Note:- Only publicly available real estate data can be worked upon.

Discover the world of property insights with APISCRAPY's user-friendly services – Realtor Property Data, Realtor Data, and Realtor API. Designed for ease of use, our platform allows anyone, from real estate professionals to researchers and businesses, to effortlessly access publicly available property listings and Property owner Data.

Our Realtor Property Data service provides comprehensive details on property listings, while Realtor API ensures easy integration for streamlined access. Additionally, we offer Zillow Property Data, enriching your property insights with information from one of the leading property platforms.

Key Features:

Realtor Property Data: Dive into detailed property listings effortlessly with our user-friendly platform.

Realtor API Integration: Seamlessly integrate our Realtor API into your systems for easy access to property data.

Zillow Property Data: Enrich your property insights with data from Zillow, one of the leading property platforms.

Publicly Available Property Listings: APISCRAPY ensures access to publicly available property listings, making property data easily accessible.

Easy Integration: Our platform is designed for simplicity, allowing for easy integration into your existing systems.

Whether you're a real estate professional, researcher, or business looking for straightforward access to property information, APISCRAPY's services cater to your needs. Choose us for simple and efficient property data services, where ease of use and accessibility come together for your convenience.

Doorda's UK Commercial Real Estate Data provides a comprehensive database of over 6 million commercial locations sourced from 20 data sources, offering unparalleled insights for business intelligence and analytics purposes.

Volume and stats: - 6M Commercial locations with internals - 1.7M Named Commercial Occupants - 1.4M Non-Domestic Energy Performance Inspections

Our Commercial Real Estate Data offers a multitude of use cases: - Market Analysis - Competitor Analysis - Lead Generation - Risk Management - Location Planning

The key benefits of leveraging our Commercial Real Estate Property Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

The BGS PSA dataset provides insurers and homeowners access to a better understanding of the shrink-swell hazard at both the individual property and/or postcode level for Great Britain. It builds upon the GeoSure shrink-swell data by mapping the hazard to the individual building polygon and considering the other susceptibility factors of building type, foundation depth, and drainage and tree proximity. The user receives GIS building polygons with an overall susceptibility to subsidence score between 1-100. Scores are also classified from non-plastic to very high. Each building polygon is also scored from 1-10 for each subsidence factor (geology, foundation, drainage, building type, building storey and tree proximity). Postcode data is also available as a table and shapefiles showing the ‘average’ PSA score for all buildings within the postcode. The identification of shrink-swell related subsidence prone areas, alongside the inclusion of potential sources to exacerbate this phenomena, can better inform insurers and homeowners and form the basis to make decisions concerning prevention and remediation. The product enhances geological information obtained from GIP and GeoSure via the inclusion of the crucial shrink-swell susceptibility factors (proximity to trees and foundation depth). This therefore allows the derivation of a risk element for the housing stock at Building level, which is then generalised to Postcode level.

This method returns the council tax band and the corresponding council tax figure.

Council tax bands are retrieved for each individual property, with tax values derived from levels set by local authorities. Figures include adult social care and parish precepts.

Address Format Recommendations

This method utilises sophisticated heuristics to match the searched property address to the council tax database. In 99% of cases, a match is found. However, please follow the recommendations below to achieve the best results:

1. The address should be sufficient to identify the exact property. If several properties correspond to the input, the method will return an error.
2. Best results are achieved when using the address derived from the SINGLE_LINE_ADDRESS field of the OS AddressBase datasets.
3. If you do not utilise OS AddressBase datasets, please include the following data: Flat, BuildingName, BuildingNumber, StreetName, Town or PostTown, Postcode.

We suggest using commas as delimiters as shown in this example. Some data points may not exist for each particular address. In this case, simply ignore them.

Address format examples:

5 WOODBINE TERRACE, EXETER, EX4 4LJ
where 5 is BuildingNumber, WOODBINE TERRACE is StreetName, EXETER is Town, EX4 4LJ is Postcode.

FLAT 1, CHARLOTTE COURT, INVERMEAD CLOSE, LONDON, W6 0WW
where FLAT 1 is Flat, CHARLOTTE COURT is BuildingName, INVERMEAD CLOSE is StreetName, LONDON is Town, W6 0WW is Postcode.

This method returns Census 2021 estimates that classify households in England and Wales by accommodation type.

Accommodation type is the type of building or structure used or available by an individual or household. This could be:

• the whole house or bungalow;
• a flat;
• a maisonette or apartment;
• a temporary or mobile structure, such as a caravan.

