House price prediction dataset

This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house

This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.

Shape of dataset : (6207, 40)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

Github Link : https://github.com/TusharPaul01/House-Price-Prediction

For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

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.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

The Synthetic House Price Prediction Datasets is a publicly available Kaggle dataset created by D.Madhan Raj for machine learning experiments. It features a single CSV file containing synthetic data on house attributes such as bedrooms, bathrooms, square footage, house age, location rating, and estimated prices in USD. Designed for regression tasks, the dataset allows users to practice predictive modeling without the constraints of real-world data privacy. It's licensed under Apache 2.0 and includes around 3,203 data rows, making it a handy resource for learning, prototyping, and fine-tuning models learning

Dataset

This dataset was created by EL-Hussein salah

Released under GPL 2

Contents

Analysis of ‘Amsterdam House Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/thomasnibb/amsterdam-house-price-prediction on 28 January 2022.

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Context

If you are like me, you might get overwhelmed when having to make big decisions such as buying a house. In such cases, I always like to go for a data driven approach, that will help me find an optimum solution. This involves two steps. First, we need to gather as much data as we can. Second, we need to define a metric for success.

Gathering housing prices requires some effort. A caveat is that the asking prices are not the prices to which the houses were actually sold. Defining a metric for success is somewhat subjective. I consider a house to be a good option if the house price is cheap compared to other listings in the area.

Content

The housing prices have been obtained from Pararius.nl as a snapshot in August 2021. The original data provided features such as price, floor area and the number of rooms. The data has been further enhanced by utilising the Mapbox API to obtain the coordinates of each listing.

Acknowledgements

Thanks to Pararius

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

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.

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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 ---

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

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

Similar Datasets

• Gender Pay Gap Dataset: LINK
• California Housing Prices Data (5 new features!): LINK
• Company Bankruptcy Prediction: LINK

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

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

Dataset

This dataset was created by johnkagglereg

Released under Apache 2.0

Contents

Analysis of ‘Delhi House Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/neelkamal692/delhi-house-price-prediction on 28 January 2022.

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

Content

This is not a comprehensive list, some of the attributes i left intentionally and some just couldn't extract. Dataset consists of 12 columns and 1259 rows. 6 of the features are numerical valued and rest are categorical. code for extracting Data is available at my Github account.

Acknowledgements

The Data has been extracted from MagicBricks (a website, provides common platform to property buyer and seller ).

Inspiration

I have done property price prediction on Boston Dataset, so i was wondering, if i can do it for Delhi properties too.

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

Dataset

This dataset was created by fehu.zone

Released under Other (specified in description)

Contents

Analysis of ‘Paris Housing Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mssmartypants/paris-housing-price-prediction on 28 January 2022.

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

Context

This is a set of data created from imaginary data of house prices in an urban environment - Paris. I recommend using this dataset for educational purposes, for practice and to acquire the necessary knowledge. What I'm trying to do next is to create a classification dataset with same data from this dataset, I'll add a new column for class attribute ofc. Here is a classification dataset ---> classification dataset <---

Content

What's inside is more than just rows and columns. You can see house details listed as column names.

Description

All attributes are numeric variables and they are listed bellow:

• squareMeters
• numberOfRooms
• hasYard
• hasPool
• floors - number of floors
• cityCode - zip code
• cityPartRange - the higher the range, the more exclusive the neighbourhood is
• numPrevOwners - number of prevoious owners
• made - year
• isNewBuilt
• hasStormProtector
• basement - basement square meters
• attic - attic square meteres
• garage - garage size
• hasStorageRoom
• hasGuestRoom - number of guest rooms
• price - predicted value

Inspiration

Idea was to create dataset that is good for regression and that gives adequate results.

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

Analysis of ‘Real estate price prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/quantbruce/real-estate-price-prediction on 28 January 2022.

--- No further description of dataset provided by original source ---

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

House price prediction Predicting house prices is a common task in data science and machine learning. Here's a high-level overview of how you might approach it:

Data Collection: Gather a dataset containing features of houses (e.g., size, number of bedrooms, location, amenities) and their corresponding prices. Websites like Zillow, Kaggle, or government housing datasets are good sources.

Data Preprocessing: Clean the data by handling missing values, encoding categorical variables, and scaling numerical features if necessary. This step ensures that the data is in a suitable format for training a model. Feature Selection/Engineering: Choose relevant features that are likely to influence house prices. You may also create new features based on domain knowledge or data analysis.

Model Selection: Select a regression model suitable for predicting continuous target variables like house prices. Common choices include Linear Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks.

Model Training: Split your dataset into training and testing sets to train and evaluate the performance of your model. You can further split the training set for validation purposes or use cross-validation techniques.

Model Evaluation: Assess the performance of your model using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).

Hyperparameter Tuning: Fine-tune your model's hyperparameters to improve its performance. Techniques like grid search or random search can be employed for this purpose.

Deployment: Once satisfied with your model's performance, deploy it to make predictions on new data. This could be as simple as saving the trained model and creating an interface for users to input house features.

Analysis of ‘HOUSE PRICE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kuntalmaity/house-price on 28 January 2022.

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

Context

The real estate markets, like those in Kolkata, present an interesting opportunity for data analysts to analyze and predict where property prices are moving towards. Prediction of property prices is becoming increasingly important and beneficial. Property prices are a good indicator of both the overall market condition and the economic health of a country. Considering the data provided, we are wrangling a large set of property sales records stored in an unknown format and with unknown data quality issues

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

"Charting the Realms of Real Estate: A Holistic and Expansive Dataset Curated for In-Depth House Price Prediction Analysis, Market Trends Evaluation, and Strategic Decision-Making in the Dynamic Landscape of Property Valuation and Investment"

Dataset

This dataset was created by Huda Imran

Contents

Title: Boston Housing Price Prediction Dataset

Description:

This dataset contains information about housing prices in Boston and is often used for regression analysis and predictive modeling. The dataset is based on the classic Boston Housing dataset, which is frequently used as a benchmark in machine learning.

Attributes:

1. CRIM (Per Capita Crime Rate): The per capita crime rate in the neighborhood.
2. ZN (Proportion of Residential Land Zoned for Large Lots): The proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS (Proportion of Non-Retail Business Acres): The proportion of non-retail business acres per town.
4. CHAS (Charles River Dummy Variable): A binary variable indicating whether the Charles River bounds the tract (1 if bounded, 0 otherwise).
5. NOX (Nitric Oxides Concentration): Nitric oxides concentration (parts per 10 million).
6. RM (Average Number of Rooms per Dwelling): The average number of rooms per dwelling.
7. AGE (Proportion of Owner-Occupied Units Built Prior to 1940): The proportion of owner-occupied units built prior to 1940.
8. DIS (Weighted Distances to Employment Centers): Weighted distances to five Boston employment centers.
9. RAD (Index of Accessibility to Radial Highways): An index representing accessibility to radial highways.
10. TAX (Full-Value Property Tax Rate per $10,000): The full-value property tax rate per $10,000.
11. PTRATIO (Pupil-Teacher Ratio): The pupil-teacher ratio by town.
12. B (1000(Bk - 0.63)^2 where Bk is the Proportion of Black Residents): A measure of the proportion of Black residents adjusted for an offset.
13. LSTAT (Percentage of Lower Status of the Population): The percentage of lower-status residents in the population.
14. MEDV (Median Value of Owner-Occupied Homes): The median value of owner-occupied homes in $1000s (Target Variable).

Objective:

Predict the median value of owner-occupied homes (MEDV) based on various features to gain insights into factors influencing housing prices.

Usage:

This dataset is suitable for regression tasks, machine learning practice, and understanding the dynamics of housing markets.

Citation:

The dataset is derived from the UCI Machine Learning Repository and can be cited as follows:

Harrison Jr., D., & Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102.

(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?

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

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

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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