Notes: The table presents the staggered dynamic difference-in-difference model with multiple events. The outcome variable is the natural logarithm of the transaction price. The independent variables are the living area (lnLA), the plot area (lnPL), the number of rooms (lnNR), the year of the building (lnBY), the distance from the metro station (lnDS) and the distance to the central business district (lnDCBD). All of them are in the natural logarithm. In addition to housing characteristics, 17 binary variables were included in the models. They represent the years before (−9 to −1) and after (1 to 8) the construction of houses. The models in Columns 1 and 2 show the estimates of the impact of new multifamily construction, and Columns 3 and 4 show the impact of new single-family construction. The model in Columns 1 and 3 (MFRobust and SFRobust) uses propensity score weights to control for potentially unbalanced data between treated and untreated groups (Rosenbaum and Rubin, 1983). Properties within the treatment group were assigned a weight of (1/propensity score), while properties in the control group were assigned a weight of [1/(1–propensity score)], as follows (Cole and Hernán, 2008). The model uses White’s heteroskedasticity-adjusted standard errors (White, 1980), and the models in Columns 2 and 4 (MFCluster and SFCluster) adjust the stand errors for clustering (Liang and Zeger, 1986). The statistics for the t-values are in parentheses

p < 0.05,

p < 0.01 and

p < 0.001. R² and adjusted R² are presented with the Aiken information criterion (AIC) and the number of observations