Rental vacancy rates across the United States showed significant regional differences in 2023, with the South experiencing the highest rate at 8.7 percent. This disparity reflects broader demographic shifts and economic factors influencing the rental market. The regional variations in vacancy rates have persisted despite an overall decline since 2014, highlighting the complex dynamics of the U.S. housing landscape. Rental demand and affordability challenges The rental market continues to face pressure from high demand, particularly among younger demographics. People under 30 comprise the largest share of American renters, with approximately 42 million in this age group. Despite softening rents in some areas, affordability remains a significant issue. In 2023, 42.5 percent of renters paid gross rent exceeding 35 percent of their income, indicating widespread financial strain among tenants. Regional disparities and market trends The Northeast and West regions, which include many large urban areas, have consistently lower vacancy rates compared to the Midwest and South. This trend aligns with population shifts towards these regions, fueling higher home prices growth. The rental market has shown signs of stabilization in 2023, with the number of vacant homes for rent slightly picking up after two years of record-low vacancy.

Graph and download economic data for Rental Vacancy Rate in the United States (RRVRUSQ156N) from Q1 1956 to Q4 2024 about vacancy, rent, rate, and USA.

Almost two years after the start of the coronavirus (COVID-19) pandemic, the occupancy rate of rental properties around college campuses in the United States has not fully recovered. In the period between 2014 and 2019, the average occupancy rate for properties within 0.5 mile reach of the campus was close to 95 percent, while in 2020 and 2021, it was about 91 percent. For properties further away from the campus, the occupancy rate was even lower.

The U.S. multifamily vacancy rate increased slightly in 2023, after reaching one of the lowest levels on record in 2022. Approximately 6.6 percent of multifamily homes were vacant in the fourth quarter of 2023. Despite the increase, this figure was notably lower than the long-term historical average. U.S. multifamily housing sector Multifamily housing, refers to a housing type where multiple apartments are contained within one housing unit, or when several buildings form a larger complex. Construction of such houses has been on the rise, as the industry struggles to meet housing demand. The average size of such a housing unit was 1,046 square feet. Popularity among investors Multifamily housing accounted for almost 15 percent of the housing stock in the United States in 2021. This type of real estate is popular among investors because it tends to generate a steady cash flow, and be easy to obtain financing for.

Graph and download economic data for Rental Vacancy Rate for the United States (USRVAC) from 1986 to 2024 about vacancy, rent, rate, and USA.

United States Housing Vacancy Rate: Rental data was reported at 6.800 % in Jun 2018. This records a decrease from the previous number of 7.000 % for Mar 2018. United States Housing Vacancy Rate: Rental data is updated quarterly, averaging 7.400 % from Mar 1956 (Median) to Jun 2018, with 250 observations. The data reached an all-time high of 11.100 % in Sep 2009 and a record low of 5.000 % in Dec 1981. United States Housing Vacancy Rate: Rental 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.

Graph and download economic data for Rental Vacancy Rate for California (CARVAC) from 1986 to 2024 about vacancy, rent, CA, rate, and USA.

Graph and download economic data for Rental Vacancy Rate for Ohio (OHRVAC) from 1986 to 2024 about vacancy, rent, OH, rate, and USA.

This table contains data described by the following dimensions (Not all combinations are available): Geography (37 items: Census metropolitan areas; Saguenay; Quebec; Edmonton; Alberta; Calgary; Alberta ...).

South Dakota was the U.S. state with the highest vacancy rate index in January 2025. Conversely, New Jersey, New York, and Illinois had the lowest vacancy rate index during that period. All three states had an index value of under five percent. Overall, apartment vacancies in the U.S. have increased since 2021, due to the increase in new supply.

The homeowner vacancy rate in the United States reached its lowest value in 2022, followed by an uptick in 2023. The rate shows what share of owner-occupied housing units were vacant and for sale. That figure peaked in 2008, when nearly three percent of homes were vacant, and gradually fell below one percent after the 2020 housing boom. Homeownership is a form of living arrangement where the owner of the inhabited property, whether apartment, house, or type of real estate, lives on the premises. Due to usually high costs associated with owning a property and perceived advantages or disadvantages associated with such a long-term investment, homeownership rates differ greatly around the world, based on both cultural and economic factors. In Europe, Romania is the country with the highest rate of homeownership, while the lowest homeownership rate was observed in Switzerland. Homeownership attitude in the U.S. Individuals may have very different opportunities or inclination to become homeowners based on nationality, age, financial status, social status, occupation, marital status, education or even ethnicity and whether one is local-born or foreign-born. In 2023, the homeownership rate among older Americans was higher than for younger Americans. In the U.S., homeownership is generally believed to be a good investment, in terms of security (no risk of eviction) and financial aspect (owning a valuable real estate property). In 2023, there were approximately 86 million owner-occupied housing units, a stark increase compared to four decades prior. Why is homeownership sentiment low? The housing market has been suffering chronic undersupply, leading to a surge in prices and eroding affordability. In 2023, the housing affordability index plummeted, reflecting the growing challenge that homeowners face when looking for property. Insufficient income, savings, and high home prices are some of the major obstacles that come in the way of a property purchase. Though affordability varied widely across different metros, just about 15 percent of U.S. renters could afford to buy the median priced home in their area.

