This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

Use Cases:

Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

Net-a-Porter web scraped data

  About the website

The e-commerce industry in the Asia Pacific region is flourishing, with a distinctive leap in Japan. Net-a-Porter, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The Japanese market, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Net.a.Porter.Product.prices.Japan.

Japan's main stock market index, the JP225, rose to 43378 points on August 15, 2025, gaining 1.71% from the previous session. Over the past month, the index has climbed 9.37% and is up 13.97% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on August of 2025.

This dataset is (surplus or shortage) imbalance price in the Japanese electricity market from March 2022. The data source is here.

• After the full liberalization of retail electricity in 2016, a new system of simultaneous balancing of planned values ​​was introduced, and retailers and power generators create demand and power generation plans for each 30-minute period, dividing a day into 48 periods, and operate to match supply and demand by one hour before actual supply and demand (gate close).
• If an imbalance occurs in actual supply and demand, the general power transmission and distribution company issues a command to the power source (adjustment power) to adjust to eliminate the imbalance.
• Power generators and retailers each formulate a power generation plan and a demand plan for the next 48 periods by 12:00 the day before. Power generation plans and demand plans can be changed up to one hour before each period.
• The difference between the plan and the actual result (imbalance) is compensated for and absorbed by the power transmission and distribution company using its adjustment power.
• The person who caused the imbalance must subsequently settle the unbalanced amount of electricity with the electricity transmission and distribution company as an imbalance price.

This dataset includes the following columns:

• date_time: Date & Time (30 minute granularity)
• Imbalance price: Prices for each of the 9 areas (Hokkaido, Tohoku, Tokyo, Chubu, Hokuriku, Kansai, Chugoku, Shikoku, Kyushu) [Yen/kWh]

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This dataset contains Food Prices data for Japan, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Burberry web scraped data

  About the website

The Fashion Industry in the Asia Pacific, particularly in Japan, has seen a significant growth in recent years. High-end, luxury brands like Burberry have established themselves firmly in the region. Its a fast-paced, highly consumer-driven industry that heavily incorporates the latest technology and trends. One noteworthy trend in Japans fashion industry is the rapid expansion of Ecommerce platforms. The dataset observed… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Burberry.Product.prices.Japan.

Loro Piana web scraped data

  About the website

Loro Piana operates within the luxury fashion industry in the Asia Pacific region, particularly Japan. This industry is noted for its high-end products and services, catering to a demographic that values exclusivity, quality, and prestige. Japan is a major market, with a sophisticated consumer base that posseses a keen interest in luxury fashion. The ecommerce sector in this region has seen substantial growth, with… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Loro.Piana.Product.prices.Japan.

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

This dataset is designed for analyzing various product categories within the Japanese market. It provides information about each product category's size, growth rate, market share, competitor market shares, average price, customer demographics, online presence, and market saturation. Here's a breakdown of each column:

Product Category: The type of products or services being analyzed within the Japanese market.

Total Market Size (in USD): The estimated total market size in terms of US dollars for each product category. This figure reflects the overall revenue potential for that category.

Market Growth Rate (%): The projected annual growth rate of each product category's market. This percentage indicates how much the market is expected to expand or contract over time.

Market Share (%): The percentage of the total market size that each product category holds. This reflects the relative importance of each category within the overall market.

Competitor 1 Market Share (%): The market share percentage of the first major competitor within each product category. This helps to understand the competitive landscape.

Competitor 2 Market Share (%): The market share percentage of the second major competitor within each product category. Similar to the previous column, this provides insight into the competitive environment.

Average Price (in USD): The average price of products or services within each product category. This information helps understand the pricing dynamics of the category.

Customer Demographics: The primary target audience or customer segments for each product category. Understanding the demographics helps in tailoring marketing efforts.

Online Presence (%): The percentage of businesses within each product category that have an online presence. This includes websites, social media, and other digital platforms.

Market Saturation (%): An estimate of how much of the potential market demand has already been captured by existing products or services within each category. A higher percentage indicates a more saturated market.

