This statistic shows the average size of newly built one-bedroom apartments in the United States from 2008 to 2018. One-bedroom apartments built in 2018 were, on average, *** square feet, down from *** square feet in 2008.

The median monthly rent for all apartment types in the U.S. has stabilized since 2022, despite some seasonal fluctuations. In August 2025, the monthly rent for a two-bedroom apartment amounted to ***** U.S. dollars. That was an increase from ***** U.S. dollars in January 2021, but a decline from the peak value of ***** U.S. dollars in August 2022. Where are the most expensive apartments in the U.S.? Apartment rents vary widely from state to state. To afford a two-bedroom apartment in California, for example, a renter needed to earn an average hourly wage of nearly ** U.S. dollars. This was approximately double the average wage in North Carolina and three times as much as the average wage in Arkansas. In fact, rental costs were considerably higher than the hourly minimum wage in all U.S. states. How did rents change in different states in the U.S.? In 2025, some of the most expensive states to rent an apartment only saw a moderate increase in rental prices. Nevertheless, rents increased in most states as of August 2025. In West Virginia, the annual rental growth was the highest, at ***** percent.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions: The average rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in average rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Average rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing. Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development.

The average monthly rent of apartments in California increased substantially 2021, followed by a period of stabilization. In May 2024, the average rent of a two-bedroom apartment cost over ***** U.S. dollars, up from ***** U.S. dollars in December 2020 before rents started to rise. Nevertheless, not all cities saw rents rise at the same pace.

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.

In 2024, New York, NY, was the most expensive rental market for one-bedroom apartments in the United States. The median monthly rental rate of an apartment in New York was ***** U.S. dollars, while in San Francisco, CA which ranked second highest, renters paid on average ***** U.S. dollars.

Average asking rent price in select Census Metropolitan Areas by rental unit type. The breakdown by number of bedrooms is provided only for apartments. The results are based on an experimental approach, meaning they are derived from recent methodologies and may be subject to revisions. Quarterly data are available starting from the first quarter of 2019.

Original Source: - Apartment Rental: https://www.kaggle.com/datasets/adithyaawati/apartments-for-rent-classified - Crime Data: https://www.kaggle.com/datasets/michaelbryantds/crimedata

Apartment Rent amenities -- 'basic' or 'luxury' bathrooms -- number of bathrooms bedrooms -- number of bedrooms has_photo -- photo of apartment pets_allowed -- True / False price -- rental price of an apartment square_feet -- size of the apartment cityname -- where the apartment is located state -- where the apartment is located latitude -- where the apartment is located longitude -- where the apartment is located source -- origin web of sourced data time -- data was sourced, originally in Unix format

Crime population -- Mean Population of the area racepctblack, racePctWhite, racePctAsian, racePctHisp-- Social background percentage of the area medIncome, medFamInc-- Median income, Median income of total family murdPerPop, rapesPerPop, robbbPerPop, assaultPerPop, burglPerPop, larcPerPop, autoTheftPerPop, arsonsPerPop, ViolentCrimesPerPop, nonViolPerPop-- Average number of each type of crimes avg_crime

This table contains data described by the following dimensions (Not all combinations are available): Geography (247 items: Carbonear; Newfoundland and Labrador; Corner Brook; Newfoundland and Labrador; Grand Falls-Windsor; Newfoundland and Labrador; Gander; Newfoundland and Labrador ...), Type of structure (4 items: Apartment structures of three units and over; Apartment structures of six units and over; Row and apartment structures of three units and over; Row structures of three units and over ...), Type of unit (4 items: Two bedroom units; Three bedroom units; One bedroom units; Bachelor units ...).

The average monthly rent of apartments in Florida increased substantially in 2021, followed by two years of slight decrease. As of ********, the average rent of a two-bedroom apartment in Florida cost ***** U.S. dollars, which was an increase of *** U.S. dollars from ******** when prices started to rise.

This dataset contains detailed information about rental properties across various locations in the UK. The data was collected by scraping Rightmove, a popular real estate platform. Each entry in the dataset includes the property's address, subdistrict code, rental price, deposit amount, letting type, furnish type, council tax details, property type, number of bedrooms and bathrooms, size in square feet, average distance to the nearest train station, and the count of nearest stations.

