Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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

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 2025, Munich was the most expensive city to rent a furnished studio among the 23 cities surveyed. At ***** euros per month, renting a studio in Munich cost nearly twice the price of a studio in Athens. For one-bedroom apartments or a furnished private room, the most expensive city was Amsterdam. Homeownership in Europe In many European countries owning your home is more commonplace than renting – for instance, in Romania, the homeownership rate is over ** 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.

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.

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.

In Germany, apartments were most expensive in Munich, with the average square meter price as high as ***** euros. In Cologne, on the other hand, the average square meter price was about ***** euros. According to the house price index in Germany, house prices in the country have seen an increase since the beginning of 2024, after declining from a peak in 2022.

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.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

This Dataset is a rich collection of real estate listings sourced from the popular real estate website, Bina.az. This dataset comprises 39,300 rows and includes 10 columns, each containing essential information about properties available for sale various locations. This valuable dataset serves as a foundational resource for comprehensive real estate market analysis, property valuation, and housing market research.

Dataset Details:

Number of Rows: 39,300 Number of Columns: 10 Column Names and Descriptions:

1.Price: This column indicates the listed price of the property, offering insights into market trends and pricing variations.

2.Location: The "Location" column specifies the geographical details of the property, including the city, district, nearest metro stations. Location is a critical factor for real estate decision-making.

3.Rooms: This column represents the number of rooms in the property. Knowing the room count is crucial for prospective buyers or renters to assess the property's suitability for their needs.

4.Square: The "Square" column contains information about the total area of the property in square meters. Property size is an essential factor for assessing space and value.

5.Floor: This column indicates the floor on which the property is situated. For those interested in apartments, the floor number can be a critical factor.

6.New Building: The "New Building" column contains binary values (e.g., 0 or 1) to indicate whether the property is in a newly constructed building. This information is valuable for those seeking modern or recently built properties.

7.Has Repair: This column contains binary values to indicate whether the property has undergone any repairs or renovations. Repair status can influence a buyer's decision.

8.Has Bill of Sale: This column contain binary values to indicate whether a legal bill of sale exists for the property, ensuring the legitimacy of the transaction.

9.Has Mortgage: The "Has Mortgage" column contains binary values to indicate whether the property has an existing mortgage.

This dataset is a powerful tool for a wide range of applications, including:

Market Analysis: Real estate professionals and analysts can leverage this dataset to conduct market research, assess pricing dynamics, and understand property preferences.

Property Valuation: Property appraisers and valuation experts can use this data to estimate property values based on attributes like location, size, and condition.

Housing Market Research: Academics, policymakers, and researchers can explore this dataset to gain insights into housing market trends, affordability, and the prevalence of mortgages and repairs.

Homebuyers and Renters: Individuals seeking properties can filter and search through the dataset to identify suitable homes based on their specific criteria, such as price, location, room count, and more.

The Bina.az Real Estate Dataset empowers data-driven decision-making within the real estate sector and serves as a valuable resource for anyone interested in the real estate market.

If you want to see scraping code follow this link: https://github.com/AzadShahvaladov/Bina.azDataScraping

This dataset contains several thousand Moscow flats from 2024-04-25

It has 12 useful columns, unfortunately some of them have missing values.

• price: The price of the property in RUB.
• min_to_metro: The approximate time, in minutes, it takes to reach the nearest metro station from the property location.
• region_of_moscow: The administrative region of Moscow where the property is located. This column is categorical with cyrillic strings. I will specify meanings of each value as well as a mapping to rename values to english.
• total_area: The total floor area of the property in square meters.
• living_area: The living area of the property in square meters, indicating the space specifically designated for living purposes.
• floor: The floor number of the property within the building.
• number_of_floors: The total number of floors in the building where the property is located.
• construction_year: The year in which the property was constructed.
• is_new: A binary indicator (yes/no or 1/0) specifying whether the property is newly constructed.
• is_apartments: A binary indicator (yes/no or 1/0) specifying whether the property is classified as an apartment. (In Russia you can be legally registered in a property that is suited for living, for legal purposes some apartments are perfectly comfortable to live in, but you can't be registered there because there are no schools nearby or something else) The apartments where you can register are labeled 0, where you can not 1.
• ceiling_height: The height of the ceiling in the property, measured in meters.
• number_of_rooms: The number of rooms in the property, including bedrooms, living rooms, and others.
• link: The URL link to access more details or information about the property online, if you are curious.

About regions: There are 9 main regions in the dataset. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9148493%2F2109f701231f891ed1e0e271b8f9b9e8%2FMoscow_all_districts.svg.png?generation=1716565819881547&alt=media" alt=""> 1. Central Administrative region (CAR) (ЦАО) 2. Northern Administrative region (NAR) (САО) 3. North-Eastern Administrative region (NEAR) (СВАО) 4. Eastern Administrative region (EAR) (ВАО) 5. South-Eastern Administrative region (SEAR) (ЮВАО) 6. Southern Administrative region (SAR) (ЮАО) 7. South-Western Administrative region (SWAR) (ЮЗАО) 8. Western Administrative region (WAR) (ЗАО) 9. North-Western Administrative region (NWAR) (СЗАО)

!Remapping no longer needed , eng version is in the file moscow_flats_datasets_eng.csv

Pandas remapping region_mapping = { 'ЦАО': 'CAR', 'САО': 'NAR', 'СВАО': 'NEAR', 'ВАО': 'EAR', 'ЮВАО': 'SEAR', 'ЮАО': 'SAR', 'ЮЗАО': 'SWAR', 'ЗАО': 'WAR', 'СЗАО': 'NWAR' } data['region_of_moscow_english'] = data['region_of_moscow'].map(region_mapping)

I hope someone will find some use for this simple small dataset. Feel free to post any comments and suggestions!

