Bibliographic record
Abstract
Farmland comprises 85% of the assets in production agriculture. Surveys show over a 32.5% increase in values from 2010 to 2011 in Iowa. An analysis of recent farmland sales data leads to a better understanding of both why prices have been increasing, and possible changes in prices in the future. The factors examined include parcel size, land productivity, and location and type of sellers and buyers. The analysis is both over time and static; it compared characteristics from year to year, and it compared characteristics in the same year.A data set with land sales from 20 randomly selected Iowa counties for five years was analyzed using a hedonic model to decompose the values various attributes of land contribute to the total sale price per acre. This was used to determine the effects of these factors and to see if these effects change over time. Next, the sales values were compared to land value surveys conducted every year. Using NPV (Net Present Value) formulas, the sales values were examined to determine an implied interest rate, and compared to rent-to-value ratios.Analysis of descriptive statistics shows approximately 85% of parcels sold are in the lower two-thirds of productivity. The percentage of ``Sole Proprietor'' buyers and sellers has fallen by over half since 1990. A higher percentage of parcels are being sold in the fourth quarter of the year. Buyers who live in-state are buying higher quality land; sellers from out-of-state are selling higher quality land. Both out-of-state buyers and sellers are buying and selling larger parcels than those in-state.The analysis reveals that land value survey results from Iowa State University are consistently higher than sale prices by an average of 9.5%. This difference is not statistically significant. Two hedonic models capture corn suitability rating (CSR), lagged cash rent, and some locational variables as statistically significant for every year modeled. Implied interest rates are 3.2% and 6.4% higher than the rent-to-value ratio for 2009 and 2011, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".