Researchers and analysts interested in the UK rental market can utilize this dataset to explore rental trends, pricing variations based on location and property type, amenities preferences, and more. The dataset provides a valuable resource for machine learning models, statistical analysis, and market research in the real estate sector.

Metadata: Source: The data was collected by scraping the Rightmove real estate platform, a leading source for property listings in the UK. Date Range: The dataset covers rental property listings available during the scraping period. Geographical Coverage: Primarily focused on various locations across the UK, providing insights into regional rental markets. Data Fields: Address: The location of the rental property. Subdistrict Code: A code representing the subdistrict or area of the property. Rent: The monthly rental price in GBP (£) for the property. Deposit: The deposit amount required for renting the property. Let Type: Indicates whether the property is available for short-term or long-term rental. Furnish Type: Describes the furnishing status of the property (e.g., furnished, unfurnished, or flexible options). Council Tax: Information about the council tax associated with the property. Property Type: Specifies the type of property, such as apartment, flat, maisonette, etc. Bedrooms: The number of bedrooms in the property. Bathrooms: The number of bathrooms in the property. Size: The size of the property in square feet (sq ft). Average Distance to Nearest Station: The average distance (in miles) to the nearest train station from the property. Nearest Station Count: The count of nearest train stations within a certain distance from the property. Data Quality: The data may contain missing values or "Ask agent" placeholders, which require direct inquiry with agents or landlords for specific information. Potential Uses: The dataset can be used for market analysis, rental price prediction models, understanding property preferences, and exploring the impact of location and amenities on rental properties in the UK.

In December 2024, the average rental price of apartments of all sizes in Poland was the highest in the capital, Warsaw. Warsaw was the only city where the average rent for a flat of **** m2 reached nearly ***** zloty, and the rent for an apartment of ***** m2 exceeded ***** zloty.

Analysis of Spanish Apartment Pricing and Size Post-COVID-19

Investigating the Impact of the Pandemic

By [source]

About this dataset

This dataset provides an in-depth insight into Spanish apartment prices, locations and sizes, offering a comprehensive view of the effects of the Covid-19 crisis in this market. By exploring the data you can gain valuable knowledge on how different variables such as number of rooms, bathrooms, square meters and photos influence pricing, as well as key details such as description and whether or not they are recommended by reviews. Furthermore, by comparing average prices per square meter regionally between different areas you can get a better understanding of individual apartment value changes over time. Whether you are looking for your dream home or simply seeking to understand current trends within this sector this dataset is here to provide all the information necessary for both people either starting or already familiar with this industry

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How to use the dataset

This dataset includes a comprehensive collection of Spanish apartments that are currently up for sale. It provides valuable insight into the effects of the Covid-19 pandemic on pricing and size. With this guide, you can take advantage of all the data to explore how different factors like housing surface area, number of rooms and bathrooms, location, number of photos associated with an apartment, type and recommendations affect price.

• First off, you should start by taking a look at summary column which summarizes in one or two lines what each apartment is about. You can quickly search some patterns which could give important information about the market current situation during COVID-19 crisis.

• Explore more in depth each individual apartment by looking at its description section for example if it refers to particular services available like swimming pool or gymnasiums . Consequently those extra features usually bumps up the prices higher since buyers are keen to have such luxury items included in their purchase even if it’s not so affordable sometimes..

• Start studying locationwise since it might gives hint as to what kind preof city we have eirther active market in terms equity investment , home stay rental business activities that suggest opportunities for considerable return on investment (ROI). Even further detailed analysis such as comparing net change over time energy efficient ratings electrical or fuel efficiency , transport facilities , educational level may be conducted when choosing between several apartments located close one another ..

• Consider multiple column ranging from price value provided (price/m2 )to size sqm surface area measure and count number of rooms & bathrooms . Doing so will help allot better understanding whether purchasing an unit is worth expenditure once overall costs per advantages estimated –as previously acknowledged apps features could increase prices significantly- don’t forget security aspect major item critical home choice making process affording protection against Intruders ..

