Dan Rushton

Assisted Living Facilities Site Selection for Elderly Care

By 2030, 1 in 6 people in the world will be aged 60 years or over. At this time the share of the population aged 60 years and over will increase from 1 billion in 2020 to 1.4 billion. To respond to the needs and requirements of this increasing proportion of the world’s population, elderly care, also known as eldercare or aged care, needs to keep pace with this growth.

Photo by In the Now Mag on Unsplash

The elderly care services market size exceeded USD 976.2 billion in 2020 and is expected to grow at a CAGR of over 10% between 2021 and 2027, surpassing USD 2,056 billion. Housing and assistive devices are expected to dominate this growth, meaning that choosing the right location for assisted living facilities (also known as nursing or residential care homes) is crucial.

The elderly care services market size is expected to grow at a CAGR of over 10% between 2021 and 2027 surpassing USD 2,056 billion

Traditional site selection analysis relied on census data for finding areas where the senior population had increased significantly or where population density was highest, 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 selection including purchasing power and behavioral trends, as we have explored in a previous blog post. Therefore, in order to identify optimal locations for new assisted living facilities, modern site selection analysts need to combine Location Intelligence with relevant, accurate, and up-to-the-minute data, which is exactly what a solution such as CARTO for Site Selection provides - whether you own a large chain of assisted living facilities or you work in Private Equity and need to understand market dynamics and potential synergies.

Site Selection for Eldery Care

CARTO for Site Selection allows assisted living facility owners and aged care investors to view current locations, understand resident demographics, run a similarity search, and analyze whitespace or greenfield opportunities, using performance metrics and geospatial data to model and simulate new locations.

Relevant data streams that can be used with such analysis include (links are to data for the United States, full listings including additional countries are available in the Spatial Data Catalog):

  • Population density by age (e.g., Experian’s sociodemographic data).
  • Land usage looking at public recreation, mixed-use, and residential sites.
  • Public transit data to identify sites close to bus stops and train stations (e.g., HERE’s Points of Interest data).
  • Hospital location data to ensure sites are located within close proximity to emergency care when required (e.g., Foursquare Places data).
  • Existing assisted living facility locations to aid within white space analysis (e.g., Precisely Points of Interest data).
  • Weather can be a significant factor in where people choose to retire, often preferring warmer and drier climates (e.g., Weather Source’s Climatology data).
  • Behavioral trends such as those mentioned earlier to determine where potential residents may prefer to retire (in terms of their lifestyle or preference to be closer to family members) (e.g., Spatial.ai’s Geosocial data)
  • Competitor data, e.g., Number of competitors, number of beds, prices proposed per type of beds, number of private beds/ financed beds, standing, personnel turnover, age of the real estate.

Within CARTO for Site Selection there are three core analytical functions: Twin Area Analysis, Whitespace Analysis and New Site Simulation.

Graphic showing the three core analytical functions

Twin Area Analysis

Twin Area Analysis is a similarity search for identifying areas in new or existing locations based on another nursing home’s performance. Performance in this case could include metrics such as occupancy rate, number of vacant beds, revenue per resident, or satisfaction levels.

The selected nursing home is used as a baseline and includes the surrounding Location Intelligence. This surrounding selection can be granular as desired and can include the demographics or POIs as mentioned previously.

Screenshot of Twin Area Analysis

Whitespace Analysis

With well over 15,000 nursing homes in the US, it is important to ensure that new sites are not located within the catchment of an existing owned or competitive site. To avoid this, Whitespace Analysis can be used along with your company’s asset data and key sales metrics to uncover new site opportunities.

Unlike Twin Area Analysis, the user does not need to define an existing location. The user simply defines target criteria to pick and choose for expansion, the ideal trade area (radius ring) boundary, as well as the target destination.

Screenshot of White Space Analysis

New Site Simulation

The final core analysis method is able to predict a site’s potential based on data science to forecast the revenue potential for new locations - considering factors such as the number of beds and occupancy rates. The common workflow for performing revenue prediction includes accessing historical revenue data for each of the nursing homes. It is important that the revenue data is of good quality in order for the data science team working on the project to find a solid model that is able to perform accurate revenue predictions.

Once the model is trained and features are identified on what is driving revenue, CARTO can utilize a predictive revenue model at an existing site or also to predict the potential revenue of a site in a location in which the nursing home operator does not have a presence (a potential site).

Screenshot of New Site Simulation

As the demand for nursing home beds continues to rise with our ageing population, Spatial Data Science can help nursing facility chains avoid million-dollar mistakes by ensuring accurate and data driven site selection strategies.

The final decisions on where to open a nursing home will depend on budgetary constraints and available real estate, but Location Intelligence provides a modern approach that eliminates the guesswork from optimal site selection.

EU Flag This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 960401.
About the author
Dan Rushton

Dan is the Content Marketing Manager at CARTO. Dan holds a Masters in Electronic Engineering with business experience in development, sales, training and marketing. Prior to joining CARTO, Dan was a Senior Product Marketing Manager at Apple.

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