The largest owner of apartments in the United States was Greystar, an international developer and manager headquartered in Charleston, SC. In 2025, 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 that 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.

The Samolet Group was the largest apartment building developer in Russia as of April 1, 2025, with an area of about **** million square meters under construction. The PIK Group and Tochno ranked second and third, with roughly **** million square meters and **** million square meters under construction, respectively.

This statistic shows the average rent in high-end apartments in selected cities in the United States in 2018. The average monthly rent for a luxury apartment in Los Angeles, the second largest city in the U.S., was ***** U.S. dollars in 2018.

Graph and download economic data for New Privately-Owned Housing Units Under Construction: Units in Buildings with 5 Units or More (UNDCON5MUSA) from Jan 1970 to Jun 2025 about 5-unit structures +, construction, new, private, housing, and USA.

The Latin American condominiums and apartments market is experiencing robust growth, driven by factors such as rapid urbanization, rising disposable incomes, and a growing preference for modern, convenient living spaces. The market, valued at approximately $XX million in 2025 (assuming a reasonable market size based on similar developing markets and the provided CAGR), is projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 5% through 2033. Key growth drivers include increasing tourism in popular destinations like Mexico and Brazil, the expansion of the middle class seeking improved housing, and government initiatives promoting real estate development. However, economic instability in certain regions, regulatory hurdles, and fluctuations in construction material costs pose potential restraints to growth. The market is segmented by country (with Brazil, Mexico, and Argentina representing the largest segments), property type (luxury vs. affordable), and location (urban vs. suburban). Significant investment from both domestic and international players like Tishman Speyer, Hines Group, and others, indicates strong investor confidence and contributes to the market's expansion. Consumption analysis reveals a strong demand for apartments, especially in larger metropolitan areas, fueled by younger demographics and rising rental costs. Import and export analyses indicate potential for increased international collaboration in construction materials and specialized building technologies. Price trends suggest that property values will continue to appreciate, though the rate may fluctuate due to macroeconomic conditions. Further analysis indicates regional disparities. While Brazil and Mexico lead in market size and development, countries like Colombia and Peru are experiencing notable growth owing to burgeoning economies and increased foreign direct investment in their real estate sectors. The competitive landscape is dynamic with both large multinational developers and local players vying for market share. The sustained growth is expected to lead to increased job creation within the construction and real estate industries and further contribute to the economic development across Latin America. The market’s future prospects are positive, though maintaining economic stability and addressing regulatory challenges will be crucial for realizing the full potential of this growing sector. Recent developments include: December 2022: Casai, a tech-driven apartment rental company, is merging with Nomah, a rental company based in Brazil. The merger will create the largest short-term rental company in Latin America, with over 3,000 units in Brazil and Mexico., December 2022: Northmarq arranged the sale of two Albuquerque apartment communities. The assets were sold by ABQ Encore LLC and Uptown Horizon Apartments LLC to Crescent Sky Real Estate Partners' CS ABQ Encore and CA ABQ Uptown. ABQ Encore, located at 810 Eubank Blvd. NE has 129 residences divided into 331-square-foot studio units and 551-square-foot one-bedroom units.. Notable trends are: Increasing Sales of Apartments Driving the Market.

High-end, or luxury apartments, are usually larger, situated in desirable locations, and provide various amenities, such as sporting facilities, recreational areas, and in-building services. In Fresno, California, which was one of the ** best large cities for renting a luxury apartment according to the source, the average monthly rent of a high-end apartment was ***** U.S. dollars as of February 2021.

The City of Peachtree Corners more closely represents an urban community than it does a traditional suburban market. Current land use within the City is almost evenly split between residential and non-residential uses (51.6%-49.4%). This level of activity is desirable from a fiscal health perspective, as non-residential uses typically generate greater revenues than the cost of services consumed. Of the approximately 55.4 million square feet of taxable building space, slightly more than 21.6 million is for singlefamily residential use (detached houses and townhouses). Warehousing accounts for the second largest total at more than 13.6 million square feet. The diversity of land uses within Peachtree Corners goes beyond the residential/ nonresidential levels. Within the residential market, nearly 25% of the developed space is in multi-family units (quadplexes, apartments, and condominiums), with the vast majority being traditional rental properties. Additionally, retail and services make up less than 13% of the City’s non-residential development. Suburban markets typically have more than 50% of non-residential development in the retail and services category

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

Korea Median Housing Price: Apartments: 6 Large Cities data was reported at 23,991.237 KRW tt in Nov 2018. This records a decrease from the previous number of 24,009.867 KRW tt for Oct 2018. Korea Median Housing Price: Apartments: 6 Large Cities data is updated monthly, averaging 18,283.086 KRW tt from Dec 2008 (Median) to Nov 2018, with 120 observations. The data reached an all-time high of 24,168.057 KRW tt in Sep 2017 and a record low of 12,743.770 KRW tt in Apr 2009. Korea Median Housing Price: Apartments: 6 Large Cities data remains active status in CEIC and is reported by Kookmin Bank. The data is categorized under Global Database’s South Korea – Table KR.EB033: Median Housing Price: Kookmin Bank.

