Exploring housing patterns and dynamics in low demand neighbourhoods using Geographically Weighted Regression

This paper will examine the geographical variation in house price determinants in areas where housing is in such low demand that there has been a total collapse in the local housing market. This will be explored through the use of a case study in the city of Manchester in the UK. Areas with concentrated housing market collapse have been referred to as low demand neighbourhoods where housing in an area is difficult or impossible to let or sell (Bramley and Pawson, 2000). In analysing housing market collapse it is important to explore patterns and dynamics within and between neighbourhoods in order to assess neighbourhood vulnerability within the wider context of urban industrial restructuring. The case of Manchester will draw on the data and variables used by MCC (Manchester City Council) within its spatial decision support system for neighbourhoods known as TNC (Tracking Neighbourhood Change). The TNC system attempts to locate, monitor and review change by integrating and mapping data from local agencies in addition to the local authority such as the Local Education Authorities, The Land Registry and Local Police Force. It has been highlighted (CLG, 2007) that the roles of such systems are important at the beginning (strategy development) and end (performance monitoring) of the design and delivery process when considering neighbourhood intervention. Similar Local Information Systems are emerging and are in use by other local authorities and agencies in many countries throughout the world. For instance, applications of information systems for many cities in the United States are available and use indicator based visual analysis that focus on neighbourhood change. Many examples of these systems have been centralised in the United States as part of a networked resource known as the National Neighbourhood Indicators Partnership (NNIP, 2007).