Housing Prices and Economic Freedom in U.S. Metropolitan Areas Tim Allen Florida Gulf Coast University Dean Stansel Southern Methodist University 1

Economic Freedom & Housing Demand Areas with greater economic freedom should be more attractive to residents & businesses. This should be reflected in higher demand for housing. All else equal, this should be reflected in HIGHER prices for housing. 2

Economic Freedom & Housing Supply The cost of producing housing in areas with greater economic freedom should be lower. This should be reflected in higher supply of housing. All else equal, this should be reflected in LOWER prices for housing. 3

Previous Literature Campbell et al. (2008) State-level data Found that EF was positively associated with housing prices Only use one control variable for supply 4

Previous Literature Cebula & Van Rensburg (2015) State-level data Uses price of new houses Found that labor market freedom (LMF) was negatively associated with housing prices Assumes that LMF is not associated with housing demand 5

An Economic Freedom Index of U.S. Metropolitan Areas Dean Stansel Journal of Regional Analysis and Policy, 43, 1 (2013):

Area 1: Government Spending 1A: General Consumption Expenditures by Government as a Percentage of Income Total Direct Expenditures MINUS: Capital Outlays; Transfers to Persons, Businesses, and Other Governments; Interest on Public Debt; and Expenditures on State Liquor Stores & Utilities 1B: Transfers & Subsidies as a Percentage of Income Includes transfers to persons & businesses such as: Welfare Payments, Grants, Agricultural Assistance, Food-stamp Payments, and Housing Assistance 1C: Insurance & Retirement Payments as a Percentage of Income Includes payments for: Employment Insurance, Workers Compensation, and various pension plans 7

Area 2: Taxes 2A: Income & Payroll Tax Revenue as a Percentage of Income Personal & Corporate Income Taxes & Payroll Taxes 2B: Top Marginal Income Tax Rate and the Income Threshold at Which It Applies 2C: Property Taxes & Other Taxes as a Percentage of Income Includes all taxes other than income, payroll, & sales taxes. 2D: Sales Tax Revenue as a Percentage of Income All Sales & Gross Receipts Taxes (including excise taxes on specific goods). 8

Area 3: Labor Market Freedom 3Ai: Minimum Wage Annual Income as a Percentage of Per Capita GDP 3Aii: Government Employment as a Percentage of Total Employment 3Aiii: Union Density 9

Calculations Each variable given value b/w 0 and 10 score = ((Max-Obs)/(Max- Min))*10 Each variable equally weighted within each area Each area equally weighted Overall score is average of 3 area scores Data is for 2002 for 384 metros 10

Housing Price Data Federal Housing Finance Agency “all- transactions” Housing Price Index (HPI) uses repeat sales prices and appraisal data on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January, 1975 We use average quarterly changes in the HPI for MSAs over the period (also & ) Only have data for 248 of the 384 metros. 11

Model MeanChange i = f [Economic Freedom i, ln(Per Capita Income i ), ln(Median Age of Population i ), ln(Population Density i ), ln(Housing Starts i )] Basic OLS 12

Results, Table 2: Full period ( housing price changes). regress mean0005 efi lnpci lnmedage00 lnpopdens lnhstarts Source | SS df MS Number of obs = F( 5, 242) = 7.13 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = mean0005 | Coef. Std. Err. t P>|t| [95% Conf. Interval] efi | lnpci | lnmedage00 | lnpopdens | lnhstarts | _cons |

Results, Table 3: Sub-period 1 ( housing price changes). regress mean0002 efi lnpci lnmedage00 lnpopdens lnhstarts Source | SS df MS Number of obs = F( 5, 242) = 8.21 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = mean0002 | Coef. Std. Err. t P>|t| [95% Conf. Interval] efi | lnpci | lnmedage00 | lnpopdens | lnhstarts | _cons |

Results, Table 4: Sub-period 1 ( housing price changes). regress mean0305 efi lnpci lnmedage00 lnpopdens lnhstarts Source | SS df MS Number of obs = F( 5, 242) = 6.38 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = mean0305 | Coef. Std. Err. t P>|t| [95% Conf. Interval] efi | lnpci | lnmedage00 | lnpopdens | lnhstarts | _cons |

Future Improvements More control variables… More sophisticated econometric model… Other suggestions?... 16

Housing Prices and Economic Freedom in U.S. Metropolitan Areas Tim Allen Florida Gulf Coast University Dean Stansel Southern Methodist University 17