Transaction Volume and Price Dispersion in the Presale and Spot Real Estate Markets

Yiu, CY; Wong, Siu Kei; Chau, Kwong Wing

doi:10.1007/s11146-008-9161-z

Cited by 18 publications

(8 citation statements)

References 46 publications

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“…Haurin (1988) compares atypical to typical dwelling units and observes that the sellers of atypical residential properties generally expect a higher value, which results in a longer listing time for such units on the market, and their final transaction price is also subject to large volatility. Yiu et al (2009) further find that square footage and house age are positively correlated with price dispersion in the housing market.…”

Section: Literature Reviewmentioning

confidence: 72%

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International Real Estate Review

Chiang¹,

Ku²,

Liu³

et al. 2019

IRER

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Due to housing stock heterogeneity, most academic discussions on price dispersion in the housing market have traditionally focused on the search behavior of consumers and neglected the housing and neighborhood characteristics that are related to price dispersion. This study applies a rich empirical data set from Taipei to explore the neighborhood characteristics that are associated with a higher degree of dispersion in housing price and associated likelihood of such. We track the housing transactions at the residential community level, and group the communities based on the coefficient of variation of the transaction prices in each community after controlling for community and housing characteristics. We apply a multinomial logistic regression to examine which neighborhood characteristics are more likely to be associated with higher price dispersion. We find that communities with higher price levels and built by government agencies are less likely to have high price dispersion, while those that are older, priced lower or have a minimum floor area of 50 pings are more likely to have higher price dispersion.

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Section: Literature Reviewmentioning

confidence: 72%

“…(2) Transaction Volume Yiu et al (2009) find that the transaction volume is negatively correlated with price dispersion. They suggest that a higher transaction volume might result in more comparable deals that consumers use as reference points which leads to reductions in price variations.…”

Section: Model Setupmentioning

confidence: 99%

International Real Estate Review

Chiang¹,

Ku²,

Liu³

et al. 2019

IRER

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“…Price dispersion is commonly measured by the variance in and skewness of the quality‐controlled price distributions (Leung et al, 2006; Yiu et al, 2009). Based on the raster‐level housing price indices (2045 cells), we test the magnitude of regional price dispersions using three different price dispersion indicators: “

{italicdispersion 1}_{t}

” denotes the variation in the regional price index (

{hpi}_{i, t}

) at time t ; “

{italicdispersion 2}_{t}

” denotes the skewness in regional price distributions; and “

{italicdispersion 3}_{t}

” denotes the “envelope” (the outer layer) of the regional price index.…”

Section: Further Discussionmentioning

confidence: 99%

Tracking the pulse of a city—3D real estate price heat maps

Agarwal

Fan

McMillen

et al. 2021

Journal of Regional Science

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This paper proposes the use of a semiparametric model based on a locally weighted approach that controls for dynamic agglomeration and diffusion effects in constructing localized housing price indices. Based on residential transaction records in Singapore, we create threedimensional interactive heat maps that allow for better measurement and visualization of spatial variations and heterogeneity in price appreciation. The heat map captures regional variations and temporal dispersions in price appreciation rates in the business cycle. Our methodology provides high-resolution information about price dynamics that could aid individual buyers, investors, and policy makers in making objective and informed decisions.

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“…Research on liquidity or trading volume effects in real estate markets was done, for example, by Kluger and Miller (1990), Sirmans et al (1995), and Stein (1995). Recently, more empirical evidence confirmed the significance of trading volume effects on the real estate pricing process (Benveniste et al 2001;Ho 2003;Yiu et al 2009Yiu et al , 2006Yiu et al , 2008. These studies established that liquidity or trading volume plays an important role in affecting the search cost of property information.…”

Section: Literature Reviewmentioning

confidence: 99%

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

2012

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Spatial dependence is often seen as a problem in econometrics rather than in economics. This study seeks to find an economic explanation for spatially correlated real estate prices. We posit spatial dependence as a process to discover price information from neighboring property transactions. Weaker spatial dependence is expected when price information in the immediate vicinity of a subject property is abundant. In the context of apartment buildings, in addition to the more commonly known horizontal dependence, there is also spatial dependence in the vertical dimension within the same building. Based on more than 18,000 transactions of highly homogeneous apartment units in Hong Kong, we found that the trading volume of a building depresses horizontal spatial dependence, but raises vertical spatial dependence. This not only confirmed the role of trading volume in the real estate price discovery process, but also questioned the validity of constant spatial autocorrelation assumption adopted in many studies.

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Transaction Volume and Price Dispersion in the Presale and Spot Real Estate Markets

Cited by 18 publications

References 46 publications

International Real Estate Review

International Real Estate Review

Tracking the pulse of a city—3D real estate price heat maps

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

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