## 6. The Impact of Property Taxes on House Price Volatility

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---

### Introduction and motivation
- Housing markets affect macroeconomic stability via aggregate demand and supply; housing wealth and housing-related expenses (mortgage payments, rents) are major components of private-sector net worth and household expenditure.
- Monetary policy and macroprudential regulation can dampen house price volatility but face limitations (monetary policy is blunt and unavailable in monetary unions; macroprudential measures can be invasive and circumvented).
- Property taxation has been used to curb house price fluctuations and bolster public revenues; evidence on its effect on house price volatility is limited.
- Objective: assess the relationship between property tax rates and house price volatility and test for causality using U.S. state and MSA data for 2005-2014.

### Theoretical framework
- Main tax and subsidy instruments affecting housing: mortgage rate deductibility, tax on imputed rents, capital gains tax, recurrent taxes on land and buildings, wealth tax, inheritance tax, VAT, stamp duties; grouped into (i) transaction taxes, (ii) recurrent property taxes, (iii) mortgage interest deductibility.
- Equilibrium condition (marginal user cost equals marginal rental value) and supply relationship imply tax treatment affects the user cost of housing and demand elasticity.
- Key theoretical prediction (Van Den Noord, 2005): lower property tax rates amplify the accelerator/overshooting mechanism after demand shocks and lead to higher short-run house price volatility; in the long run property tax rates affect amplification of shocks rather than induce volatility per se.
- Under assumptions (E[dP_t/dt]=a*[P_{t-1}-P_{t-2}], τ_c=0), the amplitude of oscillations increases with lower τ_p via a/[r·(1−τ_m)+τ_p+δ].

### Data and descriptive statistics
- Sample period: 2005-2014.
- Cross-sectional units: 51 U.S. states and 77 MSAs (two sets of regressions).
- Panel observations: 510 state-year; 770 MSA-year.
- House price data: Federal Housing Finance Agency; annual house prices = averages of four quarters.
- Effective property tax rate: ratio of median annual property tax payment to median property value for owner-occupied housing units from the American Community Survey; survey data available from 2005 onward.
- Volatility estimation: 5-year backward moving window using (i) annual growth rates and (ii) percentage deviations from HP-filtered value (HP smoothing parameter = 100 for annual series).
- Key descriptive figures:
  - Average effective property tax rate in sample: 1 percent.
  - Standard deviation of effective property tax rate: 0.5 percent.
  - Average volatility of house prices: 5-6 percent.
  - Median effective property tax rate increased from 0.8 percent pre-crisis to 1 percent in sample period.
  - Some states approach 2.5 percent property tax rate; 25-75 interquartile range of tax rates up to 1 percent.
  - Median state standard deviation of house price volatility rose from below 5 percent to above 5 percent during the crisis; cross-state variation exceeded 15 percent in some years.
  - Scatterplots indicate negative slopes between house price volatility and property tax rates and some heteroscedasticity (larger dispersion of volatility at low tax rates).

- Selected descriptive statistics (Table 2):
  - Hose price growth rate (%) — Obs. 510; Mean -1.30; Median -0.95; St. Dev. 7.42; 10th percentile -9.67; 90th percentile 6.94; 25th percentile -5.57; 75th percentile 2.98.
  - Effective property tax rate (%) — Obs. 510; Mean 1.04; Median 0.91; St. Dev. 0.48; 10th percentile 0.52; 90th percentile 1.75; 25th percentile 0.65; 75th percentile 1.35.
  - House price volatility (%, growth-based) — Obs. 510; Mean 5.13; Median 4.18; St. Dev. 3.90; 10th percentile 1.55; 90th percentile 10.28; 25th percentile 2.47; 75th percentile 6.28.
  - House price volatility (%, HP-based) — Obs. 510; Mean 6.39; Median 4.92; St. Dev. 4.93; 10th percentile 1.96; 90th percentile 12.85; 25th percentile 3.16; 75th percentile 8.15.
  - Supply restrictions index (Saiz, 2010) — Obs. 770; Mean 27.94; Median 23.29; St. Dev. 22.32; 10th percentile 3.12; 90th percentile 64.01; 25th percentile 9.28; 75th percentile 40.50.
  - Regulatory restrictions index (Wharton) — Obs. 770; Mean 0.10; Median 0.03; St. Dev. 0.69; 10th percentile -0.81; 90th percentile 0.94; 25th percentile -0.38; 75th percentile 0.61.

