The hierarchy of geography filters

The hierarchy of geography filters

When you think about market stats, geography is one of the first things that comes to mind.  Depending on the dashboard you're using, you may have one or more geography filters.  If there is more than one, then those filters work slightly different between Public and Member dashboards.

On the public dashboards, the filters are unidirectional.  That means there's an enforced hierarchy to make things simple for the public.  Said another way, the City filter looks to see what County is selected, but the County filter ignores the City filter.  The filtering only goes one direction.  In the image below, a single County is selected.  You can still see the other available Counties, but the City pick list is narrowed to the selected county.

On the member dashboards, the filters are bidirectional.  Each side of the filter honors the other side.  So when you select a County(ies), it shows you all the associated Cities.  When you select Cities it shows you all the associated Counties.  Data can be a little messier, but this structure is more flexible and powerful for agents--at the expense of sometimes having to click back and forth to get more powerful combinations.  If an agent chooses to treat it like a undirectional filter, it behaves exactly the same way as the other dashboards.  But they can get fancier if they want.  You'll see this filter also has the "reset" button on the top right above the City box, to get back to defaults in one click.  

An example of what we mean by "messy" data can be seen in the image below.  In this case, a City was chosen first, and the County list then narrowed itself down based on that City.  Fall Creek is located in Hamilton, but it's showing Marion as a selection as well, which means there is at least 1 record in the data set that was entered incorrectly the MLS. The really ambitious agent can choose to unselect the correct County of Hamilton, and the chart should then show the incorrect listing(s).  The Active Inventory chart is the best one to use in that instance. When the data gets corrected in the source, the update will flow through to the stats the next day in most cases.

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