Zhenshan Chen, Klaus Moeltner, Matthew Mair
arXiv 1 Jun 2026 · Econometrics
arXiv:2606.02795 · PDF · DOI · OpenAlex · Extracted main text
Hedonic price models are widely used to assess how environmental amenities affect property values, yet methodological guidance for estimating direct price effects remains sparse. We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches for estimating the direct unmediated price effect of spatially delineated amenities on treated properties (DUET), a conservative lower-bound approximation for welfare changes with direct applications to benefit-cost analysis. Where previous simulations rely on parametric assumptions, we retain the actual data-generating process underlying over 1 million property transactions from upstate New York (1990--2024). By randomly assigning "treatment locations" across iterations we establish a "ground truth" that allows us to precisely measure estimation error. Our results demonstrate that generalized difference-in-differences (DID) regression consistently outperforms baseline DID and two-way fixed effects models across all scenarios. Causal Machine Learning (CML) methods, particularly causal forest DID, achieve comparable performance to generalized DID in most scenarios. In larger samples (above 3,000 treated) increasingly common in contemporary hedonic studies, CML approaches offer substantial advantages when properly specified. Based on empirical simulation results, we provide a set of method-specific best practice recommendations for both traditional regression and causal machine learning approaches.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Banzhaf, H. Spencer (2021) Difference-in-differences hedonics | 1.000 | 22 | 5 | 100% |
| 2 | Athey, Susan and Tibshirani, Julie and Wager, Stefan (2019) Generalized random forests | 1.000 | 7 | 4 | 100% |
| 3 | Bishop, Kelly C. and Kuminoff, Nicolai V. and Banzhaf, H. Spencer an… (2020) Best practices for using hedonic property value models to measure willingness to pay for environmental quality | 1.000 | 6 | 3 | 100% |
| 4 | Hu, Chong and Chen, Zhuo and Liu, Pengfei and Zhang, Wei and He, Xin… (2025) Impact of large-scale solar on property values in the United States: Diverse effects and causal mechanisms self | 0.928 | 4 | 3 | 100% |
| 5 | Klaiber, H. Allen and Smith, V. Kerry (2013) Quasi experiments, hedonic models, and estimating trade-offs for local amenities | 0.811 | 4 | 2 | 100% |
| 6 | Kuminoff, Nicolai V. and Pope, Jaren C (2014) Do “capitalization effects” for public goods reveal the public's willingness to pay? | 0.737 | 3 | 2 | 100% |
| 7 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2016) Double/debiased machine learning for treatment and causal parameters | 0.644 | 2 | 2 | 100% |
| 8 | Kuminoff, Nicolai V. and Parmeter, Christopher F. and Pope, Jaren C (2010) Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities? | 0.644 | 2 | 2 | 100% |
| 9 | Chen, Zhenshan and Towe, Charles and He, Xi (2025) Heterogeneous flood zone effects on coastal housing prices-Risk signal and mandatory costs self | 0.511 | 2 | 1 | 100% |
| 10 | Cropper, Maureen L. and Deck, Leland B. and McConnell, Kenneth E (1988) On the choice of functional form for hedonic price functions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 28 scored citations.