Decomposing Residential Resale House Prices into Structure and Land Components
Bibliographic record
Abstract
The use of hedonic regression models on the sales of detached housing units is widespread in the real estate literature. However, these models do not address the need to decompose the sale price into structure and land components. For many purposes, it is necessary to obtain separate estimates for the price and quantity of housing structures and the land that these structures sit on. The builder’s model accomplishes this decomposition but it takes a producer’s perspective and requires an exogeneous structure price index. In the present paper, a consumer approach to the decomposition problem is taken and this “new” approach to the decomposition problem does not require the use of an exogenous building price index. The paper uses data on sales of detached houses in Richmond, British Columbia in order to implement the new approach. The property price indexes generated by the new approach are compared to the corresponding indexes generated by a traditional time dummy hedonic regression model. The traditional hedonic regression approach does generate reasonable overall property price indexes, but the two approaches do not generate similar land and structure subindexes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".