Forecasting House Prices through Credit Conditions: A Bayesian Approach (2024)

van der Drift, R., de Haan, J. (2024). Forecasting House Prices through Credit Conditions: A Bayesian Approach. Computational Economics. https://doi.org/10.1007/s10614-023-10542-9

van der Drift, Rosa ; de Haan, Jan ; Boelhouwer, Peter. / Forecasting House Prices through Credit Conditions : A Bayesian Approach. In: Computational Economics. 2024.

@article{6a4c6bc21106490db6c01f4e33b838d5,

title = "Forecasting House Prices through Credit Conditions: A Bayesian Approach",

abstract = "As housing development and housing market policies involve many long-term decisions, improving house price predictions could benefit the functioning of the housing market. Therefore, in this paper, we investigate how house price predictions can be improved. In particular, the merits of Bayesian estimation techniques in enhancing house price predictions are examined in this study. We compare the pseudo out-of-sample forecasting power of three Bayesian models—a Bayesian vector autoregression in levels (BVAR-l), a Bayesian vector autoregression in differences (BVAR-d), and a Bayesian vector error correction model (BVECM)—and their non-Bayesian counterparts. These techniques are compared using a theoretical model that predicts the borrowing capacity of credit-constrained and unconstrained households to affect house prices. The findings indicate that the Bayesian models outperform their non-Bayesian counterparts, and within the class of Bayesian models, the BVAR-d is found to be more accurate than the BVAR-l. For the two winning Bayesian models, i.e., the BVECM and the BVAR-d, the difference in forecasting power is more ambiguous; which model prevails depends on the desired forecasting horizon and the state of the economy. Hence, both Bayesian models may be considered when conducting research on house prices.",

keywords = "Bayesian VAR, Bayesian VECM, Cointegration, Forecasting, House prices",

author = "{van der Drift}, Rosa and {de Haan}, Jan and Peter Boelhouwer",

year = "2024",

doi = "10.1007/s10614-023-10542-9",

language = "English",

journal = "Computational Economics",

issn = "0927-7099",

publisher = "Springer",

}

van der Drift, R, de Haan, J 2024, 'Forecasting House Prices through Credit Conditions: A Bayesian Approach', Computational Economics. https://doi.org/10.1007/s10614-023-10542-9

Forecasting House Prices through Credit Conditions: A Bayesian Approach. / van der Drift, Rosa; de Haan, Jan; Boelhouwer, Peter.
In: Computational Economics, 2024.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Forecasting House Prices through Credit Conditions

T2 - A Bayesian Approach

AU - van der Drift, Rosa

AU - de Haan, Jan

AU - Boelhouwer, Peter

PY - 2024

Y1 - 2024

N2 - As housing development and housing market policies involve many long-term decisions, improving house price predictions could benefit the functioning of the housing market. Therefore, in this paper, we investigate how house price predictions can be improved. In particular, the merits of Bayesian estimation techniques in enhancing house price predictions are examined in this study. We compare the pseudo out-of-sample forecasting power of three Bayesian models—a Bayesian vector autoregression in levels (BVAR-l), a Bayesian vector autoregression in differences (BVAR-d), and a Bayesian vector error correction model (BVECM)—and their non-Bayesian counterparts. These techniques are compared using a theoretical model that predicts the borrowing capacity of credit-constrained and unconstrained households to affect house prices. The findings indicate that the Bayesian models outperform their non-Bayesian counterparts, and within the class of Bayesian models, the BVAR-d is found to be more accurate than the BVAR-l. For the two winning Bayesian models, i.e., the BVECM and the BVAR-d, the difference in forecasting power is more ambiguous; which model prevails depends on the desired forecasting horizon and the state of the economy. Hence, both Bayesian models may be considered when conducting research on house prices.

AB - As housing development and housing market policies involve many long-term decisions, improving house price predictions could benefit the functioning of the housing market. Therefore, in this paper, we investigate how house price predictions can be improved. In particular, the merits of Bayesian estimation techniques in enhancing house price predictions are examined in this study. We compare the pseudo out-of-sample forecasting power of three Bayesian models—a Bayesian vector autoregression in levels (BVAR-l), a Bayesian vector autoregression in differences (BVAR-d), and a Bayesian vector error correction model (BVECM)—and their non-Bayesian counterparts. These techniques are compared using a theoretical model that predicts the borrowing capacity of credit-constrained and unconstrained households to affect house prices. The findings indicate that the Bayesian models outperform their non-Bayesian counterparts, and within the class of Bayesian models, the BVAR-d is found to be more accurate than the BVAR-l. For the two winning Bayesian models, i.e., the BVECM and the BVAR-d, the difference in forecasting power is more ambiguous; which model prevails depends on the desired forecasting horizon and the state of the economy. Hence, both Bayesian models may be considered when conducting research on house prices.

KW - Bayesian VAR

KW - Bayesian VECM

KW - Cointegration

KW - Forecasting

KW - House prices

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U2 - 10.1007/s10614-023-10542-9

DO - 10.1007/s10614-023-10542-9

M3 - Article

AN - SCOPUS:85186630505

SN - 0927-7099

JO - Computational Economics

JF - Computational Economics

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van der Drift R, de Haan J, Boelhouwer P. Forecasting House Prices through Credit Conditions: A Bayesian Approach. Computational Economics. 2024. doi: 10.1007/s10614-023-10542-9