More information about accommodation types:

• Whole house or bungalow: This property type is not divided into flats or other living accommodation. There are three types of whole houses or bungalows:
  • Detached: None of the living accommodation is attached to another property but can be attached to a garage.
  • Semi-detached: The living accommodation is joined to another house or bungalow by a common wall that they share.
  • Terraced: A mid-terraced house is located between two other houses and shares two common walls. An end-of-terrace house is part of a terraced development but only shares one common wall.
• Flats (Apartments) and maisonettes: An apartment is another word for a flat. A maisonette is a 2-storey flat.

Accommodation types are split into 9 categories including total.

The estimates are as at Census Day, 21 March 2021.

This method returns Census 2021 estimates that classify all household spaces with at least one usual resident in England and Wales by number of bedrooms.

Number of bedrooms is the number of bedrooms in a household's accommodation. This number is not available for household spaces with no usual residents.

“Number of bedrooms” is split into 5 categories including total.

The estimates are as at Census Day, 21 March 2021.

Land and buildings owned by Canterbury City Council

We create tailor-made solutions for every customer, so there are no limits to how we can customize your scraper. You don't have to worry about buying and maintaining complex and expensive software, or hiring developers.

You can get the data on a one-time or recurring (based on your needs) basis.

Get the data in any format and to any destination you need: Excel, CSV, JSON, XML, S3, GCP, or any other.

Land and Property Assets in York.

*Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Doorda's UK Residential Building Characteristics Real Estate Data provides a comprehensive database of over 426K buildings aggregated from 15 data sources, offering unparalleled insights for insurance underwriting and analytics purposes.

Volume and stats: - 426K Buildings - Number of units in each Building - Building Height - Presence of Basements - Number of Floors

Our Residential Real Estate Data offers a multitude of use cases: - Cladding Risks - Insurance Underwriting - Compliance Checks - Flood Risk - Location Planning

The key benefits of leveraging our Residential Real Estate Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

Doorda's UK Geospatial Real Estate Data provides a comprehensive database of over 34 million addresses aggregated from 10 data sources, offering unparalleled geospatial insights for customer insights and risk analysis purposes.

Volume and stats: - 34M Addressable locations - 15M Exact Building Location - 9M derived Building Locations

Our Geospatial Real Estate Data offers a multitude of use cases: - Location Planning - Risk Analysis - Customer Insights - Data Augmentation - Market Insights

The key benefits of leveraging our Geospatial Real Estate Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage and properties depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

United Kingdom TE: RC: API: Property Income (PI) data was reported at 152,781.000 GBP mn in Mar 2018. This records an increase from the previous number of 151,851.000 GBP mn for Dec 2017. United Kingdom TE: RC: API: Property Income (PI) data is updated quarterly, averaging 129,495.000 GBP mn from Mar 1987 (Median) to Mar 2018, with 125 observations. The data reached an all-time high of 257,327.000 GBP mn in Dec 2007 and a record low of 50,921.000 GBP mn in Mar 1987. United Kingdom TE: RC: API: Property Income (PI) data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB023: ESA10: Resources and Uses: Total Economy: Primary Income.

The BGS Property Subsidence Assessment (PSA) dataset provides insurers and homeowners access to a better understanding of the shrink-swell hazard at both the individual property and/or postcode level for England and Wales. It builds upon the GeoSure shrink-swell data by mapping the hazard to the individual building polygon and considering the other susceptibility factors of building type, foundation depth, and drainage and tree proximity. The user receives GIS building polygons with an overall susceptibility to subsidence score between 1-100. Scores are also classified from non-plastic to very high. Each building polygon is also scored from 1-10 for each subsidence factor (geology, foundation, drainage, building type, building storey and tree proximity). Postcode data is also available as a table showing the ‘average’ PSA score for all buildings within the postcode. The identification of shrink-swell related subsidence prone areas, alongside the inclusion of potential sources to exacerbate this phenomena, can better inform insurers and homeowners and form the basis to make decisions concerning prevention and remediation. The product enhances geological information obtained from GIP and GeoSure via the inclusion of the crucial shrink-swell susceptibility factors (proximity to trees and foundation depth). This therefore allows the derivation of a risk element for the housing stock at Building level, which is then generalised to Postcode level.

This method returns Census 2021 estimates that classify households by tenure.

Tenure of household defines whether a household owns or rents the accommodation that it occupies.

Owner-occupied accommodation can be:

• owned outright, which is where the household owns all of the accommodation
• with a mortgage or loan
• part-owned on a shared ownership scheme

Rented accommodation can be:

• private rented, for example, rented through a private landlord or letting agent
• social rented through a local council or housing association

This information is not available for household spaces with no usual residents.

