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.

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

The rent price index in Australia in the first quarter of 2025 was *****, marking an increase from the same quarter of the previous year. Rent prices had decreased in 2020; in Melbourne and Sydney, this was mainly attributed to the absence of international students during the coronavirus outbreak. The current state of the rental market in Australia The rental market in Australia has been marked by varying conditions across different regions. Among the capital cities, Sydney has long been recognized for having some of the highest average rents. As of March 2025, the average weekly rent for a house in Sydney was *** Australian dollars, which was the highest average rent across all major cities in Australia that year. Furthermore, due to factors like population growth and housing demand, regional areas have also seen noticeable increases in rental prices. For instance, households in the non-metropolitan area of New South Wales’ expenditure on rent was around ** percent of their household income in the year ending June 2024. Housing affordability in Australia Housing affordability remains a significant challenge in Australia, contributing to a trend where many individuals and families rent for prolonged periods. The underlying cause of this issue is the ongoing disparity between household wages and housing costs, especially in large cities. While renting offers several advantages, it is worth noting that the associated costs may not always align with the expectation of affordability. Approximately one-third of participants in a recent survey stated that they pay between ** and ** percent of their monthly income on rent. Recent government initiatives, such as the 2024 Help to Buy scheme, aim to make it easier for people across Australia to get onto the property ladder. Still, the multifaceted nature of Australia’s housing affordability problem requires continued efforts to strike a balance between market dynamics and the need for accessible housing options for Australians.

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.

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

The 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.

The Price Index of Private Rents (PIPR) has shown significant growth, reaching a value of 117.9 in January 2025. This marks an increase of approximately 17.9 percent since January 2023, reflecting a robust upward trend in rental prices. Notably, the index saw a steady rise throughout 2024, with an annual percentage change peaking at 9.2 percent in March 2024. Mainstream properties are forecast to see rents further increase until 2028.

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

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

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

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

Usage

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

Acknowledgement

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

Cover Photo by: Playground.ai

The dataset you provided contains monthly price index data for private rents in Great Britain. It includes three columns: "Time period," "Price Index," and "Rental price." Here is a detailed description:

Columns: Time period: The month and year in the format MMM-YYYY. Price Index: The index value representing the price level of private rents for that particular month. Rental price: The actual rental price value corresponding to the respective month. Summary: Time Range: The data spans from January 2017 to March 2024. Observations: There are 87 monthly records in the dataset. Trends: The Price Index starts at 86.9 in January 2017 and gradually increases to 110.3 by March 2024. The Rental price starts at 982 in January 2017 and rises to 1246 by March 2024.

Israel Rental Prices: Avg: Rehovot data was reported at 4,568.300 ILS in Dec 2024. This records an increase from the previous number of 4,507.600 ILS for Sep 2024. Israel Rental Prices: Avg: Rehovot data is updated quarterly, averaging 3,843.350 ILS from Mar 2017 (Median) to Dec 2024, with 32 observations. The data reached an all-time high of 4,568.300 ILS in Dec 2024 and a record low of 3,380.675 ILS in Mar 2017. Israel Rental Prices: Avg: Rehovot data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.EB009: Average Rental Price: Dwellings.

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.

View monthly updates and historical trends for US Consumer Price Index: Rent Of Primary Residence. from United States. Source: Bureau of Labor Statistics.…

Rent Inflation in the United States remained unchanged at 3.60 percent in September. This dataset includes a chart with historical data for the United States Rent Inflation.

Price to Rent Ratio in the United States increased to 134.04 in the fourth quarter of 2024 from 133.46 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

This is the quarterly regional rental price index data of offices among commercial real estate rental trend surveys provided by the Korea Real Estate Agency (formerly the Korea Appraisal Board). - Base date: 2021.4Q = 100 - (Unit: p) - Date of publication: Quarterly (Quarterly)

The UK residential rental market is poised for significant growth, with forecasts indicating a cumulative increase of nearly **** percent by 2029. This surge is expected to be front-loaded, with a robust *****percent rise anticipated in 2025. Rental growth has accelerated notably since 2021, with August 2024 experiencing a decade-high annual percentage growth. The trend reflects the complex interplay between housing affordability, mortgage rates, and supply of rental homes, as the UK housing market navigates a period of transition.

Rent Pricing Optimization AI Market Outlook

According to our latest research, the global Rent Pricing Optimization AI market size reached USD 1.42 billion in 2024, driven by the rapid adoption of artificial intelligence to streamline rental pricing strategies. The market is poised to expand at a robust CAGR of 18.7% during the forecast period, with projections indicating a market value of USD 7.15 billion by 2033. This remarkable growth is propelled by increasing digital transformation across the real estate sector, the surge in rental property investments, and the need for dynamic, data-driven pricing models to maximize occupancy and returns in a highly competitive landscape.

