Forecast the house price index for California using different forecasting methods

Abstract

Forecasting house price index is a useful and classic problem in real estate and investment fields. Predicting house price index in a region not only helps investors make sensible decisions but also aids the government in promulgating policy. This paper will use some simple forecasting models (mean model, nave model, drift model, linear model and ARIMA model) in forecast test part and by seeing the average value of their residuals and checking whether the distribution of the residuals approximates the normal distribution, select the one with the highest accuracy among them for the final prediction. Multiple linear regression is also used to find if there is relationship between predicted data and possible influencing factors (such as income, unemployment rate and population) and then use the factors that have strong correlation with predicted data to optimize our forecasts and provide a more accurate prediction for the house price index in California in the next few years.