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.

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.

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directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.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 Housing Inventory Estimate: Renter Occupied Housing Units in the United States (ERNTOCCUSQ176N) from Q2 2000 to Q1 2025 about inventories, housing, and USA.

The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

Graph and download economic data for Zillow Home Value Index (ZHVI) for All Homes Including Single-Family Residences, Condos, and CO-OPs in the United States of America (USAUCSFRCONDOSMSAMID) from Jan 2000 to May 2025 about 1-unit structures, family, residential, housing, indexes, and USA.

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q1 2025

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.

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

Mobile Home Rental Market Outlook

The global mobile home rental market size was valued at USD 9.3 billion in 2023, and it is projected to reach USD 17.8 billion by 2032, growing at a CAGR of 7.5% during the forecast period. The growth of this market is driven by the increasing demand for affordable housing solutions and the rising trend of mobile living among different demographics. As urbanization continues to increase and housing prices soar, mobile homes present a viable and cost-effective alternative to traditional housing. The flexibility, affordability, and customization options associated with mobile homes have made them an attractive choice for many, thereby fueling the growth of the rental segment.

One of the primary growth factors for the mobile home rental market is the rising affordability crisis in urban housing. As property prices continue to skyrocket in major cities around the world, more individuals and families are turning to mobile homes as a practical solution. Mobile homes offer a lower cost of living, reduced maintenance expenses, and the ability to relocate easily, making them an appealing option for those who face financial constraints. Additionally, mobile homes are increasingly being designed with modern amenities and high-quality materials, improving their appeal and livability.

Another significant growth driver is the increasing acceptance and popularity of mobile home parks. These parks provide a community-based living environment with amenities such as recreational facilities, security, and maintenance services. This community aspect, combined with the affordability of mobile homes, attracts a diverse range of renters, from young professionals to retirees. Moreover, governments in various regions are also supporting the development of mobile home parks to address the housing shortage, further boosting the market.

The growing trend of minimalistic and sustainable living is also contributing to the market's expansion. Many individuals are prioritizing smaller, eco-friendly living spaces that reduce their carbon footprint. Mobile homes, which often employ sustainable building practices and materials, cater to this demographic. The ability to downsize and live a more sustainable lifestyle without sacrificing comfort is a strong selling point for mobile homes, increasing their popularity among environmentally conscious renters.

Regionally, North America holds the largest share of the mobile home rental market due to the high demand for affordable housing solutions and the presence of well-established mobile home communities. Europe is also witnessing significant growth, driven by similar affordability concerns and an increasing preference for flexible living options. Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by rapid urbanization, population growth, and government initiatives supporting affordable housing. Latin America and the Middle East & Africa regions are also showing promising growth potential, albeit at a slower pace.

Type Analysis

The mobile home rental market can be segmented by type into Single-Wide, Double-Wide, and Triple-Wide homes. Single-Wide mobile homes are the most traditional and common type, featuring a narrow and elongated structure that is easy to transport and set up. These homes are highly popular among individual renters and small families due to their affordability and simplicity. Despite their smaller size, many single-wide homes are equipped with modern amenities, making them a comfortable living option. The demand for single-wide homes remains strong, particularly in regions where affordable housing is scarce.

Double-Wide mobile homes consist of two sections that are joined together to create a larger living space. These homes offer more interior space and design flexibility compared to single-wide models, catering to families and individuals who require more room. The growing preference for spacious living environments without the high costs associated with traditional homes is driving the demand for double-wide mobile homes. Additionally, double-wide homes often feature more advanced amenities and higher quality finishes, further enhancing their appeal.

Triple-Wide mobile homes represent the largest and most luxurious segment within the mobile home rental market. These homes consist of three joined sections, providing a spacious and comfortable living environment that can rival traditional houses. Triple-wide homes are designed to offer maximum comfort and luxury, often featuring multiple bedrooms, large kitchens,

Graph and download economic data for Interest Rates and Price Indexes; Multi-Family Real Estate Apartment Price Index, Level (BOGZ1FL075035403A) from 1985 to 2024 about multifamily, real estate, family, interest rate, interest, rate, price index, indexes, price, and USA.

