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

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.

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.

Rental price statistics historical data time series (indices and annual percentage change). These are official statistics in development.

This dataset is no longer being updated due to redevelopment of private rental prices statistics, impact analysis, UK, please see more information here: Redevelopment of private rental prices statistics, impact analysis, UK - Office for National Statistics (ons.gov.uk).

The Index of Private Housing Rental Prices (IPHRP) is a quarterly experimental price index. It tracks the prices paid for renting property from private landlords in Great Britain.

IPHRP is produced from a number of administrative sources and is classified as experimental by ONS.

The index compares trends (rather than levels) in average private sector rents across English regions, Wales and Scotland. It uses a complex mix-adjustment and weighting process to produce a single index for each area. This index uses data on actual new and ongoing rents.

The sample ensures that the index is representative of the stock at regional level and that it isn't distorted by units dropping out of the sample because they switch to LHA or for other reasons. This is an advantage over the VOA dataset where the sample is changing over time and may not be representative.

Tables show monthly data. Data is updated once a quarter.

Index level (January 2011 = 100). Not seasonally adjusted.

See more on the ONS Website

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.

This dataset is no longer being updated due to redevelopment of private rental prices statistics, impact analysis, UK, please see more information here: Redevelopment of private rental prices statistics, impact analysis, UK - Office for National Statistics (ons.gov.uk). The Index of Private Housing Rental Prices (IPHRP) is a quarterly experimental price index. It tracks the prices paid for renting property from private landlords in Great Britain. IPHRP is produced from a number of administrative sources and is classified as experimental by ONS. The index compares trends (rather than levels) in average private sector rents across English regions, Wales and Scotland. It uses a complex mix-adjustment and weighting process to produce a single index for each area. This index uses data on actual new and ongoing rents. The sample ensures that the index is representative of the stock at regional level and that it isn't distorted by units dropping out of the sample because they switch to LHA or for other reasons. This is an advantage over the VOA dataset where the sample is changing over time and may not be representative. Tables show monthly data. Data is updated once a quarter. Index level (January 2011 = 100). Not seasonally adjusted. See more on the ONS Website

Private rent price statistics, including indices, annual percentage change and price levels.

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.

The average agreed rent for new tenancies in the UK ranged from *** British pounds to ***** British pounds, depending on the region. On average, renters outside of London paid ***** British pounds, whereas in London, this figure amounted to ***** British pounds. Rents have been on the rise for many years, but the period after the COVID-19 pandemic accelerated this trend. Since 2015, the average rent in the UK increased by about ** percent, with about half of that gain achieved in the period after the pandemic. Why have UK rents increased so much? One of the main reasons driving up rental prices is the declining affordability of homeownership. Historically, house prices grew faster than rents, making renting more financially feasible than buying. In 2022, when the house price to rent ratio index peaked, house prices had outgrown rents by nearly ** percent since 2015. As house prices peaked in 2022, home buying slowed, exacerbating demand for rental properties and leading to soaring rental prices. How expensive is too expensive? Although there is no official requirement about the proportion of income spent on rent for it to be considered affordable, a popular rule is that rent should not exceed more than ** percent of income. In 2024, most renters in the UK exceeded that threshold, with the southern regions significantly more likely to spend upward of ** percent of their income on rent. Rental affordability has sparked a move away from the capital to other regions in the UK, such as the South East (Brighton and Southampton), the West Midlands (Birmingham) and the North West (Liverpool, Manchester, Blackpool and Preston).

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?

Context

This dataset contains information on rent pricing surrounding Kuala Lumpur and Selangor region, Malaysia. The information was scraped from mudah.my

Content

There are 13 features with one unique ids (ads_id) and one target feature (monthly_rent)

• ads_id: the listing ids (unique)
• prop_name: name of the building/ property
• completion_year: completion/ established year of the property
• monthly_rent: monthly rent in ringgit malaysia (RM)
• location: property location in Kuala Lumpur region
• property_type:property type such as apartment, condominium, flat, duplex, studio, etc
• rooms: number of rooms in the unit
• parking: number of parking space for the unit
• bathroom: number of bathrooms in the unit
• size: total area of the unit in square feet
• furnished: furnishing status of the unit (fully, partial, non-furnished)
• facilities: main facilities available
• additional_facilities: additional facilities (proximity to attraction area, mall, school, shopping, railways, etc)

Acknowledgements The data was scraped from mudah.my

Inspiration I have been living in Kuala Lumpur, Malaysia since 2017, and in the past there was no easy way to understand whether certain unit pricing is making sense or not. With this dataset, I wanted to be able to answer the following questions:

• What are the biggest factor affecting the unit/rent pricing?
• Which location in Kuala Lumpur/ Selangor region that has the highest rent price? etc?