Replication code for "Local Effects of Large New Apartment Buildings in Low-Income Areas" by Brian Asquith, Evan Mast, and Davin Reed

Greystar was the biggest apartment management company in the United States in 2024. The company was responsible for the management of close to ******* apartment units in that year, which was over ***** more than the second company in the ranking - Asset Living. Additionally, Greystar was the largest apartment owner in that year.

Introduction

This dataset contains detailed data on 42,207 apartments (242,257 rooms) in 3,093 buildings including their geometries, room typology as well as their visual, acoustical, topological and daylight characteristics.

Procurement

The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).

Geometries

The dataset contains a file geometries.csv which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.

In total the datasets contains the 2D geometry of ~1.2 million separators (walls, railings), ~550,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.) and ~240,000 features (sinks, toilets, bathtubs, etc.).

Each row contains:

entity_type: The entity type (area, separator, opening, feature)

entity_subtype: The entity’s sub type (e.g. WALL)

geometry: The element’s geometry as a WKT geometry. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.

area_id: The ID of the area in which the element is spatially contained (for features)

unit_id: The ID of the unit in which the element is spatially contained (for features, areas)

apartment_id: The ID of the apartment (for features, areas)

floor_id: The ID of the floor

building_id: The ID of the building

site_id: The ID of the site

An example:

    column





    entity_type
    area


    entity_subtype
    ROOM


    geometry
    POLYGON ((-2.10406 4.02039…


    site_id
    127


    building_id
    164


    floor_id
    12864


    apartment_id
    d4438f2129b30290845ce7eef98a5ba7


    unit_id
    76643


    area_id
    684674

Simulations

Beside the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout and connectivity-related characteristics of the apartments. The file simulations.csv contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id, unit_id, apartment_id, floor_id, building_id, site_id as defined above as well as 367 simulation columns. Each simulation column is formatted as:

_

For instance. the column view_buildings_median describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.

Layout

The layout features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.

Area Basics / Geometry

    dimension
    description




    layout_area_type
    The area’s area type


    layout_net_area
    The area’s share of the apartment’s net area (e.g. 0 for a balcony)


    layout_area
    The area’s actual area


    layout_perimeter
    The area’s perimeter


    layout_compactness
    The area’s compactness (the Polsby–Popper score)


    layout_room_count
    The area’s share to the apartment’s room count


    layout_is_navigable
    True if the area is navigable by a wheelchair

Area Features

    dimension
    description




    layout_has_sink
    True if the area has a sink


    layout_has_shower
    True if the area has a shower


    layout_has_bathtub
    True if the area has a bathtub


    layout_has_toilet
    True if the area has a toilet


    layout_has_stairs
    True if the area has stairs


    layout_has_entrance_door
    True if the area is directly leading to an exit of the apartment

Area Windows / Doors

    dimension
    description




    layout_number_of_doors
    The number of doors directly leading to the area


    layout_number_of_windows
    The number of windows of the area


    layout_door_perimeter
    The sum of all door lengths directly leading to the area


    layout_window_perimeter
    The sum of all window lengths of the area

Area Walls / Railings

    dimension
    description




    layout_open_perimeter
    The sum of all of the areas boundaries that are neither walls nor railings


    layout_railing_perimeter
    The sum of all of the areas boundaries that are railings


    layout_mean_walllengths
    The mean length of the area’s sides


    layout_std_walllengths
    The standard deviation of the lengths of the area’s sides

Area Adjecency

    dimension
    description




    layout_connects_to_bathroom
    True if the area connects to a bathroom


    layout_connects_to_private_outdoor
    True if the area connects to an outside area that is private to the apartment

View

The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a certain object category occupies in the spherical field of view.

