A Nonparametric Bounds Approach for Hedonic Housing Price Indexes 12+

📙 A nonparametric bounds approach is proposed in the book for hedonic housing price models partially identified due to sample selection of an unknown form. To construct the bounds, a nonparametric hedonic model that allows spatial correlation and estimators of extreme conditional quantiles are proposed. In contrast to conventional indexes, indexes based on the bounds approach do not suffer from sample selection bias. The approach is used to construct both metro and zip code level price indexes from a sample of over one million transactions from 1996 to 2008 in Los Angeles and San Diego metropolitan areas collected by a mortgage technology firm, FNC. The bounds approach provides more reliable results because it is based on more credible assumptions. The metro level indexes show that the housing price in San Diego peaked before Los Angeles and the appreciation rate at peak in Los Angeles was higher while the depreciation rate after peak was lower. The zip code level indexes indicate that the nonparametric hedonic method may underestimate the price of high-value properties and overestimate the appreciation rate of low-value properties.