Property characteristics and parcel boundaries, shoreline features, and sea level rise inundation vulnerability for the state of Maryland. This dataset is not publicly accessible because: EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: Data on property attributes and parcel boundaries from MdProperty View can be accessed at https://planning.maryland.gov/Pages/OurProducts/PropertyMapProducts/MDPropertyViewProducts.aspx Data on shoreline features for Anne Arundel County can be accessed at http://ccrm.vims.edu/gis_data_maps/shoreline_inventories/maryland/anne_arundel/annearundel_disclaimer.html Data on sea level rise inundation vulnerability for Maryland coastal counties can be accessed at https://imap.maryland.gov/ServicesMetadata/ClimMetAtm/SeaLevelRiseVul/ELEV_2FootInundation_CGIS.htm. Format: Property sales and attribute data were obtained from MdProperty View and include numeric data as well as georeferenced parcel data. Georeferenced data on shoreline features, including adaptation structures, come from a joint program between the Virginia Institute of Marine Science, the Maryland Department of Natural Resources, and the National Oceanic and Atmospheric Agency (NOAA). Georeferenced sea level rise inundation vulnerability data were produced in a joint project between NOAA, the Maryland Commission on Climate Change, and Towson University. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

Real time pricing (RTP) is often promoted as a mechanism to improve the economic efficiency of the electricity system. However, many regulators have been hesitant to adopt RTP due to concerns about exposing customers to extreme price swings. To balance these concerns, this paper proposes a methodology for establishing price controls, based on the supply of demand-side flexibility in the system. As an illustrative example, we measure price responsiveness using an agent-based simulation model that is representative of the ERCOT market. The model is composed of a distribution feeder that has 250 customers with active agents controlling their HVAC systems in response to the historical ERCOT RTP with an artificially added high-price event. These agents are subjected to increasing electricity prices during the event, which we then use to create a supply curve for demand-side resources in our modeled scarcity event. We set potential price caps at points on the supply curve where customers’ have exhausted their flexible capacity. Using historical prices, we examine the systemic costs of these price caps, and present regulatory options for recouping them. Utilities and regulators interested in limiting consumer risk from dynamic pricing can utilize these methods to develop rate structures and encourage conservation.more » The attached data upload allows for the duplication or modification of the analysis performed in this study.« less

Cryptocurrency Market Analysis

Price Fluctuations and Market Cap Trends Over Time

By [source]

About this dataset

This dataset contains an in-depth look into the changing cryptocurrency market. It provides an insightful overview of the market capitalization, prices, circulating supplies, and symbols that make up various cryptocurrencies. This data can guide economists, financial advisors and investors to form strategies on investments they make while anticipating future cryptocurrency price movements. Researchers can utilize this data to identify correlations between different cryptocurrencies giving a clearer understanding of the market as a whole. By examining patterns and trends in the crypto market one can gain valuable information about its dynamics that could be beneficial for future investments

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How to use the dataset

This dataset provides a great opportunity to explore the cryptocurrency market and its movements. The data contained in this dataset presents key market information - such as prices, market capitalizations and symbols - that can be used in various ways to better understand the performance of different cryptocurrencies.

Here are some tips on how to use this dataset:

• Identify the cryptocurrency with highest or lowest market capitalization: Analyze the total number of coins issued by a particular cryptocurrency, then compare its associated market cap against those from other cryptocurrencies. This will give you a good indication of which cryptos are performing best or worst in terms of total value.

• Compare changes in price between different cryptos: Using our price data, users can identify how one crypto asset has performed over another over a given period of time. This will allow investors to make an informed decision on which asset might offer preferable returns for their portfolios going forward.

• Analyze circulating supply levels: Circulating supply is an important metric when analyzing any asset’s worth – simply put, if there is a higher amount of coins circulating among investors, then it’s likely that its worth will decrease slightly due to increased competition over available coins. Accordingly, observing supply levels can provide powerful insight into which assets may have more upside potential compared to others with similar volumes and prices (or visa-versa).

These are just some general guidelines – we encourage researchers and analysts alike to use this powerful tool however best suits their individual needs!

Research Ideas

• Use this dataset to develop models that predict changes in cryptocurrency prices over time, helping investors better manage their portfolios.
• Analyze trends in the cryptocurrency market to develop strategies for entering and exiting markets or improving the trading process.
• Study the relationships between different cryptocurrencies and explore correlations between them, which could help inform investment decisions or provide a deeper understanding of how cryptocurrencies are related to one another

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Cryptocurrency Historical Data Snapshot.csv | Column name | Description | |:---------------|:-----------------------------------------------------------------| | Name | The name of the cryptocurrency. (String) | | Market Cap | The total market capitalization of the cryptocurrency. (Integer) | | Price | The current price of the cryptocurrency. (Float) | | Symbol | The unique identifier of the cryptocurrency. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

When deciding on how to estimate future prices, due to influences that are likely to affect a product, we should consider two factors: the expected inflation and the real price change. The rate of real price change allows us to plot a trend line based on time series reflecting existing or past market price, that is, on "facts". Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a medium-term trend estimate, which excludes the effects of inflation. Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate. We can get the annual rate of real price change (r) from a linearized model that allows us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n, where: Pn = estimated price in year n P0= price in year 0 r = annual rate of real price change (the real compound rate) n = number of years from year 0

is transformed into that of a straight line by making a change of variables (linearization).

