This data set is a listing of all property sales by NORA through the following disposition channels. - Auction: Properties put up for auction and sold to the highest bidder. - Development: Properties offered to development partners at a discounted rate to support the development of affordable housing. - Lot Next Door: Properties sold to adjacent parcel owners, with discount opportunities for eligible participants. - Alternative Land Use: Properties sold for development of green space and community gardens. Note: this dataset contains duplicate addresses, which likely represent reversions or quitclaims that NORA sold again.

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.

Dear Scientists,

I am sharing with you this gold mine, a descriptive listing of all the Real Estate sales in France for 2022. The dataset comes from Gouvernemental website data.gouv.fr where you can access for free the past 5 years of sales of the Real Estate market.

I removed dead columns with 99% missing values and did not apply any kind of features engineering. Some columns have missing values but not worth dropping since the rows has valuable information.

Feel free to ask in comments if you need additional information concerning the French RE market, or about features meanings.

To give you some context, with the data available you can find out: - The real address of sold properties in France - The price of sold properties - The date the transaction occured - The description of sold properties (type, size, number of rooms) - The nature of the mutation (sale, swap, VEFA (Vente en l'état futur d'achèvement) etc..)

"DVF" stands for "Demande de Valeur Foncière," which translates to "Request for Property Value" in English. DVF is a system used in France to provide information about real estate transactions, particularly property sales and their associated prices.

The DVF system was established to enhance transparency in the French real estate market and make property transaction data accessible to the public. It allows individuals to inquire about property sale prices in specific areas or regions of France. This information can be valuable for various purposes, including:

Property Valuation: Homebuyers and sellers can use DVF data to get an idea of property values in a particular area, helping them make informed decisions about buying or selling real estate.

Market Analysis: Real estate professionals and analysts use DVF data to assess market trends and fluctuations in property prices. This information can inform investment decisions and market research.

Taxation: Local authorities and tax authorities use DVF data to assess property taxes, as property values are a key factor in determining tax rates.

Urban Planning: Municipalities and urban planners may use DVF data to gain insights into property transactions and trends within their regions, helping them make decisions about development and infrastructure.

It's important to note that DVF data typically includes information about the sale price, the date of the transaction, the property's location, and other relevant details. However, personal information about buyers and sellers is generally not disclosed in the publicly available DVF dataset.

DVF data has become increasingly accessible through online platforms and government websites, making it a valuable resource for those interested in the French real estate market. It provides transparency and aids in making informed decisions related to property transactions and investments.

Features (Columns):

• Date mutation (Mutation Date): The date on which the property mutation or transaction occurred.
• Nature mutation (Mutation Nature): The nature or type of property mutation, such as sale, inheritance, etc.
• Valeur fonciere (Property Value): The value of the property.
• No voie (Street Number): The street number of the property.
• Type voie (Street Type): The type of street (e.g., avenue, boulevard) where the property is located.
• Code voie (Street Code): A code associated with the street where the property is located.
• Code postal (Postal Code): The postal code of the property's location.
• Commune (Town/City): The town or city where the property is located.
• Code departement (Department Code): The code of the department where the property is situated.
• Code commune (Commune Code): A code specific to the commune where the property is located.
• Section (Section): Information about the property section.
• No plan (Plan Number): The plan number associated with the property.
• Nombre de lots (Number of Lots): The total number of lots or portions in the property.
• Type local (Local Type): The type of local or property (e.g., residential, commercial).
• Surface reelle (Actual Built Area): The actual built area of the property.
• Nombre pieces principales (Number of Main Rooms): The number of main rooms in the property.
• Surface terrain (Land Area): The total land area associated with the property.

Annual house price inflation, simple and mix-adjusted average house prices, by dwelling, type of buyer, number of transactions, mortgage advances, distribution of borrowers' ages/incomes, interest rates, land prices, average valuations, Land Registry data

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. National and Other Areas figure changed for 2015Q4 on 27/6/15 as revised data received from Local Authorities (includes houses and apartments measured in €) .hidden { display: none }

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q2 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

1. Real estate transaction information includes location, area, total price, etc. 2. This dataset is updated every 10 days.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Figure changed on the 27/6/16 as revised data received from the Local authority Prices include houses and apartments measured in € .hidden { display: none }

This dataset provides a comprehensive record of property listing price changes over time, including detailed property attributes, location information, and event types for each price change. It enables in-depth analysis of real estate market dynamics, pricing strategies, and property value trends across regions and property types.

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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.

Santa Cruz Property Insights is a premier real estate marketplace, offering an extensive range of listings and data on residential and commercial properties in the Santa Cruz area. The company's vast database provides valuable information for potential buyers, sellers, and real estate professionals alike, making it an indispensable resource for anyone involved in the local market.

