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.

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.

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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.

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 2022, the volume of commercial real estate transactions reached *** billion U.S. dollars, up from *** billion U.S. dollars in 2020. One of the reasons for the surge was the pandemic and the release of pent-up demand as the economy reopened. A real estate transaction refers to the process of passing the rights in a property unit from the seller to the buyer in return for an agreed upon sum. Effect of 2007-2008 credit crisis The U.S. real estate market reached its peak in 2007, just before the 2007-2008 credit crisis when the property market collapsed. The value of commercial property returns dropped between 2007 and 2009. Since 2010, the market has steadily recovered, and the volume of transactions climbed until 2015, and has levelled out since then. Types of commercial real estate The change in overall transaction volume is most likely impacted by the type of commercial properties which are more attractive to investors in a particular period. For instance, the interest in multifamily housing investment opportunities went down in the same period that interest in hotel investment opportunities went up.

New Home Sales in China increased to 60687.34 CNY Hundred Million in October from 55329.02 CNY Hundred Million in September of 2025. This dataset provides - China Sales Value of Commercial Residential Buildings- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Producer Price Index by Industry: Insurance Agencies and Brokerages: Sale of Commercial Property and Casualty Insurance (PCU524210524210102) from Dec 2002 to Sep 2025 about property-casualty, brokers, agency, insurance, commercial, sales, PPI, industry, inflation, price index, indexes, price, and USA.

View monthly updates and historical trends for Commercial Sales Value. from Japan. Source: Japan Ministry of Economy, Trade, and Industry. Track economic …

Indonesia Commercial Property Price Index: YoY: Semarang Municipality: Office: Sell data was reported at 0.000 % in Mar 2020. This stayed constant from the previous number of 0.000 % for Dec 2019. Indonesia Commercial Property Price Index: YoY: Semarang Municipality: Office: Sell data is updated quarterly, averaging 0.882 % from Mar 2017 to Mar 2020, with 13 observations. The data reached an all-time high of 5.891 % in Mar 2017 and a record low of 0.000 % in Mar 2020. Indonesia Commercial Property Price Index: YoY: Semarang Municipality: Office: Sell data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Indonesia Premium Database’s Construction and Properties Sector – Table ID.EF003: Commercial Property Price Index: YoY.

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].

Georgia Average Sales Price: Commercial Property: Tbilisi data was reported at 1,050.346 USD/sq m in Jun 2018. This records a decrease from the previous number of 1,139.452 USD/sq m for May 2018. Georgia Average Sales Price: Commercial Property: Tbilisi data is updated monthly, averaging 1,100.291 USD/sq m from May 2015 (Median) to Jun 2018, with 38 observations. The data reached an all-time high of 1,229.720 USD/sq m in Jul 2015 and a record low of 1,002.313 USD/sq m in Apr 2017. Georgia Average Sales Price: Commercial Property: Tbilisi data remains active status in CEIC and is reported by ISET Policy Institute. The data is categorized under Global Database’s Georgia – Table GE.P001: Average Sales Price.

Germany - Commercial property price index, office and retail buildings, whole country

Snapshot img

Commercial Real Estate Market Size 2025-2029

The commercial real estate market size is valued to increase USD 427.3 billion, at a CAGR of 4.6% from 2024 to 2029. Growing commercial sector globally will drive the commercial real estate market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 42% growth during the forecast period.
By End-user - Offices segment was valued at USD 476.50 billion in 2023
By Channel - Rental segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 43.44 billion
Market Future Opportunities: USD 427.30 billion
CAGR : 4.6%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving sector that continues to shape the global business landscape. Core technologies and applications, such as Building Information Modeling (BIM) and Real Estate Information Systems (REIS), are increasingly being adopted to streamline operations and enhance efficiency. According to a recent report, the BIM market in the real estate sector is projected to grow at a steady pace, reaching a market share of 30% by 2025. Service types and product categories, including property management, brokerage, and construction services, are also experiencing significant changes. For instance, the growing trend of remote work and online shopping is driving demand for flexible and adaptable commercial spaces.
Additionally, regulations and policies are evolving to accommodate these changes, with many governments investing in smart city initiatives and green building standards. Despite these opportunities, the market faces challenges such as economic uncertainty, changing demographics, and increasing competition. However, these challenges also present new opportunities for innovation and growth. For instance, the adoption of proptech solutions and the integration of artificial intelligence and machine learning are transforming the way commercial real estate is bought, sold, and managed. Overall, the market is a complex and dynamic ecosystem that requires constant monitoring and adaptation to stay ahead of the curve.

What will be the Size of the Commercial Real Estate Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Commercial Real Estate Market Segmented and what are the key trends of market segmentation?

The commercial real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Offices
  Retail
  Leisure
  Others


Channel

  Rental
  Lease
  Sales


Transaction Type

  Commercial Leasing
  Property Sales
  Property Management


Service Type

  Brokerage Services
  Property Development
  Valuation Consulting
  Facilities Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The offices segment is estimated to witness significant growth during the forecast period.

In the ever-evolving market, the offices segment is experiencing significant growth, driven by shifting work trends and corporate demands. Flexible work arrangements, hybrid models, and technological integration are transforming the need for office space. Businesses prioritize contemporary, adaptable, and technologically advanced workspaces to attract and retain talent. Co-working spaces like Regus and WeWork, which offer flexible office solutions, are gaining popularity. Major corporations, such as Google and Amazon, invest in innovative office designs that foster collaboration and employee satisfaction. According to recent market data, the offices end-user segment is projected to expand by 15% between 2024 and 2028, underscoring the continuous adaptation of workspaces to modern business practices.

