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by G. Sax, Director of Growth Management, RESO

Welcome to “Three Questions,” an interview series that introduces you to real estate industry professionals, their businesses and how they interact with real estate standards with a goal of humanizing the tech side of the industry, fun included.

This week, we hung out with Amy and Ed Gianos from Domus Analytics to talk about the availability of housing market data, the importance of market analytics during unique market cycles and whether or not digesting business intelligence in real estate is different from any other vertical. Enjoy!

Q1: We share something in common, which is a history of creating real estate market analytics products from MLS data. What excites you most about this angle on the industry?

Ed: There is so much more data becoming available and so many more ways to present views of the market that are different from how we’ve historically looked at market performance.

RESO: It’s so true. I’ve often noted that a business could be created from tracking available horse stables – but only if the lookup was populated with data in MLS systems.

Amy: We used to joke about reporting on alligators per acre in Florida.

Ed: I’m as excited about getting to work now as I ever have been. When times get a little tougher in terms of conducting real estate transactions, people tend to turn to stats for help. And we’ve been working on our products like, “Oohhhh, we’ve been waiting for you!” Because, now, everybody is getting as excited about stats as we are.

Q2: What is the hardest part about working in housing analytics, and is there anything that RESO can do to make it easier?

Amy: One of the hard things is that stats are important, but not critical. Practitioners need offer management, they need open houses. They can do their job without stats, but they shouldn’t. So we’re constantly justifying why they need statistics and how they should be using them.

RESO is doing a lot right now to express how important statistics are for doing business. The Data Dictionary continues to make it a lot easier, including usage statistics for fields and lookups right inside the wiki.

Ed: The Data Dictionary and Transport workgroups have helped us focus on the value add. A lot of our problems went away with Data Dictionary and Transport, especially Data Dictionary.

Amy: Agreed. We used to have to hunt and peck to find the data.

Ed: Every MLS had a series of business rules and coded it at the data layer. Now, those business rules are defined in the Data Dictionary. That’s been a huge time saver for us.

Q3: Is there anything different about business intelligence and analytics in a big vertical like real estate compared to, say, the casino and gambling industry, which you also have worked in? Or is it all just hardware and software at the end of the day?

Ed: It’s surprisingly more common than it might appear on the surface. You’re always looking for the important metrics, what’s important to monitor and how you can get the data to monitor it as best you can.

Amy: Under the covers, data is data. The skill set it takes to work with the data is what matters, not the data itself.

Ed: For example, we wrote our native client in Python. I’d never coded in Python! But having the necessary building block skills allowed me to work in this manner.

Amy: In the world of RETS, everyone had RETS Connector. In the API world, there is no equivalent, so we wrote all the code ourselves. That can be the case in any data-driven business endeavor. Whether it’s real estate data or something else.