## 1. Number of Lending Restriction Measures by Types and Across Country Groups

## Source details

**Canonical URL:** [1. Number of Lending Restriction Measures by Types and Across Country Groups](https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019045.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/wp/2019/wpiea2019045.pdf.md)
- [Structured JSON version](/-/media/files/publications/wp/2019/wpiea2019045.pdf.json)

---

### Introduction and scope
- Sample: 28 EU countries over the period 1990–2018 (sample restricted to 1990q1–2018q2).
- Focus: lending restriction measures (borrower-based), notably loan-to-value (LTV) and debt-service-to-income (DSTI) ratios.
- Data sources: Budnik and Kleibl (2018) database for measures; Bank for International Settlements and International Financial Statistics for house prices and credit; CPI used to deflate series.
- Coding of measures: categorical coding with 1 for tightening, -1 for loosening, 0 for no/neutral measure. In two instances with both tightening and loosening in same quarter the code is 0.
- Projection horizon used in analysis: h = 0,...,16 quarters.
- Event study window: j = -12,...,12.

### Key dataset statistics
- Total lending restriction measures observed: 99.
- Breakdown by measure type:
  - Loan-to-value (LTV) limits: 41 measures.
  - Debt-service-to-income (DSTI) limits (incl. interest rate stress testing): 20 measures.
  - Maturity and amortisation restrictions: 12 measures.
  - Other restrictions on lending standards: 17 measures.
  - Other income requirements for loan eligibility: 5 measures.
  - Loan-to-income (LTI) limits: 1 measure.
  - Debt-to-income (DTI) limits: 1 measure.
  - Limits on the volume of personal loans: 2 measures.
- Measure attributes:
  - Tightening measures: 82.
  - Loosening measures: 17.
  - Legally-binding measures: 54.
  - Measures without sanctions: 51.

### Empirical methodology
- Baseline estimator: local projections (Jorda 2005) estimating responses of log real house prices and log real credit to lending restriction measures (specification [1]).
- Controls: country fixed effects, time fixed effects, lagged dependent variables, GDP growth, change in monetary policy rate, crisis dummies; N lags of controls included.
- Asymmetry test: specification with separate dummies for tightening (MT) and loosening (ML) episodes (specification [2]).
- Heterogeneity tests: interactions for country groups (euro area vs other EU) and measure types (legally-binding vs recommendations; with sanctions vs without) (specification [3]).
- Robustness approaches:
  - Inverse probability weighted (IPW) estimator to mitigate endogeneity (ordered logit for propensity p_it; weights w_it = M_it/p_it + (1-M_it)/(1-p_it)).
  - Event-study methodology of Gourinchas and Obstfeld (2012).

### Main empirical findings (effects on house prices and credit)
- Direction and timing:
  - Baseline local projections: coefficients β_h largely negative for both house prices and credit; effects become statistically significant only after three years (quarter 12) and peak at -1.5 percent.
  - Baseline peak effect reported: -1.5 percent (peaking at quarter 12).
- Endogeneity-corrected effect:
  - IPW estimates: association stronger than baseline; impact peaks at -3 percent after three years (quarter 12).
  - Interpretation: baseline estimates provide a lower bound due to upward bias from reverse causality.
- Asymmetry between tightening and loosening:
  - Loosening measures tend to have a stronger association with increases in house prices and credit than tightening measures have with decreases.
  - Caveat: relatively small number of loosening observations (17) implies caution in interpretation.
- Heterogeneity across country groups:
  - Other EU countries (non-euro area): negative and largely significant association; impact peaks at around -2 percent.
  - Euro area countries: association has a wrong sign and is largely insignificant in many specifications.
  - Possible explanation: effectiveness enhanced when macroprudential measures are complemented by monetary policy; euro area countries may have less scope for national monetary policy cushioning.
- Heterogeneity across measure types:
  - Legally-binding measures vs recommendations:
    - Legally-binding measures: stronger association; impact peaks at -3 percent for house prices and -2.2 percent for credit.
  - Measures with sanctions vs without sanctions:
    - Measures with non-compliance sanctions: stronger association; impact peaks at -4 percent for house prices and -3 percent for credit.
  - Interpretation: legally-binding status and sanctions increase enforceability and compliance, making measures more “biting.”

### Dynamic patterns and event-study evidence
- Timing of significance: coefficients imprecisely estimated in the near term; significance emerges in medium term (about three years).
- Event study results:
  - Tightening measures are typically implemented when house prices and credit are rising (endogeneity present).
  - Loosening measures are typically implemented when house prices and credit are declining.
  - Post-tightening: house prices remain relatively flat short-term; credit continues growing then flattens medium-term.
  - Post-loosening: both house prices and credit rise quickly after implementation.

