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.

The District of Columbia is the most expensive U.S. state for studio apartments, with monthly rents nearly *** U.S. dollars higher than in Hawaii. As of February 2021, renters in District of Columbia paid on average ***** 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 ***** U.S. dollars, while in San Francisco, CA which ranked second highest, renters paid on average ***** U.S. dollars.

See the monthly rental prices for over 600 major cities in the United States between May 2023 and May 2024. The first two columns show the cities and states, and the rest of the columns show the monthly data. I'm thinking about pulling more data like this and would love to learn of some online sites that contain more details about apartments such as square feet, utilities, proximity to mass transit, etc. Prices are for USD.

This report provides information on studio apartments and officetels in Nam-gu, Gwangju Metropolitan City (including address, date of occupancy approval, primary and secondary use, and number of households). This data is provided by the Architecture Division of Nam-gu Office, Gwangju Metropolitan City (☎062-607-4113). Please note that data where household count information is not available is omitted. Public data may fluctuate depending on the reference point, so please contact the relevant department for further information. ※ This studio apartment and officetel data is used to understand the distribution of small residential properties in the region, market prices for sale, lease, and monthly rent, and supply trends. Studio apartments and officetels are becoming a popular housing option for young people, newlyweds, and working people in high-demand areas such as university areas, industrial complexes, and subway stations. This data allows local governments to develop housing welfare policies, rent stabilization measures, and housing supply plans, while citizens can make informed housing choices and compare costs based on this data. It can also be used for real estate market analysis, urban planning, and lifestyle SOC policies.

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.

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

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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.

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.

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.

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?

Dataset Description

This dataset contains information about real estate listings, including various features of properties and their surrounding areas. The data can be used for analysis, prediction, and insights into the real estate market.

Features

• airports_nearest: Distance to the nearest airport in meters
• balcony: Number of balconies
• ceiling_height: Ceiling height in meters
• cityCenters_nearest: Distance to the city center in meters
• days_exposition: Number of days the listing was active (from publication to removal)
• first_day_exposition: Publication date
• floor: Floor number of the property
• floors_total: Total number of floors in the building
• is_apartment: Boolean indicating if the property is an apartment
• kitchen_area: Kitchen area in square meters
• last_price: Price at the time of listing removal
• living_area: Living area in square meters
• locality_name: Name of the locality
• open_plan: Boolean indicating if the property has an open floor plan
• parks_around3000: Number of parks within a 3 km radius
• parks_nearest: Distance to the nearest park in meters
• ponds_around3000: Number of ponds/water bodies within a 3 km radius
• ponds_nearest: Distance to the nearest pond/water body in meters
• rooms: Number of rooms
• studio: Boolean indicating if the property is a studio apartment
• total_area: Total area of the property in square meters
• total_images: Number of photos in the listing

Additional Economic Data

1. Currency Exchange Rate:

   • USD to RUB exchange rate for the period 2014-2019. This data allows for analysis of how currency fluctuations impact the real estate market.

2. Central Bank of Russia Data:

   • Key interest rate: The central bank's key policy rate, which influences lending rates and overall economic conditions.
   • Inflation rate: The rate of price increases in the economy, which can affect real estate values and investment decisions.

Potential Use Cases

1. Price Prediction: Develop machine learning models to predict property prices based on various features, including economic indicators.
2. Market Trend Analysis: Analyze how different factors, including macroeconomic variables, influence property values over time.
3. Geographical Insights: Study how location and proximity to amenities affect property desirability and pricing.
4. Listing Optimization: Investigate the relationship between listing characteristics (e.g., number of images) and time-to-sell.
5. Property Type Comparison: Compare different types of properties (e.g., studios vs. multi-room apartments) in terms of pricing and demand.
6. Urban Planning: Analyze the impact of parks, water bodies, and other urban features on property values.
7. Temporal Analysis: Study seasonal trends and long-term changes in the real estate market, considering economic factors.
8. Economic Impact Analysis: Investigate how changes in currency exchange rates, interest rates, and inflation affect the real estate market.
9. Investment Strategy: Develop models to identify optimal times for real estate investment based on economic indicators.
10. Risk Assessment: Analyze the volatility of the real estate market in relation to economic factors.

