Introducing the EeMAP valuation and energy efficiency checklist

By Ursula Hartenberger, RICS Global Head of Sustainability  

The recently published EeMAP definition for energy efficient mortgages presents a breakthrough in the project and provides the basis for establishing protocols to ensure appropriate lending secured against properties which are likely to both lower credit risk and  support the climate change mitigation agenda. The definition explicitly mentions the value aspect of the energy efficiency measures of the property for which an Energy Efficient Mortgage (EEM) is being sought and as such clearly refers to the EeMAP valuation and energy efficiency checklist, developed by RICS to aid transparency to the valuation process.

What is the checklist and how does it sit with the traditional canon of mortgage lending valuation instructions?

The EeMAP valuation checklist is the result of an in-depth consultation with valuers and mortgage lending banks from across Europe and is designed to complement existing instructions given to valuers. Currently there is no standard reporting template for valuers asking them for their assessment of the relationship between value and the energy efficiency aspects of the property although much of this information may already be collected and used implicitly to inform the valuer’s opinion of market value. The checklist provides a list of property characteristics which may affect the energy demands of the building – such as heating, insulation, structure and orientation to name but a few and requires the valuer to report on each explicitly using a tick box and comment facility.  Many of the indicators on the list will look familiar to valuers and banks as they are already part of existing valuation instructions –  the difference is that they will be assessed specifically from an energy efficiency perspective.

Therefore, if the instruction allows, valuers are advised to consider and make specific reference to those indicators and observed subsequent energy efficiency characteristics and implications which potentially could impact the value of the property and, indeed, consider this relationship in their final estimate of the subject properties market and mortgage lending value.

What implications does the EeMAP checklist have for lending institutions?

Depending on the lending context and/or the property-specific information already available or recorded, lending institutions may wish to complement their existing valuation instructions with selected indicators from the checklist, or indeed use it in its entirety.

In addition, banks are advised to capture important information on the indicators contained in the checklist as this is essential for measuring the financial performance of energy efficient mortgages and for benchmarking them in relation to key risk indicators such as Probability of Default (PD) or Loss Given Default (LGD). Additionally, by collecting and recording this data, it will better inform the valuers of the energy/value relationship. This could be important for EU policy moving forward.

What is the scope of the new EeMAP checklist?

The checklist is intended to serve different lending scenarios, including:

–        the origination of a new or extension of an existing mortgage for a property undergoing    renovation,

–        the origination of a new mortgage for an already energy efficient property, and

–        re-mortgaging.

The checklist has been created with user-friendliness in mind. It comprises three assessment categories:

  1. the core indicators, such as the EPC rating, energy consumption, physical building characteristics, condition of systems, availability of building related information/documentation, etc.,
  2. commentary regarding additional energy-performance related risk considerations, including building orientation, presence of renewables, lighting, etc., and finally,
  3. an assessment summary that also benchmarks the property in question against overall market expectations with regard to energy efficiency.

Valuers are asked to rank each indicator according to a RAG (Red, Amber, Green) rating with a supplementary comment column in which they should provide a brief rationale for their respective ‘RAG’ judgement where this is not obvious.

Red: Below market ‘norm’ – value actually/potentially at risk over period of proposed loan

Amber: On or near market expectations – may be at risk in medium term

Green: Above market expectations and therefore likely to present a lower value risk moving   forward.

Grey: No data available

Are valuers in Europe ready for the new EeMAP energy efficiency valuation checklist?

The EeMAP consortium is fully aware that widespread market uptake of the EeMAP valuation checklist will depend on valuers feeling comfortable with making a professional judgement on the potential value impact of energy efficiency characteristics of a property. The consortium is also conscious of the fact that not all valuers in Europe undertaking valuations for mortgage lending purposes may currently have the necessary knowledge to assess some of the technologies that impact on energy efficiency and performance in order to appropriately complete and rate all indicators and assess their potential to impact value.

Therefore, the EeMAP consortium is now working on additional reporting guidelines to support the practical application of the checklist by providing explanatory notes on each of the checklist’s core indicators as well as the items under the commentary section and the overall assessment summary.

On the basis of these explanatory guiding notes, a tailor-made set of EeMAP energy efficient mortgage valuation training slides will be developed and integrated into existing valuation and energy efficiency training material, developed as part of the Intelligent Energy Europe funded project RenoValue. The combined training package will cover the following: the rationale and business case for integrating a building’s energy efficiency features as part of the valuation process, an introduction to energy efficiency in buildings, sources of information on energy performance, the integration of energy efficiency considerations into valuation methodology and valuing energy performance as part of valuations for mortgage lending purposes.

