A guest post by: Romi Mahajan, CMO Quantarium

Residential real estate – peoples’ homes – is the world’s largest asset class, tipping the scales at almost $200 trillion worldwide.  This number is staggering to many, including those in the housing industry.  Larger even than the sums involved are the emotions – a family’s residence is likely its largest investment and one from which, so many other life-factors radiate:  Who are your neighbors, what schools do your kids attend, are you safe, how close are you to good medical care, and so on.  Insofar as this is true, the housing sector can never be given too much attention by Economists, Sociologists and even Technologists.  Still, in many ways the sector has been given short shrift.

Consider a matter at the heart of the industry – the value of a particular house.  What appears to be a simple question with a simple answer is not.  Sure, one can look at the basics – how big it is, the year built, comparable houses in the neighborhood and so on.  One can even attempt to factor in other variables – school district, crime statistics, proximity to the beach, and a host of other things.  All said and done, all of these factors are “external” and in many ways “non-specific.”

Let’s pause for a moment.  While these factors are indeed external, we have to ask ourselves a basic question – how do I get specific?  How do I assess the value of a particular house, looking beyond these basic factors and in the process taking into account the condition of the house and the nature of its interior landscape?

For most of us this is an obvious question.  After all, if you put in a lot of money to modernize or refurbish a house, you would expect that its value rises, even if your work and effort is not recognized in the external statistics being looked at for valuation.  If you, on the other hand, paid no attention to the house and allowed it to atrophy, you’d likely expect the value to diminish.

This issue is often “solved” by Appraisers, who theoretically take into consideration all of these interior and condition-based factors when assessing the value of a house.

Now, we enter a world fraught with problems.

For the purposes of this short piece, we won’t get into the debates about the objectivity of Appraisers or even about the shortage of talent that is delaying closings in many large markets (in the US for sure.)  These issues are fertile grounds for discussion, elsewhere.

No, the main issues we intend to dissect here are the issues of scale, speed, and customer experience.

In the US, there are over 100 million residential units.  Now imagine you work at a bank or other institution that originates and/or “owns” millions of mortgages and wants to determine the value of your portfolio in toto?  Imagine, further, that you need to do so every month.  After all, you need to keep track of your assets, make decisions about where to keep houses and where to sell houses, and assess your risk in holding these mortgages.  The issues of scale and cost are enormous.  You certainly can’t send an appraiser to each house.

Imagine a different scenario.  A consumer lives in a city with a very fast market and needs to make decisions on the spot whether to buy a house.  Waiting even a few hours, not to mention days, can mean losing a house.  In this cauldron, determining the true “value” of a home has to be done instantaneously.  Here, the issues of speed are paramount.

Finally, imagine you are a real estate agent with a demanding (and rightfully so) customer who wants to buy a house.  You have visited 10 houses to determine fit and have been disappointed by their dilapidated interiors.  You are not paid for your time, only for results.  If only, there were ways to determine condition and value based on condition in a way that was easy for the customer (in this case, you.)

Enter technology, specifically AI and its offshoot, Computer Vision.  Artificial Intelligence yields a potent set of tools for real estate, starting with valuations.  First of all, AI is “better at the basics” than non AI methodologies.  To get even a basic valuation of 100+ million properties every month is not trivial; with AI, the entire US footprint can be run in hours not weeks.  The idea is simple:  Computers can learn from data sets of a critical mass, then keep improving their outputs as more data comes in.  Machine-learning is just that- machines that actually “learn” and thus can offer results and outputs that are neither obvious nor simply the result of brute-force methods.  AI can thus help with the scale and speed components.

Computer Vision comes in here in a delightful way.  If you look at house-listings, they often come with a multitude of pictures.  Computer Vision can analyze and categorize these pictures- with speed and fidelity- thereby assigning “condition” scores to kitchens, bathrooms, and other hotspots in the house.  In this way, they can help offer a “condition-adjusted” value.

Put all of this together and you get a powerful mix.  Automated Valuation Models (AVMs), powered by AI can provide accurate valuations at scale and with enormous breadth.  Add condition-adjustment, powered by Computer Vision, and you start to see technology giving its due to the vexing problems and incredible opportunities in the real estate industry.

Data is the watchword in organizations large and small. In fact, how an organization frames data is the single most important determination of future success or failure. As some put it, Data is the new “oil,” the commodity of most value in the modern age.

Many business leaders understand this intuitively. As business-users in the organization are forced to make larger number of critical decisions with larger “payloads” on a more frequent basis, the idea that these decisions must be data-driven is at the fore. Gut instinct is fine but gut instinct inflected with timely, contextual, and comprehensive knowledge of relevant data is a winning strategy.

While the idea of being “data-driven” is fundamental and powerful, most organizations fall short. Intentions are necessary but not sufficient. For most organizations, the technology and operational infrastructure that defines their “data” is predicated on notions that made sense in an earlier era in which there were simply less sources of data and less change to existing sources. The “size” of the data question makes for a complexity that is not pre-defined and therefore the solution to the data problem has to be flexible and adaptive. Data infrastructure maturity is necessary in today’s business environment and has 4 basic qualities: Governance, Security, Agility, and Automation.

Without these 4 qualifiers, 2 core facets of the solution are absent- democratizing access to data and liberating IT from the backlog and fatigue associated with constantly-changing business needs. Business-users work in the “NOW” timeframe while IT has its own rhythms. In order to truly be data-driven in a way that scales, organizations must empower business-users while simultaneously freeing IT to innovate. While there are cultural hurdles to this state, the biggest blockers are infrastructural.

Until very recently, good enough was, alas, good enough. The internecine conflict between Business and IT was considered just a fact of life, a “cost of doing business.” With automation technology, business users’ data needs can be managed on the fly and without the need for reactive hand-coding, conferring agility to the business teams and handing time back to the IT teams to innovate and more resources from lower value tasks to higher value tasks. This structural win-win is available today and harmonizes the needs of Business and IT.

If data is the new oil then an infrastructure to capitalize on it is necessary- an infrastructure that is mature and “Hub”-like. While all organizations are different, they are similar in their data needs and the data platforms that win will accommodate diversity and change inherently.

Guest post by:
Romi Mahajan
Chief Commercial Officer, TimeXtender