This rich dataset provides ample opportunities for both beginner and advanced data scientists to explore various aspects of the Russian real estate market. It allows for:

• Practice in data cleaning and preprocessing, especially in handling time series data
• Feature engineering, including creating derived features from economic indicators
• Applying different machine learning algorithms for regression and classification tasks
• Conducting time series analysis and forecasting
• Performing multivariate analysis to understand the complex relationships between property characteristics, location features, and economic factors

Researchers and analysts can use this dataset to gain insights into the dynamics of the Russian real estate market, understand the impact of macroeconomic factors on property values, and develop sophisticated models for price prediction and market analysis.

New Delhi Rental Property Dataset – Makaan.com

This dataset consists of detailed rental listings of 14,000 residential properties located in New Delhi, India, collected from the real estate portal makaan.com. The data was gathered using web scraping techniques involving BeautifulSoup4 and Regular Expressions (Regex). A total of 700 pages were scraped to compile this dataset.

This dataset is especially useful for beginners in data science looking to explore and practice concepts such as data cleaning, preprocessing, feature engineering, exploratory data analysis, and even machine learning. It contains a variety of real-world attributes related to rental properties, providing a solid foundation for understanding housing market trends in urban India.

📄 Dataset Features

Below are the features included in the dataset:

• Size : The size configuration of the property, usually indicating the number of rooms. For example: 1, 2, 3 BHK or RK (Room-Kitchen unit).

• Size_unit : The unit associated with the property size — either BHK (Bedroom-Hall-Kitchen) or RK (Room-Kitchen). Helps distinguish full apartments from studio-type accommodations.

• Property_type : The type or category of the property. Examples include Apartment, Independent House, Independent Floor, and other residential types listed on makaan.com.

• Location : The neighborhood or locality within New Delhi where the property is situated. Useful for geographic and locality-specific analysis.

• Seller_name : The name of the individual or organization who listed the property on the platform. This can help identify frequent sellers or real estate agencies.

• Seller_type : Classification of the seller into categories such as Owner, Agent, or Builder. Offers insights into listing authenticity and marketing patterns.

• Rent_price : The monthly rental cost of the property in Indian Rupees (INR). A core variable for price analysis and budget comparisons.

• Area_sqft : The built-up or carpet area of the property in square feet. Important for calculating price per square foot and comparing property sizes.

• Status : Indicates the current condition of the property. Can be one of:

  • Refurbished
  • Semi-refurbished
  • Unfurbished

• Security_deposit : The amount required as a refundable security deposit, often a multiple of the monthly rent.

• Bathroom : The total number of bathrooms in the property. Useful for assessing the comfort level, especially for families or shared accommodations.

• Facing_direction : The directional orientation of the property (e.g., East, West, North-East). This is a significant factor in Indian housing due to preferences based on sunlight, ventilation, and Vastu Shastra principles.

Feel free to use this dataset for hands-on practice in data exploration, visualization, modeling, or even creating a rental recommendation system. Let me know if you’d like help getting started! :

2025 HUD Fair Market Rents (FMR) for Section 8 Housing Choice Voucher Program in New York. Includes rent limits by city and county for studio through 4-bedroom units.

BR: FipeZap: House Asking Price Index: Rent: São Paulo: 1 Bedroom data was reported at 248.396 2010=100 in Mar 2025. This records an increase from the previous number of 245.521 2010=100 for Feb 2025. BR: FipeZap: House Asking Price Index: Rent: São Paulo: 1 Bedroom data is updated monthly, averaging 144.731 2010=100 from Jan 2008 (Median) to Mar 2025, with 207 observations. The data reached an all-time high of 248.396 2010=100 in Mar 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: FipeZap: House Asking Price Index: Rent: Rio de Janeiro: 1 Bedroom data was reported at 246.678 2010=100 in Mar 2025. This records an increase from the previous number of 241.883 2010=100 for Feb 2025. BR: FipeZap: House Asking Price Index: Rent: Rio de Janeiro: 1 Bedroom data is updated monthly, averaging 151.143 2010=100 from Jan 2008 (Median) to Mar 2025, with 207 observations. The data reached an all-time high of 246.678 2010=100 in Mar 2025 and a record low of 65.125 2010=100 in Aug 2008. BR: FipeZap: House Asking Price Index: Rent: 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 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.

Renting the typical one-bedroom apartment exceeded ***** U.S. dollars in three of United States' cities with population greater than ******* people in 2024. In May that year, the average rent for a one-bedroom apartment in Sunnyvale, California was ***** U.S. dollars.