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.

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.

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 152.24 U.S. dollars per square foot. Conversely, industrial properties had the lowest price, at 112.36 U.S. dollars per square foot.

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.

Slovenia Business Survey: sa: Retail Trade: Selling Prices data was reported at 33.000 % in Apr 2021. This records a decrease from the previous number of 34.000 % for Mar 2021. Slovenia Business Survey: sa: Retail Trade: Selling Prices data is updated monthly, averaging 12.000 % from Jan 1999 (Median) to Apr 2021, with 268 observations. The data reached an all-time high of 38.000 % in Jan 2020 and a record low of -48.000 % in Mar 2009. Slovenia Business Survey: sa: Retail Trade: Selling Prices data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.S004: Business Survey: Wholesale and Retail Trade.

Graph and download economic data for Nonfinancial Corporate Business; Cost of Sales (FSIs), Transactions (BOGZ1FA106200005Q) from Q4 1946 to Q1 2025 about cost, transactions, nonfinancial, business, sales, and USA.

Dataset

This dataset was created by sublimE009

Contents

In June 2025, commercial property sale asking prices were forecasted to witness a decrease of around **** percent. Within the given time period, the largest growth in commercial property asking prices was recorded in June 2021.

Belgium Business Survey: sa: Business Services: Trend: Selling Prices data was reported at 7.600 % Point in Mar 2025. This records an increase from the previous number of 4.700 % Point for Feb 2025. Belgium Business Survey: sa: Business Services: Trend: Selling Prices data is updated monthly, averaging 3.000 % Point from Jan 1995 (Median) to Mar 2025, with 363 observations. The data reached an all-time high of 25.600 % Point in Feb 2023 and a record low of -15.800 % Point in May 2009. Belgium Business Survey: sa: Business Services: Trend: Selling Prices data remains active status in CEIC and is reported by National Bank of Belgium. The data is categorized under Global Database’s Belgium – Table BE.S001: Business Survey: Seasonally Adjusted.

This data set contains actual sales data for a chain of Brazilian stores. I modified the names of products, customers, and employees to preserve their identity. I am making this data available so that they can help me get the most out of it, analysis such as:

• Sales forecast

• Customer segmentation

• Employee productivity

• Profitable products

• And everything else that can be extracted from it.

Columns description

Company Code - Affiliate code that sold Order Number - Unique code to identify the sale Employee - Employee who made the sale Product - Name of product sold Product Category - category the product belongs to Client - Name of the customer who made the purchase Client City - City name of the customer who made the purchase Sale Date Time - Date and time the sale was made Product Cost - Cost per unit sold Discount Amount - Total sale discount Amount - Item Quantity Total - Total item value Form of payment - Form of payment

The column values: - Client - Client City - Employee They were exchanged for fictitious names.

The category of the products was maintained, but translated into English, the name of the product consists of the name of the category to which it belongs concatenated with a random number. The rule does not apply to products in the Fuel category, for these, fictitious names were invented.

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 20.4% in Q4 2024 from the pre-pandemic rate of 16.8%. However, leasing volumes for prime office spaces are set to climb, 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 increased demand for multifamily properties and opportunities to convert underutilized properties, such as offices, into residential rentals. The industrial real estate segment is also evolving, with the boom in e-commerce necessitating the development of strategically located warehouses for quick fulfillment and last-mile delivery. Industry revenue has gained at a CAGR of 0.8% to reach $1.4 trillion through the end of 2025, including a 0.4% climb in 2025 alone. The industry is grappling with multiple challenges, including high interest rates, wide buyer-seller expectation gaps and significant disparities in demand across different geographies and asset types. The Federal Reserve's persistent high-interest-rate environment creates refinancing hurdles for properties purchased during the low-rate period of 2020-2021. Because of remote working trends, office delinquency rates are predicted to climb from 11.0% in late 2024 to 14.0% by 2026, 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 are likely to improve, and rents may observe gradual growth. The data center segment is set to 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 $1.6 trillion in 2030.

Despite the higher access to credit, business brokers endured notable declines due to high inflation, rising interest rates and an inconsistent mergers & acquisition (M&A) climate. In contrast to merger and acquisition advisers, business brokers focus on companies valued at less than $2.0 million, often finding the ultimate buyer near the company's location. According to data from the International Business Brokers Association (IBBA), there is currently an oversupply of potential small business buyers and an undersupply of high-quality businesses for sale. This resulted in higher valuations for small businesses before the pandemic, increasing commissions for successfully brokered business sales. In recent years, the acceleration of interest rates to combat high inflation has significantly curtailed small businesses’ fiscal flexibility, causing revenue to fall at a CAGR of 2.5% to an estimated $1.8 billion over the past five years, including an estimated 3.9% boost in 2024. Nearly 50.0% of business brokers are sole proprietors, typically earning between 5.0% and 10.0% of the ultimate sale price in commission. In recent years, optimism surrounding the business-for-sale market has increased among business brokers; however, the effects of high interest rates and a generally restrictive borrowing environment remains the biggest barrier to further growth, according to the IBBA. There needed to be more than the increase in volume and sustained demand for operators’ services to offset the rise in wages and other costs, causing profit to dwindle. Moving forward, the continued uncertainty surrounding interest rates, higher borrowing costs and deceleration in access to credit and the number of businesses are expected to yield slower growth in revenue. Nonetheless, the continuity of lower middle market (LMM) transaction demand, coupled with favorable demographic and private investment trends, will benefit brokers. As a more significant share of the population reaches retirement age, more small businesses will be listed for sale, increasing opportunities for business brokers. Put together, these trends are expected to cause revenue to grow at a CAGR of 1.8% to an estimated $2.0 billion over the next five years.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

