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This paper deals with the primary causes of informal housing in Greece as well as the observed differentiations in informal housing patterns across space. The spatial level of analysis is the prefectural administrative level. The results of the multinomial logistic regression analysis indicate that Greek prefectures differ in the way they experience the informal housing phenomenon. An explanation for the observed differences may be the separate development paths followed and the diverse range of economic activities in each prefecture. The Greek state has not made provisions for creating the necessary ‘urban land stock’ in each prefecture, so that everyone interested can find land parcels at an affordable price. On the contrary, the state encourages the informal housing activity by legalizing large areas of such activity sporadically and by introducing legislative initiatives of limited success in dealing with the problem.

In the past few decades, numerous structural changes regarding the socioeconomic basis of most EU countries have been profound and critical. These processes of economic restructuring have resulted in significant land use changes. As regards the agricultural sector, the overall changes in both Greece and the other European countries have been particularly intense in the last 20 years. Such changes include the massive reduction in the levels of employment in agriculture, shrinkage of the economic importance of the agricultural sector as a whole, changes in crop plants and cultivation practices, crucial implications arising from the new European Common Agricultural Policy and the growing competition due to low-cost agricultural products from developing countries. These changes have not had the same magnitude and impacts across all Greek regions. Instead, significant spatial variability relevant to the regional characteristics of each administrative prefecture can be observed. In this article, we carry out an empirical analysis focusing on agricultural land use patterns at a prefectural level for the whole country. The changes are tracked and analysed in terms of selective possible driving factors. The methodology adopted is multinomial logistic regression. Some policy implications are drawn with a regional perspective.