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 Listing Price per Square Feet in the United States (MEDLISPRIPERSQUFEEUS) from Jul 2016 to Oct 2025 about square feet, listing, median, price, and USA.

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.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Texas (MEDLISPRIPERSQUFEETX) from Jul 2016 to Oct 2025 about square feet, TX, listing, median, price, and USA.

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.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Ohio (MEDLISPRIPERSQUFEEOH) from Jul 2016 to Oct 2025 about square feet, OH, listing, median, price, and USA.

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 ...

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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.

Dataset Overview :

This dataset is created as part of a machine learning mini project on House Price Prediction in India. It includes key features commonly used to predict house prices such as:

1) Number of bedrooms 2) Property type (e.g., Apartment, House) 3) Location 4) Area in square feet 5) Price per square foot 6) Total price

Column Description :

Usage :

This dataset can be used to: --> Build a house price prediction model using ML algorithms --> Perform data visualization or feature correlation --> Understand real estate pricing trends in India

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

Content

The dataset consists of Price of Houses in King County , Washington from sales between May 2014 and May 2015. Along with house price it consists of information on 18 house features, date of sale and ID of sale.

Attribute information

1. id - Unique id for each home sold
2. date - Date of the home saled
3. price - Price of each home sold
4. bedrooms - Number of bedrooms
5. bathrooms - Number of bathrooms
6. sqft _ living - Square footage of the apartments interior living space
7. sqft _ lot - Square footage of the land space
8. floors - Number of floors
9. waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not
10. view - An index from 0 to 4 of how good the view of the property was
11. condition - an index from 1 to 5 on the condition of the apartment
12. grade - An index from 1 to 13 , where 1-3falls short of building construction and design, 7 has an average level of construction and design , and 11-13 have a high quality level of construction and design
13. sqft _ above - the square footage of the interior housing space that is above ground level
14. sqft _ basement - the square footage of the inerior housing space that is below ground level
15. yr _ built - The year of the house was initially built
16. yr _ renovated - The year of the house's last renovation
17. zipcode - What zipcode area the house is in
18. lat - Lattitude
19. long - Longitude
20. sqft _ living15 - The square footage of inerior housing living space for the nearest nearest 15 neighbours
21. sqft _ lot15 - the square footage of the land lots of the nearest 15 neighbours

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.

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.

Housing Inventory: Median Listing Price per Square Feet in Virginia was 230.00000 U.S. $ in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Listing Price per Square Feet in Virginia reached a record high of 235.00000 in May of 2025 and a record low of 131.00000 in December of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Listing Price per Square Feet in Virginia - last updated from the United States Federal Reserve on November of 2025.

United States Price per Square: Existing: Single Family Residential data was reported at 216.000 USD in Mar 2023. This records an increase from the previous number of 209.000 USD for Feb 2023. United States Price per Square: Existing: Single Family Residential data is updated monthly, averaging 139.000 USD from Jan 2012 (Median) to Mar 2023, with 135 observations. The data reached an all-time high of 234.000 USD in May 2022 and a record low of 86.000 USD in Feb 2012. United States Price per Square: Existing: Single Family Residential data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB115: Median Home Sale Price per Square Foot.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Florida (MEDLISPRIPERSQUFEEFL) from Jul 2016 to Oct 2025 about square feet, FL, listing, median, price, and USA.

The data was extracted from Zillow.. Zillow is a prominent online real estate marketplace and has data on around 100 million homes The goal is to create a rich and diverse dataset that encompasses a wide range of housing characteristics across different states, cities, and neighborhoods in the United States.This dataset provides valuable insights into real estate trends and property features. Each record represents a unique house listing and includes details such as location, property specifications, market estimates, and more. A total of 3 files are included, more about them in the file description.

Feature Description:

1. State: The state in which the property is located (AL:Alabama) . Includes all US states except Hawaii.
2. City: The city where the property is situated.
3. Street: The street address of the property.
4. Zipcode: The postal code associated with the property.
5. Bedroom: The number of bedrooms in the house.
6. Bathroom: The number of bathrooms in the house.
7. Area(sqft): The total area of the house.
8. PPSq(Price Per Square Foot): The cost per square foot of the property.
9. LotArea(acres): The total land area associated with the property.
10. MarketEstimate(Dollars $): Estimated market value of the property. This value is estimated using Zillow's own algorithm.
11. RentEstimate:(Dollars $) Estimated rental value of the property. This value is estimated using Zillow's own algorithm.
12. Latitude: The latitude coordinates of the property.
13. Longitude: The longitude coordinates of the property.
14. ListedPrice:(Dollars $) The listed price of the property.

Potential Use Cases:

• Real Estate Market Analysis: Explore trends in housing prices, market estimates, and rental values across different states and cities.
• Predictive Modeling: Build predictive models to estimate property prices or rental values based on various features.
• Feature Engineering: Create new features or derive insights to enhance machine learning models.

Dataset includes house sale prices for King County in USA. Homes that are sold in the time period: May, 2014 and May, 2015.

Columns: - ida: notation for a house - date: Date house was sold - price: Price is prediction target - bedrooms: Number of Bedrooms/House - bathrooms: Number of bathrooms/House - sqft_living: square footage of the home - sqft_lot: square footage of the lot - floors: Total floors (levels) in house - waterfront: House which has a view to a waterfront - view: Has been viewed - condition: How good the condition is ( Overall ) - grade: overall grade given to the housing unit, based on King County grading system - sqft_abovesquare: footage of house apart from basement - sqft_basement: square footage of the basement - yr_built: Built Year - yr_renovated: Year when house was renovated - zipcode: zip - lat: Latitude coordinate - long: Longitude coordinate - sqft_living15: Living room area in 2015(implies-- some renovations) - sqft_lot15: lotSize area in 2015(implies-- some renovations)

Description:

This dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.

Key Features:

• BROKERTITLE: Title of the broker
• TYPE: Type of the house
• PRICE: Price of the house
• BEDS: Number of bedrooms
• BATH: Number of bathrooms
• PROPERTYSQFT: Square footage of the property
• ADDRESS: Full address of the house
• STATE: State of the house
• MAIN_ADDRESS: Main address information
• ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
• LOCALITY: Locality information
• SUBLOCALITY: Sublocality information
• STREET_NAME: Street name
• LONG_NAME: Long name
• FORMATTED_ADDRESS: Formatted address
• LATITUDE: Latitude coordinate of the house
• LONGITUDE: Longitude coordinate of the house

Potential Use Cases:

• Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
• Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
• Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
• Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
• Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Denver-Aurora-Lakewood, CO (CBSA) (MEDLISPRIPERSQUFEE19740) from Jul 2016 to Oct 2025 about Denver, square feet, CO, listing, median, price, and USA.