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

Discover the booming rental housing market! Explore key trends, drivers, and challenges impacting this multi-trillion dollar industry. Learn about top players like Airbnb and Zillow, regional market share, and future growth projections to 2033. Get insights to inform your investment or business strategy.

The apartment rental market in the United States has been stagnating since 2019, after increasing year-on-year for several years. In 2022, the estimated market size of apartment rental was ***** billion U.S. dollars, down from ***** billion U.S. dollars in 2021. In 2023, the market is forecast to further contract by one percent, reaching ***** billion U.S. dollars.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions: The average rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in average rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Average rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing. Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

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

The size of the Rental Housing market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

As of January 2025, the rent for a two-bedroom apartment in Hawaii was about 120 U.S. dollars higher than in California. The states of Hawaii and California ranked as the most expensive within the United States for apartment renters. Conversely, an apartment in Arkansas was almost three times more affordable than one in Hawaii.In 2025, the average monthly rent in the U.S. declined slightly. Nevertheless, in rents increased in most states, with West Virginia registering the highest growth.

Build-to-Rent Housing Market Outlook

As per our latest research, the global Build-to-Rent (BTR) housing market size reached USD 74.3 billion in 2024, reflecting a robust expansion driven by rising demand for professionally managed rental communities. The market is projected to grow at a CAGR of 10.1% from 2025 to 2033, reaching an estimated USD 192.2 billion by 2033. This impressive growth trajectory is primarily fueled by evolving lifestyle preferences, increasing urbanization, and a shift in housing affordability, which are collectively redefining the residential real estate landscape worldwide.

One of the most significant growth factors for the Build-to-Rent housing market is the changing demographic profile of urban populations. Young professionals and millennials increasingly prioritize flexibility and convenience over homeownership, leading to a surge in demand for rental properties that offer modern amenities and community-centric living. The BTR model, with its professionally managed services, maintenance support, and enhanced communal facilities, appeals strongly to this demographic. Additionally, the growing number of digital nomads and remote workers is further amplifying the need for adaptable, high-quality rental housing, particularly in metropolitan areas and emerging urban centers.

Another major driver for the Build-to-Rent housing market is the ongoing affordability crisis in many global cities. Escalating property prices and stringent mortgage requirements have made homeownership unattainable for a significant portion of the population, especially in North America and Europe. As a result, institutional investors and real estate developers are capitalizing on this opportunity by expanding their BTR portfolios. The stable, long-term rental income streams offered by BTR assets are particularly attractive to pension funds, insurance companies, and private equity firms seeking diversification and resilience in their investment portfolios.

Technological advancements and innovation in construction methods are also catalyzing the growth of the Build-to-Rent housing market. The adoption of modular and prefabricated construction techniques is enabling developers to accelerate project timelines, reduce costs, and improve sustainability outcomes. These methods are particularly suited to the BTR model, where speed to market and operational efficiency are critical. Furthermore, the integration of smart home technologies and digital management platforms is enhancing tenant experiences and operational transparency, thereby increasing the appeal of BTR properties to both residents and investors.

Regionally, North America and Europe continue to dominate the Build-to-Rent housing market, accounting for a combined market share of over 65% in 2024. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, rising middle-class populations, and supportive government policies. Latin America and the Middle East & Africa are also witnessing growing interest in the BTR model, particularly in gateway cities with expanding expatriate communities and young workforces. The regional outlook for the BTR market remains highly positive, underpinned by favorable demographic trends and increasing investor appetite for income-generating real estate assets.

Property Type Analysis

The Build-to-Rent housing market is segmented by property type into single-family homes, multi-family apartments, townhouses, and others. Among these, multi-family apartments currently hold the largest market share, accounting for over 55% of the global BTR inventory in 2024. The preference for multi-family developments is rooted in their efficient land use, scalability, and ability to offer a wide array of amenities such as gyms, co-working spaces, and communal lounges. These features are highly attractive to young professionals and urban dwellers seeking community engagement and convenience. Furthermore, mul

In 2024, there were approximately **** million housing units occupied by renters in the United States. This number has been gradually increasing since 2010 as part of a long-term upward swing since 1975. Meanwhile, the number of unoccupied rental housing units has followed a downward trend, suggesting a growing demand and supply failing to catch up. Why are rental homes in such high demand? This high demand for rental homes is related to the shortage of affordable housing. Climbing the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home. How many owner occupied homes are there in the U.S.? In 2023, there were over ** million owner occupied homes. Owner occupied housing is when the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing.

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.

