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.

Cost of Living Index by Country, 2024 Mid Year data Data scraped from Numbeo: www.numbeo.com/cost-of-living/rankings_by_country.jsp All credits to Numbeo: www.numbeo.com/cost-of-living/

An index of 100 reflects the same living cost as in New York City, United States. As of 2024 Mid Year data, in NYC, A family of four estimated monthly costs are $6,074.40 without rent. A single person's estimated monthly costs are $1,640.90 without rent.

In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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This dataset contains data on monthly cost of living index in Estonia in 1919-1939.

Dataset "Monthly Cost of Living Index in Estonia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

"Cost of living and purchasing power related to average income

We adjusted the average cost of living inside the USA (based on 2021 and 2022) to an index of 100. All other countries are related to this index. Therefore with an index of e.g. 80, the usual expenses in another country are 20% less then in the United States.

The monthly income (please do not confuse this with a wage or salary) is calculated from the gross national income per capita.

The calculated purchasing power index is again based on a value of 100 for the United States. If it is higher, people can afford more based on the cost of living in relation to income. If it is lower, the population is less wealthy.

The example of Switzerland: With a cost of living index of 142 all goods are on average about 42% more expensive than in the USA. But the average income in Switzerland of 7,550 USD is also 28% higher, which means that citizens can also afford more goods. Now you calculate the 42% higher costs against the 28% higher income. In the result, people in Switzerland can afford about 10 percent less than a US citizen."

Source: https://www.worlddata.info/cost-of-living.php

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.

We adjust SNAP maximum allotments, deductions, and income eligibility standards at the beginning of each Federal fiscal year. The changes are based on changes in the cost of living. COLAs take effect on October 1 each year. Maximum allotments are calculated from the cost of a market basket based on the Thrifty Food Plan for a family of four, priced in June that year. The maximum allotments for households larger and smaller than four persons are determined using formulas that account for economies of scale. Smaller households get slightly more per person than the four-person household. Larger households get slightly less. Income eligibility standards are set by law. Gross monthly income limits are set at 130 percent of the poverty level for the household size. Net monthly income limits are set at 100 percent of poverty.

This dataset contains data on the monthly cost of living in Lithuania in 1913 and 1919-1939.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Lake County, IL (MWACL17097) from 2009 to 2023 about Lake County, IL; Chicago; adjusted; IL; average; wages; real; and USA.

Living Cost: Average per Month: SF: Republic of Kalmykia data was reported at 10,443.000 RUB in Dec 2020. This records a decrease from the previous number of 10,584.000 RUB for Sep 2020. Living Cost: Average per Month: SF: Republic of Kalmykia data is updated quarterly, averaging 5,341.000 RUB from Dec 2001 (Median) to Dec 2020, with 77 observations. The data reached an all-time high of 10,631.000 RUB in Jun 2020 and a record low of 1,399.000 RUB in Dec 2001. Living Cost: Average per Month: SF: Republic of Kalmykia data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Turkey Cost of Living Index: 63=100: Istanbul: Monthly data was reported at 517,132,174.700 1963=100 in Oct 2018. This records an increase from the previous number of 509,020,201.200 1963=100 for Sep 2018. Turkey Cost of Living Index: 63=100: Istanbul: Monthly data is updated monthly, averaging 202,839.100 1963=100 from Jan 1964 (Median) to Oct 2018, with 658 observations. The data reached an all-time high of 517,132,174.700 1963=100 in Oct 2018 and a record low of 100.400 1963=100 in Aug 1964. Turkey Cost of Living Index: 63=100: Istanbul: Monthly data remains active status in CEIC and is reported by Istanbul Chamber of Commerce. The data is categorized under Global Database’s Turkey – Table TR.I015: Cost of Living Index: Wage Earners: Istanbul: 1963=100.

Graph and download economic data for Cost of Living Index for Massachusetts (M04072US000MAM324NNBR) from Jan 1910 to Dec 1943 about MA, CPI, price index, indexes, price, and USA.

The average monthly cost for senior housing in the U.S. in 2025 was the highest for memory care and the lowest for independent living facilities. In the second quarter of the year, the average monthly cost for independent living housing was ***** U.S. dollars. Senior housing costs also vary vastly across different states.

In April 2022, the general cost of living index in Saudi Arabia was ******, implying a **** percent increase of the price level of the market basket of consumer goods and services from 2018. This was an increase in the general consumer price index compared to the previous months.

Living Cost: Average per Month: SF: Republic of Crimea data was reported at 11,074.000 RUB in Dec 2020. This records an increase from the previous number of 10,945.000 RUB for Sep 2020. Living Cost: Average per Month: SF: Republic of Crimea data is updated quarterly, averaging 9,798.500 RUB from Sep 2014 (Median) to Dec 2020, with 26 observations. The data reached an all-time high of 11,074.000 RUB in Dec 2020 and a record low of 5,786.000 RUB in Sep 2014. Living Cost: Average per Month: SF: Republic of Crimea data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

🏡 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? 🚀

**Student Data Description: ** The dataset contains data representing the financial and personal information of 200 students.

Name: The full name of the student, combining the first name and last name. Names are generated based on the gender of the student.

Age: The age of the student, randomly generated between 18 and 30 years.

Gender: The gender of the student, randomly assigned as "Male" or "Female".

Current Educational Level: The current educational level of the student, randomly assigned from "High School", "Undergraduate", or "Graduate".

Monthly Income: The monthly income of the student, randomly generated between $500 and $3000.

Rent/Room Accommodation: The monthly expense for rent or room accommodation, randomly generated between $200 and $800.

Utilities: The monthly expense for utilities, such as electricity and water, randomly generated between $50 and $200.

Groceries: The monthly expense for groceries, randomly generated between $50 and $200.

Dining Out/Eating Outside: The monthly expense for dining out or eating outside, randomly generated between $20 and $150.

Public Transportation: The monthly expense for public transportation, randomly generated between $20 and $100.

Fuel/Car Maintenance: The monthly expense for fuel and car maintenance, randomly generated between $20 and $100.

Tuition Fees: The monthly expense for tuition fees, randomly generated between $1000 and $5000.

Books and Supplies: The monthly expense for books and supplies, randomly generated between $20 and $200.

Online Courses/Subscriptions: The monthly expense for online courses and subscriptions, randomly generated between $10 and $100.

Clothing/Shoes: The monthly expense for clothing and shoes, randomly generated between $20 and $150.

Entertainment: The monthly expense for entertainment, randomly generated between $10 and $200.

Health Insurance/Medical Expenses: The monthly expense for health insurance and medical expenses, randomly generated between $20 and $200.

Gym Memberships/Physical Activities: The monthly expense for gym memberships and physical activities, randomly generated between $10 and $100.

Mobile Phone/Internet Bill: The monthly expense for mobile phone and internet bill, randomly generated between $20 and $100.

Other Miscellaneous Expenses: The monthly expense for other miscellaneous items, randomly generated between $10 and $150.

Savings/Investments Amount: The amount of money saved or invested by the student, randomly generated between $0 and $1000.

Final Monthly Expense: The calculated total monthly expense for the student, which is the sum of all individual expenses.

The dataset is intended to be used for analysis and exploration of student financial behaviors and patterns.