### Empirical analysis — methods and findings
- Baseline panel specification:
  - VOL_it = α + β TAX_it + γ′X_it + u_i + ω_t + ε_it
  - Prediction: β < 0 (higher property tax rates associated with lower volatility).

- Baseline OLS results (Table 3):
  - Property tax rate coefficients (selected columns):
    - (I) -3.542*** [0.674]
    - (II) -2.784*** [0.884]
    - (III) -3.210*** [0.789]
    - (IV) -2.802*** [0.893]
    - (V) -1.283** [0.610]
    - (VI) -1.111 [0.750]
    - (VII) -1.347** [0.626]
    - (VIII) -1.177 [0.746]
  - Lagged dependent variable coefficients range: 0.579*** to 0.474***.
  - Economic significance (response of volatility to 1 s.d. (0.48%) increase in property tax rates):
    - (I) -1.7; (II) -1.3; (III) -1.5; (IV) -1.3; (V) -0.6; (VI) -0.5; (VII) -0.6; (VIII) -0.6.
  - # observations = 459; # states = 51; R2 ranges: 0.215 to 0.598 across columns.

- Endogeneity concerns and instruments:
  - Potential reverse causality: states facing high volatility could raise property tax rates, biasing OLS toward zero.
  - Instruments used: average property tax rates of neighboring states; panel GMM using lagged values; system GMM (Blundell and Bond, 1998).

- Instrumental Variable regressions (Table 4):
  - Property tax rate coefficients (selected columns):
    - (I) -5.260*** [0.740]
    - (II) -11.081*** [3.570]
    - (III) -4.855*** [0.831]
    - (IV) -11.598*** [3.806]
    - (V) -2.113*** [0.622]
    - (VI) -6.205** [3.003]
    - (VII) -2.369*** [0.700]
    - (VIII) -6.677** [3.136]
  - Lagged dependent variable coefficients ~0.599*** to 0.469***.
  - Economic significance: (I) -2.5; (II) -5.3; (III) -2.3; (IV) -5.5; (V) -1.0; (VI) -3.0; (VII) -1.1; (VIII) -3.2.
  - # observations = 441; # states = 49; first-stage F-statistics exceed 10 in most cases.

- Dynamic panel GMM results (Table 5) — system GMM (Arellano-Bover):
  - Property tax rate coefficients (selected columns):
    - (I) -8.410*** [0.318]
    - (II) -3.083*** [0.611]
    - (III) -3.121*** [0.136]
    - (IV) -1.538*** [0.205]
    - (V) -2.704*** [0.083]
    - (VI) -2.874*** [0.229]
    - (VII) -2.136*** [0.087]
    - (VIII) -1.162*** [0.236]
  - Lagged dependent variable coefficients range: 0.825*** to 0.698***.
  - Economic significance: (I) -4.0; (II) -1.5; (III) -1.5; (IV) -0.7; (V) -1.3; (VI) -1.4; (VII) -1.0; (VIII) -0.6.
  - Model diagnostics:
    - Sargan test (p-value) reported: 0.1293, 0.1632, 1.0000, 1.0000, 0.1021, 0.2834, 1.0000, 1.0000.
    - AR(2) test (p-value) reported: 0.0660, 0.0807, 0.0789, 0.0804, 0.0533, 0.0572, 0.0500, 0.0543.
  - # observations = 459; # states = 51.

- Difference-in-difference approach (MSA-level; 77 MSAs in 29 states):
  - Specification: VOL_mt = α + β TAX_it + λ(TAX_it * SUPPLY_m) + γ′X_it + u_m + ω_t + ε_mt
  - SUPPLY indicators:
    - Geographical: share of undevelopable land area as percent total (Saiz, 2010).
    - Regulatory: Wharton Regulation Index (Gyourko et al., 2008).
  - Hypothesis: λ < 0 (higher state property tax reduces volatility more in MSAs with more rigid supply).
  - Results — Geographical supply restrictions (Table 6):
    - Interaction coefficients (Property tax rate * Geographical supply restrictions index):
      - (I) -0.057** [0.027]
      - (II) -0.074** [0.031]
      - (III) -0.076*** [0.027]
      - (IV) -0.068** [0.029]
      - (V) -0.065** [0.027]
      - (VI) -0.078** [0.030]
      - (VII) -0.080** [0.029]
      - (VIII) -0.093*** [0.031]
    - Economic significance (25-75 interquartile increases): -1.1 to -2.0 across columns.
    - # observations = 693; # MSAs = 77; R2 ranges: 0.204 to 0.565.
  - Results — Regulatory restrictions (Table 7):
    - Property tax rate coefficients (selected):
      - (I) -3.524*** [0.650]
      - (II) -1.932* [1.011]
      - (III) -2.339*** [0.538]
      - (IV) -1.596 [1.231]
    - Interaction coefficients (Property tax rate * Regulatory restrictions index):
      - (I) -3.747*** [0.582]
      - (II) -4.164*** [0.764]
      - (III) -4.602*** [0.733]
      - (IV) -4.340*** [0.846]
    - Economic significance: -2.6 to -3.2 across columns.
    - # observations = 693; # MSAs = 77; R2 ranges: 0.211 to 0.578.

