Estimated using sales prices and appraisal data.

Data Are Values Of A Fine Ounce At The Average Quotation. The Prices Are The Average Of Bar Silver In London, Per Ounce British Standard (.925 Fine), In Equivalent United States Gold Coin, Of An Ounce 1.000 Fine, Taken At The Average Price And Par Of Exchange. The London Price Is Given In Depreciated Currency After September 21, 1931. The United States Was Off The Gold Standard On And After April 19, 1933. Figure For 1933 Is For The 1932-33 Fiscal Year. Source: Annual Report Of The Directors Of The Mint, 1933, Pp.86-87. This NBER data series a04018 appears on the NBER website in Chapter 4 at http://www.nber.org/databases/macrohistory/contents/chapter04.html. NBER Indicator: a04018

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU24435240500000002). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU24435240500000002) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435240500000002). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

For more information about this series, see: XAU (https://indexes.nasdaq.com/Index/Overview/XAU) Copyright © 2025, NASDAQ, Inc.

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

The American Community Survey (ACS) and the Puerto Rico Community Survey (PRCS) ask respondents age 1 year and over whether they lived in the same residence 1 year ago. For people who lived in a different residence, the location of their previous residence is collected. ACS uses a series of monthly samples to produce estimates. The 5-year dataset is used for the county-to-county migration flows since many counties have a population less than 20,000. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435245500000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

For more information, please see the Import/Export Price Indexes web site at https://www.bls.gov/mxp/

explanatory notes (https://www.federalreserve.gov/releases/g17/About.htm) issued by the Board of Governors. For recent updates, see the announcements (https://www.federalreserve.gov/feeds/g17.html) issued by the Board of Governors.</p>

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Estimate of educational attainment for population 18 years old and over using 5 years of data. The percent of the population who is a High School Graduate or Higher includes people whose highest degree was a high school diploma or its equivalent, people who attended college but did not receive a degree, and people who received an associate's, bachelor's, master's, or professional or doctorate degree. People who reported completing 12th grade but not receiving a diploma are not included. (ACS variable S1501_C02_014E from table S1501.) For more information about the subject definitions, see: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimates include data collected over a 60-month period. The date associated with the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, the value does not describe any specific day, month, or year within that time period. Multiyear estimates require some additional considerations. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see the ACS handbook (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates," p. 13) for a comprehensive set of details and clarifications: https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Data for "Resident Population" are estimates as of July 1. Data for 1970, 1980, 1990, and 2000 are annual census. Population estimates are updated annually using current data on births, deaths, and migration to calculate population change since the most recent decennial census. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. Each vintage of estimates includes all years since the most recent decennial census.

The percentage of population below the poverty level comes from American Community Survey (ACS) variable S1701_C03_001E in table S1701. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Estimate of educational attainment for population 25 years old and over using 5 years of data. The percent of the population who has completed an Associate's degree or higher is calculated by FRED by adding the following variables from the 5-year American Community Survey (ACS) percent of the population with an Associate's Degree, percent of the population with a Bachelor's degree, and percent of the population with a Graduate or Professional degree. (ACS variables S1501_C02_011E, S1501_C02_012E, S1501_C02_013E from table S1501.) For more information about the subject definitions, see: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimates include data collected over a 60-month period. The date associated with the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, the value does not describe any specific day, month, or year within that time period. Multiyear estimates require some additional considerations. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see the ACS handbook (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates," p. 13) for a comprehensive set of details and clarifications: https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Estimate of educational attainment for population 18 years old and over whose highest degree was a bachelor’s, master’s, or professional or doctorate degree. (ACS variable S1501_C02_015E from table S1501.) For more information about the subject definitions, see: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimates include data collected over a 60-month period. The date associated with the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, the value does not describe any specific day, month, or year within that time period. Multiyear estimates require some additional considerations. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see the ACS handbook (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates," p. 13) for a comprehensive set of details and clarifications: https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.

The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records.

Personal income is the income that is received by persons from all sources. It is calculated as the sum of wages and salaries, supplements to wages and salaries, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, less contributions for government social insurance. This measure of income is calculated as the personal income of the residents of a given area divided by the resident population of the area. In computing per capita personal income, BEA uses the Census Bureau's annual midyear population estimates.

The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records.

This series represents the total number of building permits for all structure types. Structure types include 1-unit, 2-unit, 3-unit, 4-unit, and 5-unit or more.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU24435240500000003). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU24435240500000003) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435240500000003). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU24435240500000011). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU24435240500000011) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435240500000011). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435247072200001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435244245200001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The count of listings which have had their price reduced in a given market during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of listings which have had their price increased in a given market during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of listings which have had their price reduced in a given market during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median listing price in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median listing price per square foot in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435246562110001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24435245552200001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The count of listings which have had their price reduced in a given market during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of pending listings in a given market during the specified month, if a pending definition is available for that geography. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median listing price per square foot in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of new listings added to the market in a given geography during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of pending listings in a given market during the specified month, if a pending definition is available for that geography. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median listing price per square foot in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of new listings added to the market in a given geography during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median home size in square feet for listings in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of pending listings in a given market during the specified month, if a pending definition is available for that geography. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of new listings added to the market in a given geography during the month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings). With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).