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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.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

PLEASE UPVOTE IF YOU LIKE THIS CONTENT! 😍

Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

• date: Date of the home sale
• price: Price of each home sold
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_in_m2: Square meters of the apartments interior living space
• nice_view: A flag that indicates the view's quality of a property
• perfect_condition: A flag that indicates the maximum index of the apartment condition
• grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
• has_basement: A flag indicating whether or not a property has a basement
• renovated: A flag if the property was renovated
• has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
• single_floor: A flag indicating whether the property had only one floor
• month: The month of the home sale
• quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

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].

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Quarterly house price data based on a sub-sample of the Regulated Mortgage Survey.

Quarterly median house prices for metropolitan Adelaide by suburb

This dataset contains detailed information on current real estate listings in Houston, Texas, sourced from Zillow, and provides a comprehensive snapshot of the Houston housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The data includes key details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With 25,900 current listings, this dataset is ideal for in-depth analysis of the Houston housing market and the Houston real estate market. Potential use cases include:

• Comparing listing prices, price per square foot across different neighborhoods, property types
• Mapping listings to visualize the spatial distribution of for-sale inventory
• Analyzing the age of for-sale housing stock from year-built data
• Evaluating typical HOA fees, and property taxes for listings
• Identifying listings with sought-after amenities
• Assessing school quality near listings from GreatSchools ratings
• Contacting listing agents programmatically using the included agent info

Whether you're a real estate professional, market researcher, data scientist, or just curious about the Houston housing market, this dataset provides a wealth of information to explore. You can start investigating Houston real estate today.

This paper investigates the relationship between housing prices and the quality of public schools in the Australian Capital Territory. To disentangle the effects of schools and other neighbourhood characteristics on the value of residential properties, we compare sale prices of homes on either side of high school attendance boundaries. We find that a 5 percent increase in test scores (approximately one standard deviation) is associated with a 3.5 percent increase in house prices. Our result is in line with private school tuition costs, and accords with prior research from Britain and the United States. Estimating the effect of school quality on house prices provides a possible measure of the extent to which parents value better educational outcomes.

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 UK House Price Index (UK HPI) is an official statistic that captures changes in the value of residential properties in the United Kingdom. The UK HPI is calculated by the Office for National Statistics and Land & Property Services Northern Ireland. Data for the UK House Price Index is provided by HM Land Registry, Registers of Scotland, Land & Property Services Northern Ireland and the Valuation Office Agency. Geographic coverage England, Scotland, Wales and Northern Ireland License statement UK HPI data is published under Open Government Licence. When using or publishing data from the UK HPI reports, background tables in the statistical datatset: UK House Price Index: data downloads or search tool, you will need to add the following attribution statement: Contains HM Land Registry data © Crown copyright and database right [year of supply or date of publication]. This data is licensed under the Open Government Licence v3.0. When you publish the data, be sure to include information about the nature of the data and any relevant dates for the period of time covered. Neither HM Land Registry nor any third party shall be liable for any loss or damage, direct, indirect or consequential, arising from: any inaccuracy or incompleteness of the data in the UK HPI any decision made or action taken in reliance upon the data Neither shall HM Land Registry or any third party be liable for loss of business resources, lost profits or any punitive indirect, consequential, special or similar damages whatsoever, whether in contract or tort or otherwise, even if advised of the possibility of such damages being incurred.

Context

Real estate prices in Seoul are skyrocketing day by day. This may be unfortunate for someone, but it can also be a huge benefit for someone. Can you be the owner of the profit? Check it out! (Only apartment)

Content

id : Primary key for a specific apartment lat: Latitude lng: Longitude households: Number of households in residence buildDate: Date the apartment was built score: Total evaluation, maximum 5 stars m2: The area of a house(m^2) p: Number of floors min_sales, max_sales, avg_sales: Descriptive statistics of sales price

500,000+ Sample For Sale Homes Data in the US. Available fields include: "property_url","property_id","address","street_name","apartment","city","state","latitude","longitude","postcode","price","bedroom_number", "bathroom_number","price_per_unit","living_space","land_space","land_space_unit","broker_id","property_type","property_status","year_build",. "total_num_units","listing_age","RunDate","agency_name","agent_name","agent_phone","is_owned_by_zillow"

Can be modeled to analyze home sale price, geo-distribution of homes (every home has lat/lng).

Some properties are owned by zillow. Check out field is_owned_by_zillow, is_owned_by_zillow= 1 means this property is zillow owned. This dataset is for learning and educational purpose. If you are interested in building a similar dataset or for a larger scope and coverage, please contact info@barkingdata.com We specialize in web mining and web data harvesting from the world wide web (including mobile apps), we have built 5000+ datasets for researchers, analysts, scholars , retailers, ... Learn more from https://www.barkingdata.com

Dataset

This dataset was created by Nagwa Ahmed

Released under Apache 2.0

Contents

Turkey Housing Prices Dataset

This dataset contains detailed information about housing listings in Turkey, providing insights into the real estate market. It includes key attributes of properties listed for sale, such as seller type, property size, number of rooms, location details, listing date, and price. The data is ideal for analyzing housing market trends, building price prediction models, or conducting regional real estate studies in Turkey.

Columns:

satici_tip (Seller Type): Indicates the type of seller (e.g., individual or real estate agent). Metrekare (Square Meters): The total area of the property in square meters. Oda_Sayisi (Number of Rooms): The total number of rooms in the property (e.g., 2+1 for 2 bedrooms and 1 living room). il (City): The city where the property is located (e.g., Istanbul, Ankara). Ilce (District): The district within the city (e.g., Kadıköy, Çankaya). Mahalle (Neighborhood): The neighborhood where the property is located. Tarih (Date): The date when the listing was published or recorded (format: YYYY-MM-DD). fiyat (Price): The listed price of the property in Turkish Lira (TRY). Potential Use Cases:

Predicting housing prices based on location, size, and room count. Analyzing regional differences in Turkey’s real estate market. Investigating trends in property prices over time. Studying the impact of seller type on pricing strategies. Notes:

The dataset is in CSV format and has been cleaned to ensure consistency in data types. Missing values, if any, should be checked before analysis. Location data (il, Ilce, Mahalle) can be used for geospatial analysis. Users are encouraged to verify the data for specific use cases, as it may require additional preprocessing.

Tags: Turkey, Real Estate, Housing, Property Prices, Deep Learning, Machine Learning, Data Analysis, Geospatial Data

This dataset provides comprehensive information about rental house prices across various locations in India. It includes details such as house type, size, location, city, latitude, longitude, price, currency, number of bathrooms, number of balconies, negotiability of price, price per square foot, verification date, description of the property, security deposit, and status of furnishing (furnished, unfurnished, semi-furnished).

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

• House Type: Type of house (e.g., apartment, villa, duplex).
• House Size: Size of the house in square feet or square meters.
• Location: Specific area or neighborhood where the property is located.
• City: City in India where the property is situated.
• Latitude: Geographic latitude coordinates of the property location.
• Longitude: Geographic longitude coordinates of the property location.
• Price: Rental price of the house.
• Currency: Currency in which the price is denoted (e.g., INR - Indian Rupees).
• Number of Bathrooms: Total number of bathrooms in the house.
• Number of Balconies: Total number of balconies in the house.
• Negotiability: Indicates whether the price is negotiable (Yes/No).
• Price per Square Foot: Price of the house per square foot.
• Verification Date: Date when the rental information was verified.
• Description: Additional description or details about the property.
• Security Deposit: Amount of security deposit required for renting the property.
• Status: Indicates the furnishing status of the property (furnished, unfurnished, semi-furnished).

