In January 2025, apartment rents recorded an annual growth in most U.S. states. Nevertheless, the national average rent declined by about *** percent. West Virginia was the state with the largest rental increase, while Colorado measured the largest decline. California, one of the most expensive states to rent an apartment, such as California, saw an increase of about *** percent from the previous year. How much should you earn to afford to rent an apartment in different states in the U.S.? Both employment opportunities and the living costs vary widely across the country. In California, which is among the most competitive housing markets in the U.S., the hourly wage needed to afford a two-bedroom apartment rental was roughly ** U.S. dollars, more than twice higher than in North Carolina, Louisiana, or Michigan in 2024. When it comes to the median household income, on the other hand, California does not even make it in the top ten states. How much should you earn to afford a home in some of U.S. largest metros? In 2022, the annual salary needed to buy a median-priced home in the U.S. was ****** U.S. dollars. However, in some of the largest metropolitan areas in the United States, where housing prices are up to two or three times higher, homebuyers would have to earn more than 100,000 U.S. dollars to afford a home. In San Jose, which was the most expensive metro, the annual salary needed for a median-priced home was approximately ******* U.S. dollars.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUSR0000SEHA) from Jan 1981 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.

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?

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 Price Index of Private Rents (PIPR) increased gradually since 2015 and reached a value of ***** in ************. That indicates a rental increase of ** percent since ************, the baseline year when the index was set to 100. The rental rates for mainstream properties are forecast to continue to grow over the next five years.

New-tenant rent inflation rose sharply during the COVID-19 pandemic, subsequently falling. Concomitantly, consumer price index (CPI) tenant rent, which measures rent increases for both new and continuing renters, rose more gradually and, after falling somewhat, has remained elevated. To illustrate why CPI rent inflation has remained elevated, we combine a measure of new-tenant rents and annual renter mobility rates to create a simulated CPI tenant rent inflation measure. We use this simulation to define a “rent gap” that represents the difference between actual CPI tenant rent inflation and rent inflation we would observe if every tenant experienced new-tenant rent inflation. This gap has declined since hitting its peak at the end of 2022 but remains high, implying that existing rents for continuing renters may still be notably below new-tenant rent levels and that rent inflation may remain elevated. However, the future path remains uncertain because it depends on future mobility rates, future passthrough rates, and future new-tenant rent inflation.

Rent Inflation in Japan increased to 0.40 percent in October from 0.30 percent in September of 2025. This dataset includes a chart with historical data for Japan 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.

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.

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.

Rent Inflation in China decreased to -0.10 percent in October from 0 percent in September of 2025. This dataset includes a chart with historical data for China 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.

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

Price Index of Private Rents (PIPR) data chain-linked to Index of Private Housing Rental Prices. This is a historical series from January 2005 to February 2025.

Rent Inflation in Canada increased to 5.20 percent in October from 4.80 percent in September of 2025. This dataset includes a chart with historical data for Canada Rent Inflation.

This table includes the average increase of rent paid for dwellings by region. There is a breakdown regarding the rent change in- and excluding rent harmonisation. There is also a division by province and the four large cities Amsterdam, The Hague, Rotterdam and Utrecht. The rent change is given on an annual basis and is significant input for the housing price movements in the consumer price index. Data available from: 1999 Status of the figures: All values are definite. Frequency: Discontinued on 10 October 2011.

This table includes the average increase of rent paid for dwellings in the Netherlands. It shows a breakdown regarding the rent change in- and excluding rent harmonisation. Another breakdown is for the commercial and non-commercial rent movements of dwellings. The rent change is given on an annual basis and is significant input for the housing price movements in the consumer price index.

Data available from: 2009

Status of the figures: All values are definite.

Frequency: Discontinued on 10 October 2011.

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.