This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

Here's a short description of the dataset:

Serial Number: Is just a unique set of digits to identify each transaction

List year: This is the year that the particular property was put up for sale.

Date Recorded: Is the date that the transaction was completed. That is, the year the property was bought.

Town: The town where this property is located.

Address: The property's address.

Assessed Value: How much the property is generally considered to be worth.

Sale Amount: How much the property was actually sold for.

Sales Ratio: The ratio measures how close the selling price of the property is to it's assessed value.

Property Type: What kind of property it is.

Residential Type: If it is a residential property, what type is it.

Years until sold: Number of years before the property was finally sold

This dataset can be used for analysis and even machine learning projects. For those doing analysis, I invite you to try and answer these questions: * Average assessed value of properties from year to year? * Average sale amount of properties from year to year? * Average sales ratio of properties from year to year? * How long, on average, did it take for the different property types to get sold? * How long, on average, did it take for the different residential types to get sold? * Which towns saw the most property sales in 2021?

For those more interested in using this dataset in machine learning projects to forecast future property prices, I invite you also. Let's learn from your work.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

High-quality, free real estate dataset from all around the United States, in CSV format. Over 10.000 records relevant to Real Estate investors, agents, and data scientists. We are working on complete datasets from a wide variety of countries. Don't hesitate to contact us for more information.

Context and Acknowledgements This dataset is inspired by and improves upon the City of New York's NYC Property Sales dataset. The dataset contains a record of every property sold in the New York City property market since 2003 (the first year sales data was first listed on the public record) and updates monthly to include rolling sales.

Please upvote if you found the dataset or additional resources helpful. 👍

Content This dataset contains the location, address, type, sale price, tax category, and sale date of properties sold.

• BOROUGH: Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• TAX CLASSES:
  • 1: Most Residential Properties up to Three Units, Vacant Lots Zoned for Residential, Condominiums Less Than Three Stories.
  • 2: All Other Residential Properties.
  • 3: Property with equipment owned by a gas, telephone, or electric company.
  • 4: All other Properties (Garages, Factories, Warehouses...)
• EASEMENT: A right that allows an entity to make limited use of another's real property.
• $0 Sales Prices: Indicates a transfer of ownership without a cash consideration.

For further reference on the fields in this dataset see the City of New York Department of Finance's Glossary of Terms and Building Codes.

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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.

The property listings dataset contains information about real estate properties available for sale or rent in Brazil. It includes details such as property type (apartment, house, commercial property), location (city, neighborhood), size (square footage, number of rooms), price, amenities, and contact information for the property owner or real estate agent. This dataset can be used for market analysis, property valuation, and identifying trends in the real estate market.

Sales and Rental Prices Dataset: The sales and rental prices dataset provides information about the prices of real estate properties in Brazil. It includes data on property transactions, including sale prices and rental prices per square meter or per month. This dataset can be used to analyze price trends, compare property prices across different regions, and identify areas with high or low real estate market demand.

Property Characteristics Dataset: The property characteristics dataset contains detailed information about the features and attributes of real estate properties. It includes data such as the number of bedrooms, bathrooms, parking spaces, floor plan, construction year, building amenities, and property condition. This dataset can be used for property classification, identifying popular property features, and evaluating property quality.

Geographical Data: Geographical data includes information about the location and spatial features of real estate properties in Brazil. It can include data such as latitude and longitude coordinates, zoning information, proximity to amenities (schools, hospitals, parks), and neighborhood demographics. This dataset can be used for spatial analysis, identifying hotspots or desirable locations, and understanding the neighborhood characteristics.

Property Market Trends Dataset: The property market trends dataset provides information about market conditions and trends in the real estate sector in Brazil. It includes data such as the number of property listings, average time on the market, price fluctuations, mortgage interest rates, and economic indicators that impact the real estate market. This dataset can be used for market forecasting, understanding market dynamics, and making informed investment decisions.

Real Estate Regulatory Data: Real estate regulatory data includes information about legal and regulatory aspects of the real estate sector in Brazil. It can include data on property ownership, property taxes, zoning regulations, building permits, and legal restrictions on property transactions. This dataset can be used for legal compliance, understanding property ownership rights, and assessing the legal framework for real estate transactions.

Historical Data: Historical real estate data includes past records and trends of property prices, market conditions, and sales volumes in Brazil. This dataset can span several years and can be used to analyze long-term market trends, compare current market conditions with historical data, and assess the performance of the real estate market over time.

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

Embark on a comprehensive exploration of Mumbai's vibrant real estate market with our meticulously curated dataset, comprising over 12,685 entries. This extensive collection encapsulates a diverse array of properties, ranging from residential to commercial, providing invaluable insights into the dynamic landscape of Mumbai's real estate sector. Whether you are a property enthusiast, data analyst, or investor, this dataset offers a rich tapestry of information, empowering you to make informed decisions in this bustling metropolis.

