Search

Comparing Owned Homes vs. Rented Homes

About This Dataset

Discover the number of owner occupied housing units in the United States compared to the number of renter occupied housing units over time. 

This data ranges from 1975 through 2022 and displays the number of rented homes, owned homes, and the percentage of rented and owned homes. FourFront’s analytics team collected and blended this data using the US Department of Housing and Urban Development’s data via Statista’s Number of Owner Occupied Housing Units and Number of Renter Occupied Housing Units data. 

See also:

Person handing house keys to another person in front of a sign that says "sold"

Sample Data icon of a hand swiping left and right to scroll content Scroll table horizontally to view more

# Year Rented (In millions) Owned (In millions) Total % of rented %of owned
1 2017 43.28 77.66 120.94 36% 64%
2 2018 43.11 79.36 122.47 35% 65%
3 2019 43.28 80.68 123.96 35% 65%
4 2020 43 82.81 125.81 34% 66%
5 2021 44.02 83.58 127.60 34% 66%
6 2022 44.17 85.22 129.39 34% 66%

Date Created: October 2023

Date Modified: October 2023

Available Formats: CSV, XLS, JSON

Price: Free

Who Is This Data For?

This dataset may be useful to anyone interested in evaluating housing trends overtime, such as those working in housing policy, the building industry, or finance. The trends could also be helpful to audiences interested in planning for home ownership for themselves or their loved ones. 

The suggested use cases for this dataset include:
  • Urban planners can use this dataset to analyze trends and properly allocate resources
  • Banks and mortgage companies can analyze the market and tailor their products and services based off the preference of renting or buying
  • Educators can use this data as an academic tool exploring historical trends of the housing market

Enrichment Options

We utilize state-of-the-art AI and machine learning to enrich datasets further and provide predictive analytics for your important KPIs. Get in touch to learn more.

Enrichment opportunities for this dataset include:
  • Apply predictive analytics to forecast future trends, identify patterns and potential shifts in the housing market.
  • Blend data with other data sources for additional insights
  • Subscribe to a regularly updated data feed / Create a custom API
  • Request related data unique to your needs

Data Enrichment

We can provide additional services such as blending this data with your internal data, applying predictive analytics, and more. Get in touch to learn more.

Additional Questions?

If you have additional questions, or have a specific request for this dataset, contact our team at FourFront.

Date Created: October 2023

Date Modified: October 2023

Available Formats: CSV, XLS, JSON

Price: Free

Sample Data icon of a hand swiping left and right to scroll content Scroll table horizontally to view more

# Year Rented (In millions) Owned (In millions) Total % of rented %of owned
1 2017 43.28 77.66 120.94 36% 64%
2 2018 43.11 79.36 122.47 35% 65%
3 2019 43.28 80.68 123.96 35% 65%
4 2020 43 82.81 125.81 34% 66%
5 2021 44.02 83.58 127.60 34% 66%
6 2022 44.17 85.22 129.39 34% 66%

Who Is This Data For?

This dataset may be useful to anyone interested in evaluating housing trends overtime, such as those working in housing policy, the building industry, or finance. The trends could also be helpful to audiences interested in planning for home ownership for themselves or their loved ones. 

The suggested use cases for this dataset include:
  • Urban planners can use this dataset to analyze trends and properly allocate resources
  • Banks and mortgage companies can analyze the market and tailor their products and services based off the preference of renting or buying
  • Educators can use this data as an academic tool exploring historical trends of the housing market

Enrichment Options

We utilize state-of-the-are AI and machine learning to enrich the datasets further and provide predictive analytics for your important KPIs. Get in touch to learn more.

Enrichment opportunities for this dataset include:
  • Apply predictive analytics to forecast future trends, identify patterns and potential shifts in the housing market.
  • Blend data with other data sources for additional insights
  • Subscribe to a regularly updated data feed / Create a custom API
  • Request related data unique to your needs

Data Enrichment

We can provide additional services such as blending this data with your internal data, applying predictive analytics, and more. Get in touch to learn more.

Additional Questions?

If you have additional questions, or have a specific request for this dataset, contact our team at FourFront.

Scroll to Top

Sign Up for Updates

Get regular updates about what’s happening at FourFront!

Enter your full name and email to be in the know about all things SEO, data solutions, and much more.

Submit a Request