This USA Housing Market Dataset (Synthetic) contains 300 rows and 10 columns of real estate-related data designed for housing price prediction, trend analysis, and investment insights. It includes key property details such as price, number of bedrooms and bathrooms, square footage, year built, garage spaces, lot size, zip code, crime rate, and school ratings.

This dataset is ideal for: ✅ Machine Learning Models for predicting housing prices ✅ Market Research & Investment Analysis ✅ Exploring Property Trends in the USA ✅ Educational Purposes for Data Science and Analytics

This dataset provides a realistic yet synthetic view of the real estate market, making it useful for data-driven decision-making in the housing industry.

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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

The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

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].

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?

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

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

The "Dubai Real Estate Transaction First Semester 2023" dataset offers a comprehensive collection of real estate transaction data from the first six months of 2023 in Dubai. With over 81,000 entries, this dataset provides valuable insights into the dynamic and ever-evolving Dubai real estate market.

In addition to this dataset, there are two other complementary datasets available for integration: "Dubai Real Estate Explorer: Unlocking Area Coordinates" and "Dubai Real Estate Projects: Mapping Coordinates."

Integrating these datasets unlocks several advantages for comprehensive real estate analysis. By combining the "Dubai Real Estate Transaction First Semester 2023" dataset with the "Dubai Real Estate Explorer: Unlocking Area Coordinates," users gain the ability to link transaction data with specific geographical locations. This integration allows for spatial analysis, identifying transaction patterns within specific areas of Dubai and assessing the impact of location on property values and trends.

Furthermore, integrating the "Dubai Real Estate Projects: Mapping Coordinates" dataset provides valuable context to transaction data by mapping the coordinates of real estate projects. This integration allows users to identify the proximity of transactions to specific projects, understand project-based transaction trends, and assess the influence of project location and popularity on transaction dynamics.

By combining these three datasets, users can gain a comprehensive understanding of Dubai's real estate market. They can analyze transaction data in the context of area coordinates, identify transaction patterns within specific projects, explore the spatial distribution of transactions, and make data-driven decisions based on a holistic view of the market.

Integrating these datasets empowers real estate professionals, investors, researchers, and analysts with a powerful toolkit to analyze market trends, identify investment opportunities, understand spatial dynamics, and make informed decisions in Dubai's dynamic real estate landscape.

Unlock the full potential of these integrated datasets to gain deeper insights and maximize your understanding of the Dubai real estate market, enabling you to make strategic and informed decisions.

"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"

This dataset contains real estate information in the Philippines as of 2024/03/20. It includes the description, location, prices, number of bedrooms and bathrooms, floor and land area, and geographical coordinates.

The data was scraped from Lamudi using Python and Beautiful Soup 4. The geocoding was done by GeoPy.

This dataset was inspired by other real estate datasets in the PH and serves as an update to them. You can freely use this for market analysis, property valuation, and trend prediction.

ContextHousing price of Beijing from 2011 to 2017, fetching from Lianjia.comContentIt includes URL, ID, Lng, Lat, CommunityID, TradeTime, DOM(days on market), Followers, Total price, Price, Square, Living Room, number of Drawing room, Kitchen and Bathroom, Building Type, Construction time. renovation condition, building structure, Ladder ratio( which is the proportion between number of residents on the same floor and number of elevator of ladder. It describes how many ladders a resident have on average), elevator, Property rights for five years（It's related to China restricted purchase of houses policy), Subway, District, Community average price.Most data is traded in 2011-2017, some of them is traded in Jan,2018, and some is even earlier(2010,2009)All the data was fetching from https://bj.lianjia.com/chengjiao.AcknowledgementsAll the data was fetching from LianjiaInspirationIt may help you predict the housing price of Beijing.

Context This dataset is a record of every building or building unit (apartment, etc.) sold in the California property market along with the customer data.

Content Real estate is property consisting of land and the buildings on it, along with its natural resources such as crops, minerals or water; immovable property of this nature; an interest vested in this (also) an item of real property, (more generally) buildings or housing in general.

Inspiration

What can you discover about California real estate by looking at a year's worth of raw transaction records? Can you spot trends in the market, or build a model that predicts sale value in the future?

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?

🏡💰🏠Housing Market Data Attributes 🏠💰🏡

In the analysis of the housing market, it's essential to understand the various attributes that characterize each housing unit. These attributes provide valuable information for potential buyers, renters, and investors, helping them make informed decisions about their housing choices. Below, we describe the key data attributes used in our housing market analysis:

• Bedroom Count: This attribute represents the number of bedrooms in the housing unit, providing insights into its size and capacity.

• Net Square Meters (Net Sqm): Net square meters refer to the total usable interior space within the housing unit, excluding common areas like corridors and stairwells. It quantifies the size of the property.

• Center Distance: This attribute measures the distance of the housing unit from the central or downtown area of a city or town. It is a valuable metric for potential buyers or renters to assess proximity to urban amenities and activities.

• Metro Distance: Metro distance indicates the distance between the housing unit and the nearest metro or subway station. This information is particularly useful for individuals who rely on public transportation for their daily commute.

• Floor: The floor attribute specifies the level or story of the housing unit within the building, offering insights into its placement and accessibility within the structure.

• Age: The age of the property represents the number of years since its construction or renovation. It plays a crucial role in assessing the condition of the property and potential maintenance requirements.

• Price: Price is the cost associated with purchasing or renting the housing unit. It is a fundamental factor for individuals making housing decisions and can be influenced by various attributes such as bedroom count, size, location, and age.

This dataset from Zillow contains a smoothed, seasonally adjusted measure of the typical home value and market changes across across various regions in the United States from January 2000 to August 2022. The data includes monthly observations for hundreds of cities and states, offering insights into regional housing market trends over two decades.

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

Dataset

This dataset was created by Sarthak Tyagi

Contents

Dataset includes info about real estate objects in Moscow. The further purpose of this dataset is to visualize and explore the real estate market and, moreover, to make a choice which one room appertment to rent.

This dataset provides a comprehensive view of the Portuguese housing market, integrating both listing and official transaction data. Initially compiled from historical reports by Idealista, it includes €/m² prices for sales and rentals across various Portuguese regions.

Now, this dataset has been significantly enhanced with official transaction data from the Instituto Nacional de Estatística (INE) of Portugal. This addition includes quarterly values and counts of housing transactions at a national level, providing a crucial perspective on actual market activity beyond listing prices.

This consolidated dataset is a core component of a broader case study exploring housing affordability, investment potential, and regional development across Portugal. It enables a more robust analysis by allowing comparison between asking prices and actual transaction values, as well as insights into market volume.

Additional socioeconomic data will be gradually integrated to further enrich the analysis, such as:

• Minimum and average wages (INE)
• New construction volume (INE)
• Migration and population trends
• Interest rates (Banco de Portugal)

🔗 Full pipeline and source files, including data cleaning scripts and analysis notebooks, are available on GitHub: https://github.com/igor-marques/portugal-housing-market-capstone

Data Sources Included: * Idealista: Historical listing prices (€/m²) for sales and rentals across Portuguese regions. * Instituto Nacional de Estatística (INE): Official quarterly data on housing transaction values and counts for Portugal (from Q1 2009 to Q1 2025).