What We Should Know About House Reconstruction Costs?

This paper presents two important analyses, which have been derived from a rich dataset supplied by an important Property & Casualty Carrier in the Canadian Market (here we can introduce a footnote suggesting that due to the anonymity requirement we will abbreviate this important Property & Casualty Carrier in the Canadian Market as PCC). These analyses provide us with an improved understanding of house values, via a detailed analysis of their predicted reconstruction costs. In an initial step, we propose a new model that focuses on the modeling of house reconstruction cost (HRC) using 16 predictors, which include the materials and composition of the buildings. In a second step, we analyze the distribution of HRC using quantile regressions, in order to gain a better understanding of the influence of HRC skewness, which is driven by the most expensive houses. It is found that when a broad set of (16) predictors is used, the “Living Space” alone accounts for 54.87% of the cost variation, while the square of this variable accounts for another 9.4% of the variation in cost. Quantile analysis provides additional information, as the impact of certain coefficients on the cost of less expensive houses is different to that of expensive houses. In particular, the “Age of Construction” coefficient at the 25th quantile is 3 times higher (in absolute value) than at the 99th quantile, whereas the quantiles estimates for the nonlinear influence increase with quantile value, suggesting a move towards an exponential influence of age on the reconstruction cost of expensive houses. The “Living Space” predictor reveals that the living-space cost is approximately 4 times greater at the 25th than at the 95th quantile, whereas the nonlinear influence of living space varies from a negative effect (lower quantiles) to a positive effect (upper quantiles), suggesting that the cost curve changes from concave at lower quantiles to convex at higher quantiles.