Between 2008 and 2024, the average weekly rent for private renters in England has shown a significant increase. In the 2009, the average rent was 153 British pounds, and by 2024, it had risen to 237 British pounds. Excluding London, the average rent started at 130 British pounds in 2009 and reached 191 British pounds in 2024, demonstrating a similar upward trend but at a lower rate compared to the overall average in England. Rental households in England Renting is common in England. Nearly one in five households occupied a dwelling that was privately rented in 2024. While the majority of households in the country live in an owner-occupied home, this percentage has declined since the early 2000s. Meanwhile, the share of households occupying a private rental has doubled over the past decade. This shows a growing rental sector and a shift in tenure trends in the country. Buying vs renting costs For a long time, the average monthly costs of buying a home were lower than renting. In 2021, housing costs started to increase steeply, closely followed by rental costs. This resulted in the gap nearly closing in 2023. This trend can also be observed through the house price to rent ratio - an index that follows the development of house prices relative to rents, with 2015 as a baseline year. Between 2015 and 2022, the ratio grew steadily, indicating that property prices rise faster than rents. However, with rental growth accelerating and catching up with property prices in 2022, the index declined notably.

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

This dataset contains detailed information about rental properties across various locations in the UK. The data was collected by scraping Rightmove, a popular real estate platform. Each entry in the dataset includes the property's address, subdistrict code, rental price, deposit amount, letting type, furnish type, council tax details, property type, number of bedrooms and bathrooms, size in square feet, average distance to the nearest train station, and the count of nearest stations.

Researchers and analysts interested in the UK rental market can utilize this dataset to explore rental trends, pricing variations based on location and property type, amenities preferences, and more. The dataset provides a valuable resource for machine learning models, statistical analysis, and market research in the real estate sector.

Metadata: Source: The data was collected by scraping the Rightmove real estate platform, a leading source for property listings in the UK. Date Range: The dataset covers rental property listings available during the scraping period. Geographical Coverage: Primarily focused on various locations across the UK, providing insights into regional rental markets. Data Fields: Address: The location of the rental property. Subdistrict Code: A code representing the subdistrict or area of the property. Rent: The monthly rental price in GBP (£) for the property. Deposit: The deposit amount required for renting the property. Let Type: Indicates whether the property is available for short-term or long-term rental. Furnish Type: Describes the furnishing status of the property (e.g., furnished, unfurnished, or flexible options). Council Tax: Information about the council tax associated with the property. Property Type: Specifies the type of property, such as apartment, flat, maisonette, etc. Bedrooms: The number of bedrooms in the property. Bathrooms: The number of bathrooms in the property. Size: The size of the property in square feet (sq ft). Average Distance to Nearest Station: The average distance (in miles) to the nearest train station from the property. Nearest Station Count: The count of nearest train stations within a certain distance from the property. Data Quality: The data may contain missing values or "Ask agent" placeholders, which require direct inquiry with agents or landlords for specific information. Potential Uses: The dataset can be used for market analysis, rental price prediction models, understanding property preferences, and exploring the impact of location and amenities on rental properties in the UK.

The Price Index of Private Rents (PIPR) in England, Wales, and Scotland has shown significant growth, with Scotland reaching an index value of ***** points in January 2025, indicating an increase of **** percent since the baseline year of January 2023. The IPHRP measures the change in price of renting residential property from private landlords, based on an index value of 100 in January 2023. The IPHRP saw the highest growth in Wales, reaching ***** index points in January 2025 and suggesting an increase in private rents amounting to **** percent since the baseline year.

Description: This data provides a range of statistics on residential rental prices. Data is available on average rental prices (€ per month) for property types by bedroom size – all bedrooms and then 1 to 4 bedrooms.The RTB Rent Index is the most accurate and authoritative report of its kind on the private rental sector in Ireland. The index is based on the RTB’s national register of tenancies and captures actual rents being paid for rented properties, rather than asking prices. The RTB Average Rent Dataset reports on the average rent in a number of locations around the country. The dashboards provide an annual view of transactions from 2008 to 2022.Geography available in RDM: State, Regional Assembly and Strategic Planning Area (SPA), County (26), Key Settlements.Source: Residential Tenancies Board (RTB)Weblink: https://data.cso.ie/table/RIQ02Date of last source data update: August 2023Update Schedule: Annual

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.

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.