Housing and business cycles in Korea: A multi-sector Bayesian DSGE approach

Lee, Junhee; Song, Joonhyuk

doi:10.1016/j.econmod.2014.11.009

Cited by 17 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the historical shock decomposition of housing prices are similar to those noted by Iacoviello and Neri (2010), contrary to Walentin (2014) which shows that that real housing price fluctuations are negatively affected by monetary policy shocks for Sweden. Further, Ng (2015) and Lee and Song (2015) find that housing preference shocks play a key role in house prices for China and Korea, respectively.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…This shock enhances the possibility of borrowing by households who want to use their houses as collateral, thus facilitating their borrowing and consumption under the high collateral scenario. Since consumption by impatient households is higher, this has a positive effect on total consumption, even if patient consumption contracts (Lee & Song, 2015). Also, while residential investment increases after a housing preference shock, business investment declines in the no collateral and high collateral specifications, since resources in the economy shift from business investment to housing investment in the short term.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…negatively affected by monetary policy shocks for Sweden. Further, Ng (2015) and Lee and Song (2015) find that housing preference shocks play a key role in house prices for China and Korea, respectively.…”

Section: Counterfactual Analysismentioning

confidence: 99%

“…The subjective discount rate for patient households ( β ) is set to 0.9950, which shows that the average annual real interest rate over the sample is at a steady state. We set ( β' ), at 0.97, which is the standard value in the DSGE literature (see Iacoviello & Neri 2010; Lee & Song 2015). The discount factor for impatient households, β ' , is set considerably below β , as the collateral constraint is constantly binding.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Ng (2015) also reveals that monetary policy shocks drive movements in residential investments by 32 per cent. Lee and Song (2015) investigate the importance of the housing sector on business cycles in Korea and finds that housing demand shocks explain a majority of fluctuations, mainly of aggregate consumption, through the collateral constraint mechanism.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Turkish Housing Market Dynamics: An Estimated DSGE Model

Yıldırım

İvrendi

2021

Margin: The Journal of Applied Economic Research

View full text Add to dashboard Cite

In this article, we investigate the underlying driving dynamics behind house price variations in Turkey by estimating a dynamic stochastic general equilibrium (DSGE) model in which the housing market and collateral constraints are included. The model also analyses the interaction between macroeconomic variables and the housing market by making policy simulations under different loan-to-value (LTV) ratios, which are used as a housing market-specific economic policy tool. The model is extended by including the traditional Taylor rule with house prices for representing monetary policy. Our findings show that house prices in Turkey are largely explained by housing preference shocks. Besides, we find that monetary policy shock plays a small role in determining the variables of the housing market in the short-term period. However, the magnitude of the impact of housing market shocks on the rest of the economy depends on the LTV ratios. The higher the LTV ratio, the higher are the effects of the government’s housing policy instrument for stabilising the housing market on real macroeconomic variables such as consumption and output in Turkey. Finally, our findings show that the fluctuations in house prices have not played a substantial role in the monetary policy reaction function of Turkey. JEL Codes: E32, E52, E44, E51, R31

show abstract

Section: Quantitative Resultsmentioning

confidence: 99%

Section: Quantitative Resultsmentioning

confidence: 99%

Section: Counterfactual Analysismentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Turkish Housing Market Dynamics: An Estimated DSGE Model

Yıldırım

İvrendi

2021

Margin: The Journal of Applied Economic Research

View full text Add to dashboard Cite

show abstract

The Impacts of Housing Reform and Environmental Pollution on Metropolitan Housing Prices: Evidence from Guangzhou, China

2022

The Institutional Paradigm of Economic Geography

View full text Add to dashboard Cite

Impacts of demand and supply-side interventions on South Korea’s housing markets: a dynamic housing-CGE analysis

Kim,

Nam

2024

Ann Reg Sci

View full text Add to dashboard Cite

This paper examines the impacts of housing market policies in Korea by developing a dynamic computable general equilibrium model integrating regional housing markets and multiregional mobility. We compare simulation outcomes of demand- and/or supply-side approaches in housing market interventions and address how these various policy instruments affect housing prices, demand, and household welfare. Policy simulation results suggest that supply-based interventions would be more effective than housing tax policies for cooling down overheated housing markets without decreasing consumer welfare. Tax-based demand-side approaches result in a 1.8–2.2% housing price drop and a 1.1–1.2% welfare decline annually between 2021 and 2024. In the supply-side policy, investing in housing construction leads to 3.4–4.1% lower housing prices and 1.5–1.8% enhanced welfare.

show abstract

Housing and business cycles in Korea: A multi-sector Bayesian DSGE approach

Cited by 17 publications

References 6 publications

Turkish Housing Market Dynamics: An Estimated DSGE Model

Turkish Housing Market Dynamics: An Estimated DSGE Model

The Impacts of Housing Reform and Environmental Pollution on Metropolitan Housing Prices: Evidence from Guangzhou, China

Impacts of demand and supply-side interventions on South Korea’s housing markets: a dynamic housing-CGE analysis

Contact Info

Product

Resources

About