### Key quantitative summary of empirical magnitudes
- Sample period: 2005-2014.
- Cross-sections: 51 states; 77 MSAs.
- Panel observations: 510 state-year; 770 MSA-year.
- Average effective property tax rate: 1 percent; standard deviation: 0.5 percent.
- Average house price volatility: 5-6 percent.
- Estimated causal effects of increasing property tax rates by 0.5 percent (one standard deviation in total sample):
  - Overall reported range: 0.5-5.5 percent decline in house price volatility depending on specification and volatility measure.
  - Baseline (1 sd = 0.48 percent): 1.3-1.7 percent (growth-rate measure); 0.5-0.6 percent (detrended).
  - IV: 2.3-5.5 percent (growth-rate); 1.0-3.2 percent (detrended).
  - GMM: 0.7-4.0 percent (growth-rate); 0.6-1.4 percent (detrended).
  - Difference-in-difference: 1.1-2.0 percent (geographical supply restriction); 2.6-3.2 percent (regulatory supply restriction).

### Conclusions and policy implications
- Empirical evidence supports the theoretical prediction that higher property tax rates reduce house price volatility; the relationship is likely causal based on IV and GMM evidence.
- Property taxation can usefully complement monetary and macroprudential tools to dampen house price volatility.
- Transaction taxes (stamp duties) have limitations: they thin markets, discourage efficient transactions, are hard to vary frequently (legislative lags), and can adversely affect labor mobility.
- Policy reforms suggested:
  - Target recurrent property taxation and mortgage interest deductibility to ensure tax neutrality between housing and other capital and to reduce incentives for debt-financed home ownership.
  - Taxation of imputed rents is conceptually attractive but faces measurement difficulties.
  - Alternatives: increase recurrent and transaction property taxes, or (i) disallow mortgage interest deductibility and (ii) levy a lower recurrent property tax so housing remains taxed without favoring debt—both reduce incentives for debt-favored housing finance.

*Source: IMF Working Paper — "6. The Impact of Property Taxes on House Price Volatility" (chapter/section from the provided PDF content).*

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

### _wp16216 - References .............................................................................................................

### Tables

- 1. Variables and Data Sources .................................................................................................14
- 2.  Descriptive Statistics ...........................................................................................................15
- 3. Baseline Regressions ...........................................................................................................16
- 4. Instrumental Variable Regressions ......................................................................................17
- 5. Dynamic Panel GMM Regressions ......................................................................................18
- 6. Difference-in-Difference Regressions: Geographical Supply Restrictions Index ...............19
- 7. Difference-in-Difference Regressions: Regulatory Restrictions Index ...............................20

### Figures

- 1. Graphical Illustration: Demand Shock and House Prices ....................................................21
- 2. The Impact of Exogenous Demand Shock on House Prices................................................22
- 3. House Prices and Property Tax Rates ..................................................................................23
- 4. Volatility of House Prices ....................................................................................................24
- 5. House Price Volatility and Property Tax .............................................................................25

*_wp16216 - References ............................................................................................................._*

### 6. The Impact of Property Taxes on House Price Volatility ....................................................26

### 6. The Impact of Property Taxes on House Price Volatility

### Introduction and motivation
- Housing markets affect macroeconomic stability via aggregate demand and supply; housing wealth and housing-related expenses (mortgage payments, rents) are major components of private-sector net worth and household expenditure.
- Monetary policy and macroprudential regulation are common tools to dampen house price volatility but have drawbacks (monetary policy is blunt and unavailable in monetary unions; macroprudential measures can be invasive and circumvented).
- Property taxation has been used by some countries to curb house price fluctuations and bolster public revenues; evidence on its effect on house price volatility is limited.
- Objective of the analysis: provide a detailed assessment of the relationship between property tax rates and house price volatility and test for causality using U.S. state and MSA data for 2005-2014.

