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

Revenue for apartment lessors has expanded through the end of 2025. Apartment lessors collect rental income from rental properties, where market forces largely determine their rates. The supply of apartment rentals has grown more slowly than demand, which has elevated rental rates for lessors' benefit. As the Federal Reserve hiked interest rates 11 times between March 2022 and January 2024, homeownership was pushed beyond the reach of many, resulting in a tighter supply and increased demand for rental properties. Despite three interest rate cuts in 2024, mortgage rates have remained stubbornly high in 2025, encouraging consumers to rent. Revenue has climbed at a CAGR of 2.6% over the past five years and is expected to reach $295.3 billion by the end of 2025. This includes an anticipated 1.4% gain in 2025 alone. The increasing unaffordability of housing is caused by the steady climb of mortgage rates and high prices maintained by a low supply. Supply has been held down as buyers who locked in low rates stay put, and investment groups hold a strategic number of their properties empty as investments. Industry profit has remained elevated because of solid demand for apartment rentals. Through the end of 2030, the apartment rental industry's future performance will be shaped by varying factors. The apartment supply in the US, which hit a record in 2024, is expected to taper off, which will push rental prices and occupancy rates up to the lessors' benefit. Other factors, such as interest rate cuts, decreasing financial barriers to homeownership and a high rate of urbanization, will also significantly impact the industry. With an estimated 80.7% of the US population living in urban areas, demand for apartment rentals will strengthen, although rising rental prices could force potential renters to cheaper suburbs. Demand will continue to outpace supply growth, prompting a climb in revenue. Revenue is expected to swell at a CAGR of 1.7% over the next five years, reaching an estimated $321.9 billion in 2030.

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.

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.

As of January 2025, the rent for a two-bedroom apartment in Hawaii was about 120 U.S. dollars higher than in California. The states of Hawaii and California ranked as the most expensive within the United States for apartment renters. Conversely, an apartment in Arkansas was almost three times more affordable than one in Hawaii.In 2025, the average monthly rent in the U.S. declined slightly. Nevertheless, in rents increased in most states, with West Virginia registering the highest growth.

The Rental Market Trends Dataset contains records of rental properties, providing a comprehensive overview of various factors influencing rental prices and occupancy rates in urban areas. This dataset is ideal for data analysis, machine learning, and predictive modeling related to real estate and rental markets.

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.

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.

Greece - Housing cost overburden rate: Tenant, rent at market price was 37.40% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Greece - Housing cost overburden rate: Tenant, rent at market price - last updated from the EUROSTAT on November of 2025. Historically, Greece - Housing cost overburden rate: Tenant, rent at market price reached a record high of 87.50% in December of 2014 and a record low of 36.00% in December of 2010.

Rental Rate Benchmarking Tool Market Outlook

According to our latest research, the global rental rate benchmarking tool market size reached USD 1.2 billion in 2024, reflecting robust demand for data-driven solutions in the real estate sector. The market is projected to grow at a CAGR of 13.6% from 2025 to 2033, with the total market value expected to reach USD 3.8 billion by 2033. The primary growth factor driving this expansion is the increasing reliance on advanced analytics and benchmarking tools by real estate professionals and property managers to optimize rental pricing and maximize occupancy rates.

One of the core growth factors for the rental rate benchmarking tool market is the rapid digital transformation within the real estate industry. As property managers, landlords, and real estate agents face heightened competition and evolving consumer expectations, the need for accurate, real-time rental data has become paramount. These tools empower stakeholders to make informed pricing decisions by aggregating and analyzing vast datasets, including local market trends, historical rental rates, and competitive listings. This data-driven approach not only enhances profitability but also mitigates the risks of overpricing or underpricing properties. Additionally, the integration of artificial intelligence and machine learning algorithms into benchmarking tools enables predictive analytics, offering actionable insights that further streamline rental strategies and improve overall asset management.

