Journal of Forest Economics > Vol 25 > Issue 1

The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity

James Yoo, , jamyoo@calbaptist.edu Richard Ready, ,
 
Suggested Citation
James Yoo and Richard Ready (2016), "The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity", Journal of Forest Economics: Vol. 25: No. 1, pp 78-93. http://dx.doi.org/10.1016/j.jfe.2016.09.001

Published: 0/12/2016
© 0 2016 James Yoo, Richard Ready
 
Subjects
 
Keywords
JEL Codes:Q1Q18
Spatial econometric modelDeveloped and undeveloped land useAgricultural conservation easementForest land useHedonic price method
 

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In this article:
Introduction
Methodology
Data
Results and discussion
Conclusion and policy implications

Abstract

The impact of farmland under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania counties. ACE-protected farmland had a positive impact on nearby property values in one study county but a negative impact in the other. The paper also looks at the impact of forest land use, and discovers that preserved forest land had a positive impact on the nearby property values in both counties. House prices showed strong spatial correlation in both counties, and a spatial error components (SEC) model fit the data better than the OLS model, a spatial-lag model (SLM), or a spatial autoregressive error model (SEM). Geographically weighted regression (GWR) showed that the impact of ACE-protected farmland on nearby property values varied within one of the two study counties, with positive impacts in some parts of the county and negative impacts in other parts. The impact of forest cover on property values also varied, with positive impacts within both counties. A new hybrid GWR-SEC model is introduced that incorporates both spatial correlation in prices and spatial heterogeneity in the model parameters. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR model or a hybrid GWR-SEM model.

DOI:10.1016/j.jfe.2016.09.001