Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices

ArXiv ID: 2404.07132 “View on arXiv”

Authors: Unknown

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 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, to avoid spurious correlations in the analysis we transform each time series appropriately to produce stationary series for use in the GAM and GLM models. While arithmetic returns or first differences are adequate transformations for the predictor variables, for the average price response variable we utilize the series of innovations obtained from AR(q)-ARCH(1) fits. Based on the GAM results, we find that the influence of ESG factors varies markedly by city, reflecting 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 real estate models.

Keywords: Real Estate, ESG Factors, Generalized Additive Models (GAM), Time Series Analysis, Real Estate

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced statistical methods including P-spline Generalized Additive Models (GAMs), AR(q)-ARCH(1) fits for stationarity, and principal component analysis on residuals, indicating high mathematical density. However, empirical rigor is moderate due to a small sample size (23 years of annual data for 8 cities) and a lack of detailed backtesting or robustness checks typical of production-ready trading models.

  flowchart TD
    A["Research Goal<br>Predict home prices using ESG factors and construction data"] --> B{"Data Processing"}
    B --> C["Data Input: 2000-2022 US Home Prices & ESG Data"]
    C --> D["Stationarity Transformation<br>First Diffs/Returns & AR(q)-ARCH(1) innovations"]
    D --> E["Computational Modeling"]
    E --> F["GAM: P-spline Model"]
    E --> G["GLM: Linear Model"]
    F --> H{"Key Findings / Outcomes"}
    G --> H
    H --> I["ESG influence varies by city"]
    H --> J["Air conditioning is a strong factor"]
    H --> K["Time series transformation prevents spurious correlation"]