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 uses a two-color thematic shading to emphasize where areas experience the least to the most affordable housing across the US. This web map is part of the How Affordable is the American Dream story map.

Esri’s Housing Affordability Index (HAI) is a powerful tool to analyze local real estate markets. Esri’s housing affordability index measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only. For a full demographic analysis of US growth refer to Esri's Trending in 2017: The Selectivity of Growth.

The pop-up is configured to show the following 2017 demographics for each County and ZIP Code:

Total Households 2010-17 Annual Pop Change Median Age Percent Owner-Occupied Housing Units Median Household Income Median Home Value Housing Affordability Index Share of Income to Mortgage

The price of residential property in New Zealand was the highest in the Auckland region in March 2025, with an average sale price of around *********** New Zealand dollars. The most populated city in the country, Auckland, has consistently reported higher house prices compared to most other regions. Buying property in New Zealand, particularly in its major cities, is expensive. The nation has one of the highest house-price-to-income ratios in the world. Auckland residential market The residential housing market in Auckland is competitive. Prices have been slowly decreasing; the Auckland region experienced an annual decrease in the average residential house price in March 2025 compared to the same month in the previous year. The price of residential property in Auckland was the highest in the North Shore City district, with an average sale price of around **** million New Zealand dollars. Home financing Due to the rising cost of real estate, an increasing number of New Zealanders who want to own their own property are taking on mortgages. Most residential mortgage lending in New Zealand went to owner-occupier borrowers, followed by first home buyers. In addition to mortgage lending, previously under the KiwiSaver HomeStart initiative, first-home buyers in New Zealand were able to apply to withdraw all or part of their KiwiSaver retirement savings to assist with purchasing a first home. Nonetheless, the scheme was discontinued in May 2024. Furthermore, even with a large initial deposit, it may take decades for many borrowers to pay off their mortgage.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This is the look-up table for Building Age and is part of the set of District Valuation Roll (DVR) data.

The Building Age look-up table is used by the NZ Properties: National District Valuation Roll table.

Look-up tables are provided to make it easier to interpret coded DVR attributes and are given as reference data, pre-populated with fixed values defined in the Rating Valuations Rules 2008.

More information Please refer to the NZ Properties Data Dictionary for detailed metadata and information about this table.

This CSV table shows a breakdown of the dwelling stock down to a lower geographic level Lower layer Super Output Area or LSOA, categorised by the property build period and property type.

CSV metadata.

Counts in the tables are rounded to the nearest 10 with those below 5 recorded as negligible and appearing as -.

Data on build period, or age of property, has been used to create 12 property build period categories: Pre-1900, 1900-1918, 1919-1929, 1930-1939, 1945-1954, 1955-1964, 1965-1972, 1973-1982, 1983-1992, 1993-1999, 2000-2009, and 2010-2015.

Data on property type includes breakdown by bungalow, terraced, flat/maisonette, semi-detached and detached, and by the number of bedrooms.

The counts are calculated from domestic property data for England and Wales extracted from the Valuation Office Agencys administrative database on 31 March 2015.

The VOA have published data that shows homes by period built, or type, and council tax band down to MSOA and LSOA level.

The map below was created to show the average age of properties at MSOA level.

View an interactive map presentation (using Tableau) of the build period data for MSOAs and boroughs.

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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.

This map shows the average household size in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by 15-year age incrementsCount of population by marital statusThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in Ghana in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.