Details
| Original language | German |
|---|---|
| Title of host publication | FIG Working Week 2017 |
| Subtitle of host publication | Surveying the world of tomorrow - From digitalisation to augmented reality |
| Publication status | Published - 2017 |
Abstract
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FIG Working Week 2017: Surveying the world of tomorrow - From digitalisation to augmented reality. 2017.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - A Heuristic Robust Approach for Real Estate Valuation in Areas with Few Transactions
AU - Dorndorf, Alexander
AU - Soot, Matthias
AU - Weitkamp, Alexandra
AU - Alkhatib, Hamza
PY - 2017
Y1 - 2017
N2 - The German market transparency is mainly realizedby results of analyzingpurchase prices. Often, the purchases are analyzedin the context of aregression approach. The results are only reliable in areas with large numbers of purchases. However, in areas with only few transactions the solution of regression is not satisfactory. Furthermore, the purchase prices may contain outliers. Especially in areas with few transactions, the detection of outliers is a challenging task. This study presents three different estimation approaches which aredealingwith outliers. The first approach uses the data snooping to detect the outliers. The second approach is based on a heuristicRANSAC (random sample consensus) algorithm. The thirdapproach uses non–informative robust Bayesian regression techniques, in whichthe normal distribution of the likelihood data is replaced by a Student–distribution to ensure the robustness.The aim of this study is to investigate these three approachesin their efficiencyto deal with outliers in areas with few transactions. For this purpose a closed loop simulationiscarried. The results of the threerobust approachesare compared based on the knownregression coefficients and on the known observations. The results ofthe data snooping and RANSAC showthat the estimationfailmore often than the estimation by means of the robust Bayesian approach, which showsa suitable result forareas with few transactions.
AB - The German market transparency is mainly realizedby results of analyzingpurchase prices. Often, the purchases are analyzedin the context of aregression approach. The results are only reliable in areas with large numbers of purchases. However, in areas with only few transactions the solution of regression is not satisfactory. Furthermore, the purchase prices may contain outliers. Especially in areas with few transactions, the detection of outliers is a challenging task. This study presents three different estimation approaches which aredealingwith outliers. The first approach uses the data snooping to detect the outliers. The second approach is based on a heuristicRANSAC (random sample consensus) algorithm. The thirdapproach uses non–informative robust Bayesian regression techniques, in whichthe normal distribution of the likelihood data is replaced by a Student–distribution to ensure the robustness.The aim of this study is to investigate these three approachesin their efficiencyto deal with outliers in areas with few transactions. For this purpose a closed loop simulationiscarried. The results of the threerobust approachesare compared based on the knownregression coefficients and on the known observations. The results ofthe data snooping and RANSAC showthat the estimationfailmore often than the estimation by means of the robust Bayesian approach, which showsa suitable result forareas with few transactions.
M3 - Aufsatz in Konferenzband
BT - FIG Working Week 2017
ER -