Factors that influence listing prices and selling prices of owner-occupied residential properties in Germany
Notice bibliographique
Résumé
Many indicators/indices related to real estate markets are either based on list prices or selling prices whereas the latter is usually related to private data. Therefore, the relationship between these two data sets could hardly be investigated. The research in this Ph.D. work aims to enlarge the existing body of knowledge in this area.The data set in an initial part of the study comprises 1,274 transactions of owner-occupied residential properties in rural areas of Rhineland-Palatinate (Germany). The list prices are obtained from ImmoScout24, the largest German real estate brokerage website. The selling prices are acquired from official (yet private) appraisal sources that collect every real estate contract of sale in Germany. It is found that, on average, selling prices are -15.2% (-20,605 Ä) lower than the stated list prices. Moreover, 10% of the sellers had been forced to reduce the list price by more than -33.3% (-47,750 Ä) until a transaction was realized. This indicates that many sellers overestimate the value of their own property, especially in an illiquid real estate market. Several other studies from the USA or Canada came to similar conclusions. In contrast to these studies, this work found that the difference between list price and selling price is not related to common demographic, economic or location characteristics. The owner accuracy regression only indicates a strong influence of the absolute amount of the list price and the age of the dwelling structure. Besides, it is shown that list prices as such are not a perfectly reliable data source. Firstly, it is difficult to match list price and selling price of one single property because often several list prices exist for one single property. Secondly, the time-on-market and changes of the list prices are unknown, yet important to determine the selling price. The same issues applied to many house characteristics like age of the dwelling structure or quality and quantity of building appliances. In a next step, the outlined data problems will be handled with support by the data provider. Furthermore, the regression analyses (with a new sample) will be augmented by other statistical methods. Further, the set of involved variables will be extended, e.g. by owner characteristics that shall be found in surveys of ImmoScout users. The study, as the research progresses, will analyze transactions of owner-occupied residential properties in rural, urbanized and metropolitan areas. Additionally, the study contributes to the price and worth theory and to the valuation practice of residential properties. Furthermore, new scientific insights into the research field ìlist pricesî are expected.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».