Prospective homebuyers in Vancouver, British Columbia, and Toronto, Ontario, needed an annual income of over ******* Canadian dollars in June 2025 to qualify for the average priced home. In Vancouver, this figure was approximately ******* Canadian dollars. British Columbia and Ontario, are Canada's most expensive provinces for housing. According to a January 2025 forecast by the Canadian Real Estate Association (CREA), the housing market is expected to grow in the next two years, which is likely to worsen home affordability.

The house price-to-income ratio in the United States has reached concerning levels, with the index hitting ***** in the second quarter of 2025. This indicates that house prices have outpaced income growth by *****percent since 2015, highlighting a growing affordability crisis in the housing market. The widening gap between home prices and wages is putting homeownership out of reach for many Americans, particularly as real wages have remained stagnant. Rising home prices and stagnant wages While average annual real wages in the United States have increased slightly since 2014, home prices have soared. The median sales price of existing single-family homes reached a record-high in 2024, representing a substantial increase over the past five years. This disparity between wage growth and home price appreciation has led to a significant decrease in housing affordability across the country. Affordability challenges in the U.S. housing market The U.S. Housing Affordability Index, which measures whether a family earning the median income can afford a median-priced home, plummeted in 2024, marking the second-worst year for homebuyers since records began. This decline in affordability is reflected in homebuyer sentiment, with homebuyer sentiment plummeting.

In many metros in the United States, the median household income was insufficient to qualify for the median-priced home. Among the ** largest metros in the U.S., San Jose-Sunnyvale-Santa Clara, CA was the least affordable one in 2022, with the housing affordability index at **** index points. This means that the median household income, when accounting for monthly housing expenses, was less than ** percent of the necessary income to qualify for a mortgage. An index value over 100, on the other hand, shows that the median income is sufficient for a mortgage. Metros, such as Cleveland-Elyria, OH, and St. Louis, MO-IL had a median household income much higher than the income needed to buy the median-priced home.

About ** percent of homebuyers in the United States in 2024 had a median household income of over ******* U.S. dollars. This was the median income range with the largest share of homebuyers in the United States that year. The second-largest category was buyers with a median income of between 100,000 and ******* U.S. dollars, who accounted for ** percent of all buyers.

Graph and download economic data for Housing Affordability Index (Fixed) (FIXHAI) from Sep 2024 to Sep 2025 about fixed, housing, indexes, and USA.

Hungary increased 9% of House Price to Income Ratio in 2019, from a year earlier.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

The standardised house price-to-income ratio is defined as the ratio of the current price to income ratio relative to the long-term average price-to-income ratio, calculated over the period 2000 to the most recent data available. If the ratio equals 100, it means the current price-to-income ratio is equal to its long term average. House prices are provided by Eurostat, and income is calculated as adjusted household gross disposable income (B7G) per head of population based on Eurostat data.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

According to Cognitive Market Research, the global Residential Real Estate market size was USD 32651.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 13060.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 9795.48 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 7509.87 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1632.58 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 653.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
The single-family homes category is the fastest growing segment of the Residential Real Estate industry

Market Dynamics of Residential Real Estate Market

Key Drivers for Residential Real Estate Market

Increasing population drives housing demand to Boost Market Growth

Increasing population drives housing demand by creating a need for more residential spaces to accommodate growing numbers of people. As population rises, particularly in urban and suburban areas, demand for housing expands, fueling the residential real estate market. This is especially evident in countries experiencing rapid urbanization, where people move to cities seeking better job opportunities, education, and lifestyle options, further increasing housing needs. Additionally, population growth often correlates with the formation of new households, such as young families or individuals moving out on their own, intensifying the demand for housing units. In response, developers and investors are motivated to build more residential properties, ranging from single-family homes to multifamily units, contributing to market growth and driving real estate values upward. For instance, The Ashwin Sheth Group aims to broaden its residential and commercial offerings in the Mumbai Metropolitan Region (MMR) of India.

Rising incomes and economic stability to Drive Market Growth

Rising incomes and economic stability drive the residential real estate market by boosting consumers’ purchasing power and confidence in long-term investments like homeownership. As incomes increase, people can afford larger down payments, qualify for higher loan amounts, and manage mortgage payments more comfortably, making home buying a more viable option. Economic stability, characterized by low unemployment rates and steady GDP growth, reinforces this confidence, as individuals feel secure in their financial situations. With greater disposable income, many consumers seek to upgrade to larger homes, buy second properties, or invest in luxury real estate, further fueling demand. This economic backdrop attracts both local and foreign investors, leading to more housing developments, increased property values, and a flourishing residential real estate market.

Restraint Factor for the Residential Real Estate Market

High Property Prices will Limit Market Growth

High property prices restrain the residential real estate market by making homeownership unaffordable for a significant portion of the population. As prices rise, potential buyers, particularly first-time homeowners and low- to middle-income families, may find it challenging to secure adequate financing or meet the necessary down payment requirements. This affordability crisis limits the pool of qualified buyers, leading to slower sales and potential stagnation in market growth. Additionally, high property prices can prompt increased demand for rental properties, shifting focus away from home purchases. In markets where prices escalate rapidly, even affluent buyers may hesitate, fearing potential market corrections. Consequently, elevated property values can create a barrier to entry, ultimately restricting the overall health and vibrancy of the residential real estate market.

Impact of Covid-19 on the Residential Real Estate Market

The COVI...