Another significant driver of market growth is the increasing complexity and diversification of real estate portfolios. With the proliferation of mixed-use developments, co-living spaces, and flexible leasing arrangements, property owners and managers require sophisticated benchmarking solutions to navigate the intricacies of modern rental markets. The rental rate benchmarking tool market addresses these challenges by providing customizable, scalable platforms capable of handling diverse asset types and geographies. Furthermore, the growing adoption of cloud-based deployment models has made these tools more accessible to small and medium enterprises, democratizing access to advanced analytics and leveling the playing field across the real estate ecosystem. The ability to seamlessly integrate benchmarking tools with property management systems and customer relationship management platforms further enhances operational efficiency and drives market adoption.

Regulatory changes and evolving tenant expectations are also shaping the trajectory of the rental rate benchmarking tool market. In many regions, increased transparency and fairness in rental pricing have become regulatory priorities, compelling landlords and property managers to adopt benchmarking tools that ensure compliance with local laws and guidelines. Additionally, tenants are becoming more informed and discerning, often conducting their own market research before committing to leases. This shift in tenant behavior places additional pressure on landlords and agents to justify rental rates with empirical data, further fueling demand for benchmarking solutions. As sustainability and ESG (Environmental, Social, and Governance) considerations gain prominence in real estate, benchmarking tools are also being leveraged to track and compare sustainability metrics, adding another layer of value to these platforms.

From a regional perspective, North America currently dominates the rental rate benchmarking tool market, accounting for the largest share due to the high concentration of institutional real estate investors, advanced digital infrastructure, and a mature property technology ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, increasing investments in smart cities, and the digitization of real estate services. Europe also represents a significant market, characterized by a strong regulatory framework and a high demand for transparency in rental transactions. Latin America and the Middle East & Africa are witnessing steady adoption, supported by growing awareness and gradual modernization of real estate practices. The interplay of these regional dynamics is expected to shape the competitive landscape and innovation trajectory of the rental rate benchmarking tool market over the forecast period.

Component Analysis

The component segment of the rental rate benchmarking tool market is bifurcated into soft

Creating a rental market database for data analysis and machine learning.

How does it work ?

You scrape the property ads (sale or rent) on internet and you get a dataset.

Then 3 fancy solutions are possible:

• Run your webcrawler everyday for a specific place, upload the data in your data warehouse, and monitor the trends in real estate market prices.

• Apply machine learning to your database and get a sense of the relative expensiveness of the properties.

• Localize every property ads on a Google map using color-coded points in order to visualize the most cheap and expensive neighborhoods.

Original Data Source

For the sake of example, and for proximity reasons, we fetched information from a mid-sized Swiss city, called Lausanne, based in the south of Switzerland. The country has the particularity that people get often puzzled by the level of prices swarming almost everywhere in the rental markets. This is mostly related to the very high living standards prevailing over here. So we used one of the public property ads available in this french-speaking part of the country : https://www.homegate.ch/

Because the booming Swiss housing market is mainly a rental market (foreign investments have been riding high for the sales of property, and mortgage loans are closed to record low), I focused on real estate for rent ads in the Homegate website.

Building a webcrawler

In the Kernels section, you will find out how the Python looks like. I used BeautifulSoup and Urllib Python libraries to grab data from the website. As you can figure out, the code is simple, but really efficient.

What you get

In this example, I extracted data as of 03/17/2017, and I named the DataFrame "Output", available in CSV format to make the data compatible with most commonly preferred tools for analysis. It allows you to get a DataFrame with 12 columns:

• the date

• is it a rent or a buy

• the location

• the address of the property

• the zip code

• the available description of the property

• the number of rooms

• the surface

• the floor

• the price

• the source

Machine learning

In the Kernels section, you will see a very simple ML algorithm applied to the dataset in order to the "theoretical" price of each asset, at the end of the code. For the sake of simplicity, I ran a very straightforward linear regression using only 3 features (the 3 only quantitative factors I have at hand) :

• the number of rooms

• the floor

• the surface

I know what you're thinking right at the moment : those 3 features can barely explain the price of a property. Other determinants, such as the location, the neighborhood, the fact that it is outdated, badly maintained by a students roommate partying every night, ... , are of interest when it comes to assessing an appartment. But straightaway, I reduced the model to this.

