The small area model-based income estimates are the official estimates of average (mean) household income at the middle layer super output area (MSOA) level in England and Wales for 2011/12, 2013/14 and 2015/16.

For 2015-16 the figures are average annual income. For 2013/14 and 2011/12 the figures are average weekly income.

They are calculated using a model based method to produce the following four estimates of income using a combination of survey data from the Family Resources Survey, and previously published data from the 2011 Census and a number of administrative data sources. The four different measures of income are:

1. Total household income
2. Net household income
3. Net household income (equivalised) before housing costs
4. Net household income (equivalised) after housing costs

Total annual household income is the sum of the gross income of every member of the household plus any income from benefits such as Working Families Tax Credit.

Net annual household income is the sum of the net income of every member of the household. It is calculated using the same components as total income but income is net of:

• income tax payments;
• national insurance contributions;
• domestic rates/council tax;
• contributions to occupational pension schemes;
• all maintenance and child support payments, which are deducted from the income of the person making the payments; and
• parental contribution to students living away from home.

Net annual household income before housing costs (equivalised) is composed of the same elements as net household weekly income but is subject to the OECD’s equivalisation scale.

Net annual household income after housing costs (equivalised) is composed of the same elements of net household weekly income but is subject to the following deductions prior to the OECD’s equivalisation scale being applied:

• rent (gross of housing benefit);
• water rates, community water charges and council water charges;
• mortgage interest payments (net of any tax relief);
• structural insurance premiums (for owner occupiers); and
• ground rent and service charges.

For detailed information on aspects of the quality and methodology behind these statistics, "https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/methodologies/smallareaincomeestimatesmodelbasedestimatesofthemeanhouseholdweeklyincomeformiddlelayersuperoutputareas201314technicalreport " target="_blank">see the Technical Report.

This dataset is included in the Greater London Authority's Night Time Observatory. Click here to find out more.

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 data in this dataset is collected from FRED.

I decided to create this dataset while reading the research paper Factors Affecting House Prices in Cyprus: 1988-2008 by Panos Pashardes & Christos S. Savva. This research paper is extremely informative and covers a lot of details regarding the macroeconomics involved in real estate market. So I would recommend you all to go through it once.

NOTE:

This dataset will be updated over a period of time and include the following: - Macroeconomic factors with quarterly, monthly frequencies. - Microeconomic factors such as house type, age, location, size (BR, BA, carpet area/built-up area), facilities, view, disability functions, region, house prices, etc.

NOTE 2:

I recommend you all to check the file in this dataset with the title Housing_Macroeconomic_Factors_US (2).csv, it includes both the supply and demand factors associated with the housing market.

General Defintions:

1. Macroeconomic Factors

• House_Price_Index: House price change according to the index base period set (you can check the date at which this value is 100).
• Stock_Price_Index: Stock price change according to the index base period set (you can check the date at which this value is 100).
• Consumer_Price_Index: The Consumer Price Index measures the overall change in consumer prices based on a representative basket of goods and services over time.
• Population: Population of USA (unit: thousands).
• Unemployment_Rate: Unemployment rate of USA (unit: percentage).
• Real_GDP: GDP with adjusted inflation (Annual version unit: billions of chain 2012 dollars in, Monthly version unit: Annualised change).
• Mortgage_Rate: Interest charged on mortgages (unit: percentage).
• Real_Disposable_Income (Real Disposable Personal Income): Money left from salary after all the taxes are paid (unit: billions of chain 2012 dollars).
• Inflation: Decline in purchasing power over time (unit: percentage). [Forgot to remove this column in Annual version since CPI is one of the measures used to determine inflation].

What can you do with this dataset?

• Perform statistical analysis, find significant features & find the value by which these features affect the house price index (recommend to use a percentage change instead of index).
• Perform multivariate regression and predict the price of houses using microeconomic features (soon).

Thanks! If you like this dataset, I'll appreciate it if you give this dataset a vote! Discussions, suggestions & doubts are always welcome. Happy Learning!!