Economists and planners alike attribute both booms and busts to the goings-on in Real Estate. In addition, for most families, the “house” is not only the largest but also the most important investment since so much of life radiates from where one lives. As such, Real Estate is a fascinating vertical that warrants close attention.
The numbers involved are staggering. In the United States alone, there are in excess of 100 million homesteads. With the recent news of increasing average home values, the sector in aggregate is worth $40 trillion. As such, the housing sector is larger than the US GDP. Not only are the aggregate sums enormous, the transaction frequency is high as well. The NAR estimates that approximately 6 million homes will sell this year. Several million mortgages will be refinanced and tens of millions of homes will be upgraded, remodeled, or generally refurbished. Each of these millions of “events” includes wealth-exchange and transaction. With such an active and fertile ecosystem, requiring and benefiting from real time decision making, it would be challenging to overstate the compelling role of AI and large scale Data Analytics.
Amidst the complexity of this enormous marketplace, even simple questions can be hard to answer. For an individual, the question “what is my house worth?” can generate radically different answers, arrived at by vastly different methodologies. Moreover, there are hundreds of variables that impinge on both the value and the price of a house. From a bank’s point of view, this question has to be asked at scale and answered with speed- “what are these millions of houses worth?”
Enter AI. “AI” is a much-bandied phrase and many claims are made in its name. In the housing market, with the sensitivities, importance, regulation, and complex ecosystem, AI must deliver accuracy, breadth, speed, and scale- simultaneously. More than this, it must be “transparent” and not a “black box.” Assumptions cannot be opaque nor can processes or computation layers. With the fundamental importance of housing to each buyer and seller and to the economy as a whole, the “data” has to be trusted. Huge pools of multivariate data, buffeted by regulatory frameworks and macro-economic changes, require AI to make sense of in time-frames short-enough in which to make decisions.
We approached Quantarium with these principles in mind. We built it natively as an AI company- we do not invoke “AI” as a post-facto bolt-on. Our Managed Data-Set is a compelling differentiator; we see it not only as an asset but also as a form of stewardship to the real estate community in general. We used our expertise in AI, Data, Enterprise Search, and Computation to build an AVM while understanding that the housing market’s needs extended beyond valuations. We thus approached both the technical and business architecture of the solutions to be “platforms” that can extend the existing practices of Banks, Credit Unions, Mortgage Originators, Mortgage Services, Insurance Companies, Publishers, RE Marketers, and relevant Governmental and Civic Entities.
Written mutually with Romi Mahajan