This statistic shows the change in rental rates for single-family homes in selected housing markets in the United States in August 2016 and August 2017. The rents for single-family homes in Orlando, Florida increased by 4.3 percent between August 2016 and August 2017.

This dataset denotes Public Housing Authority (PHA) office locations, contact information, and program availability. Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

The detached house market, a significant segment of the residential real estate sector, is experiencing robust growth driven by several key factors. Strong population growth, particularly in suburban areas, coupled with increasing household incomes and a preference for larger living spaces, fuels demand. Low interest rates in recent years (though this is subject to change) have also stimulated buyer activity, further bolstering the market. However, supply chain constraints impacting construction materials and labor shortages have presented significant challenges, leading to higher construction costs and limited inventory. This has contributed to increased house prices and heightened competition among buyers. The market is segmented by size (e.g., single-story, multi-story), location (urban, suburban, rural), and price point (luxury, mid-range, entry-level), each segment exhibiting its own unique growth trajectory. While the current market is characterized by strong demand and higher prices, potential future economic downturns or shifts in interest rate policies represent key risks. Major players in the market, including Horton, Pulte Homes, and Invitation Homes, are adapting to these challenges through strategic land acquisitions, innovative construction techniques, and diversified rental portfolios. The forecast for the detached house market indicates continued expansion, albeit at a potentially moderated pace compared to recent years. Growth will likely be driven by ongoing population growth and the continued preference for single-family homes. Technological advancements in construction and sustainable building practices are anticipated to increase efficiency and address environmental concerns. However, affordability remains a major concern, potentially limiting market expansion, particularly for first-time homebuyers. Government regulations aimed at increasing housing affordability and addressing climate change will significantly influence the market's trajectory. The long-term outlook remains positive, contingent upon addressing supply chain challenges and managing economic volatility. Careful analysis of these factors is crucial for stakeholders to navigate the market effectively and make informed investment decisions.

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.

Real Estate Rental Market Outlook

The global real estate rental market size is projected to grow from USD 1.5 trillion in 2023 to approximately USD 2.3 trillion by 2032, reflecting a CAGR of 4.8% over the forecast period. This growth is primarily driven by urbanization, increasing disposable incomes, and the evolving nature of work environments. The market is witnessing substantial growth due to an inclination towards urban living, coupled with a significant shift towards flexible working spaces. These factors are bolstered by technological advancements and changing consumer preferences, making the real estate rental market an area of active interest and dynamic evolution.

One of the significant growth drivers of the real estate rental market is the trend of urbanization. As more people migrate to cities in search of better employment opportunities and lifestyles, the demand for rental properties surges. This urban influx requires extensive accommodation and commercial spaces, thereby driving up the rental market. Additionally, the scarcity and high cost of owned properties in urban areas make renting a more viable and attractive option for many individuals and businesses. This trend is expected to continue as cities expand and develop, creating a continual demand for rental properties.

Increasing disposable incomes and the changing dynamics of consumer spending also play a critical role in the growth of the real estate rental market. As economic conditions improve globally, more individuals and corporates have higher spending capacities, allowing them to opt for premium rental properties. This increase in disposable income is particularly noticeable in emerging economies, where rapid economic growth is leading to higher standards of living and increased demand for quality rental spaces. Additionally, the rise of a more mobile and transient workforce prefers the flexibility of renting over purchasing, further fueling market growth.

Technological advancements and digital transformation are another crucial factor contributing to the growth of the real estate rental market. The proliferation of online platforms and digital tools has revolutionized the way rental properties are marketed, managed, and leased. These innovations provide greater transparency, convenience, and efficiency, making the rental process more accessible and appealing to a broader audience. Virtual tours, online payment systems, and digital lease agreements are just a few examples of how technology is enhancing the rental experience, attracting more tenants and simplifying property management for landlords.

Regionally, the Asia Pacific region is expected to dominate the market growth, driven by rapid urbanization and economic development in countries like China and India. North America and Europe are also significant markets, with mature real estate sectors and high demand for both residential and commercial rental properties. Each region presents unique opportunities and challenges, influenced by factors such as economic conditions, regulatory environments, and cultural preferences. Understanding these regional dynamics is essential for stakeholders looking to capitalize on the growth opportunities within the global real estate rental market.

