1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

Anjuke is a leading real estate information service platform in China, providing a large amount of comprehensive, accurate, and reliable housing data. It aims to offer users a safe and convenient house-hunting experience. Therefore, this project involves scraping second-hand housing data from the Anjuke platform for the Shanghai area, to establish a regression prediction model for analysis.

After data cleaning, the final constructed dataset contains a total of 175,128 records. Each record includes nearly 30 features, covering various aspects from basic information about the property to community characteristics and living environment features. The project also attempts to utilize textual content such as titles.

安居客是国内领先的房产信息服务平台，包含大量全面、精准、可靠的房屋数据，旨在为用户提供安心、便捷的找房服务。因此，本项目爬取安居客平台上海地区二手房数据，用于建立回归预测模型分析。

经过数据清洗，最终构建的数据集共包含175,128条记录。每条记录包括近30个特征，涵盖了从房源基本信息，到小区特点，居住环境特点等多方面，还尝试利用了标题等文本内容。

A complete list of live websites using the Software Sales Prices technology, compiled through global website indexing conducted by WebTechSurvey.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

The Detroit Land Bank Authority's (DLBA) Own It Now (OIN) program offers vacant homes for sale with a minimum offer price of $1,000. Homes are available on the DLBA website for sale 24 hours a day, 7 days a week and may be offered on at any time. Homes are sold as is and buyers are required to rehab the houses and ensure that they are occupied. The dataset provides information on the properties that have been successfully sold by the DLBA through the OIN program.

The Detroit Land Bank Authority (DLBA) works directly with individual buyers, as well as Community Partner organizations and developers to achieve their mission to return the city's blighted and vacant properties to productive use. They utilize a variety of sales programs to make homeownership and land purchases accessible to Detroiters. One of these programs is Own it Now (OIN).

Each row in the dataset represents an OIN home and includes data about the sale status, closing date for sold homes, sale price, and location information such as: address; parcel ID; and neighborhood.

For more information about the DLBA's sales programs click here.

Update 10/31/2023: Sales are no longer filtered out of this data set based on deed type, sale price, or recency of sale for a given PIN with the same price. If users wish to recreate the former filtering schema they should set sale_filter_same_sale_within_365, sale_filter_less_than_10k, and sale_filter_deed_type to False.

Parcel sales for real property in Cook County, from 1999 to present. The Assessor's Office uses this data in its modeling to estimate the fair market value of unsold properties.

When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded.

Sale document numbers correspond to those of the Cook County Clerk, and can be used on the Clerk's website to find more information about each sale.

NOTE: These sales are filtered, but likely include non-arms-length transactions - sales less than $10,000 along with quit claims, executor deeds, beneficial interests are excluded. While the Data Department will upload what it has access to monthly, sales are reported on a lag, with many records not populating until months after their official recording date.

Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.

For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub.

Read about the Assessor's 2025 Open Data Refresh.

This table shows the price development of newly built and existing dwellings purchased by households. Aside from the price indices, Statistics Netherlands also publishes figures on the number, average purchase price and total sum of the purchase prices of the sold dwellings.

Data available from: 1st quarter 2015 to 3rd quarter 2023

Status of the figures: The figures in this table that are associated with existing homes (PBK) are final. The figures in this table that are associated with new dwellings (PNK) are one period provisional and the figures in this table that are associated with the number of sold dwellings and the average purchase price and related to newly built dwellings and total figures are provisional. Since this table has been discontinued, the data is no longer finalized.

Changes as of 6th of October 2022: This statistic is calculated using a European harmonized method. The method for rounding figures has changed within the European guidelines. This method change has been implemented with the result that some figures have been adjusted by a maximum of 0.1 index point or 0.1% development. The figures therefore correspond to the figures on the eurostat website.

Changes as of 25th of April 2024: This table has been discontinued. This table is followed by House Prices: new and existing dwellings price index 2020=100. See paragraph 3.

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of house prices since January 2006, then compiled the house price index based on January 2006 as 100. The Shanghai Stock Exchange Index (SSEI) data which are treated as stock market prices are derived from the CSMAR database. After that, we calculate the monthly house price and stock price return as , where are proxied by the monthly house price index and SSEI, and represent the returns series. 157 observations from January 2006 to March 2019 are obtained.

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?

