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.

We study the joint evolution of prices and rents of residential property. We construct indices for both rents and prices of renter-occupied properties and for prices of owner-occupied properties. We then decompose the change in the price of occupant-owned property into three components: (1) changes in rent, (2) changes in the relative prices of investor- and occupant-owned properties, and (3) changes in the price-rent ratio. We use a simple model to link our decomposition to different sources of variation in house prices. We argue that while the 2000s boom was plausibly driven by exuberant expectations, the boom of the 2020s more likely resulted from a preference shock.

In the first quarter of 2025, Portugal, Croatia, and the Netherlands had the highest house price to rent ratio index in Europe. The three countries ranked the highest, with house price to rent indices exceeding *** index points. The house price to rent ratio is an indicator of the affordability of owning housing over renting across European countries, with 2015 used as a base year. The higher the ratio, the more the gap between house prices and rental rates has widened since 2015 when the index amounted to 100. In terms of house price to income ratio, the top three countries were Portugal, the Netherlands, and Switzerland. Homeownership in Europe Homeownership varies widely across European countries. In some, such as Austria, Germany, and Switzerland, homeownership is relatively low, with less than ********** of people occupying a dwelling owned by a member of the household. In other countries (Iceland, the Netherlands, Norway, and Sweden), more than **** of people were owner-occupiers with a mortgage. A third group of countries with a high homeownership rate without a housing loan includes many Eastern and South European countries, among which were Serbia, Romania, North Macedonia, Italy, and Bulgaria. Dwellings as a non-financial asset Dwellings, along with structures, land, and intellectual property, are classified as non-financial assets and form an important part of household wealth. Through sale, refinancing, or renting, they can serve as an additional source of income. In 2022, France, Germany, and Norway were the European countries with the highest value of dwellings per capita as a non-financial asset with values between ****** and ****** euros per capita.

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

Mexico was one of the economies where house prices increased the most between 2016 and 2024, rising by nearly ** percent during that period. The growth rate of housing prices from 2015 to 2023 in Russia was even higher, but the 2024 data for that country was not yet available. Meanwhile, Poland and the U.S. were among the countries where rents increased the most from 2016 to 2024.

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 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

Since 2015, the gap between the cost of buying a home and renting has grown, with homeownership becoming increasingly less affordable. In the ***** ******* of 2024, the house price to rent ratio in the UK stood at *****. That meant that house price growth has outpaced rental growth by nearly ** percent between 2015 and 2024. The UK's house price to rent ratio was slightly below the average Euro area ratio. House price to income ratio in the UK Another indicator for housing affordability is the house price to income ratio, which is calculated by dividing nominal house prices by the nominal disposable income per head. The ratio saw an overall increase between 2015, which was the base year, and 2022. After that, the index declined, but remained close to the average for the Euro area. Is it more affordable to rent or buy? There are many things to be considered when comparing buying to renting, such as the ability to qualify for a mortgage and whether prospective homebuyers have sufficient savings for a deposit. Generally, purchasing a home is more affordable than renting one. However, the average monthly savings first-time buyers can achieve have been on the decline. In East of England, where house prices have increased rapidly over the past few years, it was cheaper to rent than to buy in 2022.

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.

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.

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

In this Economic Commentary , we compare characteristics of the 2000–2006 house-price boom that preceded the Great Recession to the house-price boom that began in 2020 during the COVID-19 pandemic. These two episodes of high house-price growth have important differences, including the behavior of rental rates, the dynamics of housing supply and demand, and the state of the mortgage market. The absence of changes in fundamentals during the 2000s is consistent with the literature emphasizing house-price beliefs during this prior episode. In contrast to during the 2000s boom, changes in fundamentals (including rent and demand growth) played a more dominant role in the 2020s house-price boom.

Code and data for "House Prices and Rents" House prices are from Case-Shiller and are not uploaded.

