PCAF data quality scores: Best practice for mortgage lenders

As climate regulations tighten and scrutiny on financial disclosures intensifies, mortgage lenders are under increasing pressure to measure and disclose the climate impact of their loan portfolios accurately. 

The Partnership for Carbon Accounting Financials (PCAF) provides a GHG accounting and reporting standard for financial institutions. It helps lenders assess and improve their data quality through a transparent scoring system.

What is covered:

1. Why your PCAF score matters as a mortgage lender
2. The scoring system
3. How PCAF helps to address data gaps and inconsistencies
4. The impact of outdated carbon intensity factors and the overreliance on EPC data
5. Refining emissions calculations
6. How PCAF scores support target setting
7. PCAF and transparent reporting

Why your PCAF score matters as a mortgage lender

The PCAF standard includes a data quality score that ranges from 1 (best) to 5 (worst). This is determined by the completeness, accuracy, and transparency of emissions data.

Achieving a strong PCAF score builds confidence that lenders are compliant with regulatory frameworks, such as the ISSB’s new IFRS S2 standards for climate-related disclosures, which requires reporting on financed emissions against targets and timelines.

Only 48% of UK lenders currently disclose emissions calculations in line with PCAF’s methodology. So, not only does a strong PCAF help to put lenders in the good books of regulators, but it can also help to provide a point of difference in the eyes of investors.

The scoring system

A good PCAF score helps to not only demonstrate a commitment to regulators, investors and customers that your lending is transparent and trustworthy, but also that your approach to managing climate-related risks is robust.

PCAF scores are ranked from 1 to 5, where a low PCAF score (measured from 1 to 5) indicates the use of accurate and verifiable data, enabling better climate risk assessments. 

1. Actual building energy consumption and emission factors specific to the respective energy source. Actual building energy consumption and supplier-specific emission factors are used.
2. Actual building energy consumption and average emission factors specific to the respective energy source. Actual building energy consumption and average emission factors are used.
3. Estimated building energy consumption per floor area based on official building energy labels AND the floor area are available.
4. Estimated building energy consumption per floor area based on building type and location-specific statistical data AND the floor area.
5. Estimated building energy consumption per building based on building type and location specific statistical data and the number of buildings

Baseline EPC data receives an EPC of 3, however portfolios are typically missing data for 35% of properties. If left unmodelled, these properties will receive a score of 5, hence most lenders with a disclosed PCAF score will rank between 3 and 4.

Handling data gaps and inconsistencies

As above, one of the biggest barriers to achieving a strong PCAF score is the lack of reliable property data.

Property data is notoriously dirty. In many cases, an address for the same property can differ across datasets, meaning it is difficult to gain a complete view of its environmental profile.

A way around this is to accurately match as many unique property reference numbers (UPRNs) to portfolio properties, so that you know Flat A is definitely the same property as First Floor Flat. 

While this process is oversimplified, it is the basis for matching the portfolio with an EPC. At its core, the more real data, a lender has on their mortgage book in the form of an EPC or otherwise, the stronger the PCAF data quality score. If real data cannot be used, modelling based on nearby properties or properties with shared characteristics will help to achieve a PCAF score closer to 4 for the missing properties.

The impact of outdated carbon intensity factors and the overreliance on EPC data

Standard Assessment Procedure (SAP) calculations are used to calculate an EPC rating for UK homes. This enables understanding of the energy efficiency of and carbon emissions from a home, as well as allowing comparison between different homes. However, there are a few issues with the SAP methodology that impact data accuracy:

1. Outdated baselines. The SAP methodology was first developed in 2012 and uses baseline costs of energy and construction materials from this date – energy prices are much higher, and the costs of measures like solar panels have reduced. Until 2022 the SAP calculation also used carbon intensity factors dating from 2012 which are vastly different to todays. This has led to incorrect bill estimates and energy efficiency improvement recommendations within EPCs.

2. Modelling costs not carbon impact. The output of SAP calculations is a prediction of the cost to heat a home – environmental impact is not the main focus of the methodology and so it is not a reliable indicator of energy efficiency. 

3. Unfair reflection of cost. The SAP rating is based on static, outdated fuel prices and so produces an inaccurate reflection of the costs of both energy bills and retrofit improvements.

4. Inconsistent implementation. SAP calculations are reliant on the expertise and accuracy of energy assessors who produce the inputs of the calculation e.g. thickness of roof insulation. Studies have found huge inconsistencies in the methods that different assessors use, damaging the reliability of the result.

Similarly, EPC open data suffers from significant data gaps. An estimated 35% of mortgaged properties on average still lack valid EPC data, limiting the overall accuracy of financed emissions reporting.

Using only basic, incomplete EPC data, lenders risk miscalculating their portfolio emissions, and leave large data gaps that ultimately increase PCAF scores. 

Using sophisticated address matching (mentioned above) and a range of other techniques, lenders should aim to make this data gap as small as possible. After this, rather than estimating the rest of the portfolio with a basic extrapolation, lenders should deploy predictive modelling. This helps reduce reliance on portfolio averages and produces a more accurate picture of missing properties. Kamma does this by combining its geospatial expertise with machine learning models:

Mortgage lenders like Just Group have successfully adopted these techniques to enhance their PCAF scores and boost confidence in their reported emissions.

Refining emissions calculations

Precision in emissions calculations is crucial to achieve a strong PCAF score. Beyond absolute emissions, metrics like emissions intensity also provide a comparable view for the portfolio as its composition and size changes.

Implementing a loan-to-value (LTV) weighted approach also shows the financed emissions related to outstanding loan, rather than the total asset value.

How PCAF scores support target setting

Setting realistic yet ambitious emissions reduction targets is key to building a credible climate strategy. A strong PCAF score shows that your targets are not just a finger in the air estimate, but instead built on robust data.

Lenders that take the next step to include scenario analysis into their target-setting can better account for dependencies that impact the rate of decarbonisation, such as government policy changes and grid decarbonisation. Confidence in the data makes lenders more resilient as they can accurately account and mitigate for climate risk earlier.

PCAF and transparent reporting

Transparency in climate reporting is essential for maintaining stakeholder trust. 

However, only 38% of UK lenders are confident in the effectiveness of their climate transition plans. 

To address this, lenders should regularly benchmark progress on emissions against reduction targets, providing a clear narrative that links PCAF scores with overall climate strategy.

Using PCAF scores in climate-related disclosures and adopting reporting frameworks like the Transition Plan Taskforce (TPT) can help lenders align their reporting with market expectations and demonstrate tangible progress towards net zero.

To delve deeper into this topic, download our climate transition planning report, and sign up for a personalised assessment of your plan.