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

This dataset comprises detailed information on apartment rentals, ideal for various machine learning tasks including clustering, classification, and regression. It features a comprehensive set of attributes that capture essential aspects of rental listings, such as:

Identifiers & Location: Includes unique identifiers (id), geographic details (address, cityname, state, latitude, longitude), and the source of the classified listing. Property Details: Provides information on the apartment's category, title, body, amenities, number of bathrooms, bedrooms, and square_feet (size of the apartment). Pricing Information: Contains multiple features related to pricing, including price (rental price), price_display (displayed price), price_type (price in USD), and fee. Additional Features: Indicates whether the apartment has a photo (has_photo), whether pets are allowed (pets_allowed), and other relevant details such as currency and time of listing creation. The dataset is well-cleaned, ensuring that critical columns like price and square_feet are never empty. This makes it a robust resource for developing predictive models and performing in-depth analyses on rental trends and property characteristics.

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.

The dataset contains apartment sales and rent offers from the 15 largest cities in Poland (Warsaw, Lodz, Krakow, Wroclaw, Poznan, Gdansk, Szczecin, Bydgoszcz, Lublin, Katowice, Bialystok, Czestochowa). The data comes from local websites with apartments for sale. To fully capture the neighborhood of each apartment better, each offer was extended by data from the Open Street Map with distances to points of interest (POI). The data is collected monthly and covers a timespan between August 2023 and June 2024

Related work:

• Apartment for Sale in Poland - Data analysis
• Apartment for Sale in Poland - Deep Learning Model

Description

Files

• apartments_pl_YYYY_MM.csv - monthly snapshot of sell offers
• apartments_rent_pl_YYYY_MM.csv - a monthly snapshot of rent offers

Data Fields

• city - the name of the city where the property is located
• type - type of the building
• squareMeters - the size of the apartment in square meters
• rooms - number of rooms in the apartment
• floor / floorCount - the floor where the apartment is located and the total number of floors in the building
• buildYear - the year when the building was built
• latitude, longitude - geo coordinate of the property
• centreDistance - distance from the city centre in km
• poiCount - number of points of interest in 500m range from the apartment (schools, clinics, post offices, kindergartens, restaurants, colleges, pharmacies)
• [poiName]Distance - distance to the nearest point of interest (schools, clinics, post offices, kindergartens, restaurants, colleges, pharmacies)
• ownership - the type of property ownership
• condition - the condition of the apartment
• has[features] - whether the property has key features such as assigned parking space, balcony, elevator, security, storage room
• price - offer price in Polish Zloty
  • apartments_pl_YYYY_MM.csv: sale price
  • apartments_rent_pl_YYYY_MM.csv: monthly rent

Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

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 Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to Sep 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

A list of job applications filed for a particular day and associated data. Prior weekly and monthly reports are archived at DOB and are not available on NYC Open Data.

This dataset provides monthly rental price statistics for apartments across urban neighborhoods, including average, median, minimum, and maximum rents by apartment type and location. It enables detailed market trend analysis, investment strategy development, and urban planning by offering granular insights into rental dynamics over time. The dataset is ideal for real estate professionals, investors, and researchers seeking to understand rental market fluctuations.

The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median 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.

Switzerland Real Estate Price Index: Central Swiss: Rental Apartment data was reported at 545.258 1970=100 in 2016. This records a decrease from the previous number of 555.744 1970=100 for 2015. Switzerland Real Estate Price Index: Central Swiss: Rental Apartment data is updated yearly, averaging 367.689 1970=100 from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 563.858 1970=100 in 2014 and a record low of 100.000 1970=100 in 1970. Switzerland Real Estate Price Index: Central Swiss: Rental Apartment data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.EB002: Real Estate Price Index: By Region: Residential: Annual. Rebased from 1970=100 to 2000=100 Replacement series ID: 388330597

This dataset provides comprehensive information about rental house prices across various locations in India. It includes details such as house type, size, location, city, latitude, longitude, price, currency, number of bathrooms, number of balconies, negotiability of price, price per square foot, verification date, description of the property, security deposit, and status of furnishing (furnished, unfurnished, semi-furnished).

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

• House Type: Type of house (e.g., apartment, villa, duplex).
• House Size: Size of the house in square feet or square meters.
• Location: Specific area or neighborhood where the property is located.
• City: City in India where the property is situated.
• Latitude: Geographic latitude coordinates of the property location.
• Longitude: Geographic longitude coordinates of the property location.
• Price: Rental price of the house.
• Currency: Currency in which the price is denoted (e.g., INR - Indian Rupees).
• Number of Bathrooms: Total number of bathrooms in the house.
• Number of Balconies: Total number of balconies in the house.
• Negotiability: Indicates whether the price is negotiable (Yes/No).
• Price per Square Foot: Price of the house per square foot.
• Verification Date: Date when the rental information was verified.
• Description: Additional description or details about the property.
• Security Deposit: Amount of security deposit required for renting the property.
• Status: Indicates the furnishing status of the property (furnished, unfurnished, semi-furnished).

