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Use of credit register data for statistical purposes: advantages and preconditions, current and potential future uses Violetta Damia, Vitaliana Rondonotti European Conference on Quality in Official Statistics Helsinki, 4-6 May 2010 1 Contents Background Credit register’s data: scope and coverage Central Credit Register data: advantages and drawbacks Preconditions and current limitations for the statistical use of Central Credit Register data Conclusions and way forward 2 Background Increase in ESCB data needs, in particular for: • Enhanced data content (coverage, level of details) • Higher frequency and improved timeliness • Higher flexibility while maintaining: • Comprehensive, harmonised and consistent statistics • Minimum reporting burden • High quality data Use of granular and flexible datasets maintained in microdatabases and registers, where appropriate In particular possible use of credit registers for statistical purposes 3 Credit register’s data: scope and coverage (1/2) Credit Registers: granular databases on loan information Central Credit Registers (CCR): generally maintained by National Central Banks, collect information mainly from supervised institutions, to support: 1) bank supervisors for credit risk assessment of supervised financial institutions 2) financial institutions for credit risk evaluation of transactions 3) economic analysis And, on a case by case, CCR are used for research and statistics 4 Credit register’s data: scope and coverage (2/2) Credit Registers: granular databases on loan information Private Credit Bureaus (PCB): collect information from different data sources (lenders, firms, households, etc.) to support lenders in the assessment of credit conditions for small and medium-size enterprises (also modelling consumer behaviours, or assessment of default probability by type of loans) 5 Central credit register’s data: advantages Advantages: granular information – wide coverage data updated, revised and checked on a regular basis number of attributes of interest possible links with other sources (identifiers) for euro area/EU statistics, availability of CCR data in a significant number of countries (BE, DE, ES, FR, IT, AT, PT, SI, SK, BG, CZ, LT, LV, RO) 6 Central credit register’s data: drawbacks Drawbacks: data collected mainly for supervisory purposes, therefore: differences in coverage, content, definitions and methodologies and lack of certain breakdowns (often high) thresholds for statistical purposes, no direct data influence/responsibility for euro area/EU statistics, additional lack of harmonisation cross-country and lack of CCR data in some countries data confidentiality 7 Preconditions and current limitations for the statistical use of Central Credit Register’s data (1/2) banking supervision Purposes financial stability credit risk and (in some cases) economic analysis statistical use is generally not part of the main purposes, although some statistics are compiled, mainly for internal purposes and quality checking; very limited use for ECB statistical requirements Data access for statistical use CCR often not maintained by the Statistics Department Legal framework for statistical use yes to ensure data collection and confidentiality limited access (mainly with no interface) supervised financial institutions Reporting population census but often with rather high threshold (the threshold varies from EUR 50 to 1.5m) home country principle (banks’ foreign branches included, but can be separately identified) 8 Preconditions and current limitations for the statistical use of Central Credit Register’s data (2/2) Basis of reporting mainly borrower-by-borrower (use of borrower identifiers mainly for domestic borrowers - internal assigned codes - links with internal databases, enterprise numbers); in some cases loan-by-loan (use of loan identifiers - internal assigned codes) Credit data type outstanding amounts (end-of month) (largely) consistent definition of loans both positive (loans granted) and negative (defaulting) info no information on interest rates no consistent sectoral classification of counterparties no classification by purpose in some cases classification by size of firms Credit data breakdown by countries mostly available classifications breakdown by currencies or maturity not always available little information on securitised, syndicated, back mortgage loans some information on collateral, derivatives, guarantees, credit lines, commitment credit owing to high thresholds, many CCRs hardly cover loans to Data coverage households for purposes other than financing house purchase loans to non financial corporations are generally well covered 9 Conclusions and way forward (1/2) Some examples of statistical use of Central Credit Registers’ data: compile/check Monetary and Financial Statistics and support compilation of certain statistical breakdowns build up and maintain list of attributes to support national and euro area sampling 10 Conclusions and way forward (2/2) For a wider statistical use: Need to overcome the shortcomings identified in scope, coverage, definitions, reporting framework, interoperability, links with other sources Assessment of merits and costs for statistical use Development of coherent and integrated system(s) to create statistical databases to meet various needs ensuring coverage, punctuality and timeliness, consistency and harmonisation, reliability. 11