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

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 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.

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

Taiwan CREI: Office Listing Rent Index: TB: Nanjing & Guangfu Road data was reported at 97.000 2006-2008=100 in Dec 2010. This stayed constant from the previous number of 97.000 2006-2008=100 for Sep 2010. Taiwan CREI: Office Listing Rent Index: TB: Nanjing & Guangfu Road data is updated quarterly, averaging 99.000 2006-2008=100 from Dec 2008 (Median) to Dec 2010, with 9 observations. The data reached an all-time high of 102.000 2006-2008=100 in Dec 2008 and a record low of 97.000 2006-2008=100 in Dec 2010. Taiwan CREI: Office Listing Rent Index: TB: Nanjing & Guangfu Road data remains active status in CEIC and is reported by Taiwan Real Estate Research Center. The data is categorized under Global Database’s Taiwan – Table TW.EB027: Office Rent Index and Vacancy Rate: Taiwan Real Estate Research Center, Cathay Real Estate Development Company Ltd.

Taiwan Office Rent Listing Rate: Taipei City Grade A (TA) data was reported at 2,870.000 NTD/Ping in Mar 2018. This records an increase from the previous number of 2,868.000 NTD/Ping for Dec 2017. Taiwan Office Rent Listing Rate: Taipei City Grade A (TA) data is updated quarterly, averaging 2,753.000 NTD/Ping from Mar 2004 (Median) to Mar 2018, with 57 observations. The data reached an all-time high of 2,874.000 NTD/Ping in Sep 2017 and a record low of 2,000.000 NTD/Ping in Mar 2004. Taiwan Office Rent Listing Rate: Taipei City Grade A (TA) data remains active status in CEIC and is reported by Taiwan Real Estate Research Center. The data is categorized under Global Database’s Taiwan – Table TW.EB027: Office Rent Index and Vacancy Rate: Taiwan Real Estate Research Center, Cathay Real Estate Development Company Ltd.

Taiwan CREI: Office Listing Rent Index: TA: Nanjing & Songjiang Road data was reported at 89.000 2006-2008=100 in Dec 2010. This records a decrease from the previous number of 90.000 2006-2008=100 for Sep 2010. Taiwan CREI: Office Listing Rent Index: TA: Nanjing & Songjiang Road data is updated quarterly, averaging 89.000 2006-2008=100 from Mar 2008 (Median) to Dec 2010, with 12 observations. The data reached an all-time high of 91.000 2006-2008=100 in Dec 2008 and a record low of 86.000 2006-2008=100 in Dec 2009. Taiwan CREI: Office Listing Rent Index: TA: Nanjing & Songjiang Road data remains active status in CEIC and is reported by Taiwan Real Estate Research Center. The data is categorized under Global Database’s Taiwan – Table TW.EB027: Office Rent Index and Vacancy Rate: Taiwan Real Estate Research Center, Cathay Real Estate Development Company Ltd.

Graph and download economic data for Employment for Real Estate and Rental and Leasing: Rental and Leasing Services (NAICS 532) in the United States (IPULN532W010000000) from 1987 to 2024 about leases, rent, NAICS, real estate, services, employment, and USA.

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.

In the fourth quarter of 2024, Singapore's retail real estate rental index was at 79.1. The retail real estate space rental index had started to decline after the fourth quarter of 2019.

Taiwan CREI: Office Listing Rent Index: TA: Ren'ai & Dunnan Road data was reported at 98.000 2006-2008=100 in Dec 2010. This stayed constant from the previous number of 98.000 2006-2008=100 for Sep 2010. Taiwan CREI: Office Listing Rent Index: TA: Ren'ai & Dunnan Road data is updated quarterly, averaging 98.000 2006-2008=100 from Mar 2008 (Median) to Dec 2010, with 12 observations. The data reached an all-time high of 102.000 2006-2008=100 in Jun 2008 and a record low of 96.000 2006-2008=100 in Mar 2008. Taiwan CREI: Office Listing Rent Index: TA: Ren'ai & Dunnan Road data remains active status in CEIC and is reported by Taiwan Real Estate Research Center. The data is categorized under Global Database’s Taiwan – Table TW.EB027: Office Rent Index and Vacancy Rate: Taiwan Real Estate Research Center, Cathay Real Estate Development Company Ltd.

Graph and download economic data for Employment Cost Index: Wages and salaries for Private industry workers in Real estate and rental and leasing, excluding incentive paid (CIU2025300000710I) from Q1 2006 to Q1 2025 about paid, ECI, leases, rent, real estate, salaries, workers, private industries, wages, private, industry, and USA.

