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³² For the renovations-tax policy analysis, the coefficient and standard error were 0.124 (0.083) for party members and 0.236 (0.140) for independents. For the renovations-job security analysis, the coefficient and standard error were 0.272 (0.096) for party members and 0.116 (0.139) for independents.

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⁴⁰ An MSA is a population centre as defined by the US Office of Management and Budget. MSAs typically encompass multiple counties and/or towns.

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⁴⁴ We have also analysed the almost identical index published by the Office of Housing Enterprise Oversight, and the results are similar. We use the CMHPI because it has larger coverage for some MSAs.

⁴⁵ A working paper by Justin Wolfers argues that home prices reflect voters’ future economic expectations. We considered potential implications of this interpretation of home prices, particularly the possibility that the findings could be driven by bad economic conditions that weaken voters’ economic expectations. Accordingly, we examined whether the findings held when personal income growth was higher than the median level. The results were robust to examining this subsample. For instance, the coefficient and standard error on Gubernatorial Election Year are −0.354 (0.181) for the fixed-effects estimation of Equation 1. Justin Wolfers, ‘Are Voters Rational? Evidence from Gubernatorial Elections’, Wharton Business School manuscript, 2007.

⁴⁶ Generally the two-party vote concerns Democratic and Republican candidates. However, the percentage is based on the two candidates with the largest vote shares, even in the unusual case in which one of them is a third-party candidate.

⁴⁷ For instance, if we increase the threshold to an advantage of six or seven percentage points, all of the major results hold.

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⁶⁰ The coefficients are similar in magnitude to those in Table 6 from difference-GMM and are significant at p < 0.05, two-tailed, except one case in which the significance level is p = 0.07, two-tailed.

⁶¹ In the model that estimates the average effect of a gubernatorial election year, the p-value from the Difference-in-Hansen test is p = 1.00 and from the Hansen J test is p = 0.982. In the model that estimates the effect of competitiveness, the p-value from both the Difference-in-Hansen and Hansen J tests is p = 1.00.

⁶² We tried reducing the lag structure so that it included only the second or third lag, and we even collapsed this lag, but the Hansen tests still suggested that the models were overidentifed.

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⁶⁶ For instance, if the only instrument is the collapsed second or third lag, then the Hansen J-statistic and Difference-in-Hansen test suggest the model is overidentified.

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