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

Median monthly rental prices for the private rental market in England by bedroom category, region and administrative area, calculated using data from the Valuation Office Agency and Office for National Statistics.

Correction coefficients are used to ensure equality of purchasing power of salaries of EU officials in the different duty stations. They are calculated as the ratio between the “economic parity” and the exchange rate to the Euro (where applicable). They operate as a percentage adjustment to remuneration expressed in local currency.

The method used to establish economic parities is to compare the price of a basket of goods and services purchased by the average retired international official in Belgium with the price of an equivalent basket of goods and services purchased by the average retired international official in each of the other countries.

The rent paid for an apartment or house, due to its high weight in the total household expenditure structure, plays a significant role in determining the overall correction coefficient.

Consequently, specific rent surveys are carried out annually in cooperation with relevant real estate agencies. The information obtained, and that derived from similar surveys in previous years, is used to determine the difference in rental prices between Brussels and each of the other locations where the survey is carried out, in rental prices for the same type of dwelling.

The scope of these surveys is to compare the average market rent for some specific kinds of dwellings in some pre-specified representative areas of Brussels with similar dwellings in similar (representative and comparable) areas in other EU capitals and duty stations.

The estate agency rent surveys (EARS) are organised jointly by Eurostat, the International Service for Remunerations and Pensions (ISRP) of the Co-ordinated Organisations and national statistical institutes (NSI) in each duty station, including Brussels, with the collaboration of estate agents in the duty stations concerned.

The work is done in accordance with a methodology approved by the Expert Working Group on Articles 64 & 65 of the Staff Regulations.
Whilst this rent information is collected for a specific purpose, according to a specific methodology, it may also be relevant for other purposes.

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.

In 2023, the index for residential rents in the euro area stood at ***** index points. Nevertheless, most European countries recorded a higher increase in rental prices. In Turkey, where inflation has been extremely high in recent years, the index soared to *** index points. That means that the rents increased by *** percent since 2015, the base year with index value of 100. Six other countries had an index value of over *** index points, including Lithuania, Hungary, and Slovenia. Conversely, Greece was the only country where rents have declined since 2015.

London's prime high street retail location commanded the highest rents in Europe, with monthly rates reaching a staggering 2,786 euros per square meter in the second quarter of 2025. This figure dwarfs other major European cities, with Milan coming in second at 1,667 euros per square meter. The stark difference in rental prices highlights the premium placed on prime retail spaces in Europe's financial capital, reflecting the city's status as a global shopping destination. While London boasts the highest retail rents, the UK as a whole remains a significant player in retail real estate investment. In 2023, the UK attracted 6.6 billion euros in retail real estate investment, accounting for approximately a quarter of the total investment in the sector across Europe.

Rental prices for Canada. The data give prices for 1-bedroom, 2-bedroom, 3-bedroom and Bachelor (studio) units. The data runs from 1987 until 2020.

The rental prices are average for the year, and included both vacant as well as occupied units.

This is a good beginner dataset for practicing simple data analysis or visualization.

Source - StatCan

There are 4 CSV files, one for each unit type- One Bedroom, Two Bedroom, Three Bedroom and Bachelors

The dataset has the following columns: City- Name of the City Province- Name of the province where the city is 1987- Average rental price for the unit for that year 1987 1988- Average rental price for the unit for that year 1988 .. 2020- Average rental price for the unit for that year 2020

All rental prices are in Canadian Dollars.

Median rental data for City Heights. Median rents and price changes for apartments, condos, and single-family homes across ZIP 92105 as of October 2025.

Historical market performance data including occupancy rates, average daily rates, and revenue trends

Explore Iowa City, IA rental market 2025. The average long-term prices $1,385 and short-term $1,911, with trends shaping housing in a city of 75,264 residents.

The median rent for one- and two-bedroom apartments in Los Angeles, California, amounted to about ***** U.S. dollars in January 2025. Rents soared during the COVID-19 pandemic, with rental growth hitting **** percent in March 2022. This trend has since reversed, with growth turning negative in May 2023. Among the different states in the U.S., California ranks as the second most expensive rental market after Hawaii.

The online apartment rental services industry is experiencing significant growth because of the booming apartment supply, with over half a million new rental units completed in 2024. Major cities like New York, Dallas and Austin are leading the way in this surge, causing an influx of new, predominantly high-end rental units. As a result, there is increased competition among property managers and a need for more effective digital marketing strategies to reach potential renters. This accelerated growth is predominantly benefiting online rental services, which have seen a climb in listings that, in turn, drive more traffic as renters seek opportunities and deals in markets with slowing rent growth. Overall, industry-wide revenue has climbed at a CAGR of 7.7% to $928.1 million through the end of 2025, including an 8.6% gain in 2025 alone, when profit is expected to reach 23.8%. Leading organizations, such as Zillow and Redfin, are taking advantage of this trend by forming partnerships to expand their listing networks and reach. The consolidation of these digital platforms means renters can access a broader range of apartment listings, streamlining their search process and increasing market transparency. Meanwhile, property marketers are presented with simplified operations and increased marketing leads because of enhanced exposure across major rental platforms. However, smaller markets and affordable housing are not receiving the same benefits, signaling a need for more targeted digital marketing and search tools. The online apartment rental services industry is set to face a shift from oversupply to scarcity by the end of 2030. As apartment construction slows because of high borrowing costs, tighter lending standards and rising project costs, there will be a greater demand for platforms that can help landlords maximize occupancy and optimize rents in a tightening market. To meet this demand, innovations in technology, such as predictive analytics, dynamic pricing and personalized renter experiences, will become a necessity. Amid these changes, the industry is also likely to see a gain in demand for single-family rentals, creating new opportunities for digital platforms to expand their offerings and capture a larger market share. Industry revenue will strengthen at a CAGR of 9.0% to $1.4 billion in 2030.