### Theoretical framework
- Main property tax and subsidy instruments include mortgage rate deductibility, tax on imputed rents, capital gains tax, recurrent taxes on land and buildings, wealth tax, inheritance tax, VAT, and stamp duties; grouped into: (i) transaction taxes, (ii) recurrent property taxes, (iii) mortgage interest deductibility.
- Equilibrium condition (marginal user cost equals marginal rental value) and supply relationship imply that tax treatment affects the user cost of housing and thus demand elasticity.
- Key theoretical prediction (Van Den Noord, 2005): lower property tax rates amplify the accelerator/overshooting mechanism after demand shocks and lead to higher short-run house price volatility; in the long run property tax rates affect the amplification of shocks rather than induce volatility per se.
- Under assumptions (E[dP_t/dt]=a*[P_{t-1}-P_{t-2}], τ_c=0), the amplitude of oscillations increases with lower τ_p (property tax rate) via a/[r·(1−τ_m)+τ_p+δ].

### Data and descriptive statistics
- Sample period: 2005-2014.
- Cross-sectional units: 51 U.S. states and 77 MSAs used in two sets of regressions.
- Panel observations: 510 state-year and 770 MSA-year observations.
- House price data: Federal Housing Finance Agency; annual house prices = averages of four quarters.
- Macroeconomic variables: Bureau of Economic Analysis (nominal and real GDP, GDP deflator, per capita GDP, population).
- Effective property tax rate: ratio of median annual property tax payment to median property value for owner-occupied housing units from the American Community Survey (accounts for within-state county differences and exemptions/adjustments); survey data available from 2005 onward.
- Volatility estimation: 5-year backward moving window using (i) annual growth rates and (ii) percentage deviations from HP-filtered value (HP smoothing parameter = 100 for annual series).
- Key descriptive figures:
  - Average effective property tax rate in sample: 1 percent.
  - Standard deviation of effective property tax rate: 0.5 percent.
  - Average volatility of house prices: 5-6 percent (depending on measure).
  - Median effective property tax rate increased from 0.8 percent pre-crisis to 1 percent in sample period.
  - Substantial cross-state variation: some states approach 2.5 percent property tax rate; 25-75 interquartile range of tax rates up to 1 percent.
  - Median state standard deviation of house price volatility rose from below 5 percent to above 5 percent during the crisis, with cross-state variation exceeding 15 percent in some years.
  - Scatterplots indicate negative slopes between house price volatility and property tax rates and some heteroscedasticity (larger dispersion of volatility at low tax rates).

### Empirical analysis — methods and findings
- Baseline panel specification:
  - VOL_it = α + β TAX_it + γ′X_it + u_i + ω_t + ε_it
  - Prediction: β < 0 (higher property tax rates associated with lower volatility).
- Baseline results (Table 3):
  - Slope on property tax variable negative in all specifications and in most cases statistically significant.
  - Economic significance: a 1 standard deviation increase in property tax rates (0.48 percent) leads to:
    - 1.3-1.7 percent reduction in volatility based on growth-rate measure.
    - 0.5-0.6 percent reduction in volatility based on detrended measure.
- Endogeneity concerns and instrumental variables:
  - Potential reverse causality: states facing high volatility could raise property tax rates, biasing OLS toward zero (downward bias).
  - Instruments:
    - Average property tax rates of neighboring states (motivated by strategic interaction in tax setting).
    - Panel GMM using lagged values as instruments; dynamic panel system GMM following Blundell and Bond (1998).
- Instrumental variable results (Table 4):
  - First-stage F-statistics exceed 10 in most cases.
  - Coefficient on property tax remains negative and significant.
  - A 1 standard deviation increase in property tax rates (0.48 percent) leads to:
    - 2.3-5.5 percent reduction in volatility (growth-rate measure).
    - 1.0-3.2 percent reduction in volatility (detrended measure).
- Dynamic panel GMM results (Table 5):
  - Negative coefficient on tax persists.
  - A 1 standard deviation increase in property tax rates (0.48 percent) leads to:
    - 0.7-4.0 percent reduction in volatility (growth-rate measure).
    - 0.6-1.4 percent reduction in volatility (detrended measure).
  - Model diagnostics: Arellano-Bond test for no second-order autocorrelation and Sargan test reported and confirm validity of model specification.
- Difference-in-difference approach (MSA-level, 77 MSAs in 29 states):
  - Specification: VOL_mt = α + β TAX_it + λ(TAX_it * SUPPLY_m) + γ′X_it + u_m + ω_t + ε_mt
  - SUPPLY indicators:
    - Geographical: share of undevelopable land area as percent total (Saiz, 2010).
    - Regulatory: Wharton Regulation Index (Gyourko et al., 2008).
  - Hypothesis: λ < 0 (higher state property tax reduces volatility more in MSAs with more rigid supply).
  - Results (Tables 6-7):
    - Interaction coefficient negative and significant for both supply indicators.
    - Economic significance (25-75 interquartile ranges):
      - Geographical supply restriction indicator: difference-in-difference effect of 1.1-2.0 percent.
      - Regulatory supply restriction indicator: difference-in-difference effect of 2.6-3.2 percent.

