Developing a Methodology for Identifying Urban Neighborhoods with Severe Housing Deprivation in Iran

The aim of this paper is to present a methodology for identifying urban neighborhoods with severe housing deprivation (SHD) in Iran. The study, in addition to the housing physical determinants and overcrowding, considers household’s socioeconomic characteristics as an integral dimension of SHD. It proposes a methodology that adopts census-based data and data-pooling strategy to map the spatial distribution and clustering of housing deprivation (HD) within five selected cities in Iran. First, a hybrid Factor Analysis and Analytic Network Process (F’ANP) model is used to construct a composite housing deprivation index (HDI) composed of eight HD indicators. The composite HDI is then validated using the leave-one-out cross validation method in discriminant analysis, correlation and regression analyses. Second, the composite HDI scores are classified into five categories using standard deviations. Then, spatial data analyses are performed to investigate the presence of spatial autocorrelation. The results confirm that HD in the selected cities is spatially autocorrelated and have a statistically significant pattern of spatial clustering that includes five distinct categories. The neighborhoods that belong to the HD hot spots are considered as the neighborhoods with SHD. The identification and spatial distribution of the neighborhoods characterized by different levels of HD, not only provide a deep knowledge about the existing housing situation, but also can effectively support the revision of the housing planning strategies and actions both the national and local levels. The developed methodology could be replicated for the identification of areas with SHD in the cities of Iran and other developing countries.