Last week I discussed three different kinds of data that are important to the real estate industry: property data, customer behavior data and market data. This week I want to talk about size of data.

Geeks refer to this as scale — as in,

Editor’s note: This column is the second part in a recurring series highlighting issues surrounding data and the real estate industry. Tech and real estate industry strategists Gahlord Dewald and Rob Hahn will host the first-ever Inman Data Summit, scheduled July 25-26 in San Francisco. The summit precedes the Real Estate Connect conference in San Francisco.

Last week I discussed three different kinds of data that are important to the real estate industry: property data, customer behavior data and market data. This week I want to talk about size of data.

Geeks refer to this as scale — as in, “Will that scale?” Put another way, can that idea survive if it gets much, much bigger? Or will it crush the organization to maintain it when it gets much, much bigger?

One of the fun topics of discussion among tech and marketing wonks of late is the idea of “big data.” Big data is any data set that is too large to easily manipulate from your desktop computer. It is big data that is driving the interest and funding in cloud computing — the “hard drive in the sky” applications are just a side benefit.

Those of you who read this column regularly know that I’m not a fan of gathering more data just because you can gather more data. The more data you gather, the more challenges you face in storing, sorting and using it — the classic challenges of big data.

But sometimes, in certain applications, having a lot of data is very useful. For example, in biological sciences, using extremely large data sets allows researches to understand more about the human genome. Or in finance, having extremely large data sets helps to identify trends.

From my "I don’t practice real estate but I know a lot of people who do" perspective, the real estate industry appears to be primarily a sales culture instead of a marketing culture. Please feel free to call me out on this in the comments below.

The result of having a sales culture is that gathering big data is a challenge culturally. If individual interest focused on making the next sale (or repeating the conditions that made the last sale) then longer term trending information may be lost: “Don’t waste time finding out where a lead came from — just close the sale.”

This culture has a lot of implications for the industry. I suspect it’s a leading cause of “shiny object syndrome” among practitioners. In terms of data, it means that few organizations gather enough to start unlocking the real value in real estate data.

The data sets that rely on real estate practitioners — property data and customer behavior data — tend to be small data as a result.

Small data is OK. It’s useful for a couple people and that’s nice. But it’s the trend analysis of big data that I think is most interesting for the real estate industry.

Different scales of real estate data

Much is made of the supposed value in property data. U.S. property data, as it exists today, is balkanized into a myriad of small databases spread across the country and is really only of value to the key players in the real estate sale: the person listing the house, the person buying the house, and the people who help make that transaction take place.

Certainly there is a cost in gathering that data. And that cost is what I think is being referred to when I hear real estate practitioners talk about the value of their data. They experience the value of the data directly when they have to take pictures of the property and enter in the data to their multiple listing service interface.

When we scale up from the individual listing to all the listings in an MLS region, we don’t see much value added, apparently. Evidence of this is that many MLSs outsource the database creation and maintenance to a third party — the data at this scale isn’t adding value, it’s adding expense.

Scaling up from there to the listings aggregators, we start to see value added in the form of being able to sprinkle advertising models around the property data. This is a sort of holdover from traditional media or early online models: once you’ve scaled your audience big enough you can sell this audience to businesses.

This is sort of the weak sauce version of the value of big data. But actual value in big data probably exists outside of what we currently see in existing models of media publication or real estate. The value of big data isn’t in the expense of putting it together and then sprinkling advertising around it.

Big data: shifting from expense to opportunity

When a lot of information is compiled in a way that allows for easy sifting and sorting by people with varied interests and backgrounds, the value of data shifts from an expense to an opportunity.

What kind of opportunity? Who knows. But there are examples of where data sets have the potential to unlock value:

–Hans Rossling’s work with poverty data in Africa creates opportunity in making meaningful change in health and income levels.
–The compound that sheltered Osama Bin Laden was eight times as large as most homes in the area.

These two examples of data opportunities share something in common: they don’t involve any traditional real estate industry practitioners on the value side of things. The value is truly being created and not shifted from one part of the real estate industry to another part of the real estate industry.

In these two examples, an industry other than real estate (government/nongovernmental organizations (NGOs) in the first example and law enforcement/counterterrorism in the second) are getting value from data that naturally falls within the real estate data sets.

The data available to the real estate industry as a whole — but currently locked in an overwhelming number of silos and fiefdoms with a variety of levels of data size and quality — has tremendous potential to create actual value for industries other than real estate.

To generate that value will require some changes to the way we understand, organize, store and distribute data.

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