### Key quantitative summary of empirical magnitudes
- Sample period: 2005-2014.
- Cross-sections: 51 states; 77 MSAs.
- Panel observations: 510 state-year; 770 MSA-year.
- Average effective property tax rate: 1 percent; standard deviation: 0.5 percent.
- Average house price volatility: 5-6 percent.
- Estimated causal effects of increasing property tax rates by 0.5 percent (one standard deviation in total sample):
  - Overall reported range: 0.5-5.5 percent decline in house price volatility depending on specification and volatility measure.
  - Baseline (1 sd = 0.48 percent): 1.3-1.7 percent (growth-rate measure); 0.5-0.6 percent (detrended).
  - IV: 2.3-5.5 percent (growth-rate); 1.0-3.2 percent (detrended).
  - GMM: 0.7-4.0 percent (growth-rate); 0.6-1.4 percent (detrended).
  - Difference-in-difference: 1.1-2.0 percent (geographical supply restriction); 2.6-3.2 percent (regulatory supply restriction).

### Conclusions and policy implications
- Empirical evidence supports the theoretical prediction that higher property tax rates reduce house price volatility; the relationship is likely causal based on IV and GMM evidence.
- Property taxation can usefully complement monetary and macroprudential tools to dampen house price volatility.
- Transaction taxes (stamp duties) have limitations: they thin markets, discourage efficient transactions, are hard to vary frequently (legislative lags), and can adversely affect labor mobility.
- Policy reforms suggested:
  - Target recurrent property taxation and mortgage interest deductibility to ensure tax neutrality between housing and other capital and to reduce incentives for debt-financed home ownership.
  - Taxation of imputed rents is conceptually attractive but faces measurement difficulties.
  - Alternatives: increase recurrent and transaction property taxes, or (i) disallow mortgage interest deductibility and (ii) levy a lower recurrent property tax so housing remains taxed without favoring debt—both reduce incentives for debt-favored housing finance.

*Source: IMF Working Paper — "6. The Impact of Property Taxes on House Price Volatility" (chapter/section from the provided PDF content).*

