The majority of the U.S. housing stock was between 42 and 51 years old as of 2021. According to the source, the median year was 1979, meaning that the median house age was 42 years. Housing construction in the U.S. plummeted between 2005 and 2010 and has since been slow to recover.

The majority of home buyers across all age groups in the United States purchased a detached single-family home in 2024. The share of home buyers that purchased such a home was at least ** percent across all generations.

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Average age and remaining useful service life ratio of Canadian residential housing assets. Annual estimates are available by province and territory, type of asset, and type of dwelling.

The majority of home buyers in the United States in 2024 across all age groups purchased a home between ***** and ***** square feet in size. ** percent of the young millennials (26 to 34 years old) and ** percent of the silent generation (79 to 99 years old) purchased a home about the same feet in size.

Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Age: from Age 55 to 64 (CXU800721LB0406M) from 1984 to 2023 about owned, age, market value, information, estimate, financial, housing, and USA.

Graph and download economic data for Consumer Unit Characteristics: Age of Reference Person by Housing Tenure: Homeowner with Mortgage (CXU980020LB1703M) from 2003 to 2023 about consumer unit, age, homeownership, mortgage, personal, housing, and USA.

Most of England's housing stock is owner occupied and built before 1919. Among the homes built after 2002, about 2.1 million homes were owner occupied, about 654,000 were privately rented and approximately 459,000 were social housing. The largest share of social housing was found in buildings built between 1945 and 1980. In 2024, there were around 15.8 million owner occupied households in England.

Graph and download economic data for Expenditures: Housing by Age: Age 65 or over (CXUHOUSINGLB0407M) from 1988 to 2023 about 65-years +, age, expenditures, housing, and USA.

Graph and download economic data for Expenditures: Rented Dwellings by Age: from Age 25 to 34 (CXURNTDWELLLB0403M) from 1984 to 2023 about age, 25 years +, rent, expenditures, housing, and USA.

Older housing can impact the quality of the occupant's health in a number of ways, including lead exposure, housing quality, and factors that may exacerbate respiratory conditions, like asthma. Data from the U.S. Census Bureau contains Census Tract estimates of housing age, and Allegheny County assessment data provides parcel-level information on the year residential properties were built.

About this dataset

Context

The dataset tabulates the population of House by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for House. The dataset can be utilized to understand the population distribution of House by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in House. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for House.

Key observations

Largest age group (population): Male # 60-64 years (7) | Female # 60-64 years (9). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the House population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the House is shown in the following column.
• Population (Female): The female population in the House is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in House for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for House Population by Gender. You can refer the same here

This layer shows housing costs as a percentage of household income by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the predominant housing type for householders where the householder is age 65+ and spending at least 30% of their income on housing. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25072, B25093 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

In 2022, people between 18 and 34 years of age made up the majority of homebuyers in Italy. About ** percent of homebuyers fell within this age group. Buyers aged between 35 and 44 followed with nearly ** percent. On the other hand, people aged 65 or more accounted for less than **** percent of the houses purchased.

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 paper provides estimates of the net depreciation rate for rental housing using a unique confidential data set from the Bureau of Labor Statistics that covers over 30,000 rental units from 1998 to 2009. Our data and econometric approach allow us to add to the literature in three main ways. First, we can control for unobserved quality (including cohort effects) by allowing for unit-specific fixed effects. Our results suggest that estimates of the depreciation rate for rental housing that ignore unobserved heterogeneity suffer from omitted-variable bias and potentially from selection bias, and that these biases can be large. Second, we use a dummy-variable approach to estimate aging effects, thereby avoiding ad hoc assumptions about functional form. We find that rent for a typical housing unit at first falls rapidly with age, flattens, and then begins to rise with age for older units. This nonmonotonic pattern is a feature of many other studies of both age-rent and age-price profiles for housing, and it seems likely that the upward-sloped portion of such profiles is the result of unobserved improvements and changes in style. We show that the upward slopes of our estimated profiles are only partially eliminated when we attempt to control for major improvements by excluding housing units that see very large jumps in rent. Third, we present estimates of age-rent profiles separately for different types of structures and for different regions of the country.

This indicator is defined as the median of the distribution of the share of total housing costs (net of housing allowances) in the total disposable household income (net of housing allowances) presented by age group.

The data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals. This dataset is not publicly accessible because: The data is not owned by the EPA and contains protected information in the form of residential history and thus cannot be uploaded into ScienceHub. It can be accessed through the following means: The data can be accessed by contacting Dr. Chantel Martin. Format: Data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals concentrations. This dataset is associated with the following publication: Lodge, E., C. Martin, R.C. Fry, A. White, C. Ward-Caviness, S. Martin, and A. Aiello. Objectively measured external building quality, Census housing vacancies and age, and serum metals in an adult cohort in Detroit, Michigan. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 177-186, (2023).

Real estate dataset contains 414 entries with detailed information on real estate transactions. It encompasses several features that are typically influential in real estate pricing:

Transaction date: Date of the property transaction. House age: Age of the property in years. Distance to the nearest MRT station: Proximity to the nearest Mass Rapid Transit station in meters, is a key factor considering convenience and accessibility. Number of convenience stores: Count of convenience stores in the vicinity, indicating the property’s accessibility to basic amenities. Latitude and Longitude: Geographical coordinates of the property, reflecting its location. House price of unit area: The target variable, represents the house price per unit area.

Age Friendly - Home Ownership/Renter Stats Ages 75 to 84. Data Source US Dept of Housing and Urban Development (HUD)