Determinants of house prices in Istanbul: a quantile regression approach

Akay, Ebru Çağlayan; Eban, Arikan

doi:10.1007/s11135-009-9296-x

Cited by 55 publications

(31 citation statements)

References 41 publications

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“…In contrast, estimations based on the conditional mean (such as ordinary least square (OLS)) may not be able to completely characterize the distribution of house prices and thus produce biased results. Researchers have found that quantile regression is particularly useful when examining segmented markets (such as occurs in most urban residential housing markets) because full characterization of the conditional distribution, rather than the conditional mean, of house prices is properly examined (e.g., Coulson and Mcmillen, 2007;McMillen, 2008;Zietz et al, 2008;Mak et al, 2010;Ebru and Eban, 2011). Zietz et al (2008) applied a novel spatial quantile regression estimation strategy using Orem/Provo, Utah housing data.…”

Section: Introductionmentioning

confidence: 99%

“…Zietz et al (2008) applied a novel spatial quantile regression estimation strategy using Orem/Provo, Utah housing data. Mak et al (2010), and Ebru and Eban (2011) used quantile regression technique to analyze Hong Kong and Istanbul real estate prices, respectively. All three studies found that housing attributes were valued differently across the conditional price distribution.…”

Section: Introductionmentioning

confidence: 99%

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Neighborhood impact of foreclosure: A quantile regression approach

Zhang

Leonard²

2014

Regional Science and Urban Economics

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This paper uses quantile regression, while accounting for spatial autocorrelation, to examine the simultaneous space-time impact of foreclosures on neighborhood property values. We find that negative price externalities associated with neighborhood foreclosures are greatest (1) among lower-priced homes, (2) within 250 feet of the property and (3) in the 12 months following a foreclosure auction. By using quantile regression, we are able to also investigate changes in the distribution of house prices associated with varying levels of neighborhood foreclosures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neighborhood impact of foreclosure: A quantile regression approach

Zhang

Leonard²

2014

Regional Science and Urban Economics

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“…Catalão 2010), characterized by a lack of transparency in its transactions, it is an important element of any country's economy, as an indicator of its performance (cf. Ebru, Eban 2009;Rybak, Shapoval 2011;Warren 2011;ferreira et al 2014a). In fact, since it is intrinsically associated with the functioning of the labor, financial, goods and services markets, any change in the housing market impacts those underlying markets as well.…”

Section: Measuring Tom: Fundamentals and Methodologiesmentioning

confidence: 99%

For Sale… but for How Long? A Methodological Proposal for Estimating Time-on-the-Market

Martins

Filipe

Ferreira

et al. 2015

International Journal of Strategic Property Management

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abstract. residential real estate assumes crucial importance in a country's socioeconomic development. It is an important field of study, and much work has gone into better understanding the sector and the factors determining sales within it, such as time-on-the-market (TOM). TOM can be influenced by a variety of elements; a fact that in effect raises a lot of issues, because these determinants are often interpreted in an ambiguous or unstructured way. This study aims to bring greater accuracy and structure to our understanding of these factors, by showing that the integrated use of cognitive mapping with the Analytic Hierarchy Process (AHP) can give rise to a conceptually coherent and empirically valid framework to calculate TOM indices in the residential real estate market. Because it takes into account both tangible and intangible characteristics of a house, this measurement framework also boosts strategic planning support and allows for more informed business planning, which we believe can be a real contribution to the development of the real estate market. The practical implications and limitations of this evaluation system are also discussed.

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“…For the link between housing states and economic cycles, please refer to the review inConsulting and Research (2006).…”

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confidence: 99%

“…Ebru and Eban (2011) also employ a quantile regression model to examine the relationship between the housing price and some of the housing characteristics of Istanbul by real estate agency surveys in 2007 (October-December).…”

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confidence: 99%

Housing price–volume correlations and boom–bust cycles

Wang

Zeng

2016

Empir Econ

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This paper investigates the housing price-volume nexus based on different levels of liquidity for the US new one-family housing market over the period between January 1963 and November 2009. We mainly analyze the differential responses of trading volumes to housing price changes (denoted as price elasticity), employing a quantile cointegrating approach that allows us to capture housing boom-bust cycles earlier. In addition, we also explore the long-term impacts of monetary policies on housing sales. According to our findings, the price elasticities perform differently across the booms and busts of housing markets. Shifts in price elasticities can transmit signals of oncoming upturns, recovery, and downturns of housing cycles, which possibly relate to the business cycle. On the other hand, a contractionary monetary policy exerts a much effective control over an overheated housing market, but an expansionary monetary policy has a relatively small influence on stimulating a depressed housing market. Our findings offer some important suggestions and policy implications.

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Determinants of house prices in Istanbul: a quantile regression approach

Abstract: Hedonic price model, Real estate market, Quantile regression, Outliers, Turkey, C12, C13, R31,

Cited by 55 publications

References 41 publications

Neighborhood impact of foreclosure: A quantile regression approach

Neighborhood impact of foreclosure: A quantile regression approach

For Sale… but for How Long? A Methodological Proposal for Estimating Time-on-the-Market

Housing price–volume correlations and boom–bust cycles

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