Rents for industrial real estate in the U.S. have increased since 2017, with flexible/service space reaching the highest price per square foot in 2024. In just a year, the cost of, flex/service space rose by nearly *****U.S. dollars per square foot. Manufacturing facilities, warehouses, and distribution centers had lower rents and experienced milder growth. Los Angeles, Orange County, and Inland Empire, California, are some of the most expensive markets in the country. Office real estate is pricier Industrial real estate is far from being the most expensive commercial property type. For instance, average rental rates in major U.S. metros for office space are much higher than those for industrial space. This is most likely because office units are generally located in urban areas where there is limited space and thus higher demand, whereas industrial units are more suited to the outskirts of such urban areas. Industrial units, such as warehouses or factories, require much more space because they need to house large, heavy equipment or serve as a storage unit for future shipments. Big-box distribution space is gaining in importance Warehouses and distribution may currently command the lowest average rent per square foot among industrial space types, but the growing popularity of the asset class has earned it considerable gains over the past years. In 2021 and 2022, high occupier demand and insufficient supply led to soaring taking rent of big-box buildings. During that time, the vacancy rate of distribution centers fell below ****percent. The development of industrial and logistics facilities has accelerated since then, with the new supply coming to market, causing the vacancy rate to increase and the pressures on rent to ease.

San Francisco's office rental market showcases significant variation across its submarkets, with Mission Bay commanding the highest rates at *** U.S. dollars per square foot in the third quarter of 2024. This premium location demanded nearly double the city's average rate, highlighting the stark differences in desirability and demand within the city's commercial real estate landscape. Economic powerhouse The San Francisco Bay Area's economic prowess is evident in its impressive economic growth over the past 20 years. The city's strength is fueled by the presence of major technology companies and a thriving startup ecosystem. The region's economic significance extends beyond local boundaries, contributing substantially to California's position as the state with the highest GDP in the country. This economic vitality helps explain the sustained demand for office space across various San Francisco submarkets. Offices: global context and market trends In a global context, San Francisco's office rental rates are relatively high but not the most expensive worldwide. In 2024, London, Hong Kong, and New York emerged as the top three most expensive office rental markets globally. Over the past five years, San Francisco has experienced a decline in office rents. This trend aligns with broader shifts in the office real estate sector, influenced by the COVID-19 pandemic and the rise of hybrid work. Despite these challenges, certain San Francisco submarkets like Mission Bay and The Presidio continue to command premium rates, reflecting their enduring appeal to commercial tenants.

Commercial rents services price index (CRSPI) by North American Industry Classification System (NAICS). Monthly data are available from January 2006 for the total index and from January 2019 for all other indexes. The table presents data for the most recent reference period and the last five periods. The base period for the index is (2019=100).

The average asking rent for Class A office space in Midtown Manhattan was ***** U.S. dollars per square foot in the second quarter of 2025. It was above the Manhattan average of ***** U.S. dollars but below that of Midtown South, which was the most expensive district at ***** U.S. dollars per square foot. What is Class A real estate?Class A real estate refers to the best properties in terms of appearance, age, quality of infrastructure and location. These properties usually command the highest rental rates, due to their high quality. In the U.S., Manhattan has the most expensive rents for Class A offices.Midtown vs Midtown SouthMidtown Manhattan contains the Empire State Building, MoMA, Grand Central Station, and the United Nations Headquarters. The most expensive submarket there was Plaza District in 2025. Meanwhile, Midtown South is home to Madison Square Garden, Pennsylvania Station, Hudson Yards, and Koreatown. In 2025, the most expensive submarket there was Hudson Yards, followed by Chelsea and Hudson Square.

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q1 2025 about real estate, commercial, rate, and USA.

Among the ** markets with the largest industrial and logistics real estate inventory in the U.S., Orange County, CA, had the highest rental rate in the first quarter of 2025. The square footage rent of warehouse and distribution centers was ***** U.S. dollars, while for manufacturing sites it was ***** U.S. dollars. In the largest market, Chicago, IL, rents were significantly lower, at ****U.S. dollars.

The Central Business District (CBD) was the most expensive market for office real estate rental in Houston, Texas in the second quarter of 2024. The average direct asking rental rate of office space was approximately ** U.S. dollars per square feet, compared to **** U.S. dollars per square feet in FM 1960. Among the major office markets in Texas, Dallas had the most inventory, while Austin had the highest rental rates.