Each of the following dimension is provided using the room-wise aggregations min, max, mean, std, median, p20 and p80. For instance, the column view_greenery_p20 describes the amount of greenery that can be seen from at least 20% of the positions in the area.

    dimension
    description




    view_buildings
    The amount of visible buildings


    view_greenery
    The amount of visible greenery


    view_ground
    The amount of visible ground


    view_isovist
    The amount of visible isovist


    view_mountains_class_2
    The amount of visible mountains of UN mountain class 2


    view_mountains_class_3
    The amount of visible mountains of UN mountain class 3


    view_mountains_class_4
    The amount of visible mountains of UN mountain class 4


    view_mountains_class_5
    The amount of visible mountains of UN mountain class 5


    view_mountains_class_6
    The amount of visible mountains of UN mountain class 6


    view_railway_tracks
    The amount of visible railway_tracks


    view_site
    The amount of visible site


    view_sky
    The amount of visible sky


    view_tertiary_streets
    The amount of visible tertiary_streets


    view_secondary_streets
    The amount of visible secondary_streets


    view_primary_streets
    The amount of visible primary_streets


    view_pedestrians
    The amount of visible pedestrians


    view_highways
    The amount of visible highways


    view_water
    The amount of visible water

Sun

Sun simulations help to understand the impact of the solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of sun radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of summer solstice, winter solstice and vernal equinox.

Each of the following dimension is provided using the room-wise aggregations min, max, mean, std, median, p20 and p80. For instance, column sun_201806211200_median describes the median amount of direct daylight received on the positions in the area.

Vernal Equinox

    dimension
    description




    sun_201803210800
    Daylight at 08:00 on 21st of March


    sun_201803211000
    Daylight at 10:00 on 21st of March


    sun_201803211200
    Daylight at 12:00 on 21st of March


    sun_201803211400
    Daylight at 14:00 on 21st of March


    sun_201803211600
    Daylight at 16:00 on 21st of March


    sun_201803211800
    Daylight at 18:00 on 21st of March

Summer Solstice

    dimension
    description




    sun_201806210600
    Daylight at 06:00 on 21st of June


    sun_201806210800
    Daylight at 08:00 on 21st of June


    sun_201806211000
    Daylight at 10:00 on 21st of June


    sun_201806211200
    Daylight at 12:00 on 21st of June


    sun_201806211400
    Daylight at 14:00 on 21st of June


    sun_201806211600
    Daylight at 16:00 on 21st of June


    sun_201806211800
    Daylight at 18:00 on 21st of June


    sun_201806212000
    Daylight at 20:00 on 21st of June

Winter Solstice

    dimension
    description




    sun_201812211000
    Daylight at 10:00 on 21st of December


    sun_201812211200
    Daylight at 12:00 on 21st of December


    sun_201812211400
    Daylight at 14:00 on 21st of December


    sun_201812211600
    Daylight at 16:00 on 21st of December

Noise / Window Noise

Noise level and the distribution of elements from an area helps to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered as noise blocking elements. The values are expressed in dBA (decibels).

Window Noise

The noise per window of a given area is aggregated via min and max. For instance, window_noise_train_day_max represents the maximum amount of noise received on any window of the area.

    dimension
    description




    window_noise_traffic_day
    The amount of noise received on the area’s windows

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.

Student Micro-Apartment Market Outlook

According to our latest research, the global student micro-apartment market size reached USD 9.2 billion in 2024 and is projected to grow at a robust CAGR of 8.4% from 2025 to 2033, attaining a forecasted market value of USD 18.8 billion by 2033. This significant growth is primarily driven by the increasing demand for affordable, flexible, and well-located student housing solutions in urban centers worldwide, as higher education enrollment continues to surge and urbanization intensifies.

One of the primary growth factors propelling the student micro-apartment market is the ongoing surge in global student mobility. As more students pursue higher education abroad, particularly in countries like the United States, United Kingdom, Australia, and Canada, the demand for compact, cost-effective, and conveniently located accommodation near educational institutions has soared. These micro-apartments offer students a blend of privacy, affordability, and community living, making them an attractive alternative to traditional dormitories and private rentals. Additionally, the rising costs of urban real estate have made large-scale student housing projects less viable, pushing developers to focus on micro-apartments that maximize space efficiency while minimizing rental costs.

Another key driver is the evolving lifestyle preferences of Generation Z and Millennial students, who prioritize sustainability, flexibility, and connectivity in their living arrangements. Student micro-apartments are designed to cater to these preferences by incorporating smart technology, energy-efficient appliances, and communal amenities such as study lounges, gyms, and social spaces. The modular nature of many micro-apartment projects enables rapid construction, scalability, and adaptability to changing student needs, further fueling market growth. Moreover, universities and private developers are increasingly partnering to offer purpose-built student accommodations (PBSA), integrating micro-apartments into campus master plans to enhance student satisfaction and retention.

