A geo-referenced micro-data set of real estate listings for Spain’s three largest cities
Bibliographic record
Abstract
This article presents an open data product with large geo-referenced micro-data sets of 2018 real estate listings in Spain. These data were originally published on the idealista.com real estate website. The observations were obtained for the three largest cities in Spain: Madrid ( n = 94,815 observations), Barcelona ( n = 61,486 observations), and Valencia ( n = 33,622 observations). The data sets include the coordinates of properties (latitude and longitude), asking prices of each listed dwelling, and several variables of indoor characteristics. The listings were enriched with official information from the Spanish cadastre (e.g., building material quality) plus other relevant geographical features, such as distance to urban points of interest. Along with the real estate listings, the data product also includes neighborhood boundaries for each city. The data product is offered as a fully documented R package and is available for scientific and educational purposes, particularly for geo-spatial studies.
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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 | 0.000 |
| 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".