### REFERENCES

### _wp16216 - REFERENCES

### Key literature cited
- Andrews, D., 2010, “Real House Prices in OECD Countries: The Role of Demand Shocks and Structural and Policy Factors,” OECD Economics Department Working Papers, No. 831 (Paris: OECD).
- Andrews, D., A. Caldera Sanchez, and A. Johansson, 2011, "Housing markets and structural policies in OECD countries", OECD Economics Department Working papers, No. 836 (Paris: OECD).
- Aregger, N., M. Brown and E. Ross, 2013, “Transaction Taxes, Capital Gains Taxes, and House Prices,” Swiss National Bank Working Paper No. 2 (Zurich: Swiss National Bank).
- Brueckner, J., 2003, “Strategic Interaction among Governments: An Overview of Empirical Studies,” International Regional Science Review, 26: pp. 175-188.
- Brueckner, J. and L. Saavedra, 2001, “Do Local Governments Engage in Strategic Property-Tax Competition,” National Tax Journal, 54 (2): pp. 203-30.
- Cerutti, E., S. Claessens, and L. Laeven, 2016, “The Use and Effectiveness of Macroprudential Policies: New Evidence,” Journal of Financial Stability (forthcoming, also IMF WP 15/61).
- Claessens, S., 2015, “An Overview of Macroprudential Policy Tools,” IMF Working Paper WP/14/214 (Washington, D.C.: International Monetary Fund).
- Crowe, C., G. Dell’Ariccia, D. Igan, and P. Rabanal, 2013, “How to Deal with Real Estate Booms: Lessons from Country Experiences,” Journal of Financial Stability, 9: pp. 300-319.
- Darbar, S., and X. Wu, 2015, “Experiences with Macroeconomic Policy – Five Case Studies,” IMF Working Paper WP/15/123 (Washington, D.C.: International Monetary Fund).
- Gattini, L., and I. Ganoulis, 2012, “House Price Responsiveness of Housing Investments Across Major European Economies,” ECB Working Paper No. 1461 (Frankfurt: European Central Bank).
- Gyourko, J., A. Saiz, and A. Summers, 2008, “A New Measure of the Local Regulatory Environment for Housing Markets: The Wharton Residential Land Use Regulatory Index,” Urban Studies, 45: pp. 693–729.
- He, D. 2014. “The Effects of Macroprudential Policies on Housing Market Risks: Evidence from Hong Kong.” Banque de France, Financial Stability Review No. 18 (April).
- Hilber, C. and W. Vermeulen, 2016, “The Impact of Supply Constraints on House Prices in England,” The Economic Journal (forthcoming).
- IMF. 2013. “Fiscal Monitor: Taxing Times”, October.
- Lim, C., F. Columba, A. Costa, P. Kongsamut, A. Otani, M. Saiyid, T. Wezel, and X. Wu. 2011. “Macroprudential Policy: What Instruments and How to Use Them?” IMF Working Paper WP/11/238 (Washington, D.C.: International Monetary Fund).
- Linden, A. J. and C. Gayer. 2012. “Possible Reforms of Real Estate Taxation: Criteria for Successful Policies.” European Economy, Occasional Paper No 119.
- Norregaard, J., 2015, “Taxing Immovable Property: Revenue Potential and Implementation Challenges,” IMF Working Paper WP/13/129 (Washington, D.C.: International Monetary Fund).
- OECD, 2011, “Housing and the Economy: Policies for Renovation,” Chapter 4 in Economic Policy Reforms 2011: Going for Growth (Paris: OECD).
- Poterba, J., 1992, “Taxation and Housing: Old Questions, New Answers,” American Economic Review, 82 (2): 237-242.
- Poterba, J. and T. Sinai, 2008, “Tax Expenditures for Owner Occupied Housing: Deduction for Property Taxes and Mortgage Interest and the Exclusion of Imputed Rental Income,” American Economic Review P&P, 98 (2): 84-89.
- Keen, M., A. Klemm, and V. Perry. 2010. “Tax and the Crisis.” Fiscal Studies, 31 (1), pp. 43-79.
- Kuttner, K. and I. Shim, 2013, “Can Non-Interest Rate Policies Stabilize Housing Markets? Evidence from a Panel of 57 Economies,” BIS Working Papers No. 433 (Basel: BIS).
- Rajan, R., and L. Zingales, 1998, “Financial Dependence and Growth,” American Economic Review, 88 (3): pp. 559-586.
- Saiz, A., 2010, “The Geographic Determinants of Housing Supply,” The Quarterly Journal of Economics, 125 (3): pp. 1253-96.
- Van Den Noord, P., 2005, “Tax Incentives and House Price Volatility in the Euro Area: Theory and Evidence,” Economie Internationale, 101: pp. 29-45.

### Data and variables (Table 1)
- House prices: Weighted repeated-sales indices of single family house prices (seasonally adjusted and non-adjusted); Frequency: Quarterly; Geography: State, MSA; Source: Federal Housing Finance Agency.
- Property tax rate: Effective rate = 100*Property taxes paid/Assessed value of the house (state median); Frequency: Annual; Geography: State, MSA; Source: Census bureau.
- Nominal GDP: Value added of all industries (current prices); Frequency: Annual; Geography: State; Source: Bureau of Economic Analysis.
- Real GDP: Value added of all industries (constant prices); Frequency: Annual; Geography: State; Source: Bureau of Economic Analysis.
- GDP deflator: Ratio of nominal and real GDP; Frequency: Annual; Geography: State; Source: Bureau of Economic Analysis.
- Real per capita GDP: Value added of all industries (constant prices)/Population; Frequency: Annual; Geography: State; Source: Bureau of Economic Analysis.
- Population: Number of state residents; Frequency: Annual; Geography: State; Source: Bureau of Economic Analysis.
- Geographical restrictions index: Share of undevelopable geographical area; Frequency: Annual; Geography: MSA; Source: Saiz (2010).
- Regulatory restrictions index: Index measuring zoning regulations or project approval practices that constrain new residential real estate development; Frequency: Annual; Geography: MSA; Source: Gyorko et al. (2008).