The joint EeMAP / RenoValue training material will be free and can be used either inhouse by lenders or in those cases where valuations are outsourced by firms contracted to carry out valuations on behalf of the bank.

Energy efficiency and probability of mortgage default: linkages and hidden risks

By Max Riedel, Research Fellow at Ca’ Foscari

Do borrowers become more creditworthy if they take out a mortgage on an energy efficient (EE) building? Or, to start with a more prudent question: does there exist any empirical relationship between EE and the probability of mortgage default (PD) at all? The first question is concerned with the identification of a causal link between EE and PD, while the second questions the very existence of a link between the two. The current data environment is challenging for answering either of the two questions.

To study the link between EE and PD, the necessary pre-condition is a clean, granular dataset that accurately distinguishes between loans on EE and non-EE buildings. Unfortunately, this information is seldom readily available as banks either store energy performance data in physical form, which is retrospectively costly to digitalize, or data is not being stored at all due to lack of an IT solution. Furthermore, the link has to be identified by taking into account borrower and building characteristics that might confound the empirical findings if not included. For instance, borrower’s age, building age, or location might have a confounding effect on the identification of a clear relationship between EE and PD. Fortunately, banks already collect the most relevant information in order to feed their credit risk models. Therefore, the main challenge in this respect lies in the collection of EE data and its merge with the remaining mortgage information.

When it comes to identifying causality, the requirements on the analysis become much more demanding. In order to establish a causal link, we have to understand and take into account the borrower’s consumption behaviour. Suppose a borrower buys a highly energy efficient house. One might argue that she will save money on heating and, thus, is more likely to repay her debt, which, in turn, should be reflected in an improvement of her credit rating. However, this would only hold true if she did not change her consumption behaviour. And this is where the currently unobservable risk lies: the unexpected deviation from the former consumption pattern after mortgage origination. For instance, the borrower could decide to change her heating habit and might use up the EE savings on more intense or careless heating. In the literature, this scenario is referred to as the rebound effect. The rebound effect generally refers to an increase in the request of energy services due to the decrease in the effective price paid by the consumer. However, she might also spend her EE savings on some other consumption products, such as a new car or kitchen. In addition to the shift in consumption, the borrower might also self-select herself into the data sample. In this case, not EE per se but a different factor would affect the credit risk. For instance, environmentally conscious borrowers are more likely to buy an energy efficient building and their attitude towards debt repayment might differ from other borrowers. In order to identify the causal link one has to account for such subtle factors. This is a challenging task as the bank typically does not collect soft information. However, there are solutions to overcome this issue. Regarding the consumption pattern, the bank typically possesses the borrower’s financial transaction history and can track its development over time. Additionally, utility companies collect energy consumption data, which allows to measure the rebound effect. To account for the other soft borrower characteristics, customer surveys would be helpful in order to estimate their general attitude towards the environment.

As of date, it is a challenging task to meet all data requirements for a causality analysis. The reasons being lack of data and privacy concerns. Thus, we focus our analysis on the correlation between EE and PD, and leave the causality question for a future study. In the following, we present our findings from the Dutch mortgage market.

Using loan-level data from the Dutch mortgage market, we investigate the relation between a building’s energy efficiency and the probability of mortgage default. By focusing on residential buildings exclusively, our sample consists of mortgages issued on more than 120,000 dwellings. We supplement the dataset with provisional energy efficiency ratings that are assigned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland, RVO) to all Dutch buildings that are not yet supplied with the actual energy performance certificate (EPC) rating. RVO provides rating categories for 60 pairs of different building type and construction period combinations in the Netherlands. This allows us to match the loan data with EE ratings according to building type and construction year. Additionally, we exploit the fact that the ratings change asynchronously across the different building types in order to disentangle the energy efficiency-component from building type- and building age-specific effects that are typically associated with borrower’s risk of default. We employ two empirical methodologies – the Logistic regression and the extended Cox model – and find that energy efficiency is negatively correlated with a borrower’s likelihood of default on mortgage payments. The results hold if we account for borrower, mortgage, and market control variables. The findings also survive a battery of robustness checks. As an additional exercise, we investigate to what extent the degree of energy efficiency plays a role on borrower’s credit risk. Our findings suggest that mortgages on more efficient buildings are less prone to default. However, the findings on the degree of energy efficiency are less significant than the baseline results.

To date, only few studies attempted to investigate the correlation between EE and PD. To our knowledge all these studies were focusing on the US market. Our empirical exercise is among the first ones to establish a link between EE and PD with European mortgage data. We are optimistic that this field of research will grow as more European countries and banks commit themselves to collect and share their data.