Jagdish Tripathy, Arzu Uluc, José-Luis Peydró and Francesc Rodriguez-Tous
Borrower-based macroprudential measures – such as limits on loan to income (LTI) and loan to value (LTV) ratios – have become a standard feature of the post-crisis regulatory landscape. A growing body of country-specific evidence suggests these measures are effective in moderating the self-reinforcing loop between mortgage credit and house prices, and in reducing default rates and limiting house price volatility during periods of economic stress. Yet their distributional consequences are less well understood. In a new paper, we survey the existing evidence and find that these tools deliver clear financial stability benefits, while also generating distributional effects across borrower groups. Further, we identify areas where future research is needed to provide a comprehensive welfare assessment of these measures.
A large body of literature shows that the global financial crisis was preceded by a large increase in mortgage credit, which fuelled high household leverage and house prices. Once the crisis hit, highly-indebted households cut consumption more sharply than less-leveraged households, were more likely to default and contributed to waves of foreclosures. This explains why crises preceded by household credit booms last longer and go deeper. Therefore, when housing markets and mortgage credit experience rapid growth, policies that limit excessive leverage can help mitigate the economic costs of future downturns.
Borrower-based measures (BBMs) are such policies designed to curb the build‑up of household leverage at mortgage origination and are typically introduced relative to collateral value or income. The use of these measures expanded rapidly after the global financial crisis. While they were loosened during the pandemic, their usage has picked up again in recent years (Chart 1). They sit within a broader prudential toolkit, complemented by underwriting standards and capital‑based tools – such as sectoral capital requirements, countercyclical capital buffers (CCyB) and stress testing – which are used to build system resilience and maintain lending capacity when household risks materialise.
Chart 1: Recent trends in tightening and loosening in borrower-based measures globally
Notes: The chart shows the total instances of net tightening in borrower-based measures across jurisdictions worldwide in a given year. Each instance of policy tightening is assigned +1, policy loosening is assigned -1.
Source: International Monetary Fund iMaPP.
Effects of borrower-based measures
A large empirical literature finds that BBMs are effective in limiting household leverage. Using both granular micro data and cross-country analysis, most studies show that tightening of these measures is associated with slower growth in mortgage credit and housing transactions, particularly during expansions. Early evidence from Korea demonstrates that LTV and LTI limits significantly reduced housing transactions and dampened price expectations, with speculative buyers delaying purchases in response to the policy. Cross-country studies similarly document that tightening of BBMs leads to slower credit and house price growth.
However, leverage at origination is not evenly distributed across borrower groups: younger borrowers, lower-income households and first-time buyers typically require higher leverage (refer to Figure 2 in our paper) and are more likely to be constrained by these measures. Studies using granular mortgage data find sharp reductions in high-leverage lending to these groups. However, this does not imply that credit access is necessarily a barrier to ownership: existing evidence points to deposit accumulation as the binding constraint. Moreover, lending often rebalances across borrower types and locations rather than collapsing in aggregate, indicating that these measures reduce systemic risk partly by reshaping the composition of borrowers and loan terms.
Beyond their effects on mortgage lending, BBMs also influence decisions about home ownership and location choice. Evidence shows that tighter leverage limits can delay home ownership for constrained households and influence where they choose to live. Some borrowers purchase smaller or more distant properties – often in less advantageous areas – as lenders tighten lending criteria to stay within regulatory limits, while others postpone purchasing to accumulate larger deposits, trading lower leverage for reduced post-purchase liquidity. These adjustments imply that BBMs can affect commuting patterns, job search, and households’ exposure to income shocks, extending their effects beyond housing and credit markets.
The key benefits of BBMs become apparent during downturns. Empirical evidence shows that borrowers subject to tighter leverage constraints are less likely to default when house prices or incomes decline. In the UK, low-income borrowers in areas more affected by LTI limits were less likely to default following the Brexit-induced house-price slowdown. Complementary evidence from agent-based models finds that lower leverage going into downturns reduces defaults and dampens house-price cycles.
BBMs also affect lenders’ behaviour and their balance sheets. When high-leverage lending is restricted, lenders tend to adjust the composition of loans towards unregulated segments of the portfolio. In some cases, lenders reallocate risk toward other asset classes or borrower segments as documented in Ireland. These responses underscore the potential for regulatory arbitrage and spillovers, highlighting the importance of monitoring lender behaviour alongside borrower outcomes.
Avenues for further research
While the literature has made substantial progress in understanding the effectiveness of BBMs, these policies are relatively recent, and further research is needed to build a holistic view of their consequences.
Much of the existing evidence focuses on what happens when BBMs are introduced during economic expansions. This provides an incomplete picture since the key benefits only materialise during downturns. One exception is our previous work where we study the effects during both a boom and the correction following the Brexit referendum. We find that BBMs moderated the slowdown in house-price growth and led to fewer defaults, especially among low-income borrowers, after the referendum.
Calibration
Relatedly, more work is needed to calibrate the overall costs and benefits of these measures, including their distributional consequences. While structural models of mortgage markets offer promising avenues for such calibration, none yet fully capture both the demand-side and supply-side determinants of household leverage. Agent‑based models provide a complementary approach by capturing heterogeneous borrower and lender behaviour across the full housing and credit cycle, helping to generate more realistic assessments of the net benefits of BBMs grounded in real‑world dynamics.
Policy levers
Policymakers can restrict household leverage using different BBMs, such as limits on LTV, LTI or debt-service ratio, each targeting different risks. Although these measures are correlated, they are not perfect substitutes. More research is needed to understand how these measures interact, how to choose between them, and how their effectiveness varies with macroeconomic conditions and institutional settings. In addition, understanding their interaction with monetary policy and with other prudential regulations is crucial for assessing their overall effectiveness and welfare implications.
Fintech
The growing role of fintech and non-bank lenders may alter how BBMs operate in practice. Increased use of algorithms and alternative data could change how lenders underwrite mortgages and rebalance portfolios under leverage constraints, raising questions about whether these technologies mitigate or amplify the distributional effects of BBMs.
Political economy
BBMs may also have broader political and institutional implications, given their effects on house prices, home ownership and borrower distress. Recent research links financial crises and household debt distress to political outcomes, suggesting that understanding how these measures interact with mortgage market features and voter incentives remains an important open question.
Health outcomes
Finally, a growing literature examines the relationship between household leverage, financial stress and mental health. While BBMs may reduce the likelihood that households experience severe financial distress following negative shocks, more evidence is needed to assess their broader impacts on health and wellbeing.
Conclusions
Our review points to several policy implications. First, BBMs work best when implemented early in the credit cycle, before systemic risks become entrenched. Second, because these tools bind unevenly across borrower groups, policymakers should assess distributional impacts, including effects on home ownership and location choice, alongside financial stability benefits. Finally, monitoring of lender behaviour and potential spillovers is crucial to ensuring these tools operate as intended.
Jagdish Tripathy works in the Bank’s Centre for Central Banking Studies, Arzu Uluc works in the Bank’s Macroprudential Strategy and Support Division, José-Luis Peydró works at LUISS University and EIEF, and Francesc Rodriguez-Tous works at Bayes Business School of City, University of London.
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