A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, parking etc. Regression Techniques used to predict price of the house

This dataset contains the predicted prices of the asset Cost of Living Coin over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Average House Price

Graph and download economic data for All-Transactions House Price Index for the New England Census Division (CNEWSTHPI) from Q1 1975 to Q3 2025 about New England Census Division, appraisers, HPI, housing, price index, indexes, price, and USA.

View monthly updates and historical trends for Food Price Index. Source: World Bank. Track economic data with YCharts analytics.

The price of paper is represented by a Bureau of Labor Statistics index that measures the prices received by paper manufacturers for their products. The index has a base year of 1982.

Graph and download economic data for All-Transactions House Price Index for Sussex County, DE (ATNHPIUS10005A) from 1983 to 2024 about Sussex County, DE; DE; HPI; housing; price index; indexes; price; and USA.

A "spread" can have multiple meanings, but it generally implies a difference between two comparable measures. These can be differences across space, across time, or across anything with a similar attribute. For example, in the stock market, there is a spread between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

In this dataset, spread refers to differences in prices between two locations, an origin (e.g., Illinois, Iowa, etc.) and a destination (e.g., Louisiana Gulf, Pacific Northwest, etc.). Mathematically, it is the destination price minus the origin price.

Price spreads are closely linked to transportation. They tend to reflect the costs of moving goods from one point to another, all else constant. Fluctuations in spreads can change the flow of goods (where it may be more profitable to ship to a different location), as well as indicate changes in transportation availability (e.g., disruptions). For more information on how price spreads are linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6).

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain basis (https://agtransport.usda.gov/d/v85y-3hep). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price, and there needs to be both an origin and a destination to have a price spread).

The origin and destination prices come from the grain prices dataset.

This dataset contains historical data on Tesla stock prices over a specific period of time. Includes data on the opening price, closing price, the highest and lowest price for each day, as well as trading volume. Use this dataset to analyze and forecast Tesla stock price movements and other financial research.

This dataset contains the predicted prices of the asset Used Car over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 93 companies listed on the Bucharest Stock Exchange* (XBSE) in Romania. 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 Romania:

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 Romania:

Bucharest Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Bucharest Stock Exchange. This index provides an overview of the overall market performance in Romania.

Bucharest Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Bucharest Stock Exchange. This index reflects the performance of international companies operating in Romania.

Company A: A prominent Romanian company with diversified operations across various sectors, such as manufacturing, technology, or finance. This company's stock is widely traded on the Bucharest Stock Exchange.

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

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

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Romania, 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 Romania ?

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 Romania ?

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 Romania 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,...

The S&P Case Shiller Portland Home Price Index has increased steadily in recent years. The index measures changes in the prices of existing single-family homes. The index value was equal to 100 as of January 2000, so if the index value is equal to *** in a given month, for example, it means that the house prices have increased by ** percent since 2000. The value of the S&P Case Shiller Portland Home Price Index amounted to ***** in August 2024. That was higher the national average.

All-Transactions House Price Index for Ann Arbor, MI (MSA) was 329.82000 Index 1995 Q1=100 in April of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Ann Arbor, MI (MSA) reached a record high of 329.82000 in April of 2025 and a record low of 46.01000 in April of 1978. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Ann Arbor, MI (MSA) - last updated from the United States Federal Reserve on December of 2025.

The dataset includes following fields: (Food) Item, Category, Sub Category, Item Name, Price, Cost. The purpose of this dataset is to practice data visualization in tools like power bi and python.

United States - Average Price: All Pork Chops (Cost per Pound/453.6 Grams) in U.S. City Average was 4.34900 Index in September of 2025, according to the United States Federal Reserve. Historically, United States - Average Price: All Pork Chops (Cost per Pound/453.6 Grams) in U.S. City Average reached a record high of 4.45200 in October of 2023 and a record low of 2.86600 in January of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Average Price: All Pork Chops (Cost per Pound/453.6 Grams) in U.S. City Average - last updated from the United States Federal Reserve on November of 2025.

United States - Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in the South Census Region - Urban was 1.78400 Index in September of 2025, according to the United States Federal Reserve. Historically, United States - Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in the South Census Region - Urban reached a record high of 1.87600 in January of 2024 and a record low of 0.44400 in June of 1980. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in the South Census Region - Urban - last updated from the United States Federal Reserve on November of 2025.

In Q3 2025, Malaysia, the Calcium Hydroxide Price Index rose by 0.8% quarter-over-quarter, due to construction demand. Check detailed insights for Europe and North America.

Median Sales Price of Houses Sold for the United States was 410800.00000 $ in April of 2025, according to the United States Federal Reserve. Historically, Median Sales Price of Houses Sold for the United States reached a record high of 442600.00000 in October of 2022 and a record low of 17800.00000 in January of 1963. Trading Economics provides the current actual value, an historical data chart and related indicators for Median Sales Price of Houses Sold for the United States - last updated from the United States Federal Reserve on December of 2025.

This statistic shows the average property price in the United States in 2011, by property type. Damaged REOs cost an average of ******* U.S. dollars in the U.S. that year. The abbreviation REO stands for real estate owned properties.

The Laptop Price Dataset is a comprehensive collection of data related to laptop prices, specifications, and various factors that influence the pricing of laptops. This dataset provides valuable insights into the dynamics of the laptop market, making it a valuable resource for researchers, analysts, and businesses in the technology industry.