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

Vital statistics related to Ottawa’s rental housing inventory, including cost of rental housing of different sizes and locations.

Accuracy: No know issues. For a detailed description of the methodology and sources used to compile this data set, please refer to the Rental Market Analysis. Update Frequency: One-time dataset

Contact: Emergency and Protective Services – Public Policy Development Branch

Explore Park City, UT rental market 2025. The average long-term prices $3,802 and short-term $4,285, with trends shaping housing in a city of 8,365 residents.

North Vancouver was the most expensive Canadian city for one-bedroom apartment rentals, with a median rent of about ***** Canadian dollars in June 2025. Coquitlam followed behind with a median rent of ***** Canadian dollars.

The house price to rent ratio index in the U.S. declined in the second half of 2022 and remained stable until the end of 2024, indicating that house price growth slowed down compared to rental growth. At its peak, in the second quarter of 2022, the index stood at *****. House prices increased dramatically since the coronavirus pandemic. Meanwhile, rents have grown notably, but at a slower rate. What does the house price to rent ratio index measure? The house-price-to-rent-ratio measures the evolution of house prices compared to rents. It is calculated by dividing the median house price by the median annual rent. In this statistic, the values have been normalized with 100 equaling the 2015 ratio. Consequentially, a value under 100 means that rental rates have risen more than house prices. Compared to the OECD countries average, the gap between house prices and rents in the United States was wider. The house price to rent ratio in different countries The house price to rent ratio in the United Kingdom continued to increase in the second half of 2022, but growth softened, as the housing market cooled. On the other hand, the index in Germany fell drastically between the second quarter of 2022 and the second quarter of 2023. A similar trend was observed in France.

Real Estate Breakeven Analysis for U.S. Home Types

Buy vs Rent Comparison Across Markets

By Zillow Data [source]

About this dataset

This dataset provides a comprehensive analysis of the current real estate situation in the United States. It includes breakeven analysis charts that compare buying vs renting across major U.S. markets. This dataset contains various metrics such as home types, housing stock, price-to-income ratio, cash buyers, mortgage affordability and rental affordability to name a few. This data has been compiled using Zillow's own data along with TransUnion financing survey data and the Freddie Mac Primary Mortgage Market Survey to provide an accurate understanding of each metro area’s market health and purchasing power for buyers and renters alike. By downloading this information you can compare different regions based on size rank and other factors to get full insights regarding their potential fit for your needs or investments strategies as well as any potential risks associated with each region's housing market health

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How to use the dataset

This dataset is for real estate professionals, owner-occupants, potential buyers and renters who are interested in understanding which U.S. markets offer the most favorable home buying or rental opportunities from a financial perspective over the long term.

The “Real Estate Breakeven Analysis for U.S Home Types” dataset contains data pulled from Zillow's current and forecasted housing market metrics across many different real estate regions in the United States including cities, counties, states, metro areas and combined statistical areas (CSAs). The data includes several measures of affordability such as median price-to-rent ratio (MedPR), median breakeven horizon (MedBE) - which refers to how long it takes to make up purchase costs when compared with renting; cash purchaser share; mortgage rate; mortgage affordability indices; rental affordability rates etc.

In order to analyze and compare buying vs renting decisions across various regions in the US this dataset provides breakeven analysis at various levels of geographies i.e., state names, region types (city/metro area/county) and show how long it will take homeowners to break even on their purchase costs when compared with renting in that region over a longer period of time using discounted cash flow methodology. This information helps people understand what type of transaction is a better fit for them by weighing short term vs long term goals accordingly by evaluating these different factors related to housing metrics carefully before making financial decisions about purchasing or renting properties in desired location(s).

To use this dataset one can use either basic filters like RegionType or RegionName or more detailed filter criteria like CountyName, City name , Metro area name , State Name etc . For example if someone wanted to look at properties available for rent only then they can apply filters based on Province Type =‘Rental’ Also one can further refine searches based on filtering them with defined SampleRate , Median Price – To – Rent Ratio …..etc . This could be useful if seekers would want only specific type of property like Condominium/Coop /Multifamily 5+ Units /Duplex Triplex listing etc …and then apply other parameters like Cash Buyers percent , Mortgage Affordability Rate….etc ..in order narrow down search results while looking at Breakeven scores /horizons in their target locations . One should take advantages of all relevant parameters while searching through data before making any decision related with owning rental properties so that they can make sure best possible investment decision given

Research Ideas

• Visualizing changes in real estate trends across regions by comparing price to rent ratios, mortgage affordability indices and cash buyers over time.
• Market segmentation analysis based on region-level market characteristics such as negative equity data, rental affordability, median house values and population size.
• Predicting housing demand within a particular region based on its breakeven horizon or price to rent ratio

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: BreakEven_2017-03.csv | Column name | Description | |:----------------|:----------------------------------------------------...

Explore Kansas City, MO rental market 2025. The average long-term prices $1,465 and short-term $2,562, with trends shaping housing in a city of 508,233 residents.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.