2018年-FCA英国金融行为监管局_ms16_2_2_annex_6_24页_991kb

Summary of MS16/2.2: Annex 6 - Switching Analysis Methodology

Core Content

This document outlines the methodology and approach used in the switching analysis of the UK mortgage market, focusing on the behavior of consumers on reversion rates and the potential benefits of switching to a new mortgage product. The analysis aims to understand whether consumers are harmed by not switching and how the market dynamics affect their ability to do so.

Main Objectives

• To identify consumers on reversion rates who could benefit from switching
• To assess the impact of switching behavior on mortgage costs
• To evaluate the effectiveness of internal switching policies
• To estimate potential savings from switching

Key Methodology

The switching analysis is structured into four main stages:

1. Establishing the baseline population

   • Focuses on consumers on reversion rates for at least 6 months in H2 2016
   • Excludes mortgages in arrears, those with small balances, and those near the end of their term
   • Baseline population: ~2 million mortgages on reversion rates

2. Identifying consumers who may be unable to switch

   • Uses frontier analysis to estimate the number of consumers outside the lending frontier
   • Lending frontiers are constructed based on risk characteristics: LTI, LTV, repayment type, and lending into retirement
   • Internal switching policies are considered to refine the estimate

3. Assessing benefit of switching

   • Compares base scenario (current reversion rate) and benchmark scenario (hypothetical switch to a new deal)
   • Calculates annual percentage rate of charge (APR) for both scenarios to determine savings
   • Consumers are considered inactive if they could switch but did not

4. Estimating savings for inactive consumers

   • Uses product sales data (PSD001) and mortgage status data (PSD007)
   • Adjusts for property values and income using GDP growth and Nationwide's house indices
   • Applies a conservative benchmark of 3.69% (lowest SVR in H2 2016) to determine potential savings

Data Sources

• PSD001: Data on new regulated mortgage contracts
• PSD007: Data on existing regulated mortgage contracts
• Internal switching data: From the RfI (Regulatory Financial Information)
• Product information: From third-party sources
• Qualitative evidence: From lender responses and market intelligence
• Validation: Using intermediary surveys and other FCA data

Key Findings

• Consumers unable to switch: ~50,000 consumers were identified as being outside the lending frontier and not qualifying for internal switching
• Benefit from switching: ~30,000 of these consumers were estimated to be paying reversion rates above 3.69%, and thus could benefit from switching
• Inactive consumers: ~1.75 million consumers were identified as potentially inactive, meaning they could switch but did not
• Switching behavior: Inactive consumers may be harmed due to not taking advantage of lower rates
• Benchmark scenarios: Consumers are assumed to switch to a 2-year fixed rate product or a tracker product, with the lowest SVR as the reference point
• Assumptions:
  • Consumers switch only once and revert to their lender’s reversion rate after the introductory period
  • Product fees are added to the mortgage balance and repaid over the remaining term
  • Non-monetary switching costs are not considered

Limitations and Considerations

• The analysis excludes consumers on introductory rates due to limited benefit from switching
• The frontier method may miss potential lending options not observed in the data
• Internal switching policies can significantly reduce the number of consumers unable to switch
• Income and property value are assumed to remain constant unless adjusted for GDP and house index
• The benchmark is conservative, as it assumes minimum effort switching and does not account for all switching costs

Conclusion

The methodology provides a structured approach to understanding switching behavior in the mortgage market. It highlights the importance of internal switching policies and the potential harm to inactive consumers who could benefit from switching but do not. The results suggest that a significant number of consumers could have saved money by switching, but the analysis is limited by data availability and assumptions about consumer behavior and lender risk appetite.