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House Price Expectations , Boom Bust Cycles and Implications for Monetary Policy

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Tallinn University of Technology
Rachatar Nilavongse
05-09-2019

Published in: Economy & Finance
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House Price Expectations , Boom Bust Cycles and Implications for Monetary Policy

  1. 1. House Price Expectations, Boom-Bust Cycles and Implications for Monetary Policy Tallinn University of Technology Rachatar Nilavongse 05-09-2019 1
  2. 2. Agenda 1. Introduction 2. Model features 3. Stochastic process 4. Model simulation under standard monetary policy rule 5. Model simulation under alternative monetary policy rule 6. Household welfare 7. Household welfare under different model specifications 2
  3. 3. Introduction • Keynes (1936) argues that business cycles might be driven by waves of optimism and pessimism: “Animal Spirits” • Pigou (1927) mentions that “errors of undue optimism or undue pessimism may create industrial fluctuations”. • Beaudry and Portier (2004 JME, 2006 AER) and Jaimovich and Rebelo (2009 AER) demonstrate changes in expectations regarding future productivity can| generate business cycle fluctuations in a DSGE framework. 3
  4. 4. Introduction • Case and Shiller (2003) and Shiller (2007) conclude households had unrealistic expectations about future price increases based on household surveys. • Case et al. (2012) note that households started to reverse their expectations about 2 years before the housing crisis. • Based on VARs, Lambertini et al. (2013 JECD) and Towbin and Weber (2015) show that expectations of rising house prices account for a large part of the recent U.S. housing boom. 4
  5. 5. Introduction • Ling et al. (2015, JMCB) find that a non-fundamental based housing market sentiment index predicts real house price appreciation above and beyond changes in a broad set of fundamentals. • Soo (2018, RFS) find a housing sentiment index lead prices by nearly 2 years and explain 70 percent of the variations in house price growth. 5
  6. 6. Introduction 6 Source: Soo 2013
  7. 7. Contributions • The effects of changes in expectations of future house prices are rarely studied in the context of a DSGE framework. • We therefore embed a non-fundamental stochastic processes into a DSGE model. • Changes in price expectations or housing sentiment create booms- busts instead of variations in fundamental factors. • We study the effects of changes in expectations under different financial conditions. 7
  8. 8. Research Questions 1. Can changes in household expectations about future house prices generate boom-bust cycles in housing, financial and economic activities? 2. Can monetary policy that takes into account household credit growth reduce boom-bust cycles ? 3. Can monetary policy that takes into account household credit growth improve household welfare ? 8
  9. 9. Key Findings 1. Changes in house price expectations generate boom-bust cycles in housing, financial and economic activities. 2. The standard monetary policy amplifies boom-bust cycles. 3. Monetary policy taking into account credit growth reduces the magnitude of boom-bust cycles. 4. By reacting to credit growth, this improves welfare. 9
  10. 10. Model Features • We adopt a DSGE model based on Gerali et al. (2010 JMCB). • The Gerali et al. (2010) model has been recently extended by the Bank of Finland: “Aino 2.0” and the ECB: “New Area Wide-Model II”. • We modify the model by incorporating anticipated and unanticipated shocks to generate booms-busts. 10
  11. 11. Model Features: Household Sector • Two types of households: Financially unconstrained and constrained households • Both types of households consume, work and buy houses. • Financially unconstrained households make deposits with retail banks. • The households borrow from the retail banks and use their house as collateral. 11
