Homes-uP — Single-Family Homes under Pressure?

An initial key concern here is to derive reliable estimates of the quantitative impact of long-run demographic-shifts on the demand and market prices of German SFH. The research design will integrate various empirical techniques that take advantage of both cross-sectional (i.e. regional) and time-series variation in home prices, and key demographic variables. The main variables of interest will be the long-term elasticity of house prices with respect to changes in total household population and age cohort distributions. The econometric specifications will draw upon the basic concepts of Mankiw & Weil (1989) and Ohtake & Shitani (1996), augmented by recent methodological advances documented in Takács (2012) and Saita et al. (2013). In a first step, the links between the demand for one and two-family housing and regional demographic structures at a fixed point in time will be analysed by cross-section analysis of household data (drawing on the results of Work Package 1, above). In a second step, the long-term impact of demographic shifts on home prices will be identified from regional panel data (Maennig & Dust 2008).

Next to this empirical approach, possible price rigidities are to be investigated in a third step. Given a situation of demographic change, declining population and a lower demand for single-family housing, standard economic theory predicts for a given supply that market prices will fall. Nevertheless, some studies (e.g., Spehl 2011) suggest price rigidities, especially in rural areas, leading to vacancies in the SFH sector. In order to explain potential rigidities, the analysis focuses on the suppliers’ incentives. Along with transaction costs, maintenance costs and demolition costs, real estate literature justifies price rigidities mainly with search and matching frictions (Stigler 1961, Haurin 1988, Wheaton 1990, Rogerson et al. 2005). The objective is to identify the effect of different market characteristics on frictions, as well as other mechanisms leading to price rigidities.

The impact of lower demand is supposed to have the greatest impact in rural areas. On a large scale, economic activity will shift and greatly affect private wealth. Private and social services as well as other amenities will decline along with the population. Supply routes will lengthen, and affected regions will lose attractiveness, causing demand to fall even further. Municipalities will thus have to provide infrastructure for ever more sparsely populated areas. In step 4, we will therefore examine whether SFH create higher infrastructural costs per capita, due to the effects of such current trends as demographic change (Schiller & Gutsche 2009). The question is whether municipalities will be able to reduce their spending proportionately to their shrinking populations, or to adjust service fees accordingly.

Finally and beyond economic analysis, the calculated quantitative structure of SFH stocks will be linked to a resource model, with the aid of which the implications of the developments for the use of natural resources is to be investigated. Using the methodology of lifecycle-based material flow analysis (Baccini & Brunner 2012, Schiller 2007), the effects of use changes (use density, use intensity) on the SFH stock segment with regard to the use of materials and energy and of costs of maintenance are to be quantified, and possible developments simulated both for Germany as a whole and, on a small scale, for selected regions (Sartori et al. 2008; Gruhler & Böhm 2011). In Work Package 3, contrasting scenario assumptions will be deliberated for this purpose. Investment potentials and energy savings in the SFH segment are to be simulated, in order to contrast possible futures SFH stock with the context of policy goals for the reduction of spatial heating needs by 80% by 2050. Moreover, a reduction and shift of population density in settlement patterns in Germany is to be analysed on the basis of selected smaller areas, and the effect on land consumption and the efficiency of technical infrastructure is to be calculated (Deilmann & Haug 2010); the results will be fed back into the overall analysis.