Automated Valuation Model (AVM)
Intelligent, data-driven property valuations for faster decision-making
Built using advanced modelling techniques and extensive property data, the CLSQ AVM delivers consistent, scalable property valuations across England and Wales. Designed for lenders, property professionals and data-driven organisations, the model enables faster decision-making whilst maintaining confidence in valuation outputs.
The CLSQ AVM combines historical transaction data with a wide range of proprietary property attributes and geospatial intelligence to generate robust valuation outputs that reflect real market behaviour.
Why CLSQ AVM?
Traditional valuation approaches often rely heavily on individual surveyor interpretation or limited comparable data.
CLSQ’s AVM provides a consistent, geospatial data-led alternative that can support faster decisioning, reduce operational friction, and provide an independent view of property value at scale.
Policies Available
Key Benefits
Consistent, independent valuations
Provides a standardised view of property value based on robust data analysis rather than subjective interpretation.
Built on proven transaction data
Powered by HM Land Registry Price Paid data combined with CLSQ’s extensive geospatial property datasets.
Advanced modelling techniques
Utilises Gradient Boosted Decision Trees enhanced with outlier controls and feature engineering.
Enhanced property intelligence
Incorporates a wide range of proprietary property characteristics including tenure, flood risk, lease length, floor area and more.
87.0
NPS Score
Trusted to deliver a ‘world class’ service. CLSPI's service rating from legal professionals in 2023
Product
Features
Built for the modern property ecosystem
CLSQ’s AVM forms part of a wider ecosystem of property risk and valuation solutions designed to support lenders, insurers, conveyancers and property professionals.
By combining advanced data modelling with CLSQ’s deep understanding of property risk, organisations can make faster, more confident decisions at scale.