📌 Istanbul Rental Apartments Dataset (2025)

This dataset contains rental apartment listings from Istanbul, Turkey. It includes detailed information about rental properties such as district, neighborhood, number of rooms, area in square meters, floor level, and rental price in Turkish Lira (TRY).

📊 Features:

• district: The district (e.g., Beşiktaş, Kadıköy)
• neighborhood: The neighborhood name
• room: Number of rooms
• living_room: Number of living rooms
• area_m2: Apartment size in square meters (m²)
• age: Age of the building
• floor: Floor number (Negative values indicate basement floors)
• price: Rental price in Turkish Lira (TRY)

📈 Additional Notes:

• Price per m²: Can be calculated using price / area_m2.
• Basement floors: Some apartments are below ground level (e.g., -2 means second basement).
• New buildings: Buildings with age = 0 are newly constructed.

This dataset is useful for rental market analysis, price prediction models, and urban studies in Istanbul.

🇹🇷 Türkçe Açıklama

Bu veri seti, İstanbul'daki kiralık daire ilanlarını içermektedir. Dairelerin ilçesi, mahallesi, oda sayısı, metrekare büyüklüğü, kat bilgisi ve kira fiyatı (Türk Lirası - TL) gibi detayları bulunmaktadır.

📊 Veri Seti Özellikleri:

• district: İlçe adı (Örn: Beşiktaş, Kadıköy)
• neighborhood: Mahalle adı
• room: Oda sayısı
• living_room: Salon sayısı
• area_m2: Metrekare büyüklüğü (m²)
• age: Binanın yaşı
• floor: Kat bilgisi (Negatif değerler bodrum katları ifade eder)
• price: Kira fiyatı (Türk Lirası - TL)

📈 Ekstra Notlar:

• Metrekare başına fiyat: price / area_m2 formülüyle hesaplanabilir.
• Bodrum katlar: Negatif değerler bodrum katları ifade eder. Örneğin, -2 ikinci bodrum katıdır.
• Yeni binalar: age = 0 olan binalar yeni inşa edilmiştir.

Bu veri seti, İstanbul’daki kira piyasası analizi, fiyat tahmini modelleri ve kentsel çalışmalar için kullanılabilir.

📅 Last Update: February 2025

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

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 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.

The global Housing Rental Service market is poised for significant expansion, projected to reach approximately USD 1,500 million by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of 12% through 2033. This robust growth is fueled by a confluence of evolving lifestyle preferences and economic realities. Increasing urbanization continues to drive demand for rental accommodations, particularly in major metropolitan areas. Furthermore, the growing popularity of short-term rentals, facilitated by digital platforms, caters to the burgeoning tourism and business travel sectors. Concurrently, long-term leases remain a cornerstone of the market, offering stability for both renters and property owners. Key drivers include the increasing cost of homeownership, particularly for younger demographics, and a greater emphasis on flexibility and mobility in career choices. The market is also benefiting from technological advancements that streamline the rental process, from property discovery and application to lease management and payment. The competitive landscape of the Housing Rental Service market is characterized by a dynamic mix of established property management firms and innovative digital platforms. Companies like Invitation Homes, Vacasa, and HousingAnywhere are at the forefront, leveraging technology to enhance user experience and operational efficiency. The market is segmented by application into Personal and Commercial, with Personal rentals constituting the larger share due to widespread individual housing needs. Within types, both Short-term Rental and Long-term Lease segments are experiencing healthy growth. Geographically, North America is expected to maintain a dominant market share, driven by strong economies and established rental markets in the United States and Canada. However, Asia Pacific presents a substantial growth opportunity, with rapidly expanding economies and increasing urbanization in countries like China and India. Emerging trends such as co-living spaces and the integration of smart home technologies into rental properties are further shaping the market's trajectory. This report delves into the dynamic and rapidly evolving global housing rental service market. Analyzing data from the historical period of 2019-2024, with a base year of 2025 and a forecast period extending to 2033, this study provides invaluable insights for stakeholders seeking to navigate this complex landscape. The report estimates the market size in millions of units, offering a clear quantitative perspective on growth and demand.

In 2024, Market Research Intellect valued the Rental Housing Market Report at USD 1.2 trillion, with expectations to reach USD 1.9 trillion by 2033 at a CAGR of 6.5%.Understand drivers of market demand, strategic innovations, and the role of top competitors.

Discover the booming global housing rental service market! This in-depth analysis reveals a $1.5 trillion market in 2025, growing at a 7% CAGR through 2033. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector.

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.

Market Size statistics on the Apartment Rental industry in the US