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 is a comparison of the cost of living in various cities, as gathered by popular site numbeo. All data belongs to them and has been shared with permission

Currency is Euro

In the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding ******* U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under ******* U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded **** percent in 2023.

South Korea's capital Seoul had the highest cost of living among megacities in the Asia-Pacific region in 2024, with an index score of ****. Japan's capital Tokyo followed with a cost of living index score of ****. AffordabilityIn terms of housing affordability, Chinese megacity Shanghai had the highest rent index score in 2024. Affordability has become an issue in certain megacities across the Asia-Pacific region, with accommodation proving expensive. Next to Shanghai, Japanese capital Tokyo and South Korean capital Seoul boast some of the highest rent indices in the region. Increased opportunities in megacitiesAs the biggest region in the world, it is not surprising that the Asia-Pacific region is home to 28 megacities as of January 2024, with expectations that this number will dramatically increase by 2030. The growing number of megacities in the Asia-Pacific region can be attributed to raised levels of employment and living conditions. Cities such as Tokyo, Shanghai, and Beijing have become economic and industrial hubs. Subsequently, these cities have forged a reputation as being the in-trend places to live among the younger generations. This reputation has also pushed them to become enticing to tourists, with Tokyo displaying increased numbers of tourists throughout recent years, which in turn has created more job opportunities for inhabitants. As well as Tokyo, Shanghai has benefitted from the increased tourism, and has demonstrated an increasing population. A big factor in this population increase could be due to the migration of citizens to the city, seeking better employment possibilities.

This collection contains data obtained from families of wage earners or salaried workers in industrial locales scattered throughout the United States. The purpose of the survey was to estimate the cost of living of a "typical" American family. The completed questionnaires contain information about income sources and family expenditures including specific quantities and costs of food, housing, clothing, fuel, furniture, and miscellaneous household items for the calendar year. Demographic characteristics recorded for each household member include relationship to head, age, sex, occupation, weeks spent in the household and employed, wage rate, and total earnings.

As of September 2024, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

Cost of living data based on food, housing, utilities, transportation, healthcare, and consumer discretionary spending in the United States.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

SPSS data file

SPSS output file

Excel data and sources file

Excel data only file for use with python processing (program on Github and archived on Zenodo)

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.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

The Cost of living rating evaluates how much ordinary living expenses cost in different countries, including food, housing, necessary goods, services, medical insurance and other aspects.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

1,963 (Rubles per month) in 2016. Indicator is calculated as housing and utilities services costs estimated on the basis of economically feasible tariffs to the number of housing stock habitants divided by the number of months in the reporting period.

The average transaction price of new housing in Europe was the highest in Norway, whereas existing homes were the most expensive in Austria. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, not all countries are included. To compile the ranking, the source weighed the transaction prices of residential properties in the most important cities in each country based on data from their national offices. For example, in Germany, the cities included were Munich, Hamburg, Frankfurt, and Berlin. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Key quality of life indicators - cost index, housing.

Tiny Homes Market Outlook

The global tiny homes market size was valued at approximately USD 17.4 billion in 2023 and is projected to reach USD 30.4 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.5% from 2024 to 2032. This market exhibits robust growth driven by the increasing demand for affordable housing solutions, rising awareness of sustainable living, and the growing trend towards minimalistic lifestyles. The tiny homes movement, which emphasizes smaller living spaces with efficient design, has gained considerable traction as consumers seek to reduce their carbon footprint and improve financial mobility.

One of the primary growth factors for the tiny homes market is the shift in consumer preferences towards sustainable and cost-effective living solutions. Tiny homes offer a viable alternative to traditional housing by providing affordable options for homeownership, which is particularly appealing in regions with escalating property prices. The reduced size of these homes translates into lower energy consumption and maintenance costs, making them an attractive option for environmentally conscious and cost-sensitive consumers. Additionally, the rising awareness of environmental issues and the desire to live a more sustainable lifestyle are guiding consumers towards adopting tiny homes as they seek to minimize their environmental impact.

Another significant factor contributing to the growth of the tiny homes market is the increasing trend of urbanization and the consequent reduction in available living spaces. As cities become more densely populated, the need for innovative housing solutions that maximize the use of limited space becomes imperative. Tiny homes, with their efficient and flexible design, offer a solution to the space constraints faced by urban dwellers. These homes can be strategically placed in unused urban spaces, allowing for the utilization of previously uninhabitable areas. This adaptability makes tiny homes a practical solution for meeting the housing needs of growing urban populations.

