The reason is that the expected resale price is an important component of the calculation of the return on a house investment. In the case of owner occupied housing actually, the resale proceeds represent the only source of investment return for the owner.
How can we produce house price forecasts over a particular time horizon? Well, there are simplistic methods, which in most cases are highly inaccurate and more complicated econometric methods that tend to produce more accurate forecasts.
The simplistic techniques usually start from the current level of house price and then grow them by a constant annual growth rate. So for example, if the price of the house today is Pt and we are expecting it to grow at an annual rate of r, then the formula for forecasting the price of the house after n years is:
Pt+n = Pt × (1+r)n
In order to demonstrate the application of this formula let’s assume that the price of the house today (Pt) is £250,000 and that we expect it to grow in the medium term at an average annual growth rate of 2%. With this data we can forecast the price of the house after five years as:
Pt+5 = 250,000 × (1+0.02)5
= 250,000 × 1.104 = 276,020
Of course this method relies completely on one number, the assumed yearly growth rate. In most cases, the growth rate used for the above calculation represents the average of recent or longer-term historical data, depending on whether the forecast horizon is short-term or long-term respectively. However, there is a serious problem with this approach because it assumes that the future will be exactly the same as the past, which is rarely the case. Thus, the accuracy of any house price forecasts produced using this technique is highly questionable.
Sometimes in the absence of explicit house price forecasts analysts may use forecasts of the general inflation rate as a proxy for future house price growth rates. However, there has been no empirical study indicating that house price inflation follows general price inflation.
The most appropriate methodology for producing property price forecasts is the structural econometric modeling approach with statistical equations of demand, supply, and rents/prices, which are calibrated using sufficient historical data and appropriate estimation techniques.
This technique, quantifies the effect of important exogenous economic influences (such as population, income, employment, mortgage rates, demographics) on housing demand and supply, as well as the interactions and feedback effects between the endogenous variables of the model. In this way, it allows simulation of the future behavior of the market and prices based on forecasts of the exogenous variables that drive housing demand and supply. Competent forecasts of the exogenous variables can be obtained by specialized firms that use very complex and sophisticated general equilibrium models to forecast population, employment and income changes both at the national and metropolitan level.