Dataset Highlights: The dataset encompasses an extensive array of columns, each revealing intricate details about the properties. From essential information like possession status, floor details, and pricing to more nuanced aspects such as developer details, amenities, and property uniqueness, every facet of a property transaction is meticulously documented. The dataset also features data related to maintenance, booking amounts, covered and carpet areas, and specific features like electricity and water status.

Granular Property Information: Explore nuances such as the type of property, ownership details, and the number of bedrooms and bathrooms. Uncover insights into the furnishing status, parking facilities, and the direction a property faces. The dataset delves into the transaction type, offering a glimpse into the variety of property dealings within the city. From luxury flats to standard apartments, the dataset captures the essence of Mumbai's diverse real estate offerings.

Geographic Insights: For those interested in the geographical distribution of properties, the dataset includes information on landmarks, area names, and the city itself. This geographical granularity allows users to analyze property trends across different regions of Mumbai.

Amenities and Beyond: In addition to property-specific details, the dataset includes an exhaustive list of amenities. Whether you're interested in proximity to schools, shopping centers, or specific luxury features like a swimming pool or a private terrace, this dataset provides a holistic view of the lifestyle offerings associated with each property.

Data Integrity: Carefully curated and verified, this dataset ensures data integrity, offering a reliable foundation for in-depth analyses and modeling. With information sourced meticulously, users can trust the accuracy of each entry.

Empower Your Real Estate Insights: Whether you're a real estate professional, a prospective homebuyer, or an investor seeking opportunities in Mumbai, this dataset serves as an invaluable resource. Gain a holistic understanding of the city's real estate dynamics, identify emerging trends, and make well-informed decisions with the Mumbai Real Estate Properties Dataset.

Important Points: - Data was being collected for over 10 months, since this is real estate-based data, prices can fluctuate a little bit based upon various worldwide scenarios occurred. - This dataset contains real projects ongoing and developed across Mumbai. Don't depend on prices given by us while buying any property mentioned in dataset since these are collected from various sources, prices can fluctuate a bit in real-life buying scenarios. Although there won't be big differences in price. We tried our best while dealing with collection of data in order to ensure credibility. - For amenities, we have used 0 and 1 where 0 stands for false and 1 for true. This indicates whether that property has that particular amenity or not. - NA means Not Available, the particular data was not available during collection.

Thank You

There are two files: - nsw_property_data.csv - Property data in NSW from 2001 - 3rd of April 2023 - nsw_property_archived_data.csv - Property data in NSW from 1990 - 2000

Objective - Property data is difficult to come by these days. Luckily in New South Wales - Australia, the NSW State Government has provided public dataset of the transactional property sales data (See link below) - The objective is to create a clean / comprehensive dataset with historical information of the property information in NSW Australia, based on the raw data provided by the government - Please reach out to me to provide any feedbacks / improvements and I will try my best to update the dataset as soon as possible

Disclaimer - This is a personal, non-profit project that is intended for the public to access datasets, which can potentially help people make decisions when analysing on the property market.

Copyright - NSW Property Sales Data: © Updated 24/04/2023. Crown in right of NSW through the Valuer General 2023

Data Source NSW data source

Context

This dataset contains Real Estate listings in the US broken by State and zip code.

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/usa-real-estate-dataset

Content

The dataset has 1 CSV file with 10 columns -

1. realtor-data.csv (2,226,382 entries)
   • brokered by (categorically encoded agency/broker)
   • status (Housing status - a. ready for sale or b. ready to build)
   • price (Housing price, it is either the current listing price or recently sold price if the house is sold recently)
   • bed (# of beds)
   • bath (# of bathrooms)
   • acre_lot (Property / Land size in acres)
   • street (categorically encoded street address)
   • city (city name)
   • state (state name)
   • zip_code (postal code of the area)
   • house_size (house area/size/living space in square feet)
   • prev_sold_date (Previously sold date)

NB: 1. brokered by and street addresses were categorically encoded due to data privacy policy 2. acre_lot means the total land area, and house_size denotes the living space/building area

Acknowledgements

Data was collected from - - https://www.realtor.com/ - A real estate listing website operated by the News Corp subsidiary Move, Inc. and based in Santa Clara, California. It is the second most visited real estate listing website in the United States as of 2024, with over 100 million monthly active users.

Cover Image

Image by Mohamed Hassan from Pixabay

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any data, and all rights are reserved to the respective owners.

Inspiration

• Can we predict housing prices based on the features?
• How are housing price and location attributes correlated?
• What is the overall picture of the USA housing prices w.r.t. locations?
• Do house attributes (bedroom, bathroom count) strongly correlate with the price? Are there any hidden patterns?

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 ...