The Science Behind Our Valuations

M0VE.com isn’t your average property tool. While others throw out vague estimates, we deliver sharp, localised valuations rooted in hard data and clever tech.

By blending trusted sources like Land Registry records and EPC data with our own refined methods, M0VE gives you smart, up-to-the-minute property valuations that actually reflect what’s happening on the ground.

So, how do we do it? It all comes down to our finely tuned 7-step process:

The 7-Step Valuation Process

Step 1:
Gathering the Postcodes

M0VE starts with the ONS database, pulling together a complete list of UK postcodes. For every single one, we extract:

  • The postcode
  • The street name
  • The city or town it sits within

This forms the geographic backbone of our system, allowing M0VE to deliver street-specific valuations and sharp, local market intelligence.

Step 2:
Collect Data from Sources

To build meaningful valuation models, M0VE pulls in and blends data from two powerful sources:

EPC (Energy Performance Certificates)

  • ✅ Offers detailed property info including subtypes (like bungalows or end-terraces), floor area, and energy efficiency ratings
  • ❌ Some records can be outdated or entirely missing
  • We use this to add structure and efficiency details to Land Registry records

Land Registry

  • ✅ The official record of property sales across England and Wales
  • ❌ Doesn’t include in-depth property subtypes
  • We rely on this for accurate sale prices and basic property type data at the point of transaction

🔁 These sources are aligned, cleaned, and merged. EPC adds clarity and detail, while Land Registry gives us the trusted price history that grounds every valuation.

Step 3:
Categorising & Inflating by Property Type (PT)

Raw Land Registry data only uses broad labels like “semi-detached”. That’s not enough. M0VE takes it further by:

  • Linking each property to its EPC record, revealing precise subtypes such as “semi-detached bungalow” or “end-terrace”
  • Analysing how each subtype impacts price using historic sales matched with EPC detail
  • Adjusting the original Land Registry values based on these price shifts across subtypes

This clears up the messy generalisations you often see on other sites. Instead of rough guesses, M0VE delivers subtype-specific valuations you can actually rely on.

Step 4:
Adjusting for Energy Efficiency (EPC Score)

Energy-efficient homes usually fetch more on the market — but by how much exactly?

We adjust prices based on how a property’s EPC rating stacks up against the local average. This involves:

  • Studying thousands of EPC-linked sales to see how energy efficiency truly affects value
  • Calculating region-specific multipliers for every property type
  • Normalising sale prices to strip out the inflation or deflation caused by extreme EPC ratings

🏡 For example: If a home has a B rating in an area where most homes are rated C, its price is adjusted down to reflect the local average. That way, you get a more honest view of its worth without the green premium.

The result? A fair, consistent pricing baseline that avoids EPC-driven distortion.

Step 5:
Accounting for New Builds

New builds often carry a premium — but how much that premium is depends on where you are and what kind of property it is. M0VE accounts for this by:

  • Spotting whether a property is a new build using EPC data or the year it was built
  • Calculating the local new build premium with postcode-level historic sales data
  • Adjusting older sale prices to reflect whether the property should be valued as a new build or not

🏗️ This makes sure our valuations reflect actual market expectations, not just a checkbox that says “new build”.

So whether a home went up in 2023 or 1985, users get a price that aligns with real buyer behaviour in their specific area.

Step 6:
Excluding Anomalous Data

Not every sale should shape a valuation — some are just too far off the mark. These include:

  • Auction sales
  • Commercial properties mislabelled as residential
  • Multi-unit bulk transactions
  • Dodgy or mismatched EPC data

M0VE filters out these outliers by checking for:

  • Price-per-square-foot irregularities
  • Unusual transaction frequency patterns
  • EPC validation failures

Only representative transactions make the cut. The result? Cleaner, more accurate valuations you can trust.

Step 7:
Adjusting by Historic Transactions (2021–2024)

Older sales aren’t left behind. M0VE adjusts historic transactions for inflation using local market trends:

  • Every transaction is updated using annual appreciation data at the postcode or district level
  • If postcode-level data is too limited, we switch to broader district-level trends

📈 This makes sure that every valuation reflects today’s market conditions, even if the sale happened years ago.

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