Predictive Analytics in Real Estate
The power of AI-driven predictive analytics has much to offer to real estate. Let's see how this sector can get a jump on an uncertain market through AI.
The real estate sector accounts for a significant slice of all the wealth on the planet, and of the power that accompanies that wealth. Though the global property market is calculated to be the third largest world industry after insurance provision and pension funds1, the value of all existing land and property was estimated in 2016 at US$217 trillion, with residential property accounting for 75% of that2. The figure has risen since, while the global pandemic and the move away from cities has only fueled the real estate sector further3,4.
The numerous markets and sub-markets that grew up in tandem around this huge locus of money have been seeking certainty and consolidation through technology for centuries, and were vanguard adopters of new forecasting methodologies5, construction techniques6 and rent-enabling technologies7,8.
However, the sector has recently become more conservative, as the pace of innovation has quickened9, with a smaller and more adventurous tier of organizations turning to AI software development to formulate predictive analytics solutions capable of addressing the new challenges of the market.
In this article we’ll take a look at current implementations of AI in real estate when it comes to predictive analytics and at some of the possibilities that new research might offer for the sector.
PropTech: The Growth of Real Estate Technologies
Between 1954 and 2003, the real estate tech sector was relatively stable. Traditional metrics and statistical models provided comforting continuity for the market, and innovation was relatively rare, limited to the periodic availability of new or improved metrics, or to the generalized digitization of the market after the millennium10.
As machine learning technologies grew in capability throughout the early 2000s, related growth in property technologies (PropTech) became noticeable; but after the advent of GPU-accelerated machine learning techniques around 2010, the level of commitment deepened considerably: