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

Dataset

This dataset was created by Meenakshi Sajan

Contents

Analysis of ‘Housing Prices Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/alphaepsilon/housing-prices-dataset on 13 November 2021.

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

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

Dataset

This dataset was created by abdo elsayed

Released under Apache 2.0

Contents

UCLA Housing dataset

Analysis of ‘Housing Price Data of Mumbai’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/goelyash/housing-price-data-of-mumbai on 30 September 2021.

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

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.

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

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

Hyderabad House Price Dataset source: https://www.kaggle.com/datasets/faisal012/hyderabad-house-price description This dataset contains information about house prices in Hyderabad, India. It includes features such as area, location, number of bedrooms,rate per sqft and price. It is useful for training regression models to predict housing prices.

Analysis of ‘Housing Prices in London’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arnavkulkarni/housing-prices-in-london on 30 September 2021.

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

Content

This dataset comprises of various house listings in London and neighbouring region. It also encompasses the parameters listed below, the definitions of which are quite self-explanatory. • Property Name • Price • House Type - Contains one of the following types of houses (House, Flat/Apartment, New Development, Duplex, Penthouse, Studio, Bungalow, Mews) • Area in sq ft • No. of Bedrooms • No. of Bathrooms • No. of Receptions • Location • City/County - Includes London, Essex, Middlesex, Hertfordshire, Kent, and Surrey. • Postal Code

Inspiration

This dataset has various parameters for each house listing which can be used to conduct Exploratory Data Analysis. It can also be used to predict the house prices in various regions of London by means of Regression Analysis or other learning methods.

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

Dataset

This dataset was created by Christian Noel Molina

Contents

Mumbai House Price Dataset (70k+ Entries)

Dataset Overview

This dataset provides detailed information on housing prices in Mumbai, India. It includes over 70,000 entries and is ideal for analyzing various factors affecting real estate prices in the city. The dataset captures key aspects of residential properties such as price, area, property type, and more, enabling detailed insights into the real estate market trends.

Note: This data is based on the year 2024

Sources

This dataset has been scraped from makaan.com using Python and Requests library

Potential Use Cases

• Real Estate Market Analysis: Understanding property price trends across different localities and neighborhoods in Mumbai.
• Price Prediction Models: Building machine learning models to predict housing prices based on features like area, property type, and location.

Data Quality

All columns in this dataset are fully populated with non-null values

Dataset

This dataset was created by Mohamed Jamyl

Released under Apache 2.0

Contents

Dataset

This dataset was created by Carmen Azpeitia

Released under CC BY-NC-SA 4.0

Contents

Analysis of ‘Boston housing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/altavish/boston-housing-dataset on 30 September 2021.

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

Domain: Real Estate

Difficulty: Easy to Medium

Challenges: 1. Missing value treatment
2. Outlier treatment 3. Understanding which variables drive the price of homes in Boston

Summary: The Boston housing dataset contains 506 observations and 14 variables. The dataset contains missing values.

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

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: Price in dollars Address of the property Latitude and Longitude of the address obtained by using Google Geocoding service Area Name of the property obtained by using Google Geocoding service This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas) However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes). This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/ I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

Analysis of ‘Bengaluru House price data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data on 12 November 2021.

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

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

Dataset

This dataset was created by Kat Hernandez

Contents

Dataset

This dataset was created by Masayu Anandita

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Fahad QureXhi

Released under Apache 2.0

Contents

About Dataset

Edit Context: This data gives predicted sales prices of the houses.

Content: There are only 2 variables which gives house property ID and predicted variable is in last Sales price of the house.

Acknowledgements: Please compare all the variable with respect to sales price and try to create different model, come up with the solution for sales price predictions of the house.

Technique Used: Data Cleansing Handling Categorical Features Concatenation XGBoost Regressor