TL;DR
Data doesn’t have to be Big Data in order to make a big difference. If you find the right data and ask it the right questions, the benefits go far beyond you own work projects. Here, we took two lists of data to find condominiums who may benefit from a certain kind of financing. The end result? They said, "We've wanted to do this for years!"
Sometimes data can tell us the darnedest things. The real estate world has been full of calls for Big Data. We want more of it. We want less of it. We want more of it locked down so not everyone can see it.
We want to know more stuff from more data so that the agent can find more buyers and sellers, and so that organized real estate – the trade associations – can add more value to their members.
Recently I have been working on a project to increase the availability of a certain kind of mortgage loan. Sometimes buyers really want to live in a certain place but cannot because they do not have access to the right kind of financing. Not sometimes – every single day. How can we help?
One answer lies in this project. Veterans of the US Armed Services have access to “VA Loans” which can make it easier to buy condos. For reasons beyond the scope of this article, too many condos are not certified to accept VA Loans. The reasons are based on politics, misinformation or just plain laziness. Who gets screwed? The veteran.
We asked whether the data could help us find condos who would accept VA Loans. Where better, we thought, than in a building where a Realtor is living in another unit. For example, if the condo at 123 Main St. #5 was for sale, then the Realtor living at 123 Main St. #3 might have vested interest in certifying VA Loans, because more people could get more housing. Everyone is happy.
Now to find that Realtor. This is the hard part because info for condo complexes is not (yet) publicly available. So we took two lists:
- List #1 “ForSale” is all of the condos currently for sale or just about to close escrow.
- List #2 “HomeList” is all of the Realtors living in our service area, about 4,000.
Both lists had one item in common, which is the data point we wanted the most: a street address without the unit number. 123 Main St.
For simplicity and because we had a small set – less than 6,000 rows of data – I popped it into Microsoft Access. Underlying Access is SQL, the querying language for slicing and dicing data any way you want it. With our two tables, I asked “Which street address has a Realtor living in a unit and also has a unit currently for sale?”
This is the actual query in Microsoft’s flavor of SQL:
SELECT ForSale.[Search Addr], ForSale.[Combined Street Addr], ForSale.[City], ForSale.[ML#], HomeList.[First Name Last Name] FROM ForSale, HomeList WHERE HomeList.AgentAddr LIKE '*' & ForSale.[Search Addr] & '*';
With just a few lines, we got two dozen hits. The Realtors we called were ecstatic. “I’ve wanted you to do this for years!” one said after he was told about how we can help certify his condo building for VA Loans.
The data told us what we needed to know. It was very simple and used inexpensive software. Not every data search needs a Big Data solution with trillions of data points costing millions of dollars. It may not be Hadoop or Spark or even Lumio. The simplest tool did the trick and the data backed us up.
photo credit: Charla Blue Photography
