2007
DOI: 10.1007/s11146-007-9094-y
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Regional Housing Prices in the USA: An Empirical Investigation of Nonlinearity

Abstract: Existing literature on housing prices is predominantly in a linear framework, and an important question that has not been addressed is whether housing prices exhibit nonlinearity. We examine Smooth Transition Autoregressive (STAR) model based nonlinear properties of housing prices over the 1969–2004 period for the entire US and the four regions. Our main findings are (1) housing price for the entire US and all regions except for the Midwest show non-linearity, (2) the dynamic properties implied by the nonlinea… Show more

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Cited by 68 publications
(51 citation statements)
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References 27 publications
(30 reference statements)
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“…To separately discuss each relationship of Taiwan's overall house price and each regional house price, each time we will use one of these three differences of regional house price indices -DTC, DTA, or DKA -to be the exogenous independent variable i t X  and transition variable t s . A rejection of H 02 implies the selection of the LSTR model (Kim & Bhattacharya, 2009). .…”
Section: The Choice Between Linear Model and Non-linear Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…To separately discuss each relationship of Taiwan's overall house price and each regional house price, each time we will use one of these three differences of regional house price indices -DTC, DTA, or DKA -to be the exogenous independent variable i t X  and transition variable t s . A rejection of H 02 implies the selection of the LSTR model (Kim & Bhattacharya, 2009). .…”
Section: The Choice Between Linear Model and Non-linear Modelmentioning
confidence: 99%
“…To avoid a delay that results in paying even higher prices, buyers are eager to get into the house market when prices are increasing, while sellers are often unwilling to cut prices when markets are flat (Abelson et al, 2005). The existence of lumpy transaction costs in the house market can cause important non-linearities or threshold effects in a house market's aggregate demand (Kim and Bhattacharya, 2009), but if house prices are characterized by non-linear properties, then this implies that linear models are not an appropriate tool to analyze house prices.…”
Section: Introductionmentioning
confidence: 99%
“…Although almost all previous empirical work relating to house price modelling is based on linear specifications (see for example Abraham and Hendershott, 1996) the well established literature on cyclical behavior of macroeconomic variables suggests that the nonlinearity of house prices should stem from the asymmetric properties of house price determinants like GDP, interest rate or bank lending. There are a few available empirical works which corroborate this hypothesis; for example, Kim and Bhattacharya (2009) use smooth a transition autoregressive model (STAR) to test for nonlinearity in the regional hosing market in the United States. Balcilar et al (2015) use a STAR-type model to forecast house price distributions.…”
Section: Introductionmentioning
confidence: 99%
“…And second, why should one be interested in forecasting house prices? To answer to the first question, it must be realized that the behaviour of the housing market is not the same across phases of expansion and contraction of the swings that characterize the real estate sector (Kim and Bhattacharya, 2009). Seslen (2004) argues that households exhibit forward looking behaviour and have higher probability of trading up, during the upswing with equity constraints being less binding.…”
Section: Introductionmentioning
confidence: 99%
“…The only other study that we are aware of that tests for non-linearity in the housing market is by Kim and Bhattacharya (2009) in which the authors examined the STAR models based on non-linear properties of house prices for the aggregate US economy and its four census regions (North East, Midwest, South and West) using monthly data over the period of 1969:01-2004:12. The authors concluded that house prices for the entire US and all four census regions, barring the Midwest, were nonlinear.…”
Section: Introductionmentioning
confidence: 99%