Identifying and forecasting house prices: a macroeconomic perspective

Chen, Nan‐Kuang; Cheng, Han-Liang; Mao, Ching-Sheng

doi:10.1080/14697688.2013.842650

Cited by 16 publications

(14 citation statements)

References 43 publications

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“…All in all, our work suggests that macro-prudential policies (in particular, housing finance regulation) might shield against the deleterious macroeconomic impact of housing busts and financial stability risks posed by housing booms (Arslan et al 2015a;Rubio and Carrasco-Gallego 2017;Kelly et al 2017). Additionally, it emphasizes the role that monetary policy can play, namely, by affecting interest rates and avoiding (or reducing) the likelihood of housing booms (Arslan 2014;Chen et al 2014). Moreover, as boom-bust cycles in the housing market are typically associated with the credit cycle, monetary policy can also be a key complement to macroprudential policies (Bailliu et al 2015;Falagiarda and Saia 2017;Gelain and Ilbas 2017;Rubio and Carrasco-Gallego 2014).…”

Section: Discussionmentioning

confidence: 71%

“…All in all, our work is inspired by the studies of Leung (2007Leung ( , 2014, Iacoviello (2005) and Chen and Leung (2008) and Chen et al (2014) and sheds more light on our understanding of the housing price dynamics. Specifically, in a creditless world, house prices either follows an auto-regressive process (Leung 2007) or can be described as an error-correction model (Leung 2014).…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Moreover, the effectiveness of such policies tends to be maximized when they are accompanied by countercyclical monetary policy. 5 For instance, Chen et al (2014) estimate a regime-switching model of house prices, and show that inflation and interest rate are the most robust determinants. across the various phases of the house price cycle.…”

Section: Review Of the Literaturementioning

confidence: 99%

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The Housing Cycle: What Role for Mortgage Market Development and Housing Finance?

Agnello

Castro

Sousa

2019

J Real Estate Finan Econ

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We use duration analysis to assess the impact of securitization, mortgage sector liberalization and government involvement in housing finance on the length of housing booms, busts and normal times in a panel of 20 OECD countries over the period 1970Q1-2015Q4. Our results reveal that a move towards a more liberalized mortgage sector is associated with longer housing booms, while an increase in securitization is linked with shorter housing busts. They also show that the length of housing booms and busts is particularly sensitive to housing finance characteristics, but that does not seem to be the case for normal times. Additionally, government support measures do not necessarily cushion against housing busts. A careful assessment of their distributional impact, as well as their effect on the trade-off between liquidity and guarantee/loan provision, is also required to prevent (longer) housing booms. All in all, housing finance regulation may prove especially relevant to shield against the damaging effects of housing busts and the financial stability risks associated with housing booms. Monetary policy can also be an important complement to macro-prudential policies. Finally, government participation in housing finance should be designed in a way that avoids an undesirable amplification of house price fluctuations.

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Section: Discussionmentioning

confidence: 71%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

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The Housing Cycle: What Role for Mortgage Market Development and Housing Finance?

Agnello

Castro

Sousa

2019

J Real Estate Finan Econ

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“…Common approaches to detect house prices cycles follow the business cycle literature by identifying peaks and troughs in an aggregated house price index (Igan and Loungani, 2012) and (Sinai, 2012) or by using a Markov switching model Chen et al (2014). However, these approaches focus on the level of house prices or their changes.…”

Section: Introductionmentioning

confidence: 99%

Regional composition of national house price cycles in the US

Prüser

Schmidt

2021

Regional Science and Urban Economics

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House price cycles may have considerable macroeconomic effects even if they evolve heterogeneous across local markets. In this paper we use a panel Markov switching model allowing for time-varying volatility to analyze national and state level house price regimes for the US jointly. Our approach identifies three house price regimes endogenously. A nationwide boom regime, a spatially limited bust regime and a nationwide bust regime. The spatially limited bust regime occurs in the coastal states where compared to other states the population density is high, the unemployment rate, the housing density as well as the land supply elasticity is low. This spatially limited bust regime usually follows a nationwide house price boom. Hence, house price movements in the coastal states usually determine the nationwide cycle in the US. Moreover, boom and bust cycles are accompanied by an exaggeration of house price increases during the boom in this group of states. In contrast, a bubble in the housing market occored in almost all states previous to the Great Recession. This is one explanation for the severity of the Great Recession.

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“…17 In this paper, EFP is defined as the spread between high-rating corporate bond and the low-rating one (Baa-Aaa).…”

Section: Datamentioning

confidence: 99%

Losing Track of the Asset Markets: The Case of Housing and Stock

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. , some models that emphasize imperfect capital markets outperform an AR(1) for the forecast of housing returns. After 2006, a model that includes both an external finance premium (EFP) and the TED spread "learns and adjusts" faster than competing models. Models that encompass GDP experience a significant decay in predictive power. We also demonstrate that a simulation-based approach is complementary to the EPPC methodology. Terms of use: Documents in

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Identifying and forecasting house prices: a macroeconomic perspective

Cited by 16 publications

References 43 publications

The Housing Cycle: What Role for Mortgage Market Development and Housing Finance?

The Housing Cycle: What Role for Mortgage Market Development and Housing Finance?

Regional composition of national house price cycles in the US

Losing Track of the Asset Markets: The Case of Housing and Stock

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