Comprehensive dataset of warehouse rental rates across U.S. markets including national averages, regional breakdowns, size-based pricing differentials, vacancy rates, and year-over-year growth trends for 2025.

The data was scraped from the Magicbricks website. The following are the details of the dataset:

• Title: The title of the property listing.
• Price: The monthly rent of the property.
• Area: The total area of the property in square feet.
• BHK: The number of bedrooms in the property.
• Bathrooms: The number of bathrooms on the property.
• Furnished: Whether the property is furnished or not.
• Balconies: The number of balconies in the property.
• Floor: The floor number of the property.
• Ownership: The type of ownership of the property (i.e., freehold, leasehold, etc.).
• Facing: The direction the property faces.
• Amenities: The amenities that are available in the property or the surrounding area.
• Transaction Type: Whether the property is for sale or rent.
• Property Type: The type of property (i.e., apartment, house, villa, etc.).
• Location: The location of the property.
• Year of Construction: The year the property was built.
• Is Luxury: Whether the property is considered to be a luxury property.
• Description: A brief description of the property.
• Property Image: A link to the property image.

Key points in the dataset are :

1) This dataset can be used to gain insights into the rental market in Mumbai. For example, you could use the data to analyze the average rent for different types of properties, the most popular neighborhoods for renters, or the factors that affect the price of rent. You could also use the data to identify trends in the rental market, such as the increasing popularity of furnished apartments or the rising prices of luxury properties.

2) The dataset could also be used by real estate agents to help their clients find rental properties that meet their needs and budget. Additionally, the data could be used by developers to make informed decisions about the types of properties to build in Mumbai.

3) Overall, this dataset is a valuable resource for anyone who is interested in the rental market in Mumbai. It can be used to gain insights into the market, identify trends, and make informed decisions.

(Disclaimer: The data in this dataset has been gathered from publicly available sources. While the data is believed to be reliable and all privacy policies have been observed, No personal information such as email addresses, mobile numbers, or physical addresses hasn't been collected. I scrape data from the website Magicbricks to study the real estate market of Mumbai. ) Thank you !!!

Context

Zillow operates an industry-leading economics and analytics bureau led by Zillow’s Chief Economist, Dr. Stan Humphries. At Zillow, Dr. Humphries and his team of economists and data analysts produce extensive housing data and analysis covering more than 500 markets nationwide. Zillow Research produces various real estate, rental and mortgage-related metrics and publishes unique analyses on current topics and trends affecting the housing market.

At Zillow’s core is our living database of more than 100 million U.S. homes, featuring both public and user-generated information including number of bedrooms and bathrooms, tax assessments, home sales and listing data of homes for sale and for rent. This data allows us to calculate, among other indicators, the Zestimate, a highly accurate, automated, estimated value of almost every home in the country as well as the Zillow Home Value Index and Zillow Rent Index, leading measures of median home values and rents.

Content

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels.

Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date.

Acknowledgements

The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Inspiration

What city has the highest and lowest rental prices in the country? Which metropolitan area is the most expensive to live in? Where have rental prices increased in the past five years and where have they remained the same? What city or state has the lowest cost per square foot?

The Central Business District (CBD) was the most expensive submarket for office real estate rental in Austin, Texas in the second quarter of 2024. The Class A office space rental rate was approximately **** U.S. dollars per square feet, compared to **** U.S. dollars per square foot in the South market. Among the major office markets in Texas, Dallas had the most inventory, while Austin had the highest rental rates.

Malls had the most expensive rental space among the different types of retail real estate in the United States in 2023. As of the fourth quarter of the year, the average rent in malls was ***** U.S. dollars per square foot, compared to ***** U.S. dollars for all retail. General retail space, defined as single-tenant freestanding commercial buildings with parking, such as drugstores, grocery stores, and street front urban retail stores, had some of the lowest vacancy rates.

Dataset Overview :

This dataset is created as part of a machine learning mini project on House Price Prediction in India. It includes key features commonly used to predict house prices such as:

1) Number of bedrooms 2) Property type (e.g., Apartment, House) 3) Location 4) Area in square feet 5) Price per square foot 6) Total price

Column Description :

Usage :

This dataset can be used to: --> Build a house price prediction model using ML algorithms --> Perform data visualization or feature correlation --> Understand real estate pricing trends in India

The property listings dataset contains information about real estate properties available for sale or rent in Brazil. It includes details such as property type (apartment, house, commercial property), location (city, neighborhood), size (square footage, number of rooms), price, amenities, and contact information for the property owner or real estate agent. This dataset can be used for market analysis, property valuation, and identifying trends in the real estate market.

