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Getting started with Estated Data: Property Data and API information

Our property data offers a detailed, granular look at every residential property in the United States.

For the full technical documentation, click here.

Property

Site

The site data gives you information about the land that the home sits on. This includes a standardized and parsed version of the entire street address, details about the surrounding area, and specific details like the legal descriptions and parcel ID of the property. Some examples include: street_number = ‘100’, county_name = ‘Onondaga’, parcel_id = ‘007-019-008’. From this data, you can locate the address as well as determine property zoning and lot size. You can also append your existing data with additional information if you currently only have street addresses.

Owner History

This data details the records of past owners of a property and combine it with their contact information. The data will detail the full name, phone number, email address, and address of each owner record given. It will also indicate whether the property has been corporately or privately owned, and when each owner moved and gave up ownership of the property. Some examples include: name = ‘Cheng Bernard’, phone = ‘212-123-1234’, corporate_flag = false. This data is helpful when trying to contact the current or past owners of a home.

Sales History

The sales history data details the records of the property being sold. The data includes the date, price, and the seller/buyer information. It also includes the type of sale and the HPI adjusted price. Some examples include: date = 2009-08-21, hpi_adjusted_price_2010 = 176000, buyer = ‘Cheng Bernard’, type = ‘Deed of Trust’. This data can help you benchmark sales prices for comparable homes in the area, or keep track of property transactions for other purposes.

Valuation

The valuation data comes from our proprietary AVM using advanced machine-learning algorithms. The property value comes from a number of weighted factors based on the property characteristics, market data, and other calculations. Our AVM not only gives the property value, but also a confidence interval, market value changes, and suggested rental price. Some examples of the data returned for valuation include: value = 189000, confidence = 0.90, suggested_rental = 1200.

Comparable Properties

For every property searched, we can return up to 10 comparable properties. The data returned for each comparable property includes: meta, site, structure, tax_history, valuation, and comp_score. Comparable properties are great for learning a local market and setting property value benchmarks when setting a sale price.

Structure

The structure data explains the qualities of the property. Year built, bed and bath count, square footage, construction materials and more are all included in the structure data. This is the section of the data which gets very specific about the house and it’s properties. Some examples include: year_built = 1923, beds_count = 4, finished_size = 1492, exterior_wall_type = ‘Stucco’. From here, you can identify homes which fit a specific criteria which is relevant to you and your business.

Tax History

This data details the records of property taxes paid on the property. It gives the year, the amount paid, the assess land and improvements value, and some extra tax details. Some examples include: tax_year = 2017, appraised_land = 2200, outstanding_tax = 233.16. This data is useful for investment decisions and determining if a home is a good buy.

Mortgage History

The mortgage history data covers the details of the current and past mortgages: amount, loan type, term, and more. It also covers the date issued, date due, lender name, and other particulars available. Some examples include: loan_type = ‘Conventional’, due_date = 2034-08-21, amount = 74900. This data can help identify refinancing opportunities or other profitable opportunities for financial institutions.

Postal

This is the USPS data for each property. This data categorizes properties by various USPS internal codes for delivery. It also verifies addresses as a validated USPS recorded address, and even lets you know if mail can be delivered there. Some examples of the postal data include: carrier_code = ‘R001’, validated = ‘Y’, deliverable = ‘Y’. Postal data proves to be very useful for improving the effectiveness and profitability of direct mail campaigns.

Status

Our status data detail the current residency, market, and legal status of a property. The data includes the market status, foreclosure status, vacancy, and other details. Some examples of the data include: market_status = ‘For sale’, vacant = false, foreclosure = ‘Bank pre-foreclosure’. Market and legal status data are useful for certain industries to identify new opportunities and customers.


Geo

Place

The place data is the basis for many other datasets and their returns. It includes the specific geographic location based on longitude and latitude, as well as census block and FIPS code and more. Some examples of the data include: latitude = 39.470741, census_block = 1002, county_fips = 840. Place data is associated with all of our geographic, demographic, health, and crime datasets to provide geographic context to all of the data. It is useful for any geographic analysis conducted on a property or a set of properties.

Nearby Features

Nearby features data lists the nearest geographical features, like public buildings such as fire and police departments, post offices, libraries, museums, as well as hospitals, parks, structures, and natural features. The data details the feature name, the category, the exact location, and how far it is from the property. Some examples of the data include: feature_name = ‘Foley Post Office’, type = ‘Post Office’, distance = 6.37.

School

Up to 10 nearby schools are returned on a property search. The data includes the name, type, level, district, address, and location of the school. Some examples of the data include: name = 'George Washington High', type = ‘Public’, district = 'Ohio School District 18'. School data is also a necessary factor involved in real estate purchase decisions.

Fire Jurisdiction

Similar to police jurisdictions, the fire jurisdiction data identifies the fire jurisdiction each property falls into. The fire jurisdiction data goes further in depth, however, giving more information like department type, stations count, firefighters count, and more. Some examples of the data include: fdid = ‘50009’, department_type = ‘Volunteer’, volunteer_firefighters_count = 55. Similar to police jurisdiction data, the detailed information on the department is important for insurance companies when evaluating emergency responses.

