The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting ****** U.S. dollars per square foot in 2024. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in the United States (MEDSQUFEEUS) from Jul 2016 to Oct 2025 about square feet, median, and USA.

In 2018, the average home size in the United States was ***** square feet, and the size of the average home is set to fall to ***** square feet by 2023.

In 2024, the average size of a single-family home built for sale in the United States amounted to ***** square feet. Although in the past five years American homes have been shrinking, since 1975, they have almost doubled in size. This trend towards larger homes seems illogical given that the average size of families has shrunk over the same period. Why are American homes so large? Homes in the U.S. are among the largest in the world, only surpassed by Australia. There are thought to be several reasons for this, including the concentration of wealth in the country and the deeply engrained driving culture, which means that cheaper land outside city centers is easily accessible. Where are the largest homes located? The size of homes also varies regionally, with the largest homes being located in wealthy, urban areas and in the South. Large homes, or McMansions as they’re often called, are especially popular in Texas. In 2024, Milwaukee and Omaha had the largest average home size.

House prices in the second most populous state in the United States, Texas have doubled since 2011. In 2023, the average house price reached ***** U.S. dollars per square foot, up from approximately *** U.S. dollars in 2020. Despite the increase, the median home price was still below the national average.

Prices for luxury housing in July 2024 were slightly lower than the market peak in 2021 and 2022. Luxury single-family properties had a median square footage price of *** U.S. dollars in July 2024, down from *** U.S. dollars in July 2022. Attached houses, on the other hand, had a median price of *** U.S. dollars per square foot, down from *** U.S. dollars in July 2021.

In 2022, San Mateo, San Francisco, and Santa Clara were the most expensive districts for housing in the San Francisco Bay Area. In San Francisco, the average square footage price of single-family homes exceeded 1,000 U.S. dollars per square foot. Housing in Solano, on the other hand, was most affordable, with the average square footage price for single family homes at *** U.S. dollars.

How expensive is buying a home in San Francisco? Few metros in the U.S. are more expensive than San Francisco, CA. In 2022, the median sales price of existing single-family homes in San Francisco was about *** million U.S. dollars, making it the second priciest market in the U.S. House prices in the Golden City, were not always so high: in 2014, a two-bedroom house in the Bay Area would sell for less than ******* U.S. dollars but since then, the median price has more than doubled.

How much does renting an apartment cost? Despite rents falling in 2020, renting in San Francisco is still far from cheap. Renting a two-bedroom apartment cost close to ***** U.S. dollars in 2021. California is one of the least affordable states for renters. In fact, to afford to rent such an apartment, a household needs approximately ***** full time jobs at minimum wage or *** full time jobs at mean wage.

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in Los Angeles County, CA (MEDSQUFEE6037) from Jul 2016 to Oct 2025 about Los Angeles County, CA; Los Angeles; square feet; CA; median; and USA.

In 2017, Australia had the largest homes out of the countries surveyed. Australian homes measured, on average, ***** square feet, and were closely followed by U.S. homes at ***** square feet. Canadian homes were also quite large at ***** square feet.

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in California (MEDSQUFEECA) from Jul 2016 to Oct 2025 about square feet, CA, median, and USA.

Between July 2023 and July 2024, the average size of luxury homes sold in North America decreased. In 2024, the average size of a single-family home was ***** square feet, while the average size of an attached home was ***** square feet.

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in Florida (MEDSQUFEEFL) from Jul 2016 to Oct 2025 about square feet, FL, median, and USA.

The average price per square foot of floor space in new single-family houses in North-east, United States increased from 2000 to 2021. In 2021, the average price for a new single-family house in that region was approximately *** U.S. dollars per square foot of floor space.

In 2018, the average detached single-family home size in the United States was ***** square feet, and the size of the average home is set to reach ***** square feet by 2023.

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in Chicago-Naperville-Elgin, IL-IN-WI (CBSA) (MEDSQUFEE16980) from Jul 2016 to Oct 2025 about Chicago, square feet, WI, IN, IL, median, and USA.

Between 2020 and 2022, the average square footage price of one-to-three family homes in Queens, New York increased. Existing condo prices, on the other hand, fell from ***** U.S. dollars per square foot in the ********************* to *** U.S. dollars per square foot in the *********************.

Redfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)

The dataset has the following columns: - period_begin - period_end - period_duration
- region_type
- region_type_id - table_id - is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False) - region - city - state - state_code - property_type - property_type_id - median_sale_price
- median_sale_price_mom (median sale price changes month over month) - median_sale_price_yoy (median sale price changes year over year) - median_list_price
- median_list_price_mom (median list price changes month over month) - median_list_price_yoy (median list price changes year over year) - median_ppsf (median sale price per square foot) - median_ppsf_mom (median sale price per square foot changes month over month) - median_ppsf_yoy (median sale price per square foot changes year over year) - median_list_ppsf (median list price per square foot) - median_list_ppsf_mom (median list price per square foot changes month over month) - median_list_ppsf_yoy. (median list price per square foot changes year over year) - homes_sold (number of homes sold) - homes_sold_mom (number of homes sold month over month) - homes_sold_yoy (number of homes sold year over year) - pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings - new_listings_mom
- new_listings_yoy
- inventory - inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom - months_of_supply_yoy
- median_dom (median days on market until property is sold) - median_dom_mom (median days on market changes month over month) - median_dom_yoy (median days on market changes year over year) - avg_sale_to_list (average sale price to list price ratio) - avg_sale_to_list_mom (average sale price to list price ratio changes month over month) - avg_sale_to_list_yoy (average sale price to list price ratio changes year over year) - sold_above_list
- sold_above_list_mom - sold_above_list_yoy - price_drops - price_drops_mom - price_drops_yoy - off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks) - off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month) - off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year) - parent_metro_region - parent_metro_region_metro_code - last_updated

Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html

Data Source & Credit: Redfin.com

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 ...

Housing Inventory: Median Home Size in Square Feet Year-Over-Year in South Carolina was 1.69% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Home Size in Square Feet Year-Over-Year in South Carolina reached a record high of 7.09 in February of 2022 and a record low of -9.37 in April of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Home Size in Square Feet Year-Over-Year in South Carolina - last updated from the United States Federal Reserve on December of 2025.

In 2018, the average mobile home size in the United States was ***** square feet, and the size of the average mobile home is set to fall to ***** square feet by 2023.