The number of vacant homes for rent in the United States increased for the third year in a row in 2024, after reaching a record low in 2021. In the third quarter of 2024, there were approximately 6.9 million unoccupied housing units for rent.

In 2023, there were approximately 45 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 86 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.

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

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.

This layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.This layer is symbolized to show the count and percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Multifamily Properties - AssistedThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development (HUD) feature layer, displays rental housing properties with five or more dwelling units in the United States. Per HUD, "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 be 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 designed 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". Tyler House in Washington, D.C.Data currency: current federal service (Multifamily Properties - Assisted)NGDAID: 183 (Assisted Housing - Multifamily Properties (Assisted) – National Geospatial Data Asset (NGDA))For more information, please visit: Office of Multifamily HousingSupport documentation: DD_HUD Assisted Multifamily PropertiesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

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

This layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.This layer is symbolized to show the percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

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Real Estate Market Size 2025-2029

The real estate market size is forecast to increase by USD 1,258.6 billion at a CAGR of 5.6% between 2024 and 2029.

The market is experiencing significant shifts and innovations, with both residential and commercial sectors adapting to new trends and challenges. In the commercial realm, e-commerce growth is driving the demand for logistics and distribution centers, while virtual reality technology is revolutionizing property viewings. Europe's commercial real estate sector is witnessing a rise in smart city development, incorporating LED lighting and data centers to enhance sustainability and efficiency. In the residential sector, wellness real estate is gaining popularity, focusing on health and well-being. Real estate software and advertising services are essential tools for asset management, streamlining operations, and reaching potential buyers. Regulatory uncertainty remains a challenge, but innovation in construction technologies, such as generators and renewable energy solutions, is helping mitigate risks.

What will be the Size of the Real Estate Market During the Forecast Period?

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The market continues to exhibit strong activity, driven by rising population growth and increasing demand for personal household space. Both residential and commercial sectors have experienced a rebound in home sales and leasing activity. The trend towards live-streaming rooms and remote work has further fueled demand for housing and commercial real estate. Economic conditions and local market dynamics influence the direction of the market, with interest rates playing a significant role in investment decisions. Fully furnished, semi-furnished, and unfurnished properties, as well as rental properties, remain popular options for buyers and tenants. Offline transactions continue to dominate, but online transactions are gaining traction.
The market encompasses a diverse range of assets, including land, improvements, buildings, fixtures, roads, structures, utility systems, and undeveloped property. Vacant land and undeveloped property present opportunities for investors, while the construction and development of new housing and commercial projects contribute to the market's overall growth.

How is this Real Estate Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Residential
  Commercial
  Industrial


Business Segment

  Rental
  Sales


Manufacturing Type

  New construction
  Renovation and redevelopment
  Land development


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK


  South America

    Brazil


  Middle East and Africa

By Type Insights

The residential segment is estimated to witness significant growth during the forecast period.

The market encompasses the buying and selling of properties designed for dwelling purposes, including buildings, single-family homes, apartments, townhouses, and more. Factors fueling growth in this sector include the increasing homeownership rate among millennials and urbanization trends. The Asia Pacific region, specifically China, dominates the market due to escalating homeownership rates. In India, the demand for affordable housing is a major driver, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects catering to the needs of lower and middle-income groups. The commercial real estate segment, consisting of office buildings, shopping malls, hotels, and other commercial properties, is also experiencing growth.

Furthermore, economic and local market conditions, interest rates, and investment opportunities in fully furnished, semi-furnished, unfurnished properties, and rental properties influence the market dynamics. Technological integration, infrastructure development, and construction projects further shape the real estate landscape. Key sectors like transportation, logistics, agriculture, and the e-commerce sector also impact the market.

