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How Do Property Reports Work & What Are They Used For?
How Do Property Reports Work & What Are They Used For?
Updated over 3 weeks ago

Property Reports in DAS serve as a comprehensive, data-driven tool for evaluating land and property assets. These reports provide valuable insights that help users make informed decisions in various industries, including valuation, insurance, investment, and risk management. Whether you're assessing a property's agricultural potential, environmental risks, or financial viability, DAS Property Reports offer standardized and reliable data to streamline due diligence, reporting, and strategic planning.

Each report acts as a snapshot of the property at the specific point in time of creation, providing both historical context and future projections where applicable. By leveraging DAS Property Reports, users save time, reduce uncertainty, and gain a clearer understanding of the properties they manage, assess, or invest in.

DAS Internally Calculated Data Points

DAS Property Reports include a range of internally calculated and curated data points designed to simplify the reporting process. These data points are automatically generated by DAS and can be toggled on or off depending on the user's needs. While most fields are pre-populated for accuracy and efficiency, certain fields allow manual adjustments where necessary, ensuring flexibility and customization based on specific property details.

Cover Page

The cover page provides a high-resolution geospatial image of the property's custom-defined boundaries, outlined in yellow for clear visibility. These boundaries represent the ratable area used for the report, ensuring an accurate visual reference.

Additionally Company administrators can adjust the boundary color and text color settings to align with specific corporate themes and add there logo as well for a more personalized report appearance.
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Overview

The Overview section provides key property details, including:

  • Address (editable)

  • Parcels & Land Area (ha) (editable, with a ±2% margin for accuracy)

  • Statistical Area (SA2)

  • Local Government Authority (LGA)

  • Agro-Ecological Region (AER)

  • Primary Agricultural Land Use

  • Primary Soil Type

  • Primary Crop Type

  • Average Annual Rainfall

  • Rainfall Variability

  • Growing Season

  • Potential Carrying Capacity

  • Long-Term Mean Net Primary Productivity (LTM NPP)

Manually Editable Fields

  • Property Name

  • Address

  • Property Description

  • Land Area (ha) (Users can correct discrepancies using vendor data)

Data Sources

  • Property Addresses – Sourced from G-NAF © Geoscape Australia.

  • Soil Type – Determined using national and state-specific soil data calculations:

    • New South Wales: State Government of NSW & Department of Climate Change, Energy, and the Environment (2024).

    • Australian Capital Territory: State Government of NSW & Department of Climate Change, Energy, and the Environment (2016).

    • Northern Territory: Department of Environment & Natural Resources.

    • Queensland: Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2024).

    • South Australia: Department of Environment, Water & Natural Resources (DEWNR).

    • Tasmania: Department of Primary Industries, Parks, Water & Environment (DPIPWE).

    • Victoria: Department of Environment, Land, Water & Planning.

    • Western Australia: Department of Primary Industries & Regional Development (DPIRD).

  • Crop Type – Determined using CropID technology, which leverages AI, satellite imagery, and ground truth data to classify crop types across broadacre paddocks in Australia. The system identifies nine major crop types along with vetch, pasture, and fallow land. CropID is continuously refined for improved accuracy and is used to establish the primary crop type of a property.

  • Average Annual Rainfall – Displays the long-term annual rainfall average for the property, based on SILO climate data.

    • Source: Queensland Department of Environment and Science (2024).

  • Rainfall Variability – Measured using a percentile analysis method to assess the consistency of rainfall patterns.

    • Source: Australian Bureau of Meteorology (BOM).

  • Area Calculation – DAS utilizes in-house GIS-based calculations to determine property area using polygon measurements, accounting for Earth’s curvature and disjoint polygons.

  • SA2 & LGA Boundaries – Defined by the Australian Bureau of Statistics (ABS).

  • Agro-Ecological Regions (AERs) – Sourced from CSIRO.

Agricultural Land Uses

Identifies primary and secondary land uses on the property, derived from spatial datasets and national land use classifications.

