With or without education services of English or Montessori, the Japanese government and many municipalities provide subsidies to protectors for the childcare charges of unauthorized nursery schools. To examine the adequacy of the subsidies, this paper looks into the factors that influence variations in the childcare charges. Geographically weighted regression shows that childcare charges are positively influenced by the dummy variables based on whether each nursery school offer English teaching or Montessori education as well as by the number of passengers at the nearest railway station in a wide area of Tokyo.

This study is conducted to reduce the burden of field survey work on vacant houses, which currently relies on visual surveys conducted by local governments. First, for the entire area of Maebashi city in Gunma prefecture, a machine learning model was constructed to estimate the probability of vacant houses for each detached house by utilizing the municipality owned data, which provides information on the residents of each building and water consumption. Next, we developed a method to identify detached houses that do not require on-site surveys based on the estimated probability of vacant houses for each detached house using this model. As a result, when detached houses with an estimated vacancy rate of 30% or less were estimated as non-vacant house, we were able to give a determination of non-vacant house to 79.74% of the detached house in the city. In addition, of the detached house determined to be non-vacant house, 99.02% of the buildings were truly non-vacant house. We also found that the districts with many buildings have a higher estimated number of detached houses.