Using artificial neural networks to forecast changes in national and regional price indices for the UK residential property market

Abstract

The residential property market accounts for a substantial proportion of UKeconomic activity. However, there is no reliable forecasting service to predict theperiodic housing market crises or to produce estimates of long-term sustainablevalue. This research examined the use of artificial neural networks, trained usingnational economic, social and residential property transaction time-series data, toforecast trends within the housing market.Artificial neural networks have previously been applied successfully to produceestimates of the open market value of a property over a limited time period withinsub-markets. They have also been applied to the prediction of time-series data in anumber of fields, including finance. This research sought to extend their applicationto time-series of house prices in order to forecast changes in the residential propertymarket at national and regional levels.Neural networks were demonstrated to be successful in producing time-seriesforecasts of changes in the housing market, particularly when combined in simplecommittees of networks. They successfully modelled the direction, timing and scaleof annual changes in house prices, both for an extremely volatile and difficult period(1987 to 1991) and for the period 1999 to 2001. Poor initial forecasting results forthe period 2002 onwards were linked to new conditions in the credit and housingmarkets, including changes in the loan to income ratio. Self-organising maps wereused to identify the onset of new market conditions. Neural networks trained with asubset of post-1998 data added to the training set improved their forecastingperformance, suggesting that they were able to incorporate the new conditions intothe models.Sensitivity analysis was used to identify and rank the network input variables underdifferent market conditions. The measure of changes in the house price index itselfwas found to have the greatest effect on future changes in prices. Predictionsurfaces were used to investigate the relationship between pairs of input variables.The results show that artificial neural networks, trained using national economic,social and residential property transaction time-series data, can be used to forecaststrends within the housing market under various market conditions.

Date of Award	2008
Original language	English