Factors that influence listing prices and selling prices of owner-occupied residential properties in Germany
Bibliographic record
Abstract
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 A) 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 A) 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 ilist pricesi are expected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".