Property Type Analysis

The real estate rental market is segmented by property type into residential, commercial, industrial, and others. The residential segment holds the largest share, driven by the increasing demand for housing in urban areas. As cities expand and populations grow, the need for rental housing continues to rise. This segment includes apartments, single-family homes, and multi-family units. The trend towards urban living and the high cost of homeownership in many cities make renting a more viable option for many individuals and families, thus driving the growth of the residential rental market.

Residential Real Estate remains a cornerstone of the real estate rental market, particularly as urban areas continue to expand. The demand for residential properties is driven by various factors, including population growth, urbanization, and the increasing preference for rental housing over homeownership. With cities becoming more densely populated, the need for accessible and affordable housing options is more critical than ever. Residential real estate offers a range of property

Multi Family Property Management Software Market Outlook

The global multi family property management software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 6.2 billion by 2032, growing at a significant compound annual growth rate (CAGR) of 10.5% during the forecast period. The market growth is primarily driven by the rising demand for streamlined property management solutions and the increasing adoption of cloud-based technologies.

One of the primary growth factors contributing to the expansion of the multi family property management software market is the increasing urbanization and subsequent rise in the number of rental properties. As more people migrate to urban areas, the demand for multi-family housing units has surged, necessitating efficient management solutions. This trend is particularly noticeable in rapidly growing cities across Asia Pacific and North America, where property managers are seeking advanced software solutions to handle the complexities associated with managing numerous tenants and properties.

Additionally, the integration of advanced technologies such as artificial intelligence (AI) and the Internet of Things (IoT) into property management software is significantly enhancing the functionality and appeal of these solutions. AI-driven analytics provide property managers with predictive insights, enabling them to make data-driven decisions to improve tenant satisfaction and optimize operational efficiency. Similarly, IoT devices facilitate predictive maintenance, reducing downtime and repair costs, thus bolstering the overall value proposition of these software solutions.

The increasing emphasis on tenant experience and satisfaction is another critical factor fueling market growth. Modern tenants expect quick responses and seamless interactions with property managers, which has led to the adoption of software solutions that offer features like online rent payment, instant maintenance request submissions, and real-time communication channels. These capabilities not only enhance tenant satisfaction but also contribute to higher tenant retention rates, which is a key performance indicator for property managers.

Regionally, North America is expected to maintain its dominance in the multi family property management software market over the forecast period. This can be attributed to the high level of technological adoption, the presence of a large number of multi-family housing units, and the increasing investment in real estate technology. The Asia Pacific region, however, is anticipated to witness the highest growth rate, driven by rapid urbanization, increasing disposable incomes, and a burgeoning real estate sector. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market, albeit at a slower pace compared to North America and Asia Pacific.

Component Analysis

The multi family property management software market can be segmented by component into software and services. The software segment is further divided into integrated software suites and standalone software, catering to different aspects of property management such as tenant management, lease tracking, and accounting. The services segment includes implementation, training, consulting, and support services that enhance the functionality and usability of the software solutions.

The software segment holds the largest market share and is expected to continue its dominance throughout the forecast period. This is due to the comprehensive functionalities offered by integrated software suites that address the diverse needs of property managers. These suites often include modules for accounting, tenant management, lease administration, and maintenance management, providing a one-stop solution for property management tasks. The increasing inclination towards integrated platforms that streamline operations and reduce the need for multiple disparate systems is a significant contributor to the growth of this segment.

Standalone software solutions, although witnessing slower growth compared to integrated suites, still hold substantial market value. These solutions are often preferred by property managers who require specific functionalities without the need for a full-fledged suite. For instance, a property manager might opt for a dedicated tenant management software or a specialized accounting tool based on their specific requirements. This flexibility and targeted approach make standalone solutions a viable option for smaller property management firms and