Data on resident buyers who are persons that purchased a residential property in a market sale and filed their T1 tax return form: number of and incomes of residential property buyers, sale price, price-to-income ratio by the number of buyers as part of a sale, age groups, first-time home buyer status, buyer characteristics (sex, family type, immigration status, period of immigration, admission category).

Average household income, debt-to-income ratios, and area median income comparisons for Gen Z home buyers.

The house price to income index in Europe declined in 13 of the 28 European countries in 2024, indicating that income grew faster than house prices. Portugal had the highest house price to income index ranking, with values exceeding ***** index points. Romania and Finland were on the other side of the spectrum, with less than 100 index points. The house price to income ratio is an indicator for the development of housing affordability across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. A ratio higher than 100 means that the nominal house price growth since 2015 has outpaced the nominal disposable income growth, and housing is therefore comparatively less affordable. In 2024, the OECD average stood at ***** index points.

Upfront costs involved with purchasing a residential property including deposit amounts and Stamp Duty Land Tax. The number of years of income is required to cover the upfront costs of purchasing a residential property, by income and house price decile.

Average household income, debt-to-income ratios, and area median income comparisons for Early Millennial home buyers.

From landing page:FHFA establishes annual single-family and multifamily housing goals for mortgages purchased by Fannie Mae and Freddie Mac. The Enterprise Housing Goals include separate categories for single-family mortgages on housing that is affordable to low-income and very low-income families, as well as refinanced mortgages for low-income borrowers. FHFA also establishes separate annual goals for multifamily housing. Loans that are eligible for housing goals credit are mortgages on owner-occupied housing with one to four units. The mortgages must be conventional, conforming mortgages, defined as mortgages that are not insured or guaranteed by the Federal Housing Administration or another government agency and with principal balances that do not exceed the conforming loan limits for Enterprise mortgages. This page provides data on Enterprise performance and activity related to the single-family housi​​ng goals. A full glossary of terms is provided below. Single-Family Enterprise Mortgage Acquisitions: Race and Ethnicity Data The new housing goals data tables provide insight on the racial and ethnic composition of loans acquired by the Enterprises that are eligible for housing goals credit. FHFA has provided the racial and ethnic distribution of the Enterprises' acquisitions across each of the current single-family housing goals categories. ​ Single-Family Housing Goal Loan Segments: State-Level Data FHFA is publishing state-level data for each single-family goal loan purchase and refinance segment. It is important to note that FHFA does not set state-level targets but only at the national level. These tables provide the Enterprises' share in each state along with the market share, as calculated by FHFA using the 'static' HMDA data for each year to determine Enterprise housing goals performance each year. It is important to note that HMDA state-level data are impacted by the number of HMDA-exempt reporters in each state. For more information on HMDA reporting requirements, visit the CFPB HMDA Reporting Requirements page.Low-Income Census Tracts, Minority Census Tracts and Designated Disaster Areas Data The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Safety and Soundness Act) provides for the establishment of single-family and multifamily goals each year, including a single-family purchase money mortgage goal for families residing in low-income areas. The Safety and Soundness Act defines "low-income area" for the single-family low-income areas home purchase goal as: Census tracts or block numbering areas in which the median income does not exceed 80 percent of area median income (AMI). In addition, for the purposes of this goal, "families residing in low-income areas" also include: Families with income not greater than 100 percent of AMI who reside in minority census tracts. Families with income not greater than 100 percent of AMI who reside in designated disaster areas. ​A "minority census tract" is a census tract that has a minority population of at least 30 percent and a median income of less than 100 percent of the AMI. A "low-income census tract" is census tract in which the median income does not exceed 80 percent of the AMI. Designated disaster areas are identified by FHFA based on the three most recent years' declarations by the Federal Emergency Management Agency (FEMA), where individual assistance payments were authorized by FEMA. A map of census tracts identified as minority census tracts in 2024 can be ​found here. A map of census tracts identified as low-income census tracts in 2024 can be found here. ​Learn more about low-income census tracts, minority census tracts, and designated disaster areas.

This layer was developed for public use of the most current median household income, median home value and median owner-occupied residential real estate taxes compiled by the US Census Bureau from the 2017 to 2021 American Community Survey at the Census Tract (neighborhood) level.

All data are 2020 Census Tract (neighborhood) level five-year estimates from the U.S. Census Bureau American Community Survey from 2017 to 2021. Median household income earned in the past 12 months. Includes wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income. Median home value (an estimate of how much the property would sell for if it were for sale) for properties owned, being bought, vacant for sale, or sold but not occupied at the time of the survey. Data are based on values reported by property owners. Median real estate taxes (due to all taxing jurisdictions) for owner-occupied properties are based on taxes reported by homeowners to the Census Bureau in the American Community Survey from 2017 to 2021.

All data are 2020 Census Tract (neighborhood) level five-year estimates from the U.S. Census Bureau American Community Survey from 2017 to 2021. Median household income earned in the past 12 months. Includes wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income. Median home value (an estimate of how much the property would sell for if it were for sale) for properties owned, being bought, vacant for sale, or sold but not occupied at the time of the survey. Data are based on values reported by property owners. Median real estate taxes (due to all taxing jurisdictions) for owner-occupied properties are based on taxes reported by homeowners to the Census Bureau in the American Community Survey from 2017 to 2021.

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

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How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

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

Title: Location Affordability Index - NMCDC Copy

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

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

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

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

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

UID: 73

Data Requested: Family income spent on basic need

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

Date Acquired: Map copied on May 10, 2022

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

Tags: PENDING