Robust estimation of distance effects and sub‐optimality in mixed use buildings
Bibliographic record
Abstract
Purpose The aim of this paper is to investigate whether buyers and sellers appears to take distance to the capital business district (CBD) into account in their valuation for acquisition or disposition. Design/methodology/approach Under a mono‐centric model conceptualization, applicable to the central area of many European cities, location can be represented by distance to the city center. The effect of several distance measures on selling price is investigated for income properties with mixed residential and commercial components – geometric distances, driving distance and time, and time by subway. Exponential and multiplicative models are considered and estimated using a robust estimation method. Findings The findings indicate the mono‐centric model to be a useful conceptualization, that buyers and sellers of income properties do take distance into account, and that a number of buildings operate under a suboptimal split between residential and commercial components. Research limitations/implications In social science even when a model shows good statistical fit, one will not know if it is correct, but only that it represents observed relationships well. Several models, however, may fit the data. The authors chose ones used by other researchers in similar investigations in past decades. Variable definition and measurement are always issues. In the present case “time by subway” is a mix of walking‐waiting‐riding and minutes in each activity would not be equivalent, involving inter‐personal comparisons of utility. The variable “effective age” based upon the assessment concept of “value year” may not fully capture age‐related effects on price. Practical implications The key implications are that the multiplicative model may well be a suitable functional form for these types of analyses and that robust methods are important to prevent outliers in the data from having an undue influence on the estimation. Originality/value The authors used robust estimation methods for the price models. The authors defined and studied a sub‐optimality ratio of residential area to commercial area in the mixed use buildings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".