Description

This dataset contains a set of advertisements related to properties, scraped from ikman.lk.

Copyright

Articles: The ownership of the advertisements belongs to the original owners from which this data was scraped. *Dataset image: https://www.ceylonproperty.lk/property/16547/land-for-sale-in-horana-uduwa

Citations

If you use this data in any research work / publication, please cite this dataset as,

Oshan Mudannayake. (2022). Sri Lanka Property Ads Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2369689

This dataset contains real-world property sales data from the UK, combining details from Rightmove and HM Land Registry.

You'll find: - A main property table (properties_main.csv) with info like type, location, latest price, and build type - A sale history table (price_history.csv) listing every known transaction for each property

🧠 This dataset is designed for learning and practice. It includes: - Messy fields (like missing bedrooms or bathroom info) - Currency values in text format (e.g. £280,000) - Linked tables via a unique property_id

Probate, pre-probate, and divorce real estate data offers valuable insights and opportunities for real estate professionals to identify and pursue potential leads. These datasets provide information about properties involved in probate, pre-probate, and divorce cases, enabling professionals to target motivated sellers and navigate specialized market niches. In this brief, we will explore the concept of probate, pre-probate, and divorce data, and discuss their applications across various industries.

What is Probate, Pre-Probate, and Divorce Data?

Probate Data refers to the legal process of settling the estate of a deceased person. Probate data includes information about properties owned by individuals who have passed away and are being transferred to their heirs or beneficiaries through a court-supervised process. This dataset contains details about properties that may be sold to distribute the deceased person’s assets or resolve any outstanding debts.

Pre-Probate Data includes properties owned by individuals who are alive but have designated their assets to be transferred to their heirs upon their passing. This dataset allows real estate professionals to identify potential sellers who may be interested in selling their properties before going through the probate process.

Divorce Data pertains to properties involved in divorce proceedings. When couples go through a divorce, the division of assets often includes the sale or transfer of properties. This dataset provides information on properties that may become available for sale due to a divorce settlement, providing real estate professionals with opportunities to target motivated sellers.

Gain an in-depth view of probate, pre-probate and divorce characteristics for more than 155 million properties across the country (or at the state- and country-level), including: - Property Address - Owner First & Last Name - Mailing Address - Legal Description - Property Value - Property Use - Parcel ID - Year Built - Date Of Death (Probate & Pre-Probate) - Defendant Information (Divorce) - Plaintiff Information (Divorce) - Defendant Attorney Information (Divorce) - Plaintiff Attorney Information (Divorce)

This City Owned Property data has been compiled from deeds, maps, assessor records, and other public records on file in the City of Hartford. The intent of this data layer is to depict a graphical representation of real property information relative to the planimetric features for the City of Hartford and is subject to change as a more accurate survey may disclose.

This dataset contains property tax information for the calendar year 2023. Taxes are calculated from property values, and billed in Fiscal Year 2024, which runs from July 1, 2023 to June 30, 2024. The properties include Residential, Commercial and Publicly Held parcels and structures.

The Office of the Assessor compiles property sales data to perform an annual property sales study to adjust calculated costs of property values based on local market conditions. This dataset includes property sales data obtained for annual sales studies from 2018 to the present. While only Valid Arm's Length transactions that occurred in the two years prior to when a given sales study is finalized are included in each study, this dataset includes all sales transactions obtained to perform the sales studies, whether or not the sales transactions met inclusion criteria for a study. More information about the Sales Study is available from the Office of the Assessor.

Values in categorical fields such as 'Sales Instrument' are recorded based on State of Michigan CAMA standards at the time the value was recorded. Some variation in field value codes occurs over time as a related CAMA standard is updated. CAMA standards are available from the State of Michigan Department of Treasury State Tax Commission.

Click here for the Analytics Hub visualization of Property Sales.

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 12/2025

This dataset represents real estate assessment and sales data that is updated on a quarterly basis by the Real Estate Assessor’s Office. This dataset contains information for properties in the city including: Acreage, Square footage, GPIN, Street Address, year built, current land value, current improvement values, and current total value. The information is obtained from Real Estate Assessor’s Office ProVal records database.

Every January, Finance mails New York City property owners a Notice of Property Value (NOPV). This important notice has information about your property’s market and assessed values. Finance determines your property’s value every year, according to State law. The Cityʼs property tax rates are applied to the assessed value to calculate your property taxes for the next tax year. You get your first tax bill for the year in June. If you believe the values or property descriptions on the NOPV are not correct.

This application shows comprehensive data for properties in the City of Winchester, Virginia. This data includes school district information, fire and rescue first due area, voting information, refuse and recycling and zoning information. It also shows the tax card information for each property queried.

Vietnam Rental And Sale Real Estate Dataset in Feb 2025

This dataset contains rental property listings from various cities and provinces across Vietnam. It includes details such as location, rental price, property area, number of bedrooms, and number of bathrooms. The data can be used for analyzing rental trends, comparing property prices across regions, and identifying patterns in Vietnam’s real estate market.

Dataset Overview

Geographic Coverage: Listings come from different provinces and major cities in Vietnam, including Hà Nội, Hồ Chí Minh City, Đà Nẵng, and other regions. Price (price): Represents the monthly rental cost and sale price, typically listed in Vietnamese đồng (VND). Some listings have negotiable pricing indicated as "Giá thỏa thuận". Area (area): Specifies the total available space in square meters (m²), ranging from small apartments to large commercial or industrial properties. Bedrooms (bedrooms_num) and Bathrooms (bathrooms_num): - If both values are greater than zero, the listing is likely a residential property such as an apartment, house, or villa. - If both values are zero, the listing may not be a traditional residential building but could be an office space, commercial property, warehouse, or vacant land available for rent. Example Listings

The table Historical Property 07 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 149708181 rows across 220 variables.

Residential property values by type of property for Canada, provinces and territories, annual data from 2005 to today.

The table Historical Property 02 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 153748281 rows across 220 variables.

This dataset contains over 16K+ property listings from zameen.com, a prominent online property portal in Pakistan. It includes detailed information on each property, such as city, location, price in PKR, number of bedrooms and bathrooms, and property size in square feet. This comprehensive dataset is a valuable resource for real estate analysts and professionals seeking to explore the Pakistani housing market. The data can be utilized for market and trend analysis, investment research, and other related purposes.

This data is scrapped using the zameen-com-scrapper.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 217 data sources in Washington, D.C.
3. 315,000+ properties in Washington, D.C.
4. 4 billion records (listed in the "data types" block above) in total
5. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

This dataset is a combination of attribute information from the master address table and the lot or property records table. The address points are created within a building footprint and in the case where there is no building, then the point is the center of the lot. The address information comes from a variety of sources including final subdivision plats, building permits, E-911 master street address guide (MSAG) database, Polk City Directory, and field data collection.

Please read all metadata before accessing the dataset. Note that records shown here are updated at different frequencies from data in products from MDP and SDAT. Please see the full documentation at https://opendata.maryland.gov/api/views/ed4q-f8tm/files/WtRzMltUzm25OasOCYtu7PgOGUfrplWsZTalSH4Iukg?download=true&filename=Real%20Property%20Records%20Documentation.pdf and review the dedicated metadata site (https://opendata.maryland.gov/dataset/Beta-Maryland-Statewide-Real-Property-Assessments-/ed4q-f8tm/about).

This statistic shows the average property price in the United States in 2011, by property type. Damaged REOs cost an average of ******* U.S. dollars in the U.S. that year. The abbreviation REO stands for real estate owned properties.

Database of Council owned lands, propoerties and all asociated interests including acquisition and disposal data