The market is also benefiting from the expansion of higher education institutions into secondary and tertiary cities, especially in emerging economies across Asia Pacific and Latin America. These regions are witnessing a boom in university enrollments, driven by favorable demographic trends and government initiatives to improve access to education. As a result, there is a heightened need for affordable student housing options that can be quickly deployed and managed efficiently. The growing acceptance of alternative rental models, such as co-living and shared micro-apartments, is further diversifying the market landscape, attracting investment from real estate funds, institutional investors, and proptech startups.

Regionally, Europe and North America remain the largest markets for student micro-apartments, accounting for a combined share of over 60% of global revenues in 2024. However, the Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of over 10% during the forecast period. Countries like China, India, and Australia are experiencing unprecedented growth in international student inflows and domestic university enrollments, driving robust demand for modern, affordable student housing solutions. Meanwhile, Middle East & Africa and Latin America are gradually catching up, supported by urbanization, rising middle-class incomes, and strategic investments in educational infrastructure.

In addition to the growing demand for student micro-apartments, there is an increasing focus on Staff Housing Solutions within educational institutions. As universities expand and modernize their campuses, the need for accommodating faculty and administrative staff becomes paramount. Staff housing solutions are designed to attract and retain top talent by providing convenient, affordable, and high-quality living options close to campus. These solutions often include a mix of housing types, from single-family homes to apartments, tailored to meet the diverse needs of university staff. By investing in staff housing, institutions not only enhance employee satisfaction and productivity but also strengthen their community ties and institutional reputation.

The US luxury residential market, a sector characterized by high-value properties like apartments, condominiums, villas, and landed houses, is experiencing robust growth. Driven by factors such as increasing high-net-worth individuals, a preference for larger living spaces, and a desire for premium amenities, the market exhibits a Compound Annual Growth Rate (CAGR) exceeding 3.00%. Key cities like New York, Los Angeles, San Francisco, Miami, and Washington D.C. dominate the market, attracting both domestic and international buyers. The segment encompassing apartments and condominiums currently holds the largest market share, reflecting a trend towards urban luxury living. However, the villas and landed houses segment is also demonstrating strong growth, fueled by demand for larger properties and privacy. The market faces constraints such as fluctuating interest rates, limited inventory in prime locations, and the overall economic climate. Nevertheless, the long-term outlook remains positive, with continued growth expected throughout the forecast period (2025-2033). Leading developers like Toll Brothers, D.R. Horton, and several high-end custom builders are actively shaping the market, contributing to the overall expansion and diversification of luxury housing options. This market's expansion is further influenced by evolving architectural trends emphasizing sustainability and smart-home technology. The increasing popularity of eco-friendly materials and designs, along with the integration of advanced technological features, is attracting environmentally conscious high-net-worth individuals. Furthermore, the market's regional distribution showcases a strong concentration in North America, particularly the United States, although international markets, including key regions in Europe and Asia, are also showing promising growth potential. The competitive landscape is dynamic, with both large national builders and smaller, specialized custom home builders vying for market share. This leads to innovative design and construction approaches, thereby enhancing the overall quality and appeal of luxury residential properties. Future growth will depend on maintaining a balance between catering to evolving consumer preferences, addressing market constraints, and adapting to broader economic conditions. This comprehensive report provides an in-depth analysis of the US luxury residential market, encompassing historical data (2019-2024), current estimations (2025), and future projections (2025-2033). We examine market dynamics, key players, emerging trends, and growth catalysts to offer a 360° perspective on this lucrative sector. The report is crucial for investors, developers, real estate professionals, and anyone seeking to understand the intricacies of the high-end residential landscape. High-value keywords used throughout the report include: luxury homes, luxury real estate, high-end residential, luxury condos, luxury apartments, prime real estate, US luxury housing market, luxury home builders, luxury real estate investment. Key drivers for this market are: Energy efficiency in construction, Flexibility and customization options. Potential restraints include: Limited availability of suitable land for construction, Lower quality compared to traditional construction. Notable trends are: Home Automation Becoming a Pre-requisite for Luxury Real Estate.

