Filling the Gaps with MICE: Addressing Missing Data in Real Estate Price Indices

Missing data are a common feature of micro-level transaction data used to construct hedonic real estate price indices. Missingness typically occurs in the descriptive characteristics required for quality adjustment rather than in transaction prices. Since these characteristics are central to hedonic quality adjustment, complete-case analysis can skew measured price dynamics through sample-selection and composition effects. This paper proposes multiple imputation as a way to handle missing characteristic values in index construction. The aim is not to recover individual missing values, but to restore incomplete observations and reduce variability in the estimation sample. We employ multiple imputation by chained equations (MICE) as a flexible imputation framework. Since conventional aggregation rules for multiple imputation, Rubin’s rules, do not align with the multiplicative chaining structure of price indices, we introduce an alternative aggregation method based on pooled growth rates. Empirical evidence from two applications, a large dataset of Vienna apartment transactions and a smaller, more heterogeneous Austrian office market, shows that index estimates are relatively robust to missing data in large, homogeneous settings. In contrast, in thinner and more heterogeneous markets, imputation can materially affect index dynamics. In both settings, flexible MICE specifications with rich predictor sets perform better than simpler imputation methods.