Housing is a commodity which is a bundle of characteristics specific to a housing market, tenure, income and other household characteristics. Voluminous literature exists on housing demand in which a quantitative measure defined as housing services is used to measure housing demand. Housing or, more precisely, the service stream from a housing unit, is a heterogeneous commodity. Some dimensions, such as age or size of structure, are measured on a continuous scale; others, such as tenure or type of structure. are discrete properties. Measuring the volume of housing services as housing expenditure, as is done in previous research, essentially ignores the heterogeneity, and, for large number of policy purposes like impact of tax on tenure choice, choice between owning and renting, etc., the distribution of housing consumption into qualitatively different categories, is of more interest than an aggregate qualitative measure of housing expenditure alone. Earlier research has addressed the issues of methodologies in housing demand estimation and different market and related differences in demand elasticities. The econometric theory of joint discrete/continuous models is well studied, and there exist a variety of applications. However, there is a paucity of research applications to analyse housing demand using discrete models. The limited research in this area has focused only on American or German housing markets. There is no research on housing markets, which treats housing demand as discrete choices for Japan, despite the economic importance of the Japanese economy. We model housing demand in Japan using a discrete choice model. A nested multinomial logit model (NMNL) is the basic analytical tool for our analysis. The microeconomic and econometric foundations of NMNL models encompass the elegant theory of housing economics of a utility, maximizing household. NMNL models impose a hierarchical structure on the choice set that can be visualized in the form of a decision tree. Three dimensions of choice, tenure, dwelling size (as number of rooms) and structure type (as type of unit) generate these steps of clustering. This paper estimates the choice probabilities and demand elasticities of various housing alternatives for Tokyo using 1993 housing survey data for 23 wards.
- housing demand
- discrete choice model
- nested multinomial logit model