data warehousing, based on collected variables from potential buyers and renters, machine learning technology is making it increasingly possible to think about buyers and sellers in terms of probabilities. Those probabilities count, though, when thinking about the strength of any given market relative to any given product — the more purchase-probable the market, the more price-accurate the product is relative to the person buying it. In other words, machine learning technologies are making predicted price outcomes much more likely within addressable markets and time constraints.
In addition to the personalized pricing capability, the other important aspect of machine learning is simply speed to market — adjusting and aggregating data used to take a lot of manual time and machines (and the algorithms within them) are rapidly automating the process in a way that human beings simply cannot do in any commercial way. When we combine personal purchase knowledge of potential buyers with speed in trend analysis, we have a price disruption capability that we are only beginning to see.
To be fair, the potential for profound positive changes within the industry should be placed strongly in context with the equally as profound challenges within commercial machine learning: namely, maintaining appropriate data privacy for smartphone/app users and also highlighting fairness around consumer equity — meaning, in other words, the balance between probability of purchases not being skewed toward specific demographic groups.
As we look to the future to assess the more strategic impacts of large-scale machine learning technology, it’s useful to note that despite the awareness around the potential for this kind of automated technology, a recent survey by Duke University‘s Fuqua School of Business suggested that only about 3% of survey respondents indicated their firms deployed AI/ML in their marketing regularly. Clearly, we are only seeing the early edge of this transformative technology reach our lives as we consider the buying and selling of real estate.
Churchill once remarked that a moment was only the end of the beginning in a larger global struggle. In short, with the explosion of the real estate market intersecting as it did with the initial deployments of machine learning technology, we have perhaps only seen the end of the beginning of price disruption on a global scale. The disruption may very well have been born in real estate pricing.