Table 3

Variance-Adjusted Cluster-Weighted Least Squares Meta-regression Results

Variable(1)(2)(3)(4)(5)(6)
Waterfronta0.1457***
(0.046)		0.0170
(0.023)		−0.0677
(0.052)		0.1010**
(0.039)		−0.1126 (0.086)0.1251***
(0.042)
Midwesta−0.1056
(0.087)		−0.1488*
(0.082)		−0.2059 (0.132)−0.2561***
(0.096)		−0.3156***
(0.116)
Southa−0.2248***
(0.076)		−0.2797***
(0.084)		−0.3209*
(0.171)		−0.2889***
(0.108)		−0.3945***
(0.131)
Westa−0.1803
(0.120)		−0.1803
(0.121)		−0.1747
(0.147)		−0.3745**
(0.180)		−0.3772**
(0.174)
Estuarya−0.0401**
(0.016)		−0.1223***
(0.039)
Waterfront × estuary0.1274**
(0.055)		0.1721*
(0.088)
Mean clarity0.0375
(0.030)		0.0799**
(0.035)
Waterfront × mean clarity−0.0684**
(0.029)		−0.1061**
(0.050)
Elasticity variance8.97E-07***
(1.52E-07)		6.26E-07**
(2.56E-07)		6.26E-07**
(2.57E-07)		5.10E-07
(3.30E-07)		6.02E-07**
(2.56E-07)		5.02E-07
(3.65E-07)
Time trend0.0183***
(0.006)		0.0223***
(0.006)
Linear-loga0.1973
(0.161)		0.1987
(0.159)
Lineara0.2198**
(0.086)		0.1159
(0.083)
Log-lineara−0.0020
(0.008)		−0.0059
(0.008)
Constant0.0414*
(0.021)		0.2474***
(0.077)		0.3320***
(0.090)		0.3092*
(0.182)		0.1681
(0.122)		0.0493
(0.180)
Observations260260260260260260
Adjusted R-squared0.0800.1480.1480.1560.1700.176

  • Note: The dependent variable is the home price elasticity with respect to water clarity (Secchi disk depth). Clustered-robust standard errors are in parentheses and are clustered according to the K = 66 study-housing market combinations. Weighted least squares regressions are estimated using the “regress” routine in Stata 16 and defining analytical weights equal to the variance-adjusted cluster weights (see equation [3]).

  • ↵a Independent variables that are dummy variables.

  • ↵* p < 0.1;

  • ↵** p < 0.05;

  • ↵*** p < 0.01;