## CHAPTER 2 dOwNSIdE RISkS TO HOuSE PRICES

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### Introduction
- Housing serves as both a long-term investment and a consumption good that generates considerable utility for households.
- Housing makes up a large share of households’ wealth; higher house prices increase households’ net worth and can boost consumption.
- Housing is an important source of collateral for borrowing and financing small businesses.
- Rising house prices can lock out potential buyers and reduce disposable income if mortgage or rental outlays increase.
- Housing consumption and investment accounted for about one-sixth of the US and the euro area economies in 2017.
- Mortgages and other housing-related lending make up a large fraction of banks’ assets in many countries; changes in house prices can significantly affect banks’ portfolio quality and profitability.
- Recessions are deeper and last longer when house prices fall more and more quickly.
- More than two-thirds of the nearly 50 systemic banking crises in recent decades were preceded by boom-bust patterns in house prices.

### Methodology: measuring downside risks to house prices (House Prices at Risk, HaR)
- Framework:
  - Builds on the growth-at-risk (GaR) framework to model large house price declines with a given (low) probability and horizon (house prices at risk).
  - Downside risks quantified within the lowest quantiles (typically the 5th percentile) of the conditional distribution of future house price growth.
  - Quantile regression is used to study how HaR moves when conditioning variables change.
- Analytical questions addressed:
  - How factors affecting expected house prices or housing vulnerabilities forecast downside risks and how this varies with forecast length.
  - Relationships among macroprudential policy, monetary policy, capital account openness, capital flow measures, and HaR.
  - What elevated HaR implies for downside risks to economic growth and financial stability.
- Data coverage:
  - 22 major advanced economies and 10 emerging market economies; city-level analysis includes 31 cities (largest cities for 32 countries, except South Africa).
  - Sample period reference: 1990:Q1 to 2017:Q4 for average growth metrics.

### Main findings (summary)
- HaR responds to pricing and vulnerability factors:
  - Deteriorates with tightening of financial conditions, declines in real GDP growth, higher credit growth, and greater house price overvaluation.
  - Effects generally more pronounced in the short term and stronger at the 5 percent quantile than at the 50 percent quantile.
  - Effects appear stronger at the city level than the country level.
  - Downside risks to house prices move together across advanced economies ahead of major crises.
- Usefulness for surveillance:
  - Adding HaR to standard GaR and crisis-prediction models enhances predictive power.
  - HaR can gauge financial stability risks and inform prospective policy action, though house price levels should not be direct policy targets.
- Policy interactions:
  - Tightening macroprudential housing measures lowers downside risks to house prices.
  - Easier monetary policy improves HaR in the short term (up to one year) in advanced economies and may have longer-lasting effects through financial conditions.
  - Capital inflows increase downside risks in advanced economies; surges raise the likelihood of high short-term house price growth and increased medium-term downside risks.
  - City-level relationships between capital flows and HaR vary by flow type and country.

### Conceptual framework linking vulnerabilities and downside risks
- Vulnerabilities (macro-financial imbalances) increase during good times via excessive risk taking.
- Easy financial conditions and easy credit can lead to household overborrowing and overvalued house prices.
- Adverse shocks with heightened vulnerabilities can be amplified by cutbacks in or higher prices of credit, producing feedback loops of:
  - Large house price declines → weakened household balance sheets → declines in real activity → increased credit risk → declines in collateral values for banks → tighter financial conditions.
- Relationships between determinants (financial conditions, leverage, GDP growth, overvaluation) and downside risks are nonlinear and horizon-dependent.

### Historical developments and data summary
- Average (annualized) one-year and three-year growth rates of real house prices from 1990:Q1 to 2017:Q4:
  - About 2 percent a year in advanced economies.
  - 2.6 percent a year in emerging market economies.
- Frequency of negative real growth over one-year horizon:
  - Negative real growth occurs in about half of observations in advanced economies.
  - In a third of observations in emerging market economies.
- Tail declines (5 percent probability):
  - Advanced economies: a 10.5 percent decline in average (annualized) real house prices occurs once every 20 years (associated with a 5 percent probability of downside risk).
  - Emerging market economies: the 5 percent greatest decline corresponds to a 12 percent decline in real house prices.

### Determinants of HaR: empirical quantile-regression findings
- Key variables: financial conditions (FCI), real GDP growth, credit-to-GDP ratio (detrended), price-to-GDP per capita ratio (house price misalignment), lagged house price growth.
- Variable standardization: mean zero, standard deviation one.
- Distributional findings (5th, 50th, 95th quantiles):
  - Financial conditions:
    - Tightening associated with lower future house prices, stronger effect in the lower tail (5th percentile).
    - Magnitudes: a one-standard-deviation tightening associated with 0.3 to 0.7 percentage point higher downside risk to house prices in the short term (stronger in emerging market economies).
    - Over longer horizons, effect diminishes to 0.1 percentage point in advanced economies and becomes insignificant in emerging market economies.
    - Note: the global financial crisis entailed a 2.3 standard deviation shock to financial conditions in advanced economies in the underlying decomposition.
  - Real GDP growth:
    - A one-standard-deviation higher real GDP growth associated with an insignificant reduction in downside risks one to three quarters ahead in advanced economies, but an opposite and significant relationship over longer horizons.
    - In emerging market economies, the association is positive but not statistically significant.
    - Referenced GDP growth shocks: 2.2 standard deviations in advanced economies and 1.7 in emerging market economies.
  - Overvaluation (price-to-GDP per capita):
    - A one-standard-deviation increase consistently and significantly related to higher downside risks over time.
    - Estimated corrections signaled: between 0.5 and 1.0 percentage point in advanced economies and 0.7 to 1.0 percentage point in emerging market economies.
    - Overvaluation shock cited: about 0.2 standard deviation across both groups.
  - Credit booms (credit-to-GDP above long-term mean):
    - Tend to worsen HaR by up to 0.5 percentage point at short horizons in advanced economies (three quarters ahead).
    - In emerging market economies, effects up to 1 percentage point at medium-term horizons (up to seven quarters).
- Distributional insights:
  - Effects of fundamentals and overvaluation are more pronounced at the lower tail (5th percentile) than at the median (50th percentile).
  - Credit booms more strongly related to large negative house price corrections at short- and medium-term horizons than to median outcomes.

### Dynamics over time and cross-country patterns
- HaR displays cyclical short-term comovement and substantial fluctuations.
  - Advanced economies: high downside risks in early 1990s (Nordic banking crises) and immediately before the global financial crisis in 2008; cross-country distribution widened during those periods and recently.
  - Emerging market economies: heterogeneity in early 1990s, around the Asian and Russian crises of 1997 and 1998, and before 2008.
- Evolution 2007:Q4 to 2017:Q4:
  - Many countries at high risk in 2007 have lower downside risk in 2017 and vice versa; several countries remain high risk in both periods.
  - On a GDP-weighted basis, short- and medium-term HaR decreased in advanced economies and increased in emerging market economies between 2007:Q4 and 2017:Q4, especially in the short term.
  - In advanced economies, downside house price risk in 2017:Q4 remains above the overall median.
- Predictive distribution behavior:
  - Before the global financial crisis (2006:Q3–2008:Q3), both the mean and left tail of the predictive distribution shifted left in advanced and emerging market economies.
  - Short-term HaR tends to be higher than medium-term HaR in both country groups.

### Country-level examples: United States and China
- United States:
  - HaR deteriorated gradually beginning in the early 2000s leading up to the global financial crisis, initially related to overvaluation.
  - Over time, past house price movements and credit exerted negative effects; loose financial conditions partially offset these forces.
  - During the global financial crisis, tightening financial conditions weighed negatively on HaR.
  - Since late 2016, US HaR gradually deteriorated due to overvaluation concerns and high credit growth, partly offset by easy financial conditions and house price momentum.
- China:
  - HaR more volatile, following volatility in house price growth.
  - Easy financial conditions contained risks until 2010; after 2010, high credit-to-GDP gaps and tightening financial conditions increased downside risks.
  - Since 2016, overvaluation also contributed to deterioration in HaR.

### City-level versus country-level analysis
- City-level results confirm the model but show larger responses of HaR to financial conditions than country-level estimates, particularly in emerging market economies.
  - Possible reasons: sampled cities are major financial centers, heightened responsiveness to financial factors, and greater supply constraints.
- Box 2.2 findings (United States and Canada):
  - Estimation: Five percent HaR, four quarters ahead.
  - Comparative periods: 2008 and 2018; one-year-ahead estimates refer to 2019:Q1 and 2009:Q1 respectively.
  - US cities: housing downside risks more volatile, large drop around the GFC; valuations came down for most US cities in 2018 versus 2008.
  - Canadian cities: cycles relatively stable on average; amid stretched valuations several cities (Hamilton, Toronto, Vancouver) were particularly vulnerable in 2018.
  - City-level house-price-to-income ratios linked with one-year-ahead HaR deterioration.
  - Capital flows at city level:
    - FDI inflows tend to mitigate risk in tech-friendly US cities.
    - Other capital inflows (largely bank transactions) amplify downside risks in certain US and Canadian cities.
  - Example: additional property taxes on foreign home buyers in Vancouver and Toronto are targeted responses where nonresident buyers contributed to overvaluation and downside risk.

### Forecasting, surveillance, and predictive links with GaR and crises
- Forecasting performance:
  - Out-of-sample predictions closely track in-sample estimates for one-year-ahead HaR in country-specific examples (United States and China).
- HaR and Growth at Risk (GaR):
  - An increase in downside risks to house prices (a lower/more negative HaR) is associated with an increase in future downside risk to GDP growth.
  - The strongest impact of HaR on GaR occurs four to eight quarters ahead.
  - A 1 percent improvement in the HaR measure precedes on average a 0.3 percentage point improvement in growth at risk.
- Crisis prediction:
  - Adding HaR to standard crisis prediction models improves accuracy across one-, two-, and three-year horizons and country groups.
  - An annualized HaR measure of −12 percent (an estimated 5 percent probability of a 12 percent decline in real house prices two years ahead) implies:
    - a 31 percent probability of a financial crisis two years ahead in advanced economies,
    - a 10 percent probability of a financial crisis two years ahead in emerging market economies.
  - Financial crises correspond to systemic banking crises as identified by Laeven and Valencia (2018).

### Policy effects: macroprudential, monetary, and capital-flow measures
- Macroprudential policy:
  - Measured using an IMF iMaPP-based variable combining loan-to-value and debt-service-to-income measures (three-year rolling window, purged for credit-to-GDP).
  - Tightening macroprudential measures has a statistically significant level-shifting effect that reduces downside risk (shifts entire term structure of HaR upward).
  - Timing and magnitudes:
    - In advanced economies, maximum impact occurs between four and eight quarters ahead.
    - In emerging market economies, impact highest in the short term and remains mostly steady until about 12 quarters ahead.
    - Example: A one-unit tightening of macroprudential measures during a credit boom could lower the one-year-ahead average HaR by up to 2 percentage points (annualized), from −9.4 percent to −7.4 percent.
  - Borrower-based measures (loan-to-value, debt-service-to-income) are highlighted as especially effective.
- Monetary policy:
  - Identified as residuals from an expanded Taylor-rule regression.
  - In advanced economies, monetary policy shocks have a short-lived negative relationship with HaR; unexpected loosening reduces HaR for a few quarters.
  - Monetary policy shocks have no significant impact on HaR over longer horizons, nor in emerging markets.
  - Monetary policy's ability to mitigate downside risks is limited beyond its effect on financial conditions.
- Capital inflows and capital flow management measures:
  - Aggregate effects (advanced economies):
    - An increase in capital inflows of 1 percent of GDP would raise upside risks by 1.5 percent one quarter ahead.
    - An increase in capital inflows of 1 percent of GDP would raise downside risks by 0.5 percent eight quarters ahead.
  - Heterogeneity by flow type and region:
    - Emerging markets: no robust relationship for overall inflows; portfolio flows reduce downside risks at some horizons; foreign direct investment increases downside risks at some horizons.
    - City evidence: a 1 percentage point increase in foreign direct investment inflows to the United States is significantly associated with a reduction of about 1–2 percentage points in HaR in San Francisco, Seattle, and Portland, Oregon.
    - Other inflows (often foreign bank transactions) amplify downside risks in cities such as Las Vegas, Miami, Los Angeles, Toronto, Vancouver, and Calgary.
  - Capital flow management measures:
    - Tightening may briefly increase downside risks in the very short term (one to three quarters ahead) but reduces downside risks between one and two years ahead by about 3 percent (annualized) in each quarter in advanced economies.
    - No significant effect at longer horizons.
    - Tightening of real estate inflow restrictions not significantly related to HaR; the lack of significance may reflect only 12 episodes of tightening in the data.
  - Policy caveats:
    - Capital flow measures should be assessed case-by-case, weighing benefits of capital mobility against potential financial-stability gains.

### Synchronization and international implications
- Short-term cross-country comovement of HaR increases sharply before major recessions or financial crises, especially among advanced economies.
- Greater financial openness is associated with higher synchronization of downside risk.
- Capital flow management measures related to real estate may help mitigate synchronization of downside risk around major recessions and financial crises.
- Recommendation: consider international policy coordination when episodes of increased synchronization of downside risks emerge.

### Policy guidance and conclusion
- House-prices-at-risk is a useful early-warning indicator for financial stability surveillance.
- Key policy guidance:
  - Use HaR as part of financial stability monitoring and to inform activation of macroprudential policies when downside risks heighten.
  - Emphasize borrower-based macroprudential measures (loan-to-value, debt-service-to-income) in toolkits.
  - Do not target house price levels directly with macroprudential or monetary policy; target the buildup of vulnerabilities.
  - Recognize monetary policy’s limited direct effect on downside risks beyond financial conditions; rely primarily on macroprudential and, where appropriate, targeted capital flow measures.
  - Assess capital flow measures case-by-case, balancing reduced downside risks against benefits of capital mobility.
  - Monitor synchronization and consider international coordination as needed.
- Overall conclusion:
  - HaR reflects fundamentals, overvaluation, and past price dynamics.
  - Recent data point to an increase in downside risks to house prices over the next one to three years in some countries, posing concerns for financial stability and the global macroeconomic outlook over the medium term.
  - HaR has a significant impact on growth at risk and improves crisis-prediction models when included.

*International Monetary Fund | April 2019 — Chapter 2, "Downside Risks to House Prices".*

### Introduction

### ch2 - Introduction

### Importance of housing for households, firms, and banks
- Housing serves as both a long-term investment and a consumption good that generates considerable utility for households.
- In most countries, housing makes up a large share of households’ wealth; higher house prices increase households’ net worth and can boost consumption.
- Housing is an important source of collateral that homeowners can use to borrow when facing temporary income shocks and to obtain financing for small businesses.
- Rising house prices can also lock out potential buyers who cannot come up with a down payment or reduce households’ disposable income if mortgage or rental outlays increase, dampening economic growth and depressing firm sales and profits.
- Housing consumption and investment accounted for about one-sixth of the US and the euro area economies in 2017.
- Mortgages and other housing-related lending make up a large fraction of banks’ assets in many countries; changes in house prices can significantly affect banks’ portfolio quality and profitability.

### Linkages between house prices, macroeconomic outcomes, and financial stability
- Recessions are deeper and last longer when house prices fall more and more quickly.
- More than two-thirds of the nearly 50 systemic banking crises in recent decades were preceded by boom-bust patterns in house prices; the 2007–08 global financial crisis is a key example.
- Housing market characteristics associated with increased risks of crises include higher loan-to-value ratios and greater reliance on wholesale markets.
- Interactions between house prices and credit volumes can create self-reinforcing feedback loops: rising house prices facilitate credit expansion (via collateral effects) that can further raise house prices; the reversal can produce large declines in house prices followed by a collapse in credit and GDP growth.
- The simultaneous increase in house prices in many countries in recent years and increased synchronization of house prices raise concerns about coordinated, large declines; heightened synchronicity can signal downside risks to economic activity, especially when leverage is high.
- The decades-long decline in interest rates combined with rising household leverage may have increased the likelihood of large house price declines (downside risks), increasing macroeconomic and financial stability risks.

### Methodology: measuring downside risks to house prices (house prices at risk)
- The chapter develops a methodology to model large house price declines with a given (low) probability and horizon—referred to as house prices at risk—building on the growth-at-risk (GaR) framework.
- Downside risks are quantified within the lowest quantiles (typically the 5th percentile) of the conditional distribution of future house price growth.
- Quantile regression is used to study how house prices at risk move when conditioning variables change, allowing analysis of how factors affect different parts (quantiles) of the distribution of future house price growth.
- The methodology addresses:
  - How factors affecting expected house prices or related to housing vulnerabilities help forecast downside risks to future house price growth and how this varies with forecast length.
  - Relationships among macroprudential policy, monetary policy, capital account openness, capital flow measures, and house prices at risk, including whether these policies reduce downside risks even when not their primary aim.
  - What elevated house prices at risk imply for downside risks to economic growth and financial stability.

### Main findings
- House prices at risk move in response to pricing factors:
  - The house-prices-at-risk measure deteriorates in response to changes in fundamental factors, which include tightening of financial conditions, a decline in real GDP growth, and higher credit growth.
  - It worsens with greater house price overvaluation (deviation from fundamentals).
  - These effects vary with the horizon and are generally more pronounced in the short term.
  - The relationship between these variables and house prices at risk (at the 5 percent quantile) is stronger than with median house prices (at the 50 percent quantile).
  - Effects appear stronger at the city level than the country level.
  - Additional results suggest downside risks to house prices move together across advanced economies ahead of major crises.
- The house-prices-at-risk measure is a useful early-warning indicator for financial stability surveillance:
  - Adding the house-prices-at-risk measure to standard growth-at-risk and financial-crisis-prediction models enhances the predictive power of those models.
  - While house price levels should not be targets for monetary, macroprudential, or capital flow management policies, the house-prices-at-risk measure can gauge financial stability risks and inform prospective policy action.
- Macroprudential and monetary policies can reduce downside risks to house prices:
  - Tightening macroprudential housing measures in response to rising vulnerabilities (for example, tighter constraints on loan-to-value and debt-service-to-income ratios) lowers downside risks to house prices.
  - Easier monetary policy improves house prices at risk in the short term (up to one year) in advanced economies and may have longer-lasting effects through financial conditions.
- Capital inflows and capital flow management:
  - Capital inflows seem to increase downside risks to house prices in advanced economies, which may justify capital flow management measures in specific cases.
  - A surge in capital inflows simultaneously raises the likelihood of high house price growth in the short term and increases downside risks to house prices in the medium term.
  - City-level evidence suggests the relationship between various capital flows and house prices at risk varies with the type of capital flows and across countries.

### Conceptual framework linking vulnerabilities and downside risks
- Financial stability risks arise from the interaction of macro-financial imbalances (vulnerabilities) and negative shocks; vulnerabilities increase during good times because of excessive risk taking by lenders and borrowers.
- In the housing sector, easy financial conditions and easy credit (low price of risk) can lead to household overborrowing (excessive household leverage) and overvalued house prices (deviations from fundamentals).
- With heightened vulnerabilities, adverse shocks can be amplified by cutbacks in or higher prices of credit (binding borrowing constraints), producing a feedback loop of large house price declines, weakened household balance sheets, declines in real activity, increased credit risk, declines in collateral values for banks, and tighter financial conditions that reinforce one another.
- The relationship between determinants (financial conditions, leverage, GDP growth, overvaluation) and downside risks is nonlinear and may change with the horizon of analysis because of endogenous accumulation of vulnerabilities; loose financial conditions today may reduce short-term downside risks but increase future vulnerabilities and downside risks.

### Overview of historical developments and data summary
- Data cover 22 major advanced economies and 10 emerging market economies (four in Latin America, three in east Asia, plus Russia, South Africa, and Turkey) and their major cities; city-level analysis includes 31 cities (the largest cities for each of 32 countries in the sample, except South Africa).
- Average (annualized) one-year and three-year growth rates of real house prices from 1990:Q1 to 2017:Q4 are about 2 percent a year in advanced economies and 2.6 percent a year in emerging market economies.
- Negative real growth in house prices occurs in about half of the observations in advanced economies and in a third of the observations in emerging market economies over a one-year horizon.
- In advanced economies, a 10.5 percent decline in average (annualized) real house prices occurs once every 20 years (associated with a 5 percent probability of downside risk).
- In emerging market economies, the 5 percent greatest decline in average growth in real house prices corresponds to a 12 percent decline in real house prices.

*International Monetary Fund | April 2019*

### CHAPTER 2 dOwNSIdE RISkS TO HOuSE PRICES

### CHAPTER 2 dOwNSIdE RISkS TO HOuSE PRICES

### Determinants of house price downside risk
- Key variables examined: financial conditions (FCI), real GDP growth, credit-to-GDP ratio (detrended), and price-to-GDP per capita ratio (house price misalignment).
- Distributional findings (Figure 2.4): estimated relationships shown at the 5th, 50th, and 95th quantiles.
  - Financial conditions: tightening is associated with lower future house prices, with a stronger effect in the lower tail (5th percentile).
  - Real GDP growth: lower real GDP growth is generally associated with lower house price growth.
  - Credit-to-GDP ratio: when above its long-term mean, the negative relationship with future house prices is more pronounced.
  - Price-to-GDP per capita ratio: a valuation metric showing a more negative relationship with future house prices in the lower tail than for median or high growth.

### Empirical analysis: quantile regression of "House Prices at Risk" (HaR)
- Models run separately for advanced economies and emerging market economies; models include lagged house price growth to capture momentum.
- Variables standardized for comparability (mean zero, standard deviation one).
- Short- vs. medium-term impacts (one-year and three-year horizons) and tail (5th percentile) focus.

Findings on magnitudes and significance:
- Financial conditions:
  - A one-standard-deviation tightening of financial conditions is associated with 0.3 to 0.7 percentage point higher downside risk to house prices in the short term (stronger in emerging market economies).
  - Over longer horizons, effect diminishes to 0.1 percentage point in advanced economies and becomes insignificant for emerging market economies.
  - Note: the global financial crisis entailed a 2.3 standard deviation shock to financial conditions in advanced economies in the underlying decomposition.
- Real GDP growth:
  - A one-standard-deviation higher real GDP growth is associated with an insignificant reduction in downside risks to house prices one to three quarters ahead in advanced economies, but an opposite and significant relationship over longer horizons.
  - In emerging market economies, the association is positive but not statistically significant.
  - GDP growth shocks referenced: 2.2 standard deviations in advanced economies and 1.7 in emerging market economies.
- Overvaluation (house price misalignment: price-to-GDP per capita ratio):
  - A one-standard-deviation increase is consistently and significantly related to higher downside risks over time.
  - Estimated corrections signaled: between 0.5 and 1.0 percentage point in advanced economies and 0.7 to 1.0 percentage point in emerging market economies.
  - Overvaluation shock magnitude cited: about 0.2 standard deviation across both groups.
- Credit booms:
  - Credit booms tend to worsen the HaR measure by up to 0.5 percentage point at short horizons in advanced economies (three quarters ahead).
  - In emerging market economies, effects up to 1 percentage point at medium-term horizons (up to seven quarters).
  - Credit booms defined as periods when the credit-to-GDP ratio is above the long-term trend.

Distributional and comparative insights:
- Effects of fundamentals and overvaluation are generally more pronounced at the lower tail (5th percentile) than at the median (50th percentile).
  - Financial tightening has larger negative impacts on HaR than on median house prices in both groups.
  - Overvaluation shocks are more strongly related to downside house prices than to median house prices.
  - Credit booms are more strongly related to large negative house price corrections at short- and medium-term horizons than to median outcomes.

### Dynamics over time and cross-country patterns
- HaR fluctuates substantially and displays cyclical short-term comovement.
  - Advanced economies: high downside risks in early 1990s (Nordic banking crises) and immediately before the global financial crisis in 2008; cross-country distribution widened during those periods and recently.
  - Emerging market economies: significant heterogeneity in early 1990s, around the Asian and Russian crises of 1997 and 1998, and before 2008.
- Evolution between 2007:Q4 and 2017:Q4:
  - Many countries at high risk in 2007 appear to have lower downside risk in 2017 and vice versa; several countries remain high risk in both periods (one- and three-year-ahead HaR).
  - On a GDP-weighted basis, short- and medium-term HaR decreased in advanced economies and increased in emerging market economies between 2007:Q4 and 2017:Q4, especially in the short term.
  - In advanced economies, downside house price risk in 2017:Q4 remains above the overall median.

Predictive distribution behavior:
- Before the global financial crisis (2006:Q3–2008:Q3), both the mean and left tail of the predictive distribution shifted left in advanced and emerging market economies, indicating rising downside risks.
- Short-term HaR tends to be higher than medium-term HaR in both country groups.

### Country-level examples: United States and China
- United States:
  - HaR deteriorated gradually beginning in the early 2000s leading up to the global financial crisis, initially related to overvaluation.
  - Over time, past house price movements and credit exerted negative effects; loose financial conditions partially offset these forces.
  - During the global financial crisis, tightening financial conditions weighed negatively on HaR.
  - Since late 2016, US HaR gradually deteriorated due to overvaluation concerns and high credit growth, partly offset by easy financial conditions and house price momentum.
- China:
  - HaR more volatile, following volatility in house price growth.
  - Easy financial conditions contained risks until 2010; after 2010, high credit-to-GDP gaps and tightening financial conditions increased downside risks.
  - Since 2016, overvaluation has also contributed to deterioration in HaR.

### City-level versus country-level analysis
- City-level results confirm the model but show larger responses of HaR to financial conditions than country-level estimates, particularly in emerging market economies.
  - Possible explanations: many cities sampled are major financial centers, heightened responsiveness to financial factors, and greater supply constraints (regulations, land shortages).

### Forecasting and surveillance
- New-data analysis indicates the HaR model can be used for forecasting and surveillance of housing market downside risks.
- The model captures momentum, financial conditions, overvaluation, and credit dynamics to inform one- and three-year-ahead downside risk assessments.

*International Monetary Fund | April 2019*

### 1. United States: Contributions to One-Year HaR2. China: Contributions to One-Year HaR

### ch2 - 1. United States: Contributions to One-Year HaR2. China: Contributions to One-Year HaR

### Factors affecting House Prices at Risk (HaR) in the United States and China
- The figure decomposes the estimated one-year-ahead HaR at the 5th percentile into contributions of:
  - past house prices,
  - financial conditions,
  - real GDP growth,
  - house price misalignment,
  - credit boom.
- The (negative) constant term is not shown.
- Observed patterns:
  - Since late 2016, US HaR appears to have deteriorated gradually.
  - HaR in China was more volatile and was driven mainly by financial conditions.
- Terminology:
  - FCI = financial conditions index.
  - HaR = house prices at risk.

### Out-of-sample forecasting accuracy for one-year-ahead real house price growth
- Comparison approach:
  - Compare quantiles estimated using full sample (in-sample) with those using information only up to a given point in time (out-of-sample, recursive estimation).
  - For illustration, country-specific models used for United States and China reduce degrees of freedom; figures show results for the 20th and 25th percentiles for the United States and China, respectively.
- Key finding:
  - Out-of-sample predictions closely track the in-sample estimates for one-year-ahead house prices at risk.
- Implication:
  - The model accurately signals downside house price vulnerabilities in real time despite structural changes in financing.

### HaR’s predictive relationship with Growth at Risk (GaR) and financial crises
- Empirical associations:
  - An increase in downside risks to house prices (a lower/more negative HaR) is associated with an increase in future downside risk to GDP growth.
  - The association with downside risks is stronger than with median growth.
- Quantified effects:
  - The highest impact of HaR on GaR occurs four to eight quarters ahead.
  - A 1 percent improvement in the HaR measure precedes on average a 0.3 percentage point improvement in growth at risk.
- Crisis prediction:
  - Adding HaR to standard crisis prediction models (with fixed effects, output growth, FCI, credit-to-GDP gap) improves accuracy across horizons (one, two, three years) and country groups (advanced and emerging market economies).
  - An annualized HaR measure of −12 percent (an estimated 5 percent probability of a 12 percent decline in real house prices two years ahead) implies:
    - a 31 percent probability of a financial crisis two years ahead in advanced economies,
    - a 10 percent probability of a financial crisis two years ahead in emerging market economies.
- Notes:
  - Financial crises correspond to systemic banking crises as identified by Laeven and Valencia (2018).

### Policy effects on House Prices at Risk
- Overview:
  - Policies that tame accumulation of vulnerabilities or increase buffers can reduce downside risks to house prices.
  - Policy tools considered: macroprudential policy, monetary policy, fiscal measures (property taxes, tax base, interest deductibility), capital flow management measures.
- Theoretical model insights:
  - Household debt typically exceeds socially optimal levels due to pecuniary externalities.
  - Simulations suggest:
    - Tightening macroprudential policy in response to higher household debt can prevent or mitigate housing crises by curbing pre-crisis debt run-ups.
    - Monetary policy that responds to high credit-to-GDP ratios can mitigate adverse effects on GDP but less effectively than macroprudential policy because it does not fully curb household debt and affects broader output.
  - Conclusion: Containing accumulation of vulnerabilities via macroprudential policy more effectively reduces downside risks than monetary policy in theory; in practice, incomplete macroprudential toolkits may require monetary policy to consider HaR.

### Empirical effects of macroprudential policy, monetary policy, and capital flows on HaR
- Macroprudential policy:
  - Measured using an IMF iMaPP-based variable combining loan-to-value and debt-service-to-income measures (three-year rolling window, purged for credit-to-GDP).
  - Effects:
    - Tightening macroprudential measures has a statistically significant level-shifting effect that reduces downside risk (shifts entire term structure of HaR upward).
    - In advanced economies, maximum impact occurs between four and eight quarters ahead.
    - In emerging market economies, impact is highest in the short term and remains mostly steady until about 12 quarters ahead.
    - Quantified example: A one-unit tightening of macroprudential measures during a credit boom could lower the one-year-ahead average HaR by up to 2 percentage points (annualized), from −9.4 percent to −7.4 percent.
  - Broader credit-related measures show qualitatively similar results in advanced economies; not significant in emerging market economies.
- Monetary policy:
  - Captured by predicted residuals of a feedback rule (details in Online Annex 2.1).
  - Effects:
    - In advanced economies, monetary policy has a significant effect: initially increases downside risks, but less so over time.
    - Monetary policy shocks have no significant impact on HaR over longer horizons, nor in emerging markets.
- Capital flows and capital flow management measures:
  - Total capital inflows (percent of GDP) at the 95th percentile tend to increase upside risks to house prices; at the 5th percentile they tend to increase downside risks.
  - Proxies for changes in capital flow management measures appear to reduce downside risks to house prices over some intermediate horizons.
- Confidence and significance:
  - Figures report 95 percent confidence bounds and statistical significance at the 10 percent level or higher where indicated.

*Source: IMF staff calculations; figures and notes as presented in the chapter.*

### 3. Advanced Economies: Monetary Policy

### ch2 - 3. Advanced Economies: Monetary Policy

### Monetary policy shocks and house prices at risk
- Monetary policy tightening contributes to a deterioration of house prices at risk over a short horizon in advanced economies.
- Monetary policy shocks are identified by regressing a country’s short-term policy rate on a set of controls and using the residuals as the identified shocks. The set of controls includes contemporaneous and lagged values for inflation, log GDP, and log foreign GDP (to capture external risks), as well as lagged values of the short-term rate and a quadratic time trend.
- A Taylor rule is a reduced-form approximation of the response of the central bank’s policy rate to changes in inflation, output, or other economic conditions.
- The analysis focuses on “shocks” to traditional monetary policy, understood as unexpected deviations of the short-term policy rate from an expanded Taylor rule.
- Results:
  - These shocks have a short-lived, negative relationship with house prices at risk only in advanced economies.
  - The inclusion of monetary policy shocks weakens the short-term relationship between financial conditions and house prices at risk, indicating that part of that relationship was associated with changes in the short-term policy rate.
  - Conventional monetary policy shocks seem to have only a short-term influence in advanced economies, where an unexpected loosening reduces the house-prices-at-risk measure for a few quarters.
- Policy implication:
  - The ability of monetary policy to mitigate downside risks to housing prices, beyond its relationship with financial conditions, seems more limited. Monetary policy would influence downside risks to house prices mainly through its impact on financial conditions.

### Capital inflows, types of flows, and house prices at risk
- Capital inflows can increase downside risks to house prices in advanced economies and have heterogeneous effects by type of flow and country group.
- Quantified effects (advanced economies, aggregate):
  - An increase in capital inflows of 1 percent of GDP would raise upside risks by 1.5 percent one quarter ahead.
  - An increase in capital inflows of 1 percent of GDP would raise downside risks by 0.5 percent eight quarters ahead.
- Findings by flow type and region:
  - Among emerging market economies, no robustly significant relationship between overall capital inflows and downside or upside risks to house prices was found.
  - When opening by type of capital for emerging markets:
    - Portfolio flows reduce downside risks to house prices at some horizons.
    - Foreign direct investment increases downside risks to house prices at some horizons.
  - City and country evidence:
    - Foreign direct investment is generally associated with less future risk in several US and Canadian housing markets (example: a 1 percentage point increase in foreign direct investment inflows to the United States is significantly associated with a reduction of about 1–2 percentage points in house prices at risk in San Francisco, Seattle, and Portland, Oregon).
    - Other capital inflows (not foreign direct investment or portfolio flows), generally attributed to foreign bank transactions, amplify downside risks in cities such as Las Vegas, Miami, Los Angeles, Toronto, Vancouver, and Calgary.
- Heterogeneity:
  - Consequences of various types of capital flows for downside risks to house prices vary across groups of countries, with a clearer relationship in the most highly financially integrated economies.
  - The weaker relationship in emerging markets is consistent with these housing markets being less globally integrated.

### Capital flow management measures and their effects
- Tightening of capital flow management measures may improve house prices at risk in advanced economies at some horizons, though short-run effects can differ.
- Empirical results (advanced economies):
  - Tightening of capital flow management measures seems to briefly increase downside risks to house prices in the very short term (one to three quarters ahead)—possibly because of reverse causality.
  - Tightening reduces downside risks (improves house prices at risk) between one and two years ahead by about 3 percent (annualized) in each quarter.
  - No significant effect at longer horizons.
- Specifics on restrictions:
  - The overall indicator used does not distinguish measures tightened for macroprudential reasons (capital flow management/macroprudential measures).
  - Tightening of real estate inflow restrictions, more likely motivated by macroprudential considerations, is not significantly related to downside risks to house prices; the lack of significance could reflect fewer instances (there are only 12 episodes of tightening of real estate restrictions in the data).
- Policy caveats:
  - Capital flow management measures can support macroeconomic policy adjustment and financial stability in certain circumstances, such as during capital inflow surges, when other policy options are limited or timing is crucial.
  - These measures should be assessed case-by-case and weighed against benefits of free and unrestricted capital flows (better smoothing of consumption, diversification of financial risks, development of the financial sector).

### Macroprudential policies and house prices at risk
- Macroprudential policies should not be targeted to house price levels but to the buildup of financial vulnerabilities signaled by downside risks to house prices.
- Findings on effectiveness:
  - Some macroprudential policies appear effective in reducing house prices at risk.
  - Macroprudential measures have a significant additional effect on house prices at risk beyond other factors.
  - The relationship is especially significant for borrower-based measures, such as restrictions on loan-to-value and debt-service-to-income ratios.
- Policy recommendations:
  - Countries should add borrower-based measures to their macroprudential toolkit and monitor their development over time.
  - Macroprudential policy should target some level of downside risk (risk management approach), not levels of asset prices.
  - Policymakers should be mindful of broader systemic-risk implications to avoid precipitating declines in house price levels when the economy and housing market are fragile.
  - Where macroprudential toolkits are incomplete or decision-making is inadequate, monetary policy’s short-term association with house-prices-at-risk may be a consideration, given the link between house prices at risk and downside risks to GDP growth.

### Synchronization and city-level evidence
- Synchronization across countries:
  - Short-term cross-country comovement of downside risks to house prices increases sharply immediately before major recessions or financial crises, especially among advanced economies.
  - Greater financial openness is associated with higher synchronization of downside risk.
  - Capital flow management measures related to real estate may help mitigate synchronization of downside risk around major recessions and financial crises.
- City-level (United States and Canada) findings:
  - Downside risks to house prices across US and Canadian cities have changed substantially over time; US housing markets show larger swings on average than Canada.
  - In Canada, downside risks have increased over the past two years, approaching levels seen around the global financial crisis in some cities (Hamilton, Toronto, Vancouver).
  - City-level overvaluation, proxied by the house-price-to-income ratio, is associated with deterioration in one-year-ahead house prices at risk.
  - Capital flows affect city housing markets heterogeneously:
    - Foreign direct investment tends to reduce future house-price risk in tech-friendly US cities.
    - Other inflows (largely bank transactions) amplify downside risks in certain US and Canadian cities.
  - Policy example: additional property taxes on foreign home buyers in Vancouver and Toronto reflect targeted measures where nonresident buyers contributed to overvaluation and downside risk.

### Conclusion and policy guidance
- New methodology:
  - A panel quantile regression approach based on the growth-at-risk model provides a useful early-warning indicator—house prices at risk—for financial stability surveillance.
- Key conclusions:
  - House prices at risk reflect fundamental factors, overvaluation, and past price dynamics.
  - Recent data point to an increase in downside risks to house prices over the next one to three years in some countries, which is a concern for financial stability and the global macroeconomic outlook over the medium term.
  - House prices at risk have a significant impact on growth at risk.
- Policy guidance summary:
  - Use house-prices-at-risk as part of financial stability monitoring and for activation of macroprudential policies when downside risks heighten.
  - Emphasize borrower-based macroprudential measures (loan-to-value, debt-service-to-income) in toolkits.
  - Recognize monetary policy’s limited direct effect on downside risks beyond financial conditions; rely primarily on macroprudential and, where appropriate, targeted capital flow measures.
  - Assess capital flow measures on a case-by-case basis, balancing reduced downside risks against benefits of capital mobility.
  - Consider international policy coordination when episodes of increased synchronization of downside risks emerge.

*International Monetary Fund | April 2019 — Chapter 2, "Downside Risks to House Prices" (section: Advanced Economies: Monetary Policy).*

### Box 2.2. City-Level House Prices at Risk in the United States and Canada

### Box 2.2. City-Level House Prices at Risk in the United States and Canada

### Downside risks and timing
- Estimation: Five percent HaR, four quarters ahead.
- Distributional markers shown: 10th percentile; Median; 90th percentile.
- Comparative periods highlighted: 2008 and 2018.
- One-year-ahead estimates in panels 3 and 4 refer to 2019:Q1 and 2009:Q1, respectively.
- City-level house-price-to-income ratios refer to 2008 and 2018, respectively.

### Key findings — United States
- Housing downside risks across US cities are more volatile, with a large drop around the GFC.
- Valuations came down for most US cities in 2018 compared with 2008, partially mitigating tail risks.
- Panel results display city heterogeneity (10th, median, 90th percentiles) in downside risk across the sample of US cities.

### Key findings — Canada
- On average, cycles of downside risks across Canadian cities are relatively stable.
- Amid stretched valuations, several Canadian cities were particularly vulnerable to downside risks in 2018.
- Panel results display city heterogeneity (10th, median, 90th percentiles) in downside risk across the sample of Canadian cities.

### House-price-to-income and HaR relationship
- Panels 3 and 4 link one-year-ahead house prices at risk (5 percent) with house-price-to-income ratios.
- City-level house-price-to-income ratio comparisons are made between 2008 and 2018.

### Capital flows as drivers of HaR
- Finding: FDI inflows have a positive role in mitigating risk, whereas other capital inflows are a negative driver.
- Panels 5 and 6 show coefficients (10th percentile; HaR four quarters ahead) for selected American and Canadian cities; dark green and red bars refer to coefficients significant at the 10 percent level.
- Note: "Other capital flows" refers to capital flows other than foreign direct investment or portfolio capital flows.

### Visualization and sample coverage (selected cities shown in figures)
- United States (examples shown): New York; Las Vegas; Boston; Phoenix; Philadelphia; Houston; Minneapolis; Charlotte; Dallas; Portland; Denver; San Francisco; Atlanta; Seattle; Tampa; Austin; Cleveland; Chicago; Washington, DC; Detroit; San Diego; Los Angeles; Miami.
- Canada (examples shown): Quebec City; St. Catharines; Victoria; London; Sudbury; Regina; Montreal; Winnipeg; Windsor; Ottawa; Hamilton; Edmonton; Vancouver; Toronto; Calgary.

*Source: IMF staff calculations. Note: Panels 1 and 2 show the 10th and 90th percentiles of the cross-sectional city distribution in each period. In panels 3 and 4, one-year-ahead estimates of 5 percent HaR refer to 2019:Q1 and 2009:Q1, respectively. City-level house-price-to-income ratios refer to 2008 and 2018, respectively. Other capital flows refers to capital flows other than foreign direct investment or portfolio capital flows. Dark green and red bars refer to coefficients significant at the 10 percent level. FDI = foreign direct investment; GFC = global financial crisis; HaR = house prices at risk.*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2019/april/english/ch2.pdf_