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The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 64% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The Asia Pacific region holds the largest share of The market, dr

The Inventory of Owned and Leased Properties (IOLP) allows users to search properties owned and leased by the General Services Administration (GSA) across the United States, Puerto Rico, Guam and American Samoa. The Owned and Leased Data Sets include the following data except where noted below for Leases: Location Code - GSA’s alphanumeric identifier for the building Real Property Asset Name - Allows users to find information about a specific building Installation Name - Allows users to identify whether a property is part of an installation, such as a campus Owned or Leased - Indicates the building is federally owned (F) or leased (L) GSA Region - GSA assigned region for building location Street Address/City/State/Zip Code - Building address Longitude and Latitude - Map coordinates of the building (only through .csv export) Rentable Square Feet - Total rentable square feet in building Available Square Feet - Vacant space in building Construction Date (Owned Only) - Year built Congressional District - Congressional District building is located Senator/Representative/URL - Senator/Representative of the Congressional District and their URL Building Status (Owned Only) - Indicates building is active Lease Number (Leased Only) - GSA’s alphanumeric identifier for the lease Lease Effective/Expiration Dates (Leased Only) - Date lease starts/expires Real Property Asset Type - Identifies a property as land, building, or structure

Vacancy rates across the office real estate sector in the U.S. increased during the coronavirus pandemic. Before 2020, the quarterly vacancy rate was around 12 percent, but as the pandemic unfolded, it climbed to above 15 percent. In the fourth quarter of 2023, about 16.9 percent of office space across the country was vacant. In some of the major U.S. markets, vacancies reached up to 30 percent. With a considerable part of the workforce working from home or following a hybrid working model, businesses are cautious when it comes to upscaling or renewing leases. Workplaces may never be the same again The COVID-19 pandemic has changed the way that companies operate, and working from home has become the new normal for many U.S. employees. The function of the office has evolved from the primary workplace to a space where employees collaborate, exchange ideas, and socialize. That has shifted occupiers’ attention toward spaces with modern designs that can accommodate the office of the future. Many businesses used the pandemic time to revisit their office guidelines, remodel or do a full or partial fit-out. With so much focus on quality, older buildings with poorer design or energy performance are likely to suffer lower demand, resulting in a two-speed market. What do higher vacancy rates mean for investors? Simply put, if landlords do not have tenants, their income stream is disrupted, and they cannot service their debts. April 2023 data shows that several U.S. metros had a significantly high share of distressed office real estate debt. In Charlotte-Gastonia-Concord, NC-SC, more than one-third of the commercial mortgage-backed securities for offices were delinquent, in special servicing, or a combination of both. Nevertheless, offices had a lower delinquency rate compared to other commercial property types, such as lodging or retail properties.

Rents for industrial real estate in the U.S. have increased since 2017, with flexible/service space reaching the highest price per square foot in 2024. In just a year, the cost of, flex/service space rose by nearly five U.S. dollars per square foot. Manufacturing facilities, warehouses, and distribution centers had lower rents and experienced milder growth. Los Angeles, Orange County, and Inland Empire, California, are some of the most expensive markets in the country. Office real estate is pricier Industrial real estate is far from being the most expensive commercial property type. For instance, average rental rates in major U.S. metros for office space are much higher than those for industrial space. This is most likely because office units are generally located in urban areas where there is limited space and thus higher demand, whereas industrial units are more suited to the outskirts of such urban areas. Industrial units, such as warehouses or factories, require much more space because they need to house large, heavy equipment or serve as a storage unit for future shipments. Big-box distribution space is gaining in importance Warehouses and distribution may currently command the lowest average rent per square foot among industrial space types, but the growing popularity of the asset class has earned it considerable gains over the past years. In 2021 and 2022, high occupier demand and insufficient supply led to soaring taking rent of big-box buildings. During that time, the vacancy rate of distribution centers fell below six percent. The development of industrial and logistics facilities has accelerated since then, with the new supply coming to market causing the vacancy rate to increase and the pressures on rent to ease.