With a focus on providing accurate and up-to-date information, Santa Cruz Property Insights has established itself as a trusted authority in the real estate industry. From property listings to market trends and analysis, the company's comprehensive data sets enable users to make informed decisions and navigate the complex landscape of real estate with confidence.

Please note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.Overview of the Data:This dataset is a polygon feature layer with the boundaries of all tax parcels owned by the City of Rochester. This includes all public parks, and municipal buildings, as well as vacant land and structures currently owned by the City. The data includes fields with features about each property including property type, date of sale, land value, dimensions, and more.About City Owned Properties:The City's real estate inventory is managed by the Division of Real Estate in the Department of Neighborhood and Business Development. Properties like municipal buildings and parks are expected to be in long term ownership of the City. Properties such as vacant land and vacant structures are ones the City is actively seeking to reposition for redevelopment to increase the City's tax base and economic activity. The City acquires many of these properties through the tax foreclosure auction process when no private entity bids the minimum bid. Some of these properties stay in the City's ownership for years, while others are quickly sold to development partners. For more information please visit the City's webpage for the Division of Real Estate: https://www.cityofrochester.gov/realestate/Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Real Estate.

Context

I have previously shared a classification based dataset to classify the gender which is liked by those who are new to machine learning as it give a pretty good accuracy, which encouraged me to create a regression dataset to predict continues values. I have tried many real world datasets for regression problems which are predicting with lower accuracy and high error rate. As a beginner, I have struggled and worried why and how the dataset performs poorly. This is another main reason why I created this dataset. Although this is a made up dataset, I have considered all the features when deciding the price of the property. If you are a beginner, you would love to try this as the results are stunning..

Content

Since this is a populated data, I will straightaway explain the features and the label. FEATURES 1. land_size_sqm - This the total size of the land in square meters. 2. house_size_sqm - This is the area in which house is located within the land. This is measured in square meters. 3. no_of_rooms - This indicates the number of rooms available in the house. 4. no_of_bathrooms - This shows the number of total bathrooms made in the house. 5. large_living_room - This indicates whether the house includes a larger living room or not. The assumption is that all the houses contain a living room. This feature attempts to classify whether it's large or small where '1' means large and '0' means small. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 6. parking_space - This indicates whether there is a parking space or not. '1' represents the parking available while '0' represents no parking space available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 7. front_garden - This shows whether there is a garden available in front of the house. '1' means the garden available and '0' means no garden available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 8. swimming_pool - This shows the availability of the swimming pool at the house. 1 represents the availability of the swimming pool while 0 represents the non availability of the same. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 9. distance_to_school_km - This shows the distance from the house to the nearest school in Kilometers. 10. wall_fence - This shows whether there is a wall fence or not. '1' mean there is wall fence and '0' means no wall fence. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 11. **house_age_or_renovated **- This is either the age of the house in years or the period from the date of renovation. 12. water_front - this indicates whether the house is located in front of the water or not. 1 means waterfront and 0 means its not located near the water. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 13. distance_to_supermarket_km - what is the distance to the nearest supermarket in kilometers.

LABEL property_value - This is the price of the property

Following features are only available in the "house price dataset original v2 cleaned" and "house price dataset original v2 with categorical features" data only. 14. crime_rate - its in float and falls between 0 and 7. lesser the better 15. room_size - As the name suggests, it explains the size of the room. 0 is being 'small', 1 is being 'medium', 2 is 'large' and 3 is being 'Extra large'. However in the categorical dataset, these values are categorical and self explanatory.

Acknowledgements

I spent around 3 hours creating this dataset. Enjoy..

Inspiration

Share your notebooks to see which algorithm predicts the house price precisely.

Real estate sales case transaction information, including the location of the subject property (de-identified), area, total price, and other information. (Tucheng District)

Graph and download economic data for State and Local Governments; Nonresidential Structures, Equipment, and Intellectual Property Products, Current Cost Basis, Transactions (BOGZ1FA215013865Q) from Q4 1946 to Q2 2025 about retirement, intellectual property, cost, state & local, nonresidential, transactions, buildings, equipment, production, government, employment, and USA.

Graph and download economic data for State and Local Governments; Nonresidential Intellectual Property Products, Current Cost Basis, Transactions (BOGZ1FU215013765A) from 1946 to 2024 about retirement, intellectual property, cost, state & local, nonresidential, transactions, production, government, employment, and USA.