In 2023, the average vacancy rate index for rental apartments in different metros in Texas ranged between 6.6 percent and 9.1 percent. Dallas-Fort Worth-Arlington, the most populated metropolitan area, had a vacancy rate index of 7.6 percent in December 2023. Meanwhile, Killeen-Temple was the metro with the lowest index, at 6.59. According to the source, the index is calculated based on data on apartments listed on the Apartment List platform and changes in availability.

What is Rental Data?

Rental data encompasses detailed information about residential rental properties, including single-family homes, multifamily units, and large apartment complexes. This data often includes key metrics such as rental prices, occupancy rates, property amenities, and detailed property descriptions. Advanced rental datasets integrate listings directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.

Additional Rental Data Details

The rental data is sourced from over 20,000 property managers via direct feeds and property management platforms, covering over 30 percent of the national rental housing market for diverse and broad representation. Real-time updates ensure data remains current, while verified listings enhance accuracy, avoiding errors typical of survey-based or scraped datasets. The dataset includes 14+ million rental units with detailed descriptions, rich photography, and amenities, offering address-level granularity for precise market analysis. Its extensive coverage of small multifamily and single-family rentals sets it apart from competitors focused on premium multifamily properties.

Rental Data Includes:

• Property Types
• Single-Family Rentals
• Small Multi-family Units
• Premium Apartments
• 16,000+ ZIP Codes
• 800+ MSAs
• Pricing Trends
• Lease Terms Amenities

Graph and download economic data for Rental Vacancy Rate for Texas (TXRVAC) from 1986 to 2024 about vacancy, rent, TX, rate, and USA.

Graph and download economic data for Rental Vacancy Rate for Utah (UTRVAC) from 1986 to 2024 about UT, vacancy, rent, rate, and USA.

--- DATASET OVERVIEW --- This dataset captures detailed performance data for individual vacation rental properties, providing a complete picture of operational success metrics across different timeframes and market conditions. With weekly updates and four years of historical data, it enables both point-in-time analysis and long-term trend identification for property-level performance.

The data is derived from OTA platforms using advanced methodologies that capture listing, calendar and quote details. Our algorithms process this raw information to produce standardized and enriched performance metrics that facilitate accurate comparison across different property types, locations, and time periods. By leveraging our other datasets and machine learning models, we are able to accurately detect guest bookings, revenue generation, and occupancy patterns.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Occupancy Metrics: Daily, weekly, and monthly occupancy rates based on actual bookings - Revenue Generation: Total revenue, average daily rate (ADR), and revenue per available day (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Seasonality Indicators: Performance variations across seasons, months, and days of week - Competitive Positioning: Performance relative to similar properties in the same market - Historical and Forward Looking Trends: Year-over-year and month-over-month performance changes

--- USE CASES --- Property Performance Optimization: Property managers can leverage this dataset to evaluate the performance of individual listings against market benchmarks. By identifying properties that underperform relative to similar listings in the same area, managers can implement targeted improvements to pricing strategies, property amenities, or marketing approaches. The granular performance data enables precise identification of specific improvement opportunities at the individual property level.

Competitive Benchmarking: Property owners and managers can benchmark their listings against competitors with similar characteristics in the same market. The property-level performance metrics enable detailed comparison of occupancy rates, ADR, and revenue generation across comparable properties. This competitive intelligence helps identify realistic performance targets and market positioning opportunities.

Portfolio Optimization: Vacation rental portfolio managers can analyze performance variations across different property types and locations to optimize investment and management decisions. The dataset supports identification of high-performing property configurations and locations, enabling strategic portfolio development based on actual performance data rather than assumptions.

Seasonal Strategy Development: The historical performance data across different seasons enables development of targeted seasonal strategies. Property managers can analyze how different property types perform during specific seasons or events, informing marketing focus, pricing adjustments, and operational planning throughout the year.

Performance Forecasting: Historical performance patterns can be leveraged to develop accurate forecasts for future periods. By analyzing year-over-year trends and seasonal patterns, property managers can anticipate performance expectations and set realistic targets for occupancy and revenue generation.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data processing methodology ensures high-quality, reliable performance metrics that accurately represent actual property performance. The raw booking and revenue data undergoes extensive validation and normalization processes to address inconsistencies, identify anomalies, and ensure comparability across different pro...

In December 2024, the Australian city of Melbourne had a rental property vacancy rate of 2.2 percent. In contrast, the rental property vacancy rate in Hobart was estimated at 0.6 percent in the same month.

United States Housing Vacancy Rate: Rental: West data was reported at 5.100 % in Jun 2018. This records a decrease from the previous number of 5.200 % for Mar 2018. United States Housing Vacancy Rate: Rental: West data is updated quarterly, averaging 6.800 % from Mar 1956 (Median) to Jun 2018, with 250 observations. The data reached an all-time high of 12.600 % in Jun 1965 and a record low of 4.200 % in Dec 2016. United States Housing Vacancy Rate: Rental: West 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.

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Annual Rental Facility Occupancy Survey is conducted from April 1-30 each year and tracks vacancies, turnover rates, average rents, and amenities. Facilities located within Montgomery County's unincorporated areas as well as the municipalities of Rockville, Gaithersburg, and Takoma Park participate in the survey. This dataset includes the average reported rent by bedroom count for each community surveyed.