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.

Rents in the United States declined year-on-year for the first time in June 2023, after surging for two years in a row. In November 2021, rents soared by over ** percent annually — the highest increase on record, and in August 2022, the average rental price reached an all-time high of over ***** U.S. dollars. Rental growth has since mellowed, with January 2025 recording a decline of about *** percent from the same period one year ago. Despite the softening of the market, many states still experienced rising rents.

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.

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.

The monthly median asking rent for unfurnished apartments in the United States in the first quarter of 2025 amounted to ***** U.S. dollars. This was an increase of about *** U.S. dollars in just five years. In 2020, the median rent stood at ***** U.S. dollars. The U.S. rental market As rental apartment vacancy rates fall, rents are on the rise. This makes it more difficult for Americans to, first, find an apartment to rent, and second, find an apartment which they can afford. Nevertheless, renting has become much more common in recent years, with the number of renter households having substantially increased in the past two decades. In 2025, there were approximately **** million renter households in the U.S. Rents in different states Of course, rents vary from state to state. The most expensive rents are found in Hawaii, California, District of Colombia, New Jersey, and Florida. Following the COVID-19 pandemic, growth was the strongest in the Sun Belt states, and especially in states with lower costs of living, such as Texas. In Austin, TX, the average rent soared by nearly ** percent in 2021, and remained elevated, despite a slight decline in 2023.

The average monthly rent of apartments in Florida increased substantially in 2021, followed by two years of slight decrease. As of ********, the average rent of a two-bedroom apartment in Florida cost ***** U.S. dollars, which was an increase of *** U.S. dollars from ******** when prices started to rise.

Context

The purpose of this dataset is to provide updated data on the Zillow Observed Rent Index (ZORI). Most of the Zillow datasets on Kaggle have not been updated in four years, and no other dataset except one contains information related to rent. Providing updated data on this will also allow the community to analyze the effects of COVID-19 on rent prices, which could not be done with previous available data sets.

Content

Zillow Observed Rent Index (ZORI): A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is once again weighted to reflect the rental housing stock. Details available in ZORI methodology. https://www.zillow.com/research/methodology-zori-repeat-rent-27092/

This dataset contains two files. The Metro dataset looks at the median rent prices for large US cities. The ZIP code dataset breaks the US cities down by their ZIP codes. Note that the region IDs in both datasets are only used for tracking purposes. Also, some of the ZIP codes under the Region Name are less than the standard five-digit zip code and unreliable. Even if you add zeros in accounting for possible formatting mistakes. It is recommended to remove these entries since there is no way to identify which ZIP code the entry actually represents. These entries are left in here in case some analyst can solve the issue.

Acknowledgements

Zillow provides many useful open source datasets that relate to housing, which can be found at Zillow Research Data. https://www.zillow.com/research/data/ This dataset was also prompted by an older dataset I came across that only lacked updated data. https://www.kaggle.com/zillow/rent-index Thumbnail and banner picture is from this pixabay artist https://pixabay.com/users/pexels-2286921/

Inspiration

1. Where are the cheapest and most expensive ZIP codes to live?
2. We all know rent increases overtime, but has it been increasing at a faster rate since 2014?
3. If rent has been increasing at a faster rate, what year did it increase the fastest?
4. What cities or ZIP codes are increasing rent the fastest and by how much?
5. Did rent continue to increase during the COVID-19 pandemic, and was it at a faster or slower rate than previous years?

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.

South Korea Rent Price Index: sa data was reported at 109.245 2015=100 in 2025. This records an increase from the previous number of 108.443 2015=100 for 2024. South Korea Rent Price Index: sa data is updated yearly, averaging 79.204 2015=100 from Dec 1985 (Median) to 2025, with 41 observations. The data reached an all-time high of 109.245 2015=100 in 2025 and a record low of 36.533 2015=100 in 1985. South Korea Rent Price Index: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual.

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

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.

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.

United States US: Price to Rent Ratio: sa data was reported at 133.530 2015=100 in 2024. This records an increase from the previous number of 133.173 2015=100 for 2023. United States US: Price to Rent Ratio: sa data is updated yearly, averaging 89.750 2015=100 from Dec 1970 (Median) to 2024, with 55 observations. The data reached an all-time high of 137.339 2015=100 in 2022 and a record low of 89.750 2015=100 in 1997. United States US: Price to Rent Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by rent price indices

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.

Mexico MX: Rent Price Index: sa data was reported at 132.256 2015=100 in 2025. This records an increase from the previous number of 127.702 2015=100 for 2024. Mexico MX: Rent Price Index: sa data is updated yearly, averaging 69.117 2015=100 from Dec 1980 (Median) to 2025, with 46 observations. The data reached an all-time high of 132.256 2015=100 in 2025 and a record low of 0.087 2015=100 in 1980. Mexico MX: Rent Price Index: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Mexico – Table MX.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual.

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

By [source]

About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

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How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permi...

Turkey TR: Rent Price Index: sa data was reported at 1,791.325 2015=100 in Dec 2025. This records an increase from the previous number of 1,654.611 2015=100 for Sep 2025. Turkey TR: Rent Price Index: sa data is updated quarterly, averaging 31.232 2015=100 from Mar 1982 (Median) to Dec 2025, with 176 observations. The data reached an all-time high of 1,791.325 2015=100 in Dec 2025 and a record low of 0.002 2015=100 in Mar 1982. Turkey TR: Rent Price Index: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Turkey – Table TR.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Quarterly.

Israel Rental Prices: Avg: Jerusalem data was reported at 5,064.600 ILS in Dec 2024. This records an increase from the previous number of 5,037.000 ILS for Sep 2024. Israel Rental Prices: Avg: Jerusalem data is updated quarterly, averaging 4,347.900 ILS from Mar 2017 (Median) to Dec 2024, with 32 observations. The data reached an all-time high of 5,064.600 ILS in Dec 2024 and a record low of 4,048.865 ILS in Mar 2017. Israel Rental Prices: Avg: Jerusalem 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.

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.

The average rental costs across Ireland increased steadily over the past decade. The National Rent Index, using the 2012 average rental prices as its baseline, reached ***** index points in June 2024. That was an increase of **** index points from the same period the year before. Across the country, rents increased the most in Longford, Laois, and Kilkenny between the second quarter of 2023 and the second quarter of 2024.