Modeling the impact of distance between offices and borrowers on agricultural loan volume
Modeling the impact of distance between offices and borrowers on agricultural loan volume
复制标题
模拟办事处和借款人之间的距离对农业贷款额的影响
DOI：
10.1108/afr-01-2015-0005
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发表时间：
2015
影响因子：
1.6
通讯作者：
Rodney D. Jones
中科院分区：
文献类型：
--
作者：
T. Witte;E. DeVuyst;Brian E. Whitacre;Rodney D. Jones
目的- -农业信贷是俄克拉荷马州和全国农业生产者信贷的主要提供者。设立新的农业信贷办公室的决定减少了借款人的搜索和旅行成本，并应增加贷款量。本文的目的是建立新的贷款量的函数，从东中部俄克拉荷马州县的质心到农业信贷办公室的距离。然后，该模型被用来预测在服务不足的地区设立新办事处的影响。设计/方法/方式-县总的新贷款量回归距离农业信贷分支和外地办事处和其他变量预计会影响农业贷款量。估计模型用于预测在没有分支和外地办事处的州增设这些办事处对新贷款量的影响。置信区间用于衡量预测贷款量的重要性。调查结果-距离县中心的分支和外地办事处被发现显着减少新的贷款量。结果被用来模拟增加新的分支和外地办事处。模拟预测了与办公室增加相关的年度新增贷款量。实际影响-使用空间模型，农业信贷中东部俄克拉荷马州和其他农业贷款机构可以更好地规划扩张（或整合）。这些模型表明，年度新增贷款量可能高于（或低于合并）附近其他县的县。其结果可以改善借款人的准入和系统的财务业绩。独创性/价值-虽然空间建模已被用于其他部门，几乎没有做相对于农业信贷的获得和贷款量的影响。这里的模型明确模拟了到农业信贷办公室的距离对年度新贷款量的影响。
Purpose - – Farm Credit is a major provider of credit to agricultural producers in Oklahoma and nationally. The decision to place a new Farm Credit office reduces borrower search and travel costs and should increase loan volume. The purpose of this paper is to model the new loan volume as function of distance from east central Oklahoma county centroids to Farm Credit offices. The model is then used to predict the impact of placing new offices in underserved areas. Design/methodology/approach - – County aggregate new loan volume is regressed on distances to Farm Credit branch and field offices and other variables expected to impact agricultural loan volume. The estimated model is used to predict new loan volume impact of adding additional branch and field offices in counties that did not have these offices. Confidence intervals are used to measure the significance of predicted loan volumes. Findings - – Distances from county centroids to both branch and field offices were found to significantly reduce new loan volume. The results were used to simulate the addition of new branch and field offices. The simulation predicted the added annual new loan volume associated with office additions. Practical implications - – Using spatial models, Farm Credit of east central Oklahoma and other agricultural lenders can better plan for expansion (or consolidation). These models indicate counties where annual new loan volume will likely be higher (or lower for consolidation) than other nearby counties. The result can be improved borrower access and system financial performance. Originality/value - – While spatial modeling has been utilized in other sectors, little has been done relative to agricultural credit access and impact on loan volume. The model here explicitly models the impact that distance to Farm Credit offices have on annual new loan volume.