• An interesting but tricky part is Num Photos how many were included –possibly indicates quality build high end projects appreciate additional gallery mentioning quite informative panorama around property itself - while recomendation customarily assumes certain guarantees warranties unique promise provided providing aside prospective buyer safety issues impose trustworthiness matters shared among other future residents …

• Finally type & region column should be taken into account reason enough different categories identifies houses versus flats diversely built outside suburban villas contained inside specially designed mansion areas built upon special requests .. Therefore usage those two complementary field help finding right desired environment accompaniments beach lounge bar attract nature lovers adjacent mountainside

Research Ideas

• Creating an interactive mapping tool that showcases the average prices per square meter of different cities or regions in Spain, enabling potential buyers to identify the most affordable areas for their desired budget and size.
• Developing a comparison algorithm that recommends the best options available depending on various criteria such as cost, rooms/bathrooms, recommended status, etc., helping users make informed decisions when browsing for apartments online.
• Constructing a model that predicts sale prices based on existing data trends and analyses of photos and recommendations associated wit...

Among the largest cities in the United States, renting an apartment was most affordable in Wichita, KS, in 2024. On average, renters in Wichita could rent an ***** square foot apartment for ***** U.S. dollars. The average apartment rent varies widely across different metros and states, with Hawaii, California, and Washington D.C. fetching the most expensive rents.

See the average Airbnb revenue & other vacation rental data in Apt in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

A more recent version of these indicators can be found on this page: https://www.data.gouv.fr/fr/datasets/carte-des-loyers-indicateurs-de-loyers-dannonce-par-commune-en-2022/ Due to the evolution of the methodology and the communal mesh size, successive versions of the indicators cannot be compared to provide information on the evolution of rents. ### Context of the project Knowledge of the level of rents is important to ensure the proper functioning of the rental market and the conduct of national and local housing policies. The Directorate-General for Planning, Housing and Nature (DGALN) launched in 2018 the “rent map” project by partnering on the one hand with a research team in economics of Agrosup Dijon and the National Institute of Research in Agronomics (INRAE), and on the other hand with SeLoger, leboncoin and PAP. This innovative partnership has rebuilt a database with more than 9 million rental ads. On the basis of these data, the research team developed a methodology for estimating indicators, at the communal scale, of rent (including charges) per m² for apartments and houses. These experimental indicators are put online in order to be usable by all: state services, local authorities, real estate professionals, private donors and tenants. In a second phase of the project, the methodology will need to be consolidated and sustained, in order to provide for a regular update of these indicators. This project provides additional information to that offered by the Local Land Observatorys (OLL), deployed since 2013 and reinforced since 2018 by the Elan law. Today, this associative network of 30 OLL publishes every year precise information on the rents practiced in 51 of the main French agglomerations. ### Presentation of the dataset The data disseminated are indicators of ad rents, at the level of the municipality. The field covered is the whole of France, outside of Mayotte. The geography of the municipalities is the one in force on 1 January 2017. Rent indicators are calculated using ad data published on leboncoin, SeLoger and PAP over the period 2015-2019. Rent indicators are provided including charges for standard properties leased in the 3 rd quarter of 2018 with the following reference characteristics: — For an apartment: 49 m² and average area per room of 22.1 m² — For a house: 92 m² area and average area per room of 22.5 m² ### Data terms and conditions These indicators can be freely used, provided that the source is indicated as follows: “UMR 1041 CESAER estimates (AgroSup Dijon-INRAE) from SeLoger, leboncoin, PAP”. ### Precautions for use Rent indicators are calculated including charges, on ad data, so measure flow rents only. The data were duplicated but could not rely on very discriminating photos and characteristics. For municipalities with no housing leased through an advertisement on at least one of the three sites during the period considered, the rent indicator is that estimated for a larger grid comprising neighbouring municipalities with similar characteristics. Moreover, since the data do not make it possible to distinguish with certainty furnished and tourist rentals, biases in the rent indicators can be observed locally. Users are advised to consider rent indicators with caution in municipalities where the coefficient of determination (R2) is less than 0.5, the number of observations in the municipality is less than 30 or the prediction interval is very wide.

Housing and residential status. Residential area and social structure. Mobility and economic situation. Employment. Election decision and participation.

