Spatial Dependence in Urban Housing Prices: Evidence from Zagreb

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning.