Optimal prediction in loglinear models

Garderen, Kees Jan van

doi:10.1016/s0304-4076(01)00061-6

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…Log prices appear, therefore, to be the better choice-provided that adjustment is made for the bias introduced by undoing the log transformation to obtain appraisals. We find that log predictions corrected with the adjustment suggested by Garderen (2001) produce the best out-of-sample performance.…”

Section: Resultsmentioning

confidence: 82%

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Automated valuation modelling: a specification exercise

Schulz

Wersing

Werwatz

2013

Journal of Property Research

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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. Terms of use: Documents in AbstractMarket value predictions for residential properties are important for investment decisions and the risk management of households, banks, and real estate developers. The increased access to market data has spurred the development and application of Automated Valuation Models (AVMs), which can provide appraisals at low cost. We discuss the stages involved when developing an AVM. By reflecting on our experience with md*immo, an AVM from Berlin, Germany, our paper contributes to an area that has not received much attention in the academic literature. In addition to discussing the main stages of AVM development, we examine empirically the statistical model development and validation step. We find that automated outlier removal is important and that a log model performs best, but only if it accounts for the retransformation problem and heteroscedasticity.

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Section: Resultsmentioning

confidence: 82%

“…A longer estimation window provides more observations, but assuming that the coefficients are constant over the longer period is too restrictive. Appraisals from a statistical model fitted to log prices and log predictions corrected with the adjustment suggested by Garderen (2001) produce the best out-of-sample performance.…”

Section: Introductionmentioning

confidence: 99%

Automated valuation modelling: a specification exercise

Schulz

Wersing

Werwatz

2013

Journal of Property Research

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“…Theorem below is an extension of Propositions 2 and 3 in van Garderen (2001). The proof is relegated to the Appendix.…”

Section: Exact Minimum Variance Unbiased Estimatormentioning

confidence: 92%

Exact interpretation of dummy variables in semilogarithmic equations

Garderen

Shah

2002

The Econometrics Journal

185

105

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This paper considers the percentage impact of a dummy variable regressor on the level of the dependent variable in a semilogarithmic regression equation with normal disturbances. We derive an exact unbiased estimator, its variance, and an exact unbiased estimator of the variance. The main practical contribution lies in a convenient approximation for the unbiased estimator of the variance, which can be reported together with Kennedy's approximate unbiased estimator of the percentage change. The two approximations are very simple, yet highly reliable. The results are applied to teacher earnings and further illustrated by examples from the literature.

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