The proposed method is easy to reproduce and seems more orthodox than apply projections made using a simple straight-line model. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. In Figure 1, you can see the differences between these approaches. If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table I). In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.

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The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average

Vegetable and Fruits Prices in India (Year by Year)

Exploring Factors Influencing Price Dynamics of Agricultural Produce

By Raghunath [source]

About this dataset

This dataset provides an in-depth look into the prices of fruits and vegetables in India from 2010 to 2018. With this data, you can gain insight into the ebb and flow of vegetable and fruit prices throughout this period, ultimately uncovering how supply, demand forces, weather changes, or regional events can influence the cost of these items. For anyone looking to research best times for crop cultivation or just better understand India’s agricultural market trends -this dataset is a must have. Be amazed as you witness trends in produce pricing form up right before your eyes! Dig deep with Datesk, Item_Name, and Price columns- they lead you on your way to greater agricultural knowledge!

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How to use the dataset

This dataset is a great source of information for anyone interested in understanding the prices of various fruits and vegetables in India between the years 2010 to 2018. With this dataset, users can gain insights into trends in different markets over time and analyze the changes in prices over a period of 8 years.

The data is provided by Dateksk,Item_Name and Price columns. The Datesk column specifies the unique identifier for each row which also doubles up as an indicator for specific dates. The Item_Name column provides details on which fruit or vegetable pertains to a given record, whereas the Price column provides an insight on what that produce was sold at on that particular date.

Here are some creative ways users can use this dataset:

• Identify key periods throughout the 8 year period where certain produces became significantly more expensive or cheaper – What led to those changes?

• Compare average prices between produces across different months & years – Is there any correlation?

• Locate potential areas/ regions within India where certain produces are most affordable – When should you plan your trips accordingly?

• Analyze seasonal variations within different regions across India – What could be some main factors driving these changes?

Research Ideas

• Predictive analytics to forecast future pricing trends of vegetables and fruits in India.
• Analysis of growing seasons and correlations between fruit/vegetable prices across the country.
• Studying the impact of transportation costs on overall production cost, as transport is a major factor for farmers when selling their produce

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Vegetable and Fruits Prices in India.csv | Column name | Description | |:--------------|:-----------------------------------------| | Datesk | Unique identifier for each day (Integer) | | Item_Name | Name of the item (String) | | Price | Price of the item (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Raghunath.

This dataset contains the predicted prices of the asset We Rise 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.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

This dataset contains the predicted prices of the asset Rise Above the Red 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.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

This dataset contains the predicted prices of the asset Rise of PNUT 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.

Abstract of associated article: I show theoretically that applying the model of Kőszegi and Rabin (2006) to a simple purchasing decision where consumers are ex ante uncertain about the price realisation, gives – when changing the underlying distribution of expected prices – rise to counterintuitive predictions in contrast with a “good deal model” where consumers are predicted to be disappointed (rejoice) when the realised price is perceived as being worse (better) than the other possible realisation. While the underlying ideas of both models are similar with respect to expectation-based reference points, the different results come from the concept of Personal Equilibrium in Kőszegi and Rabin (2006). The experimental results show some support for the simpler good deal model for a number of different real consumption goods though the support is weaker for goods that either have a salient market price or no market price outside of the experiment.

About this dataset

Context

The dataset tabulates the Price township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Price township was 3,741, a 0.38% increase year-by-year from 2021. Previously, in 2021, Price township population was 3,727, an increase of 0.98% compared to a population of 3,691 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price township increased by 1,065. In this period, the peak population was 3,741 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price township is shown in this column.
• Year on Year Change: This column displays the change in Price township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price township Population by Year. You can refer the same here

This dataset contains the predicted prices of the asset Rise Industries 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.

United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data is updated weekly, averaging 0.000 % from Apr 2019 (Median) to 12 May 2025, with 320 observations. The data reached an all-time high of 0.000 % in 12 May 2025 and a record low of 0.000 % in 12 May 2025. United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Personal Consumption Expenditure (PCE) Inflation: Core.

1) Data Introduction • The Twitter Stock Prices Dataset contains stock price data for Twitter from November 2013 to October 2022. This dataset is a time series dataset that provides daily stock trading information. • The key attributes include the stock's opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and volume (Volume).

2) Data Utilization (1) Characteristics of the Twitter Stock Prices Data • This dataset is a time series, offering daily stock price fluctuations and allows tracking of price changes over time. • It includes 7 main attributes related to stock trading, allowing for analysis of price movements (open, high, low, close) and volume, to better understand Twitter’s stock price dynamics. • This data helps analyze market trends, price volatility patterns, and price fluctuation analysis, providing insights into the dynamics of the stock market.

(2) Applications of the Twitter Stock Prices Data • Predictive Modeling: This dataset can be used to develop stock price prediction models, including predicting price increases/decreases or forecasting future stock prices using machine learning models. • Business Insights: Investment experts can use this dataset to evaluate Twitter’s stock performance, and it provides useful information for optimizing investment strategies in response to market changes. This dataset can be used for trend forecasting and investor analysis. • Trend Analysis: By analyzing stock upward/downward trends, this dataset can help evaluate the company's market performance and develop trend-based investment strategies.