Forecasting US home prices with artificial neural networks and fuzzy methods combination and single forecasts

ABSTRACT: Recent studies have shown that there is a link between the housing market and economic activity. Also, they suggest that house-price fluctuations lead to real activity, inflation, or both. Therefore the existence of good model to forecast is very crucial for policy makers. The main objective of this thesis is to forecast the housing price indices for US and four Census regions of the US, namely, Northeast, South, Midwest and West by using relevant time series techniques. The purpose is to forecast out-of-sample period, from 2001:1 to 2010:5 according to the monthly data covering the in-sample period from 1968:1 to 2000:12 by using four advanced valuation method artificial neural networks and fuzzy methods multi layer perception (MLP), nonlinear autoregressive neural network (NAR), adaptive Neuro-fuzzy inference systems (ANFIS) and genetic algorithm (GA) as well as the forecast combination method. Also, the 24-step-ahead price indices will be predicted covering 2010:6-2012:6 period. The result of this study showed that both MLP and NAR separately had better answer in all parts of the data (US and four census regions) and they could have better forecast accuracy. Similarly, the results of ANFIS have a better forecast power especially in the initial steps than MLP and NAR. The results of this research also posits that both the neural network (MLP and NAR) and ANFIS have a suitable ability to model and forecast especially when there is a non- linear relationship between the data .On the other hand the results of the GA (as a linear model) in all parts of the data were not desirable. The results also showed that the nonlinear models like neural networks are better at longer horizons while the GA (as a linear model) is better at short horizons. Keywords: Forecasting, Neural Networks, US and Census Housing Price Indexes, Adaptive Neuro Fuzzy Inference Systems (ANFIS), Genetic Algorithm. …………………………………………………………………………………………………………………………………………………………………………………………………………