Housing Investment: What Makes it so Volatile? Theory and Evidence from OECD Countries

Nguyen, Hung

doi:10.2139/ssrn.1690462

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The negative income elasticity for housing demand is also reported by previous work (Durkaya and Yamak, 2004;Fontenla and Gonzalez, 2009;Goodman and Thibodeau, 2008;Tiwari, 2000). The housing collateral constraint gives information and conceives an association between the housing market and borrowing capacity that strengthens the response of housing demand to technology shocks in economies with more liberalized mortgage markets (Nguyen, 2013;Oikarinen, 2012). Han (2010) found that households with lower income are more elastic to shocks in monthly cost that decreases their affordability of larger types of houses.…”

Section: Discussionmentioning

confidence: 99%

Determinants of housing demand in the Erzurum province, Turkey

Oktay

Karaaslan

Alkan

et al. 2014

International Journal of Housing Markets and Analysis

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Purpose – The main aim of this study is to determine the factors that influence the housing demand of households in Erzurum, northeastern Turkey. Housing demand is generally affected by several factors including housing prices, individuals’ income, expectations and choices and so on, as a means of its demographic and socio-psychological contexts. Design/methodology/approach – A questionnaire-based cross-sectional survey was carried out, in which the outcome variable had binary responses such as whether to invest in housing or not. A binary logistic regression analysis was performed to estimate the underlying data. Findings – The questionnaire was conducted in 2,927 households living in Erzurum city center, and 47 per cent of the respondents claimed that they would consider investing in housing in the future. The estimation results reveal that demographic or socio-economic factors that may possibly influence housing demand of the respondents are as follows: household head’s and spouse’s occupation, monthly income, the number of individuals in the family and car ownership. Originality/value – This paper involves the most comprehensive survey addressing the housing demand in the East Anatolian Region, Turkey. Additionally, this paper aims to contribute to the existing housing literature through establishing the statistical analysis of housing demand in an unstudied territory of the world.

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Section: Discussionmentioning

confidence: 99%

Determinants of housing demand in the Erzurum province, Turkey

Oktay

Karaaslan

Alkan

et al. 2014

International Journal of Housing Markets and Analysis

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“…Research on real estate investment strategy and management has been conducted on mixed-asset portfolios (Hoesli et al ., 2004, Bekkers et al , 2009), real estate only (Byrne and Lee, 2001) and within real estate contexts (Lee and Devaney, 2007, Viezer, 2000). The housing investment literature has mainly addressed the role of housing in the composition of individuals’ wealth and portfolio choices (Cocco, 2005; Quigley, 2006), the level and behaviour of the related mortgage market (Nguyen, 2013) and general investment in housing analysis (Topel and Rosen, 1988; Zahirovich-Herbert and Gibler, 2014). In this paper, we concentrate on within housing real estate allocation in the context of market heterogeneity.…”

Section: Literature Reviewmentioning

confidence: 99%

Market heterogeneity, investment risk and portfolio allocation

Amédée‐Manesme

Baroni

Barthélémy

et al. 2017

IJHMA

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Purpose: With Paris' apartment market as a case study, this research addresses the heterogeneity of real estate assets with regard to investment and portfolio allocation. It demonstrates how the analysis per quantile may draw more suitable information about market risk and return. Design/methodology/approach: Quantile regression is used to handle the fact that willingness to pay for housing attributes may vary greatly over both space and asset value categories. The method is alternately applied on central and peripheral districts of Paris, or "arrondissements", with hedonic indices built for nine deciles over a 17-year period (1990-2006). Portfolio allocation is subsequently analysed through the analysis of risk and return metrics. Findings: The findings suggest that during the slump, peripheral districts show better resilience while also exhibiting a lower volatility. In addition, higher returns are observed for lower-priced apartments, both central and peripheral. During the recovery and boom stages of the cycle, the highest returns are experienced for the cheapest apartments in central locations, whereas upper-priced, centrally located units yield the lowest returns.

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“…For private households, housing investment often constitutes their largest lifetime investment and accounts for most of their wealth. For forecasters and analysts, housing investment is a significant and volatile component of aggregate demand (Nguyen, 2013) and an important driver of the business cycle (Huang et al, 2020; Leamer, 2015; Piazzesi & Schneider, 2016), including for the prediction of recessions (Aastveit et al, 2019; Kohlscheen et al, 2018). Changes to housing wealth have significant implications for private consumption spending (de Bondt et al, 2020, 2021), and, as the Great Recession of 2008–09 has shown once again, housing booms usually develop into a recession, and when combined with a credit boom, they predict a harder landing (Cerutti et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Forecasting housing investment

Martínez

Bondt

Gieseck

2023

Journal of Forecasting

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This study applies a model averaging approach to conditionally forecast housing investment in the largest Euro area countries and the Euro area. To account for substantial modeling uncertainty, it estimates a large and diverse number of vector error correction models using a wide set of long‐ and short‐run determinants and applies subset selection based on in‐sample and out‐of‐sample criteria. First, a pseudo out‐of‐sample forecast exercise shows that our model averaging approach consistently beats a battery of distinguished benchmark models, including BVARs, FAVARs, LASSO, and Ridge regressions. This evidences that model averaging provides more accurate forecasts also in the case of housing investment. Second, we find remarkable cross‐country heterogeneity in the drivers of housing investment. Overall, these findings guide forecasters and modelers on improving housing investment models and policymakers on implementing country‐specific housing market policies.

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Housing Investment: What Makes it so Volatile? Theory and Evidence from OECD Countries

Cited by 3 publications

References 21 publications

Determinants of housing demand in the Erzurum province, Turkey

Determinants of housing demand in the Erzurum province, Turkey

Market heterogeneity, investment risk and portfolio allocation

Forecasting housing investment

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