### Descriptive statistics (Table 2)
- Obs. = 510 for most state-level variables; Supply restrictions index and Regulatory restrictions index have Obs. = 770.
- Hose price growth rate (%) — Obs. 510; Mean -1.30; Median -0.95; St. Dev. 7.42; 10th percentile -9.67; 90th percentile 6.94; 25th percentile -5.57; 75th percentile 2.98.
- Effective property tax rate (%) — Obs. 510; Mean 1.04; Median 0.91; St. Dev. 0.48; 10th percentile 0.52; 90th percentile 1.75; 25th percentile 0.65; 75th percentile 1.35.
- House price volatility (%, growth-based) — Obs. 510; Mean 5.13; Median 4.18; St. Dev. 3.90; 10th percentile 1.55; 90th percentile 10.28; 25th percentile 2.47; 75th percentile 6.28.
- House price volatility (%, HP-based) — Obs. 510; Mean 6.39; Median 4.92; St. Dev. 4.93; 10th percentile 1.96; 90th percentile 12.85; 25th percentile 3.16; 75th percentile 8.15.
- Real GDP per capita growth (%) — Obs. 510; Mean 1.27; Median 1.40; St. Dev. 2.66; 10th percentile -1.69; 90th percentile 4.16; 25th percentile 0.16; 75th percentile 2.54.
- GDP deflator growth (%) — Obs. 510; Mean 2.23; Median 2.10; St. Dev. 1.88; 10th percentile 1.09; 90th percentile 3.50; 25th percentile 1.67; 75th percentile 2.84.
- Population growth (%) — Obs. 510; Mean 0.84; Median 0.75; St. Dev. 0.75; 10th percentile 0.11; 90th percentile 1.73; 25th percentile 0.35; 75th percentile 1.24.
- Supply restrictions index (Saiz, 2010) — Obs. 770; Mean 27.94; Median 23.29; St. Dev. 22.32; 10th percentile 3.12; 90th percentile 64.01; 25th percentile 9.28; 75th percentile 40.50.
- Regulatory restrictions index (Wharton) — Obs. 770; Mean 0.10; Median 0.03; St. Dev. 0.69; 10th percentile -0.81; 90th percentile 0.94; 25th percentile -0.38; 75th percentile 0.61.

### Main empirical findings — baseline and robustness (Tables 3–5)
- Baseline OLS regressions (Table 3) — dependent variable: house price volatility:
  - Property tax rate coefficients:
    - (I) -3.542*** [0.674]
    - (II) -2.784*** [0.884]
    - (III) -3.210*** [0.789]
    - (IV) -2.802*** [0.893]
    - (V) -1.283** [0.610]
    - (VI) -1.111 [0.750]
    - (VII) -1.347** [0.626]
    - (VIII) -1.177 [0.746]
  - Lagged dependent variable coefficients range: 0.579*** to 0.474*** with robust standard errors as reported.
  - Economic significance (response of volatility to 1 s.d. (0.48%) increase in property tax rates):
    - (I) -1.7; (II) -1.3; (III) -1.5; (IV) -1.3; (V) -0.6; (VI) -0.5; (VII) -0.6; (VIII) -0.6.
  - # observations = 459; # states = 51; R2 ranges: 0.215 to 0.598 across columns.

- Instrumental Variable regressions (Table 4) — instrument: average property tax rate of neighboring states:
  - Property tax rate coefficients:
    - (I) -5.260*** [0.740]
    - (II) -11.081*** [3.570]
    - (III) -4.855*** [0.831]
    - (IV) -11.598*** [3.806]
    - (V) -2.113*** [0.622]
    - (VI) -6.205** [3.003]
    - (VII) -2.369*** [0.700]
    - (VIII) -6.677** [3.136]
  - Lagged dependent variable coefficients ~0.599*** to 0.469***.
  - Economic significance: (I) -2.5; (II) -5.3; (III) -2.3; (IV) -5.5; (V) -1.0; (VI) -3.0; (VII) -1.1; (VIII) -3.2.
  - # observations = 441; # states = 49; R2 ranges: 0.206 to 0.467.