Google Map display of the property ads and their relative expensiveness

cf Capture.PNG file

Upcoming improvements

• Add new features to machine learning process, especially a dummy variable accounting for the neighborhood to which the property pertains.

• See to what extent a logistic regression could overcome a linear regressor.

• Test more complex machine learning algorithms.

• Display trends in rental property prices, for each neighborhood, after establishing a larger database (with a few weeks of scraped data).

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.

Rent estimates at the 50th percentile (or median) are calculated for all Fair Market Rent areas. Fair Market Rents (FMRs) are primarily used to determine payment standard amounts for the Housing Choice Voucher program, to determine initial renewal rents for some expiring project-based Section 8 contracts, to determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), and to serve as a rent ceiling in the HOME rental assistance program. FMRs are gross rent estimates. They include the shelter rent plus the cost of all tenant-paid utilities, except telephones, cable or satellite television service, and internet service. The U.S. Department of Housing and Urban Development (HUD) annually estimates FMRs for 530 metropolitan areas and 2,045 nonmetropolitan county FMR areas. Under certain conditions, as set forth in the Interim Rule (Federal Register Vol. 65, No. 191, Monday October 2, 2000, pages 58870-58875), these 50th percentile rents can be used to set success rate payment standards.

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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For more datasets, click here.

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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 | |:----------------|:----------------------------------------------------...

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

The data was scraped from the Magicbricks website. The following are the details of the dataset:

• Title: The title of the property listing.
• Price: The monthly rent of the property.
• Area: The total area of the property in square feet.
• BHK: The number of bedrooms in the property.
• Bathrooms: The number of bathrooms on the property.
• Furnished: Whether the property is furnished or not.
• Balconies: The number of balconies in the property.
• Floor: The floor number of the property.
• Ownership: The type of ownership of the property (i.e., freehold, leasehold, etc.).
• Facing: The direction the property faces.
• Amenities: The amenities that are available in the property or the surrounding area.
• Transaction Type: Whether the property is for sale or rent.
• Property Type: The type of property (i.e., apartment, house, villa, etc.).
• Location: The location of the property.
• Year of Construction: The year the property was built.
• Is Luxury: Whether the property is considered to be a luxury property.
• Description: A brief description of the property.
• Property Image: A link to the property image.

Key points in the dataset are :

1) This dataset can be used to gain insights into the rental market in Mumbai. For example, you could use the data to analyze the average rent for different types of properties, the most popular neighborhoods for renters, or the factors that affect the price of rent. You could also use the data to identify trends in the rental market, such as the increasing popularity of furnished apartments or the rising prices of luxury properties.

2) The dataset could also be used by real estate agents to help their clients find rental properties that meet their needs and budget. Additionally, the data could be used by developers to make informed decisions about the types of properties to build in Mumbai.

3) Overall, this dataset is a valuable resource for anyone who is interested in the rental market in Mumbai. It can be used to gain insights into the market, identify trends, and make informed decisions.

(Disclaimer: The data in this dataset has been gathered from publicly available sources. While the data is believed to be reliable and all privacy policies have been observed, No personal information such as email addresses, mobile numbers, or physical addresses hasn't been collected. I scrape data from the website Magicbricks to study the real estate market of Mumbai. ) Thank you !!!

Malta - Housing cost overburden rate: Tenant, rent at market price was 19.70% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Malta - Housing cost overburden rate: Tenant, rent at market price - last updated from the EUROSTAT on November of 2025. Historically, Malta - Housing cost overburden rate: Tenant, rent at market price reached a record high of 31.00% in December of 2009 and a record low of 12.10% in December of 2018.

