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Alteryx Use Cases

Read Alteryx customer stories to learn how they transform their organizations into becoming a data-driven business.

Buy Your House with Alteryx

Alteryx Alumni (Retired)
2019 Excellence Awards Entry: Buy Your House with Alteryx
Name: Mike Nealey
Title: Enterprise Account Executive
Company: Alteryx
Overview of Use Case:

A couple of years ago, my family and I relocated from the Twin Cities to Denver. After spending some time renting up near the Alteryx office in Boulder, we decided to take the plunge and enter the buying market. Neither my wife nor I were very familiar with the Denver metro, however we had certain criteria that were important to us in the buying process, such as price (of course) but also factors like the # of young families in the neighborhood, diversity of the neighborhood etc.
Describe the business challenge or problem you needed to solve:

When your knowledge of a metro area is limited, narrowing down the choice of areas to search for a house is daunting. There are websites that grade cities according to livability. Tie information like livability with access to real estate database websites and you may think you're in a good spot.
The downside of livability rankings however is that they cover too large an area to be helpful when choosing a specific neighborhood to live in. Furthermore, the rankings use a methodology that, whilst broadly fair (low crime, amenities, education), doesn't allow for customization. As of the time of writing, the #1 place to live in the US according to livability.com is Ann Arbor, MI (population 116,194). An area that size will have neighborhoods with varying characteristics. If I decided to buy a house in Ann Arbor, MI, based off of its livability score, and perhaps another metric like house price, then my young family (2 kids under six) could end up in a neigbourhood with ~zero other kids. 
Being able to isolate neighborhoods within a well-ranked city was critical to both our being able to find the right house, but also to be as efficient with our time as possible. Buying a house is stressful. Driving around an unfamiliar city, with two young kids in tow, means there is limited time to spend looking at houses before the kids call for playtime. So, in short: we needed to figure out how to narrow down our choices to a few, very specific neighborhoods, based on criteria that were important to us.
Describe your working solution:
1. I use Alteryx's Experian data packages to first select the metro areas of interest, and then perform a spatial join against all of state's "block groups" (the smallest US Census-published geographical unit), which eliminates areas outside the metros.
2. I next find the central point (centroid) of the block group - this allows me to identify the nearest address to the centre of the group: I use this information to pass that address into as a dynamic URL later on, so I can see homes for sale in my real estate database of choice later on.
3. I next generate the specific variable metrics my family and I are interested in for these block groups, using the Allocate Append tool. 
4. Using the Allocate Metainfo tool, I can rename the variables easily - I then create calculated fields: performing calculations in Alteryx and passing them into the visualization tool of choice is almost always the best approach: it allows for the viz tool to do what it does best: visualizing the data, rather than using its engine for calculations
5. A final reshape of the data (viz tools tend to prefer a long rather than wide data table), then I create polygon shapes and use an output tool to directly pass the data into a Tableau .hyper file, which lets me immediately start visualizing in Tableau.
6. Create the visualization (this was the bit that my wife liked the most...though she certainly understood that steps 1-5 were the critical analytical elements!)



Describe the benefits you have achieved:

We bought our house at the end of July. Without this solution, we would of course have bought a home, however the time and effort it would have taken would likely have been much greater. Our neighborhood is perfect: the variables we identified, specifically our most important one (# of families with children aged 0-5), were spot on: knowing that you're buying a house in a neighborhood with 1 in 4 household having potential friends for your kids was a major win for us, and our kids.
LI_neighborhood_photo_2 (2).jpg
Related Resources
7 - Meteor

Great use case for everyday life! 

Alteryx Alumni (Retired)

Indeed! In fact, I was just able yesterday to change the parameters to look at a different city in the US...changed and ran the workflow, updated the Tableau workbook in ~1 minute.

Not thinking about leaving Denver just yet (!), but was curious to look at neighborhoods elsewhere.

Alteryx Alumni (Retired)

@MikeN Great use-case! You should package that up as App :)

8 - Asteroid

great idea.  I love the family pics.

5 - Atom


OSM was used as the Geo dataset here. I'm trying to work with Singapore standards maintained with Singapore Land Authority. Has anyone had any experience with Singapore? Or even in Asia? It appears that the address formats have to be localised and that is creating its own challenges.