AHedonic Price Model of Self-Assessed Agricultural Land Values

The hedonic price model assumes that land prices contain information in relation to the value that consumers put on characteristics of the land. Variations in prices may then be used to measure the productive value of those characteristics. There is a small literature on hedonic price models of agricultural land, including a study by Kostov (2009). Kostov deals with the impact of land characteristics on price in Northern Ireland and puts the emphasis on solving problems related to spatial dependency which can lead to biased results they are ignored. Latruffe and Le Mouel (2007) studied the capitalization of farm subsidies into higher land prices, while Myles et al. (2008) assess the influence of direct payments on the rental value of the land by. Urbanization can also have an impact on land prices because of an increased expected value of the land due to land use changes as discussed by Cavailhès and Wareski (2003). The aim of this paper is to understand what drives the farm land market in terms of price making and value of the land and to what extent. The main objective of this study is to evaluate the impact of certain groups of factors on the agricultural land market, namely:  Policy Capitalisation  Local Markets  Environmental and Agronomic Drivers of Land Productivity  Land Use In order to estimate a hedonic price model with the four agronomic, market, land use and policy elements, we require a dataset that contains both land values and relevant explanatory variables. In order to capture market capitalization, it requires information on policy changes over time, while capturing local market and agronomic characteriststics requires georeferenced information. The Teagasc National Farm Survey, which is the Irish component of the EU Farm Accountancy Data Network (FADN) is a detailed farm datasetthat has been conducted annually since 1972. Given the selection bias associated actual sales or purchases, we have chosen to use selfassessed land prices from the NFS as our dependent variable. 3 Models are estimated of increasing complexity  Land use  Land use plus policy plus environment  Land use plus policy plus environment plus local land market


Issue Date:
2015
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/212639




 Record created 2017-04-01, last modified 2017-08-28

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