Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

Graph and download economic data for NBER based Recession Indicators for the United States from the Period following the Peak through the Trough (USREC) from Dec 1854 to Jul 2025 about peak, trough, recession indicators, and USA.

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

United States Recession Probability data was reported at 14.120 % in Oct 2019. This records a decrease from the previous number of 14.505 % for Sep 2019. United States Recession Probability data is updated monthly, averaging 7.668 % from Jan 1960 (Median) to Oct 2019, with 718 observations. The data reached an all-time high of 95.405 % in Dec 1981 and a record low of 0.080 % in Sep 1983. United States Recession Probability data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

United States NBER: Recorded Recession data was reported at 0.000 Unit in Oct 2018. This stayed constant from the previous number of 0.000 Unit for Sep 2018. United States NBER: Recorded Recession data is updated monthly, averaging 0.000 Unit from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 1.000 Unit in Jun 2009 and a record low of 0.000 Unit in Oct 2018. United States NBER: Recorded Recession data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability. An interpretation of US Business Cycle Expansions and Contractions data provided by The National Bureau of Economic Research (NBER). A value of 1 is a recessionary period, while a value of 0 is an expansionary period.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

United States FRB Recession Risk data was reported at 0.178 % in Apr 2025. This records a decrease from the previous number of 0.192 % for Mar 2025. United States FRB Recession Risk data is updated monthly, averaging 0.193 % from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 1.000 % in Oct 2008 and a record low of 0.022 % in Jul 2003. United States FRB Recession Risk data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

Abstract (en): In 1995-1996, the MacArthur Midlife Research Network carried out a national survey of over 7,000 Americans aged 25 to 74 [ICPSR 2760]. The purpose of the study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. The study was innovative for its broad scientific scope, its diverse samples (which included siblings of the main sample respondents and a national sample of twin pairs), and its creative use of in-depth assessments in key areas (e.g. daily diary of stressful experiences [ICPSR 3725] and cognitive functioning [ICPSR 3596]) on a subset of participants. A detailed description of the study and findings generated by it are available at: http://www.midus.wisc.edu With support from the National Institute on Aging, a follow-up of the original Midlife Development in the United States (MIDUS) sample was conducted in 2004 (MIDUS 2 [ICPSR 4652]). The daily stress and cognitive functioning projects were repeated and expanded at MIDUS 2; in addition the protocol was expanded to include biomarkers and neuroscience. In 2013 a third wave (MIDUS 3) of survey data was collected on longitudinal participants. Data collection for this follow-up wave largely repeated baseline assessments (e.g., phone interview and extensive self-administered questionnaire), with additional questions in selected areas such as economic recession experiences. Cognitive functioning data were also collected at the same time, while data collection for the daily diary, biomarker, and neuroscience projects commenced in 2017. MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required. The Aggregate Data dataset contains 2,613 variables and 3,294 cases. This dataset includes information about the following types of variables: recession experience, health, education, occupation, marital status, household roster, children, caregiving, living arrangements, race and ethnicity, life satisfaction, health insurance, personal beliefs, finances, community involvement, neighborhood, social networks, sexuality, religion and spirituality, discrimination, childhood family background, images of life change, and psychological turning points. The Disposition Codes dataset contains 6 variables and 7,108 cases. This dataset contains final disposition codes for the M3 phone interview. The Coded Text Data dataset contains 183 variables and 3,137 cases. This dataset contains coded text responses to open-ended questions and those which had "Other-specify" responses. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: For information regarding scales, please refer to the Psychosocial Constructs and Composite Variables. Response Rates: 77 percent of living longitudinal participants completed the M3 phone survey. Details of fielding and final disposition codes for Phone, SAQ, as well as Cognitive data collection projects can be found in the Field Report. Datasets:DS0: Study-Level FilesDS1: Aggregate DataDS2: Disposition CodesDS3: Coded Text DataDS4: National Survey of Midlife Development in the United States (MIDUS 3), 2013-2014, Coded Text Data The noninstitutionalized, English-speaking population of the United States. Smallest Geographic Unit: none Only living respondents who completed the M2 phone interview were eligible for participation in the M3 survey. 2019-04-30 This collection has been updated to include new data supplied by the P.I. The resupplied data includes new cases for the Self-Administered Questionnaire (SAQ) as part of the MIDUS M3 re-fielding effort undertaken to increase participant completion during 2015 fielding. The re-fielding cases can be identified and/or filtered with C1STATUS or the new M3RE_FILTER variables. Ten post-stratification weight variables (C1PWGHT1 through C1PWGHT10), and a new occupation variable based on Standard Occupation Classification system, have b...

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.