### Robustness and identification
- Endogeneity concern: macroprudential activation is endogenous to target developments (reverse causality), biasing baseline estimates upward (coefficients interpreted as lower bounds).
- IPW approach details:
  - First stage: ordered logit for probability p_it that a lending restriction measure is implemented using lagged observables.
  - Second stage: local projections weighted by inverse propensity score.
  - Result: stronger negative association (peak -3 percent for house prices after three years), supporting presence of upward bias in baseline.
- Event study confirms pre-trends consistent with endogenous deployment of measures and supports asymmetry findings.

### Policy implications and recommendations
- Effectiveness: lending restriction measures (LTV, DSTI, and other borrower-based tools) implemented in EU countries have been broadly effective in curbing house prices and credit, with caveats on timing and magnitude.
- Timing: policymakers should expect a lag before full effects materialize; peak effects observed around three years after implementation.
- Asymmetry and leakage:
  - Tightening measures are less effective than loosening measures, possibly due to regulatory arbitrage and leakages to non-bank lenders or foreign branches.
  - Reciprocation agreements across home and host supervisory authorities can help reduce cross-border leakages.
- Design and enforcement:
  - Legally-binding measures and measures that include sanctions are more effective; policymakers should consider legal force and enforcement mechanisms when choosing instruments.
- Coordination with macroeconomic policy:
  - Macroprudential measures may be more effective when complemented by supportive monetary policy actions; country context (e.g., euro area membership) affects available policy mix.
- Future research:
  - Assess effectiveness of other macroprudential categories (bank-based restrictions such as capital buffers, loan-loss provisioning, concentration limits) in addressing financial stability risks.

*Source: wpiea2019045*

### 1. Number of Lending Restriction Measures by Types and Across Country Groups..........15

### 1. Number of Lending Restriction Measures by Types and Across Country Groups

### Overview
- Section 1: Number of Lending Restriction Measures by Types and Across Country Groups..........15
- Section 2: Number of Tightening and Loosening Lending Restriction Measures: Over Time.........16
- Section 3: Number of Tightening and Loosening Lending Restriction Measures: Across Countries..17

### Dynamics and Target Variables
- Section 4: Dynamics of House Price and Credit Growth.....................................................18
- Section 5: Results: Response of Target Variables to Lending Restriction Measures.....................19

### Asymmetry and Heterogeneity of Effects
- Section 6: Results: Asymmetric Response of Target Variables with Respect to Tightening and Loosening Measures....................................................................................20
- Section 7: Results: Differential Effects Across Euro Area and Other EU Countries.....................21
- Section 8: Results: Differential Effects Across Legally–Binding Measures and Recommendations...22
- Section 9: Results: Differential Effects Across Measures with and Without Sanctions..................23

### Robustness Checks and Event Studies
- Section 10: Robustness Check: Inverse Probability Weighted Estimator..................................24
- Section 11: Robustness Check: Event Study Analysis for Tightening Measures.........................25
- Section 12: Robustness Check: Event Study Analysis for Loosening Measures.........................26

### Tables (Inventory)
- TABLE 1. Cross–Country Empirical Studies on the Effectiveness of Macroprudential Policies.......27
- TABLE 2. List of Lending Restriction Measures Implemented in 28 EU Countries Over 1990–2018 28
- TABLE 3. Variables and Their Sources........................................................................29
- TABLE 4. Estimation Results: Baseline Specification (House Prices).....................................30
- TABLE 5. Estimation Results: Baseline Specification (Credit).............................................31
- TABLE 6. Estimation Results: Asymmetric Effects of Tightening and Loosening Measures (House Prices).....................................................................................................32
- TABLE 7. Estimation Results: Asymmetric Effects of Tightening and Loosening Measures (Credit) 33
- TABLE 8. Estimation Results: Differential Effects Across Euro Area and Other EU Countries (House Prices).....................................................................................................34
- TABLE 9. Estimation Results: Differential Effects Across Euro Area and Other EU Countries (Credit)....................................................................................................35
- TABLE 10. Differential Effects Across Legally-Binding Measures and Recommendations (House Prices).....................................................................................................36
- TABLE 11. Differential Effects Across Legally-Binding Measures and Recommendations (Credit) 37
- TABLE 12. Estimation Results: Differential Effects Across Measures with and Without Sanctions (House Prices)............................................................................................38
- TABLE 13. Estimation Results: Differential Effects Across Measures with and Without Sanctions (Credit)....................................................................................................39

*Source: wpiea2019045 - 1. Number of Lending Restriction Measures by Types and Across Country Groups..........15*

### References................................................................................................40

### wpiea2019045 - References................................................................................................40

### Introduction and scope
- Sample: 28 EU countries over the period 1990–2018 (sample restricted to 1990q1–2018q2).
- Focus: lending restriction measures (borrower-based), notably loan-to-value (LTV) and debt-service-to-income (DSTI) ratios.
- Data: Budnik and Kleibl (2018) database for measures; Bank for International Settlements and International Financial Statistics for house prices and credit; CPI used to deflate series.
- Measures coded as categorical: 1 for tightening, -1 for loosening, 0 for no/neutral measure. In two instances with both tightening and loosening in same quarter the code is 0.

### Key dataset statistics
- Total lending restriction measures observed: 99.
- Breakdown by measure type:
  - Loan-to-value (LTV) limits: 41 measures.
  - Debt-service-to-income (DSTI) limits (incl. interest rate stress testing): 20 measures.
  - Maturity and amortisation restrictions: 12 measures.
  - Other restrictions on lending standards: 17 measures.
  - Other income requirements for loan eligibility: 5 measures.
  - Loan-to-income (LTI) limits: 1 measure.
  - Debt-to-income (DTI) limits: 1 measure.
  - Limits on the volume of personal loans: 2 measures.
- Measure attributes:
  - Tightening measures: 82.
  - Loosening measures: 17.
  - Legally-binding measures: 54.
  - Measures without sanctions: 51.
- Projection horizon used in analysis: h = 0,...,16 quarters.
- Event study window: j = -12,...,12.

### Empirical methodology
- Baseline: local projections (Jorda 2005) estimating responses of log real house prices and log real credit to lending restriction measures (specification [1]).
- Controls: country fixed effects, time fixed effects, lagged dependent variables, GDP growth, change in monetary policy rate, crisis dummies; N lags of controls included.
- Asymmetry test: specification with separate dummies for tightening (MT) and loosening (ML) episodes (specification [2]).
- Heterogeneity tests: interactions for country groups (euro area vs other EU) and measure types (legally-binding vs recommendations; with sanctions vs without) (specification [3]).
- Robustness: inverse probability weighted (IPW) estimator to mitigate endogeneity (ordered logit for propensity p_it; weights w_it = M_it/p_it + (1-M_it)/(1-p_it)); event-study methodology of Gourinchas and Obstfeld (2012).

### Main empirical findings (effects on house prices and credit)
- Direction and timing:
  - Baseline local projections: coefficients β_h largely negative for both house prices and credit; effects become statistically significant only after three years (quarter 12) and peak at -1.5 percent.
  - Baseline peak effect reported: -1.5 percent (peaking at quarter 12).
- Endogeneity-corrected effect:
  - IPW estimates: association stronger than baseline; impact peaks at -3 percent after three years (quarter 12).
  - Interpretation: baseline estimates provide a lower bound due to upward bias from reverse causality.
- Asymmetry between tightening and loosening:
  - Loosening measures tend to have a stronger association with increases in house prices and credit than tightening measures have with decreases.
  - Caveat: relatively small number of loosening observations (17) implies caution in interpretation.
- Heterogeneity across country groups:
  - Other EU countries (non-euro area): negative and largely significant association; impact peaks at around -2 percent.
  - Euro area countries: association has a wrong sign and is largely insignificant in many specifications.
  - Possible explanation: effectiveness enhanced when macroprudential measures are complemented by monetary policy; euro area countries may have less scope for national monetary policy cushioning.
- Heterogeneity across measure types:
  - Legally-binding measures vs recommendations:
    - Legally-binding measures: stronger association; impact peaks at -3 percent for house prices and -2.2 percent for credit.
  - Measures with sanctions vs without sanctions:
    - Measures with non-compliance sanctions: stronger association; impact peaks at -4 percent for house prices and -3 percent for credit.
  - Interpretation: legally-binding status and sanctions increase enforceability and compliance, making measures more “biting.”

### Dynamic patterns and event-study evidence
- Timing of significance: coefficients imprecisely estimated in the near term; significance emerges in medium term (about three years).
- Event study results:
  - Tightening measures are typically implemented when house prices and credit are rising (endogeneity present).
  - Loosening measures are typically implemented when house prices and credit are declining.
  - Post-tightening: house prices remain relatively flat short-term; credit continues growing then flattens medium-term.
  - Post-loosening: both house prices and credit rise quickly after implementation.

### Robustness and identification
- Endogeneity concern: macroprudential activation is endogenous to target developments (reverse causality), biasing baseline estimates upward (coefficients interpreted as lower bounds).
- IPW approach:
  - First stage: ordered logit for probability p_it that a lending restriction measure is implemented using lagged observables.
  - Second stage: local projections weighted by inverse propensity score.
  - Result: stronger negative association (peak -3 percent for house prices after three years), supporting presence of upward bias in baseline.
- Event study confirms pre-trends consistent with endogenous deployment of measures and supports asymmetry findings.

### Policy implications and recommendations
- Effectiveness: lending restriction measures (LTV, DSTI, and other borrower-based tools) implemented in EU countries have been broadly effective in curbing house prices and credit, with caveats on timing and magnitude.
- Timing: policymakers should expect a lag before full effects materialize; peak effects observed around three years after implementation.
- Asymmetry and leakage:
  - Tightening measures are less effective than loosening measures, possibly due to regulatory arbitrage and leakages to non-bank lenders or foreign branches.
  - Reciprocation agreements across home and host supervisory authorities can help reduce cross-border leakages.
- Design and enforcement:
  - Legally-binding measures and measures that include sanctions are more effective; policymakers should consider legal force and enforcement mechanisms when choosing instruments.
- Coordination with macroeconomic policy:
  - Macroprudential measures may be more effective when complemented by supportive monetary policy actions; country context (e.g., euro area membership) affects available policy mix.
- Future research:
  - Assess effectiveness of other macroprudential categories (bank-based restrictions such as capital buffers, loan-loss provisioning, concentration limits) in addressing financial stability risks.

*Source: wpiea2019045 - References................................................................................................40*

### References

### References

### Macroprudential policy empirical studies
- Aiyar, S., Calomiris, C., and T., Wieladek 2014, “Does Macroprudential Regulation Leak? Evidence from a UK Policy Experiment,” Journal of Money, Credit and Banking, 46 (1): pp. 181–214.  
- Ahuja, A. and M. Nabar 2011, “Safeguarding Banks and Containing Property Booms: Cross-Country Evidence on Macroprudential Policies and Lessons from Hong Kong SAR,” IMF Working Paper WP/11/284 (International Monetary Fund: Washington, D.C.).  
- Akinci, O., and J. Olmstead-Rumsey 2018, “How Effective are Macroprudential Policies? An Empirical Investigation,” Journal of Financial Intermediation, 33: pp. 33–57.  
- Bruno, V., Shim, I., and H-S. Shin 2017, “Comparative Assessment of Macroprudential Policies,” Journal of Financial Stability, 28: pp. 183–202.  
- Carreras, O., Davis, P., and R. Piggot 2018, “Assessing Macroprudential Tools in OECD Countries within A Cointegrating Framework,” Journal of Financial Stability, 37: pp. 112–30.  
- Cerutti, E., Claessens, S., and L. Laeven, 2017, “The Use and Effectiveness of Macroprudential Policies: New Evidence,” Journal of Financial Stability, 28: pp. 203–24.  
- Cerutti, E., Dagher, J.., and G. Dell’Ariccia 2017, “Housing Finance and Real-Estate Booms: A Cross–Country Perspective,” Journal of Housing Economics, 38: pp. 1–13.  
- Cerutti, E., Correa, R., Fiorentino, E., and E. Segalla 2017, "Changes in Prudential Policy Instruments —A New Cross–Country Database," International Journal of Central Banking, 13 (1): pp. 477–503.  
- Fendoglu, S. 2017, "Credit Cycles and Capital Flows: Effectiveness of the Macroprudential Policy Framework in Emerging Market Economies," Journal of Banking and Finance, 79: pp. 110–28.  
- Galati, G. and R. Moessner 2018, “What Do We Know About the Effects of Macroprudential Policy?” Economica, 85: pp. 735–70.  
- Kuttner, K. and I. Shim 2016, “Can Non-Interest Rate Policies Stabilize Housing Markets? Evidence from a Panel of 57 Economies,” Journal of Financial Stability, 26: pp. 31– 44.  
- Lim, C., Costa, A., Columba, F., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T., and X. Wu, 2011, “Macroprudential Policy: What Instruments and How to Use Them? Lessons from Country Experiences,” IMF Working Paper WP/11/238 (International Monetary Fund: Washington, D.C.).  
- Reinhardt, D., and R. Sowerbutts 2015, “Regulatory Arbitrage in Action: Evidence from Banking Flows and Macroprudential Policy,” Bank of England Staff Working Paper 546.  
- Richter, B., Schularick, M., and I. Shim 2018, “The Costs of Macroprudential Policy,” NBER Working Paper 24989.  
- Vandenbusche, J., Vogel, U., and E. Detragiache 2015, “Macroprudential Policies and Housing Prices: A New Database and Empirical Evidence for Central, Eastern, and Southeastern Europe,” Journal of Money, Credit, and Banking, 47(1): pp. 343–77.  
- Zhang, L., and E. Zoli, 2014, “Leaning Against the Wind: Macroprudential Policy in Asia,” IMF Working Paper WP/14/22 (International Monetary Fund: Washington, D.C.).  
- AEO/GEO: Alam, Z., Alter, A., Eiseman, J., Gelos, G., Kang, H., Narita, M., Nier, E. and N. Wang 2018, “Digging Deeper—Evidence on the Effects of Macroprudential Policies from a New Database,” International Monetary Fund (IMF) Working Paper (forthcoming).

