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.

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.

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.

Dataset

This dataset was created by Fagr Ahmed

Released under MIT

Contents

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.

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 UK house price index (UK HPI) represents changes in the value of UK residential properties (i.e., detached houses, semi-detached houses, terraced houses, flats and maisonettes) and indicates trends in the UK housing market. The UK HPI applies a hedonic regression model that utilises the various sources of data on property price (e.g., HM Land Registry's Price Paid dataset) to allow for a true comparison of UK property prices in each period. The data is sourced from the Office for National Statistics (ONS) and HM Land Registry, using house sales data from HM Land Registry, Registers of Scotland, and Land and Property Services Northern Ireland. Forecast data is estimated by IBISWorld, with reference to Office for Budget Responsibility (OBR) forecasts submitted in its 'Economic and fiscal outlook – March 2022' publication. The figures are presented with a base month in 2015 (i.e., January 2015 = 100) and are averages of the UK HPI over each financial year (i.e., April-March).

During the considered period, the commercial property price index in Portugal reached its lowest value in 2013 at *****. From then on, this index continuously rose, reaching ****** in 2024. This value was the highest during the period considered, with the base year 2015 equaling 100.

Japan - Commercial property price index, all properties, Tokyo

Denmark - Commercial property price index, all business

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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.

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

Commercial real estate prices in Europe plummeted in 2023 as uncertainty due to macroeconomic headwinds and tighter monetary policy continued to suppress investor sentiment and transaction volume. The office sector was most severely affected, with property prices declining by nearly *** percent year-on-year as of October 2024, according to Green Street's pan-European commercial property price index. The index measures the development of commercial real estate prices across ** of the most liquid property markets in Europe, with August 2007 as a base month and an index value of 100. In May 2023, commercial real estate prices declined by approximately ** percent, which was the largest year-on-year decrease recorded.

The purpose of this study was to understand the relationship between happiness and housing prices in Canada. The happiness data were obtained from the General Social Survey between 2009 and 2013, asking respondents to report overall happiness level by using scale ranging between 1 to 10 points. House Price Indexes at the provincial level were constructed to cover the same period. The relationship between average house price change and average happiness was estimated using Ordinary Least Square and Logistic Regression techniques. Individual's characteristics were used as control variables. The study found that average happiness level is positively and significantly related to the change in housing prices for one group and not for another - for homeowners but not for renters. In addition, individuals with better health are much happier than individuals with poor health. Similarly, individuals with higher income are happier than individuals with less income. The implication of this study is that the government should design attractive policies to encourage homeownerships.

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 the second quarter of 2025, the preliminary commercial property price index for offices in Japan stood at ***, up from ***** points in the previous quarter.Apart from offices, the commercial property price index comprises retail properties, warehouses, factories, apartment buildings, and commercial and industrial land.

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

In the second quarter of 2025, the preliminary commercial property price index for warehouses in Japan stood at ***** in the three metropolitan areas of Tokyo, Osaka, and Nagoya. In all other areas, the property price index reached **** points.Apart from warehouses, the commercial property price index comprises retail properties, offices, factories, apartment buildings, and commercial and industrial land.

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