For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). Copyright © 2016, S&P Dow Jones Indices LLC. All rights reserved. Reproduction of Home Price Index for Boston, Massachusetts in any form is prohibited except with the prior written permission of S&P Dow Jones Indices LLC "S&P". S&P does not guarantee the accuracy, adequacy, completeness or availability of any information and is not responsible for any errors or omissions, regardless of the cause or for the results obtained from the use of such information. S&P DISCLAIMS ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE. In no event shall S&P be liable for any direct, indirect, special or consequential damages, costs, expenses, legal fees, or losses (including lost income or lost profit and opportunity costs) in connection with subscriber's or others' user of Home Price Index for Boston, Massachusetts. Permission to reproduce this series can be requested from index_services@spdji.com. More contact details are available from http://us.spindices.com/contact-us/, including phone numbers for all of its regional offices.

Estimated using sales prices and appraisal 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/).

All electricity. Average consumer prices are calculated for household fuel, motor fuel, and food items from prices collected for the Consumer Price Index (CPI). Average prices are best used to measure the price level in a particular month, not to measure price change over time. It is more appropriate to use CPI index values for the particular item categories to measure price change. Prices, except for electricity, are collected monthly by BLS representatives in the 75 urban areas priced for the CPI. Electricity prices are collected for the BLS for the same 75 areas on a monthly basis by the Department of Energy using mail questionnaires. All fuel prices include applicable Federal, State, and local taxes; prices for natural gas and electricity also include fuel and purchased gas adjustments. For more information, please visit the Bureau of Labor Statistics (https://www.bls.gov/cpi/factsheets/average-prices.htm).

The All industry total includes all Private industries and Government. Real GDP by metropolitan area is an inflation-adjusted measure of each metropolitan area's gross product that is based on national prices for the goods and services produced within the metropolitan area. For more information about this release go to http://www.bea.gov/newsreleases/regional/gdp_metro/gdp_metro_newsrelease.htm.

The All industry total includes all Private industries and Government. Gross domestic product (GDP) by metropolitan area is the measure of the market value of all final goods and services produced within a metropolitan area in a particular period of time. In concept, an industry's GDP by metropolitan area, referred to as its "value added", is equivalent to its gross output (sales or receipts and other operating income, commodity taxes, and inventory change) minus its intermediate inputs (consumption of goods and services purchased from other U.S. industries or imported). GDP by metropolitan area is the metropolitan area counterpart of the nation's, BEA's featured measure of U.S. production. For more information about this release go to http://www.bea.gov/newsreleases/regional/gdp_metro/gdp_metro_newsrelease.htm.

All observations are July 1 estimates of each year.

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

The median number of days property listings spend on the market in a given geography during the specified month (calculated from list date to closing, pending, or off-market date depending on data availability). 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/).

Estimated using sales prices and appraisal data. Metropolitan Division refers to a county or group of counties within a Metropolitan Statistical Area that has a population core of at least 2.5 million. This series was discontinued because the source uses revised Metropolitan Statistical Areas (MSAs) as announced by the Office of Management and Budget (OMB) on February 28, 2013.

All piped gas. Average consumer prices are calculated for household fuel, motor fuel, and food items from prices collected for the Consumer Price Index (CPI). Average prices are best used to measure the price level in a particular month, not to measure price change over time. It is more appropriate to use CPI index values for the particular item categories to measure price change. Prices, except for electricity, are collected monthly by BLS representatives in the 75 urban areas priced for the CPI. Electricity prices are collected for the BLS for the same 75 areas on a monthly basis by the Department of Energy using mail questionnaires. All fuel prices include applicable Federal, State, and local taxes; prices for natural gas and electricity also include fuel and purchased gas adjustments. For more information, please visit the Bureau of Labor Statistics (https://www.bls.gov/cpi/factsheets/average-prices.htm).

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 '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/BOST625BPPRIV). 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 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/).

This series represents the total number of building permits for 1-unit structure types. The 1-unit structure type is a single-family home or house. A single-family home or house types include fully detached, semidetached (semidetached, side-by-side), row houses, and townhouses. For attached units, each must be separated from the adjacent unit by a ground-to-roof wall in order to be classified as a single-family structure. These units also must not share heating/air-conditioning systems or utilities. Units built on top of one another and those built side-by-side that do not have a ground-to-roof wall and/or have common facilities (i.e., attic, basement, heating plant, plumbing, etc.) are not included under single-family structures. 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/BOST625BP1FH). 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 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 All industry total includes all Private industries and Government. Real GDP by metropolitan area is an inflation-adjusted measure of each metropolitan area's gross product that is based on national prices for the goods and services produced within the metropolitan area. Gross Domestic Product of a given area divided by the resident population of the area. For more information about this release go to http://www.bea.gov/newsreleases/regional/gdp_metro/gdp_metro_newsrelease.htm.

