Context

Insurance companies collect multiple features of a House and select which houses can be insured and what amount they can charge the Premium from them. So here I have collected data from multiple insurance companies in USA where features with house prices are given

Content

This data set has many property details from address to their location co ordinates nad many other features, use them to predict the House price

Inspiration

Multiple regression datasets have been published every one unique in their own way, Use of location coordinates and some other co-ordinates are new here.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

Existing Home Sales in the United States increased to 4010 Thousand in July from 3930 Thousand in June of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This file provides data about residential property sold in Ireland from 2010 to 2025 (as of June 12, 2025). It has a lot of rows (over 700,000), but not many columns, just the date of sale, address, county, price and a couple of other columns.

This is a very clean dataset which provides the opportunity to practice some basic skills.

As I play with it, I will add code and explain things in the Discussion. Hopefully someone else will read some of it and give some of these things a try.

House Price Index YoY in the United States decreased to 2.80 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

(https://www.kaggle.com/c/house-prices-advanced-regression-techniques) About this Dataset Start here if... You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.

Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting Acknowledgments The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Housing Index in the United States decreased to 434.40 points in May from 435.10 points in April of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

New Home Sales in the United States increased to 627 Thousand units in June from 623 Thousand units in May of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. The dwelling data is based on the Council's property rates database, using a simplified classification schema of Residential Apartment, House/Townhouse and Student Apartment. The count of dwellings per residential building is shown.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

This dataset is a snapshot from October 2022 of all 48 homes in a section of a neighborhood nearby a large university in Central Florida. All of the homes are single family homes featuring a garage, a driveway, and a fenced-in backyard. Data was gathered by hand (keyboard) via a collection of sites, including Zillow, Realtor, Redfin, Trulia, and Orange County Property Appraiser. All homes were built in the same year in the early 2000's and feature central air and all other utilities typical of contemporary suburban homes in the United States. The area is close to a university and a large portion of renters are college students and young professionals, as well as families and older adults.

There are 30 columns:

• HID: House ID, a unique identifier for each house (int from 1 to 48, not the actual address number) -Sqft: The Square Footage of the Interior of the house (int) -LandSqft: The Total Square Footage of the land (int) -Neighbors: The number of homes directly adjacent to each house (int) -Stories: The number of stories in each house (int) -Pool: Does the house have a pool (int, 0 for 'No', 1 for 'Yes') -Bedrooms: The number of bedrooms in each house (int) -Bathrooms: The number of bathrooms (full or half) in each house (int) -DateLastSold: The date on which the house was last sold (datetime) -PropertyTaxes2022: The annual property taxes for 2022 (float) -OwnedByBank: Is the house owned by a bank (int, 0 for 'No', 1 for 'Yes') -OuterPortion: Is the house on the Outer Portion of the Neighborhood (int, 0 for 'No', 1 for 'Yes') -NextToLoudRoad: Is the house directly adjacent to a loud road (int, 0 for 'No', 1 for 'Yes') -PriceLastSold: Price that the house was last sold for (float) -Zestimate: Zillow's Price Estimate for the house (float) -RentZestimate: Zillow's Estimate for the Monthly Price of rent for the house (float) -RealtorcomEstimate: Realtor dot com's Estimate for the house (float) -RedfinEstimate: Redfin's Estimate for the house (float) -TruliaEstimate: Trulia's Estimate for the house (float) -OCPALandValue2022: The Land Value on the county's 2022 records (float) -OCPABuildingValue2022: The Building Value on the county's 2022 records (float) -OCPAFeaturesValue2022: The Features Value on the county's 2022 records (float) -OCPAMarketValue2022: The Market Value on the county's 2022 records (float) -OCPAAssessedValue2022: The Assessed Value on the county's 2022 records (float), AKA what homeowners are taxed on -OCPALandValue2021: The Land Value on the county's 2021 records (float) -OCPABuildingValue2021: The Building Value on the county's 2021 records (float) -OCPAFeaturesValue2021: The Features Value on the county's 2021 records (float) -OCPAMarketValue2021: The Market Value on the county's 2021 records (float) -OCPAAssessedValue2021: The Assessed Value on the county's 2021 records (float), AKA what homeowners are taxed on -Notes: any notes on any of the homes (str)

Note that while the dataset is exhaustive in that it has all of the houses, some homes are missing some columns, typically because a home did not feature a estimate on a site or the one home not found on the property appraiser's site. This also is therefore not a randomized dataset, so the only population of homes that it can be used to infer on are those within this specific portion of the neighborhood. Personally, I am going to use the dataset to practice a couple of aspects of real-world data: Cleaning, Imputing, and Exploratory Data Analysis. Mainly, I want to compare different approaches to filling in the missing values of the dataset, then do some Model Building with some additional Dimensionality Reduction.

Analysis of ‘Jiffs house price prediction dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/elakiricoder/jiffs-house-price-prediction-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

I have previously shared a classification based dataset to classify the gender which is liked by those who are new to machine learning as it give a pretty good accuracy, which encouraged me to create a regression dataset to predict continues values. I have tried many real world datasets for regression problems which are predicting with lower accuracy and high error rate. As a beginner, I have struggled and worried why and how the dataset performs poorly. This is another main reason why I created this dataset. Although this is a made up dataset, I have considered all the features when deciding the price of the property. If you are a beginner, you would love to try this as the results are stunning..

Content

Since this is a populated data, I will straightaway explain the features and the label. FEATURES 1. land_size_sqm - This the total size of the land in square meters. 2. house_size_sqm - This is the area in which house is located within the land. This is measured in square meters. 3. no_of_rooms - This indicates the number of rooms available in the house. 4. no_of_bathrooms - This shows the number of total bathrooms made in the house. 5. large_living_room - This indicates whether the house includes a larger living room or not. The assumption is that all the houses contain a living room. This feature attempts to classify whether it's large or small where '1' means large and '0' means small. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 6. parking_space - This indicates whether there is a parking space or not. '1' represents the parking available while '0' represents no parking space available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 7. front_garden - This shows whether there is a garden available in front of the house. '1' means the garden available and '0' means no garden available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 8. swimming_pool - This shows the availability of the swimming pool at the house. 1 represents the availability of the swimming pool while 0 represents the non availability of the same. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 9. distance_to_school_km - This shows the distance from the house to the nearest school in Kilometers. 10. wall_fence - This shows whether there is a wall fence or not. '1' mean there is wall fence and '0' means no wall fence. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 11. **house_age_or_renovated **- This is either the age of the house in years or the period from the date of renovation. 12. water_front - this indicates whether the house is located in front of the water or not. 1 means waterfront and 0 means its not located near the water. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 13. distance_to_supermarket_km - what is the distance to the nearest supermarket in kilometers.

LABEL property_value - This is the price of the property

Following features are only available in the "house price dataset original v2 cleaned" and "house price dataset original v2 with categorical features" data only. 14. crime_rate - its in float and falls between 0 and 7. lesser the better 15. room_size - As the name suggests, it explains the size of the room. 0 is being 'small', 1 is being 'medium', 2 is 'large' and 3 is being 'Extra large'. However in the categorical dataset, these values are categorical and self explanatory.

Acknowledgements

I spent around 3 hours creating this dataset. Enjoy..

Inspiration

Share your notebooks to see which algorithm predicts the house price precisely.

--- Original source retains full ownership of the source dataset ---

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data marked with n/a over the period 1969 and 1973 are not available. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Figure changed on the 27/6/16 as revised data received from the Local authority Includes houses and apartments, measured in EUR

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in EUR Figure changed on the 27/6/16 as revised data received from the Local authority