Which factors capitalize into house prices? A Bayesian averaging approach

Stadelmann, David

doi:10.1016/j.jhe.2010.07.002

Cited by 36 publications

(28 citation statements)

References 72 publications

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“…The willingness-to-pay for several public services, amenities or other communal characteristics can be affected by leaving out significant variables and/ or including unnecessary estimators that add noise to our model. Therefore, the selection of the independent variables has to be rather careful (Stadelmann, 2010).…”

Section: Methodology Hedonic Regression Methodsmentioning

confidence: 99%

“…Stadelmann (2010) mentioned that according to the theory of housing market, the housing prices in communities with more attractive characteristics such as the proximity to the amenities of the city centres, access to public transportation and other services, school quality, etc. are expected to be higher than in communities without these attributes.…”

Section: Literature Review the Housing Market Attributesmentioning

confidence: 99%

“…The overall concept of capitalisation could be summarised that every attribute/characteristic of the market can be capitalised (Oates, 1969(Oates, , 1973Pollakowski, 1973;Abelson and Markandya, 1985;Reback, 2005;Clapp et al, 2008;Stadelmann, 2010).…”

Section: Literature Review the Housing Market Attributesmentioning

confidence: 99%

See 2 more Smart Citations

A Hedonic Model of House Prices in the Greek Islands

2013

Proceedings of the 20th Annual European Real Estate Society Conference - Vienna, Austria

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Section: Methodology Hedonic Regression Methodsmentioning

confidence: 99%

Section: Literature Review the Housing Market Attributesmentioning

confidence: 99%

See 1 more Smart Citation

A Hedonic Model of House Prices in the Greek Islands

2013

Proceedings of the 20th Annual European Real Estate Society Conference - Vienna, Austria

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“…And in connection with housing price fore-casting, it was used in a variety of empirical analyses on the United States (Dua et al, 1999), Switzerland (Stadelmann, 2010), South…”

Section: Housing Price Forecastingmentioning

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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“…The algorithm of Bayesian Averaging of Classical Estimators (BACE) was developed by Sala-i-Martin, Doppelhofer, and Miller (2004) and later elaborated by Stadelmann (2010). The general principle for this model can be summarized by the following assumptions: let Y be a regressor and X={X 1 , X 2 ,...,X s } be an s-element set of potential predictor variables.…”

Section: Bayesian Averaging Of Classical Estimatorsmentioning

confidence: 99%

Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates

Białowolski¹,

Kuszewski²,

Witkowski³

2012

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The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators (BACE) is employed. The models are atheoretical (i.e. they do not reflect causal relationships postulated by the macroeconomic theory) and the role of regressors is played by business and consumer tendency survey-based indicators. Additionally, survey-based indicators are included with a lag that enables to forecast the variables of interest (GDP, unemployment, and inflation) for the four forthcoming quarters without the need to make any additional assumptions concerning the values of predictor variables in the forecast period. Bayesian Averaging of Classical Estimators is a method allowing for full and controlled overview of all econometric models which can be obtained out of a particular set of regressors. In this paper authors describe the method of generating a family of econometric models and the procedure for selection of a final forecasting model. Verification of the procedure is performed by means of out-of-sample forecasts of main economic variables for the quarters of 2011. The accuracy of the forecasts implies that there is still a need to search for new solutions in the atheoretical modelling. IntroductionThe main aim of this paper is to construct a model that would accurately forecast basic macroeconomic indicators, i.e. the GDP growth rate (GDP), the consumer price inflation (CPI), and the unemployment rate (UNE) measured in line with the methodology of the Polish Labour Force Survey (BAEL). The model presented in this paper is by definition atheoretical, i.e. its specification does not intend to reflect the causal relations driven from the economic theory. Additionally, it is based on the results of business and consumer tendency surveys.The events of the crisis period proved that Diebold's concern about macroeconomic forecasting was justified.Dynamic simultaneous equations models, general equilibrium models, dynamic stochastic general equilibrium models and vector autoregressive (VAR) models were predominantly unable to generate accurate short-term macroeconomic predictions in that period.The concept of an atheoretical model construction was proposed by Sargent andSims (1977) andSims (1980). The idea was either acknowledged or criticised for being "atheoretical macroeconomics" (Cooley & LeRoy, 1985

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Which factors capitalize into house prices? A Bayesian averaging approach

Cited by 36 publications

References 72 publications

A Hedonic Model of House Prices in the Greek Islands

A Hedonic Model of House Prices in the Greek Islands

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

Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates

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