Korea Median Housing Price: Apartments: 6 Large Cities: Gwangju data was reported at 18,270.274 KRW tt in Nov 2018. This records an increase from the previous number of 18,174.950 KRW tt for Oct 2018. Korea Median Housing Price: Apartments: 6 Large Cities: Gwangju data is updated monthly, averaging 16,028.350 KRW tt from Apr 2013 (Median) to Nov 2018, with 68 observations. The data reached an all-time high of 18,270.274 KRW tt in Nov 2018 and a record low of 12,602.957 KRW tt in May 2013. Korea Median Housing Price: Apartments: 6 Large Cities: Gwangju data remains active status in CEIC and is reported by Kookmin Bank. The data is categorized under Global Database’s South Korea – Table KR.EB033: Median Housing Price: Kookmin Bank.

The online apartment rental services industry is experiencing significant growth because of the booming apartment supply, with over half a million new rental units completed in 2024. Major cities like New York, Dallas and Austin are leading the way in this surge, causing an influx of new, predominantly high-end rental units. As a result, there is increased competition among property managers and a need for more effective digital marketing strategies to reach potential renters. This accelerated growth is predominantly benefiting online rental services, which have seen a climb in listings that, in turn, drive more traffic as renters seek opportunities and deals in markets with slowing rent growth. Overall, industry-wide revenue has climbed at a CAGR of 7.7% to $928.1 million through the end of 2025, including an 8.6% gain in 2025 alone, when profit is expected to reach 23.8%. Leading organizations, such as Zillow and Redfin, are taking advantage of this trend by forming partnerships to expand their listing networks and reach. The consolidation of these digital platforms means renters can access a broader range of apartment listings, streamlining their search process and increasing market transparency. Meanwhile, property marketers are presented with simplified operations and increased marketing leads because of enhanced exposure across major rental platforms. However, smaller markets and affordable housing are not receiving the same benefits, signaling a need for more targeted digital marketing and search tools. The online apartment rental services industry is set to face a shift from oversupply to scarcity by the end of 2030. As apartment construction slows because of high borrowing costs, tighter lending standards and rising project costs, there will be a greater demand for platforms that can help landlords maximize occupancy and optimize rents in a tightening market. To meet this demand, innovations in technology, such as predictive analytics, dynamic pricing and personalized renter experiences, will become a necessity. Amid these changes, the industry is also likely to see a gain in demand for single-family rentals, creating new opportunities for digital platforms to expand their offerings and capture a larger market share. Industry revenue will strengthen at a CAGR of 9.0% to $1.4 billion in 2030.

The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

Henderson, Chesapeake, and Virginia Beach were the cities in the United States where the average size of rental apartments was the largest in 2024. In Henderson, NV, this measured at *** square feet, whereas in Seattle, WA, the average apartment was much smaller at *** square feet. When it comes to affordability, Wichita, KS, was the city where 1,500 U.S. dollars would get renters the largest apartment.

Introduction This dataset contains detailed data on over 42,500 apartments (250,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance. Changelog v2.2.0 (2023-03-10): A file, location_ratings.csv, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by Fahrländer Partner AG, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below. v2.1.0 (2022-12-23): A file, locations.csv, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated v2.0.0 (2022-10-17): Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field unit_usage describes whether an area belongs to a commercial, residential, janitor or public part of the building Added the fields elevation and height to geometries.csv to describe the elevation above the terrain surface and the height of objects. Added the field plan_id which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan Improved the ordering of fields in the CSV files (instead of alphabetic order) Minor changes to individual sites Procurement The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%). Geometries The dataset contains a file geometries.csv which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment. In total, the datasets contain the 2D geometry of ~1.5 million separators (walls, railings), ~670,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.), and ~290,000 features (sinks, toilets, bathtubs, etc.). Each row contains: apartment_id: The ID of the apartment (for features, areas), note: an apartment id is only unique per site site_id: The ID of the site building_id: The ID of the building floor_id: The ID of the floor plan_id: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan unit_id: The ID of the unit in which the element is spatially contained (for features, areas) area_id: The ID of the area in which the element is spatially contained (for features) unit_usage: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR entity_type: The entity type (area, separator, opening, feature) entity_subtype: The entity’s sub-type (e.g. WALL) geometry: The element’s geometry as a WKT geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards. elevation: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights. height: The height of the entity in meters, note: In many cases, a default height is assumed An example: column apartment_id d4438f2129b30290845ce7eef98a5ba7 site_id 127 building_id 164 plan_id 492 floor_id 861 unit_id 63777 area_id 767676 unit_usage RESIDENTIAL entity_type area entity_subtype LIVING_ROOM geometry POLYGON ((-6.1501158933490139 -4.8490786654693... elevation 0 height 2.6 Simulations Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file simulations.csv contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id, unit_id, apartment_id, floor_id, building_id, site_id as defined above as well as 367 simulation columns. Each simulation column is formatted as: