The era of Big Data

15 years into the information age, Big Data is quickly becoming the lingua franca of the business world. Are real estate brokers keeping up? How does data play in your office model?

Big Data — you’d better get to know it

In his great dystopian novel, 1984, George Orwell famously remarked: “He who controls the past controls the future.” That is precisely what the business leaders of today are hoping will be accomplished through the use of Big Data.

If you have your ear tuned to the business world you will quickly find we are now entering the era of Big Data. In fact, New York Times columnist David Brooks has termed the dominant philosophy of our day as Dataism.

Related article:

“The philosophy of data” from the New York Times

Big Data has been touted as the new engine driving the next phase of American economic growth, with real estate transactions playing an integral role. But what is it, exactly?

Surely businesses big and small, large scale real estate brokerages to moonlighting agents, have long used some form of data in their daily operations. Picture the chalkboard in Glengarry Glen Ross!

Related article:

Glengarry Glenn Ross from Reference.com

But Big Data is different, however.  It is uniquely now. We are just beginning to understand how to use the billions of bytes of data at our fingertips as the age of information enters maturity. For a managing real estate broker, this data will be marshaled to include:

• generating leads of myriad variety;
• brand development and marketing;
• creating agent assignments; and
• developing team strategy,

all of which is made possible through centralized data gathering and analysis.

Along with our increased stock of readily available data, the art of gathering and analyzing data is also changing. Data used to be thought of as a stock of information — a static repository of numbers that reveal past performance and yesterday’s trends, as MLS data is consistently presented.

As data becomes available more quickly, often in real time, it is more important to pay attention to what Big Data expert Tom Davenport refers to as dataflows.

Related article:

“How ‘Big Data’ is Different” from MIT

A dataflow refers to the behavior of one set of data in context with other sources of data. For instance, we know an increase in the employment rate creates more end-user demand for real estate. But what happens when this employment data is placed in conversation with interest rate fluctuations? Ultimately, both put substantial pressure on real estate prices. It is the ability to read this flow of information that will determine one’s success in anticipating prices.

One must look at the flow of data from multiple sources in order to gain an understanding of the forces forging the big picture and thus driving tomorrow’s action.  Binary up and down, yes and no, this and that thinking might be a fine approach for a sales pitch, but it has no place in this world of data.

This moment in Big Data

Companies have high hopes for Big Data, but are currently dissatisfied with the results, according to a recent study by the Harvard Business Review. The survey asked Fortune 1000 companies about their experience with Big Data. The majority had great expectations:

• 85% reported Big Data initiatives in the works;
• 85% of the initiatives are being spearheaded by C-level executives; and
• 80% believe that their Big Data initiatives will impact multiple income sources.

Although hopes are high, the present moment in Big Data applications does not seem to be up to par:

• 15% of respondents ranked their present access to data — the gathering — as merely “adequate”;
• 21% ranked their analytic capabilities — the thinking — as just “adequate”; and
• 17% ranked their businesses ability—a broker’s control – to use data to transform their business as “more than adequate.”

So where’s the disconnect? The discipline of Big Data gathering, analysis and controlled use is new and going through a process of trial and error. What’s more, the data itself isn’t the only thing that’s changing; the analytic approach to its use is changing as well.

Changing the status quo of a broker’s business operations is always difficult.  Brokers need nimble, change-oriented managers to shift the mindsets and actions of employed brokers and agents.

The take-away here is important: it’s not too late to implement solid data gathering and analysis plans into your real estate practice. Once you figure out the data you need and set out to get it, managing the controlled use of it is equally pressing. With that said, starting NOW is imperative to maintaining competitive relevance.

Related article:

“Who’s really using Big Data?” from the Harvard Business Review

Not so big on Big Data

Not everyone is singing Big Data’s praises. A recent Op-Ed in the New York Times deftly lays out the deficiencies of the newly burgeoning trend.

The central critique of the new data movement is that it ignores human intuition and reduces unpredictable human behavior to a mathematical model. That can be dangerous indeed; animal instincts are always at work in humans. Many analysts blame the 2008 financial crisis on a failure to recognize the behavioral impact of Big Data, not Big Data itself.

Wall Street was among the first to implement the highly sophisticated mathematical models said to predict the future based on the past. But such models failed the global financial system in a big way in 2008.  Many Wall Street cases offer a false sense of market stability and risk through their deceptive bundling of mortgages then fractionalized and peddled to millions of investors.  Their fancy algorithms were used out of context and without an understanding by Wall Street of such truisms as the real estate market’s mean price level.

Of course, the failure was not in the data itself. The humans behind Wall Street simply ignored the data’s import, or willfully misinterpreted it to meet their own financial goals.

The nature of Big Data is massive, and messy.  Key to avoiding the chaos that comes with the Big Data revolution is to maintain a critical approach. When positioning data at the center of a real estate brokerage business model, one must keep five questions in mind:

• How do you define the challenge you want to resolve?
• What data do you need?
• Where do the data come from?
• What are the assumptions behind the analysis the data is subjected to?
• How is the analysis (model for reviewing the data) different from reality?

Where to begin

We can’t all hire rocket scientists or purchase the latest cutting edge software in order to grow our real estate business. Fortunately, the best data for real estate just also happens to be the simplest.

Aside from starting with good numbers, any attempt to integrate data analysis into your real estate operation ought to include:

• dataflows; and
• healthy skepticism.

To get started, we’ve developed this dataflow to provide a clear picture of how a few of our real estate market charts work together. This diagram puts several data-based charts into conversation with one another, showing how these primary drivers of the real estate market work together.

The dataflow above puts several real estate market charts into conversation with one another to draw a picture of demand in the real estate market. Demand is at the very core of the market, determining sales volume and prices.  This is a near absolute truth in a market of zero-bounded interest rates, as we have had and will have for a few more years. Supply takes a distant back seat in periods of compounded recession and financial crisis environments such as we now have.

There are two market factors that affect demand:

• jobs; and
• interest rates.

The Fed ultimately controls both.

Employment and income-related buyer purchasing power as steered by interest rates on mortgages have a direct correlation to the performance of the real estate market (sales). Follow the links to each individual chart and the accompanying analysis to get a clearer picture of demand in the California real estate market.

Of course, all quantitative analysis of human behavior – data – includes its assumptions. Also, other ancillary economic factors are at work here.

What assumptions do you see at play in this diagram, if any? Let us know your take in the comments section below!