Sales and Rental Prices Dataset: The sales and rental prices dataset provides information about the prices of real estate properties in Brazil. It includes data on property transactions, including sale prices and rental prices per square meter or per month. This dataset can be used to analyze price trends, compare property prices across different regions, and identify areas with high or low real estate market demand.

Property Characteristics Dataset: The property characteristics dataset contains detailed information about the features and attributes of real estate properties. It includes data such as the number of bedrooms, bathrooms, parking spaces, floor plan, construction year, building amenities, and property condition. This dataset can be used for property classification, identifying popular property features, and evaluating property quality.

Geographical Data: Geographical data includes information about the location and spatial features of real estate properties in Brazil. It can include data such as latitude and longitude coordinates, zoning information, proximity to amenities (schools, hospitals, parks), and neighborhood demographics. This dataset can be used for spatial analysis, identifying hotspots or desirable locations, and understanding the neighborhood characteristics.

Property Market Trends Dataset: The property market trends dataset provides information about market conditions and trends in the real estate sector in Brazil. It includes data such as the number of property listings, average time on the market, price fluctuations, mortgage interest rates, and economic indicators that impact the real estate market. This dataset can be used for market forecasting, understanding market dynamics, and making informed investment decisions.

Real Estate Regulatory Data: Real estate regulatory data includes information about legal and regulatory aspects of the real estate sector in Brazil. It can include data on property ownership, property taxes, zoning regulations, building permits, and legal restrictions on property transactions. This dataset can be used for legal compliance, understanding property ownership rights, and assessing the legal framework for real estate transactions.

Historical Data: Historical real estate data includes past records and trends of property prices, market conditions, and sales volumes in Brazil. This dataset can span several years and can be used to analyze long-term market trends, compare current market conditions with historical data, and assess the performance of the real estate market over time.

The Canadian commercial real estate market, valued at $77.09 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 7.59% from 2025 to 2033. This expansion is driven by several key factors. Firstly, Canada's strong economy and increasing population fuel demand for office, retail, and industrial spaces. Urbanization and population growth, particularly in major cities like Toronto, Vancouver, and Calgary, are significant contributors. Furthermore, ongoing investments in infrastructure and technological advancements are enhancing the attractiveness of commercial properties. The growth is segmented across various property types, with office spaces benefiting from a return to the workplace following the pandemic, and the industrial sector experiencing sustained growth fueled by e-commerce expansion and supply chain optimization initiatives. The hospitality sector is also poised for recovery, driven by increased tourism and business travel. However, the market is not without its challenges. Rising interest rates and inflation present significant headwinds, impacting construction costs and potentially reducing investment activity. Government regulations and environmental concerns related to sustainable development also influence market dynamics. Competition among developers and brokerage firms remains intense, impacting pricing and profitability. Despite these restraints, the long-term outlook for the Canadian commercial real estate market remains positive, driven by fundamental economic strengths and a growing population. Strategic investments in key areas, such as sustainable building practices and technological integrations, will be crucial for developers and investors to succeed in this evolving landscape. The diverse market segments, from office towers to industrial parks, each offer unique opportunities for growth and investment within the Canadian commercial real estate sector. Recent developments include: June 2023: Prologis, Inc. and Blackstone announced a definitive agreement for Prologis to acquire nearly 14 million square feet of industrial properties from opportunistic real estate funds affiliated with Blackstone for USD 3.1 billion, funded by cash. The acquisition price represents an approximately 4% cap rate in the first year and a 5.75% cap rate when adjusting to today's market rents., May 2023: An experiential real estate investment trust, VICI Properties Inc., announced that it had signed agreements to buy the real estate assets of Century Casinos, Inc.'s Century Downs Racetrack and Casino in Calgary, Alberta, Century Casino St. Albert in Edmonton, Alberta, and Century Casino St. Albert in St. Albert, Alberta, for a total purchase price of USD 164.7 million. This move demonstrates both their continued drive to grow abroad and their faith in the Canadian gaming industry. They are also excited to assist Century's asset monetization strategy, which will open up new opportunities for their cooperation.. Key drivers for this market are: Evolution of retail sector driving the market, Office spaces in Toronto and Vancouver are increasing. Potential restraints include: Evolution of retail sector driving the market, Office spaces in Toronto and Vancouver are increasing. Notable trends are: Evolution of retail sector driving the market.