E-wallets, online banking, and cryptocurrency verified accounts are some of the most expensive illegal digital products for sale on the dark web. As of April 2023, details of a credit card with up to ***** U.S. dollars on balance could sell at around *** U.S. dollars. Crypto accounts on N26, for instance, had an average selling price of ***** U.S. dollars. Social media followers can also be bought on the dark web, for example, at **** dollars per ***** followers on Instagram. In turn, AirBNB.com verified accounts averaged *** U.S. dollars.

Graph and download economic data for Producer Price Index by Industry: Internet Publishing and Web Search Portals: Internet Publishing and Web Search Portals - Subscription, Content Access, and Licensing Sales (PCU5191305191302) from Dec 2009 to Dec 2022 about licenses, internet, printing, sales, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for All-Transactions House Price Index for North Carolina (NCSTHPI) from Q1 1975 to Q3 2025 about NC, appraisers, HPI, housing, price index, indexes, price, and USA.

In 2022, there were **** major site acquisition deals for data centers in the main European markets. Two of the deals took place in Frankfurt. Only *** of the deals had an announced acquisition price: The purchase of the ***** acre site on Wilhelm-Fay-Strasse 31-37, Frankfurt from Corum cost the buyer Cyrus *** over ** million U.S. dollars.

This statistics shows by region in Germany the minimum to maximum sales price in euros per square meter (sqm) of fully-serviced logistic sites in 2016. Munich had the highest price range for logistic sites in any region in Germany from *** euros per sqm to *** euros per sqm.

Croatia New Dwellings Sold: Cost: Building Sites: Zagreb data was reported at 2,198.000 HRK/sq m in Dec 2022. This records a decrease from the previous number of 2,491.000 HRK/sq m for Jun 2022. Croatia New Dwellings Sold: Cost: Building Sites: Zagreb data is updated semiannually, averaging 2,159.000 HRK/sq m from Jun 2001 (Median) to Dec 2022, with 44 observations. The data reached an all-time high of 2,969.000 HRK/sq m in Dec 2009 and a record low of 840.000 HRK/sq m in Jun 2006. Croatia New Dwellings Sold: Cost: Building Sites: Zagreb data remains active status in CEIC and is reported by Croatian Bureau of Statistics. The data is categorized under Global Database’s Croatia – Table HR.EB006: Average Price and Cost of New Dwellings Sold: HRK (Discontinued).

In 2020, the average selling price (ASP) of products available on over three fourths (***** percent) of e-commerce websites in Brazil amounted to less than 100 Brazilian reals. Around ** percent of online shopping sites in the South American country had an ASP above 1,000 reals. Male shoppers spent on average ***** Brazilian reals per online checkout in Brazil.

This page is no longer being updated. Please use the UK House Price Index instead.

Mix-adjusted house prices, by new/pre-owned dwellings, type of buyer (first time buyer) and region, from February 2002 for London and UK, and average mix-adjusted prices by UK region, and long term Annual House Price Index data since 1969 for London.

The ONS House Price Index is mix-adjusted to allow for differences between houses sold (for example type, number of rooms, location) in different months within a year. House prices are modelled using a combination of characteristics to produce a model containing around 100,000 cells (one such cell could be first-time buyer, old dwelling, one bedroom flat purchased in London). Each month estimated prices for all cells are produced by the model and then combined with their appropriate weight to produce mix-adjusted average prices. The index values are based on growth rates in the mix-adjusted average house prices and are annually chain linked.

The weights used for mix-adjustment change at the start of each calendar year (i.e. in January). The mix-adjusted prices are therefore not comparable between calendar years, although they are comparable within each calendar year. If you wish to calculate change between years, you should use the mix-adjusted house price index, available in Table 33.

The data published in these tables are based on a sub-sample of RMS data. These results will therefore differ from results produced using full sample data. For further information please contact the ONS using the contact details below.
House prices, mortgage advances and incomes have been rounded to the nearest £1,000.
Data taken from Table 2 and Table 9 of the monthly ONS release.

Download from ONS website

Certain forged documents in physical format usually sell at a higher average price than scanned versions on dark web marketplaces. For example, an original Maltese passport could sell for ***** U.S. dollars as of March 2023, while passports from various other European Union countries averaged *****. Regarding scanned versions, the price for a Alberta driver's license could reach *** U.S. dollars.

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.