Annual updates to MicroBuild data enables the most accurate trade area analysis available.

As summer begins we have just released our annual update to our MicroBuild family of demographic data products.  This update reflects 2014 demographics, and while there’s plenty to see in this update as we analyze what key demographic shifts are taking root across America, now is a great time to talk about how MicroBuild tracks growth and change.

MicroBuild can find 2014 demographic changes like no other dataset can.  That’s because MicroBuild is built on a different methodology than any other demographic database.  While the old-school approach uses Census and Postal data from large geographies, and crams these data into small levels like a block group, MicroBuild works from the bottom up.  It starts with the smallest geo-demographic unit: an individual household.

MicroBuild’s patented approach has several advantages over traditional, old-school demographic estimates.  One of them is the ability to accurately track population and household growth, and to do so at micro-levels such as a Census block or a ZIP+4.  Drawing on multitude of household-level data sources, MicroBuild can identify areas of change (whether growth or decline) and quantify the magnitude of the shift.

With this latest release, we have continued to pursue increased precision to the geocoding process for our source data.   A significant addition to our process includes the adding of another national address data source which assisted in improving the number of households available at multi-family properties (2 units and above).  The source also assists in identifying household vacancies.  These changes are continuing to enhance the accuracy and quality of the product.


MicroBuild excels at finding change, even at sub-neighborhood levels.  Here’s just one example from last year’s release, where MicroBuild identified a huge new development in a formerly industrial area.
Every day, we see examples of massive growth, sometimes in the most unpredictable places.  Because MicroBuild is aware of new household formation (whether it’s in new construction or not), it’s ideal for tracking obvious growth such as tract development, or more subtle growth like urban gentrification.  Because it’s built using household-level data, MicroBuild natively measures growth in even the tiniest Census blocks.  Datasets that take a top-down approach have trouble making accurate growth estimates, even at larger Census block groups.

There’s something else that only MicroBuild can do: generate population-weighted block centroids.  Because MicroBuild is aware of the lat/long coordinates of all the households it measures, it can fine-tune where the “center of mass” of a particular block is located.  Doesn’t sound like a big deal?  Analysts are finding that it’s a huge deal, especially with smaller trade areas.  A weighted centroid can help you include truly populated areas, and keep out phantom population that actually resides outside your boundaries.

While other centroids would be placed in the middle of nowhere, MicroBuild’s pop-weighted centroid (green dot) is placed in the center of the housing cluster.  This makes a huge difference if you use block-level allocation.

MicroBuild also does something else that other datasets struggle with: seasonal household estimates.  Identifying seasonally-occupied households is a natural part of our process, that is invaluable when researching locations in heavy seasonal areas such as Myrtle Beach, SC or Gatlinburg, TN.  Retailers are finding that they don’t have to guess about what the in-season population might be; instead, they use MicroBuild to come up with reliable, accurate readings on high-season and low-season household counts.

We’ll be covering more about MicroBuild’s 2014 demographic estimates on this blog, so check back often.  You may also be interested in reading about some of our past results from 2013, which we covered in a number of blog posts.  I hope this “refresher” is a great lead-in to how MicroBuild is constructed differently from other demographic data solutions, and why these differences matter to sophisticated location research teams.