Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices

Miles, William

doi:10.1007/s11146-007-9067-1

Cited by 85 publications

(59 citation statements)

References 20 publications

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“…Moreover, we also examine this issue of the forecast performance of non-linear versus linear models for interval and density forecasts, in addition to point forecasts. Miles (2008) considers linear and nonlinear forecasts of house prices in five US states -California, Florida, Massachusetts, Ohio, and Texas -using the generalized autoregressive (GAR) model. He concludes that the "GAR does a better job at out-of-sample forecasting … in many cases, especially in those markets traditionally associated with high home-price volatility."…”

Section: Introductionmentioning

confidence: 99%

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

2015

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This paper provides out-of-sample forecasts of linear and non-linear models of US and four Census subregions' housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The non-linear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and non-linear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and non-linear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the US, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.

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Section: Introductionmentioning

confidence: 99%

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

2015

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“…Mills (2008) showed that housing business is a critical factor in a close relation to a country's economy from the viewpoint of policy-making authorities, citing that 20 housing business recessions between 1970 and 2000 led to 19 economic recessions in OECD countries. Crawford (1995) showed that housing price volatility is a major factor behind mortgage default and loan prepayment, and is particularly very important to financial institutions heavily relying on housing mortgage loans.…”

Section: Background Of Studymentioning

confidence: 99%

A Study on Forecasting Model for Apartment Housing Price Index Reflecting Model Uncertainty*: Focused on BMS, BMA

Kim¹,

Kim²

2015

J. Real Estate Anal.

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This paper suggests a new housing price index prediction model reflecting model uncertainty and improving information loss arising from Frequentists approach by using Bayesian Model Selection (BMS) and Bayesian Model Averaging (BMA) that are based on Bayesian approach. We used 11 variables including macroeconomic variables and regional variables of Seoul and Busan from the 1st quarter of 2007 to the 4th quarter of 2010, and found out that Seoul's best model consists of fluctuation of land price (Seoul), consumer price index, market interest, mortgage rate, and CD rate, and Busan's best model consists of nominal GDP. In addition, we derived top ranked model with Occam's Window. With selected models and AR model, we predicted housing trade price index from the 1st quarter of 2011 to the 4th quarter of 2014, and both BMS and BMA showed better performance in prediction than AR.

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“…Ou Tinghao (2007) W. Miles (2008) proves that the GAM model surpasses ARMA and the GARCH model in the forecast aspect when analyzing house price which is nonlinear system through experiment. Particularly, its application has better effect in the high price tradition housing market which is in existence of soaks the desert.…”

Section: Regression Time Series Stochastic Process and Fuzzymentioning

confidence: 99%

The Review on Non-linear Analysis and Forecasting Methods of the Real Estate Market in China

Fan¹,

Xu²

2009

ASS

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The real estate market is a complex system .The scientific analysis and forecast are the premise of making correct decision .This paper gives a review of the research and application on the non-linear quantitative analysis and the forecast methods of the real estate market. Especially, its development during the recent years in China is discussed. It is in hopes of providing references to the researchers on this aspect.

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Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices

Abstract: Bubbles, Forecasting models, Generalized autoregressive model, Nonlinear techniques,

Cited by 85 publications

References 20 publications

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

A Study on Forecasting Model for Apartment Housing Price Index Reflecting Model Uncertainty*: Focused on BMS, BMA

The Review on Non-linear Analysis and Forecasting Methods of the Real Estate Market in China

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