Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q3 2024 about real estate, commercial, rate, and USA.

Commercial property prices in the U.S. plateaued in 2024 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. 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 weighed 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 retails reading the pack.

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

Japan - Commercial property price index, all properties, Tokyo

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

United States - Commercial property price index, all properties

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.

Commercial Real Estate Prices for United States was -10.47280 % Chg. from Yr. Ago in July of 2024, according to the United States Federal Reserve. Historically, Commercial Real Estate Prices for United States reached a record high of 16.13922 in April of 2006 and a record low of -30.24535 in October of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Commercial Real Estate Prices for United States - last updated from the United States Federal Reserve on August of 2025.

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. Green Street's pan-European commercial property price index, which 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, stood at ** index points in ************. That was a decline of about *** percent from the same month a year ago. Among the different property sectors, offices were most severely affected.

CPPI: YoY: Jabodebek: Retail: Rent data was reported at -0.465 % in Mar 2019. This records a decrease from the previous number of -0.333 % for Dec 2018. CPPI: YoY: Jabodebek: Retail: Rent data is updated quarterly, averaging 2.993 % from Mar 2013 (Median) to Mar 2019, with 25 observations. The data reached an all-time high of 17.940 % in Jun 2013 and a record low of -3.143 % in Mar 2018. CPPI: YoY: Jabodebek: Retail: Rent data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.EF003: Commercial Property Price Index: YoY. Prior Q2-2014, the scope of Commercial Property Price Index was Jakarta, Bogor, Depok, Bekasi (Jabodebek), Banten, and Bandung. Since Q2-2014 onwards, the index adds Makassar in its coverage. Sebelum Q2-2014, cakupan Indeks Harga Properti Komersial adalah Jakarta, Bogor, Depok, Bekasi (Jabodebek), Banten, dan Bandung. Sejak Q2-2014 dan seterusnya, indeks menambahkan Makassar dalam cakupannya.

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.

Germany - Commercial property price index, office and retail buildings, 7 large cities

This metadata document provides details of the data used for the dissertation: “Improving Commercial Property Price Statistics”. The study explores data related and methodological challenges in the construction of price statistics for commercial real estate.

Short abstract of the dissertation

Since the financial crisis of 2008, National Statistical Institutes (NSIs) have worked to develop commercial real estate (CRE) indicators for official statistics. These indicators are considered essential in financial stability monitoring and may help contain the consequences of future crises or even prevent future crises. However, progress at NSIs to develop these indicators has been slow due to challenges like low observation numbers and high heterogeneity. This dissertation addresses these challenges by exploring data issues and suggesting methodological improvements.

The first three studies focus on data challenges regarding share deals and portfolio sales. Both are real estate trading constructions that are specific to CRE. The results show that share deals and portfolio sales significantly differ from the rest of the market. Therefore, under specific circumstances, CRE indicators could benefit from including these trading types. The final two studies focus on methodological challenges regarding index construction methods and the role of sustainability in real estate pricing. The results show that, by combining established techniques, it is possible to construct price indices that meet official statistics’ standards. Furthermore, the results uncover a complex relationship between sustainability and prices: while energy efficiency generally involves price premiums, others aspects like health and environment display a discount for low sustainable properties.

Overall, this dissertation contributes to the legislative framework that is currently being developed for EU countries to publish official statistics for commercial real estate and adds to the academic discussion by presenting innovative techniques for data analyses and index construction.

