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Record W2097765773 · doi:10.31274/etd-180810-320

Farmland price determinants in Iowa

2012· dissertation· en· W2097765773 on OpenAlexaboutno aff
Matthew Stinn

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural economicsAcreQuarter (Canadian coin)ProductivityDescriptive statisticsValue (mathematics)Land ValuesAgricultural scienceQuality (philosophy)AgricultureProduction (economics)BusinessEconomicsLand useGeographyStatisticsMathematicsEnvironmental scienceEngineeringMicroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.016
GPT teacher head0.237
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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