“Tenure of household” is split into 15 categories including total.

The estimates are as at Census Day, 21 March 2021.

Data on the physical properties (transmissivity, storage coefficient, porosity and permeability) of aquifers in England and Wales. Compiled by BGS staff from paper records of field and laboratory testing held by BGS, the Environment Agency and other organisations. Contains summary data on approximately 20,000 pump tests at over 2000 discrete locations. The majority of BGS and EA pump test data is included for both major and minor aquifers, but in minor aquifers this is complemented by data on specific yield. Laboratory determinations of porosity and permeability are limited to open file BGS data only. All data subject to similar processing and interpretation, but raw data highly variable.

Data from the Birmingham City Council Housing team on council owned social housing stock.Data is provided at individual property level and shows the following property attributes;Heating typeConstruction dateAgeProperty typeOccupancy statusOwnerNumber of BedroomsSheltered typeArchitectureAffordable housingWard level geographic locationConstituency locationThe location for use in our mapping application will display the Ward.Data is updated weekly.

Note:- Only publicly available data can be worked upon

APISCRAPY collects and organizes data from Zillow's massive database, whether it's property characteristics, market trends, pricing histories, or more. Because of APISCRAPY's first-rate data extraction services, tracking property values, examining neighborhood trends, and monitoring housing market variations become a straightforward and efficient process.

APISCRAPY's Zillow real estate data scraping service offers numerous advantages for individuals and businesses seeking valuable insights into the real estate market. Here are key benefits associated with their advanced data extraction technology:

1. Real-time Zillow Real Estate Data: Users can access real-time data from Zillow, providing timely updates on property listings, market dynamics, and other critical factors. This real-time information is invaluable for making informed decisions in a fast-paced real estate environment.

2. Data Customization: APISCRAPY allows users to customize the data extraction process, tailoring it to their specific needs. This flexibility ensures that the extracted Zillow real estate data aligns precisely with the user's requirements.

3. Precision and Accuracy: The advanced algorithms utilized by APISCRAPY enhance the precision and accuracy of the extracted Zillow real estate data. This reliability is crucial for making well-informed decisions related to property investments and market trends.

4. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can access the desired Zillow real estate data without unnecessary delays.

5. User-friendly Interface: APISCRAPY provides a user-friendly interface, making it accessible for individuals and businesses to navigate and utilize the Zillow real estate data scraping service with ease.

APISCRAPY provides real-time real estate market data drawn from Zillow, ensuring that consumers have access to the most up-to-date and comprehensive real estate insights available. Our real-time real estate market data services aren't simply a game changer in today's dynamic real estate landscape; they're an absolute requirement.

Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

APISCRAPY's data extraction is a key element, and it is an automated and smooth procedure that is at the heart of the platform's operation. Our platform gathers Zillow real estate data quickly and offers it in an easily consumable format with the click of a button.

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This method returns Census 2021 estimates that classify households by occupancy rating based on the number of rooms in the household.

Occupancy rating for rooms defines whether a household's accommodation is overcrowded, ideally occupied or under-occupied. This is calculated by comparing the number of rooms the household requires to the number of available rooms.

The number of rooms the household requires uses a formula which states that:

• one-person households require three rooms comprised of two common rooms and one bedroom
• two-or-more person households require a minimum of two common rooms and a bedroom for each person inline with the Bedroom Standard

People who should have their own room according to the Bedroom Standard are:

• married or cohabiting couple
• single parent
• person aged 16 years and over
• pair of same-sex persons aged 10 to 15 years
• person aged 10 to 15 years paired with a person under 10 years of the same sex
• pair of children aged under 10 years, regardless of their sex
• person aged under 16 years who cannot share a bedroom with someone in 4, 5 or 6 above

An occupancy rating of:

• -1 or less: implies that a household's accommodation has fewer rooms than required (overcrowded)
• +1 or more: implies that a household's accommodation has more rooms than required (under-occupied)
• 0: suggests that a household's accommodation has an ideal number of rooms

The number of rooms is taken from Valuation Office Agency (VOA) administrative data for the first time in 2021. The number of rooms is recorded at the address level. This means that for households that live in a shared dwelling, the available number of rooms are counted for the whole dwelling in VOA, and not each individual household.

VOA's definition of a room does not include bathrooms, toilets, halls or landings, kitchens, conservatories or utility rooms. All other rooms, for example, living rooms, studies, bedrooms, separate dining rooms and rooms that can only be used for storage are included.

“Occupancy rating for rooms” is split into 6 categories including total.

The estimates are as at Census Day, 21 March 2021.