Usage

This dataset aims to provide valuable insights into the rental housing market in India, enabling analysis of rental trends, comparison of prices across different locations and property types, and understanding the impact of various factors on rental prices. Researchers, analysts, and policymakers can utilize this dataset for a wide range of applications, including real estate market analysis, urban planning, and economic research.

Acknowledgement

This Dataset is created from https://www.makaan.com/. If you want to learn more, you can visit the Website.

Cover Photo by: Playground.ai

Results of the lag order test of the quantity of residential supply on the residential price model.

Metadata The ‘Requests for land values’ database, or DVF, lists all sales of land over the last five years, in mainland France and in the overseas departments and territories — except in Mayotte and Alsace-Moselle. The properties concerned can be built (apartment and house) or unbuilt (plots and farms). The data are produced by Bercy, i.e. by the Directorate-General for Public Finance. They come from the deeds registered with notaries and the information contained in the cadastre. Legal framework: The DVF database does not contain personal data, such as the name of the seller or the buyer of a good. It contains only information on transactions: type of property sold, area, selling price and so on. As these data can be cross-checked with other data already online, the Directorate-General for Public Finance recalls that the use of data from the DVF database cannot have the purpose or effect of allowing the re-identification of data subjects, nor should it be indexed on online search engines. Consult the general conditions of use: https://static.data.gouv.fr/resources/request-de-valeurs-foncieres/20190419-091643/conditions-generales-dutilisation.pdf Fields code_service_ch: not provided reference_document: not entered articles_cgi1: not entered articles_cgi2: not entered articles_cgi3: not entered articles_cgi4: not entered articles_cgi5: No_provision: Each provision of a document has a number. Only the provisions concerning transfers for consideration are returned to the file. The provisions concerning transfers free of charge are removed from the register by the application. The disposition numbers used do not therefore necessarily follow the numerical order date_mutation: Date of signature of nature_mutation document: Sale, sale in the future state of completion, sale of building land, tendering, expropriation or exchange of land value: This is the price or valuation declared in the context of a transfer for consideration. It can correspond to several properties. The details are not traced in the information system no_voie: Number in track b_t_q: Repetition index type_of_way: Track type (example: Street, Avenue,...) code_voie: Track code: Wording of the code_postal route: Common postal code: Wording of the commune code_departement: Common_code department code: Common code prefix_of_section: Prefix of cadastral section section: Cadastral section no_plan: Cadastral plan no_volume: Cadastral volume A condominium lot consists of a private part (apartment, cellar, etc.) and a share of the common part (tenths). Only the first 5 lots are mentioned. If the number of lots exceeds 5, they will not be returned. 1st lot surface_carrez_du_1er_lot: surface area CARREZ of the 1st lot 2nd_lot: 2nd lot surface_carrez_du_2eme_lot: surface area CARREZ of the 2nd lot 3rd_lot: 3rd lot surface_carrez_du_3eme_lot: CARREZ surface area of the third lot, fourth lot: 4th lot surface_carrez_du_4eme_lot: surface area CARREZ of the 4th lot 5th_lot: 5th lot surface_carrez_du_5eme_lot: surface area CARREZ of the 5th lot number_of_lots: Total number of lots per layout code_type_local: Local type code type_local 1: House, 2: apartment, 3: dependency (isolated), 4: Industrial and commercial premises or similar identifier_local: This is the number that identifies each room. The local is a tax concept of built property. The file includes one line per number (per local) with the corresponding real area surface_reelle_bati next to it: The real area is attached to the local identifier. This is the sum of the actual surface area of the premises and the surface areas of the outbuildings (see real estate lexicon) number_pieces_principal: Number of main nature_culture parts: Nature of culture nature_culture_speciale: Nature of special crop surface_terrain: Building land capacity: indicates the presence of racks (non-zero local_type) nb_line: Number of lines of the transaction (number of lines on grouping of a single value of the department code set, common code, date of transfer, nature of transfer, land value, no_disposition) id_parcelle: PCI-type parcel identifier

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