West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Around 64 percent of U.S. consumers spend less on non-essentials amidst the ongoing cost of living crisis in 2023. This is according to a survey conducted by We are Social and Statista Q, which shows that rising inflation rates have caused around a similar percentage of customers to pay more attention to bargains, good deals, or offers (when going shopping). Furthermore, around 39 percent of U.S. consumers do not go out for dinner/lunch anymore to deal with the situation.

The cost of living is spiraling. Prices are going up, household expenses are rising, and the U.S. inflation rate reached a 40-year record high in 2023. Many consumers are looking for new ways to deal with this situation and refer to social media for support. So, which social media platforms have the most helpful content to deal with the current cost of living crisis in the U.S.? According to an exclusive survey by We Are Social and Statista Q, around 61 percent of TikTok users in the United States find helpful content there. Coming on number second is YouTube, as 56 percent of YouTube users find life hacks, tricks, money saving tips and other suitable advice to deal with inflation in 2023.

According to an April 2023 survey by We Are Social and Statista Q, 40 percent of U.S. consumers feel highly affected by the ongoing cost of living crisis, whereas only 6 percent don't feel affected at all.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

In October 2024, approximately 39 percent of people in the Republic of Ireland thought that inflation / the rising cost of living was one the two most important issues facing the country. This was down from 65 percent in July 2022, and 55 percent in November 2023.

🏡 Cost of Living in Nairobi, Kenya

📌 Overview

This dataset provides a detailed time-series estimate of the monthly cost of living across 20 different areas in Nairobi, Kenya from 2019 to 2024. It covers essential expenses such as rent, food, transport, utilities, and miscellaneous costs, allowing for comprehensive cost-of-living analysis.

This dataset is useful for:
✅ Individuals planning to move to Nairobi
✅ Researchers analyzing long-term cost trends
✅ Businesses assessing salary benchmarks based on inflation
✅ Data scientists developing predictive models for cost forecasting

📊 Data Summary

• Total Records: 60,000 (5 years of monthly data)
• Columns:
  • 🏠 Area: The residential area in Nairobi
  • 💰 Rent: Estimated monthly rent (KES)
  • 🍽️ Food: Grocery and dining expenses (KES)
  • 🚕 Transport: Public and private transport costs (KES)
  • ⚡ Utilities: Water, electricity, and internet bills (KES)
  • 🎭 Misc: Entertainment, personal care, and leisure expenses (KES)
  • 🏷️ Total: Sum of all expenses
  • 📆 Date: Monthly timestamp from January 2019 to December 2024

📍 Areas Covered

This dataset provides cost estimates for 20+ residential areas, including:
- High-End Areas 🏡: Kileleshwa, Westlands, Karen
- Mid-Range Areas 🏙️: South B, Langata, Ruaka
- Affordable Areas 🏠: Embakasi, Kasarani, Githurai, Ruiru, Umoja
- Satellite Towns 🌿: Ngong, Rongai, Thika, Kitengela, Kikuyu

🛠️ How the Data Was Generated

This dataset was synthetically generated using Python, incorporating realistic market variations. The process includes:

✔ Inflation Modeling 📈 – A 2% annual increase in costs over time.
✔ Seasonal Effects 📅 – Higher food and transport costs in December & January (holiday season), rent spikes in June & July.
✔ Economic Shocks ⚠️ – A 5% chance per record of external economic effects (e.g., fuel price hikes, supply chain issues).
✔ Random Fluctuations 🔄 – Expenses vary slightly month-to-month to simulate real-world spending behavior.

🔍 Potential Use Cases

• 📊 Cost of Living Analysis – Compare affordability across different Nairobi areas.
• 💵 Salary & Real Estate Benchmarking – Businesses can analyze salary expectations by location.
• 📉 Time-Series Forecasting – Train predictive models (ARIMA, Prophet, LSTM) to estimate future living costs.
• 📈 Inflation Impact Studies – Measure how economic conditions influence cost variations over time.

⚠️ Limitations

• Synthetic Data – The dataset is not based on real survey data but follows market trends.
• No Lifestyle Adjustments – Differences in household size or spending habits are not factored in.
• Inflation Approximation – While inflation is simulated at 2% annually, actual inflation rates may differ.

📁 File Format & Access

• nairobi_cost_of_living_time_series.csv – 60,000 records in CSV format (time-series structured).

📢 Acknowledgments

This dataset was generated for research and educational purposes. If you find it useful, consider citing it in your work. 🚀

📥 Download and Explore the Data Now!