  12. 12. Model Features: Production Sector • Entrepreneurs use capital as collateral to obtain loans from the retail banks. • Entrepreneurs employ labor and capital to produce intermediate goods and sell them to retail firms. • Retail good producers use intermediate goods to produce consumption goods and are price setters. • The retail good sector is the source of sticky prices in the economy. 12
  13. 13. Model Features: Banking Sector • The banking sector comprises: wholesale and retail banks. • Wholesale banks conduct business activities with retail banks. • The wholesale banks manage its capital to asset ratio to meet the capital requirement. • The retail banks conduct business activities with households and entrepreneurs. • The first retail unit collects deposits from financially unconstrained households. • The second retail unit provides loans to financially constrained households and entrepreneurs. 13
  14. 14. FINANCIALLY UNCONSTRAINED HOUSEHOLDS FINANCIALLY CONSTRAINED HOUSEHOLDS RETAIL LOAN UNIT WHOLESALE UNIT MACRO- PRUDENTIAL AUTHORITY Deposits Loans ENTREPRENEURS Wholesale funding Wholesale loans Bank capital ratio req. Loans Banking Sector Policy rate CENTRAL BANK RETAIL DEPOSIT UNIT
  15. 15. Main Features: Wholesale Banking • A change in the policy rate affects the supply of wholesale loans. • If the capital to asset ratio deviates from the capital requirement, the bank incurs a cost that is imposed by the macroprudential regulator. • A change in the bank capital requirement can affect the credit supply. 15
  16. 16. Model Features: Retail Banking • The retail loan unit operates in a monopolistically competitive market. • The unit obtains the interbank loans from the wholesale unit and then transforms them to differentiated loans (consumer loans). • The retail interest rate on household/business loans is a markup over the wholesale loan rate. • The unit also faces an adjustment cost of varying loan rates. • The retail loan rates do not immediately adjust to the policy rate. 16
  17. 17. Central Bank • The central bank sets the policy rate by following a Taylor rule. 1. Monetary policy reacting to inflation and output is referred to as a standard Taylor rule. 2. Monetary policy reacting to household credit growth in addition to inflation and output is referred to as an alternative Taylor rule. 17
  18. 18. Financially Unconstrained Household • Utility function of the financially unconstrained household: • 𝐸0 σ 𝑡=0 ∞ 𝛽U 𝑡 𝑙𝑛𝐶U,𝑡 + 𝜐ℎ 𝑙𝑛𝐻U,𝑡 − 𝑁U,𝑡 𝜂+1 𝜂+1 (1) • s.t. the budget constraint: • 𝐶U,𝑡 + 𝐷 𝑈,𝑡 + 𝑞𝑡 𝐻U,𝑡 − 𝐻U,𝑡−1 = (1+𝑅 𝐷,𝑡−1) 𝜋 𝑡 𝐷 𝑈,𝑡−1 + 𝑊U,𝑡 𝑃𝑡 𝑁U,𝑡 + ΠU,𝑡 (2) • Income: interest income from deposits (1+𝑅 𝐷,𝑡−1) 𝜋 𝑡 𝐷 𝑈,𝑡−1, labor income 𝑊U,𝑡 𝑃𝑡 𝑁U,𝑡 and lump-sum transfers ΠU,𝑡. • Expenditure: consumption 𝐶U,𝑡, deposits 𝐷 𝑈,𝑡, and investment in a housing market 𝑞𝑡 𝐻U,𝑡 − 𝐻U,𝑡−1 . 18
  19. 19. Financially Unconstrained Household: Housing Demand Financially uncontrained housing demand equation comprises: • Marginal cost of buying a house: 𝑞 𝑡 𝐶 𝑈𝑡 • Marginal benefit of living in a house: 𝜈ℎ 𝐻U,𝑡 • Marginal benefit from the expected resale of the house: 𝛽 𝑈 𝐸𝑡 𝑞 𝑡+1 𝐶U,𝑡+1 • Housing demand condition is: • 𝑞 𝑡 𝐶U,𝑡 = 𝜈ℎ 𝐻U,𝑡 + 𝛽 𝑈 𝐸𝑡 𝑞 𝑡+1 𝐶U,𝑡+1 19
  20. 20. Financially Constrained Households • 𝐸0 σ 𝑡=0 ∞ 𝛽F 𝑡 𝑙𝑛𝐶F,𝑡 + 𝜐ℎ 𝑙𝑛𝐻F,𝑡 − 𝑁F,𝑡 𝜂+1 𝜂+1 (3) • s.t. • Budget constraint: • 𝐶F,𝑡 + 𝑞𝑡 𝐻F,𝑡 − 𝐻F,𝑡−1 + 1+𝑅F,𝑡−1 𝜋 𝑡 𝐿F,𝑡−1 = 𝐿F,𝑡 + 𝑊F,𝑡 𝑃𝑡 𝑁F,𝑡 (4) • Collateral constraint: • 𝐸𝑡 (1+𝑅F,𝑡) 𝜋 𝑡+1 𝐿F,𝑡 = 𝑚 𝐹 𝐸𝑡 𝑞𝑡+1 𝐻F,𝑡 (5) • The amount of loans depends on the expected value of housing collateral. 20
  21. 21. Financially Constrained Households: Housing Demand • Marginal benefit of using house as a collateral: 𝑚 𝐹 𝜆F,𝑡 𝐸𝑡 𝑞𝑡+1 • The shadow value of house: 𝜆F,𝑡 • Higher expected house prices relaxes the collateral constraint. • A higher 𝑚 𝐹 (loan-to-value ratio) amplifies the housing demand. • 𝑞 𝑡 𝐶F,𝑡 = 𝜈ℎ 𝐻F,𝑡 + 𝛽 𝐹 𝐸𝑡 𝑞 𝑡+1 𝐶F,𝑡+1 + 𝑚 𝐹 𝜆F,𝑡 𝐸𝑡 𝑞𝑡+1 21
  22. 22. Stochastic Process: Boom-Bust Cycles • Piazzesi and Schneider (2009 AER) and Bolt et al. (2019 JEDC): data support heterogeneity in expectations. • Financially constrained households believe that prices will deviate from fundamentals. • Add an exogenous shock 𝑧𝑡 to the financially constrained household’s housing demand equation: • 𝑞 𝑡 𝐶F,𝑡 = 𝜈ℎ 𝐻F,𝑡 + 𝛽 𝐹 𝐸𝑡 𝑞 𝑡+1 𝐶F,𝑡+1 + 𝑚 𝐹 𝜆F,𝑡 𝐸𝑡 𝑞𝑡+1 + 𝑧𝑡. 22
  23. 23. Stochastic Process: Boom-Bust Cycles • The exogenous shock to the housing demand 𝑧𝑡 evolves according to • 𝑧𝑡 = 𝜌 𝑧 𝑧𝑡−1 + 𝑒 𝑧,𝑡 + 𝜉 𝑧,𝑡−𝑝, • where 0 < 𝜌 𝑧 < 1. • The error terms comprise: – an unanticipated shock 𝑒 𝑧,𝑡 – an anticipated shock 𝜉 𝑧,𝑡−𝑝 that is observed 𝑝 quarters in advance. 23
  24. 24. Stochastic Process: Boom-Bust Cycles • In period 𝑡 the households receive a news about future house prices 𝜉 𝑧,𝑡 that suggests that there will be a positive shock to future house prices/housing market at 𝑡 + 𝑝. • When period 𝑡 + 𝑝 occurs, the news is revealed to be incorrect. • The positive shock to the housing market does not take place: • 𝑒 𝑧,𝑡+𝑝 = −𝜉 𝑧,𝑡. • The high value of 𝑧𝑡+𝑝 does not materialize. • This unfulfilled news comes as a surprise to the households. 24
  25. 25. Solving Model • We solve the model by log-linearizing the non-lineaar system around the unique steady state. • Model value parameters originate from the study of Gerali et al. (2010). • Motivated by Soo (2018), an anticipated shock 𝜉 𝑧,𝑡−𝑝 is observed 8 quarters in advance. • We estimate the persistence of house price shock to be 0.95. 25
  26. 26. Simulation: Boom-Bust Cycles • Can waves of optimism and pessimism about future house prices generate boom-bust cycles ? • In the following case, the central bank follows the standard Taylor rule. 26
  27. 27. Simulation: Boom-Bust Cycles 27
  28. 28. Simulation: Boom-Bust Cycles 28
  29. 29. Simulation: Boom-Bust Cycles 29
  30. 30. Main Findings • Boom-bust cycles in financial and economic activities follow house price patterns. • A rise in expected future house prices boosts the housing demand and house prices. • The rise in the value of housing collateral induces the households to borrow more as a result household debt surges. • The housing boom induces wholesale banks to expand their credit supply. • The decline in wholesale loan rate induces retail banks to reduce the retail rates. 30
  31. 31. Main Findings • When the households do not longer expect the house prices to go up in the future, they reverse their expectations. • The reversal of expectations triggers a collapse in house prices. • As the housing market plummets, banks cut their operation as a result this leads to a contraction of credit supply. • This effect has a negative impact on the real economic activity. 31
  32. 32. Housing Boom and Inflation Dynamics • Inflation declines during a house price boom period. • Over-indebtedness reduces the ability for financially constrained households to smooth consumption. • As a result, the households rise their labor supply to facilitate consumption-smoothing and payment of mortgage loans. • The increase in the labor supply creates downward pressure on the marginal cost and inflation. 32
  33. 33. Housing Boom and Inflation Dynamics • The central bank observes that there is downward pressure on inflation. • Hence, the central bank cuts the policy rate to boost inflation. • A reduction in the policy rate amplifies the housing boom. 33
  34. 34. Boom-Bust Cycles under Alternative Policy Rule • We compare boom-bust cycles under two different monetary policy rules. • 1) The standard monetary policy rule where the central bank reacts to inflation and output growth. • 2) The alternativeTaylor rule where the central bank reacts to household credit growth in addition to inflation and output growth. 34
  35. 35. Boom-Bust Cycles under Alternative Policy Rule 35 The standard monetary policy is represented by the line with squares. The alternative Taylor rule is represented by the solid line.
  36. 36. Boom-Bust Cycles under Alternative Policy Rule 36 The standard monetary policy is represented by the line with squares. The alternative Taylor rule is represented by the solid line.
  37. 37. Boom-Bust Cycles under Alternative Policy Rule • The alternative policy dampens booms-busts in housing demand and household debt. • This improves the ability of the households to smooth consumption over boom-bust cycles. • Monetary policy taking into account credit growth enhances the stability of the real economy and financial stability. 37
  38. 38. Household Welfare • Based on Schmitt-Grohe and Uribe (2004, 2007), we calculate welfare by using a second-order approximation. • We refer i as U or F: • 𝑊𝑒𝑙𝑓𝑖,𝑡 ≡ 𝐸t σ 𝑚=0 ∞ 𝛽 𝑈 𝑚 𝑙𝑛𝐶𝑖,𝑡+𝑚 + 𝜈ℎ 𝑙𝑛𝐻𝑖,𝑡+𝑚 − 𝑁 𝑖,𝑡+𝑚 𝜂+1 𝜂+1 • 𝑊𝑒𝑙𝑓𝑖,𝑡 = 𝑈 𝐶𝑖,𝑡, 𝐻𝑖,𝑡, 𝑁𝑖,𝑡 + 𝛽𝑖 𝑚 𝐸t 𝑊𝑒𝑙𝑓,𝑡+1 • A welfare gain can be intrepreted as households are willing to pay in consumption units for the implementation of an alternative policy. 38
  39. 39. Household Welfare 39 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Welfaregain% Credit growth cofficient in Taylor rule Total Household Welfare
  40. 40. Household Welfare • When the central bank reacts to credit growth, this policy dampens the excessive optimism in the housing market as well as household debt. • The reduction of household debt helps financially constrained households to smooth their consumption. 40
  41. 41. Household Welfare under Different Model Specifications • 1) Does the case for taking account of credit growth becomes more pronounced in an economy that has a strict regulatory banking capital requirement or a weak regulatory banking capital requirement ? • 2) Does the case for taking account of credit growth becomes stronger in an economy with flexible or fixed interest rates ? • 3) Does the case for taking account of credit growth becomes stronger in an economy with a highly household leverage? • 4) Full Monty case: low banking capital, flexible rates and household leverage 41
  42. 42. Bank Capital Requirements and Implications for Household Welfare 42 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Welfaregain% Credit growth cofficient in Taylor rule Total Household Welfare and Bank Capital Ratio Requirements benchmark bank capital ratio low bank capital ratio high bank capital ratio
  43. 43. Bank Capital Requirements and Implications for Household Welfare • A higher bank capital ratio requirement forces banks to reduce their leverage and thus credit supply. • Thus, monetary policy that takes into account household credit growth will not significantly improve household welfare when a strict regulatory banking capital requirement is already in place. 43
  44. 44. Flexible Mortgage Rates and Welfare 44 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Welfaregain% Credit growth cofficient in Taylor rule Total Household Welfare and Flexible rates flexible rates benchmark
  45. 45. Flexible Interest Rates and Welfare • The welfare gains are amplified with flexible interest rates. • The reasons are that: • Under the flexible interest rate environment, changes in the policy rate will have a big and immediate impact on the household loan rate. • This effect has a larger impact on household cash flows and ultimately household welfare relative to the case of the sticky interest rate environment. 45
  46. 46. Household Leverage and Welfare 46 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Welfaregain% Credit growth cofficient in Taylor rule Total Household Welfare under High Household Leverage a high LTV calibration benchmark
  47. 47. Full Monty Case 47 0 0.05 0.1 0.15 0.2 0.25 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Welfaregain% Credit growth cofficient in Taylor rule Total Household Welfare under Multiple Cases Multiple Cases benchmark
  48. 48. Conclusions • The role of expectations is matter for boom-bust cycles in house prices, business activities and financial activities. • Monetary policy that takes into account household credit growth reduces the magnitude of boom-bust cycles; thereby improving household welfare. • The case for monetary policy reacting to credit growth becomes stronger in an economy with a weak banking capital regulation, flexible interest rates or a highly indebted household sector. 48

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