The vacancy rate for rental apartments in the United States fell to about *** percent in October 2021, followed by a steady increase until 2025. In January that year, the vacancy index stood at **** percent.

In 2023, the average vacancy rate index for rental apartments in different metros in Texas ranged between *** percent and *** percent. Dallas-Fort Worth-Arlington, the most populated metropolitan area, had a vacancy rate index of *** percent in December 2023. Meanwhile, Killeen-Temple was the metro with the lowest index, at ****. According to the source, the index is calculated based on data on apartments listed on the Apartment List platform and changes in availability.

Graph and download economic data for Rental Vacancy Rate for Pennsylvania (PARVAC) from 1986 to 2024 about vacancy, rent, PA, rate, and USA.

Graph and download economic data for Rental Vacancy Rate in the United States (RRVRUSQ156N) from Q1 1956 to Q2 2025 about vacancy, rent, rate, and USA.

The residential vacancy rate is the percentage of residential units that are unoccupied, or vacant, in a given year. The U.S. Census Bureau defines occupied housing units as “owner-occupied” or “renter-occupied.” Vacant housing units are not classified by tenure in this way, as they are not occupied by an owner or renter.

The residential vacancy rate serves as an indicator of the condition of the area’s housing market. Low residential vacancy rates indicate that demand for housing is high compared to the housing supply. However, the aggregate residential vacancy rate is lacking in granularity. For example, the housing market for rental units in the area and the market for buying a unit in the same area may be very different, and the aggregate rate will not show those distinct conditions. Furthermore, the vacancy rate may be high, or low, for a variety of reasons. A high vacancy rate may result from a falling population, but it may also result from a recent construction spree that added many units to the total stock.

The residential vacancy rate in Champaign County appears to have fluctuated between 8% and 14% from 2005 through 2022, reaching a peak near 14% in 2019. In 2023, this rate dropped to about 7%, its lowest value since 2005. However, this rate was calculated using the American Community Survey’s (ACS) estimated number of vacant houses per year, which has year-to-year fluctuations that are largely not statistically significant. Thus, we cannot establish a trend for this data.

The residential vacancy rate data shown here was calculated using the estimated total housing units and estimated vacant housing units from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Occupancy Status.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (4 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table SB25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Hong Kong HK: Private Domestic: Units: Vacancy Rate: to Total Stock %: Large Units data was reported at 8.200 % in 2017. This records a decrease from the previous number of 9.200 % for 2016. Hong Kong HK: Private Domestic: Units: Vacancy Rate: to Total Stock %: Large Units data is updated yearly, averaging 8.800 % from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 10.700 % in 2012 and a record low of 6.000 % in 1999. Hong Kong HK: Private Domestic: Units: Vacancy Rate: to Total Stock %: Large Units data remains active status in CEIC and is reported by Rating and Valuation Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.EB031: Residential: Private: Vacancy & Vacancy Rate.

Rental Vacancy Rate for North Dakota was 7.80% in January of 2024, according to the United States Federal Reserve. Historically, Rental Vacancy Rate for North Dakota reached a record high of 16.70 in January of 2017 and a record low of 4.80 in January of 1993. Trading Economics provides the current actual value, an historical data chart and related indicators for Rental Vacancy Rate for North Dakota - last updated from the United States Federal Reserve on December of 2025.

This statistic shows the forecasted growth in rental rates of office space in selected cities in the Americas between 2017 and 2019. The average rents of office space in Toronto, Canada are forecast to grow by *** percent in that period.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show age, type, vacancy rates, and owner/renter tenure of housing units by Regional Commission in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

Hong Kong HK: Private Domestic: Units: Vacancy Rate: to All Other Units % data was reported at 2.300 % in 2017. This records a decrease from the previous number of 2.600 % for 2016. Hong Kong HK: Private Domestic: Units: Vacancy Rate: to All Other Units % data is updated yearly, averaging 2.700 % from Dec 1979 (Median) to 2017, with 39 observations. The data reached an all-time high of 4.700 % in 2003 and a record low of 0.800 % in 1979. Hong Kong HK: Private Domestic: Units: Vacancy Rate: to All Other Units % data remains active status in CEIC and is reported by Rating and Valuation Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.EB031: Residential: Private: Vacancy & Vacancy Rate.

