Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung

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  • Technische Universität Dresden
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Titel in ÜbersetzungAggregation and weighting technique of different real estate valuation data in regions with few transactions by means of variance component estimation
OriginalspracheDeutsch
Seiten (von - bis)123-136
Seitenumfang14
FachzeitschriftAVN Allgemeine Vermessungs-Nachrichten
Jahrgang124
Ausgabenummer5
PublikationsstatusVeröffentlicht - 2017

Abstract

In Germany, real estate valuation is realized by three standardized methods, among which the approach based on sales comparison is considered as being closest to the real estate market. With this approach, regression analysis is frequently used to solve the linear relationship. The regression model, however, normally requires approximately 15 purchases per independent variable for an accurate estimate in real estate valuation. This causes a problem for areas with few transactions in which only 10 to 30 purchases are available. To solve this problem, the current paper presents a mathematical-statistical model for an accurate estimation of regression coefficients in areas with few transactions. This aim is realized by using additional market data, which are combined with the available purchase cases. Challenges with this approach are the acquisition of the additional market data and the optimal combination of the data in the adjustment model. In this paper, survey data from real estate experts and offer prices are used as additional market data. The combination of these data with the purchase cases is carried out by means of variance component estimation (VCE). The quality assessment of the various market data shows a good conformity between the purchase cases and the experts' survey data. The offer prices differ from the other two datasets. The investigation concerning the VCE for the weighting of the different datasets within the adjustment model has two results: VCE yields more precise estimates of the regression coefficients, but this does not allow for a better prediction of the market values.

Schlagwörter

    Expert knowledge, Regions with few transactions, Regression analysis, Variance component estimation

ASJC Scopus Sachgebiete

Zitieren

Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung. / Dorndorf, Alexander; Soot, Matthias; Weitkamp, Alexandra et al.
in: AVN Allgemeine Vermessungs-Nachrichten, Jahrgang 124, Nr. 5, 2017, S. 123-136.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Dorndorf, A, Soot, M, Weitkamp, A & Alkhatib, H 2017, 'Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung', AVN Allgemeine Vermessungs-Nachrichten, Jg. 124, Nr. 5, S. 123-136.
Dorndorf, A., Soot, M., Weitkamp, A., & Alkhatib, H. (2017). Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung. AVN Allgemeine Vermessungs-Nachrichten, 124(5), 123-136.
Dorndorf A, Soot M, Weitkamp A, Alkhatib H. Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung. AVN Allgemeine Vermessungs-Nachrichten. 2017;124(5):123-136.
Dorndorf, Alexander ; Soot, Matthias ; Weitkamp, Alexandra et al. / Aggregation und gewichtung von unterschiedlichen wertermittlungsdaten in kaufpreisarmen lagen mittels varianzkomponentenschatzung. in: AVN Allgemeine Vermessungs-Nachrichten. 2017 ; Jahrgang 124, Nr. 5. S. 123-136.
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abstract = "In Germany, real estate valuation is realized by three standardized methods, among which the approach based on sales comparison is considered as being closest to the real estate market. With this approach, regression analysis is frequently used to solve the linear relationship. The regression model, however, normally requires approximately 15 purchases per independent variable for an accurate estimate in real estate valuation. This causes a problem for areas with few transactions in which only 10 to 30 purchases are available. To solve this problem, the current paper presents a mathematical-statistical model for an accurate estimation of regression coefficients in areas with few transactions. This aim is realized by using additional market data, which are combined with the available purchase cases. Challenges with this approach are the acquisition of the additional market data and the optimal combination of the data in the adjustment model. In this paper, survey data from real estate experts and offer prices are used as additional market data. The combination of these data with the purchase cases is carried out by means of variance component estimation (VCE). The quality assessment of the various market data shows a good conformity between the purchase cases and the experts' survey data. The offer prices differ from the other two datasets. The investigation concerning the VCE for the weighting of the different datasets within the adjustment model has two results: VCE yields more precise estimates of the regression coefficients, but this does not allow for a better prediction of the market values.",
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AU - Dorndorf, Alexander

AU - Soot, Matthias

AU - Weitkamp, Alexandra

AU - Alkhatib, Hamza

N1 - Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017

Y1 - 2017

N2 - In Germany, real estate valuation is realized by three standardized methods, among which the approach based on sales comparison is considered as being closest to the real estate market. With this approach, regression analysis is frequently used to solve the linear relationship. The regression model, however, normally requires approximately 15 purchases per independent variable for an accurate estimate in real estate valuation. This causes a problem for areas with few transactions in which only 10 to 30 purchases are available. To solve this problem, the current paper presents a mathematical-statistical model for an accurate estimation of regression coefficients in areas with few transactions. This aim is realized by using additional market data, which are combined with the available purchase cases. Challenges with this approach are the acquisition of the additional market data and the optimal combination of the data in the adjustment model. In this paper, survey data from real estate experts and offer prices are used as additional market data. The combination of these data with the purchase cases is carried out by means of variance component estimation (VCE). The quality assessment of the various market data shows a good conformity between the purchase cases and the experts' survey data. The offer prices differ from the other two datasets. The investigation concerning the VCE for the weighting of the different datasets within the adjustment model has two results: VCE yields more precise estimates of the regression coefficients, but this does not allow for a better prediction of the market values.

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