Hospitality properties had the highest square footage price in the U.S. commercial real estate sector in the fourth quarter of 2024. Hospitality properties sold during that period had an average price of ****** U.S. dollars per square foot. Conversely, industrial properties had the lowest price, at ****** U.S. dollars per square foot.

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.

Commercial property prices in the U.S. plateaued in 2025 after declining in 2023. Between 2014 and 2021, commercial real estate prices nearly doubled, with the index reaching ***** index points. Following a slowdown in the market, the index declined, falling to ****** index points in the second quarter of 2025. Despite the correction, this indicated an increase of almost ** percent in prices since 2010, which was the baseline year for the index. How have prices of different property types developed over the past years? After more than a decade of uninterrupted growth, office real estate prices started to decline in 2022, reflecting a decline in occupier demand and a tougher lending environment. Industrial real estate prices, which have grown rapidly over the past few years, also experienced a correction in late 2022. Retail real estate prices displayed most resilience amid the difficult economic environment, with the equal-weighted repeat sales index remaining stable. How much is invested in new commercial properties? The value of commercial real estate construction has been on the rise since 2010 in the United States. This trend mirrors the recovery seen across all economic sectors after the 2007-2009 recession. However, investment volumes in commercial property vary by type, with private office space, warehouses, and retail leading the pack.

The Commercial Real Estate (CRE) industry is exhibiting significant variations across markets, with persistently high office vacancy rates juxtaposed against thriving prime office spaces. Hard hit by the widespread adoption of remote and hybrid work models, the overall office vacancy rate rose to 20.7% in Q2 2025, up from the pre-pandemic rate of 16.8%. However, leasing volumes for prime office spaces are climbing, providing opportunities for seasoned investors. On the other hand, the multifamily sector is gaining from a prominent move towards renting, primarily driven by housing affordability concerns and changing lifestyle preferences. This has strengthened demand for multifamily properties and opportunities to convert underutilized properties, such as offices, into residential rentals. The industrial real estate segment is also moderating, with the boom in e-commerce and industrial construction activity in 2021 and 2022 moderating more recently. Industry revenue has gained at a CAGR of 1.7% to reach $1.5 trillion through the end of 2025, including a 1.0% climb in 2025 alone. The industry is grappling with multiple challenges, including wide buyer-seller expectation gaps and significant disparities in demand across different geographies and asset types. Despite interest rate cuts in 2024 and 2025, economic uncertainty and labor market weakness have resulted in tighter credit and lending conditions. Because of remote working trends, office delinquency rates swelled to above 14.0% in 2025, leading to a job market increasingly concentrated in certain urban centers. Through the end of 2030, the CRE industry is expected to stabilize as the construction pipeline shrinks, reducing new supply and, in turn, rebalancing supply and demand dynamics. With this adjustment, occupancy rates will likely improve, and rents may gradually climb. The data center segment will witness accelerating demand propelled by the rapid expansion of artificial intelligence, cloud computing and the Internet of Things. Likewise, mixed-use properties are poised to gain popularity, driven by the growing appeal of flexible spaces that accommodate diverse businesses and residents. This new demand, coupled with the retiring baby boomer generation's preference for leisure-centric locales, is expected to push the transformation of traditional shopping plazas towards destination centers, offering continued opportunities for savvy CRE investors. Industry revenue will expand at a CAGR of 1.9% to reach $1.7 trillion in 2030.

In 2023, the average price of properties for business purposes in Beijing surpassed ** thousand yuan per square meter. The capital, together with major municipalities of Shanghai, and the southern provinces of Guangdong and Hainan are the regions with the most expensive commercial real estate in China, where the average price increased slightly to ****** yuan per square meter in 2023.

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

China Property Price: YTD Avg: Commercial Bldg: Existing House: Overall data was reported at 8,550.513 RMB/sq m in Mar 2025. This records a decrease from the previous number of 8,738.992 RMB/sq m for Feb 2025. China Property Price: YTD Avg: Commercial Bldg: Existing House: Overall data is updated monthly, averaging 8,677.458 RMB/sq m from Jan 2006 (Median) to Mar 2025, with 230 observations. The data reached an all-time high of 10,824.073 RMB/sq m in Mar 2019 and a record low of 4,227.000 RMB/sq m in Jun 2006. China Property Price: YTD Avg: Commercial Bldg: Existing House: Overall data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Commercial Building: Monthly.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

Real Estate Sales 2001-2020 GL Metadata Updated: August 12, 2023

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment.

Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Access & Use Information Public: This dataset is intended for public access and use. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.

In the second quarter of 2025, the preliminary commercial property price index in Japan stood at *****, up by *** percent compared to the previous quarter.The commercial property price index comprises offices, warehouses, factories, apartment buildings, and commercial and industrial land.

Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_f333bf427c827efb484cf57a73ff700a/view

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment.

Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019.

The United States real estate brokerage market, valued at $197.33 billion in 2025, is projected to experience steady growth, driven primarily by a robust housing market, increasing urbanization, and the growing preference for professional real estate services. The market's Compound Annual Growth Rate (CAGR) of 2.10% from 2025 to 2033 indicates a consistent, albeit moderate, expansion. Key market segments include residential and non-residential properties, with sales and rental services as primary revenue streams. Major players such as Keller Williams, RE/MAX, and Coldwell Banker dominate the market, leveraging extensive networks and advanced technological tools to enhance client services. While competition is fierce, the market's growth is fueled by factors like rising home prices, increasing investor interest in real estate, and the continuing need for expert guidance in navigating complex real estate transactions. The market faces challenges such as fluctuating interest rates which can impact buyer affordability and economic downturns that can reduce both sales and rental activity, thereby influencing the overall market expansion. However, the long-term outlook remains positive, supported by the enduring demand for housing and the critical role of brokerage firms in facilitating real estate transactions. The increasing use of online platforms and proptech solutions is also expected to further shape the market landscape in the coming years. The segmentation by property type (residential and non-residential) and service type (sales and rental) provides valuable insights into market dynamics. Residential sales are likely to remain the largest segment, driven by demographic shifts and population growth. The non-residential segment, encompassing commercial properties, will likely experience growth influenced by business expansion and investment activities. The rental segment is expected to continue its growth, particularly in urban areas facing housing shortages. The competitive landscape features established national brands alongside smaller, localized firms. The success of individual firms will depend on their ability to adapt to technological advancements, offer specialized services, and build strong client relationships. Furthermore, government regulations and economic conditions will also continue to play a significant role in shaping the market's trajectory. Recent developments include: May 2024: Compass Inc., the leading residential real estate brokerage by sales volume in the United States, acquired Parks Real Estate, Tennessee's top residential real estate firm that boasts over 1,500 agents. Known for its strategic acquisitions and organic growth, Compass's collaboration with Parks Real Estate not only enriches its agent pool but also grants these agents access to Compass's cutting-edge technology and a vast national referral network., April 2024: Compass has finalized its acquisition of Latter & Blum, a prominent brokerage firm based in New Orleans. Latter & Blum, known for its strong foothold in Louisiana and other Gulf Coast metros, has now become a part of Compass. This strategic move not only solidifies Compass' presence in the region but also propels it to a significant market share, estimated at around 15% in New Orleans.. Key drivers for this market are: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Potential restraints include: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Notable trends are: Industrial Sector Leads Real Estate Absorption, Retail Tightens Vacancy Rates.

Discover the latest trends and insights into the booming US residential real estate market. Our comprehensive analysis reveals a steady CAGR of 2.04%, key drivers, market segmentation, and leading players. Learn about growth projections through 2033 and understand the opportunities and challenges shaping this dynamic sector. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Notable trends are: Existing Home Sales Witnessing Strong Growth.

Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each model group, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website. The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar. One KeyPIN is one Commercial Entity. Each KeyPIN (entity) can be comprised of one single PIN (parcel), or multiple PINs as designated in the pins column. Additionally, each KeyPIN might have multiple rows if it is associated with different class codes or model groups. This can occur because many of Cook County's parcels have multiple class codes associated with them if they have multiple uses (such as residential and commercial). Users should not expect this data to be unique by any combination of available columns. Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied. This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map. For more information on the sourcing of attached data and the preparation of this datase

Reach commercial property professionals across North America. Verified emails, phones, titles, and real estate firm info. Ideal for PropTech, B2B outreach, and investment platforms.