One of the main factors driving high rents across European cities is the same as any other consumer-driven business. If demand outweighs supply, prices will inflate. The drive for high paid professionals to be located centrally in prime locations, mixed with the low levels of available space, high land, and construction costs, all keep rental prices increasing. Renting in European cities In the third quarter of 2023, Munich was the most expensive city to rent a furnished studio. For one-bedroom apartments or a furnished private room, the most expensive city was Amsterdam. At almost 1,650 euros per month, renting a studio in Munich cost about 1,000 euros more than a studio in Budapest. Owning a home In many European countries owning your home is more commonplace than renting – for instance, in Romania, the homeownership rate is over 95 percent. In the UK, affordability of housing is one of the leading housing concerns, with the majority of adults agreeing that first-time buyers getting on a property ladder is a very or somewhat serious problem. More in-depth information on the topic can be found in the report on residential real estate in Europe.

The District of Columbia is the most expensive U.S. state for studio apartments, with monthly rents nearly 300 U.S. dollars higher than in Hawaii. As of February 2021, renters in District of Columbia paid on average 1,625 U.S. dollars monthly for a studio apartment. In comparison, studios in Arkansas were approximately three times more affordable.

Between 2020 and 2021, the average monthly rent in the U.S. saw an overall increase. Nevertheless, this was not the case in some states that experienced dramatic negative rental growth.

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 4,280 U.S. dollars, while in San Francisco, CA which ranked second highest, renters paid on average 3,160 U.S. dollars.

As of October 2024, Cluj-Napoca had the highest rent for one-room apartments, on average, renting a studio apartment costs 400 euros per month. Arad was the most affordable city to live in on the given list — 220 euros per month, even reaching an average of 180 euros in January and February 2024.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Graph and download economic data for Condo Price Index for New York, New York (NYXRCSA) from Jan 1995 to Jan 2025 about New York, HPI, housing, price index, indexes, price, and USA.

Brazil FipeZap House Asking Price Index: Rent: Average Price: Rio de Janeiro: 1 Bedroom data was reported at 37.790 BRL/sq m in Jun 2019. This records an increase from the previous number of 37.772 BRL/sq m for May 2019. Brazil FipeZap House Asking Price Index: Rent: Average Price: Rio de Janeiro: 1 Bedroom data is updated monthly, averaging 38.309 BRL/sq m from Jan 2008 (Median) to Jun 2019, with 138 observations. The data reached an all-time high of 50.504 BRL/sq m in Aug 2014 and a record low of 16.810 BRL/sq m in Aug 2008. Brazil FipeZap House Asking Price Index: Rent: Average Price: Rio de Janeiro: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EK014: Real Estate: FipeZap House Asking Price Index: Rent: Average Price.

BR: FipeZap: House Asking Price Index: Rent: MoM: São Paulo: 1 Bedroom data was reported at 1.045 % in Jan 2025. This records a decrease from the previous number of 1.046 % for Dec 2024. BR: FipeZap: House Asking Price Index: Rent: MoM: São Paulo: 1 Bedroom data is updated monthly, averaging 0.591 % from Feb 2008 (Median) to Jan 2025, with 204 observations. The data reached an all-time high of 8.583 % in Mar 2008 and a record low of -1.604 % in Jan 2010. BR: FipeZap: House Asking Price Index: Rent: MoM: São Paulo: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB006: Real Estate: FipeZap House Asking Price Index: Rent: Month-on-Month. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

BR: FipeZap: House Asking Price Index: Rent: São Paulo: 1 Bedroom data was reported at 242.303 2010=100 in Jan 2025. This records an increase from the previous number of 239.798 2010=100 for Dec 2024. BR: FipeZap: House Asking Price Index: Rent: São Paulo: 1 Bedroom data is updated monthly, averaging 144.109 2010=100 from Jan 2008 (Median) to Jan 2025, with 205 observations. The data reached an all-time high of 242.303 2010=100 in Jan 2025 and a record low of 69.277 2010=100 in Jan 2008. BR: FipeZap: House Asking Price Index: Rent: São Paulo: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB005: Real Estate: FipeZap House Asking Price Index: Rent. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

BR: Residential: Condominium: Average Price: USD: Metropolitan Region: Sao Paulo: 1 Bedroom data was reported at 2,129.000 USD/sq m in 2021. This records an increase from the previous number of 1,933.000 USD/sq m for 2020. BR: Residential: Condominium: Average Price: USD: Metropolitan Region: Sao Paulo: 1 Bedroom data is updated yearly, averaging 2,230.500 USD/sq m from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 5,194.000 USD/sq m in 2011 and a record low of 994.000 USD/sq m in 2002. BR: Residential: Condominium: Average Price: USD: Metropolitan Region: Sao Paulo: 1 Bedroom data remains active status in CEIC and is reported by Brazilian Enterprise for Equity Studies. The data is categorized under Global Database’s Brazil – Table BR.RKA005: Real Estate: Condominium Stock.

BR: FipeZap: House Asking Price Index: Sales: YoY: São Paulo: 1 Bedroom data was reported at 6.787 % in Feb 2025. This records an increase from the previous number of 6.676 % for Jan 2025. BR: FipeZap: House Asking Price Index: Sales: YoY: São Paulo: 1 Bedroom data is updated monthly, averaging 5.876 % from Jan 2009 (Median) to Feb 2025, with 194 observations. The data reached an all-time high of 29.839 % in Oct 2011 and a record low of -0.498 % in Feb 2017. BR: FipeZap: House Asking Price Index: Sales: YoY: São Paulo: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB003: Real Estate: FipeZap House Asking Price Index: Sales: Year-on-Year. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

In the first quarter of 2024, Amsterdam was the most expensive city to rent a furnished one-bedroom apartment among the 23 leading European cities surveyed. At 2,300 euros per month, rent in Amsterdam was more than twice as high as in Brussels. Amsterdam was also the most expensive city to rent a private room.One of the main factors driving high rents across European cities is the same as any other consumer-driven business. If demand outweighs supply, prices will inflate. The drive for high paid professionals to be located centrally in prime locations, mixed with the low levels of available space, high land, and construction costs, all help keep rental prices increasing.