In the first quarter of 2025, London West End - Bond Street was the most expensive location for prime high street rents in the UK, with prices reaching 2,500 British pounds per square foot. The West End was ranked ahead of London City, which came in third. In Manchester, the annual costs of rental per square foot of prime retail real estate amounted to 235 British pounds. Retail warehouses Retail warehouses typically range from 50,000 to hundreds of thousands of square feet. They are used for keeping and distributing inventory. Retail warehouses include loading docks, truck doors and large parking lots; also, they may contain a limited amount of office space. Prime retail warehouse properties belong to the wider category of industrial property, along with other real estate types, such as distribution buildings, showroom facilities, manufacturing buildings, cold storage facilities, telecom or data hosting centers, "flex" buildings denoting more than one industrial or commercial facility housed in the same building, and finally R&D buildings. Prime yields of high street retail across Europe Retail real estate prime yields in Europe were the lowest in Zurich, Switzerland, and the highest in Bucharest, Romania in 2025. As could be expected, larger cities in Europe tended to produce lower yields, due to the lower risk associated with these markets. Locations with lower yields tend to have steady occupancy rates and rental growth.

Redfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)

The dataset has the following columns: - period_begin - period_end - period_duration
- region_type
- region_type_id - table_id - is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False) - region - city - state - state_code - property_type - property_type_id - median_sale_price
- median_sale_price_mom (median sale price changes month over month) - median_sale_price_yoy (median sale price changes year over year) - median_list_price
- median_list_price_mom (median list price changes month over month) - median_list_price_yoy (median list price changes year over year) - median_ppsf (median sale price per square foot) - median_ppsf_mom (median sale price per square foot changes month over month) - median_ppsf_yoy (median sale price per square foot changes year over year) - median_list_ppsf (median list price per square foot) - median_list_ppsf_mom (median list price per square foot changes month over month) - median_list_ppsf_yoy. (median list price per square foot changes year over year) - homes_sold (number of homes sold) - homes_sold_mom (number of homes sold month over month) - homes_sold_yoy (number of homes sold year over year) - pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings - new_listings_mom
- new_listings_yoy
- inventory - inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom - months_of_supply_yoy
- median_dom (median days on market until property is sold) - median_dom_mom (median days on market changes month over month) - median_dom_yoy (median days on market changes year over year) - avg_sale_to_list (average sale price to list price ratio) - avg_sale_to_list_mom (average sale price to list price ratio changes month over month) - avg_sale_to_list_yoy (average sale price to list price ratio changes year over year) - sold_above_list
- sold_above_list_mom - sold_above_list_yoy - price_drops - price_drops_mom - price_drops_yoy - off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks) - off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month) - off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year) - parent_metro_region - parent_metro_region_metro_code - last_updated

Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html

Data Source & Credit: Redfin.com

The generated dataset, "US Real Estate", simulates housing attributes across various cities in the United States. It encompasses 5000 entries and 10 distinct columns representing crucial real estate metrics. The dataset was designed synthetically for academic or analytical purposes and is not derived from actual market data.

The "Price" column signifies the property cost, ranging between $100,000 and $1,000,000, reflecting diverse market values. Bedroom and bathroom counts are denoted by the "Bedrooms" and "Bathrooms" columns, respectively, spanning from 1 to 5 bedrooms and 1 to 3 bathrooms per property. "SqFt" signifies the square footage of each property, ranging between 800 and 4000 square feet, indicative of varied property sizes.

The dataset incorporates a range of American cities such as New York, Los Angeles, Chicago, Houston, and Phoenix, outlined in the "City" column. The respective states for these cities are represented in the "State" column, including NY, CA, IL, TX, and AZ. Each property's construction year is cataloged in the "Year_Built" column, spanning from 1950 to 2022, offering a glimpse into the vintage and modernity of properties within the dataset.

