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.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 km Agencies and private organizations designing conservation efforts must contend with the fundamental scarcity of available resources, while simultaneously confronting a vast array of potential conservation strategies. Accurately predicting costs of competing strategies, then, is crucial to achieving the cost-effective use of scarce conservation dollars. Where conservation by fee simple acquisitions and land easement contracts is the dominant approach, the market value of land is a primary determinant of such costs. Yet, academics and planners have not often had access to reliable estimates of fair market land value, which constitute a high-priority resource for global change science and policy in general . Some stSeveral studies have attempted more accurate, higher-resolution land value estimates with national coverage. Larson 2015) uses a patchwork of USDA county-level agricultural land value estimates and existing hedonic estimates of urban land value  uses a p. UtiliziThere is reason to believe that expanding both the set of agriculturally and economically relevant predictors as well as the geographic scale of analysis could improve the coverage and quality of land value estimates. The market value of undeveloped land is in many cases driven by the value of its actual or potential use in agriculture. It can also be driven by demand in related, residential land markets. Climate and soil variables, absent from the set of predictors in Nolte , are welWhile many of the land quality attributes that determine economic value are fixed, or change only very slowly over time, many drivers of supply and demand—income, preferences, technology, and the availability of substitutes—vary over time. Failing to account for time-varying predictors can lead to an erosion of predictive power. When sales prices exhibit trends over time, not only will overall estimation accuracy suffer, but models might systematically over- or under-predict sales values for observations before or after the median sales year if they do not adequately control for such trends. To address these issues, we add median home price indices to our set of predictors. Home price indices capture changing dynamics in residential land markets that can exert pressure on (or otherwise correlate with) the price of undeveloped land. Accounting for variation in local home prices might thus improve the predictive power of models aimed at estimating the value of agricultural or other undeveloped land.Beyond incorporating additional predictors, we consider the possibility that modeling at scales larger than the county can ease local observation constraints, improving both the coverage and accuracy of resulting estimates. Many counties have few sales records in the ZTRAX database. Nolte (2020) implements a 1000-observation minimum for modeling parcel land values within a county, supplementing a focal county’s observations with those drawn from neighboring counties where possible. In replicating this supplementation procedure with vacant, undeveloped, and agricultural parcels, we find that 27 percent of counties in the study sample fail to meet the threshold, and prediction accuracies for counties with sufficient-but-low observation densities are notably poorer than those for other counties.Indeed, national performance aggregations mask substantial heterogeneity across county characteristics. In particular, a county’s observation density is strongly correlated with both performance measures. We attempt to resolve this trade-off between model size and observation homogeneity by specifying models at the scale of USDA farm resource regions . Farm refurther reduces prediction error by 5 percent . By modeling at the farm resource region level, we extend coverage in areas that were dropped in county modeling due to insufficient density by 376 counties while preserving comparable predictive accuracy to county-level approaches.Overall, our modeling approach, which both expands the set of predictors and models at a regional scale, reduces prediction error (MSE) by 15 percent. Decomposing this effect, we see that adding variables alone  reduces prediction error by approximately 9 percent, while modeling at a regional scale ex ante land value predictions can improve conservation outcomes by helping agencies achieve better targeted, more cost-effective contracting.In addition, we assess the practical benefits of our modeling approach by simulating the protection of 30 percent of US lands via government purchasing, modeled after the Biden administration’s “30x30” initiative. Using our proposed modeling strategy saves the purchasing agency approximately $15 million per year, or 4 percent of the USDA’s annual budget for land easements (a conservation strategy related to but distinct from direct land acquisition wherein property does not change hands but does become subject to certain land use restrictions and stewardship mandates). The provision of more accurate In short, our approach enhances the quality and quantity of parcel-level estimates of fair market value.p is the total set of predictive features, and a required minimum leaf size of 3. Nolte (2020) estimates models for