📌 Source: ABARES 2024, Catchment Scale Land Use of Australia
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Remnant Vegetation

Classifies remnant vegetation into:

  • Forest (≥20% canopy cover)

  • Sparse-Woody Vegetation (5–19% canopy cover)

  • Non-Woody Areas

📌 Source: Department of Climate Change, Energy, the Environment, and Water.

Soil Type

Categorizes soil using the Australian Soils Classification (ASC) system, aligning with national mapping datasets.

📌 Source: Department of Planning, Industry & Environment.
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Slope & Elevation

Measures terrain characteristics:

  • Slope (%) – Indicates land inclination, affecting erosion risk and land usability.

  • Elevation (m) – The average height above sea level.

📌 Source: Geoscience Australia.

Potential Carrying Capacity

Estimates the livestock sustainability of the land under optimal conditions, using:

  • Climate & soil data

  • Feed availability

  • Pasture composition

  • Economic grazing models

📌 Source: CSIRO.

Climate Risk

Breaks down climatic data relevant to property management and risk assessment:

  • Average Annual Rainfall – Derived from SILO Climate Database.

  • Rainfall Variability – Measures consistency of rainfall patterns over time.

  • Growing Season – Identifies the predominant season for crop growth.

  • Fire, Flood & Frost Risk – Uses historical data to assess environmental risks.

📌 Source: Bureau of Meteorology (BoM), CSIRO, SILO Climate Database.

Sections That Are Manually Editable & User-Provided Data

These sections contain user-input data and can be modified before generating the final report.

Nearby Sales

The Nearby Sales section provides insights into recent property transactions in the surrounding area. This data helps users assess market trends, property valuations, and regional sales activity, supporting informed decision-making for investment, insurance, and lending purposes.

How Nearby Sales Are Managed

Unlike other datasets, Nearby Sales are not automatically populated. Users must either:

  1. Manually create custom sales within the system (custom sales are auto-approved).

  2. Approve sales from the DAS sales layer—this dataset is sourced from PropTrack and provides official historical valuer general sales data (Present up until 10 years old) for review.

Only approved sales—whether user-created or validated from the PropTrack feed—can be used as comparable nearby sales in reports.

Key Data Points Included

Each Nearby Sale includes the following key details to support property valuation and market analysis:

  • Sale Price – The recorded transaction price of the property.

  • Sale Date – The official date of sale.

  • Land Area (ha) – The total area of the sold property, enabling direct comparisons with the subject property.

  • Price per Hectare – The sale price divided by the land area, providing a standardized value for comparison.

  • Distance to Subject Property – The proximity of the sold property to the property being assessed.

📌 Source: Prop Track
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Structures

The Structures section provides an interactive way to manage and review buildings detected within a property. While DAS provides automatic structure detection through Geoscape Australia's building dataset, users have full control over updating and supplementing this data.
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How Structures Are Identified

DAS receives quarterly updates from Geoscape, Data accuracy varies based on imagery availability, update frequency, and environmental conditions at the time of capture. While this automated dataset provides a strong foundation, users can manually refine or expand upon the structures recorded.

User-Editable Fields

While DAS provides the building layer itself, all other structure details can be manually added or adjusted by users to ensure data is relevant and accurate.

  • Type – Users can categorize structures based on their primary use (e.g., residential, agricultural, commercial).

  • Area (m²) – Pre-filled based on detected footprints, but can be modified.

  • Construction Year – Allows users to input the build year for historical tracking.

  • Condition – Users can assign a condition rating for each structure.

  • Value ($) – Users can input an estimated or appraised value.

  • Insured Value ($) – Allows for tracking insured amounts for risk and finance assessments.

  • Description – Users can add notes for context or clarification.

  • Images – Users can upload photos of each structure for documentation.

Adding & Editing Structures

Users can manually add new structures by selecting the "Add Structure" button, placing a pin on the map, and entering relevant details. This feature allows users to:

  • Record newly built structures not yet detected in Geoscape’s dataset.

  • Improve accuracy by correcting structure types, sizes, and conditions.

  • Track additional property assets, including fencing, sheds, silos, water tanks, and farm equipment.

📌 Source: Geoscape Australia.

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