Dataset Description

This dataset contains rental property listings scraped from Tonaton.com, one of Ghana's leading online classifieds platforms. It provides valuable information on rental prices across various regions in Ghana, along with other property details. The dataset is designed to support analysis, visualization, and modeling of rental prices in the Ghanaian real estate market.

Research paper: https://arxiv.org/abs/2501.06241 Analysis source code: https://github.com/epigos/house-prices-prediction

Features

The dataset includes the following columns:
- url: The link to the listing.
- name: The headline or title of the rental property listing.
- price: The rental price of the property in Ghanaian Cedis (GHS).
- category: The type of rental property (e.g., apartment, house, room, office).
- bedrooms: The number of bedrooms available in the property.
- bathrooms: The number of bathrooms available in the property.
- floor_area: The floor area of the property in square meters.
- location: The address location where the property is located.
- condition: Condition of the property e.g new, used, off-plan etc. - amenities: Amenities provided in the property. - region: Geographic administrative region of the property location. - locality: Represent the town or city where the property is located. - parking_space: Indicates if there is parking space available. - is_furnished: Indicates if the property is furnished. - lat: Longitude location of the property. - lng: Latitude location of the property.

Potential Use Cases

1. Real Estate Market Analysis: Analyze rental price trends across different locations and property types in Ghana.
2. Price Prediction Models: Train machine learning models to predict rental prices based on features like location, property type, and number of bedrooms.
3. Geographical Insights: Investigate how location impacts rental prices across urban and rural areas in Ghana.
4. Consumer Trends: Study patterns in rental property preferences, such as the most popular types of properties or regions.

Source

The data was scraped from Tonaton.com as of November, 2024. Please note that this dataset reflects the listings available during that period and may not include all rental properties in Ghana.

Disclaimer

This dataset is shared for educational and research purposes only. It is not intended for commercial use or to reproduce Tonaton.com’s proprietary information. Users are responsible for ensuring their use complies with Tonaton.com’s terms and conditions.

Conflict of Interest

There are no conflicts of interest associated with the creation or use of this dataset.

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

The house price to rent index in Mexico has risen quarter on quarter since 2015, suggesting that property prices have grown faster than rents. The index is calculated by dividing the median house price by the median annual rent, with 2015 chosen as a base year. In the second quarter of 2025, the house price to rent index amounted to ***** index points, meaning that house price growth outpaced rental growth by ** percent.

Context

Zillow has a lot of data about housing prices in America.

Content

Data about housing prices and rental prices broken down according to city and state and number of bedrooms. More detail can be found at https://www.zillow.com/research/data/ and at https://www.zillow.com/research/home-sales-methodology-7733/.

Acknowledgements

The data was downloaded from https://www.zillow.com/research/data/. Banner photo from Ian Keefe on Unsplash. Dataset license described at https://www.zillow.com/research/data/.

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 a comprehensive collection of features related to houses in California, with the primary aim of facilitating the prediction of house rent prices. It includes 80 columns and 1460 rows, offering a rich set of information for model training and evaluation. Target Variable: The dataset aims to predict the house rent prices, making it suitable for regression models. The 'SalePrice' column can be used as the target variable for training and evaluating predictive models.

Columns:

1. Id: Unique identifier for each record.
2. MSSubClass: The building class
3. MSZoning: The general zoning classification of the property.
4. LotFrontage: Linear feet of street connected to property.
5. LotArea: Lot size in square feet.
6. Street: Type of road access to property.
7. Alley: Type of alley access to property.
8. LotShape: General shape of the property.
9. LandContour: Flatness of the property. ... (and many more)

Use Case: Ideal for exploring and implementing regression models, particularly Linear Regression, to predict house rent prices based on various features associated with the properties.

Dataset Size: 80 columns 1460 rows

Source: This dataset is based on houses in California, making it relevant for studying the factors influencing house rent prices in this region.

Note: Please refer to the dataset documentation for details on each column and additional information regarding the data. Feel free to use this dataset for your machine learning projects, research, or educational purposes. Happy coding!

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.