Public Information and Abnormal Returns in Real Estate Investment

Gau, George W.

doi:10.1111/1540-6229.00338

Cited by 58 publications

(30 citation statements)

References 18 publications

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“…They also applied statistical methodology of the RWH in investigating single -family residential prices. This study shows advancement in its evaluation of the market when compared to that of analysis of Gau ( ,1985 who tested the weak form efficiency of residential markets using Canada as evidence and reported that Canadian income generating residential markets are efficient. Case and Shiller results did not confirm the presence of weak form efficiency for housing markets this was contrary to the study of Gau ( ,1985 and Linneman (1986).…”

Section: Empirical Evidence On Market Efficiencymentioning

confidence: 95%

“…This study shows advancement in its evaluation of the market when compared to that of analysis of Gau ( ,1985 who tested the weak form efficiency of residential markets using Canada as evidence and reported that Canadian income generating residential markets are efficient. Case and Shiller results did not confirm the presence of weak form efficiency for housing markets this was contrary to the study of Gau ( ,1985 and Linneman (1986). By applying exchange tactics to further proof the absence of the weak form efficiency for US single-family homes Case and Shiller (1989) highlight uncertainties as proof of whether or not housing markets are efficient.…”

Section: Empirical Evidence On Market Efficiencymentioning

confidence: 95%

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Efficiency of Real Estate Market: Evidence from Istanbul Residential Market

Sunjo¹,

Yılmaz²

2017

IOSR JBM

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Section: Empirical Evidence On Market Efficiencymentioning

confidence: 95%

Section: Empirical Evidence On Market Efficiencymentioning

confidence: 95%

Efficiency of Real Estate Market: Evidence from Istanbul Residential Market

Sunjo¹,

Yılmaz²

2017

IOSR JBM

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“…However, Gau (1985) points out some caveats with respect to data problems inherent in his study. Linneman (1986) focuses his study on the efficiency of the housing market on the housing market of Philadelphia for two points in time (1975 and 1978) using observations on individual homeowner assessments of their house values.…”

Section: Literature Reviewmentioning

confidence: 96%

“…His results are in support of the random walk-fair game model and thus, in support of the weak-form version of the efficient market hypothesis. In a subsequent study by Gau (1985) -based on the same series of apartment transactions from 1971 to 1980 as in Gau (1984) -the semi-strong form version of the efficient market hypothesis is considered by applying the asset pricing framework of the capital asset pricing model and the arbitrage pricing theory. These models are utilized to estimate the abnormal returns resulting from two types of public information, major changes in government tax shelter and rent control policies as well as unanticipated changes in mortgage interest rates.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Third, Case and Shiller (1989) extend the geographical area by using data from the Society of Real Estate Appraisers for Atlanta, Chicago, Dallas, and San Francisco / Oakland. Fourth, and most importantly for a theoretical perspective, the applied statistical methodology shows several improvements over the analysis by Gau (1984Gau ( , 1985. The methodology improvements concern testing the random walk hypothesis for housing prices by regressing the change in the index on lagged changes in the index.…”

Section: Literature Reviewmentioning

confidence: 99%

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Further Evidence on the (In-) Efficiency of the U.S. Housing Market

Schindler

2010

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Terms of use: Documents in EconStor mayDownload this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp10004.pdf Non-technical SummaryResearch strategy. In general, we can conclude that investors might be likely to earn excess returns by using past information in the U.S. housing market, in particular when standardized derivatives of the indices are traded on exchange markets. However, due to data limitations, the analysis does not conduct the tests based on prices and price changes in derivatives. This analysis would give further empirical evidence whether inefficiencies in the U.S. housing market are exploitable or whether they are incorporated into the pricing process of tradable products and are thus not exploitable by investors.-II - we apply technical trading strategies to test whether or not the inefficiencies can be exploited by investors earning excess returns. The empirical findings suggest that investors might be able to obtain excess returns from both autocorrelation-and moving average-based trading strategies compared to a buy-and-hold strategy. Das Wichtigste in Kürze

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Forecasting Australia's real house price index: A comparison of time series and machine learning methods

Milunovich

2020

Journal of Forecasting

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We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out-of-sample predictions. The algorithms, which are specified in both singleand multi-equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one-and two-quarter-ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne-quarter-ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.

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Public Information and Abnormal Returns in Real Estate Investment

Cited by 58 publications

References 18 publications

Efficiency of Real Estate Market: Evidence from Istanbul Residential Market

Efficiency of Real Estate Market: Evidence from Istanbul Residential Market

Further Evidence on the (In-) Efficiency of the U.S. Housing Market

Forecasting Australia's real house price index: A comparison of time series and machine learning methods

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