Japan Consumer Price Index (CPI): Goods: Rice data was reported at 195.300 2020=100 in Mar 2025. This records an increase from the previous number of 182.600 2020=100 for Feb 2025. Japan Consumer Price Index (CPI): Goods: Rice data is updated monthly, averaging 100.300 2020=100 from Jan 1970 (Median) to Mar 2025, with 663 observations. The data reached an all-time high of 195.300 2020=100 in Mar 2025 and a record low of 41.700 2020=100 in Jan 1970. Japan Consumer Price Index (CPI): Goods: Rice data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.I002: Consumer Price Index: 2020=100.

Gross-Profit Time Series for KakakuCom Inc. Kakaku.com, Inc., together with its subsidiaries, engages in the provision of purchase support, restaurant review, and other services in Japan. The company operates Kakaku.com, that provides prices, specifications, and user reviews, on various products and services, such as computers, home appliances, fashion, interior goods, and finance and communications; and Tabelog.com, a restaurant search and reservation site. It also operates Kyushu Box and Jobcube, job classified websites; Smaity, a residential real estate website; 4 travel, a travel review and comparison site; Sumaity, an online travel site; icotto, an online travel Information media site; Bus Comparison Navi, a comparison search site for nationwide express buses and night buses and bus tours; and low-price trips, a price comparison site for domestic travel and overseas airline tickets. In addition, the company operates kinarino; eiga.com; and webCG. Kakaku.com, Inc. was incorporated in 1997 and is headquartered in Tokyo, Japan.

Producer Prices in Japan increased to 126 points in March from 125.50 points in February of 2025. This dataset provides - Japan Producer Prices - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Japan JP: Standardised Price-Income Ratio: sa data was reported at 87.536 Ratio in 2024. This records a decrease from the previous number of 89.289 Ratio for 2023. Japan JP: Standardised Price-Income Ratio: sa data is updated yearly, averaging 113.262 Ratio from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 163.202 Ratio in 1973 and a record low of 73.471 Ratio in 2009. Japan JP: Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

Food Prices for Japan.

Contains data from the FAOSTAT bulk data service covering the following categories: Consumer Price Indices, Deflators, Exchange rates, Producer Prices

This dataset is about book series. It has 1 row and is filtered where the books is The price of prosperity, lessons from Japan. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 4 companies listed on the Nagoya Stock Exchange (XNGO) in Japan. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Japan:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Japan:

Nikkei 225: The main index that tracks the performance of major companies listed on the Tokyo Stock Exchange. This index provides an overview of the overall market performance in Japan.

TOPIX: The index that tracks the performance of all domestic companies listed on the Tokyo Stock Exchange. This index reflects the performance of a broader range of companies in the Japanese market.

Company A: A prominent Japanese company with diversified operations across various sectors, such as automotive, electronics, or manufacturing. This company's stock is widely traded on the Tokyo Stock Exchange.

Company B: A leading financial institution in Japan, offering banking, insurance, or investment services. This company's stock is actively traded on the Tokyo Stock Exchange.

Company C: A major player in the Japanese consumer goods sector or other industries, involved in the production and distribution of consumer products. This company's stock is listed and actively traded on the Tokyo Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Japan, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Japan ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Japan?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Japan exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive...

Graph and download economic data for Residential Property Prices for Japan (QJPN628BIS) from Q1 1955 to Q1 2025 about Japan, residential, HPI, housing, price index, indexes, and price.

This dataset contains information about world's coal price from 1987. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes:- Source: IHS Northwest Europe prices for 1990-2000 are the average of the monthly marker, 2001-2016 the average of weekly prices. IHS Japan prices basis = 6,000 kilocalories per kilogram NAR CIF.- The Asian prices are the average of the monthly marker.- Chinese prices are the average monthly price for 2000-2005, weekly prices 2006 -2016, 5,500 kilocalories per kilogram NAR, including cost and freight (CFR)- Source: Platts. Prices are for CAPP 12,500 Btu, 1.2 SO2 coal, fob. - CAPP = Central Appalachian; cif = cost+insurance+freight (average prices); fob = free on board. &am

Index Time Series for Xtrackers MSCI Japan ESG UCITS ETF 1C GBP. The frequency of the observation is daily. Moving average series are also typically included. NA