Year: The year of the observation.

Month: The month of the observation.

Interest Rate: The prevailing interest rate for the given month.

Unemployment Rate: The unemployment rate in percentage terms for that time period.

Index Price: A synthetic stock market index price representing overall market trends.

United States Import Value Index data was reported at 126.779 2015=100 in 2021. This records an increase from the previous number of 103.958 2015=100 for 2020. United States Import Value Index data is updated yearly, averaging 51.384 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 126.779 2015=100 in 2021 and a record low of 11.319 2015=100 in 1982. United States Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

United States Export Value Index data was reported at 116.753 2015=100 in 2021. This records an increase from the previous number of 94.833 2015=100 for 2020. United States Export Value Index data is updated yearly, averaging 47.271 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 116.753 2015=100 in 2021 and a record low of 15.076 2015=100 in 1983. United States Export Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's export value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

The NYSE Health Care Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the health care industry. Between January 2004 and February 2025, the NYSE Health Care Index increased overall and reached a value of *********.

The NYSE U.S. Large Cap Equal Weight Index tracks the performance of the largest *** highly capitalized companies listed on U.S. stock exchanges. Between November 2001 and September 2024, the index fluctuated but increased overall, reaching a value of ********* index points.

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

Libya LY: Import Value Index data was reported at 284.030 2000=100 in 2016. This records a decrease from the previous number of 348.339 2000=100 for 2015. Libya LY: Import Value Index data is updated yearly, averaging 152.463 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 723.473 2000=100 in 2013 and a record low of 100.000 2000=100 in 2000. Libya LY: Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Libya – Table LY.World Bank: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2000). UNCTAD's import value indexes are reported for most economies. For selected economies for which UNCTAD does not publish data, the import value indexes are derived from import volume indexes (line 73) and corresponding unit value indexes of imports (line 75) in the IMF's International Financial Statistics.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

The NYSE Energy Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the energy industry by market capitalization. Between January 2004 and February 2025, the index fluctuated significantly and reached its highest value in May 2008, with a value of *********, whereas it reached its lowest value in October 2020, at ******** index points. As of February 2025, the value of the NYSE Energy Index amounted to ********* index points.

The topographic index is a hydrological quantity describing the propensity of the soil at landscape points to become saturated with water as a result of topographic position (i.e. not accounting for other factors such as climate that also affect soil moisture but are accounted for separately). Modern land surface models require a characterisation of the land surface hydrological regime and this parameter allows the use of the TOPMODEL hydrological model to achieve this .This Geographic Information System layer is intended for use as topographic ancillary files for the TOPMODEL routing model option within the Joint UK Land Environment Simulator (JULES) land surface model. The topographic index variable here is directly comparable to the compound topographic index available from United States Geological Survey's Hydro1K at 30 sec resolution. PLEASE NOTE: This dataset is a correction to a previous version which was found to contain errors ( https://doi.org/10.5285/ce391488-1b3c-4f82-9289-4beb8b8aa7da ). In the previous version all pixels north of 4.57 degrees south were shifted consistently 9.3 km to the west. This version is correctly aligned at all points. Full details about this dataset can be found at https://doi.org/10.5285/6b0c4358-2bf3-4924-aa8f-793d468b92be

This dataset provides values for INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Unidad Indexada (UI) is a unique monetary unit in Uruguay that serves as a hedge against inflation. Introduced in 2003, it is tied to the country's Consumer Price Index (CPI). The UI's value is adjusted daily based on inflation rates, making it a reliable store of value. It is commonly used for long-term contracts, such as real estate transactions and loans, providing stability and protecting borrowers from inflation risks. The UI has been successful in reducing financial uncertainty, allowing individuals and businesses to plan and budget effectively in an inflationary environment, thus contributing to Uruguay's economic stability.

The source is the National Institute of Statistics. The link is: https://www.gub.uy/instituto-nacional-estadistica/datos-y-estadisticas/estadisticas/series-historicas-ui

The name of the file is: Unidad Indexada (06/2002 - )

Graph and download economic data for Value Added by Gross Domestic Product (Chain-Type Price Index) (VAPIAI) from Q1 2005 to Q2 2025 about value added, chained, GDP, price index, indexes, price, and USA.

The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receives, on a monthly basis, Q indices from Sodankyla. These data are available from July 1957 to the present. Sodankyla indices are the most complete records of Q indices that NCEI has. The archive also contains Q indices from 15 other observatories. The Q index is a measure of geomagnetic activity assigned by high latitude (geomagnetic latitude > 58 degrees) geomagnetic observatories for each 15 minute interval. The index is designed to study auroral and ionospheric phenomena over a time scale smaller than that possible with KP. The Q scale is loosely logarithmic, with possible values from 0 to 11.

Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to 2024.

Export value index (2000 = 100) in Japan was reported at 115 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Export value index (2000 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Graph and download economic data for ICE BofA Public Sector Issuers Emerging Markets Corporate Plus Index Total Return Index Value (BAMLEMPBPUBSICRPITRIV) from 1998-12-31 to 2025-11-06 about sub-index, return, emerging markets, public, sector, corporate, indexes, and USA.

Graph and download economic data for ICE BofA Public Sector Issuers US Emerging Markets Liquid Corporate Plus Index Total Return Index Value (BAMLEMPUPUBSLCRPIUSTRIV) from 2003-12-31 to 2025-12-01 about sub-index, return, emerging markets, public, liquidity, sector, corporate, indexes, and USA.

Graph and download economic data for ICE BofA US Emerging Markets Corporate Plus Index Total Return Index Value (BAMLEMUBCRPIUSTRIV) from 1998-12-31 to 2025-11-06 about sub-index, return, emerging markets, corporate, indexes, and USA.

Index Time Series for Vanguard Small-Cap Value Index Fund ETF Shares. The frequency of the observation is daily. Moving average series are also typically included. The fund advisor employs an indexing investment approach designed to track the performance of the CRSP US Small Cap Value Index, a broadly diversified index of value stocks of small U.S. companies. The advisor attempts to replicate the target index by investing all, or substantially all, of its assets in the stocks that make up the index, holding each stock in approximately the same proportion as its weighting in the index.

In the period from April 2020 to April 2025, the reported index value development of sales turnover in cosmetics has fluctuated significantly. Sales of cosmetics was reported to have an index value of ** in April 2020, before hitting an all-time high with a value of ***** in December 2024. As of April 2025, the index value of cosmetics in Norway was *****.