Estimating Transaction-Based Price Indices of Local Commercial Real Estate Markets Using Pubic Assessment Data
Bibliographic record
Abstract
This study examines the feasibility of constructing reliable commercial property price indices using property tax records. We employ the Clapp and Giacotto (1992) assessed-value method to estimate price indices for commercial properties in Florida. The estimated Florida commercial property price index is compared to the Moody's/REAL Commercial Property Price Index (CPPI) and to the transaction-based index (TBI) produced at MIT. Our results are promising, suggesting that this widely-available data source can be used to produce commercial property price indices for a variety of precise market locations and specific investor segments.A secondary but interesting objective of this paper is to use our rich and comprehensive database to examine the price performance of two specific subsets of properties in more detail. First, we narrow our range to focus on just the office sector for Florida. We compare price movements providing support to both methods. Second, we contrast the price performance of higher-and lower-valued properties and reject the hypothesis that their periodic price index levels are equal. The mean price changes of Florida commercial properties assessed at $2.5 million and above are observed to be slightly higher than for properties assessed below $2.5 million, although not statistically different. In particular, higher-valued properties had higher mean price changes rleative to lower-valued properties during periods of economic expansion. This economic difference represents an importnat contribution toward beginning to understand the relative peformance of smaller and investment-grade commercial properties.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".