ImmobilienScout24 is the largest real estate internet platform in Germany. Properties for private as well as commercial use are offered on the website. However, the data only cover residential properties. The dataset covers most characteristics collected on the platform like price, size and characteristics of the housing unit but also automatically generated items like the duration of the advertisement spell.

In June 2019, apartment rental prices peaked in Budapest at nearly four thousand forints per square meter and subsequently decreased to 3.2 thousand by June 2021. However, the average rental price of apartments in the whole country maintained rather similar values over the considered time period.

BR: FipeZap: House Asking Price Index: Rental Yield: São Paulo: 1 Bedroom data was reported at 0.566 % in Jan 2025. This records an increase from the previous number of 0.562 % for Dec 2024. BR: FipeZap: House Asking Price Index: Rental Yield: São Paulo: 1 Bedroom data is updated monthly, averaging 0.506 % from Jan 2008 (Median) to Jan 2025, with 205 observations. The data reached an all-time high of 0.944 % in Oct 2008 and a record low of 0.453 % in Mar 2016. BR: FipeZap: House Asking Price Index: Rental Yield: São Paulo: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB009: Real Estate: FipeZap House Asking Price Index: Rent: Rental Yield. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources.

Context

The dataset consists of lists of unique objects of popular portals for the sale of real estate in Russia. More than 540 thousand objects. The dataset contains 540000 real estate objects in Russia.

Content

The Russian real estate market has a relatively short history. In the Soviet era, all properties were state-owned; people only had the right to use them with apartments allocated based on one's place of work. As a result, options for moving were fairly limited. However, after the fall of the Soviet Union, the Russian real estate market emerged and Muscovites could privatize and subsequently sell and buy properties for the first time. Today, Russian real estate is booming. It offers many exciting opportunities and high returns for lifestyle and investment. The real estate market has been in a growth phase for several years, which means that you can still find properties at very attractive prices, but with good chances of increasing their value in the future.

Dataset

The dataset has 13 fields. - date - date of publication of the announcement; - time - the time when the ad was published; - geo_lat - Latitude - geo_lon - Longitude - region - Region of Russia. There are 85 subjects in the country in total. - building_type - Facade type. 0 - Other. 1 - Panel. 2 - Monolithic. 3 - Brick. 4 - Blocky. 5 - Wooden - object_type - Apartment type. 1 - Secondary real estate market; 2 - New building; - level - Apartment floor - levels - Number of storeys - rooms - the number of living rooms. If the value is "-1", then it means "studio apartment" - area - the total area of ​​the apartment - kitchen_area - Kitchen area - price - Price. in rubles

Attention.

The dataset may contain erroneous data due to input errors on services, as well as outliers, and so on.

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Using this dataset, we offer Kagglers algorithms that use a wide range of functions to predict real estate prices. Competitors will rely on a vast dataset that includes housing data and macroeconomic models. An accurate forecasting model provides more confidence to its clients in a volatile economy.

Virginia (VA) has the 19th highest rent in the country out of 56 states and territories. The Fair Market Rent in Virginia ranges from $701 for a 2-bedroom apartment in Grayson County, VA to $1,765 for a 2-bedroom unit in Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area.

For FY 2024, the Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area (Arlington County) rent for a studio or efficiency is $1,772 per month and $3,015 per month to rent a house or an apartment with 4 bedrooms. The average Fair Market Rent for a 2-bedroom home in Virginia is $1,056 per month.

Approximately 15% of Americans qualify for some level of housing assistance. The population in Virginia is around 2,038,847 people. So, there are around 305,827 people in Virginia who could be receiving housing benefits from the HUD. For FY 2025, the Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area (Arlington County) rent for a studio or efficiency is $2,012 per month and $3,413 per month to rent a house or an apartment with 4 bedrooms. The average Fair Market Rent for a 2-bedroom home in Virginia is $1,059 per month.

Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data was reported at 178.027 Jun2012=100 in Jan 2025. This records an increase from the previous number of 176.754 Jun2012=100 for Dec 2024. Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data is updated monthly, averaging 130.435 Jun2012=100 from Jan 2008 (Median) to Jan 2025, with 205 observations. The data reached an all-time high of 178.027 Jun2012=100 in Jan 2025 and a record low of 39.466 Jun2012=100 in Jan 2008. Brazil BR: FipeZap: House Asking Price Index: Sales: Broad: 1 Bedroom data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Global Database’s Brazil – Table BR.RKB001: Real Estate: FipeZap House Asking Price Index: Sales. The FipeZap Index uses announcements of sale or rental of apartments ready registered in many websites as data sources. Broad Index: Is composed by 20 cities (São Paulo, Rio de Janeiro, Belo Horizonte, Distrito Federal, Salvador, Recife, Fortaleza, Santo André, São Bernardo do Campo, São Caetano do Sul, Niterói, Vitória, Vila Velha, Porto Alegre, Curitiba, Florianópolis, Goiânia, Campinas, Santos and Contagem) and the historical data started in 2012.

Median prices for dwellings/townhouses, and apartments by their year of settlement for the City of Melbourne by CLUE Small area.