Explaining drivers of housing prices with nonlinear hedonic regressions

Journal Article · Fri Jul 04 00:00:00 EDT 2025 · Machine Learning with Applications

DOI:https://doi.org/10.1016/j.mlwa.2025.100707· OSTI ID:2572674

Wan, Heng ^[1]; Chowdhury, Pranab K. Roy ^[1]; Yoon, Jim ^[1]; Bhaduri, Parin ^[2]; Srikrishnan, Vivek ^[2]; Judi, David ^[1]; Daniel, Brent ^[1]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
2. Cornell Univ., Ithaca, NY (United States)

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 2572674
• Report Number(s): PNNL-SA--185736
• Journal Information: Machine Learning with Applications, Journal Name: Machine Learning with Applications Vol. 21; ISSN 2666-8270
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Baltimore
Hedonic regression
Interpretable machine learning
Partial dependence plot
Sobol sensitivity analysis