Bulgaria - Housing cost overburden rate: Tenant, rent at market price was 27.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Bulgaria - Housing cost overburden rate: Tenant, rent at market price - last updated from the EUROSTAT on December of 2025. Historically, Bulgaria - Housing cost overburden rate: Tenant, rent at market price reached a record high of 51.00% in December of 2017 and a record low of 27.80% in December of 2024.

Homeownership Rate: 35 to 39 Years data was reported at 56.400 % in 2017. This records an increase from the previous number of 55.300 % for 2016. Homeownership Rate: 35 to 39 Years data is updated yearly, averaging 63.500 % from Dec 1982 (Median) to 2017, with 36 observations. The data reached an all-time high of 67.600 % in 1982 and a record low of 55.300 % in 2016. Homeownership Rate: 35 to 39 Years data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB008: Housing Vacancy and Home Ownership Rate.

The median monthly rent for all apartment types in the U.S. has stabilized since 2022, despite some seasonal fluctuations. In August 2025, the monthly rent for a two-bedroom apartment amounted to ***** U.S. dollars. That was an increase from ***** U.S. dollars in January 2021, but a decline from the peak value of ***** U.S. dollars in August 2022. Where are the most expensive apartments in the U.S.? Apartment rents vary widely from state to state. To afford a two-bedroom apartment in California, for example, a renter needed to earn an average hourly wage of nearly ** U.S. dollars. This was approximately double the average wage in North Carolina and three times as much as the average wage in Arkansas. In fact, rental costs were considerably higher than the hourly minimum wage in all U.S. states. How did rents change in different states in the U.S.? In 2025, some of the most expensive states to rent an apartment only saw a moderate increase in rental prices. Nevertheless, rents increased in most states as of August 2025. In West Virginia, the annual rental growth was the highest, at ***** percent.

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

United States Homeownership Rate: 45 to 54 Years data was reported at 69.300 % in 2017. This stayed constant from the previous number of 69.300 % for 2016. United States Homeownership Rate: 45 to 54 Years data is updated yearly, averaging 75.550 % from Dec 1982 (Median) to 2017, with 36 observations. The data reached an all-time high of 77.400 % in 1982 and a record low of 69.300 % in 2017. United States Homeownership Rate: 45 to 54 Years data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB008: Housing Vacancy and Home Ownership Rate.

United States Homeownership Rate: 50 to 54 Years data was reported at 71.100 % in 2017. This records a decrease from the previous number of 71.600 % for 2016. United States Homeownership Rate: 50 to 54 Years data is updated yearly, averaging 77.200 % from Dec 1982 (Median) to 2017, with 36 observations. The data reached an all-time high of 78.800 % in 1983 and a record low of 71.100 % in 2017. United States Homeownership Rate: 50 to 54 Years data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB008: Housing Vacancy and Home Ownership Rate.

Canada SEPH: Job Vacancy Rate: Real Estate, Rental & Leasing data was reported at 1.500 % in Aug 2019. This records an increase from the previous number of 1.400 % for Jul 2019. Canada SEPH: Job Vacancy Rate: Real Estate, Rental & Leasing data is updated monthly, averaging 1.200 % from Mar 2011 (Median) to Aug 2019, with 87 observations. The data reached an all-time high of 1.800 % in May 2011 and a record low of 0.400 % in Jul 2015. Canada SEPH: Job Vacancy Rate: Real Estate, Rental & Leasing data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.G026: Job Vacancy and Wage Survey: Job Vacancies: NAICS 2017. Replacement series ID: 446801237