Usage

This dataset aims to provide valuable insights into the rental housing market in India, enabling analysis of rental trends, comparison of prices across different locations and property types, and understanding the impact of various factors on rental prices. Researchers, analysts, and policymakers can utilize this dataset for a wide range of applications, including real estate market analysis, urban planning, and economic research.

Acknowledgement

This Dataset is created from https://www.makaan.com/. If you want to learn more, you can visit the Website.

Cover Photo by: Playground.ai

📌 Istanbul Rental Apartments Dataset (2025)

This dataset contains rental apartment listings from Istanbul, Turkey. It includes detailed information about rental properties such as district, neighborhood, number of rooms, area in square meters, floor level, and rental price in Turkish Lira (TRY).

📊 Features:

• district: The district (e.g., Beşiktaş, Kadıköy)
• neighborhood: The neighborhood name
• room: Number of rooms
• living_room: Number of living rooms
• area_m2: Apartment size in square meters (m²)
• age: Age of the building
• floor: Floor number (Negative values indicate basement floors)
• price: Rental price in Turkish Lira (TRY)

📈 Additional Notes:

• Price per m²: Can be calculated using price / area_m2.
• Basement floors: Some apartments are below ground level (e.g., -2 means second basement).
• New buildings: Buildings with age = 0 are newly constructed.

This dataset is useful for rental market analysis, price prediction models, and urban studies in Istanbul.

🇹🇷 Türkçe Açıklama

Bu veri seti, İstanbul'daki kiralık daire ilanlarını içermektedir. Dairelerin ilçesi, mahallesi, oda sayısı, metrekare büyüklüğü, kat bilgisi ve kira fiyatı (Türk Lirası - TL) gibi detayları bulunmaktadır.

📊 Veri Seti Özellikleri:

• district: İlçe adı (Örn: Beşiktaş, Kadıköy)
• neighborhood: Mahalle adı
• room: Oda sayısı
• living_room: Salon sayısı
• area_m2: Metrekare büyüklüğü (m²)
• age: Binanın yaşı
• floor: Kat bilgisi (Negatif değerler bodrum katları ifade eder)
• price: Kira fiyatı (Türk Lirası - TL)

📈 Ekstra Notlar:

• Metrekare başına fiyat: price / area_m2 formülüyle hesaplanabilir.
• Bodrum katlar: Negatif değerler bodrum katları ifade eder. Örneğin, -2 ikinci bodrum katıdır.
• Yeni binalar: age = 0 olan binalar yeni inşa edilmiştir.

Bu veri seti, İstanbul’daki kira piyasası analizi, fiyat tahmini modelleri ve kentsel çalışmalar için kullanılabilir.

📅 Last Update: February 2025

The average price of purpose-built rental (PRS) apartments in Tallinn, the capital of Estonia, ranged from ** to ** euros per square meter per month on average in the first half of 2024. That was the highest range compared to other capitals in the Baltic region. In the prime co-living apartment segment, Vilnius recorded the highest price range. The average monthly rent in the private apartment market was the lowest in Riga, varying from ** to **** euros per square meter.

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.

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

Context

This dataset contains information on rent pricing surrounding Kuala Lumpur and Selangor region, Malaysia. The information was scraped from mudah.my

Content

There are 13 features with one unique ids (ads_id) and one target feature (monthly_rent)

• ads_id: the listing ids (unique)
• prop_name: name of the building/ property
• completion_year: completion/ established year of the property
• monthly_rent: monthly rent in ringgit malaysia (RM)
• location: property location in Kuala Lumpur region
• property_type:property type such as apartment, condominium, flat, duplex, studio, etc
• rooms: number of rooms in the unit
• parking: number of parking space for the unit
• bathroom: number of bathrooms in the unit
• size: total area of the unit in square feet
• furnished: furnishing status of the unit (fully, partial, non-furnished)
• facilities: main facilities available
• additional_facilities: additional facilities (proximity to attraction area, mall, school, shopping, railways, etc)

Acknowledgements The data was scraped from mudah.my

Inspiration I have been living in Kuala Lumpur, Malaysia since 2017, and in the past there was no easy way to understand whether certain unit pricing is making sense or not. With this dataset, I wanted to be able to answer the following questions:

• What are the biggest factor affecting the unit/rent pricing?
• Which location in Kuala Lumpur/ Selangor region that has the highest rent price? etc?

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, LMR, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 05/2024 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 05/2024 (municipality, district, LMR, and grid level); third, the time-region fixed effect between 2008 and 2024, which is used to determine the price changes for all three region types to the base year of 2008.

An experimental price index tracking the prices paid for renting property from private landlords in the United Kingdom

VITAL SIGNS INDICATOR
Rent Payments (EC8)

FULL MEASURE NAME
Median rent payment

LAST UPDATED
January 2023

DESCRIPTION
Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 2 (1970)
Form STF1 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B25058 (2005-2021; median contract rent)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Rent data reflects median rent payments rather than list rents (refer to measure definition above). American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

1970 Census data for median rent payments has been imputed from quintiles using methodology from California Department of Finance as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) 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.