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We used the quasi-experimental difference-in-differences method to estimate the impact that the Endangered Species Act’s (ESA’s) critical-habitat (CH) regulation had on developed and undeveloped parcel prices throughout the United States between 2000 and 2019. At the national level we find that, on average, the prices of parcels in CH areas were not statistically different from the prices of similar nearby parcels not in CH. However, limiting our analysis to specific subsets of CH areas, we find mixed results. Previous empirical estimates have consistently found that the regulation reduces parcel prices. We offer several potential explanations for our contradictory results. 

Planning for cost-effective conservation requires reliable estimates of land costs, spatially-differentiated at high resolution. Nolte (2020) provides a county-by-county, parcel-level estimation approach that dramatically improves estimates of fair market value for undeveloped land across the contiguous Unites States. Much undeveloped land of conservation interest is under threat of conversion to agricultural use or is already agricultural. This paper demonstrates the value of accounting for additional variables that affect agricultural productivity and demand for undeveloped land, as well as the benefit of modeling at scales corresponding to regional agricultural markets. We find that countywide median home value, climatic variables, and several parcel-level soil type variables contribute substantially to predictive power. Enlarging the set of predictors and the geographical scale of modeling improves accuracy by approximately 15 percent and, relative to a more restricted modeling benchmark adapted from Nolte (2020), extends coverage into 376 counties occupying 1.35 million km². To assess the practical benefits of our modeling approach, we simulate the protection of 30 percent of US lands via government purchasing, modeled after the Biden administration’s “30x30” initiative. Using our proposed modeling strategy, the purchasing agency saves approximately $15 million per year, or 4 percent of the USDA’s annual land easement budget. 

We discuss data quality and modeling issues inherent in the use of nationwide property data to value environmental amenities. By example of ZTRAX, a U.S.-wide real estate database, we identify challenges and propose guidance for: (1) the identification of arm’s-length sales, (2) the geo-location of parcels and buildings, (3) temporal linkages between transaction, assessor, and parcel data, (4) the identification of property types, such as single-family homes and vacant lands, and (5) dealing with missing or mismeasured data for standard housing attributes. We review current practice and show that how researchers address these issues can meaningfully influence research findings.