Data sources

The following data sources were used:

1. Bussiness Register (Statistics Netherlands)
2. Transactions linked to the Register of Adresses and Buildings (BAG)
3. Linking table buildings and companies (Dutch Land Registry Office)
4. Property Transfer Tax data (Dutch Tax Authorities)
5. Building sustainability scores (W/E advisors)Commercial real estate transactions (Dutch Land Registry Office)
6. Commercial real estate transactions (Dutch Land Registry Office)

Processing methodology

1. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_2_ABR_Bedrijfsinfo. The data is used for deriving company transfers by comparing ownership states of various periods. The first period that an ownership differs of the same company indicates an ownership transfer.
2. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_6_ABR_CompleetMicro. The data is used for calcuting the size of real estate share deals and estimating price developments by applying appropriate filters and counting the output.
3. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is SPE_KADASTER. The data is used for finding real estate information that corresponds to company transfers by linking the company register (ABR) to the real estate register (BAG).
4. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_SPE_3_OVB_Bedrijfsinfo. The data is used for deriving real estate share deals by linking this table (Kadaster) to the real estate register (BAG).
5. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is duurzaamheid_input_regressie2. The data is used for finding the relationship between sustainabilty measures and real estate transaction prices by linking sustainabilty scores from a consultancy (WE) to transaction prices (Cadastre) and running regression analyses.
6. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_OV20_pand. The data is used for 4 purposes (separate studies).

• (1) Chapter 3: Determining the price effect of portfolio sale by running regression analyses
• (2) Chapter 4: Developing methods to include portfolio sales in CPPI calcutions by using auxilary data of the real estate properties.
• (3) Chapter 5: Developing a price index method for small domains by using these data to test the outcomes
• (4) Chapter 6: Determining the relationship between sustatinability by running regression analyses

Data restrictions

As part of the CBS law, sharing micro-data outside of the CBS-environment is prohibited. Furthermore, CBS manages the data, but in some cases other parties are still formal owners of the data. The 2 other parties are The Land Registry Office and WE consultancy. Ownership and intellectual property rights are managed in contracts with both owners. It was agreed upon that the data can only be used for the purpose of the PhD study and that the microdata will never be externally disseminated. The data is still owned by them and the intellectual property rights of the analyses belong to me. An intended use of the microdata should be approved by both Statistics Netherlands and the formal data owner. Because of the above, no data can be publicly shared.

If one intends to do research on these data, an application for data use can be requested at CBS. CBS will charge costs for anonymising the data and providing a closed environment to work with the data. More information on this can be found at: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research

Contact information

Author: Farley Ishaak

Statistics Netherlands | Henri Faasdreef 312 | P.O. Box 24500 | 2490 HA The Hague

TU Delft | Delft University of Technology | Faculty of Architecture and the Built Environment

Department of Management in the Built Environment | P.O. Box 5043 | 2600 GA Delft

M +31 6 46307974 | ff.ishaak@cbs.nl | f.f.ishaak@tudelft.nl

Germany Commercial Property Market Index: 127 Cities data was reported at 127.680 1990=100 in 2019. This records an increase from the previous number of 123.710 1990=100 for 2018. Germany Commercial Property Market Index: 127 Cities data is updated yearly, averaging 101.500 1990=100 from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 127.680 1990=100 in 2019 and a record low of 93.330 1990=100 in 2004. Germany Commercial Property Market Index: 127 Cities data remains active status in CEIC and is reported by Bulwiengesa AG. The data is categorized under Global Database’s Germany – Table DE.EB004: Property Market Index.

As of the fourth quarter of 2024, the commercial property price index in Indonesia stood at ******. This indicated an increase compared to the first quarter in the same year.

Switzerland - Commercial property price index, residential investment properties

Available headline data are generally compiled from commercial data supplied by MSCI (www.msci.com). For the experimental European aggregates, when a country prefers a different source these data are included replacing that reported by MSCI and using the appropriate code. The data are heterogeneous in terms of the transaction or valuation based methodologies used. Therefore, comparisons between countries or of different sources within individual countries should be made with caution. The indicators are generally available at quarterly frequency. Breakdowns of the data are also available but these are strictly shown by the type of data collection i.e. transaction based or valuation based.

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

Graph and download economic data for Commercial Real Estate Prices for Brazil (COMREPBRQ159N) from Q1 2005 to Q1 2025 about Brazil, real estate, commercial, and rate.