One of the primary growth factors for the Rent Pricing Optimization AI market is the escalating demand for advanced analytics and machine learning tools among property owners and managers. As the rental market becomes more dynamic and competitive, stakeholders are seeking innovative solutions to accurately predict optimal rent prices based on a multitude of variables such as local demand, seasonality, competitor pricing, and economic indicators. The integration of AI-driven pricing platforms enables property managers to automate the rent-setting process, minimize vacancies, and enhance profitability. The growing sophistication of these AI models, which can process vast datasets in real time, is significantly improving pricing accuracy and driving adoption across both residential and commercial segments.

Another crucial growth driver is the increasing penetration of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness for end-users. Cloud deployment enables seamless integration with property management systems, access to real-time market data, and the ability to update pricing strategies remotely. This has proven invaluable for large real estate agencies and property management firms overseeing diverse portfolios across multiple geographies. Moreover, the proliferation of smart property technologies and IoT devices is generating granular data on tenant behavior and property performance, further enhancing the predictive power of AI-based rent pricing optimization tools.

The market is also benefiting from the global shift towards data-driven decision-making in real estate. With rising property values and increased investor interest in rental properties, there is mounting pressure to optimize revenue and reduce operational inefficiencies. AI-powered rent pricing solutions are empowering landlords, real estate agencies, and hospitality providers to respond swiftly to market fluctuations, regulatory changes, and tenant expectations. The ability to generate actionable insights and forecast future trends is positioning AI as an indispensable tool in the evolving rental ecosystem.

Regionally, North America remains the dominant force in the Rent Pricing Optimization AI market, accounting for the largest share in 2024 due to widespread adoption of digital technologies and a highly mature rental sector. However, rapid urbanization and digitalization in Asia Pacific are catalyzing market expansion in the region, with countries such as China, India, and Australia witnessing increased investments in AI-powered property management solutions. Europe is also emerging as a significant market, driven by regulatory reforms and growing awareness of the benefits of dynamic rent pricing, while Latin America and the Middle East & Africa are gradually embracing these technologies as part of broader real estate modernization efforts.

In the realm of airport management, Gate Allocation Optimization AI is emerging as a transformative force, akin to the advancements seen in rent pricing optimization. This technology leverages sophisticated algorithms to streamline the allocation of gates at airports, ensuring efficient use of resources and minimizing delays. By analyzing real-time data on flight schedules, passenger volumes, and aircraft types, Gate Allocation Optimization AI can dynamically adjust gate assignments to optimize airport operations. This not only enhances the passenger experience by reducing wait times but also improves the overall efficiency of airport logistics. As the aviation industry continues to recover and expand, the integration of AI-driven solutions like gate allocation optimization is becoming increasingly vital for maintaining operational excel

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in Seattle-Tacoma-Bellevue WA (CBSA) (CUURA423SEHA) from Dec 1914 to Sep 2025 about Seattle, primary, rent, WA, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

This dataset contains Real Estate Rents listings in the Canada broken by Province and City. Data was collected via web scraping using python libraries.

You may use the dataset for Canada rents houses trend analysis (with respect to the location - province/city/longitude/latitude), regression analysis (price prediction), correlation analysis, etc.,

Content

The dataset has 1 CSV file with 18 columns -

rentfaster.csv (25k+ entries)

-**'rentfaster_id'** - id of property on https://www.rentfaster.com . Can be explore with www.rentfaster.ca/rentfaster_id -**'city'** - city of property like 'Toronto', 'Calgary', 'Vancuver' and etc. -**'province'** - province of property like 'Alberta', 'Ontario' and etc. -**'address'** - address of property like '333 Seymour St' and etc -**'latitude'** - latitude coordinate of rental property -**'longitude'** - longitude coordinate of rental property -**'lease_term'** - category of rental period like 'Long Term', 'Negotiable' and etc -**'type'** - category of type a rental property like 'House', 'Apartment', 'Basement' and etc -**'price'** - price in CAD -**'beds'** - count of bedrooms -**'baths'** - count of bathrooms -**'sq_feet'** - area of rental property in square feets -**'link'** - right side of url for getting full details of the property rentfaster.com+'link' -**'furnishing'** - Furnished or not -**'availability_date'** - Date of availability -**'smoking'** - is allow smoke -**'cats'** - is allow cats -**'dogs'** - is allow dogs