No abstract

We adopt a novel method to deal with omitted spatial heterogeneities in hedonic house price analysis. A Gaussian variant of the conditional autoregressive (CAR) model is used to study the impact of spatial effects. In a general linear modeling framework, we include zone‐specific random effects that are allowed to interact spatially with neighboring zones. The results demonstrate that this estimator accounts for missing spatial information, producing more reliable results on estimated spatially related coefficients. The CAR model is benchmarked against a fixed effects model. Socioeconomic neighborhood characteristics are found to have only modest impact on spatial variation in housing prices.

In this paper, we study how concentrations and diversity of different groups of households were reflected in the housing prices of neighbourhoods in the Oslo urban area, Norway. The focus is primarily on the settlement pattern of immigrants, but the analysis controls for socioeconomic and demographic characteristics. Based on a hedonic conditional autoregressive spatial model formulation, we find that households on average prefer neighbourhoods with a high concentration of natives, many immigrants from Western countries, and, at the same time, a diverse, thin representation of neighbours from a wide range of countries. We do not find that immigrants from specific countries or continents have a substantial negative impact on housing prices in a neighbourhood.