Topics: 1. Housing and housing status: size of the place of residence (degree of urbanisation); location; duration of residence; satisfaction with the place of residence; length of residence in the apartment; number of moves in the last ten years; previous place of residence; residential status of the previous apartment; living space of the previous apartment; reasons for moving; main reason for moving; residential status of the current apartment; one or more households in the house; monthly contribution costs; type of purchase of house/flat; construction of the house/flat by public subsidies; amount of monthly mortgage repayment and interest; amount of monthly ancillary costs; amount of heating costs in the last calendar year; amount of maintenance costs in the last calendar year; monthly burden subsidy received from the state; housing entitlement certificate required; owner of the flat; rent amount; rent including costs for heating and hot water; amount of lump sum for heating and hot water (or. for heating and hot water separately); average costs for heating and hot water and payment period; rent includes modernisation charge; amount of modernisation charge in total or per sqm; type of modernisation measures for which a modernisation charge is paid; adequacy of rental costs; receipt of housing benefit; amount of monthly housing charge; living space; number of rooms; assessment of apartment size; apartment furnishing; apartment equipment meets needs; preferred living standard; year of construction of the house; assessment of the structural condition of the house; satisfaction with the apartment.

1. Residential area and social structure: satisfaction with the immediate residential environment; satisfaction with the environmental conditions at the place of residence; walking distance to selected facilities (e.g. public transport stops, shopping facilities, doctors, kindergarten, primary school, etc.); social structure: social differences in the immediate living environment; relationship with neighbours; satisfaction with the neighbourhood; development of personal living situation; greatest loss after possible relocation (local connection); preferred home; preferred residential area; foreigners in the residential environment; proportion of foreigners in the residential area compared to other residential areas; foreigners who have been living in the residential area or have recently moved in; newly arrived foreigners are predominantly ethnic Germans, refugees or have been living in Germany for some time; relationship between foreigners and Germans in the residential environment; attitude towards the spatial separation of Germans and foreigners; personal contacts with foreigners or Germans in the family, at work, in the neighbourhood or among friends and acquaintances; assessment of assistance for foreigners (simple entry aids, more extensive integration measures or renouncement of such assistance).

2. Mobility: intention to move; reasons for moving; most important reason for moving; preference for moving (target area); plans for the current apartment within the next two years or changes already carried out in the last two years (new furnish, renovate, modernise, add-on or conversion); classification on a ladder best form of living / worst imaginable apartment (own apartment, in comparison own apartment 5 years ago, best accessible apartment, justly entitled apartment, average apartment of friends and acquaintances, apartment of an average German citizen); assessment of the current personal economic situation.

3. Employment: employment status; job security; length of working distance; longest accepted working distance in minutes; willingness to commute.

4. Election decision and participation: eligibility to vote in the last federal election; participation in the last federal election and election decision (second vote); party preference (Sunday question) or party most likely to be considered.

Demography: sex; age (month of birth and year of birth); highest school leaving certificate or targeted school leaving certificate; age at school leaving certificate; vocational education or training certificate; current or former employment; full-time or part-time employment; current or last professional position; current or last professional activity; marital status; cohab...

Content

The dataset consists of Price of Houses in King County , Washington from sales between May 2014 and May 2015. Along with house price it consists of information on 18 house features, date of sale and ID of sale.

Attribute information

1. id - Unique id for each home sold
2. date - Date of the home saled
3. price - Price of each home sold
4. bedrooms - Number of bedrooms
5. bathrooms - Number of bathrooms
6. sqft _ living - Square footage of the apartments interior living space
7. sqft _ lot - Square footage of the land space
8. floors - Number of floors
9. waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not
10. view - An index from 0 to 4 of how good the view of the property was
11. condition - an index from 1 to 5 on the condition of the apartment
12. grade - An index from 1 to 13 , where 1-3falls short of building construction and design, 7 has an average level of construction and design , and 11-13 have a high quality level of construction and design
13. sqft _ above - the square footage of the interior housing space that is above ground level
14. sqft _ basement - the square footage of the inerior housing space that is below ground level
15. yr _ built - The year of the house was initially built
16. yr _ renovated - The year of the house's last renovation
17. zipcode - What zipcode area the house is in
18. lat - Lattitude
19. long - Longitude
20. sqft _ living15 - The square footage of inerior housing living space for the nearest nearest 15 neighbours
21. sqft _ lot15 - the square footage of the land lots of the nearest 15 neighbours

This statistic shows the median size of new residential units in Manhattan and Brooklyn, New York in the first quarter 2018. In that quarter, the median size of newly developed two-bed units sold in Manhattan amounted to ***** square feet, whereas the median size of those sold in Brooklyn was ***** square feet.