This updated version makes your documentation more detailed and actionable for users interested in forecasting and economic analysis. Would you like help building a cost prediction model? 🚀

Approximately 81 percent of people in the Republic of Ireland thought that the state of the global economy was the main contributing factor to the rising cost of living in the country. By contrast, just 49 percent of people in Ireland believed that workers demanding pay rises was the main reason.

Explore the impact of inflation in Japan on living costs, with households and businesses facing increasing financial pressures amid rising prices for essentials.

Monthly indexes and percentage changes for all components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

In March 2025, 66 percent of households in Great Britain reported that their cost of living had increased in the previous month, compared with 45 percent in July 2024. Although the share of people reporting a cost of living increase has generally been falling since August 2022, when 91 percent of households reported an increase, the most recent figures indicate that the Cost of Living Crisis is still ongoing for many households in the UK. Crisis ligers even as inflation falls Although various factors have been driving the Cost of Living Crisis in Britain, high inflation has undoubtedly been one of the main factors. After several years of relatively low inflation, the CPI inflation rate shot up from 2021 onwards, hitting a high of 11.1 percent in October 2022. In the months since that peak, inflation has fallen to more usual levels, and was 2.5 percent in December 2024, slightly up from 1.7 percent in September. Since June 2023, wages have also started to grow at a faster rate than inflation, albeit after a long period where average wages were falling relative to overall price increases. Economy continues to be the main issue for voters Ahead of the last UK general election, the economy was consistently selected as the main issue for voters for several months. Although the Conservative Party was seen by voters as the best party for handling the economy before October 2022, this perception collapsed following the market's reaction to Liz Truss' mini-budget. Even after changing their leader from Truss to Rishi Sunak, the Conservatives continued to fall in the polls, and would go onto lose the election decisively. Since the election, the economy remains the most important issue in the UK, although it was only slightly ahead of immigration and health as of January 2025.

The study of Jürgen Nautz deals with selected aspects of tariff autonomy and wage development during the years of inflation in the Weimar Republic. First the development of wages will be presented in the context of cost of living. To investigate the question of tariff autonomy in the inflation period it is of special interest to analyze the usage of arbitration instruments by unions, management and the state. Another central subject of this study is the fundamental position concerning the question of the design of important relations. Two themes are in the focus of interest; the ideas of the further refinement of the collective bargaining principle and the arbitration of labor disputes.Especially concerning tariff autonomy legal positions were developed during the inflation years which had an important impact on the discussion about tariff autonomy during the entire period the Weimar Republic. Data tables in HISTAT:A.1 Development of cost of living: Index of the statistical office of the German Empire (1920-1923)A.2 Index of average real weekly wages per collective agreement Index (1913-1923)A.3 Real weekly and real hourly wages of unskilled and skilled workers (1919-1923)A.4 Strikes and lockouts (1918-1924) A.5 Number of collective agreements (1918-1929)

In August 2024, the consumer price index for personal expenses in Brazil increased by 0.25 percent compared to the previous month. Since August 2020, the CPI for personal expenses has remained with a positive monthly variation rate.

Inflation Rate in Japan remained unchanged at 3.60 percent in April. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Consumer Price Index (IPC-FIPE): Smoothed: Median data was reported at 3.910 % in 28 Jun 2019. This records an increase from the previous number of 3.830 % for 27 Jun 2019. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Consumer Price Index (IPC-FIPE): Smoothed: Median data is updated daily, averaging 4.850 % from Dec 2001 (Median) to 28 Jun 2019, with 4406 observations. The data reached an all-time high of 11.020 % in 13 Dec 2002 and a record low of 3.360 % in 06 Aug 2007. Brazil Market Expectation: Inflation: Accumulated Over Next 12 Months: Consumer Price Index (IPC-FIPE): Smoothed: Median data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA036: Market Expectation: Inflation: Accumulated Over Next 12 Months: Consumer Price Index (IPC-FIPE): Smoothed. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Researched in the city of São Paulo, reflects the cost of living of families with income from 1 to 20 minimum wages.

Inflation Rate in India decreased to 3.16 percent in April from 3.34 percent in March of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Germany inflation rate for 2023 was 5.95%, a 0.93% decline from 2022.

<ul style='margin-top:20px;'>

<li>Germany inflation rate for 2022 was <strong>6.87%</strong>, a <strong>3.81% increase</strong> from 2021.</li>
<li>Germany inflation rate for 2021 was <strong>3.07%</strong>, a <strong>2.92% increase</strong> from 2020.</li>
<li>Germany inflation rate for 2020 was <strong>0.14%</strong>, a <strong>1.3% decline</strong> from 2019.</li>
</ul>Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.

Inflation Rate in Thailand decreased to -0.57 percent in May from -0.22 percent in April of 2025. This dataset provides the latest reported value for - Thailand Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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. 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 and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is