Analysis of Consumers’ Perceptions of Buying Conditions for Houses

Dua, Pami

doi:10.1007/s11146-007-9084-0

Cited by 26 publications

(20 citation statements)

References 28 publications

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“…Households, presumably the least sophisticated and most informationally constrained agents in the market, appear to rely in part on past price movements to form their nonfundamentals‐based expectations of future house price movements. This is consistent with the findings of Dua () and Piazzesi and Schneider (). Past house price movements are also weakly predictive of changes in builder sentiment (equation (5)) and highly predictive of lender sentiment (equation (8)).…”

Section: Resultssupporting

confidence: 93%

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Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

Ling¹,

Ooi²,

Le³

2015

J of Money Credit Banking

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This paper examines the role of nonfundamentals-based sentiment in house price dynamics, including the well-documented volatility and persistence of house prices during booms and busts. To measure and isolate sentiment's effect, we employ survey-based indicators that proxy for the sentiment of three major agents in housing markets: home buyers (demand side), home builders (supply side), and lenders (credit suppliers). After orthogonalizing each sentiment measure against a broad set of fundamental variables, we find strong and consistent evidence that the changing sentiment of all three sets of market participants predicts house price appreciation in subsequent quarters, above and beyond the impact of changes in lagged price changes, fundamentals, and market liquidity. More specifically, a one-standard-deviation shock to market sentiment is associated with a 32-57 basis point increase in real house price appreciation over the next two quarters. These price effects are large relative to the average real price appreciation of 71 basis points per quarter observed over the full sample period. Moreover, housing market sentiment and its effect on real house prices is highly persistent. The results also reveal that the dynamic relation between sentiment and house prices can create feedback effects that contribute to the persistence typically observed in house price movements during boom and bust cycles.JEL codes: R3, R21

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Section: Resultssupporting

confidence: 93%

“…Dua () also uses the University of Michigan's Surveys of Consumer Attitudes to identify the factors influencing home buyers’ perceptions. Interestingly, impulse response functions (IRFs) show that rising prices predict increased sentiment in the short run, but negative sentiment over longer horizons.…”

Section: Literature Reviewmentioning

confidence: 99%

Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

Ling¹,

Ooi²,

Le³

2015

J of Money Credit Banking

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“…Croce and Haurin () show that the GTTB index outperforms other indexes in predicting housing market dynamics. Dua () utilizes the GTTB index in a Granger‐causality analysis of macroeconomic variables and consumer house‐buying attitudes. The above‐mentioned literature motivates the selection of the GTTB index as the proxy for households’ expectations in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk diversification gains from metropolitan housing assets

Huang

2019

Rev Financ Econ

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The study analyzes the roles of metropolitan housing assets in risk diversification by assessing intertemporal hedging demands for multi‐asset portfolios, which include metropolitan houses, REITs, stocks, bonds, and riskless assets. Investors substitute housing assets in high‐population MSAs with those in low‐population cities, and they switch their holdings of housing assets to less risky bonds in the 2007–2008 housing bust. The findings from the multi‐period portfolio choice problem provide evidence for momentum reversal since forward‐looking investors substitute bottom metropolitan housing assets for top ones in the housing boom, and the GTTB index and the lagged REIT price return have negative impacts on various asset returns.

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“…Traditionally, the University of Michigan Consumer Sentiment Index has been used by several studies to show improvement in explanatory power (Carroll et al (1994), Bram and Ludvigson (1998), Howrey (2001), Easaw and Heravi (2004)) The majority of sentiment focused studies in real estate, such as Goodman (1994), Weber and Devaney (1996), Dua (2008), Nanda (2007), Croce and Haurin (2009) or Ling et al (2014) tend to concentrate on the residential sector. Baker and Saltes (2005), Clayton et al (2009), and Marcato and Nanda (2016) analyse the role of sentiment data in explaining market dynamics in commercial real estate.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring sentiment in real estate – a comparison study

Heinig

Nanda

2018

JPIF

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Purpose -In mainstream economics and finance literature, market sentiment is considered "irrational". This leads to significant challenges in capturing the effect of sentiment on economic relationships. Real estate is even more complex due to the fact that the sector exhibits several market inefficiencies. The purpose of this paper is to explore the literature and present a simple test for the potential of using three different sentiment indicators to improve a basic cap rate model. The authors establish the case using commercial real estate (CRE) data for London West End. Design/methodology/approach -The three indicators differ in their underlying source and method. The authors used orthogonalisation and principal component analysis for a macroeconomic sentiment indicator. Furthermore, online search volume data have been used to mirror the market sentiment for the London West End market. Finally, textual analysis based on word lists has been applied to corpus of market reports. Findings -The results indicate considerable improvement in the authors' ability to capture the effect of sentiment. Furthermore, the consideration of a human factor leads to improvement in the basic yield model. Practical implications -The methods suggest that sentiment extracted from more forward-looking sources, such as online searches, could be a significant information gain for investors, lenders or other market participants. The additional information could be used to adjust their behaviour within the market. Originality/value -To the authors' knowledge, this is the first study that applies textual analysis to market reports for the CRE market in the UK.

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Analysis of Consumers’ Perceptions of Buying Conditions for Houses

Cited by 26 publications

References 28 publications

Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

Risk diversification gains from metropolitan housing assets

Measuring sentiment in real estate – a comparison study

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