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.

This dataset, prepared for Final Datavidia 9.0, provides an extensive collection of Airbnb listing information, offering a rich resource for deep analysis into short-term rental markets. With over 260,000 entries and a diverse range of features, it enables researchers, data scientists, and enthusiasts to explore various aspects of the Airbnb ecosystem, from pricing dynamics to host behavior and geographical distribution.

Dataset Overview

The dataset comprises 261,894 individual Airbnb listings across multiple cities, described by 55 distinct features. It captures a snapshot of properties available, details about their hosts, location specifics, pricing structures, availability, and comprehensive review scores. The data types span categorical, numerical (float64, int64), providing a versatile base for various analytical and machine learning tasks.

Key Features and Columns:

The columns in this dataset can be broadly categorized as follows:

• Listing Identification & Details:

  • id: Unique identifier for each listing.
  • name, description: Textual descriptions of the listing.
  • property_type: Type of property (e.g., apartment, house, private room).
  • room_type: The type of room offered (e.g., Entire home/apt, Private room).
  • accommodates: Number of guests the listing can accommodate.
  • bathrooms, bathrooms_text, bedrooms, beds: Details on the property's physical attributes.
  • amenities: A list of amenities provided by the listing.

• Host Information:

  • host_id, host_name: Unique identifiers and names for hosts.
  • host_since: Date when the host joined Airbnb.
  • host_location, host_about: Information about the host's location and self-description.
  • host_response_time, host_response_rate, host_acceptance_rate: Metrics on host responsiveness and booking acceptance.
  • host_is_superhost: Indicates if the host is a "Superhost."
  • host_neighbourhood: The neighborhood where the host resides (if provided).
  • host_listings_count, host_total_listings_count: Number of listings by the host.
  • host_verifications, host_has_profile_pic, host_identity_verified: Verification status of the host.

• Location Data:

  • latitude, longitude: Geographical coordinates of the listing.
  • neighbourhood, neighbourhood_overview, neighbourhood_cleansed: Information about the listing's neighborhood, with neighbourhood_cleansed likely being a standardized version.
  • city: The city where the listing is located.

• Pricing & Availability:

  • price: The nightly price of the listing.
  • has_availability: Indicates if the listing has any availability.
  • availability_30, availability_60, availability_90, availability_365: Number of available days in various future periods.
  • availability_eoy: Availability at the end of the year.

• Review Scores & Activity:

  • number_of_reviews, number_of_reviews_ltm, number_of_reviews_l30d: Total reviews and reviews in the last 12 months (ltm) and 30 days (l30d).
  • number_of_reviews_ly: Number of reviews in the last year.
  • first_review, last_review: Dates of the first and last reviews.
  • review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value: Detailed breakdown of review scores.
  • reviews_per_month: Average number of reviews per month.
  • estimated_occupancy_l365d, estimated_revenue_l365d: Estimated occupancy and revenue over the last 365 days.

Potential Use Cases:

This dataset is ideal for a wide range of analytical and predictive modeling tasks, including but not limited to:

• Price Prediction: Building models to predict Airbnb listing prices based on features like location, amenities, and property type.
• Market Analysis: Identifying trends in listing availability, host behavior, and property distribution across different cities and neighborhoods.
• Host Performance Analysis: Evaluating factors that contribute to host success, superhost status, and review scores.
• Geospatial Analysis: Visualizing listing density, pricing heatmaps, and neighborhood characteristics.
• Recommendation Systems: Developing systems that recommend listings based on user preferences or similar properties.
• Demand Forecasting: Analyzing availability trends to understand demand patterns over different periods.
• Feature Importance: Determining which features are most influential in determining price, reviews, or occupancy.

The presence of non-null counts indicates varying levels of data completeness across columns, which may require data imputation or careful handling during analysis.