The demographic shift towards smaller household sizes and the rise of remote work have also played a pivotal role in the market's expansion. With more people working from home, there is an increasing demand for flexible living arrangements that accommodate both personal and professional needs. Tiny homes, with their customizable designs, cater to this demand by offering multifunctional spaces that can easily be adapted to suit various lifestyle requirements. Furthermore, as more individuals and couples choose to live alone or with fewer dependents, the demand for smaller, more manageable living spaces continues to grow, further fuelling the tiny homes market.

From a regional perspective, North America has been a pioneer in the tiny homes movement, driven by factors such as high property prices and a cultural inclination towards environmental sustainability and minimalist lifestyles. The market in this region is expected to continue its rapid growth, supported by favorable government policies and increasing consumer awareness. Meanwhile, Europe is also experiencing significant growth, driven by similar trends and a strong emphasis on green living. The Asia Pacific region presents considerable potential for market expansion due to its large population base and rapid urbanization, although the market is still in its nascent stages in this region.

Product Type Analysis

The tiny homes market is segmented by product type into mobile tiny homes and stationary tiny homes, each offering distinct advantages and appealing to different consumer needs and preferences. Mobile tiny homes, as the name suggests, are designed for mobility, allowing homeowners to relocate easily. This segment has gained popularity among individuals seeking a nomadic lifestyle, providing the flexibility to travel without the constraints of a fixed property. The rise of the "digital nomad" lifestyle, wherein individuals work remotely while traveling, has further bolstered the demand for mobile tiny homes, making this segment a significant contributor to the market's growth.

Mobile tiny homes are built on trailers, making them easy to transport and set up in various locations. This aspect not only appeals to those seeking adventure but also to those who wish to live in natural settings without the need to invest in land. Moreover, mobile tiny homes cater to the growing trend of off-grid living, as many are equipped with self-sustaining features such as solar panels and composting toilets. This self-reliant aspect makes mobile tiny homes an attractive optio

This feature service contains data from the American Community Survey: 5-year Estimates Subject Tables for the greater Bozeman, MT area. The attributes come from the Financial Characteristics table (S2503). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases. For example: Rent_35kto50k_20to29pcnt is equal to the percentage of the population living in a renter-occupied household, with an annual household income of $35,000 to $50,000, spending between 20% to 29% of their income on housing costs in the past 12 months. Data DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyOwn: Percent of population living in an Owner-occupied householdRent: Percent of population living in a Renter-occupied householdAnnual Household Income20kto35k: Annual household income of $20,000 to $34,99935kto50k: Annual household income of $35,000 to $49,99950kto75k: Annual household income of $50,000 to $74,999Over75k: Annual household income of over $75,000Housing Cost BurdenUnder_20pcnt: Monthly housing costs under 20% of household income in the past 12 months20to29pcnt: Monthly housing costs of 20-29% of household income in the past 12 months30pcntOrMore: Monthly housing costs of over 30% of household income in the past 12 monthsDownload ACS Financial Characteristics data for the greater Bozeman, MT areaAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

The survey charted the housing conditions of higher education students in Tampere, Finland. The survey was divided into three main themes: current housing situation, housing preferences, and housing situation during studies. Regarding housing situation at the time of the survey, the questions surveyed type of housing the respondents lived in (housing type and housing tenure), household composition (number of roommates, children), size of housing (number of rooms and surface area), housing costs, distance to own university and city centre, travel time from home to university, and satisfaction with and opinions on housing. Concerning housing preferences, the respondents were asked which type of housing they would have preferred to live in, whether they were looking for new accommodation and what kind, what their attitude towards student housing was, how important they thought different things when looking for accommodation, in which neighbourhoods they would have wanted to live during their studies, how much they would have been prepared to pay for housing of their preference per month, and how much they thought reasonable housing costs for a student would be per month. Finally, with regard to housing situation during studies, the respondents were asked whether they had first moved out of their parents' house for studies, whether they had had diffculties in finding accommodation at the beginning of studies, how many different places and student apartments they had lived in during studies, how many months in total they had lived in student apartments, and, if they had moved house, reasons for moving. Background variables included the respondent's employment situation, marital status, disposable income per month, expected year of graduation, education level, and education and occupations of parents. Furthermore, the data contain background variables based on register data. These include the respondent's higher learning institution, age, gender, nationality, municipality of domicile and place of residence, the year R began their studies, faculty, and degree.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.