As part of our AI research series, we explore the potential for pricing differences by demographic characteristics in the mortgages market.
Read the Research Note (PDF)[2]
As part of our AI research series, we explore the potential for pricing differences by demographic characteristics in the mortgages market.
This research note seeks to understand whether there are differences in the prices paid for mortgage products by a series of demographic characteristics.
We built a statistical model to predict interest rates and examined whether, after controlling for risk factors and mortgage type, there were any differences by 4 demographic characteristics. These were: ethnicity, sex, sexual orientation and having a health condition (as a characteristic of vulnerability). We conclude that after adjusting for mortgage type and controlling for risk factors, there appears to be no difference in mortgage pricing across these groups.
Instead, we find that groups appear to have different types of mortgage products. For example, those with a health condition appear to have mortgages with higher initial gross rates of interest on average, but lower upfront lender fees, lower property values and lower household incomes. This could suggest they are more likely to take out products where payments are spread over time resulting in slightly higher overall prices paid. It was unclear if this difference was driven by consumer choice or due to the types of mortgages these consumers were able to access.
From this research, whilst we observe that there is no evidence of a lack of direct pricing fairness (through differences in pricing by demographic characteristics alone), we cannot conclude that there are not issues with ‘demand fairness’ via the availability of products to different groups. There are other potential drivers of differential outcomes, which have been discussed in this research note, like the impact of society on the financial conditions of individuals at the point of taking out a mortgage. Future attempts to build upon this research could include further analysis of what drives the differences in outcomes and other work to determine causality.
Our AI research series
We want to enable the safe and responsible use of AI in UK financial markets, driving growth, competitiveness and innovation in the sector. As part of our effort to deepen our understanding of AI and its potential impact on financial services, our team is undertaking research in the area of AI bias.
We are publishing a series of research notes on how AI intersects with financial services to spark discussion on these issues, drawing on a variety of academic and regulatory perspectives. We hope that these notes are of interest to those who build models, financial firms, and consumer groups in understanding complex debates on building and implementing AI systems.
Disclaimer
Research notes contribute to the work of the FCA by providing rigorous research results and stimulating debate. While they may not necessarily represent the position of the FCA, they are one source of evidence that the FCA may use while discharging its functions and to inform its views. The FCA endeavours to ensure that research outputs are correct, through checks including independent referee reports, but the nature of such research and choice of research methods is a matter for the authors using their expert judgement. To the extent that research notes contain any errors or omissions, they should be attributed to the individual authors, rather than to the FCA.
Authors
Daniel Bogiatzis-Gibbons, Lawrence Charles, Luke Lawlor, Bjorn Limani, Eoghan O’Brien, Ned Staniland.