Daniel Calano, Prospectus, LLC
As I said in the first part of this article, two weeks ago previous writing, Big Data is not just data collection. It is data correlation focused on better, more efficient decision making (i.e., Big Algorithm). The revelation today is that now we can do something with data. Collection is done with purpose.
Part I of my article explored the state of the art today. There may also be a day when the real estate professional can predict, or find third party sources to predict, the probability and amount of demand for a certain type of real estate, as these types relate to seemingly random data sets such as over population of schools, municipal problems causing tax increases, or perhaps cuts in police departments, and how that will all fit together to be predicative about whether an area would be popular some five year hence. Taking it a step further, perhaps even the broker or the buyer can "cross-tab" all of that information with real time traffic jams, global weather patterns, ice cap melting, public transportation - all seemingly random independent data sets, brought together in a correlative linking to help us better understand what the client needs and will want in the future.
While things seem to be going slowly, there are entrepreneurs trying concepts, that may develop into models to help specific areas of real estate. For example, according to New York City Real Estate News, one new company called "Site Compli" allows property managers to track their buildings' compliance with city laws and codes. The model mines data from many N.Y. agencies to find and monitor violations, thus helping owners stay ahead of budding problems.
Another example is "Block Avenue" which "grades" areas on the basis of parks, restaurants, schools, public transportation, and other metrics. The goal is to link these metrics with customer preferences to help developers decide how/what/where to build. There are many more such ventures already launched or in the wings, some seemingly very helpful, with great potential for helping us make sound decisions. So, keep an eye out.
So what's holding us back from embracing Big Data? To name just a few, there is a normal resistance to change, concern for privacy with data collection, need for more advances in technology, and of course funding and budgets. Additionally, as one real estate blogger has noted, with only 5 million transactions per year in U.S. real estate, as opposed to one million per hour at Walmart's, perhaps in the end we in real estate have less need; perhaps there is less impetus to be predictive. It is also possible that being predictive in real estate is more difficult, less quantifiable, more art than technology, as many transactions are rooted as much in emotion and even whimsy, as they are in rationale.
But also heed this warning: like a few notable industries before us, for example the decline of the newspapers succumbing to digital news, or the music CD obliterated by internet music, the move to Big Data in real estate may seem to occur slowly over time ... until all of a sudden, it happens.
Daniel Calano, CRE, is the managing partner and principal of Prospectus, LLC, Cambridge, Mass.