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Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

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Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

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Graph and download economic data for Business Tendency Surveys: Selling Prices: Economic Activity: Manufacturing: Future Tendency for Netherlands (BSSPFT02NLM460S) from Jan 1991 to Apr 2025 about business sentiment, Netherlands, business, sales, manufacturing, and price.

We study the impact of targeted price controls on supermarket products in Argentina between 2007 and 2015. Using web-scraping methods, we collected daily prices for controlled and non-controlled goods and examined the differential effects of the policy on inflation, product availability, entry and exit, and price dispersion. We first show that price controls have only a small and temporary effect on inflation that reverses itself as soon as the controls are lifted. Second, contrary to common beliefs, we find that controlled goods are consistently available for sale. Third, firms compensate for price controls by introducing new product varieties at higher prices, thereby increasing price dispersion within narrow categories. Overall, our results show that targeted price controls are just as ineffective as more traditional forms of price controls in reducing aggregate inflation.

We address how using different censoring thresholds and imputation procedures affects the baseline results of Coibion, Gorodnichenko, and Hong (2015). Higher censoring thresholds introduce measurement error and outliers that generate wide variability in results across weighting schemes, but methods that explicitly control for outliers confirm the results of Coibion et al. (2015) for all censoring thresholds. We also illustrate how the BLS's approach to imputing missing prices can introduce a cyclical bias into measures of posted price inflation when store-switching is present in the data.

This dataset was generated manually from an existing bar business. I was contracted to analyze the sales of different alcoholic and non-alcoholic products. This data was generated within a period of two months from the day-to-day business activities entered in a sales book, and it was manually entered into an excel spreadsheet.

Column description: 1. Drinks: refers to the different types of beverages or products. 2.**Old Stock**: this is the stock before sales. 3. Supply: this is the total supplied products. 4. Total Old Stock: this is the sum of the old stock and the supplies. 5. New Stock: this refers to remaining products after sales. 6. Sold: this is the number of sales per product. 7. Purchase Price: this refers to the price of the product from the supplier. 8. Total Amount P.P: this is the total amount sold with purchasing price. 9. Selling Price: this refers to the selling price of the product from the bar. 10. Total Amount S.P: this refers to the total amount sold with selling price. 11. Profit: this was derived by subtracting the* Total Amount P.P* from the Total Amount S.P. 12. Date: this refers to the date of every sales activity.

Introduction The Price Indexes of New Single-Family Houses Sold Including Value of Lot are a set of price indexes designed to illustrate inflation in new houses built for sale. These indexes do not include contractor-built houses, owner-built houses, or houses built for rent.

Data Collection The data used to compute these indexes are obtained from the U.S. Census Bureau's Survey of Construction. This survey gathers information on the physical characteristics and prices of new single-family houses through monthly interviews with the builders or owners of a national sample of new houses.

Price Index Design – Laspeyres Type Indexes The Constant Quality Price Indexes of New Single-Family Houses Sold Including Value of Lot are Laspeyres type indexes. The basic form of a Laspeyres type price index is:

∑ 𝑖 ( 𝑞 0 𝑖 ⋅ 𝑝 𝑡 𝑖 ) ∑ 𝑖 ( 𝑞 0 𝑖 ⋅ 𝑝 0 𝑖 ) ∑ i ​ (q 0i ​ ⋅p 0i ​ ) ∑ i ​ (q 0i ​ ⋅p ti ​ ) ​

where 𝑝 0 𝑖 p 0i ​ and 𝑝 𝑡 𝑖 p ti ​ are the prices in the base and current periods, respectively, and 𝑞 0 𝑖 q 0i ​ are the quantities in the base period. This ratio represents the current cost of the quantity of goods purchased in the base year compared to the cost in base year prices of the same quantity of goods. The denominator is the price of the average base period house. To compute this index, the prices must be derived from a regression model since only the total house and land price are collected.

Regression Model Experience has shown that regression estimation of the price in the following multiplicative model is superior to estimation for the above additive model:

Apollo Commercial Real Est Finance reported $105.06M in Cost of Sales for its fiscal quarter ending in March 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 July in 2025.