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 map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

This map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

This map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies.

The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale.

Scale Range: 1:591,657,528 down to 1:72,224.

For more information on this map, including the terms of use, visit us online.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

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.

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.

This map shows the median household income in the U.S. in 2017 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Median household income is estimated for 2017 in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Median household incomeMedian household income by age of householderCount of households by income level (Householder age 15 to 24)Count of households by income level (Householder age 25 to 34)Count of households by income level (Householder age 35 to 44)Count of households by income level (Householder age 45 to 54)Count of households by income level (Householder age 55 to 64)Count of households by income level (Householder age 65 to 74)Count of households by income level (Householder age 75 plus)The data shown is from Esri's 2017 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2017/2022) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Data Note: The median household income value divides the distribution of household income into two equal parts. Pareto interpolation is used if the median falls in an income interval other than the first or last. For the lowest interval, <$10,000, linear interpolation is used. If the median falls in the upper income interval of $500,000+, it is represented by the value of $500,001.

According to a recent study, Colombia had the lowest monthly cost of living in Latin America with 546 U.S. dollars needed for basic living. In contrast, four countries had a cost of living above one thousand dollars, Costa Rica, Chile, Panama and Uruguay. In 2022, the highest minimum wage in the region was recorded by Ecuador with 425 dollars per month.

Can Latin Americans survive on a minimum wage? Even if most countries in Latin America have instated laws to guarantee citizens a basic income, these minimum standards are often not enough to meet household needs. For instance, it was estimated that almost 22 million people in Mexico lacked basic housing services. Salary levels also vary greatly among Latin American economies. In 2022, the average net monthly salary in Brazil was lower than Ecuador's minimum wage.

What can a minimum wage afford in Latin America? Latin American real wages have generally risen in the past decade. However, consumers in this region still struggle to afford non-basic goods, such as tech products. Recent estimates reveal that, in order to buy an iPhone, Brazilian residents would have to work more than two months to be able to pay for it. A gaming console, on the other hand, could easily cost a Latin American worker several minimum wages.

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.

At **** U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was **** dollars in the U.S., and **** U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

This web mapping service contains data from the American Community Survey (ACS), which is an ongoing survey that provides data every year in order to give communities the current information they need to plan investments and services. Information from the survey generates data that help determine how more than $400 billion in federal and state funds are distributed each year. This survey contains information about the age, sex, race, family and relationships, income and benefits, health insurance, education, veteran status, disabilities and the cost of living of the communities surveyed. The Census ACS 2014 WMS web mapping service contains data as of January 1, 2014.

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance for holders of federally regulated mortgages. In addition, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Cell Sizes: 10 meters (default), 30 meters, and 90 metersUnits: NoneSource Type: ThematicPixel Type: Unsigned integerSource: Federal Emergency Management Agency (FEMA)Update Frequency: AnnualPublication Date: December 18, 2024This layer is derived from the December 18, 2024 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 88 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 88 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census' boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "flood hazard areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "flood hazard areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel.Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Dataset SummaryPlease note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.About this data:The operational purpose of the vacant land dataset is to facilitate the tracking and mapping of vacant land for the purposes of promoting redevelopment of lots to increase the City's tax base and spur increased economic activity. These properties are both City owned and privately owned. The vast majority of vacant lots are the result of a demolition of a structure that once stood on the property. Vacant lots are noted in the official tax parcel assessment records with a class code beginning with 3, which denotes the category vacant land.Related Resources:For a searchable interactive mapping application, please visit the City of Rochester's Property Information explorer tool. For further information about the city's property tax assessments, please contact the City of Rochester Assessment Bureau. To access the City's zoning code, please click here.Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Business and Zoning.

Please note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.Overview of the Data:This dataset is a polygon feature layer with the boundaries of all tax parcels owned by the City of Rochester. This includes all public parks, and municipal buildings, as well as vacant land and structures currently owned by the City. The data includes fields with features about each property including property type, date of sale, land value, dimensions, and more.About City Owned Properties:The City's real estate inventory is managed by the Division of Real Estate in the Department of Neighborhood and Business Development. Properties like municipal buildings and parks are expected to be in long term ownership of the City. Properties such as vacant land and vacant structures are ones the City is actively seeking to reposition for redevelopment to increase the City's tax base and economic activity. The City acquires many of these properties through the tax foreclosure auction process when no private entity bids the minimum bid. Some of these properties stay in the City's ownership for years, while others are quickly sold to development partners. For more information please visit the City's webpage for the Division of Real Estate: https://www.cityofrochester.gov/realestate/Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Real Estate.

In March 2025, inflation amounted to 2.4 percent, while wages grew by 4.3 percent. The inflation rate has not exceeded the rate of wage growth since January 2023. Inflation in 2022 The high rates of inflation in 2022 meant that the real terms value of American wages took a hit. Many Americans report feelings of concern over the economy and a worsening of their financial situation. The inflation situation in the United States is one that was experienced globally in 2022, mainly due to COVID-19 related supply chain constraints and disruption due to the Russian invasion of Ukraine. The monthly inflation rate for the U.S. reached a 40-year high in June 2022 at 9.1 percent, and annual inflation for 2022 reached eight percent. Without appropriate wage increases, Americans will continue to see a decline in their purchasing power. Wages in the U.S. Despite the level of wage growth reaching 6.7 percent in the summer of 2022, it has not been enough to curb the impact of even higher inflation rates. The federally mandated minimum wage in the United States has not increased since 2009, meaning that individuals working minimum wage jobs have taken a real terms pay cut for the last twelve years. There are discrepancies between states - the minimum wage in California can be as high as 15.50 U.S. dollars per hour, while a business in Oklahoma may be as low as two U.S. dollars per hour. However, even the higher wage rates in states like California and Washington may be lacking - one analysis found that if minimum wage had kept up with productivity, the minimum hourly wage in the U.S. should have been 22.88 dollars per hour in 2021. Additionally, the impact of decreased purchasing power due to inflation will impact different parts of society in different ways with stark contrast in average wages due to both gender and race.