the group of all parcels greater than 1 acre in size as well as separate models for the subset of stringently defined “vacant” (undeveloped) parcels. Parcels of interest in the present study include all “vacant” parcels plus any additional parcels coded as agricultural, even if they contain a building footprint. Our filter (fully described in n = 1.26 million) and training (n = 3.78 million) sets balanced on the outcome variable  , which tvariable . In counOur approach to dealing with insufficient county-level observations differs in two ways from that of Nolte (2020). First, Nolte (2020) accepts donations from all counties, beginning with those nearest the focal county and extending the search outward until 1000 observations are achieved. We suspend the search if the directly adjacent neighbors of the focal county cannot achieve 1000 observations. Second, we supply the focal county with exactly the difference between its native sample size and 1000, whereas Nolte (2020) donates the entirety of the sample from the neighboring county, regardless of the neighboring county’s sample size. Further, because we restrict the generation of predictions to within each modeled county, our “base” model  does not produce predictions for all counties in CONUS, unlike Nolte (2020), which estimates land values for all parcels, even if that requires the utilization of a model specified in a distant county. Our county-level modeling approach allows for the specification of 1571 county models. In speaking of the extension in prediction geography our region-scale modeling offers, this approach is the benchmark to which we compare the results of our farm resource region models.We restrict the benchmark modeling approach to each focal county and its immediately adjacent neighbors to better preserve the value of specifying models at the county level: the ability to capture locally specific dynamics. If county modeling did, in fact, perform better than modeling at the regional-scale, then this restricted approach would better evince such performance by not eroding the improvements from locally specific relationships by importing observations from great distances. Further, in counties with no sales observations, model validation is infeasible , hinderiAmong the set of predictors in Nolte (2020)  are variSoil classifications, indicating a map unit’s suitability for agricultural use, were compiled from the Natural Resources Conservation Service (NRCS)’s high-resolution SSURGO soil survey database . The NRCPrime. Optimal site composition and availability for producing agricultural product;Unique. Soil producing high-value crops ;Statewide Importance. State-defined agricultural land that fails to meet prime criteria;Local importance. Locally defined agricultural land that fails to meet prime or statewide criteriaConditional classes. Land that would be considered prime, of statewide importance, or of local importance conditional on a specified improvement We operationalize farmland classifications as the percent of each parcel containing a given classification . Aggregation is applied to overlapping classifications containing multiple conditions. For instance, “Prime if drained or protected from flooding” gets assigned to “Prime if drained” and “Prime if protected from flooding.” For sales records covering multiple parcels, our soil aggregation strategy is described in the To incorporate information on local real estate markets, we add yearly all-transaction house price index values at the county level . At the R code for all of the methods described above is available at https://github.com/binders1/fmv.Altogether, we estimate fair market value in four primary model runs, corresponding to different combinations of predictor set (our “Full” set of predictors or the “Restricted” set including only variables from Nolte (2020)) and geographic scale of analysis (county-level or farm resource region-level). To assess our model’s comparability to the results of Nolte (2020), we predict the year-2020 fair market land value of all 31.35 million parcels in our base dataset, including those for which we do not have sales records . The pre2 hexagonal tessellation cells.Our results largely replicate geographic patterns in Nolte (2020)’s CONUS-wide prediction map. Per-hectare values greatly increase in metropolitan areas, with the urban centers of Washington D.C., Boston, and Los Angeles reaching predicted prices of well over $1 million. Because the model predicts land value alone, not including built improvements, we observe a gentler gradient between rural and urban regions than is shown in Nolte (2020)’s Fig 1. This gradient is further smoothed visually by our mapping method, which averages predicted values over 10 kmWhile the geographic pattern and general order of magnitude of our fair market value estimates roughly match the Nolte (2020) results, our added predictors and regional modeling strategy deliver important improvements in accuracy and coverage.Individually and collectively, the additional predictors in our “Full” set offer modest and meaningful improvements to model performance relative to the “Restricted” set. Figs Accounting for changes in related real estate markets plays an especially important role in better predicting fair market value. Altogether, our Full model offers modest but consistent improvements in model performance at the county level. 