A comparison of the forecasting ability of ARIMA models (original) (raw)

An evaluation of the performance of UK real estate forecasters

Real Estate & Planning …, 2005

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. It compares the performance of real estate forecasters with non-real estate forecasters. Using the Investment Property ...

Prediction of housing prices: an application of the Arima model

2012

This study uses an ARIMA model to provide out-of-sample forecasts for United States housing prices as represented by the Case Schiller Index during the financial crises timeline ( between 2005 and 2009). The major findings are that the model fails to predict the peak/tuming point o f the financial crisis but successfully predicted declining prices since 2006:6; therefore the magnitude o f the loss realized during that period could have been reduced had the models prediction been considered. The model predicted extremely negative one year ahead prices in 2008:2, 2008:3 and 2008:9 which explains the timeline of the collapse o f the Bear Stearns and Lehman Brothers as well as the subsequent global financial meltdown.

The evaluation of the Australian office market forecast accuracy

Journal of Property Investment & Finance, 2018

Purpose Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events. Design/methodology/approach This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the fol...

The accuracy of housing forecasting in Australia

2003

Abstract: Forecasting is an integral part of all business planning, and forecasting the outlook for housing is of interest to many firms in the housing construction sector. This research measures the performance of a number of industry forecasting bodies; this is done to provide users with an indicator of the value of housing forecasting undertaken in Australia.

Assessing the Accuracy and Dispersion of Real Estate Investment Forecasts

International Review of Financial Analysis, 2015

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to underestimate growth rates during strong market conditions and overestimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns.

Forecasting Office Building Rental Growth Using a Dynamic Approach

Numerous econometric models have been proposed for forecasting property market performance, but limited success has been achieved in finding a reliable and consistent model to predict property market movements over a five to ten year timeframe. This research focuses on office rental growth forecasts and overviews many of the office rent models that have evolved over the past 20 years. A model by DiPasquale and Wheaton is selected for testing in the Brisbane office market. The adaptation of this model did not provide explanatory variables that could assist in developing a reliable, predictive model of office rental growth. In light of this result, this paper suggests a system dynamics framework that includes an econometric model based on historical data as well as user input guidance for the primary variables. The rent forecast outputs would be assessed having regard to market expectations and probability profiling undertaken for use in simulation exercises.

Forecasting Housing Supply: Empirical Evidence from the Irish Market

European Journal of Housing Policy, 2007

This paper compares the performance of three alternative models in forecasting housing supply in the Irish Republic. The results highlight key behavioural issues in the dynamics of housing supply that the OLS and VAR models fail to adequately capture due to the inclusion of fundamental variables in their specification. The behaviour of developers in delaying projects means that housing supply can often respond slowly to demand shocks. For this reason the OLS and VAR models substantially overestimate housing supply during the period 1998-2001. In comparison the simple ARIMA model provides generally accurate forecasts of supply.

Comparison between ARIMA and DES Methods of Forecasting Population for Housing Demand in Johor

MATEC Web of Conferences, 2016

Forecasting accuracy is a primary criterion in selecting appropriate method of prediction. Even though there are various methods of forecasting however not all of these methods are able to predict with good accuracy. This paper presents an evaluation of two methods of population forecasting for housing demand. These methods are Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Both of the methods are principally adopting univariate time series analysis which uses past and present data for forecasting. Secondary data obtained from Department of Statistics, Malaysia was used to forecast population for housing demand in Johor. Forecasting processes had generated 14 models to each of the methods and these models where evaluated using Mean Absolute Percentage Error (MAPE). It was found that 14 of Double Exponential Smoothing models and also 14 of ARIMA models had resulted to 1.674% and 5.524% of average MAPE values respectively. Hence, the Double Exponential Smoothing method outperformed the ARIMA method by reducing 4.00 % in forecasting model population for Johor state. These findings help researchers and government agency in selecting appropriate forecasting model for housing demand.

Analysing Uk Real Estate Market Forecast Disagreement

2005

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters, these real estate forecasts are compared with actual real estate performance to assess a number of real estate forecasting issues in the UK over 1999-2004, including real estate forecast error, bias and consensus. The results suggest that real estate forecasts are biased, less volatile compared to market returns and inefficient in that forecast errors tend to persist. The strongest finding is that real estate forecasters display the characteristics associated with a consensus indicating herding.

Examination of property forecasting models - accuracy and its improvement through combination forecasting

2012

This paper investigates property forecasting accuracy and its improvement. The research suggests that despite increased sophistication of property market modelling and forecasting, there still remains a degree of inaccuracy between model outputs and actual property market performance. Subsequently, the paper presents the principle of combination forecasting as a medium helping to achieve greater predictive outcomes. The research implements combination forecasting principle. It then assesses whether combination forecasts from different forecasting techniques are better than single model outputs. It examines which of them-combination or single forecast-fits the UK property market better, and which of these options forecasts best.