Surveying Housing Market Supply Affordability Using A Spatial Data Mining Approach
Free (open access)
125 - 137
Over the last decade, a consistent increase in real-estate prices, both in Italy and others OECD member countries, has been registered. This rise in price and the following subprime mortgage scandal have resulted in a significant fluctuation in market prices and/or market activity and in a consequent spreading of concerns about future market trends. These factors and the actual economic crisis often preclude people from buying residential properties, especially for those who want to set up house for the first time. Considering the limited public purchasing power and the unemployment increase, social housing and other related public policies must be assisted by appropriate decision support systems in order to provide affordable housing solutions to local citizens in financial need. These systems need to be able to identify in which nationwide contexts it is necessary to act with more incisive determination. Such a need also has to be considered in terms of support for the local population in order to get a better awareness of real estate acquisition risks.
This work aims to analyse the general affordability level of residential properties in urban environments, through a spatial decision support system (SDSS). At present, the system is still in the development phase, but is able to generate a qualitative overview of the housing market supply, especially in those contexts where the acquired dataset is characterized by a suitable spatial density. Using this information, it is possible to assess the income level required to affordably meet the current housing market supply and compare it to the official average income registered within the area of study.
housing affordability, spatial data mining, real estate appraisal, housing market, econometrics