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.

This statistic shows the best states to make living in the United States in 2019. In 2019, Wyoming was ranked as the best state to make a living in the United States, with the cost of living index at **** value and the median income of ****** U.S. dollars.

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

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.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

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.

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.

Cost comparison table showing community type costs by location

Quality of Life by State 2024

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This dataset provides insights into the quality of life across different states in the United States for the year 2024. Quality of life, encompassing aspects like comfort, health, and happiness, is evaluated through various metrics including affordability, economy, education, and safety. Dive into this dataset to understand how different states fare in terms of overall quality of life and its individual components.

Columns Description

• State: The name of the U.S. state.
• QualityOfLifeTotalScore: The total score representing the overall quality of life for the respective state. This score is calculated based on various quality of life metrics.
• QualityOfLifeQualityOfLife: The score representing the quality of life aspect for the respective state. This aspect may include subjective factors related to happiness, satisfaction, and overall well-being. Higher scores may indicate a higher level of subjective well-being, happiness, or overall satisfaction among residents. Lower scores could suggest lower levels of subjective well-being.
• QualityOfLifeAffordability: The score representing the affordability aspect of the quality of life for the respective state. This aspect evaluates factors such as cost of living, housing affordability, and income levels. Higher scores typically indicate greater affordability of housing, cost of living, and basic necessities. Lower scores may suggest that these essentials are less accessible or more expensive for residents.
• QualityOfLifeEconomy: The score representing the economic aspect of the quality of life for the respective state. This aspect assesses factors such as employment opportunities, economic growth, and income distribution. Higher scores may reflect a stronger economy with more job opportunities, higher incomes, and lower levels of poverty. Lower scores might indicate economic challenges such as unemployment or income inequality.
• QualityOfLifeEducationAndHealth: The score representing the education and health aspect of the quality of life for the respective state. This aspect considers factors such as access to quality education, healthcare facilities, and overall public health indicators. Higher scores generally signify better access to quality education, healthcare services, and overall public health. Lower scores may indicate deficiencies in these areas, such as limited access to healthcare or lower educational attainment levels.
• QualityOfLifeSafety: The score representing the safety aspect of the quality of life for the respective state. This aspect evaluates factors such as crime rates, public safety measures, and community well-being initiatives. Higher scores suggest lower crime rates, better community safety, and a higher sense of security among residents. Lower scores may indicate higher crime rates or concerns about safety.

These descriptions provide an overview of what each column represents and the specific aspects of quality of life they assess for each U.S. state.

A dataset comprising various variables around housing and demographics for the top 50 American cities by population.

Variables:

Zip Code: Zip code within which the listing is present.

Price: Listed price for the property.

Beds: Number of beds mentioned in the listing.

Baths: Number of baths mentioned in the listing.

Living Space: The total size of the living space, in square feet, mentioned in the listing.

Address: Street address of the listing.

City: City name where the listing is located.

State: State name where the listing is located.

Zip Code Population: The estimated number of individuals within the zip code. Data from Simplemaps.com.

Zip Code Density: The estimated number of individuals per square mile within the zip code. Data from Simplemaps.com.

County: County where the listing is located.

Median Household income: Estimated median household income. Data from the U.S. Census Bureau.

Latitude: Latitude of the zip code. ** Data from Simplemaps.com.**

Longitude: Longitude of the zip code. Data from Simplemaps.com.

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.

About This Dataset

This dataset is the original 70-city version used in my first published research paper: “A Data-Driven Survey on Cost of Living and Salary Affordability in Indian Cities” (IJRASET, 2025) Link: https://www.ijraset.com/best-journal/a-datadriven-survey-on-cost-of-livingsalary-affordability-in-indian-cities

It was created using web-scraping techniques from LivingCost.org and converted to INR using a consistent USD→INR exchange rate. This dataset forms the foundational base for affordability analysis, exploratory data analysis (EDA), and benchmarking cost-of-living patterns across India.

The dataset includes 70+ Indian cities, with fields covering living cost, rent, salary, affordability ratio (“months covered”), and derived financial indicators. It is clean, structured, and suitable for beginner to intermediate analytics projects.

Why This Dataset?

This dataset is ideal for:

EDA practice for college & school projects

Correlation and regression analysis

Basic ML tasks (predicting salary, affordability, rent, etc.)

Urban economics mini-projects

Dashboard creation (PowerBI, Tableau)

Data cleaning and preprocessing assignments

It is designed to be simple enough for students but structured enough for real-world analysis.

Features Included

Each row represents a city/state-level affordability profile with:

Cost of living (USD & INR)

Rent for a single person (USD & INR)

Monthly after-tax salary (USD & INR)

Income after rent

“Months Covered” affordability ratio

Source URLs for verification

Exchange rate used

This makes the dataset both transparent and reliable for academic usage.

Data Quality

Web-scraped directly from LivingCost.org

Cleaned and standardized

Currency converted uniformly

Non-city entries flagged

Fully reproducible from the source

This dataset served as the master input for my peer-reviewed paper and has been validated through statistical analysis.

Intended Audience

Students (school, undergraduate, postgraduate)

Data science beginners

Educators needing real datasets for teaching

Analysts looking for quick EDA practice

Researchers exploring affordability or urban economics

Note

A more comprehensive 200+ city enhanced dataset (used in my second paper) will be uploaded soon, including ICT metrics, GDP, and extended affordability indicators.

Drinking water affordability affects all residents including those with piped water connections and self-supply, private wells. Self-supply drinking water well users make up 12–15% of the United States population and are often overlooked during affordability studies. To the best of our knowledge, this study is the first attempt to quantify statewide water affordability costs for all residents, both utility customers and private well users. This study applies several geospatial methodologies: percent median household income (MHI), number of households beneath affordability thresholds, and hours worked at minimum wage. The greatest determinant of private well affordability was found to be initial capital costs. These costs were represented as a monthly loan payment plus operations and maintenance costs; a range of results were found. For operations and maintenance costs only, 9% of private well serviced homes exceeded the 2.5% MHI threshold while for monthly loan payments, corresponding to a $13,500 capital cost, 51% of households exceeded the same affordability metric. There are 3.17% of centralized water utility serviced households spending greater than 2.5% of their median household income on water while 16.3% of utility-serviced households fall below an income threshold representing 2.5% of the block group MHI. This range of results highlights the need for a multifaceted solution to ensure the human right to affordable water.

Cost comparison table showing community type costs by location

In 2024, the annual cost for a private room in an assisted living facility in the U.S. amounted to ****** U.S. dollars - the national median price. However, cost varied greatly from one state to another. The least expensive states for a private room in assisted living were South Dakota, and Mississippi. While the most expensive states for assisted living were Hawaii and Alaska.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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For more datasets, click here.

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

Cost comparison table showing community type costs by location

Alabama cost of living for the counties with the lowest and highest MHI in Alabama (2022 U.S. Dollars).

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Cost comparison table showing community type costs by location