Among the 30 markets with the largest inventory of industrial and logistics real estate, Orange County, CA had the lowest vacancy rate of one percent in the first quarter of 2024. Chicago, IL, the largest market by total inventory, ranked 13th, with 4.6 percent of industrial and logistics real estate vacant. Phoenix, AZ, was the market with the highest rate at almost 11 percent. Overall, the share of vacant industrial and logistics properties has increased since 2022.

Multifamily Properties Insured by Housing and Urban DevelopmentThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development (HUD) feature layer, displays insured rental housing properties with five or more dwelling units. Per HUD, "the Federal Housing Administration (FHA) insured Multifamily Housing portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land". Gateway Village (Capitol Heights, MD)Data currency: current federal service (HUD Insured Multifamily Properties)NGDAID: 123 (HUD Insured Multifamily Properties - National Geospatial Data Asset (NGDA))For more information: Multifamily HousingSupport documentation: HUD Insured Multifamily PropertiesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

The primary reasons for purchasing a home in the United States in 2023 varied among home buyers. Approximately one in four homebuyers bought a home because they desired to have their own home. Having one's own home was mainly considered by millennial buyers during their home buying process.

Oslo was the Norwegian city with the most expensive apartments and houses in 2024. In March that year, the average price per residential property in the Norwegian capital was approximately 6.4 million Norwegian kroner. The city above the polar circle, Tromsø ranked second, with housing units costing on average nearly 4.6 million Norwegian kroner. In 2019, there were over nine thousand dwellings sold in Norway. Housing types The largest share of Norwegian residential housing units in 2023 were detached houses, accounting for nearly half of the total housing market in the country. Moreover, a quarter of all occupied and vacant dwellings that year were blocks of flats and over one fifth were houses with two dwellings or row houses. Where are properties the most expensive? Within selected global property markets, Hong Kong had the most expensive housing prices in 2020. An average property would cost roughly 1.25 million U.S. dollars in the former British colony. Munich ranked second, where the average property price amounted to roughly one million U.S dollars.

The average American household consisted of 2.51 people in 2023.

Households in the U.S.

As shown in the statistic, the number of people per household has decreased over the past decades.

The U.S. Census Bureau defines a household as follows: “a household includes all the persons who occupy a housing unit as their usual place of residence. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live and eat separately from any other persons in the building and which have direct access from outside the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated persons who share living arrangements. (People not living in households are classified as living in group quarters.).”

The population of the United States has been growing steadily for decades. Since 1960, the number of households more than doubled from 53 million to over 131 million households in 2023.

Most of these households, about 34 percent, are two-person households. The distribution of U.S. households has changed over the years though. The percentage of single-person households has been on the rise since 1970 and made up the second largest proportion of households in the U.S. in 2022, at 28.88 percent.

In concordance with the rise of single-person households, the percentage of family households with own children living in the household has declined since 1970 from 56 percent to 40.26 percent in 2022.

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. 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 (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 12/2023

Retail properties had the highest capitalization rates in the United States in 2023, followed by offices. The cap rate for office real estate was 6.54 percent in the fourth quarter of the year and was forecast to rise further to 7.39 percent in 2024. Cap rates measure the expected rate of return on investment, and show the net operating income of a property as a percentage share of the current asset value. While a higher cap rate indicates a higher rate of return, it also suggests a higher risk. Why have cap rates increased? The increase in cap rates is a consequence of a repricing in the commercial real estate sector. According to the National NCREIF Property Return Index, prices for commercial real estate declined across all property types in 2023. Rental growth was slow during the same period, resulting in a negative annual return. The increase in cap rates reflects the increased risk in the investment environment. Pricing uncertainty in the commercial real estate sector Between 2014 and 2021, commercial property prices in the U.S. enjoyed steady growth. Access to credit with low interest rates facilitated economic growth and real estate investment. As inflation surged in the following two years, lending policy tightened. That had a significant effect on the sector. First, it worsened sentiment among occupiers. Second, it led to a decline in demand for commercial spaces and commercial real estate investment volumes. Uncertainty about the future development of interest rates and occupier demand further contributed to the repricing of real estate assets.