On the uncertainty of real estate price predictions

Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analyzing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from nonconformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighborhoods, as well as those on the market for less than one year are more challenging to evaluate.