Hedonic Models Incorporating Environmental, Social, and Governance Factors for Time Series of Average Annual Home Prices

Abstract

Using data from 2000 through 2022, we analyze the predictive capability of the annual numbers of new home constructions and four available environmental, social, and governance (ESG) factors on the average annual price of homes sold in eight major U.S. cities. We contrast the predictive capability of a P-spline generalized additive model (GAM) against a strictly linear version of the commonly used generalized linear model (GLM). As the data for the annual price and predictor variables constitute non-stationary time series, we transform each time series appropriately to produce stationary series for use in the GAMs and GLMs in order to avoid spurious correlations in the analysis. While arithmetic returns or first differences are adequate transformations for the predictor variables, we utilize the series of innovations obtained from AR(q)-ARCH(1) fits for the average price response variable. Based on the GAM results, we find that the influence of ESG factors varies markedly by city and reflects geographic diversity. Notably, the presence of air conditioning emerges as a strong factor. Despite limitations on the length of available time series, this study represents a pivotal step toward integrating ESG considerations into predictive time series models for real estates.