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.

翻译：享乐价格模型广泛应用于评估环境舒适度对房产价值的影响，但关于估计直接价格效应的方法论指导仍较为稀缺。我们通过经验蒙特卡洛模拟，评估传统方法与因果机器学习方法在估计空间划定舒适度对受处理房产的直接非中介价格效应（DUET）时的表现。该效应是福利变化的一种保守下限近似，可直接应用于成本效益分析。与以往依赖参数假设的模拟不同，我们保留了纽约州北部1990年至2024年超过100万笔房产交易背后的实际数据生成过程。通过在各次迭代中随机分配“处理区位”，我们建立了一个“基准真相”，从而能够精确衡量估计误差。结果显示，在所有场景中，广义双重差分（DID）回归一致优于基线DID及双向固定效应模型。因果机器学习（CML）方法，尤其是因果森林DID，在多数场景下实现了与广义DID相当的性能。在当代享乐研究中日益常见的大样本（超过3000个受处理观测）条件下，当适当设定时，CML方法展现出显著优势。基于经验模拟结果，我们为传统回归方法与因果机器学习方法分别提供了一系列针对特定方法的最佳实践建议。