- Dynamic panel GMM regressions (Table 5) — system GMM (Arellano-Bover):
  - Property tax rate coefficients:
    - (I) -8.410*** [0.318]
    - (II) -3.083*** [0.611]
    - (III) -3.121*** [0.136]
    - (IV) -1.538*** [0.205]
    - (V) -2.704*** [0.083]
    - (VI) -2.874*** [0.229]
    - (VII) -2.136*** [0.087]
    - (VIII) -1.162*** [0.236]
  - Lagged dependent variable coefficients range: 0.825*** to 0.698***.
  - Economic significance: (I) -4.0; (II) -1.5; (III) -1.5; (IV) -0.7; (V) -1.3; (VI) -1.4; (VII) -1.0; (VIII) -0.6.
  - # observations = 459; # states = 51.
  - Sargan test (p-value) reported: 0.1293, 0.1632, 1.0000, 1.0000, 0.1021, 0.2834, 1.0000, 1.0000.
  - AR(2) test (p-value) reported: 0.0660, 0.0807, 0.0789, 0.0804, 0.0533, 0.0572, 0.0500, 0.0543.

### Heterogeneity: Supply and regulatory constraints (Tables 6–7)
- Difference-in-difference: Geographical supply restrictions index (Table 6)
  - Interaction: Property tax rate * Geographical supply restrictions index coefficients:
    - (I) -0.057** [0.027]
    - (II) -0.074** [0.031]
    - (III) -0.076*** [0.027]
    - (IV) -0.068** [0.029]
    - (V) -0.065** [0.027]
    - (VI) -0.078** [0.030]
    - (VII) -0.080** [0.029]
    - (VIII) -0.093*** [0.031]
  - Economic significance (25-75 interquartile increases): reported as -1.1 to -2.0 across columns.
  - # observations = 693; # MSAs = 77; R2 ranges: 0.204 to 0.565.

- Difference-in-difference: Regulatory restrictions index (Table 7)
  - Property tax rate coefficients:
    - (I) -3.524*** [0.650]
    - (II) -1.932* [1.011]
    - (III) -2.339*** [0.538]
    - (IV) -1.596 [1.231]
    - (V) -1.516** [0.681]
    - (VI) -0.229 [1.301]
    - (VII) -1.493** [0.686]
    - (VIII) -0.484 [1.132]
  - Interaction: Property tax rate * Regulatory restrictions index coefficients:
    - (I) -3.747*** [0.582]
    - (II) -4.164*** [0.764]
    - (III) -4.602*** [0.733]
    - (IV) -4.340*** [0.846]
    - (V) -3.710*** [0.438]
    - (VI) -3.800*** [0.581]
    - (VII) -4.087*** [0.844]
    - (VIII) -4.189*** [0.787]
  - Economic significance: -2.6 to -3.2 across columns.
  - # observations = 693; # MSAs = 77; R2 ranges: 0.211 to 0.578.

### Figures and illustrative exercises
- Figure 1: Graphical illustration of demand shock and house prices showing conceptual panels for less vs. more generous tax treatment of housing.
- Figure 2: Simulated impact of an exogenous demand shock on house price levels and house price changes over time for property tax = 1%, 2%, 3%.
- Figure 3: Box-plot style depiction of variation ranges across 51 U.S. states (2005–2014) for Property taxes (annual effective rates) and House prices (real growth rates).
- Figure 4: Volatility of house prices (5-year backward moving window) for real growth rates and deviation from HP-filtered values (2005–2014).
- Figure 5: Scatterplots of House price volatility versus Property tax rate across 51 U.S. states for 2005–14.
- Figure 6: Distribution exercise showing:
  - Original sample: mean = -1.3%, st. dev. = 7.4.
  - With higher property tax rate: mean = -1.3%, st. dev. = 4.4.
  - Note on calculation: new standard deviation computed as 7.4-0.48*3, where 3 is the average economic significance of the property tax rate impact across all regressions.

*Source: _wp16216 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp16216.pdf_