Residential Real Estate is the biggest asset class in the world. In the United States itself, the residential real estate market is in aggregate worth over $30 Trillion. As such, it dwarfs other asset classes and industries.
In many ways, this is widely understood. The US Government releases Housing reports. The Government and large financial institutions carefully manage rules related to mortgages and mortgage rates. An entire financial industry has grown, predicated on securities themselves back by mortgages.
At a personal level, for most individual families, their residence constitutes the single largest economic transaction they’ll make. These families understand that the choice of home- location, cost, and other features- has huge downstream implications, including where the kids go to school, the types of civic services and amenities they’ll be able to avail of, and what sort of lifestyle they’ll be able to live.
Yes, the importance of the real estate market is fundamental to the entire economy and to the lives of each family. Booms and recessions are both tied to the opportunities and woes of this fascinating market.
Still, despite its importance, it remains opaque and complex.
To address this, there has been a burgeoning of technology companies seeking to optimize some or many aspects of real estate valuation, transaction, and decision-making- for consumers, mortgage professionals, banks, and others as well.
These companies – we consider Quantarium among them – all rely on data as their lifeblood. Arriving at the proper valuation of not only one residential property but millions of them requires enormous amounts of data, gleaned from hundreds of sources. More than that, the data has to be accurate, clean, decipherable, interoperable, and usable in order to enable decisions. Patterns have to be discerned at scale, with enormous speed. To do this, Machine-Learning and Artificial Intelligence are key.
This surfeit of real estate data- powered by these new technologies, has many downstream uses and applications. Any production or economic process that is derived from this data is fair game. Real estate data is indeed a bellwether.
It doesn’t require too vivid an imagination to identify some of these downstream use cases. Imagine that you are in charge of opening new stores for a luxury retailer or high-end restaurant chain. Clearly, the “location, location, location” you pick is fundamentally tied to house-prices and their fluctuations because your clientele likely skews steady and affluent. If you are determining where to open a new office or plant, the reigning home-prices in the region impinge on our employees’ ability to afford the new location. If your government job has you tasked with creating a “livability index” for cities in the United States, delving deeply into real estate data is necessary.
You get the point.
There are also a variety of offshoot-uses of this data even in the real estate industry. If a bank holds a million mortgage notes, being able to determine who might refinance, default, or buy a second home and when they might do any of these, is golden. Understanding whether you should divest from one area and reinvest in another could be the difference between wild profits and bankruptcy.
Real estate data is thus a relevant category for all manner of organizations. The advances in AI and data-technologies can put the power derived from understanding this data right at your fingertips.
