In 2024, there were approximately **** million housing units occupied by renters in the United States. This number has been gradually increasing since 2010 as part of a long-term upward swing since 1975. Meanwhile, the number of unoccupied rental housing units has followed a downward trend, suggesting a growing demand and supply failing to catch up. Why are rental homes in such high demand? This high demand for rental homes is related to the shortage of affordable housing. Climbing the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home. How many owner occupied homes are there in the U.S.? In 2023, there were over ** million owner occupied homes. Owner occupied housing is when the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing.

Following a period of stagnation over most of the 2010s, the number of owner occupied housing units in the United States started to grow in 2017. In 2023, there were over 86 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2023, the number of renter occupied housing units amounted to almost 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.

Graph and download economic data for Housing Inventory Estimate: Renter Occupied Housing Units in the United States (ERNTOCCUSQ176N) from Q2 2000 to Q1 2025 about inventories, housing, and USA.

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

In 2023, single-family homes and apartments in buildings with five or more units were the most popular structure for renters in the United States. Approximately *** million people lived in a rental home, with about ** million occupying an apartment in a multifamily building. That corresponded to about ** million households in total and ** million households living in an apartment in a large residential building.

Residential property managers have seen revenue growth in recent years; demand for management services is countercyclical, as more consumers switch to rentals when the economy worsens and the price of home ownership increases. Managers experienced growth during the economic downturn brought on by the COVID-19 pandemic, carried by improved residential constructions. Rental vacancy rates declined as property owners needed to fill more apartments to maximize revenue during a time of uncertainty, as the eviction moratorium prevented them from pushing out renters who couldn't pay. Revenue has grown at a CAGR of 7.3% over the five years to 2024, when revenue is set to reach $113.8 billion, when revenue is set to grow 1.6% and profit is set to have seen overall growth. Homeownership provides the most substantial competition to residential property managers. Interest rates were lowered to spur economic growth during the COVID-19 pandemic, leading to increased homeownership. The Federal Reserve then hiked interest rates multiple times to combat persistent inflation, pushing many residents back to renting. The rental vacancy rate accordingly fell over the past five years. While this may provide more immediate revenue, many property owners purposefully keep a certain quantity of units empty to maintain higher value, supporting profit by increasing the return per unit. Moving forward, the value of residential construction will grow, increasing the profitability of opening rental facilities. Falling interest rates, with cuts having begun in 2024, will have a mixed impact on the industry. Disposable income will grow while this happens, meaning capable renters will not be in short supply. Altogether, revenue is set to grow at a CAGR of 1.7% over the five years to 2029, when revenue is set to reach $122.7 billion.

The majority of rental properties in the private sector in England were owned by individuals in 2024. Out of the total rental housing stock in the country, approximately ******* homes were owned by individuals, while ****** were owned as part of a company and *****, as both. Large portfolios of more than ** properties were more likely to be owned by companies or a combination of individuals and companies.

A. SUMMARY Beginning in 2022, the law requires owners of residential housing units in San Francisco to report certain information about their units to the San Francisco Rent Board on an annual basis. For units (other than condominium units) in buildings of 10 residential units or more, owners were required to begin reporting this information to the Rent Board by July 1, 2022, with updates due on March 1, 2023 and every March 1 thereafter. For condominium units and units in buildings with less than 10 residential units, reporting began on March 1, 2023 with updates due every March 1 thereafter. Owners are also required to inform the Rent Board within 30 days of any change in the name or business contact information of the owner or designated property manager. The Rent Board uses this information to create and maintain a “housing inventory” of all units in San Francisco that are subject to the Rent Ordinance.

B. HOW THE DATASET IS CREATED The Rent Board has developed a secure website portal that provides an interface for owners to submit the required information (The Housing Inventory). The Rent Board uses the information provided to generate reports and surveys, to investigate and analyze rents and vacancies, to monitor compliance with the Rent Ordinance, and to assist landlords and tenants and other City departments as needed. The Rent Board may not use the information to operate a “rental registry” within the meaning of California Civil Code Sections 1947.7 – 1947.8.

