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| Content Provider | Springer Nature Link |
|---|---|
| Author | Brunauer, Wolfgang A. Lang, Stefan Feilmayr, Wolfgang |
| Copyright Year | 2013 |
| Abstract | This article proposes a procedure that exploits two heterogeneous data sets to derive spatial house price predictions for Austria in a semiparametric hierarchical regression framework. The first data set contains a large number of house price data, but only a small number of house characteristics (a long data set), giving rise to the omitted variable bias. In contrast, the second data set contains a small number of observations with a wide range of house price attributes (a wide data set). Although the latter allows for detailed estimation of the effects of house price characteristics, the sparse and uneven distribution over the research area leads to volatility in spatial effects. To circumvent these disadvantages, we propose a two-step approach: In the first step, we model the long data set, being aware of potential bias in the estimated effects. We apply a multilevel version of structured additive regression (STAR) models in order to account for nonlinearity in price functions as well as hierarchical spatial heterogeneity not explicitly modeled by covariates. The spatial prediction from this model is used as an explanatory covariate for the wide price data in the second stage. This novel modeling approach, a hybrid multilevel STAR model, avoids the bias from omitted variables on the one hand and yields robust spatial predictions on the other hand. We present detailed comparisons of nonlinear covariate effects and spatial heterogeneity from both stages, contrasting them with estimated effects from a reference model that is only based on the wide data set. The presented results support the approach taken in this paper, which proves particularly useful for spatial prediction of house prices.In diesem Aufsatz beschreiben wir einen neuartigen Ansatz, mit dem zwei heterogene Datensätze für räumliche Hauspreisvorhersagen nutzbar gemacht werden. Der erste Datensatz enthält eine große Anzahl von Hauspreisbeobachtungen, allerdings nur wenige Immobilieneigenschaften (ein langer Datensatz). In der Modellschätzung kann dies zu Verzerrungen aufgrund fehlender Variablen (Omitted Variable Bias) führen. Andererseits verfügen wir über einen Datensatz mit wenigen Beobachtungen, der jedoch eine Vielzahl von Immobilieneigenschaften enthält (ein breiter Datensatz). Aufgrund der geringen Beobachtungszahl kann es hier zu volatilen räumlichen Preisschätzungen kommen. Um die geschilderten Nachteile zu umgehen, schlagen wir eine zweistufige Vorgehensweise vor: In der ersten Stufe wenden wir eine hierarchische Version von Strukturiert Additiven Regressionsmodellen (STAR) auf den langen Datensatz an. In der zweiten Stufe wird die (möglicherweise verzerrte) räumliche Preisschätzung als erklärende Variable zur Modellierung des breiten Datensatzes verwendet. Dadurch werden die räumlichen Preisvorhersagen stabilisiert und gleichzeitig mögliche Verzerrungen aus der ersten Stufe modellimplizit korrigiert – wir nennen dies ein hybrides hierarchisches STAR Modell. Es werden detaillierte Vergleiche zwischen den unterschiedlichen Modellen gezogen, welche die erhöhte Vorhersagequalität durch die neue Modellvariante belegen. |
| Starting Page | 151 |
| Ending Page | 172 |
| Page Count | 22 |
| File Format | |
| ISSN | 01737600 |
| Journal | Jahrbuch für Regionalwissenschaft |
| Volume Number | 33 |
| Issue Number | 2 |
| e-ISSN | 16139836 |
| Language | German |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2013-02-15 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Bayesian hierarchical models Hedonic pricing models Hybrid models P-Splines Regional/Spatial Science Environmental Economics Population Economics Geography (general) Landscape/Regional and Urban Planning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Geography, Planning and Development Economics and Econometrics |
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