### Databases, indicators, and cross-country compilations
- Budnik, K. and J. Kleibl 2018, “Macroprudential Regulation in the European Union in 1995– 2014: Introducing a New Data Set on Policy Actions of a Macroprudential Nature,” ECB Working Paper, 2123.  
- Cerutti, E., Correa, R., Fiorentino, E., and E. Segalla 2017, "Changes in Prudential Policy Instruments —A New Cross–Country Database," International Journal of Central Banking, 13 (1): pp. 477–503.  
- Jacome, L. and S. Mitra 2015, “LTV and DTI Limits—Going Granular,” IMF Working Paper WP/15/154 (International Monetary Fund: Washington, D.C.).  
- Lo Duca, M., Koban, A., Basten, M., Bengtsson, E., Klaus, B., Kusmierczyk, P., Lang, j, Detken, C., and T. Peltonen 2017, “A New Database for Financial Crises in European Countries,” ECB Occasional Paper, 194.  
- Vandenbusche, J., Vogel, U., and E. Detragiache 2015, “Macroprudential Policies and Housing Prices: A New Database and Empirical Evidence for Central, Eastern, and Southeastern Europe,” Journal of Money, Credit, and Banking, 47(1): pp. 343–77.  

### IMF, BIS, FSB and policy guidance
- BIS 2018, “Annual Economic Report,” June 2018 (Bank for International Settlements: Basel).  
- International Monetary Fund (IMF) 2012, “The Interaction of Monetary and Macroprudential Policies—Background Paper,” International Monetary Fund, Washington.  
- International Monetary Fund (IMF), 2014, “Staff Guidance Note on Macroprudential Policy,” IMF Policy Paper (Washington: International Monetary Fund).  
- International Monetary Fund (IMF), 2018, “The IMF’s Annual Macroprudential Policy Survey—Objectives, Design and Policy Responses,” IMF Policy Paper and Note to G20, April, (Washington: International Monetary Fund).  
- International Monetary Fund (IMF)–Financial Stability Board (FSB) – Bank for International Settlements (BIS) 2016, “Elements of Effective Macroprudential Policies: Lessons from International Experience,” (available here).  

### Methodology and theoretical contributions
- Jorda, O. 2005, “Estimation and Inference of Impulse Responses by Local Projections,” American Economic Review, 95(1): pp. 161–182.  
- Gourinchas, P.-O. and M. Obstfeld 2012, “Stories of the Twentieth Century for the Twenty-First,” American Economic Journal: Macroeconomics, 4(1): pp. 226–65.  
- Romer, C., and D. Romer 1989, “Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz,” NBER Macroeconomics Annual, 4: pp. 121–170.  
- Claessens, S. 2014, “An Overview of Macroprudential Policy Tools,” IMF Working Paper WP/14/214 (International Monetary Fund: Washington, D.C.).  
- Galati, G. and R. Moessner 2018, “What Do We Know About the Effects of Macroprudential Policy?” Economica, 85: pp. 735–70.  
- Reinhardt, D., and R. Sowerbutts 2015, “Regulatory Arbitrage in Action: Evidence from Banking Flows and Macroprudential Policy,” Bank of England Staff Working Paper 546.  

*Source: wpiea2019045 - References (IMF PDF).*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019045.pdf_