C. UPDATE PROCESS The Housing Inventory is continuously updated as it receives submissions from the public. The portal is available to the public 24/7. The Rent Board Staff also makes regular updates to the data during regular business hours, and the data is shared to DataSF every 24 hours.

D. HOW TO USE THIS DATASET It is important to note that this dataset contains information submitted by residential property owners and tenants. The Rent Board does not review or verify the accuracy of the data submitted. Please note that historical data is subject to change.

Notes for Analysis - Addresses have been anonymized to the block level - Latitude & Longitude are the closest mid-block point to the unit - Each row is a unit. To count total units, first select a year then count unique ids. Do not sum unit count.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller thelevel of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock· Population: Total Population (B01003)· Households: Total number of households (B25003)· OwnHH: Total number of owner-occupied households (B25003)· RentHH: Total number of renter-occupied households (B25003)· TotalU: Total number of housing units (B25001)· VacantU: Total number of vacant units (B25004)· SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)· ForSale: Total number of units currently for sale (B25004)· ForRent: Total number of units currently for rent (B25004)· MedianHI: Median Household Income (B25119)· OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· Poverty: Population below poverty level. (B17001)· PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)· MYearBuilt: Median structure year of construction. (B25035)

Vacation Rental Property Management System Market Outlook

The global vacation rental property management system market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 7.3 billion by 2032, growing at a CAGR of 12.1% from 2024 to 2032. This substantial growth is driven by the increasing demand for vacation rentals due to a shift in consumer preference towards unique and personalized accommodations. Additionally, advancements in technology and the integration of AI and IoT in property management systems are further propelling the market growth.

One of the primary growth factors in the vacation rental property management system market is the surge in the tourism industry. As travelers seek more personalized and home-like experiences, vacation rentals are becoming a preferred choice over traditional hotels. This trend is encouraging property managers and homeowners to adopt sophisticated management systems that can streamline operations, enhance guest experiences, and maximize revenue. The ease of booking and managing properties through integrated platforms not only attracts more guests but also simplifies the management process for property owners. Moreover, the ability to manage multiple properties from a single dashboard is significantly appealing to property managers.

Another significant growth driver is the increasing adoption of digital technologies. The integration of AI, machine learning, and IoT in property management systems is revolutionizing the way vacation rentals are managed. These technologies enable predictive maintenance, personalized guest experiences, and efficient resource management. For instance, AI can help in dynamic pricing, ensuring that property owners can maximize their rental income based on demand fluctuations. IoT devices, on the other hand, can monitor and control various aspects of the property, such as lighting, heating, and security, thereby enhancing the overall guest experience and property security.

The rise of the sharing economy is also contributing to the growth of the vacation rental property management system market. Platforms like Airbnb, Vrbo, and Booking.com have popularized vacation rentals and made them accessible to a broader audience. These platforms provide property owners with the tools and visibility needed to reach potential guests, while also offering guests a wide range of accommodation options. This increased visibility and accessibility have led to a surge in the number of vacation rentals, further driving the demand for advanced property management systems that can handle the complexities of managing multiple bookings and maintaining high service standards.

Regionally, North America holds a significant share of the vacation rental property management system market, driven by a well-established tourism industry and high internet penetration rates. The presence of major market players and the early adoption of advanced technologies in this region are also contributing to the market growth. Europe follows closely, with countries like France, Spain, and Italy being popular vacation destinations. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rising disposable incomes, increasing tourism activities, and a growing inclination towards vacation rentals among travelers.

Component Analysis

The vacation rental property management system market is segmented by component into software and services. The software segment holds a significant share of the market due to the increasing need for efficient property management solutions. These software solutions offer various features such as automated booking, guest communications, payment processing, and reporting, which help in streamlining operations and enhancing guest experiences. Advanced software solutions also integrate with third-party platforms, enabling property managers to manage their listings across multiple channels from a single interface.

