Spatial data are often aggregated into spatial units and differences between spatial units can complicate the analysis of the data. One solution to this problem is spatial unit conversion, also called areal interpolation. Of the many areal interpolation methods proposed thus far, few method are based on spatial econometrics: a subset of econometrics which is concerned with the role of spatial autocorrelation (a general property of spatial data that implies that data in nearby locations are similar) in the regional economic model response. In this article, an areal interpolation method that considers both the spatial autocorrelation and the pycnophylactic property (a most basic premise of areal interpolation that the sum of the data given in a specific area must be constant) is proposed by combining a spatial econometric model and a linear regression-based areal interpolation method. Parameters of the proposed method are estimated using the expectation-maximization algorithm. The performance of the proposed method was examined through empirical analysis using real data and ratios on aging populations. The results indicate the importance of considering both the pycnophylactic property and the spatial autocorrelation in areal interpolation. The results also show the applicability of spatial econometrics to areal interpolation problems.