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.

People in Great Britain's experiences of and actions following increases in their costs of living, and how these differed by a range of personal characteristics.

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.

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.

Cost of Living - Country Rankings Dataset

Context:

The "Cost of Living - Country Rankings Dataset" provides comprehensive information on the cost of living in various countries around the world. Understanding the cost of living is crucial for individuals, businesses, and policymakers alike, as it impacts decisions related to travel, relocation, investment, and economic analysis. This dataset is intended to serve as a valuable resource for researchers, data analysts, and anyone interested in exploring and comparing the cost of living across different nations.

Content:

This dataset comprises four primary columns:

1. Countries: This column contains the names of various countries included in the dataset. Each country is identified by its official name.

2. Cost of Living: The "Cost of Living" column represents the cost of living index or score for each country. This index is typically calculated by considering various factors, such as housing, food, transportation, healthcare, and other essential expenses. A higher index value indicates a higher cost of living in that particular country, while a lower value suggests a more affordable cost of living.

3. 2017 Global Rank: This column provides the global ranking of each country's cost of living in the year 2017. The ranking is based on the cost of living index mentioned earlier. A lower rank indicates a lower cost of living relative to other countries, while a higher rank suggests a higher cost of living position.

4. Available Data: The "Available Data" column indicates whether or not data for a specific country and year is available.

This dataset is designed to support various data analysis and visualization tasks. Users can explore trends in the cost of living, identify countries with high or low cost of living, and analyze how rankings have changed over time. Researchers can use this dataset to conduct in-depth studies on the factors influencing the cost of living in different regions and the economic implications of such variations.

Please note that the dataset includes information for the year 2017, and users are encouraged to consider this when interpreting the data, as economic conditions and the cost of living may have changed since then. Additionally, this dataset aims to provide a snapshot of cost of living rankings for countries in 2017 and may not cover every country in the world.

Link: https://www.theglobaleconomy.com/rankings/cost_of_living_wb/

Disclaimer: The accuracy and completeness of the data provided in this dataset are subject to the source from which it was obtained. Users are advised to cross-reference this data with authoritative sources and exercise discretion when making decisions based on it. The dataset creator and Kaggle assume no responsibility for any actions taken based on the information provided herein.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

A December 2023 study looked at the Britons' main responses to tackle the rising cost of living when planning a holiday. While ** percent of the survey sample reported intending to travel outside peak periods, ** percent of respondents mentioned reducing the number of nights spent on vacation.

The FCA presents the findings from a survey undertaken in January 2024 to understand the financial impact of the increased cost of living on adults across the UK. Key findings include: Since January 2023 there has been an improvement in the number of people finding it hard to manage the higher costs of living, although challenges remain for some groups. The cost of living continues to have an impact on the financial lives of some adults in the UK. In January 2024: 7.4m (14%) felt heavily burdened by their domestic bills and credit commitments 5.5m (11%) had missed any of these bills in the previous 6 months 14.6m (28%) were not coping financially or finding it difficult to cope 5.9m (11%) had no disposable income

SIA200 - Impact of Cost of Living Measures on Poverty Rates. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Impact of Cost of Living Measures on Poverty Rates...

Impact of Cost of Living Measures on Income and Poverty Rates

Description and Purpose This data companion pack is a resource intended to frame and be read alongside the linked rapid review of evidence for interventions to address the cost of living crisis (available on the Institute of Health Equity website) . The resource provides intelligence and context on the cost of living crisis in London only, while the accompanying rapid review of evidence for interventions to mitigate the impacts of the rising cost of living on London, contains the recommendations for action.​ This pack is intended to provide a high-level overview of the impacts of the costs of living crisis on London and the need Londoners have for support to deal with the cost of living crisis through intelligence available in the public domain.  This pack identifies how certain groups in the population already experiencing health inequalities are at greatest risk of poverty and worsening health due to the cost of living crisis. Given there are significant gaps in intelligence available, this pack also highlights these gaps and limitations in our understanding. Audience It will be useful for health leaders, analysts, officers, and policy makers from local and regional government, integrated care systems, NHS, academia, VCS organisations and partners across London to support their work to address the costs of living crisis by​ Advocating for the need for action to address the rising cost of living given impacts on health and health inequalities ​ Framing the context for the interventions highlighted in the linked rapid review of interventions​ Engaging communities ​ ​Development of this resource The Institute of Health Equity (IHE), Greater London Authority (GLA) Health, GLA City Intelligence Unit, Office for Health Improvement and Disparities London (OHID), Association of Directors of Public Health London (ADPH), and NHSE have collaboratively produced this report, as part of the Building the Evidence (BTE) programme of work The sources of data available and topics included have been identified from existing published data, working in partnership through iterative discussion The resource is provided in PDF and PowerPoint format to support colleagues in their work to There is no current plan for periodic updates of this resource, though this will be discussed on completion of this programme of work

This data companion pack is a resource intended to frame and be read alongside the linked rapid review of evidence for interventions to address the cost of living crisis (available on the Institute of Health Equity website) . The resource provides intelligence and context on the cost of living crisis in London only, while the accompanying rapid review of evidence for interventions to mitigate the impacts of the rising cost of living on London, contains the recommendations for action.​ This pack is intended to provide a high-level overview of the impacts of the costs of living crisis on London and the need Londoners have for support to deal with the cost of living crisis through intelligence available in the public domain.

According to a survey conducted in April 2023, around ** percent of French people considered that their living standards had being impacted by the economic situation, and a further ** percent of them were very impacted. On the other hand, ** percent of the respondents claimed they were only slightly impacted by the current economic situation, and ***** percent of said they were not impacted at all.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

This work looks at the spiralling cost of living and the challenges facing Londoners including the rising poverty levels in the capital. The latest update is dated August 2022. The report and public attitudes and behaviour charts (published 1 February 2022) were republished (7 April 2022) to correct a calculation error. This error was due to manual calculation.

How different groups in the population have been affected by an increase in their cost of living, using data from the Opinions and Lifestyle Survey, November 2021 to March 2022.

This work looks at the spiralling cost of living and the challenges facing Londoners including the rising poverty levels in the capital. The latest update is dated August 2022. The report and public attitudes and behaviour charts (published 1 February 2022) were republished (7 April 2022) to correct a calculation error. This error was due to manual calculation.

🏡 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? 🚀