The software segment is further divided into various types, including booking management software, customer relationship management (CRM) software, and property management software. Booking management software helps in automating the reservation process, reducing manual errors, and improving efficiency. CRM software enables property managers to maintain detailed guest profiles, personalize communications, and enhance guest satisfaction. Property management software provides a comprehensive solution for managing all aspects of the property, from maintenance to financial management.

On the other hand, the serv

Online Detached House Rental Market Outlook

The global online detached house rental market size is expected to grow from USD 50 billion in 2023 to USD 85 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of 6%. This growth is largely driven by increasing urbanization, the proliferation of digital platforms, and the evolving preferences for rental accommodations over homeownership. Additionally, the convenience and transparency provided by online rental platforms have significantly contributed to the market's expansion.

One of the primary growth factors for the online detached house rental market is the increasing acceptance and reliance on digital technology. As more people become comfortable with online transactions and digital platforms, the ease of finding, comparing, and renting houses online has led to a surge in demand. Furthermore, advancements in virtual reality and augmented reality technologies have enhanced the house viewing experience, allowing potential tenants to tour properties remotely, which has widened the geographical scope of rental markets.

Another significant growth driver is the shifting attitude towards renting versus owning property, especially among younger generations. Millennials and Gen Z are more inclined towards flexible living arrangements that accommodate travel, career mobility, and lifestyle changes. The economic uncertainty post-COVID-19 has also made many wary of long-term financial commitments associated with homeownership, thus driving the rental market. The rising cost of homeownership in urban areas also contributes to this trend, making renting a more feasible option.

Additionally, the global urbanization trend plays a crucial role in fueling the market. As more people move to cities for better employment opportunities, the demand for rental housing, including detached houses, increases. Urban areas are witnessing a higher influx of professionals and families looking for spacious accommodations, driving the need for detached rental homes. Moreover, property owners are increasingly listing their properties on online platforms to reach a broader audience and ensure higher occupancy rates.

From a regional perspective, North America is expected to dominate the online detached house rental market due to its advanced digital infrastructure and high internet penetration rates. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. This is attributed to rapid urbanization, growing middle-class population, and the increasing popularity of digital services in countries like China, India, and Southeast Asian nations. The integration of advanced technologies and the rising number of internet users in these regions further bolster market growth.

The concept of Vacation Rental has become increasingly popular in recent years, especially with the rise of platforms like Airbnb and Vrbo. Vacation rentals offer a unique opportunity for travelers to experience a home-like environment while exploring new destinations. These rentals often provide more space, privacy, and personalized amenities compared to traditional hotel accommodations. For property owners, vacation rentals present a lucrative opportunity to generate income, particularly during peak travel seasons. This trend has been further fueled by the growing preference for unique and immersive travel experiences, as well as the flexibility that vacation rentals offer in terms of location and duration of stay. As the vacation rental market continues to expand, it is expected to play a significant role in shaping the broader rental market landscape.

Property Type Analysis

In the online detached house rental market, property types are segmented into luxury detached houses and standard detached houses. The luxury detached houses segment caters to high-net-worth individuals and expatriates seeking premium accommodations with superior amenities. This segment often features properties with exclusive locations, extensive grounds, and high-end finishes, attracting a niche market willing to pay a premium for luxury and comfort. Although this segment represents a smaller portion of the overall market, it commands higher rental prices and contributes significantly to the market's revenue.

Standard detached houses comprise the larger segment, catering to the broader population including middle-income families and professionals. These houses offer essential am

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 2012-2016, to show comparison of housing ownership costs and rental costs to income, by census tract 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 2012-2016). 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, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Units_Owner_Costs_Computed = # Housing units for which Selected Monthly Owner Costs as a percentage of household income are computed, 2016 Units_Owner_Costs_Computed_MOE = # Housing units for which Selected Monthly Owner Costs as a percentage of household income are computed (Margin of Error), 2016 Units_OwnCost_30pct_Income = # Owner-occupied housing units, costs 30.0% of income or more, 2016 Units_OwnCost_30pct_Income_MOE = # Owner-occupied housing units, costs 30.0% of income or more (Margin of Error), 2016 Pct_OwnCost_30pct_Income = % Owner-occupied housing units, costs 30.0% of income or more, 2016 Pct_OwnCost_30pct_Income_MOE = % Owner-occupied housing units, costs 30.0% of income or more (Margin of Error), 2016 Units_RentToIncome_Computed = # Occupied units for which rent as % of income can be computed, 2016 Units_RentToIncome_Computed_MOE = # Occupied units for which rent as % of income can be computed (Margin of Error), 2016 Units_Rent_More30Pct_Income = # Gross rent 30.0% of income or greater, 2016 Units_Rent_More30Pct_Income_MOE = # Gross rent 30.0% of income or greater (Margin of Error), 2016 Pct_Rent_More30Pct_Income = % Gross rent 30.0% of income or greater, 2016 Pct_Rent_More30Pct_Income_MOE = % Gross rent 30.0% of income or greater (Margin of Error), 2016 Num_Tot_HH_RentOwnCosts = # Total households paying for housing (rent or owner costs), 2016 Num_Tot_HH_RentOwnCosts_MOE = # Total households paying for housing (rent or owner costs) (Margin of Error), 2016 Units_HsCosts_30pct_Income = # Occupied units for which costs exceed 30% of income, 2016 Units_HsCosts_30pct_Income_MOE = # Occupied units for which costs exceed 30% of income (Margin of Error), 2016 Pct_HsCosts_30pct_Income = % Occupied units for which costs exceed 30% of income, 2016 Pct_HsCosts_30pct_Income_MOE = % Occupied units for which costs exceed 30% of income (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

This dataset denotes HUD subsidized Multifamily Housing properties excluding insured hospitals with active loans. HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

Housing Rental Service Market Outlook

The global housing rental service market size was valued at $1.56 trillion in 2023 and is projected to reach $2.56 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.6% during the forecast period. This growth is primarily driven by increasing urbanization, rising population density in metropolitan areas, and the shift in consumer preference towards rental accommodations over homeownership. The demand for housing rental services is also fueled by the flexibility and cost-effectiveness they offer compared to buying properties, particularly in economically volatile environments.

One of the primary growth factors influencing the housing rental service market is the rapid urbanization happening globally. As more people move to urban centers in search of better employment opportunities, the demand for rental housing rises significantly. Urban areas often come with high property prices, making homeownership less feasible for many individuals. Consequently, the rental market becomes an attractive alternative, providing more affordable and flexible living arrangements. Additionally, the increasing number of single-person households and young professionals seeking mobility and convenience further propels the market.

Another significant driver is the growing popularity of the sharing economy, which has revolutionized the way people perceive and utilize property. Platforms like Airbnb have normalized short-term rentals, contributing to the market's growth. These platforms offer homeowners the opportunity to monetize vacant properties and provide renters with cost-effective and flexible options. This shift towards embracing short-term rentals is also supported by advancements in technology, which make it easier for users to find, book, and manage rental properties online, thus enhancing the overall user experience.

Economic factors also play a crucial role in the growth of the housing rental service market. In regions with high costs of living and economic uncertainty, renting becomes a more viable option compared to purchasing a home. Renting allows for better financial flexibility, avoiding the long-term commitment and financial burden that comes with a mortgage. Moreover, the trend towards remote work, accelerated by the COVID-19 pandemic, has led to changes in housing preferences, where people are no longer constrained to live near their workplaces, allowing them to choose rental properties that better suit their lifestyle and budget.

