Built to make property decisions clearer

Indeval was built because too many property decisions still rely on fragmented data, patchy local knowledge and avoidable guesswork. We bring together the signals behind an area into one clearer view, helping property professionals assess locations faster, ask better questions and spot risks earlier.

Meet the team

The team behind Indeval

Indeval is built by a small team spanning product, development and data science. That combination helps us turn fragmented location data into something clearer, more usable and more commercially relevant.

  • Dan Tarbuck

    Dan leads product positioning, website experience and the AI-driven report workflow behind Indeval. His role is to make sure the platform is clear, commercially useful and able to turn complex location data into outputs clients can actually use.

    Product, Marketing & AI Systems
  • Josh Dale

    Josh leads front-end development and is responsible for the product experience users interact with day to day. He turns complex data and model outputs into a clear, usable interface, helping make Indeval practical rather than just technically capable.

    Lead Front-End Developer
  • Ralph Bradley

    Ralph leads the data science behind Indeval’s scoring and modelling approach. He focuses on the structure, logic and interpretation that sit behind the platform, helping ensure the product is built on a more useful and defensible view of location data.

    Lead Data Scientist
  • How Indeval took shape

    What started as a data-led idea has developed into a clearer product focused on helping property professionals assess locations with more confidence.

    How Indeval turns complex data into clearer decisions

    Our Methodology

    Indeval brings together multiple location and property signals into one clearer view. By combining trusted datasets, structured comparison and model-supported interpretation, we help users assess areas faster and with more confidence.

    Data inputs

    STAGE one

    We gather relevant signals from trusted public and commercial datasets, including property, demographic, infrastructure, planning and local context data.

    Structured comparison

    stage two

    Raw data on its own is fragmented and difficult to compare. We organise the inputs into consistent layers so areas can be assessed more clearly and more fairly.

    Scoring and interpretation

    stage three

    We apply scoring logic, modelling and reporting rules to surface patterns that are more useful in real decision-making. The aim is not to reduce everything to a single number, but to make the wider picture easier to understand.

    Decision-ready output

    stage four

    The result is a clearer output designed to support faster, more defensible property decisions. Indeval is built to reduce guesswork, not replace judgement.

    Built using signals such as:

    Property data · Demographics · Crime & safety · Schools · Connectivity · Planning context
    Get Started

    Want to see how Indeval fits your workflow?

    If you work in property and want a clearer way to assess areas, compare locations and support decision-making, we’d be happy to show you how Indeval works.

    Contact Us