United States - Employment Cost Index: Total compensation for Private industry workers in Real estate and rental and leasing, excluding incentive paid was 168.20000 Index: Dec 2005=100 in January of 2025, according to the United States Federal Reserve. Historically, United States - Employment Cost Index: Total compensation for Private industry workers in Real estate and rental and leasing, excluding incentive paid reached a record high of 168.20000 in January of 2025 and a record low of 100.50000 in April of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employment Cost Index: Total compensation for Private industry workers in Real estate and rental and leasing, excluding incentive paid - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Employment Cost Index: Total compensation for Private industry workers in Real estate and rental and leasing, excluding incentive paid (CIU2015300000710I) from Q1 2006 to Q4 2021 about ECI, paid, leases, compensation, rent, real estate, private industries, workers, private, industry, and USA.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the AI-Powered Rental Price Index market size reached USD 1.7 billion in 2024, reflecting the rapid adoption of artificial intelligence technologies in the real estate sector. The market is projected to grow at a robust CAGR of 18.9% from 2025 to 2033, with the forecasted market size anticipated to reach USD 8.5 billion by 2033. This impressive growth trajectory is driven by the increasing demand for data-driven rental pricing solutions, the proliferation of smart property management systems, and the need for real-time market intelligence among property stakeholders.

One of the key growth factors fueling the expansion of the AI-Powered Rental Price Index market is the escalating complexity and dynamism of global rental markets. Traditional pricing models often fail to capture the nuanced shifts in demand and supply, especially in urban and high-growth regions. AI-powered solutions leverage vast datasets, including historical rental data, economic indicators, neighborhood trends, and even social sentiment, to provide highly accurate and adaptive rental price indices. This enables property managers, landlords, and real estate agencies to optimize pricing strategies, reduce vacancy rates, and maximize returns. The ability to harness predictive analytics and machine learning for rental price forecasting is increasingly seen as a competitive differentiator in the industry.

Another significant driver is the digital transformation sweeping through the real estate sector. The integration of AI-powered rental price indices with property management platforms, listing services, and financial analytics tools is streamlining operations and enhancing decision-making. Cloud-based deployment models are making these advanced analytics accessible to a broader range of users, from large real estate agencies to individual landlords. The automation of rental price assessments not only reduces human error but also accelerates the leasing process, providing a seamless experience for both property owners and tenants. Furthermore, the growing emphasis on transparency and fairness in rental pricing is prompting regulatory bodies and public sector organizations to adopt AI-driven solutions for market monitoring and policy formulation.

The surge in urbanization and the proliferation of rental properties, especially in emerging economies, are also contributing to market growth. As cities expand and rental housing becomes a primary option for a growing segment of the population, the need for accurate, real-time rental price indices becomes critical. AI-powered platforms are uniquely positioned to capture hyper-local trends, adjust for seasonality, and factor in external events such as economic shocks or policy changes. This level of granularity and agility is essential for navigating the increasingly competitive and fragmented rental market landscape. Additionally, the COVID-19 pandemic has accelerated the adoption of digital solutions in real estate, further boosting the demand for AI-powered rental price indices.

Regionally, North America currently dominates the AI-Powered Rental Price Index market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, has witnessed widespread adoption of AI-driven property management tools, supported by a mature real estate ecosystem and high digital literacy. Europe is rapidly catching up, driven by regulatory initiatives and a strong focus on data-driven urban planning. The Asia Pacific region is expected to exhibit the highest CAGR over the forecast period, fueled by rapid urbanization, rising investments in proptech startups, and the digitalization of real estate services in countries like China, India, and Australia. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as local governments and private players recognize the value of AI in addressing housing market inefficiencies.

Component Analysis

The AI-Powered Rental Price Index market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. The software segment comprises AI algorithms, analytics engines, and user interfaces that enable stakeholders to access, interpret, and act on rental price data. These platforms are increasingly incorporating advanced features such as n

This layer contains the vacancy status of vacant housing units by tract, 2012-2016. Data from American Community Survey. Margins of error included.Vacancy status information is used by public- and private-sector organizations to evaluate the need for new housing programs and initiatives. As a component of the index of leading economic indicators, the rental vacancy rate is used by the federal government and economic forecasters to gauge the economic climate.Nationally, the vacancy status breakdown is below. Second to "other vacant," the most common vacancy type is for seasonal, recreational, or occasional use. United StatesEstimateMargin of ErrorTotal:16,338,662+/-214,426For rent2,855,844+/-47,433Rented, not occupied616,696+/-12,934For sale only1,395,797+/-29,630Sold, not occupied636,952+/-14,367For seasonal, recreational, or occasional use5,368,085+/-40,254For migrant workers35,398+/-1,372Other vacant5,429,890+/-78,831Accompanying web map also available.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the global AI-Powered Rental Price Index market size reached USD 1.84 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.2% projected through the forecast period. By 2033, the market is anticipated to achieve a value of USD 8.19 billion, driven by increasing demand for data-driven pricing strategies, rapid digital transformation in real estate, and the growing adoption of artificial intelligence across property valuation and management. As per our comprehensive analysis, the market is witnessing exponential growth due to the need for accurate, real-time rental price insights, supporting both property owners and tenants in making informed decisions.

