A carregar...
Publicação
On the uncertainty of real estate price predictions
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 2.33 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Real estate Automated valuation model Conformal prediction Quantile regression Machine learning
Contexto Educativo
Citação
Bastos, João A. e Jeanne Paquette (2024). "On the uncertainty of real estate price predictions". REM Working paper series, nº 0314/2024
Editora
ISEG – REM (Research in Economics and Mathematics)