Storm Events

Storm events data details the most recent 25 storm events in the county. These storms range from snow, flood, hurricane, tornado, and wildfire. The data goes in-depth by detailing the damage, magnitude, and cause of the storms. It also includes the injury and fatality count. Every storm comes with a narrative written about the weather episode to give context. Some examples of the data include: event_type = ‘Winter Storm’, magnitude = 98, injuries_direct = 6. Storm events data is also used by insurance companies when assessing risk in a given area.

Geographic Entities

Geographical entities data details the various districts which a property belongs to based on location. This includes: school, congressional, legislative, police, and fire districts. Some examples of the data include: congressional_district = ‘19th District’, state_legislative_district_upper = ‘District 26’. (fleshed out in school/fire/police, otherwise marketing reasons)This dataset is used to categorize properties into their various districts.

School District

The school district data is able to match the correct school district to the property. The data includes the name of the district, the grade range, and geographic placement of the district. Some examples of the data include: name = 'Clovis Unified School District', type = ‘Secondary’, high_grade = ‘12’. School district data is a necessary factor involved in real estate purchase decisions.

Police Jurisdiction

The police jurisdiction data identifies exactly which police jurisdiction each property falls into. The data includes the jurisdiction name, the FIPS codes, and geographic identifiers. Some examples of the data include: name = 'Seattle Police Department', city = ‘Seattle’, county_fips = 33. Police jurisdiction data factors into emergency preparation which mainly comes in handy for insurance evaluation. It is important to know the police jurisdiction which will be responding to emergencies at the property to estimate their response time and more.

Natural Disaster Risk

The natural disaster risk data estimates the likelihood of a natural disaster occurrence based off historical events. The data covers two things: number of events since 1980 and occurrence likelihood rating. Natural disasters included in the dataset are floods, wildfires, earthquakes, tornadoes, hurricanes, snow/ice/hail storms, extreme heat events, and landslides. Some examples of the data include: earthquake.frequency = 6, earthquake.risk_rating = 77. Natural disaster risk data is used by insurance providers to assess the risk of issuing insurance in different areas.


Health

Life Expectancy

Life expectancy data is reported in 5 year intervals from 1985 to 2010 and is split between male and female. The data can reveal trends in lifestyle and health changes in geographic areas. Some examples of the data include: year = 2010, male = 73.3, female = 78.8. Life expectancy data is a useful indicator of overall health in a given area, and is a good factor when scoring areas for a potential move.

Obesity

Obesity data presents the obesity prevalence in a given area and is split between male and female. Values are returned for years 2001, 2009, 2011. The data can reveal trends in lifestyle and health changes in geographic areas. Some examples of the data include: year = 2011, male = 37.1, female = 39.2. Obesity data is a useful indicator of overall health in a given area, and is a good factor when scoring areas for a potential move.

Diabetes

Diabetes data presents the diabetes prevalence in a given area and is split between male and female. Values are returned for every year from 1999 to 2012. The data can reveal trends in lifestyle and health changes in geographic areas. Some examples of the data include: year = 2011, male = 16.67, female = 13.92. Diabetes data is a useful indicator of overall health in a given area, and is a good factor when scoring areas for a potential move.

Physical Activity

Physical activity data presents the obesity prevalence in a given area and is split between male and female. Values are returned for years 2001, 2009, 2011. The data can reveal trends in lifestyle and health changes in geographic areas. Some examples of the data include: year = 2011, male = 52.9, female = 47.5. Physical activity data is a useful indicator of overall health in a given area, and is a good factor when scoring areas for a potential move.

Alcohol Consumption

Alcohol consumption data presents the binge drinking and heavy drinking prevalence in a given area and is split between male and female. Values are returned for every year from 2005 to 2012. The data can reveal trends in lifestyle and health changes in geographic areas. Some examples of the data include: year = 2011, binge_male = 38.6, binge_female = 25.0. Diabetes data is a useful indicator of overall health in a given area, and is a good factor when scoring areas for a potential move.


Crime

Aggregated Crime Stats

Aggregated crime statistics data presents the volume of different crime events happening from the most recent census year. It also includes the rate per capita and reporting agency. Some examples of the data includes: type = ‘larceny_theft’, rate = 16.1, value = 48. Aggregated crime statistics are useful for determining the safeness of an area when considering a move, investment, or insurance decision.

Sex Offenders

Sex offenders data shows a list of up to 100 sex offenders in the area. The data provides their physical description, crimes committed, home address, and more. Some examples of the data include: name = James Matthew Hinkle, crimes = [{"Date Convicted":"27 June 2013","Offense\/Statute":"Sexual Abuse 2nd"}], address = 735 County Rd. Sex offender data and frequency are useful for determining the safeness of an area when considering a move, investment, or insurance decision.

Crime Events

Crime events data provides the most recent 25 crime events from the area. The data is combined with latitude/longitude coordinates to pinpoint the location of the crime. Some examples of the data include: incident_category = Theft, incident_robbery = Robbery, case_number = 13-08-0815-1. Detailed crime events data gives an in-depth look into the crime in a specific neighbourhood, which can be useful in identifying problem areas and safe areas.