One of the primary growth factors fueling the AI-Powered Rental Price Index market is the escalating need for transparency and precision in rental pricing, especially in highly dynamic urban real estate environments. Traditional pricing methodologies often fall short in accounting for rapidly shifting market variables, such as sudden changes in demand, local economic trends, or emerging neighborhood developments. AI-powered solutions leverage advanced algorithms and machine learning models to process vast datasets, including historical rental prices, property attributes, neighborhood analytics, and even social sentiment. This enables real estate stakeholders to arrive at more accurate and competitive rental prices, minimizing vacancies and maximizing returns. Further, the integration of AI with Internet of Things (IoT) and smart city initiatives is enhancing the granularity and timeliness of rental data, solidifying the value proposition of AI-powered rental indices.

Another significant growth driver is the increasing adoption of digital platforms by real estate agencies, property managers, and institutional investors. The transformation from manual, spreadsheet-based assessments to automated, AI-driven platforms is streamlining operations, reducing human error, and enabling scalable portfolio management. Financial institutions are also leveraging AI-powered rental indices for risk assessment, loan underwriting, and investment analysis, further expanding the addressable market. Additionally, the proliferation of proptech startups and increased venture capital investments in real estate technology are accelerating the innovation cycle, resulting in more sophisticated and customizable AI-powered pricing solutions. The rising consumer expectation for transparency and fairness in rental pricing, particularly among younger, tech-savvy renters, is further catalyzing market growth.

Furthermore, regulatory developments and government initiatives aimed at improving housing affordability and market efficiency are positively impacting the AI-Powered Rental Price Index market. In many regions, public sector agencies are collaborating with technology providers to develop standardized rental indices, which support policy-making, rent control measures, and urban planning. These collaborations are fostering an environment where AI-powered analytics are not only a competitive advantage for private enterprises but also a tool for public good. However, market expansion is somewhat tempered by challenges related to data privacy, algorithmic transparency, and the need for standardized data formats across jurisdictions. Addressing these issues will be crucial for sustained growth and broader adoption in the coming years.

Regionally, North America continues to dominate the AI-Powered Rental Price Index market, accounting for the largest share in 2024, owing to its mature real estate sector, high digital adoption, and strong presence of leading proptech firms. Europe is experiencing rapid growth, particularly in countries with high urbanization rates and regulatory support for digital transformation in real estate. Asia Pacific is emerging as a high-growth region, driven by urban expansion, smart city projects, and a burgeoning middle class seeking reliable rental information. While Latin America and Middle East & Africa are currently smaller markets, they present significant long-term potential as digital infrastructure and real estate investment accelerate. Overall, regional dynamics are shaped by varying levels of technological maturity, regulatory frameworks, and the pace of urbanization.

United States - Employment Cost Index: Wages and salaries for Private industry workers in Real estate and rental and leasing was 165.80000 Index: Dec 2005=100 in January of 2025, according to the United States Federal Reserve. Historically, United States - Employment Cost Index: Wages and salaries for Private industry workers in Real estate and rental and leasing reached a record high of 165.80000 in January of 2025 and a record low of 83.90000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employment Cost Index: Wages and salaries for Private industry workers in Real estate and rental and leasing - last updated from the United States Federal Reserve on July of 2025.

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.

The Location Affordability Index (LAI) estimates the percentage of a family’s income dedicated to the combined cost of housing and transportation in a given location. Because what is “affordable” is different for everyone, users can choose among a diverse set of family profiles—which vary by household income, size, and number of commuters—and see the affordability landscape for each in a given neighborhood, city, or region. The Location Affordability Index (LAI) estimates three dependent variables of transportation behavior (auto ownership, auto use, and transit use) as functions of 14 independent variables (median income, per capita income, average household size, average commuters per household, residential density, gross density, block density, intersection density, transit connectivity, transit frequency of service, transit access shed, employment access, job diversity, and average commute distance). To hone in on the built environment’s influence on transportation costs, the independent household variables (income, household size, and commuters per household) are set at fixed values to control for any variation they might cause. The LAI also estimates two dependent variables of housing costs (Selected Monthly Owner Costs and Gross Rent) as functions of 16 independent variables: regional median selected monthly owner costs and regional median gross rent in addition to the 14 variables used in the transportation model.

To learn more about the Location Affordability Index (v.1.0) visit: https://www.locationaffordability.info/LAPMethods.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Location Affordability Indev v.1.0. Date of Coverage: 2005-2009 https://www.locationaffordability.info/LAPMethodsV2.pdf