基于宏观审慎借款人的措施下的家庭行为

Household Behaviorunder MacroprudentialBorrower-BasedMeasures Jaunius Karmelavičiusand Julia Otten WP/26/66 IMF Working Papersdescribe research inprogress by the author(s) and are published toelicit comments and to encourage debate.The views expressed in IMF Working Papers arethose of the author(s) and do not necessarilyrepresent the views of the IMF, its Executive Board,or IMF management. 2026APR IMF Working Paper Monetary and Capital Markets Department Household Behavior under Macroprudential Borrower-Based Measures*Prepared byJaunius KarmelavičiusandJulia Otten Authorized for distribution by David HofmanApril2026 IMF Working Papersdescribe research in progress by the author(s) and are published to elicitcomments and to encourage debate.The views expressed in IMF Working Papers are those of theauthor(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT:This paper develops a life cycle model to study household choice under macroprudential borrower-based measures (BBMs). The model is extended to multiple heterogeneous households, allowing to assess bothaggregate and distributional effects of BBMs on mortgage and housing demand. The framework is applied toLithuanian and Slovak distributional data to quantify the impact of various BBM configurations. We find that thepresence of binding BBMs can usefully dampen mortgage and house price growth. However, tight regulationmay also redirect demand towards lower-valued housing, while pushing households into the rental market. Inparticular, loan-to-value (LTV) limits are most constraining for households with little or no initial wealth. Thishighlights the distributional consequences of BBMs and the importance of designing regulation to account forborrower characteristics. RECOMMENDED CITATION:Karmelavičius, J., Otten, J. (2026). Household Behavior under MacroprudentialBorrower-Based Measures. Working Papers WP/26/66, International Monetary Fund. *The authors would like to thank the Bank of Lithuania (LB) and the National Bank of Slovakia (NBS) for sharing their data. Weare grateful to Erlend Nier, David Hofman, and TengTeng Xu for their support and guidance, and to Gunes Kamber, JeromeVandenbussche, Kazuhiro Hiraki, and reviewers for helpful comments and suggestions. We thank participants of seminars atthe IMF, LB, NBS, and the Joint Vienna Institute. Executive Summary Quantifying the impact of macroprudential borrower-based measures (BBMs) requires a rigorousframework to analyze borrower behavior under changing conditions. While significant progresshas been made in understanding how BBMs contribute to household resilience, models thatassess their effects on credit uptake and housing choices remain limited. Existing frameworksare often stylized and lack strong microfoundations. This paper presents a micro-founded, heterogeneous-agent model designed to quantify thedirect effects of BBMs—loan-to-value (LTV), debt-service-to-income (DSTI), debt-to-income(DTI), and maturity limits—on credit and housing decisions at both individual and aggregatelevels.The framework is based on a life-cycle model of a resource-constrained householdthat makes rental, home purchase, and borrowing decisions. The household chooses not onlymonetary quantities—such as the price of housing and the amount to borrow—but also theduration of renting, saving, and borrowing. The model nests corner cases, including the decisionto remain a renter for life or to purchase a home without prior saving or borrowing. The single-household model is extended to a setting with many heterogeneous households.Although each household solves the same optimization problem, differences in preferences, en-dowments, and constraints generate variation in optimal decisions. This results in heterogeneityin loan parameter choices, which can be aligned with observed distributions in data over time.The model thus enables construction of relevant counterfactuals for any chosen period. We apply the model to Lithuania and Slovakia—two European countries with well-establishedmacroprudential BBM frameworks—and estimate it using rich distributional data on mortgagecharacteristics, including LTV, DSTI, DTI ratios, and loan maturities. Using the estimated models,we conduct a series of counterfactual policy experiments. For Lithuania, we first show that the adoption of appropriate BBMs during the pre-global-financial-crisis (GFC) buildup of financial imbalances could have restrained credit and houseprice growth. Second, our counterfactual analysis suggests that, in the absence of BBMs followingthe GFC, housing demand would have been front-loaded, leading to a substantial increase inboth credit demand and house prices. Finally, we examine alternative BBM configurations thatcould have helped moderate the post-Covid increase in credit and house price imbalances. In Slovakia, BBMs include various exemptions and speed limits that banks can apply at theirdiscretion. Howev