Conferences

The purchase of a residential property is the largest single expenditure of a typical middle-class household. The trends in the house prices are therefore of interest to professionals and agencies with remit in town planning, welfare, construction, taxation policy and the like, in both the private and public sphere. A house price index (HPI) is a summary of the trends in house prices. It compares the price of a typical property at one time point with the price of a similar property at another time point, such as a year ago. HPI has some features of a consumer price index (CPI). The principal difference is that a residential property is a complex product which includes not only the structure (a building or its part) and its immediate surroundings (garden, paving, fencing and the like), but also its environment, interpreted widely: access to the property, proximity of shops, schools, sources of entertainment and location for leisure activity, and other services, absence of dilapidation, criminal activity and (industrial) pollution, and the like. In contrast, the items in the basket of goods for a CPI are standardised, with their qualities and functions subject to no alteration.

A HPI is based on the census of all transactions of residential properties (houses, flats, etc.), with the attributes of the properties and circumstances of the transaction (market or private sale, mortgage or paid in cash, type of tenure, etc.). The ideal HPI compares like-with-like transactions in one period with the transactions in another. This is impossible to arrange, because we can exercise no controll over the attributes of the properties involved in the transactions. This problem is addressed by the established methods by regression adjustment (hedonic house price indices) and repeated-sales analysis. The presentation will discuss the deficiencies of these, and will propose the potential outcomes framework (POF) as an approach to constructing HPI to a greater standard of integrity. In POF, the time of the transaction is regarded as the treatment, and the treatment effect is the difference in the prices paid for an identical property at one time point and another. However, a property at a fixed address is not the same consumer item as it was, say, five years ago, because of the wear and tear, maintenance and other factors (including fashion) that bring about changes, including the attributes of the environment (a new park, school, employment opportunities, reputation for petty crime, and the like). The strength of POF is that it can respond to this concern, at least in principle. A reference stock of properties is considered together with the effects of time for each property. The HPI is defined as the average treatment effect for the stock. This is practical to define on the multiplicative (log) scale, so that the calculus of percentages is simplified.

An example of a proposal of HPI for New Zealand will be discussed, together with the various options of how POF can be implemented, and their connection with the methodology for missing data, and multiple imputation in particular.