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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.

Context

So this data set is collected for completing a college project ,which is an android app for calculating the price of houses.

Content

This data is scraped from magic bricks website between june 2021 and july 2021 .

Acknowledgements

magicbricks.com

Inspiration

With the help of the data available one can make a regression model to predict house prices.

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

Description:

This dataset contains various features of residential properties along with their corresponding prices. It is suitable for exploring and analyzing factors influencing housing prices and for building predictive models to estimate the price of a property based on its attributes.

Usage:

• This dataset can be used for exploratory data analysis to understand the relationships between different housing features and prices.
• It can also be used to build machine learning models for predicting housing prices based on the given features.

License: This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Average House Price

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 ...

Context

House Price prediction Mini Dataset For Begging notebooks

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

House Prices Dataset

Subtitle:

Detailed Real Estate Data for Predicting House Prices and Analyzing Market Trends

Description:

This dataset contains information on 21,613 properties, making it a comprehensive resource for exploring real estate market trends and building predictive models for house prices. The data includes various features capturing property details, location, and market conditions, providing ample opportunities for data exploration, visualization, and machine learning applications.

Key Features:

• General Information:

  • id: Unique identifier for each property.
  • date: Date of sale.

• Price Details:

  • price: Sale price of the house.

• Property Features:

  • bedrooms: Number of bedrooms.
  • bathrooms: Number of bathrooms (including partials as fractions).
  • sqft_living: Living space area in square feet.
  • sqft_lot: Lot size in square feet.
  • floors: Number of floors.
  • waterfront: Whether the property has a waterfront view.
  • view: Quality of the view rating.
  • condition: Overall condition of the house.
  • grade: Grade of construction and design (scale of 1–13).

• Additional Metrics:

  • sqft_above: Square footage of the property above ground.
  • sqft_basement: Basement area in square feet.
  • yr_built: Year the property was built.
  • yr_renovated: Year of last renovation.

• Location Coordinates:

  • zipcode: ZIP code of the property.
  • lat and long: Latitude and longitude coordinates.

• Neighbor Comparisons:

  • sqft_living15: Average living space of 15 nearest properties.
  • sqft_lot15: Average lot size of 15 nearest properties.

Use Cases:

• Predicting house prices using regression models.
• Identifying the impact of various features (e.g., number of bedrooms, location) on property prices.
• Analyzing market trends and spatial distribution of real estate prices.

This dataset is a valuable resource for anyone interested in real estate analytics, machine learning, or geographic data visualization.

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.

The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.

Key Features:

• Record Count: 2 million housing listings from across the USA.
• Data Fields: Property address, price, property type, bedrooms, bathrooms, square footage, lot size, year built, and availability.
• Format: CSV format for easy integration with data analysis platforms, machine learning models, and real estate tools.
• Source: Directly sourced from Homes.com’s USA real estate listings.
• Geographical Focus: Comprehensive coverage of properties across all regions of the United States.

Use Cases:

• Real Estate Market Research: Analyze property prices, market trends, and housing demand in various U.S. regions.
• Investment Analysis: Use data to identify high-potential properties and regions for real estate investments.
• Property Comparison: Compare listings by price, location, and features to evaluate market conditions across different cities and states.
• Machine Learning Models: Build predictive models for price forecasting, property valuation, and real estate recommendation systems.
• Content Creation: Create real estate-related content, reports, and insights for the U.S. housing market using up-to-date data.

UK house prices since 1953 as monthly time-series. Data comes from the Nationwide.

Data can be found in the data/data.csv file. See datapackage.json for source info.

Source: http://www.nationwide....

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

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

This dataset contains 2,000 entries of house price data from all states in Malaysia, providing a comprehensive overview of the country’s real estate market for 2025. Sourced from Brickz, a trusted platform for property transaction insights, it includes detailed information such as property location, tenure, type, median prices, and transaction counts. This dataset is ideal for real estate market analysis, predictive modeling, and exploring trends across Malaysia’s diverse property market.

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Data Columns (Total 8 Columns):

1. Township: The specific township where the property is located (e.g., Cheras, Subang Jaya).
2. Area: The locality or broader area encompassing the township (e.g., Klang Valley, Penang Island).
3. State: The Malaysian state where the property is situated (e.g., Selangor, Johor, Penang).
4. Tenure: The property ownership type (e.g., Freehold, Leasehold).
5. Type: The category of property (e.g., Terrace, Condominium, Semi-Detached).
6. Median_Price: The median price (in MYR) for properties in the specified township or area.
7. Median_PSF: The median price per square foot (in MYR) for properties.
8. Transactions: The number of recorded property transactions.

Future Plans:

• Expanded Coverage: This dataset will be regularly updated with additional property data to make it even more versatile.
• Enhanced Features: Future updates may include rental prices, amenities, or property-specific details to offer deeper insights into Malaysia’s housing market.

Dataset

This dataset was created by 173050055

Released under Other (specified in description)

Contents

A dataset comprising various variables around housing and demographics for the top 50 American cities by population.

Variables:

Zip Code: Zip code within which the listing is present.

Price: Listed price for the property.

Beds: Number of beds mentioned in the listing.

Baths: Number of baths mentioned in the listing.

Living Space: The total size of the living space, in square feet, mentioned in the listing.

Address: Street address of the listing.

City: City name where the listing is located.

State: State name where the listing is located.

Zip Code Population: The estimated number of individuals within the zip code. Data from Simplemaps.com.

Zip Code Density: The estimated number of individuals per square mile within the zip code. Data from Simplemaps.com.

County: County where the listing is located.

Median Household income: Estimated median household income. Data from the U.S. Census Bureau.

Latitude: Latitude of the zip code. ** Data from Simplemaps.com.**

Longitude: Longitude of the zip code. Data from Simplemaps.com.