A spatial econometric analysis of residential land prices in Kuwait

Abstract

Land price mapping has recently drawn considerable attention from academics and practitioners alike. This paper investigates the factors influencing residential land prices in a rather underrepresented part of the world. Owing to land prices’ spatial association and heterogeneity, the study uses both traditional and Bayesian spatial regression techniques to test the impact of population density, the percentage of Kuwaitis among the total population, the total number of schools, traffic accidents, and air pollution as measured by the prevalence of both carbon monoxide (CO, ppm) and ground-level or tropospheric ozone level (O3, ppb) on residential land prices in Kuwait. The general pattern of the results shows that land prices are driven positively by density, the percentage of Kuwaitis and the existence of educational amenities, while air pollution has a negative impact on prices. The analysis also reveals that land prices in Kuwait tend to cluster in groups/hotspots. It is argued that such an accurate identification of hotspots and the correct understanding of their relation to explanatory variables can help decision-makers to make sound decisions in areas as diverse as planning for amenities and zoning. land prices; spatial analysis; spatial autocorrelation; Bayesian spatial analysis; hotspots; Kuwait.