Which Counties Have the Highest Single-Person Households?

Identifying markets with a high concentration of single-person households is critical for industries from real estate development to consumer packaged goods. However, accessing this specific demographic data at a county level often involves navigating complex government websites, downloading large datasets, and writing SQL queries. This process can take hours or even days, delaying important business decisions. The traditional methods of data extraction are a bottleneck for agile market analysis, requiring specialized skills to translate raw census files into actionable insights. This article outlines a more efficient method for obtaining this data, reducing research time from days to seconds using natural language queries to analyze public datasets directly.

Why Single-Person Household Data is a Key Market Indicator

The number of people living alone is a significant demographic trend in the United States. According to the U.S. Census Bureau, single-person households now constitute nearly 30% of all U.S. households, a substantial increase over the past few decades. Understanding where these households are concentrated is essential for accurate market segmentation and strategic planning.

This demographic has distinct consumption patterns and lifestyle needs that differ from those of multi-person households. Businesses and organizations that can accurately identify and understand these markets gain a considerable advantage.

• For Marketers and Retailers: This group often has higher discretionary spending per capita. They prioritize convenience, smaller product sizes, and experiences. A CPG company can use this data to identify prime locations for launching single-serving meal options, while a furniture retailer might target these areas with marketing for compact, multi-functional furniture.

• For Real Estate Developers and Investors: A high concentration of solo dwellers signals strong demand for smaller residential units like studios and one-bedroom apartments. Investors can use this data to pinpoint counties with underserved rental markets or identify opportunities for new micro-living developments.

• For Public Policy Analysts: Government agencies and nonprofits use this data to plan for social services, housing assistance, and public transportation. For example, a high percentage of elderly individuals living alone in a specific county might indicate a need for increased funding for senior centers or in-home care services.

Access to precise, county-level data on this demographic is not just useful, it's a requirement for effective decision-making.

The Traditional Challenge of Accessing Granular ACS Data

The primary source for detailed demographic information is the U.S. Census Bureau's American Community Survey (ACS). The ACS provides a wealth of data, but accessing and analyzing it is a multi-step, technical process. For a market researcher or business analyst trying to find the percentage of single-person households per county, the typical workflow looks like this:

1. Navigate data.census.gov: First, the user must find the correct data table. For household information, this might be table S1101 or DP02, but knowing which table contains the exact required variables is a challenge in itself.

2. Apply Complex Filters: The user then needs to filter this data for the correct geographic level (all counties in the United States) and for the specific variables (e.g., "households with one person" and "total households").

3. Download and Clean: This process results in a large CSV file that often contains thousands of rows and hundreds of columns with cryptic headers. The file must be opened in a tool like Excel, R, or Python for cleaning and processing. Extraneous columns must be removed, and data types corrected.

4. Perform Manual Calculations: The raw data doesn't provide the percentage. The analyst must write formulas or code to divide the number of one-person households by the total number of households for each county and then multiply by 100.

5. Visualize the Results: Finally, to make the data understandable, it must be imported into another tool to create a chart or map.

This entire process is time-consuming, prone to human error, and requires a certain level of data engineering skill. It creates a barrier between the business question and the data-driven answer.

Find Single-Person Household Data in Seconds with Cambium AI

Cambium AI removes these technical barriers by providing a no-code interface to query public datasets directly. The platform connects to sources like the ACS, allowing users to ask questions in plain English and receive instant visualizations. Instead of downloading files and writing formulas, you can get the exact answer you need in one step.

Here is how you can use Cambium AI to perform the same analysis that traditionally takes hours.

Example 1: Find the Top Counties Nationwide

To get a high-level overview, you can ask a broad question. 

Your Query:

"Show me the top 20 US counties by percentage of single-person households."

Cambium AI processes this request and instantly generates a bar chart or list showing the counties that meet your criteria. 

Example 2: Focus on a Specific State or Region

If your research is focused on a particular market, you can easily narrow the geographic scope of your query.

Your Query:

"Which counties in Texas have the highest percentage of householders living alone?"

This query provides a focused list, allowing a regional manager or local marketing team to understand their specific area of operation without sorting through national data.

Example 3: Compare Key Markets Directly

You can also use the platform for comparative analysis to benchmark different markets against each other.

Your Query:

"Compare the percentage of one-person households in San Francisco County, CA vs. Suffolk County, MA vs. Denver County, CO."

This query returns a simple chart comparing these major urban centers, providing immediate context for strategic decisions. The ability to perform such analyses on the fly allows for rapid iteration and deeper exploration of the data.

Practical Applications for Marketers and Analysts

The speed and simplicity of this approach enable professionals to integrate demographic data directly into their daily workflows.

• Scenario for a CPG Brand: A brand manager for a frozen foods company is considering a new line of premium, single-serving dinners. To guide the launch strategy, they need to identify metropolitan areas with a high density of their target demographic. They use Cambium AI to ask: "Map of US counties showing the percentage of one-person households, for counties with a population over 500,000." The resulting map visualizes the top urban markets, helping them allocate their initial marketing budget.

• Scenario for a Real Estate Firm: A commercial real estate analyst is evaluating investment opportunities in the Sun Belt. They are specifically interested in markets with growing demand for smaller rental units. Their query is: "List the counties in Florida and Arizona with the highest growth in single-person households ." This data helps them identify emerging hotspots and prioritize sites for acquisition and development.

• Scenario for a Consulting Project: A management consultant is advising a national retail bank on optimizing its branch locations. Part of the analysis involves understanding the household composition around underperforming branches. For a specific branch in a Chicago suburb, they ask: "What is the household composition breakdown for Cook County, IL?" The resulting data, obtained in seconds, informs their recommendation to reconfigure the branch to better serve a growing population of single professionals.

In each scenario, the user gets a direct answer to a business question without needing to become a data expert.

Conclusion

Accessing granular demographic data like the prevalence of single-person households should not be a technical hurdle. By enabling users to query complex datasets in plain English, Cambium AI streamlines the entire research workflow. 

To see how you can query complex demographic data in seconds and get instant visualizations for your own projects, start your free trial here.