In spatial econometrics, we usually assume that the spatial dependence structure is known and that all information about it is contained in a spatial weights matrix W. However, in practice, the structure of W is unknown a priori and difficult to obtain, especially for asymmetric dependence. In this paper, we propose a data-driven method to obtain W, whether it is symmetric or asymmetric. This is achieved by calculating the area overlap of the adjacent regions/districts with a given shape (a pizza-like shape, in our case). With W determined in this way, we estimate the potentially asymmetric spatial autoregressive dependence on irregular lattices. We verify our method using Monte Carlo simulations for finite samples and compare it with classical approaches such as Queen’s contiguity matrices and inverse-distance weighting matrices. Finally, our method is applied to model the evolution of sales prices for building land in Brandenburg, Germany. We show that the price evolution and its spatial dependence are mainly driven by the orientation towards Berlin.

We present a model to estimate the technical requirements, including the photovoltaic area and battery capacity, along with the costs, for a four-person household to be 100% electrically self-sufficient in Germany. We model the hourly electricity consumption of private households with quasi-Fourier series and an autoregressive statistical model based on data from Berlin in 2010. Combining the consumption model and remote-sensed hourly solar irradiance data from the ERA5 data set, we find the optimal photovoltaic area and battery capacity that would have been necessary to be self-sufficient in electricity from July 2002 to June 2022. We show that it is possible to build a self-sufficient household with today’s storage technology for private households and estimate the costs expected to do so.