From a regional perspective, North America and Europe are major markets for housing rental services due to the high rate of urbanization and a substantial population of expatriates and young professionals. The Asia Pacific region is anticipated to witness significant growth, driven by rapid urbanization in countries like China and India. The Middle East & Africa and Latin America are also expected to see moderate growth, supported by improving economic conditions and increased foreign investments in real estate. These regional dynamics highlight the varied but robust demand for rental housing services worldwide.

The luxury rental market is an intriguing segment within the broader housing rental service market. This niche caters to high-net-worth individuals and expatriates who seek premium accommodations with top-tier amenities and services. Luxury rentals often include features such as concierge services, private gyms, and high-end finishes, appealing to those who prioritize comfort and exclusivity. In urban centers, luxury apartments and penthouses are particularly popular, offering breathtaking views and proximity to cultural and business hubs. The demand for luxury rentals is also driven by the increasing number of affluent individuals and the global mobility of professionals who prefer renting over purchasing properties in foreign locations.

Type Analysis

The housing rental service market can be segmented by type into short-term rentals and long-term rentals. Short-term rentals, including vacation rentals and corporate housing, have gained significant traction due to the popularity of platforms like Airbnb and VRBO. These rentals are appealing to travelers and business professionals seeking temporary accommodation without the commitment of a long-term lease. The flexibility and convenience provided by short-term rentals, coupled with the ability to experience different neighborhoods and properties, have made them an attractive option for many consumers.&

This table measures the price development in the transaction price of a dwelling that was purchased for own-use and the cost of all goods and services that households purchase in their role as owner occupiers of dwellings. Acquisition costs concerns the purchase of a newly built dwelling, self-built dwelling and a former rental dwelling. Costs of possession a dwelling concerns costs of (large) maintenance and property insurance.

Data available from: 1st quarter 2010

Status of the figures: The figures in this table are one period provisional; the sub-series ‘Acquisition formally rented dwelling’ and ‘Structural costs: insurance’ are final directly.

Changes as of July 4th 2025: The figures for period 1st quarter 2025 have been added and the figures for the 4th quarter and the year 2024 are now final.

Changes as of June 27th 2024: The figures in this table for the period 2015-2023 have been corrected as a result of the application of an improved methodology.

When will new figures be published? New figures are published in October 2025.

70% of White British households owned their own homes – the highest percentage out of all ethnic groups.

The largest owner of apartments in the United States was Greystar, an international developer and manager headquartered in Charleston, SC. In 2024, Greystar owned nearly ******* units. MAA, a Tennessee-based real estate investment trust, ranked second, with ****** apartments owned. Real estate investment trusts The majority of the largest owners of apartments in the U.S. are real estate investment trusts (REITs), which are companies who own (and usually operate) income producing real estate. REITs were created in 1960, when the Cigar Excise Tax Extension permitted investment in large-scale diversified real estate portfolios through the purchase and sale of liquid securities. This effectively aligned investment in real estate with other asset classes. In 2023, there were approximately 200 REITs in the United States with a market capitalization of *** trillion U.S. dollars. Apartments in the United States The rental return for apartments in the U.S. has been steadily climbing in recent times, with the national monthly median rent for an unfurnished apartment steadily increasing since 2012. Over this period, apartment vacancy rates have been decreasing across the country, suggesting that demand outweighs supply. Accordingly, large-scale investment in apartments by REITs is likely to continue into the foreseeable future.

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, and USA.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock

·
Population: Total Population (B01003)

·
Households: Total number of households (B25003)

·
OwnHH: Total number of owner-occupied households (B25003)

·
RentHH: Total number of renter-occupied households (B25003)

·
TotalU: Total number of housing units (B25001)

·
VacantU: Total number of vacant units (B25004)

·
SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)

·
ForSale: Total number of units currently for sale (B25004)

·
ForRent: Total number of units currently for rent (B25004)

·
MedianHI: Median Household Income (B25119)

·
OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
Poverty: Population below poverty level. (B17001)

·
PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)

·
MYearBuilt: Median structure year of construction. (B25035)