Human behaviour - an underappreciated factor in real estate transaction analyses
Bibliographic record
Abstract
Most valuation approaches examine the ups and downturns of real estate prices mainly focussing on the variables lot size, living space, dwelling age and furniture. These approaches disregard the importance of the individual behaviour of private sellers and buyers. As their decisions are often influenced by uncertainty due to poor market transparency it can be assumed that these decisions are - to some extent - not congruent with rational behaviour. Hence, regression analyses should take both factors - objective and behavioural variables - into account. The empirical study (N=413) in this research analyses the behaviour of brokers and sellers who offer their dwellings through the leading brokerage website in Germany, ImmobilienScout24. They are surveyed directly after the initial listing of the dwelling and then again 5 months later. Human behaviour should be explained by real estate related knowledge, expectations, personal situation and heuristic processes. Heuristic processes reduce the complexity and lead to results which do not coincide with rational behaviour. The heuristic process ´adjustment and anchoring´ is especially important for real estate transactions. Generally, a seller starts from an initial value, e.g listing prices of comparable houses at brokerage websites, and adjusts that value to yield her own dwellings´ value. However, this approach is often led by false individual assumptions. First empirical results show that 25% of private sellers significantly overestimate the appropriate listing price. Three-quarter of private sellers admit, that they have only rudimentary knowledge of the property market and one quarter is forced to sell the property urgently. These results indicate that selling and listing prices of residential properties are also influenced by human behaviour.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".