Editor’s note: This column is the latest in a 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, leading up to the Real Estate Connect conference, which runs from July 27-29.

As most of my real estate practitioner friends remind me, there are lot of people who would like to muscle in on a piece of those big, fat, juicy real estate commission checks. And the data people are definitely a part of that.

But if real estate data sets were to be realigned as a revenue source instead of a way to shuffle the deck chairs among the existing real estate interests, then it might be good to identify what groups of people might have to get along well enough to achieve that. And even if nothing is to change about the nature of real estate data, identifying the kinds of mindsets that are applied to data-related problems is going to be beneficial. So here goes.

Data producers

Facts exist, whether we record them or not. The tree definitely falls in the forest, even if no one is there to hear it. However, for facts to become useful someone has to record them and share them with other people. These people are the data producers.

In each data set, data producers might go by different job titles. For the property data set, that job title is probably "listing agent." For the economic data set, there are probably several job titles, ranging from "government employee" to "economist" to "industry analyst."

For the customer data set the job title might be varied as well, ranging from "Web analyst" to "demographic researcher" and all points in between.

Regardless of job title, data producers typically share some things in common. Understanding their perspective will help other stakeholders get the most from data producers.

  • Data collection is a bottleneck. There are many points of data to collect. Too many, in fact. Without any kind of controls, data collection can end up making so many bits of data that it is a challenge to use — as any first-time user of Google Analytics knows. Every point of data that is gathered takes time. Data producers never have enough time. You can make data producers happy by limiting the amount of data they need to produce.
  • Data producers rarely see the full scope. The individuals who gather and produce the data rarely get much input into the larger scheme of what and why the data is being gathered. Combined with the hassle of gathering data, this can lead to a data-quality issue. You can make data producers happy by being sure they understand how the data they make adds value to the system.

Data analyzers

A pile of data is just that: a pile of data. It doesn’t have much meaning or value unless some sense is made of it. This happens when bits of data are compared and contrasted.

Again, depending on which data set market, property or customer, the people who analyze the data may have various job titles: "broker," "Web analyst," "business analyst," "economist," "government employee," "reporter," and things like that.

Notice the similarity between data producers. Over time the production and analysis of data will likely diverge. But so long as the value of data is primarily locked to real estate transactions, these two roles will probably continue to be shoehorned into one job title.

Analyzers of data are focused on turning piles of data into something meaningful for someone. Hopefully that meaning adds enough value that they generate revenue off of the efforts of the data production. Here are some ways to get the most from your data analyzers.

  • Data analyzers rarely get enough insight into business models. Data is fascinating to analysts. They like lots of it. They like to figure out what it means. If they understand specific business models they will focus their meaning-generation on ways to augment the business model (or, hopefully, work to identify serious flaws/improvements in the existing models). A spreadsheet and numbers are not enough to do a good job with analysis.
  • Data analyzers are less useful if they sugarcoat the meaning of the data. This one should be obvious, of course, but it’s rarely a given. Data analyzers for vendors will be encouraged to gloss over traffic volume and quality flaws. Data analyzers for economic data will be encouraged to only talk about why now is a good time to buy or sell a house. Making analysis conform to marketing goals will short-circuit any profound and game-changing insights that analysis can bring to light.

Decision makers

CEOs. Presidents. Founders. Team leaders. Anyone who might be making big decisions based on available data is a decision maker. They may not be intimately involved with the creation or analysis of the data, but they will throw switches based on what insights are available to them.

Whether that data leads them to build a franchise Internet Data Exchange (IDX) website, fine-tune their search engine optimization plans, or adjust financial models based on economic analysis, decision makers are all the same: They want to know the future.

Here are tips for the care and feeding of decision makers.

  • Decision makers don’t trust the data. Most decision makers got where they are by following their gut instincts. And over the course of their careers their gut instinct has been good enough to serve them well (they are the deciders, after all). In a world where access to data was limited, gut instincts were the only thing available. This isn’t that world anymore. If you make something using data for a decision maker, be absolutely clear about how that data was produced and analyzed. Acknowledge shortcomings at the least, and perhaps overplay them. Decision makers need assistance and clarification about the positive and negative qualities of any data set with which they interact.
  • Decision makers don’t have time. They don’t have time on two fronts. On the obvious front, they don’t have time for 10-page reports. They need things clear and quick. On the less obvious front, the world is changing. Data is becoming increasingly available to everyone: customers, competitors, employees. Skill sets that were advantageous in a world of data opacity may be liabilities in a world of data transparency. Decision makers need assistance navigating opacity/transparency from a strategic point of view.


The first round of the real estate data conversation is focused on aggregation. The meaning and value of real estate data, according to the business models, is in piling together as much data (on your agent site, on your broker site, on your franchise site, on your advertising site) and trying to make money off it through lead generation or through advertising (or both).

The next round of the real estate data conversation will likely have to get beyond this in order to generate actual value (beyond the shuffling of deck chairs between current real estate participants). But the aggregators will continue to play a role, for certain.

The people in organizations that are focused on gathering this data together have a wide variety of challenges.

  • Aggregation is hard without data standardization. Part of the reason the boring old data standards conversation continues to happen is because of conflicts between those hanging on to opaque data skills/business models and those who want to generate a larger pile of data. Aggregators need motivation to deal with lack of data standardization in all of the real estate data sets.
  • Aggregation is a technologically expensive activity. Issues of storage, compliance, frequency, bandwidth and so on are at the core of aggregation activities. As the scale of data moves from small data to big data, the brunt of this expense will be borne by aggregators. Identifying data sets that can "go big" clearly can help the aggregation process.


Oh yeah, this data is supposedly valuable to someone, right? Even defining who the customer is can be tricky. In one type of data conversation, the customers for the data were primarily the advertisers on aggregation sites — just like newspaper advertisers were before this whole Internet "fad" started.

But who is going to be the customer for real estate data beyond that? Is there a market for real estate data beyond those people directly involved in real estate transactions?

And then there are the customers who provide the data points to begin with: the homeowners; the people who take out loans and pay back loans; the people who surf around on websites and use mobile devices; and so on. These people are customers in a way, too. And they have needs.

  • Customers are concerned with security and privacy. Any time we discuss where people live and how they spend their money, those people are concerned about who knows how much. Customers need to feel safe with the data being produced, or the well dries up. This can happen through government regulation or entire new business models springing up to circumvent security/privacy flaws in data production and analysis.
  • Customers are concerned with data quality. People who may find value in real estate data sets but aren’t involved in the real estate transaction have different needs related to the data. They want to know what, if any, biases exist in the production and analysis process. They need this in order to figure out how to apply the data to their own goals and objectives.

If all data always point to "Now is a good time to fulfill the American Dream of homeownership," then the data or analysis, or both, may be questionable.

These different types of people, some with directly opposing needs, will need to be able to have meaningful conversations if we’re going to start moving into the next phase of the real estate data conversation.

I hope to see solid representatives of each group at the Data Summit. In fact, I know that many will be on the stage. But as with any conference, it’s the conversations in the hallways and lunches and breakfasts and dinners that will be where some of the best insights occur.

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