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 (O 3 , 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.

Islamic Bank Deposits during COVID-19 Pandemic: A Spatial Finance Approach

“…The Variant Inflation Factor (VIF) test also showed a multicollinearity relationship between variables with a condition number value of 16.33 or VIF > 10 (Shrestha, 2020). In contrast to OLS, Anselin (2005) stated that spatial lag model (SLM) is indicated through the OLS model that does not pass regression diagnostic test because there is a spatial relationship between neighboring areas and a particular area based on weighted spatial lag and uniformity of patterns and values between regions (Anselin et al, 2006;Mostafa, 2018;Putra et al, 2020).…”

“…In the second phase, Regression Diagnostics testing was conducted based on the Ordinary Least Square (OLS) approach through normality, heteroskedasticity, and multicollinearity tests to determine spatial relationships (Anselin, 2005;J. LeSage & Pace, 2009;Mostafa, 2018). According to preliminary studies, the inability of the three analyses to meet the rules of the classic assumption indicates the potential for spatial influence in the model, therefore necessitating the application of spatial analysis.…”

Factors affecting residential land prices A review of the literature

“…Therefore, many cities' high and continuously rising residential land prices pose significant challenges to economic development and social stability (Yang et al, 2017). Due to current market price distortions and economic abnormalities brought on by the high demand, Kuwait has some of the highest land prices in the world (Mostafa, 2018), as well as China's expensive housing prices have emerged as a phenomenon that troubles the entire country (Zhang, 2021).…”

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

“…Adegoke found a significant relationship between land price and mainly the population density and level of development. Other studies have linked proximity to urban facilities, such as schools, parks, and sports-related venues with mainly residential prices in Singapore (Murakami, 2018), China (Liu et al, 2007), Kuwait (Mostafa, 2018), Spain (Chica-Olmo et al, 2019), and the United States (Clapp et al, 2008;Espey and Owusu-Edusei, 2001; Kiel and Zabel, 2008). In conclusion, the selection process of suitable factors depends mainly on various elements, such as the setting of the designated target area, type of land price (e.g., residential and commercial), and the availability of aspatial or spatial data.…”