Graph and download economic data for All-Transactions House Price Index for Great Falls, MT (MSA) (ATNHPIUS24500Q) from Q4 1992 to Q1 2025 about Great Falls, MT, appraisers, HPI, housing, price index, indexes, price, and USA.

In 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

Graph and download economic data for All-Transactions House Price Index for Wichita Falls, TX (MSA) (ATNHPIUS48660Q) from Q3 1986 to Q1 2025 about Wichita Falls, appraisers, HPI, TX, housing, price index, indexes, price, and USA.

Falls Church City, VA - All-Transactions House Price Index for Falls Church city, VA was 344.62000 Index 2000=100 in January of 2024, according to the United States Federal Reserve. Historically, Falls Church City, VA - All-Transactions House Price Index for Falls Church city, VA reached a record high of 344.62000 in January of 2024 and a record low of 18.58000 in January of 1976. Trading Economics provides the current actual value, an historical data chart and related indicators for Falls Church City, VA - All-Transactions House Price Index for Falls Church city, VA - last updated from the United States Federal Reserve on July of 2025.

All-Transactions House Price Index for Twin Falls County, ID was 328.62000 Index 2000=100 in January of 2024, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Twin Falls County, ID reached a record high of 328.62000 in January of 2024 and a record low of 40.93000 in January of 1981. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Twin Falls County, ID - last updated from the United States Federal Reserve on July of 2025.

All-Transactions House Price Index for Fall River County, SD was 363.41000 Index 2000=100 in January of 2024, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Fall River County, SD reached a record high of 363.41000 in January of 2024 and a record low of 91.16000 in January of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Fall River County, SD - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for All-Transactions House Price Index for Idaho Falls, ID (MSA) (ATNHPIUS26820Q) from Q2 1986 to Q1 2025 about Idaho Falls, ID, appraisers, HPI, housing, price index, indexes, price, and USA.

All-Transactions House Price Index for Great Falls, MT (MSA) was 401.98000 Index 1995 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Great Falls, MT (MSA) reached a record high of 410.73000 in October of 2024 and a record low of 88.62000 in October of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Great Falls, MT (MSA) - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for All-Transactions House Price Index for Glens Falls, NY (MSA) (ATNHPIUS24020Q) from Q2 1991 to Q1 2025 about Glens Falls, appraisers, NY, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for All-Transactions House Price Index for Jefferson County, ID (ATNHPIUS16051A) from 1991 to 2024 about Jefferson County, ID; Idaho Falls; ID; HPI; housing; price index; indexes; price; and USA.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

Graph and download economic data for All-Transactions House Price Index for Waterloo-Cedar Falls, IA (MSA) (ATNHPIUS47940Q) from Q3 1980 to Q1 2025 about Waterloo, IA, appraisers, HPI, housing, price index, indexes, price, and USA.

The CoStar Commercial Repeat-Sales Index (CCRSI) for office real estate in the United States started to fall in 2022, after more than a decade of steady growth. The index measures the development of sales prices of office properties with 2000 chosen as a base year. An index value of *** means that sales prices have doubled since 2000. In March 2024, the value-weighed index, which is more representative of the high-value deals in core markets, hit *** index points, down from a market peak of *** in December 2021. The equal-weighed index is more influenced by the lower-priced deals that comprise the higher share of transactions. It stood at *** index points in March 2024, down from a market peak of *** in June 2022.

All-Transactions House Price Index for Idaho Falls, ID (MSA) was 420.20000 Index 1995 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Idaho Falls, ID (MSA) reached a record high of 434.05000 in October of 2024 and a record low of 76.33000 in April of 1986. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Idaho Falls, ID (MSA) - last updated from the United States Federal Reserve on July of 2025.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

In March 2024, the agricultural input price index in Israel fell to around *** points. The price of inputs in the country's agricultural sector fell by over * percent in October 2023. In the months following the Israel-Hamas war, which started on October 7, 2023, prices decreased by *** percent.

All-Transactions House Price Index for Wichita Falls, TX (MSA) was 310.35000 Index 1995 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Wichita Falls, TX (MSA) reached a record high of 310.35000 in January of 2025 and a record low of 79.98000 in April of 1989. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Wichita Falls, TX (MSA) - last updated from the United States Federal Reserve on July of 2025.

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
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Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

Raw materials price index (RMPI) by North American Product Classification System (NAPCS) 2017 Version 2.0. Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (202001=100).

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes