## _wp1338

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---

### Introduction and motivation
- House prices in many advanced countries moved in tandem prior to, during, and after the global financial crisis, with pronounced booms before 2006 and collapses over 2006–11.
- Two central questions:
  - How synchronized are housing cycles across countries?
  - What are the main shocks driving movements in global house prices?
- Importance of housing:
  - Satisfies shelter needs.
  - Accounts for an important fraction of GDP and household expenditures.
  - Is typically households’ main asset while mortgage debt is their main liability.
- Financial amplification mechanisms (financial accelerator) can magnify house price shocks through balance-sheet effects, affecting borrowing, investment, consumption, and thereby reinforcing house price movements.

### Data and sample
- Main dataset:
  - Quarterly series of GDP, house prices, equity prices, credit, short- and long-term interest rates for 18 advanced OECD countries for the period 1971:1–2011:3.
  - Sample accounts for slightly more than 60 percent of global GDP over the 1971–2011 period (in PPP exchange rates).
- Sub-periods:
  - “Pre-globalization period”: 1971:1–84:4.
  - “Globalization period”: 1985:1–2011:3.
- Variable details and sources (selected):
  - House Prices: Real house prices; 1971:1-2011:3 — OECD.
  - Credit: Nominal credit deflated using consumer price index; 1971:1-2011:3 — IFS and Datastream.
  - Uncertainty: Standard deviation of daily equity price index in each month; 1971:1-2011:3 — GFD.
  - Short-term Interest Rate and Long-term Interest Rate: 1971:1-2011:3 — IFS, GDS, OECD, Haver Analytics as applicable.
- Transformations:
  - Four-quarter growth rates for house prices, credit, equity prices, and GDP; variables seasonally adjusted and expressed in percentages.

### Key facts on growth, volatility, and comovement
- Long-run averages and volatilities:
  - Real house prices grew at an average rate of 2¼ percent per year over the past four decades.
  - Average standard deviation of house price growth: almost 7½ percent per year.
- Comovement and correlations (averages over 1971–2011 unless noted):
  - Average correlation between house prices and output: close to 0.5 (Table 3: Output with House Prices Full Sample 0.46).
  - Average cross-country correlation of house prices: close to 0.2 (Table 5: House Prices Full Sample 0.18).
  - House prices are procyclical.
  - Strong comovement between house prices and credit; credit often leads house prices (Table 3: Credit with House Prices Full Sample 0.20; Table 4 lead/lag patterns).
  - Virtually no contemporaneous correlation between housing and equity prices or between housing and interest rates, though house prices often lead equity prices.

### Synchronization metrics and trends
- Concordance index (fraction of time two series are in same phase):
  - Fraction of synchronized housing cycles increased from 51 percent (pre-globalization) to more than 63 percent (globalization) (text summary; Table 6 Concordance: House Prices Pre-Globalization 52.23, Globalization 63.89; Full Sample 59.22).
- Variance explained by common (global) factors:
  - Global factor explains almost one-third of the variation in the growth rate of house prices (full sample).
  - Share of house price variance explained by the common factor increased from about 20 percent (pre-globalization) to about 35 percent (globalization) (Table 5: House Prices Pre-Globalization 0.21, Globalization 0.35, Full Sample 0.29).
  - Country heterogeneity example: in the globalization period the global factor explains about 75 percent of house price movements in the United Kingdom and the United States, but only about 10 percent in New Zealand (text).
- Cross-factor correlations:
  - The common factor of house prices is highly correlated with factors of credit and output for the full sample (Table 8: House Prices with Output Full Sample 0.59; Credit with Output Full Sample 0.53).
  - Correlations between the common factor of house prices and the factors of credit and output have declined over time, suggesting weaker links between housing markets and fundamentals at the global level (text).

### Methodology overview
- Global factors estimated as the first principal component of each variable.
- FAVAR models (lag length 4) used to analyze importance of various shocks; two sets of FAVARs:
  - One with only estimated global factors.
  - One mixing estimated global factors with some country-specific variables (e.g., spreads, default rates).
- Two shock-identification strategies:
  - Recursive (Cholesky-type) ordering using global components ordered as: output, house prices, interest rates, credit, equity prices.
  - Sign-restriction approach (Uhlig 2005) with 5,000 accepted impulse response draws and horizon for sign restrictions set at four quarters.
- Specific sign-restriction schemes applied for monetary policy, productivity, credit supply, and uncertainty shocks following referenced literature.

### Evidence from recursive identification (reduced-form implications)
- Global interest rate shocks:
  - Have a significant but delayed negative impact on global house prices.
  - Global interest rate shocks account for close to 30 percent of movements in global house prices (full sample, Table 9).
- Global credit shocks (reduced-form):
  - Positive and significant contemporaneous effect on house prices in reduced-form recursive specifications.
  - Credit contributes about 10 percent of house price variance in recursive specifications (Table 9).
- Equity and output shocks:
  - Shocks to equity returns do not elicit a significant response in global house prices.
  - Shocks to global output have a small positive impact on house prices.
- Interpretation cautions:
  - Reduced-form interest rate shocks reflect all surprises to rates, not only monetary policy actions.
  - Recursive credit results capture correlation between credit and house prices, not necessarily exogenous credit-supply effects.

### Evidence from sign-restrictions (structural interpretation)
- Monetary policy shocks (sign restrictions):
  - Global monetary policy shocks do not appear to have a significant impact on global house prices (Figure 5A).
  - Monetary policy shocks explain 12 percent of house price variation in the full sample (Table 10A).
  - Implication: the monetary policy component of interest-rate innovations is not the primary driver of global house price movements.
- Credit-supply shocks (Meeks (2012) sign restrictions):
  - Responses of house prices to identified exogenous negative credit supply shocks are essentially zero.
  - Credit supply shocks account for 14 percent of house price growth and 12 percent of output growth in the sign-restriction FAVAR (text).
  - Interpretation: exogenous credit-supply shocks are not a dominant driver of average house price fluctuations; however, credit shocks can matter during periods of severe financial stress.
- Productivity shocks:
  - Positive responses in output but small and statistically insignificant effects on house prices.
- Uncertainty shocks (recursive identification on global uncertainty factor):
  - Uncertainty shocks reduce output for the full sample and globalization period.
  - Uncertainty shocks account for around 10 percent of output variation (Table 11A).
  - For the G-7 sample, uncertainty shocks:
    - Become significant drivers of house prices.
    - Account for 30 percent of house price variation in the G-7 during the globalization period (Table 11B).
  - Correlations:
    - Average correlation of country-level uncertainty with the G-7 uncertainty factor: 0.81.
    - Average correlation of uncertainty across G-7 country pairs: 0.62.
  - Possible mechanism for uncertainty increasing house prices in G-7:
    - Portfolio reallocation toward relatively less volatile assets (housing perceived as safer than equities), combined with declines in interest rates in response to uncertainty shocks, which can support house prices.

### Variance decomposition headline numbers
- Fraction of global house price variance explained (selected entries preserved exactly as reported):
  - Global interest rate shocks: close to 30 percent (Table 9).
  - Credit (reduced-form): about 10 percent (Table 9).
  - Monetary policy shocks (sign restrictions): 12 percent (Table 10A).
  - Credit-supply shocks (sign restrictions): account for 14 percent of house price growth and 12 percent of output growth (text).
  - Uncertainty shocks (G-7, globalization period): 30 percent of house price variation (Table 11B).
  - Global factor explains almost one-third of house price variation in the full sample; increased from about 20 percent (pre-globalization) to about 35 percent (globalization).

### Main conclusions
- House prices in advanced economies move together and have become more synchronized over time:
  - Concordance of housing cycles increased from 51 percent (pre-globalization) to more than 63 percent (globalization).
  - Share of house price variance explained by global factor increased from about 20 percent to about 35 percent.
- Four major findings on sources of global house price fluctuations:
  1. Global interest-rate shocks (reduced-form) have significant effects on house prices in many specifications, but these shocks are not equivalent to global monetary policy shocks.
  2. Global monetary policy shocks identified with sign restrictions do not have a sizeable impact on global house price movements (monetary policy shocks explain 12 percent of house price variation).
  3. Neither productivity nor exogenous credit-supply shocks appear to have large average impacts on house price movements.
  4. Uncertainty shocks play an important role, especially for the G-7: one third of the global cycle in house prices for the G-7 can be attributed to uncertainty shocks in the globalization period.

### Policy implications and suggested research directions
- Policy implication:
  - Low global interest rates alone (as monetary policy actions) cannot fully explain the global boom-bust in house prices; non-monetary-rate innovations and common uncertainty shocks are important contributors.
- Suggested avenues for future research:
  - Consider a wider range of shocks (including fiscal and financial policy shocks) in time-series models that allow for heterogeneity and nonlinearities.
  - Analyze why house prices have become more synchronized over time, focusing on differential effects of shocks and country structural features, including banking-system linkages.
  - Study how shocks affect house prices during distinct episodes (e.g., credit booms/crunches, housing booms/busts) using counterfactuals.
  - Use dynamic stochastic multi-country general equilibrium models that can account for cross-country housing market spillovers.

*Source: _wp1338 — 3. Distribution of Cross-Country Correlations; Appendix I: Database; Authors' calculations*

### References .............................................................................................................

### _wp1338 - References .............................................................................................................

### Tables
- 1. Housing Cycles, Recessions and Recoveries ...................................................................... 29
- 2. Summary Statistics .............................................................................................................. 30
- 3. Correlations Across Variables Within Countries ................................................................ 31
- 4. Lead/Lag Correlations Between House Prices and Other Variables .................................. 32
- 5. Cross-Country Correlations ................................................................................................ 33
- 6. Concordance Across Countries ........................................................................................... 33
- 7. Variance Explained by the Global Factors ......................................................................... 33
- 8. Correlations Among Principal Components of Variables .................................................. 34
- 9. Variance Decompositions (Recursive Identification) ......................................................... 35
- 10A. Variance Decompositions for Credit Shocks (Identification with Sign Restrictions) ........ 36
- 10B.  Variance Decompositions for Monetary Policy Shocks (Identification with Sign Restrictions) ........................................................................................................................ 36
- 10C.  Variance Decompositions for Productivity Shocks (Identification with Sign  
         Restrictions)         ........................................................................................................................ 36
- 11A. Variance Decompositions for Uncertainty Shocks (Recursive Identification,  
         18         Countries)                  ...................................................................................................................... 37
- 11B.  Variance Decompositions for Uncertainty Shocks (Recursive Identification,  
         G7         Countries)         ...................................................................................................................... 37

### Figures
- 1. Coincidence of House Price Downturns and Recessions ................................................... 38
- 2. Global Factors of Financial Variables and Output ............................................................. 39

*Source: _wp1338 - References .............................................................................................................*

### 3. Distribution of Cross-Country Correlations........................................................................ 40

### 3. Distribution of Cross-Country Correlations........................................................................ 40

### Introduction and motivation
- House prices in many advanced countries moved in tandem prior to, during, and after the global financial crisis, with pronounced booms before 2006 and collapses over 2006–11.
- Two central questions: First, how synchronized are housing cycles across countries? Second, what are the main shocks driving movements in global house prices?
- Housing matters because it (i) satisfies shelter needs, (ii) accounts for an important fraction of GDP and household expenditures, and (iii) is typically households’ main asset while mortgage debt is their main liability.
- Financial amplification mechanisms (financial accelerator) can magnify house price shocks through balance-sheet effects, affecting borrowing, investment, consumption, and thereby reinforcing house price movements.

### Data and sample
- Main dataset: quarterly series of GDP, house prices, equity prices, credit, short- and long-term interest rates for 18 advanced OECD countries for the period 1971:1–2011:3.
- Sample accounts for slightly more than 60 percent of global GDP over the 1971–2011 period (in PPP exchange rates).
- Two sub-periods:
  - “Pre-globalization period”: 1971:1–84:4.
  - “Globalization period”: 1985:1–2011:3.
- House prices: various national indices of house or land prices depending on country source.
- Credit: aggregate claims on the private sector by deposit money banks.
- Uncertainty: constructed using the volatility of daily equity prices of the G-7 countries following Bloom (2009).
- Transformations: four-quarter growth rates for house prices, credit, equity prices, and GDP; variables seasonally adjusted and expressed in percentages.

### Key facts on growth, volatility, and comovement
- Real house prices over the past four decades grew at an average rate of 2¼ percent per year, slightly slower than output.
- Average standard deviation of house price growth: almost 7½ percent per year.
- Average correlation between house prices and output over 1971–2011: close to 0.5.
- Average cross-country correlation of house prices: close to 0.2.
- Comovement patterns:
  - House prices are procyclical (rise in expansions, fall in recessions).
  - Strong comovement between house prices and credit; credit often leads house prices.
  - Virtually no contemporaneous correlation between housing and equity prices or between housing and interest rates, though house prices often lead equity prices.

### Synchronization metrics and trends
- Concordance index (fraction of time two series are in same phase):
  - Fraction of synchronized housing cycles increased from 51 percent (pre-globalization) to more than 63 percent (globalization).
- Variance explained by common (global) factors:
  - Global factor explains almost one-third of the variation in the growth rate of house prices (full sample).
  - Share of house price variance explained by the common factor increased from about 20 percent (pre-globalization) to about 35 percent (globalization).
  - Country heterogeneity: in the globalization period the global factor explains about 75 percent of house price movements in the United Kingdom and the United States, but only about 10 percent in New Zealand.
- Cross-factor correlations:
  - The common factor of house prices is highly correlated with factors of credit and output for the full sample.
  - Correlations between the common factor of house prices and the factors of credit and output have declined over time, suggesting weaker links between housing markets and fundamentals at the global level.

### Methodology overview
- Global factors estimated as the first principal component of each variable.
- FAVAR models (lag length 4) used to analyze the importance of various shocks; two sets of FAVARs:
  - One with only estimated global factors.
  - One mixing estimated global factors with some country-specific variables (e.g., spreads, default rates).
- Two shock-identification strategies:
  - Recursive (Cholesky-type) ordering using global components ordered as: output, house prices, interest rates, credit, equity prices.
  - Sign-restriction approach (Uhlig 2005) with 5,000 accepted impulse response draws and horizon for sign restrictions set at four quarters.
- Specific sign-restriction schemes applied for monetary policy, productivity, credit supply, and uncertainty shocks following references in the literature (e.g., Peersman and Straub (2009); Meeks (2012); Bloom (2009)).

### Evidence from recursive identification (reduced-form implications)
- Global interest rate shocks:
  - Have a significant but delayed negative impact on global house prices.
  - Global interest rate shocks account for close to 30 percent of movements in global house prices (full sample, Table 9).
- Global credit shocks (reduced-form):
  - Positive and significant contemporaneous effect on house prices in reduced-form recursive specifications; credit contributes a more modest 10 percent of house price variance (Table 9).
- Equity and output shocks:
  - Shocks to equity returns do not elicit a significant response in global house prices.
  - Shocks to global output have a small positive impact on house prices.
- Interpretation caution:
  - Reduced-form interest rate shocks reflect all surprises to rates, not only monetary policy actions; country heterogeneity in mortgage-to-GDP ratios affects cross-country responses.
  - Recursive credit results capture correlation between credit and house prices, not necessarily exogenous credit-supply effects.

### Evidence from sign-restrictions (structural interpretation)
- Monetary policy shocks (sign restrictions):
  - Global monetary policy shocks do not appear to have a significant impact on global house prices (Figure 5A).
  - Monetary policy shocks explain 12 percent of house price variation in the full sample (Table 10A).
  - Implication: the monetary policy component of interest-rate innovations is not the primary driver of global house price movements; many interest-rate surprises driving house prices originate outside global monetary policy shocks.
- Credit-supply shocks (Meeks (2012) sign restrictions):
  - Responses of house prices to identified exogenous negative credit supply shocks are essentially zero.
  - Credit supply shocks account for 14 percent of house price growth and 12 percent of output growth in the sign-restriction FAVAR (as reported in the text).
  - Interpretation: exogenous credit-supply shocks are not a dominant driver of average house price fluctuations; however, credit shocks can matter during periods of severe financial stress.
- Productivity shocks:
  - Positive responses in output but small and statistically insignificant effects on house prices.
  - Interpretation: productivity-driven income effects on house prices are modest.
- Uncertainty shocks (recursive identification on global uncertainty factor):
  - Uncertainty shocks reduce output for the full sample and globalization period.
  - Uncertainty shocks account for around 10 percent of output variation (Table 11A).
  - For the G-7 sample, uncertainty shocks:
    - Become significant drivers of house prices.
    - Account for 30 percent of house price variation in the G-7 during the globalization period (Table 11B).
  - Correlations:
    - Average correlation of country-level uncertainty with the G-7 uncertainty factor: 0.81.
    - Average correlation of uncertainty across G-7 country pairs: 0.62.
  - Possible mechanisms for uncertainty increasing house prices in G-7:
    - Portfolio reallocation toward relatively less volatile assets (housing perceived as safer than equities), combined with declines in interest rates in response to uncertainty shocks, which can support house prices.

### Variance decomposition headline numbers (as reported)
- Fraction of global house price variance explained (full sample and by shock type):
  - Global interest rate shocks: close to 30 percent (Table 9).
  - Credit (reduced-form): about 10 percent (Table 9).
  - Monetary policy shocks (sign restrictions): 12 percent (Table 10A).
  - Credit-supply shocks (sign restrictions): account for 14 percent of house price growth and 12 percent of output growth (text).
  - Uncertainty shocks (G-7, globalization period): 30 percent of house price variation (Table 11B).
  - Global factor explains almost one-third of house price variation in the full sample; increased from about 20 percent (pre-globalization) to about 35 percent (globalization).

### Main conclusions
- House prices in advanced economies move together and have become more synchronized over time.
  - Concordance of housing cycles: increased from 51 percent (pre-globalization) to more than 63 percent (globalization).
  - Share of house price variance explained by global factor: increased from about 20 percent to about 35 percent.
- Four major findings on sources of global house price fluctuations:
  1. Global interest-rate shocks (reduced-form) have significant effects on house prices in many specifications, but these shocks are not equivalent to global monetary policy shocks.
  2. Global monetary policy shocks identified with sign restrictions do not have a sizeable impact on global house price movements (monetary policy shocks explain 12 percent of house price variation).
  3. Neither productivity nor exogenous credit-supply shocks appear to have large average impacts on house price movements.
  4. Uncertainty shocks play an important role, especially for the G-7: one third of the global cycle in house prices for the G-7 can be attributed to uncertainty shocks in the globalization period.

### Policy implications and suggested research directions (from conclusions)
- Policy implication: Low global interest rates alone (as monetary policy actions) cannot fully explain the global boom-bust in house prices; non-monetary-rate innovations and common uncertainty shocks are important contributors.
- Suggested avenues for future research:
  - Consider a wider range of shocks (including fiscal and financial policy shocks) in time-series models that allow for heterogeneity and nonlinearities.
  - Analyze why house prices have become more synchronized over time, focusing on differential effects of shocks and country structural features, including banking-system linkages.
  - Study how shocks affect house prices during distinct episodes (e.g., credit booms/crunches, housing booms/busts) using counterfactuals.
  - Use dynamic stochastic multi-country general equilibrium models that can account for cross-country housing market spillovers.

*Source: _wp1338 - 3. Distribution of Cross-Country Correlations........................................................................ 40*

### REFERENCES

### _wp1338 - REFERENCES

### Housing markets and housing cycles
- Alvarez, Luis J., G. Bulligan, A. Cabrero, L. Ferrara and H. Stahl, 2010, “Housing cycles in the major euro area countries,” Working Paper, Banque de France.  
- Case, Bradford, William Goetzmann, and Geert Rouwenhorst, 1999, “Global Real Estate Markets: Cycles and Fundamentals,” Yale International Center for Finance, WP No. 99-03.  
- Cesa-Bianchi, Ambrogio, 2011, “Housing Cycles in the Global Economy,” Working Paper, Inter-American Development Bank.  
- Cunningham, Rose, and Ilan Kolet, 2007, “Housing Market Cycles and Duration Dependence in the United States and Canada,” Working Paper, Bank of Canada.  
- Hall, V.B., C. J. McDermott, and J, Tremewan, 2006, “The Ups and Downs of New Zealand House Prices,” Motu Working Paper 06–03.  
- Moench, Emanuel and Serena Ng, 2011, “A hierarchical factor analysis of U.S. housing market dynamics,” The Econometrics Journal, Vol. 14, Issue 1, pp. C1–C24.  
- Silver, Mick. 2012, “Why House Price Indices Differ: Measurement and Analysis,” IMF Working Paper, No. 12/125.  
- Terrones, Marco E. and Christopher Otrok, 2004, “The Global House Price Boom,” World Economic Outlook, September, pp.71–89.

### Monetary policy, house prices, and financial transmission
- Aoki, Kosuke, James Proudman, and Gertjan Vlieghe, 2004, “House Prices, Consumption, and Monetary Policy: A Financial Accelerator Approach,” Journal of Financial Intermediation, Vol. 13, No, 4, pp. 414–35.  
- Bernanke, Ben, Jean Boivin, and Piotr S. Eliasz, 2005, “Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach,” The Quarterly Journal of Economics, Vol. 120, No.1, pp. 387–422, January.  
- Bernanke, Ben, Mark Gertler, and Simon Gilchrist,1999, “The Financial Accelerator in a Quantitative Business Cycle Framework,” in Handbook of Macroeconomics, Vol.1C, Handbooks in Economics, Vol. 15., pp. 1341–93.  
- Calza, Alessandro, Tommaso Monacelli, and Livio Stracca, 2009, “Housing finance and monetary policy,” ECB Working Paper.  
- Del Negro, Marco, and Otrok, Christopher. 2007, “99 Luftballons: Monetary policy and the house price boom across U.S. states,” Journal of Monetary Economics, Vol.54 (7), pp. 1962–85.  
- Jarocinski, M., and F. R. Smets, 2008, “House prices and the stance of monetary policy,” Review, Vol. 1, pp. 339–66.  
- Kuttner, Kenneth N., 2012, “Low Interest Rates and Housing Bubbles: Still No Smoking Gun,” Mimeo, Williams College.  
- Monacelli, Tommaso, 2009, “New Keynesian Models, Durable Goods, and Collateral Constraints,” Journal of Monetary Economics, Vol.56, No. 2, pp. 242–54.  
- Vansteenkiste, Isabel 2007, “Regional housing market spillovers in the US - lessons from regional divergences in a common monetary policy setting,” Working Paper Series 708, European Central Bank.  
- Feroli, Michael, Ethan S. Harris, Amir Sufi, and Kenneth D. West, 2012, “Housing, Monetary Policy, and the Recovery,” Chicago Booth Research Paper, No. 12–16.  
- Goodhart, Charles and Hofmann, Boris, 2008, “House prices, money, credit, and the macroeconomy,” Oxford Review of Economic Policy, Vol. 24, No. 1, pp.180–205.

### Financial cycles, credit shocks, and crises transmission
- Bernanke, Ben and Mark Gertler, 1989, “Agency Costs, Net Worth, and Business Fluctuations,” American Economic Review, Vol. 79, pp.14–31.  
- Gertler, Mark, 1988, “Financial Structure and Aggregate Economic Activity: An Overview,” Journal of Money, Credit and Banking, Vol. 20, No. 3, pp. 559–88.  
- Gilchrist, Simon, Vladimir Yankov, and Egon Zakrajšek, 2009, “Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets,” NBER Working Papers 14863.  
- Helbling, Thomas, Raju Huidrom, M. Ayhan Kose and Chris Otrok, 2011, “Do Credit Shocks Matter? A Global Perspective,” European Economic Review, Vol. 55, pp. 340–53.  
- Helbling, Thomas, and Marco E. Terrones, 2003, “Real and Financial Effects of Bursting Asset Price Bubbles,” IMF World Economic Outlook, April, pp.61–94.  
- Meeks, Roland, 2012, “Do credit market shocks drive output fluctuations? Evidence from corporate spreads and defaults,” Journal of Economic Dynamics & Control, Vol.36 pp. 568–84.  
- Mendoza, Enrique and Marco E. Terrones, 2008, “An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data,” NBER Working Paper No. 14049.  
- Sa, Filipa, Towbin, Pascal and Wieladek, Tomasz. 2011. Low Interest Rates and Housing Booms: The Role of Capital Inflows, Monetary Policy, and Financial Innovation. Working paper.

### Uncertainty, policy uncertainty, and macroeconomic effects
- Baker, Scott, and Nicholas Bloom, 2011, “Does Uncertainty Drive Business Cycles? Using Disasters as a Natural Experiment,” Working Paper, (Stanford University).  
- Baker, Scott, Nicholas Bloom, and Steven J. Davis, 2012, “Measuring Economic Policy Uncertainty,” Working Paper, (Stanford University).  
- Bloom, Nicholas, 2009, “The Impact of Uncertainty Shocks,” Econometrica, Vol. 77(3), pp. 623–85.  
- Bloom, Nicholas, Max Floetotto, Nir Jaimovich, Itay Saporta-Eksten, and Stephen Terry, 2012, “Really Uncertain Business Cycles,” NBER Working Paper No. 18245.  
- Carrière–Swallow, Yan, and Luis Felipe Céspedes, 2011, “The Impact of Uncertainty Shocks in Emerging Economies,” Working Paper No. 646, (Santiago: Central Bank of Chile).  
- Fatas, Antonio, and Ilian Mihov, 2012, “Policy Volatility, Institutions and Economic 24 Growth,” Review of Economics and Statistics, (forthcoming).  
- Leduc, Sylvain and Zheng Liu, 2012, Uncertainty Shocks are Aggregate Demand Shocks, Federal Reserve Bank of San Francisco, Working Paper 2012–10.

### Global business cycles, international comovement, and integration
- Beltratti, A., and C. Morana, 2010, “International house prices and macroeconomic fluctuations,” Journal of Banking & Finance, Vol. 34(3), pp.533–45.  
- Claessens, Stijn, M. Ayhan Kose, and Marco Terrones, 2012, “How do Business and Financial Cycles Interact?” Journal of International Economics, Vol. 87, pp.178–90.  
- Claessens, Stijn, M. Ayhan Kose, and Marco Terrones, 2010, “Financial Cycles: What? How? When?” NBER International Seminar on Macroeconomics 2010, in Richard Clarida and Francesco Giavazzi eds., University of Chicago Press, pp.303–43.  
- Claessens, Stijn, M. Ayhan Kose, and Marco Terrones, 2009, “What Happens During Recessions, Crunches, and Busts?” Economic Policy, October, pp. 653–700.  
- Crucini, Mario, M. Ayhan Kose, and Chris Otrok, 2011. “What Are the Driving Forces of International Business Cycles?” Review of Economic Dynamics, Vol. 14, pp. 156–75.  
- Kose, M. Ayhan, Christopher Otrok, and Eswar Prasad, 2012, “Global Business Cycles: Convergence or Decoupling?” International Economic Review, Vol. 53, pp. 511–38.  
- Kose, M. Ayhan, Prakash Loungani, and Marco E. Terrones, 2009, “Out of the Ballpark: The Global Recession of 2007–08 in Historical Context,” Finance & Development, Vol. 25 pp. 25–8.  
- Kose, M. Ayhan, Christopher Otrok, and Charles H. Whiteman, 2008, “Understanding the Evolution of World Business Cycles,” Journal of International Economics, Vol. 75, No. 1, pp. 110–30.  
- Kose, M. Ayhan, Eswar Prasad, and Marco E. Terrones, 2006, “How Do Trade and Financial Integration Affect the Relationship between Growth and Volatility,” Journal of International Economics, Vol. 69, pp. 176–202.  
- Kose, M. Ayhan, Christopher Otrok, and Charles H. Whiteman, 2003, “International Business Cycles: World, Region, and Country Specific Factors,” American Economic Review, Vol. 93, pp. 1216–39.  
- Liu, Zheng, Pengfei Wang, and Tao Zha, 2011, “Land-price dynamics and macroeconomic fluctuations,” NBER Working Papers 17045, National Bureau of Economic Research.  
- Eickmeier, Sandra, Leonardo Gambacorta, Boriss Hofmann, 2012, “Understanding Global Liquidity,” BIS Working Paper.

### Methodology, identification, and empirical techniques
- Bernanke, Ben, 1983, “Irreversibility, Uncertainty and Cyclical Investment,” Quarterly Journal of Economics, Vol. 98, pp. 85–106.  
- Jarocinski, M., and F. R. Smets, 2008, “House prices and the stance of monetary policy,” Review, Vol. 1, pp. 339–66.  
- Kiyotaki, Nobuhiro, and John Moore, 1997, “Credit Cycles,” Journal of Political Economy, Vol. 105, No. 2, pp. 211–48.  
- Otrok, Christopher and Panayiotis M. Pourpourides, 2011, “On the Cyclicality of Real Wages and Wage Differentials,” Working Papers 2011-4, Central Bank of Cyprus.  
- Peersman, Gert, and Roland Straub, 2009, “Technology Shocks And Robust Sign Restrictions In A Euro Area Svar,” International Economic Review, Vol. 50, No. 3, pp. 727–50, 08.  
- Uhlig, Harald, 2005, “What are the effects of monetary policy on output? Results from an agnostic identification procedure,” Journal of Monetary Economics, Vol. 52, No. 2, pp.381–419.  
- Stock, James H. and Mark W. Watson, 2009, “The Evolution of National and Regional Factors in U.S. Housing Construction,” in Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle, Tim Bollerslev, Jeffrey Russell and Mark Watson (eds), Oxford: Oxford University Press.  
- Stock, James H. and Mark W. Watson, 2012. “Disentangling the Channels of the 2007-2009 Recession,” Brookings Papers on Economic Activity (forthcoming).  
- Moench, Emanuel and Serena Ng, 2011, “A hierarchical factor analysis of U.S. housing market dynamics,” The Econometrics Journal, Vol. 14, Issue 1, pp. C1–C24.  

### Other contributions and working papers
- Beltratti, A., and C. Morana, 2010, “International house prices and macroeconomic fluctuations,” Journal of Banking & Finance, Vol. 34(3), pp.533–45.  
- Bradford, Case, William Goetzmann, K. Rouwenhorst, 1999, “Global Real Estate Markets: Cycles And Fundamentals,” Yale School of Management Working Papers.  
- Carstensen, K., Hulsewig, O. and Wollmershauser, T., 2009, “Monetary policy transmission and house prices: European cross country evidence,” Working paper d.7.4, European Commission.  
- Gupta, Rangan, Marius Jurgilas, Alain Kabundi, and Stephen M. Miller, 2012, “Monetary policy and housing sector dynamica in a large-scale Bayesian vector autoregressive model,” International Journal of Strategic Property Management, Vol. 16, pp. 1–20.  
- Hubrich, Kirstin, and Robert J. Tetlow, 2011, “Financial Stress and Economic Dynamics: The Transmission of Crises,” unpublished working paper.  
- Kose-related collaborative works and IMF/central bank working papers cited across the list.

* _wp1338 - REFERENCES*

### Appendix I: Database

### Appendix I: Database

### Variable definitions and sources
- Output: Gross Domestic Product, volume; 1971:1-2011:3 — OECD
- House Prices: Real house prices; 1971:1-2011:3 — OECD
- Equity Prices: Share price (index) deflated using consumer price index; 1971:1-2011:3 — IFS
- Credit: Nominal credit deflated using consumer price index; 1971:1-2011:3. Notes: Nominal credit from IFS (titled "Claims on Private Sector", "Claims on Other Resident Sector", etc.). Nominal credit from Datastream (titled "Loans to Resident Private Sector", "Lending to Enterprises and Individuals", etc.). For Canada, New Zealand, and United States, the average of last four quarters is used for 2011:3. — IFS and Datastream
- Short-term Interest Rate: Treasury bill rate (IFS), short-term interest rate (GDS), and three months interbank offer rate (Haver Analytics) deflated using inflation rate; 1971:1-2011:3 — IFS, GDS, and Haver Analytics
- Long-term Interest Rate: Government bond yield (IFS), long-term interest rates on government bonds (OECD), and long-term interest rates (GDS); 1971:1-2011:3 — IFS, OECD, and GDS
- Uncertainty: Standard deviation of daily equity price index in each month; 1971:1-2011:3 — GFD
- Reserves: Total reserves, 1971:1-2011:3 (exceptions: Denmark: 1992:1-2011:3, Spain: 1979:4-2011:3, Sweden: 2001:4-2011:3) — IFS, FRED, and Haver Analytics
- Credit Spread: Corporate bond spreads: difference between Moody's seasoned Aaa and Baa corporate bonds for the U.S.; 1971:1-2011:3 — Moody's Investor Services
- Default Rate: Rated U.S. speculative-grade corporate bonds; 1971:1-2011:3 — Moody's Investor Services

### Housing cycles, recessions and recoveries (Table 1 — sample medians unless noted)
- A. Recessions
  - Recessions without House Price Busts: Number of Events 73; Duration 3.27; Amplitude -1.72; Cumulative Loss -2.43; Slope -0.51
  - Recessions with House Price Busts: Number of Events 40; Duration 4.28**; Amplitude -2.35; Cumulative Loss -3.57**; Slope -0.52
  - Recessions with Severe House Price Busts: Number of Events 24; Duration 4.38*; Amplitude -2.64**; Cumulative Loss -5.23***; Slope -0.72
- B. Recoveries
  - Recoveries without House Price Booms: Number of Events 10; Duration 24.79; Amplitude 2.97; Cumulative Loss 0.75
  - Recoveries with House Price Booms: Number of Events 13; Duration 2.08***; Amplitude 6.25***; Cumulative Loss 1.45***
  - Recoveries with Strong House Price Booms: Number of Events 8; Duration 2.13***; Amplitude 7.36***; Cumulative Loss 1.59***
- Notes: Duration for recessions = quarters between peak and trough (means reported). Amplitude for recessions = decline in output from peak to trough. Recoveries amplitude = one year change in output after the trough. Cumulative loss expressed in percent. Booms = top 25 percent of upturns by amplitude; Busts = worst 25 percent of downturns by amplitude. Recessions, recoveries, housing busts and booms identified following Claessens, Kose, and Terrones (2012). ***, **, * denote significance at 1 percent, 5 percent, and 10 percent, respectively.

### Summary statistics (Table 2 — full sample 1971:1-2011:3; sub-periods 1971:1-1984:4 pre-globalization, 1985:1-2011:3 globalization)
- Output
  - Full Sample: Mean 2.51; Volatility 2.53; Coefficient of Variation 0.99; Maximum 9.34; Minimum -6.39
  - Pre-Globalization: Mean 2.82; Volatility 2.74*; Coefficient of Variation 1.03; Maximum 8.90; Minimum -3.29
  - Globalization: Mean 2.35; Volatility 2.30; Coefficient of Variation 1.02; Maximum 6.80; Minimum -5.64
- House Prices
  - Full Sample: Mean 2.20; Volatility 7.48; Coefficient of Variation 0.29; Maximum 26.40; Minimum -14.63
  - Pre-Globalization: Mean 1.23**; Volatility 8.05; Coefficient of Variation 0.15; Maximum 22.09; Minimum -12.94
  - Globalization: Mean 2.70; Volatility 6.75; Coefficient of Variation 0.40; Maximum 21.04; Minimum -12.27
- Equity Prices
  - Full Sample: Mean 4.64; Volatility 24.15; Coefficient of Variation 0.19; Maximum 79.50; Minimum -49.49
  - Pre-Globalization: Mean -0.02***; Volatility 23.22; Coefficient of Variation 0.00; Maximum 62.13; Minimum -41.74
  - Globalization: Mean 7.08; Volatility 23.95; Coefficient of Variation 0.30; Maximum 74.74; Minimum -46.56
- Credit
  - Full Sample: Mean 5.30; Volatility 6.66; Coefficient of Variation 0.80; Maximum 26.46; Minimum -10.01
  - Pre-Globalization: Mean 4.80; Volatility 6.45; Coefficient of Variation 0.74; Maximum 20.25; Minimum -7.36
  - Globalization: Mean 5.56; Volatility 6.32; Coefficient of Variation 0.88; Maximum 22.56; Minimum -8.02
- Short-term interest rate
  - Full Sample: Mean -0.15; Volatility 2.29; Coefficient of Variation -0.07; Maximum 8.38; Minimum -8.02
  - Pre-Globalization: Mean 0.21***; Volatility 2.76***; Coefficient of Variation 0.08; Maximum 7.08; Minimum -6.13
  - Globalization: Mean -0.34; Volatility 1.88; Coefficient of Variation -0.18; Maximum 5.47; Minimum -6.52
- Long-term interest rate
  - Full Sample: Mean -0.10; Volatility 1.30; Coefficient of Variation -0.08; Maximum 4.01; Minimum -3.95
  - Pre-Globalization: Mean 0.27***; Volatility 1.42*; Coefficient of Variation 0.19; Maximum 3.55; Minimum -3.11
  - Globalization: Mean -0.30; Volatility 1.14; Coefficient of Variation -0.26; Maximum 3.11; Minimum -3.27
- Notes: Mean = average growth rate. Volatility = standard deviation of growth rate. Coefficient of variation = mean/volatility. ***, **, * denote pre-globalization t-tests statistically different from globalization at 1 percent, 5 percent, and 10 percent.

### Correlations within countries (Table 3 — average within-country correlations)
- Selected cells (full sample / pre-globalization / globalization)
  - Output with House Prices: Full Sample 0.46; Pre-Globalization 0.45; Globalization 0.48
  - Output with Equity Prices: Full Sample 0.29; Pre-Globalization 0.21***; Globalization 0.38
  - Output with Credit: Full Sample 0.15; Pre-Globalization 0.08*; Globalization 0.16
  - House Prices with Equity Prices: Full Sample 0.11; Pre-Globalization 0.08*; Globalization 0.16
  - Credit with House Prices: Full Sample 0.20; Pre-Globalization 0.10**; Globalization 0.32
  - Short-term interest rate: Full Sample 0.64; Pre-Globalization 0.65; Globalization 0.62
- Note: Average within-country correlations across variables; ***, **, * denote significance of difference between pre-globalization and globalization.

### Lead/Lag correlations between house prices and other variables (Table 4 — lags i = -6 to +6)
- Output (average correlations at leads/lags shown across -6..+6):
  - Full Sample examples: 0.21, 0.24, 0.28, 0.34, 0.39, 0.43, 0.46, 0.43, 0.37, 0.28, 0.18, 0.09, 0.02
  - Pre-Globalization notable entries with significance: 0.16*, 0.28**, 0.05***, -0.05***, -0.11***
  - Globalization entries: 0.26, 0.28, 0.31, 0.35, 0.39, 0.43, 0.48, 0.48, 0.45, 0.38, 0.29, 0.19, 0.11
- Equity Prices and Credit series also reported across leads/lags with values and significance differences between sub-periods.
- Note: Correlations computed keeping house prices at {t} and other variables at {t-i}, i=-6..+6.

### Cross-country correlations and concordance (Tables 5 and 6)
- Cross-country average correlations (selected):
  - Output: Full Sample 0.43; Pre-Globalization 0.32***; Globalization 0.52
  - House Prices: Full Sample 0.18; Pre-Globalization 0.14***; Globalization 0.23
  - Equity Prices: Full Sample 0.56; Pre-Globalization 0.43**; Globalization 0.64
  - Credit: Full Sample 0.26; Pre-Globalization 0.24***; Globalization 0.30
  - Short-term interest rate: Full Sample 0.33; Pre-Globalization 0.29***; Globalization 0.42
  - Long-term interest rate: Full Sample 0.51; Pre-Globalization 0.44**; Globalization 0.54
- Concordance (median across country pairs, with averages in parentheses)
  - Output: Full Sample 80.17 (79.54); Pre-Globalization 73.49 (73.30); Globalization 83.22 (83.01)
  - House Prices: Full Sample 59.22 (58.16); Pre-Globalization 52.23 (50.93); Globalization 63.89 (61.50)
  - Equity Prices: Full Sample 71.21 (69.33); Pre-Globalization 63.14 (63.21); Globalization 75.26 (72.59)
  - Credit: Full Sample 69.89 (69.97); Pre-Globalization 63.24 (59.46); Globalization 74.12 (74.52)
- Variance explained by global factors (median and averages reported)
  - Output: Full Sample 0.47 (0.55); Pre-Globalization 0.38*** (0.41); Globalization 0.56 (0.64)
  - House Prices: Full Sample 0.29 (0.27); Pre-Globalization 0.21** (0.13); Globalization 0.35 (0.36)
  - Equity Prices: Full Sample 0.59 (0.59); Pre-Globalization 0.46*** (0.47); Globalization 0.66 (0.69)
  - Credit: Full Sample 0.32 (0.33); Pre-Globalization 0.31; Globalization 0.37 (0.41)
  - Short-term interest rate: Full Sample 0.39 (0.38); Pre-Globalization 0.37 (0.43); Globalization 0.47 (0.53)
  - Long-term interest rate: Full Sample 0.55 (0.53); Pre-Globalization 0.51 (0.56); Globalization 0.59 (0.57)

### Correlations among principal components (Table 8)
- Selected correlations (Full Sample / Pre-Globalization / Globalization)
  - House Prices with Output: Full Sample 0.59; Pre-Globalization 0.81***; Globalization 0.58
  - Equity Prices with Output: Full Sample 0.44 / Pre-Globalization 0.33* / Globalization 0.53
  - Credit with Output: Full Sample 0.53; Pre-Globalization 0.86***; Globalization 0.44
  - Short-term interest rate with Output: Full Sample 0.49; Pre-Globalization 0.38*; Globalization 0.57
  - Long-term interest rate with Output: Full Sample 0.22; Pre-Globalization 0.05*; Globalization 0.28
- Note: Correlation between pairs of principal components; Fisher's Z test used for pre-globalization vs globalization differences.

### Variance decompositions (Table 9 and Table 10 series — recursive and sign-restriction identifications)
- Table 9 (Recursive identification): Proportion of forecast error variance of factors explained by shocks (examples, Full Sample)
  - House Prices shock contributions to factors at forecast horizons:
    - Horizon 1 quarter: 91.77; 3 quarters: 3.71; 10 quarters: 0.91; 13 quarters: 3.36; 60.25; 0.25 (table entries presented across horizons)
  - Output shock contributions (selected horizons): 18.41; 84.76; 1.30; 0.67; 4.87 (values presented per horizon and factor)
  - Note: Model includes variables: output, house prices, interest rates, credit, equity prices.
- Table 10.A (Credit shocks — sign restrictions): Proportion of forecast error variance explained by credit shocks (examples, Full Sample)
  - House Prices, Output, Short-term Interest Rates, Credit, Inflation, Spread, Default — values across horizons such as 18.49, 9.53, 8.44, 13.07, 5.85, 2.57, 17.53 (Full Sample, horizon 1) and sequences for horizons 4, 8, 12.
- Table 10.B (Monetary policy shocks — sign restrictions): Values reported across horizons for House Prices, Output, Short-term Interest Rates, Credit, Inflation, Long-term Interest Rates, Reserves with Full Sample, Pre-Globalization, Globalization panels.
- Table 10.C (Productivity shocks — sign restrictions): Proportion of forecast error variance of factors explained by productivity shocks for different forecast horizons; tables report Full Sample, Pre-Globalization, Globalization panels with numeric entries per horizon.
- Notes: Pre-globalization variance decompositions not available for some shocks due to data limitations (spreads and defaults).

### Variance decompositions for Uncertainty shocks (Table 11.A and 11.B)
- Table 11.A (18 countries, recursive identification) — selected entries (Full Sample / Pre-Globalization / Globalization)
  - At forecast horizons (example layouts): House Prices, Output, Equity Prices entries such as 11.57, 5.74, 2.20; 42.04, 10.63, 7.75; 85.05, 9.89, 7.93; 126.42, 10.12, 7.86
- Table 11.B (G7 countries) — similar structure with distinct numeric entries for Full Sample / Pre-Globalization / Globalization.
- Note: These tables show proportions of forecast error variance explained by uncertainty shocks for different horizons.

### Figures and impulse-response evidence (selected descriptive findings)
- Figure 1: Coincidence of house price downturns and recessions — series shown across 1971:1 to 2011:1; gray bars mark global recessions in years 1975, 1982, 1991, and 2009 and adjacent periods 1974, 1981, 2008, 1992.
- Figure 2: Global factors (first principal components) of financial variables and output plotted (growth rates) across 1971.01–2011.01 for House Prices, Equity Prices, Credit, Output.
- Figure 3: Distributions of cross-country correlations — Kolmogorov-Smirnoff tests indicate statistically significant differences across pre-globalization and globalization periods for house prices, equity prices, and credit.
- Figure 4.A & 4.B: Impulse responses of house prices to various shocks (house price shocks, output shocks, short-term interest rate shocks, credit shocks, equity price shocks) — median responses with 16 percent and 84 percent error bands shown for Full Sample, Pre-Globalization, Globalization; Figure 4.B presents country-specific median impulse responses to short-term interest rate shocks (country list: Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, United Kingdom, United States) with scales up to approximately ±3.6 across horizons 0..11 quarters.
- Figure 5.A–5.C: Impulse responses to Monetary Policy Shocks, Credit Shocks, and Productivity Shocks for 18 countries — median estimates and 16/84 error bands for variables: House Price, Output, Short-term Interest Rate, Credit, Inflation, Long-term Interest Rate, Reserves, Spread, Default (where available). Panels provide Full Sample, Pre-Globalization, Globalization comparisons.
- Figure 6.A–6.B: Impulse responses to Uncertainty Shocks for 18 countries and G7 countries — median responses and error bands for House Prices, Output, Equity Prices across horizons 0..11 quarters; scales show larger equity and output responses (e.g., equity price responses range up to ±40 in Full Sample panels).

*Source: _wp1338 - Appendix I: Database — Authors' calculations*

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