Diversity in property types is depicted in the "Type" column, encompassing apartments, houses, and condos. The presence of a garage on the property is indicated by the binary "Garage" column, specifying whether the property has a garage (1) or not (0). Additionally, the "Lot_Area" column signifies the area size of the property, varying between 1000 and 10000 square feet, reflecting the diversity in land sizes.

This dataset could serve as a foundation for various analytical pursuits within the realm of real estate research, including market trend analysis, property value prediction models, or urban housing dynamics. Researchers or analysts may employ this dataset to derive insights into housing patterns, pricing determinants, or regional housing market disparities within the United States.

This is a dataset about the properties in New York City. The data was scraped from Trulia Website with the help of selenium Webdriver. This dataset contains the Name of the property, the city where the property belongs, the Neighborhood, the Price of the Property in USD, the year the property was built in, the beds, baths , Area per Square Feet and the status Air Conditioning.

What to do? - Data Cleaning - Changing the Strings to Integers wherever necessary - Data Visualization - Prediction of House Prices

(NOTE: The data size is insufficient so it should be used for educational purpose only)

Overview

This residential real estate data set was created by Redfin, an online real estate brokerage. Published on January 9th, 2022, this data summarize the monthly housing market for every State, Metro, and Zip code in the US from 2012 to 2021. Redfin aggregated this data across multiple listing services and has been gracious enough to include property type in their reporting. Please properly cite and link to RedFin if you end up using this data for your research or project.

Source: RedFin Data Center

Property Type

Property type defined by RedFin

• All Residential: All properties defined as single-family, condominium, co-operative, townhouses, and multi-family (2-4 units) homes with a county record.
• Single Family Home (SFH): are homes built on a single lot, with no shared walls. Sometimes there’s a garage, attached or detached.
• Condominium (Condo): Usually a single unit within a larger building or community. Generally come with homeowners’ associations (HOAs), which require the residents to pay monthly or yearly dues.
• Cooperatives (Co-op): Usually a single unit within a larger building or community, but with a different way of holding a title to a shared building. You join a community and everyone in the community owns the building together.
• Townhouse: a hybrid between a condo and a single-family home. They are often multiple floors, with one or two shared walls, and some have a small yard space or rooftop deck. They’re generally larger than a condo, but smaller than a single-family home.
• Multifamily (2-4 units): They are essentially a home that has been turned into two or more units but the units cannot be purchased individually. There is one owner for the whole building.
• Land: Just land, no home of any type for sale.

Source: Building Types

Property Type

For more definitions, please visit RedFin Data Center Metrics

• Average sale to list: The mean ratio of each home's sale price divided by their list price covering all homes with a sale date during a given time period. Excludes properties with a sale price of 50%.
• Home sales: Total number of homes with a sale date during a given time period.
• Inventory: Total number of active listings on the last day of a given time period.
• Median active list ppsf: The median list price per square foot of all active listings.
• Median active list price: The median list price of all active listings.
• Median active listings: The median of how many listings were active on each day within a given time period.
• Median days on market: The number of days between the date the home was listed for sale and when the home went off-market/pending sale covering all homes with an off-market date during a given time period where 50% of the off-market homes sat longer on the market and 50% went off the market faster. Excludes homes that sat on the market for more than 1 year.
• Median days to close: The median number of days a home takes to go from pending to sold.
• Median list price: The most recent listing price covering all homes with a listing date during a given time period where 50% of the active listings were above this price and 50% were below this price.
• Median list price per square foot: The most recent listing price divided by the total square feet of the property (not the lot) covering all homes with a listing date during a given time period where 50% of the active listings were above this price per sqft and 50% were below this price per sqft.
• Median listing with price drops: The median of how many listings were active on each day and whose current list price is less than the original list price within a given time period.
• Median sale price: The final home sale price covering all homes with a sale date during a given time period where 50% of the sales were above this price and 50% were below this price.
• Median sale price per square foot: The final home sale price divided by the total square feet of the property (not the lot) covering all homes with a sale date during a given time period where 50% of the sales were above this price per sqft and 50% were below this price per sqft.
• Months of supply: When data are monthly, it is inventory divided by home sales. This tells you how long it would take supply to be bought up if no new homes came on the market.
• New listings: Total number of homes with a listing added date during a given time period.
• Off market in two weeks: The total number of homes that went under contract within two weeks of their listing date.
• Pending home sales: Total homes that went under contract during the period. Excludes homes that were on the market longer than 90 ...