Meanwhile, tenant occupancy rates remain a critical concern for commercial property owners. Lease agreement terms, negotiation strategies, and rent collection efficiency are essential factors in maintaining a healthy portfolio. Building lifecycle costs, code compliance, and investment return metrics are other essential considerations for property managers. Environmental impact assessments, construction cost estimating, and property tax appeals are also crucial elements in the market. Property value depreciation, commercial property insurance, and portfolio risk management are essential aspects of property management. Property management software, energy efficiency upgrades, and property tax assessments are key tools for optimizing o

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).

File descriptions

• train.csv - the training set
• test.csv - the test set
• data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here
• sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms

Data fields

Here's a brief version of what you'll find in the data description file.

• SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.
• MSSubClass: The building class
• MSZoning: The general zoning classification
• LotFrontage: Linear feet of street connected to property
• LotArea: Lot size in square feet
• Street: Type of road access
• Alley: Type of alley access
• LotShape: General shape of property
• LandContour: Flatness of the property
• Utilities: Type of utilities available
• LotConfig: Lot configuration
• LandSlope: Slope of property
• Neighborhood: Physical locations within Ames city limits
• Condition1: Proximity to main road or railroad
• Condition2: Proximity to main road or railroad (if a second is present)
• BldgType: Type of dwelling
• HouseStyle: Style of dwelling
• OverallQual: Overall material and finish quality
• OverallCond: Overall condition rating
• YearBuilt: Original construction date
• YearRemodAdd: Remodel date
• RoofStyle: Type of roof
• RoofMatl: Roof material
• Exterior1st: Exterior covering on house
• Exterior2nd: Exterior covering on house (if more than one material)
• MasVnrType: Masonry veneer type
• MasVnrArea: Masonry veneer area in square feet
• ExterQual: Exterior material quality
• ExterCond: Present condition of the material on the exterior
• Foundation: Type of foundation
• BsmtQual: Height of the basement
• BsmtCond: General condition of the basement
• BsmtExposure: Walkout or garden level basement walls
• BsmtFinType1: Quality of basement finished area
• BsmtFinSF1: Type 1 finished square feet
• BsmtFinType2: Quality of second finished area (if present)
• BsmtFinSF2: Type 2 finished square feet
• BsmtUnfSF: Unfinished square feet of basement area
• TotalBsmtSF: Total square feet of basement area
• Heating: Type of heating
• HeatingQC: Heating quality and condition
• CentralAir: Central air conditioning
• Electrical: Electrical system
• 1stFlrSF: First Floor square feet
• 2ndFlrSF: Second floor square feet
• LowQualFinSF: Low quality finished square feet (all floors)
• GrLivArea: Above grade (ground) living area square feet
• BsmtFullBath: Basement full bathrooms
• BsmtHalfBath: Basement half bathrooms
• FullBath: Full bathrooms above grade
• HalfBath: Half baths above grade
• BedroomAbvGr: Bedrooms above grade (does NOT include basement bedrooms)
• KitchenAbvGr: Kitchens above grade
• KitchenQual: Kitchen quality
• TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
• Functional: Home functionality rating
• Fireplaces: Number of fireplaces
• FireplaceQu: Fireplace quality
• GarageType: Garage location
• GarageYrBlt: Year garage was built
• GarageFinish: Interior finish of the garage
• GarageCars: Size of garage in car capacity
• GarageArea: Size of garage in square feet
• GarageQual: Garage quality
• GarageCond: Garage condition
• PavedDrive: Paved driveway
• WoodDeckSF: Wood deck area in square feet
• OpenPorchSF: Open porch area in square feet
• EnclosedPorch: Enclosed porch area in square feet
• 3SsnPorch: Three season porch area in square feet
• ScreenPorch: Screen porch area in square feet
• PoolArea: Pool area in square feet
• PoolQC: Pool quality
• Fence: Fence quality
• MiscFeature: Miscellaneous feature not covered in other categories
• MiscVal: $Value of miscellaneous feature
• MoSold: Month Sold
• YrSold: Year Sold
• SaleType: Type of sale
• SaleCondition: Condition of sale

Acknowledgments

Using data from: House Prices: Advanced Regression Techniques

2 attributes corrected from the description: KitchenAbvGr and BedroomAbvGr

Apollo Commercial Real Est Finance reported $19.45M in Cost of Sales for its fiscal quarter ending in September of 2025. Data for Apollo Commercial Real Est Finance | ARI - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Context

This data gives different sales prices with respect to type of houses in USA

Content

There are 72 Variables gives house property and predicted variable is in last Sales price of the house

Acknowledgements

Please compare all the variable with respect to sales price and try to create different model, come up with the solution for Sales price predictions of the house

Inspiration

business probes is predicting sales price

Commercial Metals reported $1.72B in Cost of Sales for its fiscal quarter ending in June of 2025. Data for Commercial Metals | CMC - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Dataset Overview

This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.

Data Science Applications

This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.

Column Descriptors

1. type: The type of property (e.g., single-family home, condo).
2. text: A textual description of the property.
3. year_built: The year in which the property was constructed.
4. beds: The number of bedrooms.
5. baths: Total number of bathrooms (including full and half).
6. baths_full: Number of full bathrooms.
7. baths_half: Number of half bathrooms.
8. garage: Garage capacity (number of cars).
9. lot_sqft: Size of the lot in square feet.
10. sqft: Living area size in square feet.
11. stories: Number of stories/floors in the property.
12. lastSoldPrice: The price at which the property was last sold.
13. soldOn: The date on which the property was last sold.
14. listPrice: The listing price of the property at the time of data collection.
15. status: The current status of the listing (e.g., for sale, sold).

Ethically Mined Data

This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.

Acknowledgements

We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.

Usage Policy

This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.