Assessing localization impact on land values: a spatial hedonic study

Natividad Guadalajara,María-Teresa Caballer,Jose M Osca

doi:10.5424/sjar/2019173-14961

Natividad Guadalajara, María-Teresa Caballer + Show 1 more

Open Access

https://doi.org/10.5424/sjar/2019173-14961

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Abstract

Aim of study: To obtain spatial land valuing models using Geographic Information Systems (GIS), which collect spatial autocorrelation and improve the conventional models estimated by OLS (Ordinary Least Squares) to determine and quantify the factors explaining these values.Area of study: The Spanish Autonomous Community of Aragón, Spain.Material and methods: The mean land values per municipality and the land uses published by the Aragonese Statistics Institute were used, as well as the geographic, agricultural, demographic, economic and orographic characteristics of these municipalities. The Spatial Lag Model and the Spatial Error Model were compared with OLS in general terms and for uses.Main results: The statistics (R2, log likelihood, Akaike’s information criterion, Schwarz’s criterion) demonstrated that spatial models always outperformed conventional models. The tests based on the Lagrange Multiplier and Likelihood Ratio tests were significant at 99%. The importance of both agricultural and non-agricultural factors for determining the arable land value was confirmed. The land value increased with irrigation availability (by a mean of 2.2-fold for the set of all land uses), plot size (by 5.7% for each 1 ha increase), population size, income and location in nature reserves (11.02-12.89%).Research highlights: Results indicate the need to develop spatial models when modeling land prices by implementing GIS.

Highlights

Introduction& Cañero, 2000; García & Grande, 2003; Gracia et al., 2004; Caballer & Guadalajara, 2005)
The origin of hedonic regression lies in valuing land of agricultural use (Haas, 1922) which, at the end of the 20th century and the start of the present century and with computers, has been well applied to value land worldwide (Xu et al, 1993; Shi et al, 1997; Maddison, 2000), and in Spain (Caballer, 1973; Calatrava& Cañero, 2000; García & Grande, 2003; Gracia et al., 2004; Caballer & Guadalajara, 2005)
This paper shows that spatial effects are significant on land values in the Spanish Autonomous Community of Aragón (SACA), which coincides with previous studies conducted in other areas (Patton & McErlean, 2003; Huang et al, 2006; Seo, 2008; Maddison, 2009; Mallios et al, 2009; Zygmunt & Gluszak, 2015; Uberti et al, 2018)

Summary

Introduction

& Cañero, 2000; García & Grande, 2003; Gracia et al., 2004; Caballer & Guadalajara, 2005) In all these works, valuing models has been estimated by Ordinary Least. Spatial data, e.g. land values, present two properties that make meeting requirements and fulfilling the hypothesis of hedonic regression estimated by OLS difficult (Guadalajara, 2018): (1). Autocorrelation, association or spatial dependence refers to the concentration or dispersion of the values of a variable (land prices in our case) in a land or geographic space. This implies that the value of a variable is conditioned by the value that this same variable takes in one neighboring region or in several

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