Radio maps provide radio frequency metrics, such as the received signal strength, at every location of a geographic area. These maps, which are estimated using a set of measurements collected at multiple positions, find a wide range of applications in wireless communications, including the prediction of coverage holes, network planning, resource allocation, and path planning for mobile robots. Although a vast number of estimators have been proposed, the theoretical understanding of the radio map estimation (RME) problem has not been addressed. The present work aims at filling this gap along two directions. First, the complexity of the set of radio map functions is quantified by means of lower and upper bounds on their spatial variability, which offers valuable insight into the required spatial distribution of measurements and the estimators that can be used. Second, the reconstruction error for power maps in free space is upper bounded for three conventional spatial interpolators. The proximity coefficient, which is a decreasing function of the distance from the transmitters to the mapped region, is proposed to quantify the complexity of the RME problem. Numerical experiments assess the tightness of the obtained bounds and the validity of the main takeaways in complex environments.