The unprecedented growth in Americans aged 65 and over is driving demand in the senior housing subsector. The Census Bureau expects the U.S. senior population to increase by 60 percent between 2016 and 2040, and preparing for this unprecedented population growth presents challenges for deciding when, where, and what type of medical facilities to open.
In the past, site analysts relied on census data for finding areas where the senior population had increased significantly, and then selected sites in those areas. Correlating market growth to population growth can work in certain industries, but there are more factors involved in healthcare site planning. Today, for instance, more seniors are moving to cities and metropolitan areas so they can retire closer to their adult children. These recent behavioral trends are hard to notice with traditional site planning workflows, like the one above, because sources like the U.S. Census update demographic data only periodically.
Location Intelligence provides a modern solution for site planning, however, that applies different types of spatial analysis for identifying origin and destination patterns among retirees, determining what constraints exist in these areas, and then deciding the optimal site for new facilities.
Site planning with Location Intelligence in Washington, D.C.
If seniors are more likely to retire near their adult children, then identifying areas with a high population density of “adult children” can help determine growth market corridors where demand for senior care services is likely to grow. Erickson Living has had success with this strategy, and as senior vice president Adam E. Kane recently explained, even if “there’s not really a plethora of aging demographics in a local area, but its a growing market where you have a lot of adult children moving to and living there.”
One growth corridor market is Washington, D.C., which has experienced a drastic population increase. But, as D.C. Policy Center reports, from 2010 to 2016 population growth outpaced the growth of new housing with the number of residents increasing 13.2 percent while housing stock increased only 5 percent. This situation has led to increases in property values as the map below illustrates:
In the map below, the 19 current nursing homes within the District of Columbia are represented with proportional circles whose size corresponds to the residential capacity for each site. In addition, each census tract is styled based on the total population count.
As expected, most nursing homes are located in densely populated areas, but to identify growth market corridors let’s segment total population by age and style each tract by total population to men and women ages 35 to 49.
Taking the higher end of the distribution of adults ages 30-49 gives a better sense of possible growth market corridors for new nursing homes. Next, set some criteria for each site to help locate optimal sites among these areas. Ideally, potential sites should be (1) easily accessible by public transportation, (2) have low crime rates, and (3) have high population density of adult children at lowest possible cost. Now let’s add enriched data from our Data Observatory and run some basic types of spatial analysis to refine site selection based on established criteria.
- Import transportation data to find tracts with accessible public transit based on bus ridership rates
- Run an intersect and aggregate analysis to add data on crime incidents
- Run another intersect and aggregate analysis to add average real estate listing price for each tract
These steps added a lot of criteria-related data to the map below, but there’s no meaningful information just yet for determining new nursing home locations.
Making sense of this data requires running a few more types of analysis for finding areas where our desired criteria overlap.
- Add the create centroid of geometries analysis to find the center point of areas where our criteria overlap
- Next create clusters so points are grouped around the centroids created in the last step and set the cluster count to three
- Finally, run create centroid of geometry analysis again and categorize by the number of clusters column created in the last step
These steps produce three sets of coordinates that identify which census tracts and where in those census tracts are most suitable for new nursing homes. In this case, our three neighborhoods of interest are Mount Pleasant, Arboretum, and Congress Heights. In the map below, these coordinates are displayed and the map has been styled by average real estate listing price.
The most expensive neighborhood by far is Mount Pleasant with listing prices between $1 and $1.1 million dollars. In the other two neighborhoods, however, prices were substantially less ranging between $370,000-$450,000 in Arboretum and $370,000-$400,000 in Congress Hill.
Finally, let’s check to see which, if any, of these sites are located in growth market corridors by styling this map by population of adult children in the map below.
All three neighborhoods are on the higher end of target segment distribution, with the highest concentration of adult children located in Mount Pleasant and an equal share in Arboretum and Congress Hill.
The final decisions on where to open a nursing home will depend on budgetary constraints and available real estate, but Location Intelligence provided a modern approach that eliminates the guesswork from site planning.
Discover how Sanitas, an international leader in the healthcare field, implemented a successful site planning strategy working with our modern Location Intelligence stack in our recent case studyDownload Today!