blockgrouphomevalues # Context A home purchase is among the most import decisions, and potentially risk investments, in a person's life. Their choice can reflect interest in long term gains, housing costs and, in the U.S., part of the American Dream. Analytics of home values and rental costs, however, are commonly limited to highest level geographic aggregates and broad, even annual, periods of time. This publication produces a data file shared in the Block Groups Datasets dataverse hosted on https://dataverse.harvard.edu/dataverse/blockgroupdatasets. The data is shared under a Common Commons, open source license, without warranties, share alike, non commercial and by attribution. Method This publication attempts to cast home values down to U.S. Census block group geographies, by inheriting and averaging the measures from ZIP code level estimates. On the whole, block groups with a few hundred households are considerably smaller than ZIP code areas with several thousand. In addition, the two geographies are managed by separate Federal agencies, the U.S. Postal Service and the Census Bureau, so they are inherently dissimilar. The simplest method of projection involves overlaying the two geographies, having a block group inherit the estimates of the ZIP code level that covers it. When the block group spans ZIP code boundaries, an average is appropriate, weighted by land area lying in each parent. Data Zillow is recognized as an innovator in predicting home values, serving real estate agents, home buyers, and home sellers. Their research service publishes several estimates at a ZIP code level including measures of home value (Zillow Home Value Index ZHVI) and rental costs (Zillow Observed Rent Index ZORI). The ZHVI is broken down by housing type: single family homes and condominiums. And, each of their publications has monthly frequency dating, in some cases, to 2000. Block group geographic boundariess are maintained by the US Census' TIGER (Topologically Integrated Geographic Encoding and Referencing) publication. ZIP code boundaries are not generally published, but shared from a private company, Dotlas, in various retail marketing solutions. ZIP codes, also, have long been problematic for demographic analytics. Their boundaries span counties and states, so you cannot tiethem to familar geographies including Census tracts and block groups. The Census Bureau tries to address this by using ZIP Code Tabulation Areas (ZCTAs). These are coded very much like 5 digit ZIP codes and are equal to them most of the time. When A ZIP code geography crosses a county line, though, new ZCTAs are invented to represent each side of the split area. So, while ZIP codes cannot be aggregated, ZCTAs can total into counties, states, divisions and regions. The blockgrouphomevalues dataset offers the following columns: Column Data Type Description STATEFP string The 2-digit State FIPS code of the block group COUNTYFP string The 3-digit County FIPS code of the block group TRACTCE string The 6-digit Census Tract of the block group BLKGRPCE string The 1-digit Block Group of the block group GEOID string 12 digit concatenation of State, County, Tract and Block Group codes GEOIDFQ string The 'fully qualified' GEOID with US country prefix ALAND integer The land area if the block group in square meters AWATER integer The area if the block group, covered by water, in square meters INTPTLAT float Latitude of the block groups centroid point INTPTLON float Longitude of the block groups centroid point ZIP Codes Overlaying list List of the ZIP codes that overlay the block group ZHVI All Housing Types float Zillow Home Value Index, attributed to the block group, all housing types ZHVI Single Family Homes float Zillow Home Value Index, attributed to the block group, single family homes ZHVI Condos/Coops float Zillow Home Value Index, attributed to the block group, condominiums and cooperatively owned ZORI All Housing Types float Zillow Observed Rent Index, attributed to the block group Additional Notes When the Block Group Code BLKGRPCE is '0', that block group is under water. Block groups cover the Great Lakes, for example, making a confusing visual for chloropleth maps. To support visualization, the code also uses Census definitions of cities called Combined Statitical Areas, which group counties together. The CSA for New York includes 22 counties, distinguished as Central or Outlying. The Delineation Files publication includes the geographic IDs of state and county FIPS codes in each major city. Maps of these results may be visually biased. New York City and San Francisco Bay areas have extreme housing values, but they have small land areas. Denver by contrast has higher then median housing values with very large land areas. As a result, western Colorado looks like the dominating location of home values. When more than one ZIP code overlays a block group, values are attributed by the shared land area. This assumes that housing is uniform over...

Use this map to show where households are renting single-family detached homes. The darker areas have high percentages of single-family detached homes that are rented. The size of the symbol depicts the count of single-family detached rentals. Pop-up displays more context. Map opens in Las Vegas and has national coverage. Zoom out to see map render data for counties and states. Often, households renting single-family detached homes are renting from a private owner rather than a rental company, sometimes via informal agreements rather than a formal lease. As such, these households don't always get the same tenant protections that those in formal leases do, such as the recent eviction moratorium during COVID-19.Real estate developers are responding to the demand for single-family rentals. For example, newer developments such as Cactus Cliff in Las Vegas are communities of single-family detached homes built as professionally-managed rentals.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.