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 United States Office of Management and Budget (OMB) delineates metropolitan and micropolitan statistical areas according to published standards that are applied to Census Bureau data. The boundaries of statistical areas are periodically revised and are subject to change. For additional information, see here (https://www.census.gov/programs-surveys/metro-micro.html)

This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

All unleaded regular gasoline. Average consumer prices are calculated for household fuel, motor fuel, and food items from prices collected for the Consumer Price Index (CPI). Average prices are best used to measure the price level in a particular month, not to measure price change over time. It is more appropriate to use CPI index values for the particular item categories to measure price change. Prices, except for electricity, are collected monthly by BLS representatives in the 75 urban areas priced for the CPI. Electricity prices are collected for the BLS for the same 75 areas on a monthly basis by the Department of Energy using mail questionnaires. All fuel prices include applicable Federal, State, and local taxes; prices for natural gas and electricity also include fuel and purchased gas adjustments. For more information, please visit the Bureau of Labor Statistics (https://www.bls.gov/cpi/factsheets/average-prices.htm).

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/SMU25716546054120001). 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 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/).

Average weekly wages are the wages paid by unemployment insurance covered employers during the calendar quarter, regardless of when the services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). 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. In some cases, the NSA data will be updated but the SA data will not be updated. The reason is usually that the data series has not accumulated enough new seasonal factors to trigger an adjustment. The NSA series can be located here (https://fred.stlouisfed.org/series/ENUC144640010) The FRED team is currently working on a new procedure to replace SA data that has not yet be updated with NSA data that has been updated. 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 data were retrieved from the BLS API on the "Updated" date referenced above the graph. BLS.gov cannot vouch for the data or analyses derived from these data after the data have been retrieved from BLS.gov. "

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

For questions on the data, please contact the data source (https://www.federalreserve.gov/apps/ContactUs/feedback.aspx?refurl=/releases/h41/%). For questions on FRED functionality, please contact us here (https://fred.stlouisfed.org/contactus/).

Regional price parities (RPPs) are regional price levels expressed as a percentage of the overall national price level for a given year. The price levels are determined by the average prices paid by consumers for the mix of goods and services consumed in each region. Taking the ratio of RPPs shows the difference in price levels across regions. The term "all items" refers to all the detailed consumption goods and services used in the estimation of the RPPs. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

Real personal income is personal income at RPPs divided by the national PCE price index. It is estimated for states, state metro/nonmetro portions, metropolitan statistical areas, and the combined nonmetropolitan portion of the United States. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

Real per capita personal income is the real personal income divided by midyear population. Real personal income is personal income at RPPs divided by the national PCE price index. It is estimated for states, state metro/nonmetro portions, metropolitan statistical areas, and the combined nonmetropolitan portion of the United States. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

The economic activity index measures average economic growth in the metropolitan area. It is computed using a dynamic factor model that includes 12 variables measuring various aspects of economic activity in the MSA. The index is calibrated to Gross Metropolitan Product (GMP) growth and variance to allow for comparison across metro areas. For additional details, see Arias, M.A., C.S. Gascon and D.E. Rapach (2016), “Metro Business Cycles,” Journal of Urban Economics 94, 90-108, http://www.sciencedirect.com/science/article/pii/S009411901630016X. Federal Reserve Bank of St. Louis Working Paper 2014-046C, https://research.stlouisfed.org/wp/more/2014-046.

Indeed calculates the index change in seasonally-adjusted job postings since February 1, 2020, the pre-pandemic baseline. Indeed seasonally adjusts each series based on historical patterns in 2017, 2018, and 2019. Each series, including the national trend, occupational sectors, and sub-national geographies, is seasonally adjusted separately. Indeed switched to this new methodology in December 2022 and now reports all historical data using this new methodology. Historical numbers have been revised and may differ significantly from originally reported values. The new methodology applies a detrended seasonal adjustment factor to the index change in job postings. For more information, see Frequently Asked Questions (https://www.hiringlab.org/indeed-data-faq/) regarding Indeed Data. Copyrighted: Pre-approval required. Contact Indeed to request permission to use the data at their contact information provided here (https://github.com/hiring-lab/data#readme). End Users are excluded of any warranty and liability on the part of Indeed for the accuracy of the Indeed Data. End Users will refrain from any external distribution of Indeed Data except in oral or written presentations, provided that such portions or derivations are incidental to and supportive of such presentations and, provided further that the End Users shall not distribute or disseminate in such presentations any amount of Indeed Data which could cause such presentations to be susceptible to use substantially as a source of, or substitute for Indeed Data. End Users agree to credit Indeed as the source and owner of the Indeed Data when making it available to third parties in any permissible manner as well as in internal use. End Users agree to not sell or otherwise provide the Indeed Data obtained from Licensee to third parties.

The All industry total includes all Private industries and Government. A chained-type index is based on the linking (chaining) of indexes to create a time series. Annual chained-type Fisher indices are used in BEA's national income and product accounts (NIPAs) whereby Fisher ideal price indices are calculated using the weights of adjacent years. Those annual changes are then multiplied (chained) together, forming the chained-type index time series. Chain-type indexes or chain-dollar estimates are used when you want to show how output or spending has changed over time. The percent changes in quantity indexes exactly match the percent changes in chained dollars, so they can be used interchangeably for making comparisons. Real estimates remove the effects of price changes, which can obscure changes in output or spending in current dollars. For more information about this release go to http://www.bea.gov/newsreleases/regional/gdp_metro/gdp_metro_newsrelease.htm.