Annual New Property prices by cities from 1969 to 2015 Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of Mortgage financed existing house transactions. Average house prices are derived from data supplied by the Mortgage lending agencies on loans approved by them rather than loans paid. In Comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas The most current data is published on these Sheets. Previously published data may be subject to revision. Any change from the Originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in EUR

Overview

This residential real estate data set was created by Redfin, an online real estate brokerage. Published on January 9th, 2022, this data summarize the monthly housing market for every State, Metro, and Zip code in the US from 2012 to 2021. Redfin aggregated this data across multiple listing services and has been gracious enough to include property type in their reporting. Please properly cite and link to RedFin if you end up using this data for your research or project.

Source: RedFin Data Center

Property Type

Property type defined by RedFin

• All Residential: All properties defined as single-family, condominium, co-operative, townhouses, and multi-family (2-4 units) homes with a county record.
• Single Family Home (SFH): are homes built on a single lot, with no shared walls. Sometimes there’s a garage, attached or detached.
• Condominium (Condo): Usually a single unit within a larger building or community. Generally come with homeowners’ associations (HOAs), which require the residents to pay monthly or yearly dues.
• Cooperatives (Co-op): Usually a single unit within a larger building or community, but with a different way of holding a title to a shared building. You join a community and everyone in the community owns the building together.
• Townhouse: a hybrid between a condo and a single-family home. They are often multiple floors, with one or two shared walls, and some have a small yard space or rooftop deck. They’re generally larger than a condo, but smaller than a single-family home.
• Multifamily (2-4 units): They are essentially a home that has been turned into two or more units but the units cannot be purchased individually. There is one owner for the whole building.
• Land: Just land, no home of any type for sale.

Source: Building Types

Property Type

For more definitions, please visit RedFin Data Center Metrics

• Average sale to list: The mean ratio of each home's sale price divided by their list price covering all homes with a sale date during a given time period. Excludes properties with a sale price of 50%.
• Home sales: Total number of homes with a sale date during a given time period.
• Inventory: Total number of active listings on the last day of a given time period.
• Median active list ppsf: The median list price per square foot of all active listings.
• Median active list price: The median list price of all active listings.
• Median active listings: The median of how many listings were active on each day within a given time period.
• Median days on market: The number of days between the date the home was listed for sale and when the home went off-market/pending sale covering all homes with an off-market date during a given time period where 50% of the off-market homes sat longer on the market and 50% went off the market faster. Excludes homes that sat on the market for more than 1 year.
• Median days to close: The median number of days a home takes to go from pending to sold.
• Median list price: The most recent listing price covering all homes with a listing date during a given time period where 50% of the active listings were above this price and 50% were below this price.
• Median list price per square foot: The most recent listing price divided by the total square feet of the property (not the lot) covering all homes with a listing date during a given time period where 50% of the active listings were above this price per sqft and 50% were below this price per sqft.
• Median listing with price drops: The median of how many listings were active on each day and whose current list price is less than the original list price within a given time period.
• Median sale price: The final home sale price covering all homes with a sale date during a given time period where 50% of the sales were above this price and 50% were below this price.
• Median sale price per square foot: The final home sale price divided by the total square feet of the property (not the lot) covering all homes with a sale date during a given time period where 50% of the sales were above this price per sqft and 50% were below this price per sqft.
• Months of supply: When data are monthly, it is inventory divided by home sales. This tells you how long it would take supply to be bought up if no new homes came on the market.
• New listings: Total number of homes with a listing added date during a given time period.
• Off market in two weeks: The total number of homes that went under contract within two weeks of their listing date.
• Pending home sales: Total homes that went under contract during the period. Excludes homes that were on the market longer than 90 ...

Despite a slowdown in the country's economy, property prices remained relatively high across China in 2023. In Shanghai, the average prices for residential housing exceeded ****** yuan per square meter, making the metropolis one of the most expensive cities to live in globally. Meanwhile, many less developed regions, such as the provinces of Guizhou, Gansu, and Guangxi, had average housing prices below ***** yuan per square meter. High property prices in major cities The commodification of real estate in the 1990s led to a rapid rise in property prices across China over the last three decades. Between 1998 and 2023, average property prices in China ************************* to more than ****** yuan per square meter. The cost of housing in core areas of major urban centers such as Shenzhen, Shanghai, and Beijing can often reach unaffordable levels, even for the middle class. Key drivers behind the housing price rise Due to the regional disparities in the country, China's rapid urbanization resulted in a high influx of internal migrants into its eastern cities, resulting in a short housing supply across many regions. At the same time, due to China's unique land and tax system, local governments are often highly dependent on land transfer revenues for their finances. As a result, many regional authorities tend to restrict the supply of available land in the market, further exacerbating property price rises across the country.

This dataset contains information related to housing sales, in the form of individual properties. Here's a breakdown of the columns:

The dataset contains 2413 entries and has a mixture of numerical and categorical data. It's likely used for analyzing various factors influencing housing sale prices, such as location, size, condition, and amenities.