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 data includes information on the current status of apartment sales, sale prices, number of units supplied, land costs, construction costs, value-added tax, and sale prices, allowing citizens to objectively verify the actual sale price structure and detailed items of apartments and other multi-family housing. This allows consumers to make rational housing choices, while administrative agencies can use it to review the application of sale price caps, establish regional market stabilization policies, and manage supply. Furthermore, researchers and the private sector can utilize it for various analyses, such as analyzing housing price trends, comparing the ratio of construction costs to land costs, and verifying the effectiveness of sale policies. Ultimately, this data is crucial for enhancing transparency in the multi-family housing market, ensuring housing stability, and establishing sound real estate policies.

Updated on 26 June 2024 - Statistics up to 1st quarter 2024.

Since September 2013, the Observatoire de l’Habitat and STATEC have jointly disseminated quarterly statistics on the sale prices of apartments, making it possible to distinguish between existing apartments (assimilated to the old market) and apartments under construction (assimilated to the new market, for properties sold under a contract for sale in the future state of completion, or VEFA).

These statistics are based on the notarial acts, as included in the Land Advertising data sent by the Registration and Domains Administration. The different stages of processing the raw data to arrive at the statistics of the sale prices of apartments are described in the methodological document joint STATEC and the Habitat Observatory.

The prices carried forward for apartments under construction (VEFA) correspond to a VAT rate of 3%, within the limit of the total tax favor of 50,000 €.

The sale of an apartment frequently includes annexes such as one or more garages, indoor spaces, outdoor parking spaces and cellars. It is important to evaluate the contribution of these different annexes, in particular to be able to compare, when searching for housing, apartments with different numbers of annexes.

The Housing Observatory of the Ministry of Housing has therefore developed a methodology to estimate the contribution of the various annexes to the sale, then to evaluate the price per m2 of the apartments by excluding from the transaction the contributions of these annexes (garages, outdoor locations and cellars). The statistical model made it possible to estimate the implicit prices of garages, outdoor locations and cellars associated with the sale of an apartment, depending on the location of the property. On average, the estimated valuation for the model over the year 2022 is estimated at 67,000 € for a garage or an indoor location in a residence, 32,000 € for an outdoor location, and 15,000 € for a cellar. However, there are large variations within the territory, especially for the implicit prices of outdoor locations. These implicit prices are updated once a year (for Q4 statistics).

The downloadable Excel tables show the sales prices per m2 "refined", in municipalities for which at least 10 transactions are recorded (after selection and processing).

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.

Dataset Presentation: Prices and Characteristics of Apartments in São Paulo

This dataset contains detailed information about prices and various characteristics of apartments in São Paulo, Brazil. The data was collected through a personal search, covering variables such as neighborhood, number of bedrooms, square footage, sale price, among other attributes contained in the dictionary file, for analysis of the real estate market in the region.

The dataset can be used for several purposes, including exploratory analyses to identify price patterns by region, correlations between property characteristics and their values, and the construction of predictive models for price inference.

Enjoy! I hope it is useful for some purpose in your Data Science studies.

Pursuant to Article 70 of the Law on the State Real Estate Cadastre, the State Land Service accumulates and analyses the market prices of the real estate. Information on real estate purchase transactions is regularly received from Land Registers and is collected in the real estate market database maintained by the Cadastre Information System. On 1 March 2021, around 955 000 transactions were registered in the real estate market database. Data on transactions with residential space groups registered in the Cadastre Information System in the composition of apartment properties are collected in the files. _ Taking into account price differences in the real estate market for multi-apartment buildings built after 2000, separate apartment transactions_: 1) in buildings built before 2000; 2) Buildings built after 2000 or new project houses. Average transaction prices are presented by different groups of territories: 1) Apartments in buildings built before 2000 — in Riga, Jurmala, other cities of the Republic, towns of Pieriga impact region, cities of regional significance and small towns; 2) New project apartments in Riga, Jurmala and Pierīga districts. Data available from 2011

France New Apartment Sold: Average Sales Price data was reported at 4,075.646 EUR/sq m in Sep 2018. This records an increase from the previous number of 4,055.257 EUR/sq m for Jun 2018. France New Apartment Sold: Average Sales Price data is updated quarterly, averaging 3,422.609 EUR/sq m from Dec 2000 (Median) to Sep 2018, with 72 observations. The data reached an all-time high of 4,075.646 EUR/sq m in Sep 2018 and a record low of 2,025.000 EUR/sq m in Mar 2001. France New Apartment Sold: Average Sales Price data remains active status in CEIC and is reported by Ministry of Ecology, Sustainable Development and Energy. The data is categorized under Global Database’s France – Table FR.P005: New Apartments: Sales Price.

Dataset

This dataset was created by Beverlyne Akoth

Contents

Depending on the location, the average price for a two-bedroom apartment in Mexico City could exceed ******* U.S. dollars in 2024. In Miguel Hidalgo, the most expensive borough to buy an apartment in Mexico City, the average apartment price was nearly ******* U.S. dollars for a two-bedroom apartment, while a three-bedroom apartment cost over ******* U.S. dollars. Among the boroughs ranked in the statistic, Coyoacán had the most affordable prices for a one-bedroom apartment, averaging ******* U.S. dollars. Overall, Mexico City has the highest average house price per square meter in Mexico.