2 (the added properties make up 818.7 km2). Across these added counties, model performance remains well within the previously observed range, with a median mean squared error of 0.92 and a mean of 1.53.Specifying models at the scale of the farm resource region enhances accuracy and enables FMV predictions for parcels in counties that, even after drawing on observations from neighbors, do not meet the minimum observation threshold for county-level modeling. In MSE = 1.02 versus MSE = 1.07 using the Full predictor set). The benefits of modeling at the FRR scale remain even at higher observation densities. In counties with greater than 1,000 observations, the FRR model with the Full predictor set produces an average mean squared of error of 0.75, compared to 0.77 from the county model.Elsewhere, we see improvements to predictive accuracy. In counties with fewer than 1000 sales observations (before drawing on observations from neighbors), the farm resource region model outperforms the county model  and median home value (farm resource region models), capture broader market dynamics rather than capitalized attribute values. The consistent feature importance exhibited by both real estate predictors suggests that having access to high quality indicators of spatiotemporal variation in local and regional residential real estate markets may greatly improve conservation planners’ ability to estimate the cost of non-residential properties, including the vacant, undeveloped, and agricultural parcels of interest to the present study.Regardless of predictor set, we observe that the farm resource region models improve model performance, relative to county modeling. Enlarging the geographic scale of modeling allows for the incorporation of more data points. The improvement in performance is likely due to the greater number of observations on which the models draw, consistent with sample size-performance relationships observed in county-level modeling in However, having more observations does not guarantee better performance. The predictive benefit of more observations will not always outweigh the predictive cost of pooling parcels across areas that could exhibit fundamentally different relationships among variables. That tension could explain the relatively similar performance of our models across regions of very different sizes . Take foThe broader coverage afforded by our farm resource region modeling allows for conservation planning in areas that would otherwise need to rely on cruder estimates. For instance, across the entire state of Montana, our sample contains only 507 observations, all of which are discarded under the county-level modeling paradigm. Farm resource region models allow for such observations to be grouped and modeled alongside observations in either the Basin and Range or Northern Great Plains which bear a resemblance to those otherwise discarded Montana observations. In the absence of the farm resource region approach, policy analysts and conservation planners would be forced to use lower-resolution estimates, which have been shown to lead to substantial mischaracterization of cost-effective conservation strategies (Nolte 2020). The expansion of model predictions into rural areas afforded by the farm resource region models , approaching the general problem of land value estimation, established a methodology and database that greatly improves value estimates of undeveloped land, employing parcel-level sales data and a large set of predictors. As developers and agricultural producers look to undeveloped or partially agricultural land for conversion to lucrative but environmentally damaging use, conservation planners require a thorough and reliable picture of property valuation in order to cost-effectively build conservation agendas.In this paper, we improve the accuracy and coverage of previous estimation models for undeveloped land value, leveraging an added set of economically-relevant, high resolution climatic and ecological predictors, as well as incorporating time series data on county-specific residential housing markets. Modeling at larger scales that correspond to regional agricultural markets expands spatial coverage and accuracy, offering a new standard for applications and further improvements.We test our modeling approach using one such exemplary application: the ambitious target of protecting 30 percent of US lands by 2030. Using the improved modeling methods introduced here, we find that an implementing agency could save approximately $15 million annually–around 4 percent of the Agricultural Conservation Easement Program’s annual budget–from more cost-effective targeting of land purchases.S1 FigClimatic variables grouped for clarity. Features added by this paper denoted in bold. Each dot represents a single county.(TIF)Click here for additional data file.S2 FigClimatic variables grouped for clarity. Features added by this paper denoted in bold.(TIF)Click here for additional data file.S3 FigPrediction accuracy varies little across farm resource regions and shows no apparent correlation with the number of observations in a region.(TIF)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.